The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data are currently lacking for urological cancers. This study provides the methodological foundation for the prospective CONCORDIA trial, which will generate this data for the first time. In this preliminary work, we evaluated the proposed measurement tool for the CONCORDIA study -the System-Causability-Scale (SCS) and its modified version (mSCS) - based on recommendations from ChatGPT-4 and an MTB for 40 urological cancer-scenarios. Both scales demonstrated strong validity, reliability (all aggregated Cohen’s K > 0.74), and internal consistency (all Cronbach’s Alpha > 0.9), with the mSCS showing superior reliability, internal consistency, and clinical applicability (p<0.01). Two Delphi processes were used to define the LLMs to be tested in the CONCORDIA study (ChatGPT-4 and Claude 3.5 Sonnet) and to establish the acceptable non-inferiority margin for LLM recommendations compared to MTB recommendations. The forthcoming ethics-approved and registered CONCORDIA non-inferiority trial will require 110 urological cancer scenarios, with an mSCS difference threshold of 0.15, a Bonferroni corrected alpha of 0.025, and a beta of 0.1. Blinded mSCS assessments of MTB recommendations will then be compared to those of the LLMs. In summary, this work establishes the necessary prerequisites prior to initiating the CONCORDIA study and validates a modified score with high applicability and reliability for this and future trials.

Background: Over the past two decades, the Copenhagen Psychosocial Questionnaire (COPSOQ) has been established as a valid instrument to measure psychosocial stress at work. Currently, the COPSOQ international network is responsible for monitoring and improving COPSOQ. In 2019, a new questionnaire was published, and the Greek version is now being validated. Methods: The measurement qualities of the Greek COPSOQ III have been explored in accordance with the usual requirements of a validation study as defined by DIN EN ISO 10075-3. A sample of observations from 2,189 participants surveyed with the COPSOQ in Greece was used to validate the current version with appropriate statistical analyses. Exploratory factor analysis was used to assess the statistical relationships for many scales. Results: With its 108 items and 40 scales, the Greek COPSOQ III includes all psychosocial work factors that have been internationally validated and remain compatible with approximately 72% of the COPSOQ III German version content. In addition to the primary results, congruence with widely used theoretical approaches such as the demand-control (−support) model (DCM) or the job demands-resources model (JDR) is generally satisfactory. In summary, our validation study for the Greek COPSOQ III version showed adequate reliability and validity, which is in line with the findings of the COPSOQ III questionnaire from other European countries, and it is also compatible with the validation of the German COPSOQ III (Lincke, 2021). Our regression analysis revealed that 34 psychosocial work factors (34 “context” scales) could adequately predict the scores of the satisfactory and health scales (6 “outcome” scales). The analysis also revealed the top five (5) predictors (context variables) for each of the six (6) “effect” scales (outcome variables). Conclusions: With the launch of COPSOQ III in Greece, current and new workplace psychosocial aspects could be explored. COPSOQ III (GR) is a beneficial instrument for enterprise research and risk assessment.

Currently available seasonal influenza vaccines confer variable protection due to antigenic changes resulting from accumulation of diverse mutations. Analysis of new seasonal influenza vaccines is challenging in part due to limitations of the traditional hemagglutination inhibition (HAI) assay with A/H3N2 strains. An improved and objective novel HAI assay was developed using recombinant virus-like particles (VLPs) and egg-derived virus as agglutinins, oseltamivir treatment of VLPs, human red blood cells, and using an automated image reader-based analysis of hemagglutination. HAI validation was demonstrated using four VLP and egg-derived strains, with 46–56 serum samples tested 12 times in duplicate per strain. The validated HAI assay was precise as indicated by the percent geometric coefficient of variation for intra-, inter-, and total-assay precision, and accurate as evidenced by % bias measurements. The assay exhibited linearity, specificity for homologous type/subtype strains, and sensitivity with a starting dilution of 1:10. Assay robustness and sample stability were demonstrated as a % difference compared to reference condition. Validated HAI results were equivalent for singleton and duplicate sample testing and correlated well with a qualified live wild-type influenza microneutralization assay. These findings demonstrate the suitability of this high-throughput novel modified validated HAI assay, for evaluating vaccine immunogenicity and efficacy.

HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥8 (IC50 values ≤10 nM). Our svm-based ensemble model predicted IC50 values <10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

Remote sensing (RS) and Geographic Information Systems (GIS) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in-situ validation methods. This article reviews a range of image analysis tools applicable to natural resource management, including agriculture, water, forests, soil, and natural hazards, and compares their functionalities. Our study highlights that Google Earth Engine (GEE) is favored for wide-area analysis due to its extensive coverage and free access. Global Mapper excels in 3D and light detection and ranging (LIDAR) data, environment for visualizing images (ENVI) specializes in multi- and hyperspectral image processing, ERDAS IMAGINE is optimal for radar data, and eCognition is used for object-based image analysis. The article emphasizes the importance of in-situ validation data, which provides essential ground truth information to calibrate and validate RS models, thereby enhancing accuracy. Understanding these tools and integrating them with in-situ validation techniques enables natural resource managers to improve their monitoring and decision-making processes, facilitating effective collaboration with RS researchers.

The aim of this study was to evaluate the psychometric properties of the Communication Skills Scale (CSS) among Peruvian nurses and its association with job insecurity during the COVID-19 pandemic. We explored five models of confirmatory factor analysis for the CSS and its four subscales and assessed the convergent validity and criterion validity of the scale by analyzing its connection with job insecurity through stepwise multiple regression. We used insights from a focus group for the cultural adaptation of the scale. In the psychometric phase, 225 nurses participated through a virtual survey. The psychometric analysis revealed that the CSS and its subscales have a robust internal structure—similar to the original questionnaire—and are optimally reliable in the Peruvian population. Furthermore, the results show that job insecurity was associated with contract type, communication skills, empathy, and job satisfaction. The CSS and its subscales are valid and reliable to be applied to Peruvian nurses. Initiatives should be undertaken to strengthen communication skills and increase job satisfaction among nursing professionals by improving their working conditions, especially in times of crisis, to reduce job insecurity and promote well-being.

Enhancing the acoustical quality in learning environments is mandatory, especially for Hearing Aids (HAs) users. When in-field evaluations cannot be performed, Virtual Reality (VR) can be adopted for the acoustical quality assessment of existing and new buildings, contributing to the acquisition of subjective impressions in lab settings. To ensure an accurate spatial reproduction of the sound field in VR also for HAs users, multispeaker-based systems can be employed to auralize a given environment. However, most systems are effortful due to costs, size and construction. This work deals with the validation of a VR-system based on a 16-speaker-array synced with a VR-headset, arranged to be easily replicated in small non-anechoic spaces and suitable for HAs users. Both objective and subjective validations are performed against a real university lecture room of 800 m3 and with 2.3 s of Reverberation Time at mid-frequencies. Comparisons of binaural and monoaural room acoustic parameters are performed between measurements in the real lecture room and its lab reproduction. To validate the AudioVisual experience, 32 normal-hearing subjects were administered the Igroup Presence Questionnaire (IPQ) on the overall sense of perceived presence. The outcomes confirm that the system is a promising and feasible tool to predict the perceived room acoustical quality.

The Supportive and Palliative Indicators Tool (SPICT) was developed for timely identifying patients who may benefit from supportive and palliative care for a better treatment review, care plan discussion, and end-of-life care. Although the SPICT has been validated in different languages and patients living in different settings, it has not been validated in patients receiving home-based medical care (HBMC), or in the context of using traditional Chinese characters. The present study aimed to validate the Taiwan version of SPICT (SPICT-TW) and to measure its ability to predict six-month mortality in patients who received HBMC in Taiwan. Seven HBMC agents (five clinics and two hospitals) participated in this validation study. We recruited 129 patients aged ≥50 years who had been consistently receiving HBMC for &gt; two months. The results revealed that the SPICT-TW demonstrated similar reliability and validity compared to other language versions of SPICT. It may be an appropriate tool for healthcare professionals to timely detect the needs of palliative care in older people who received home healthcare. Furthermore, we found that a combination of four general indicators and one clinical indicator in SPCIT-TW has the best prediction ability at predicting six-month mortality in these HMBC recipients. This multicenter study validated the SPICT-TW among HBMC recipients in Taiwan. SPICT-TW demonstrated high reliability and validity and may be a practical tool for identifying older people at risk of dying within six months who would benefit from palliative care.

Adverse medical events not only harm patients and families, but also cause significant negative impact on healthcare providers, with potential to compromise future professional functioning. These “second victims” may need organizational support and rehabilitation to return to functionality. We analyzed the validity of an adapted tool, the Second Victim Experience and Support Tool (SVEST), to a population in Israel, H-SVEST. The H-SVEST was completed by 172 nurse participants working in a variety of patient care settings. The H-SVEST was assessed for content validity, internal consistency, and construct validity with confirmatory factor analysis (CFA). The CFA run on the initial model with 10 factors and 36 items did not meet criteria for goodness of fit. After removing five items based on their low factor loadings and the correlation, The model fit significantly improved with acceptable CFI, TLI, RMSEA, and SRMR; χ2(389) =626.939, p<.001; χ2/df=1.61; TLI=0.901; CFI=0.917; RMSEA [90% CI]=0.060 [0.051, 0.068]; SRMR=0.064. The final version included 31 items and 10 factors with Cronbach α values ranging from 0.66 to 0.94. The H-SVEST demonstrates robust psychometric properties and valuable insights into the second victim experience in the Israeli context. Comparative analysis with other versions highlights potential cultural influences and areas for further investigation. Implementing this tool and developing evidence-based interventions based on its results can significantly improve well-being and resilience of healthcare providers in Israel and other countries with diverse cultural populations. This study was not registered.

The Surface Water and Ocean Topography (SWOT) mission was launched on December 16, 2022 to measure water levels over both open ocean and inland waters. To achieve these objectives, the SWOT Payload contains an innovative Ka-band radar interferometer, called KaRIn, completed with a conventional nadir altimeter called POSEIDON-3C that has been switched on a month after launch and a few days before KaRIn. POSEIDON-3C measurements provide a link between large scale phenomena and high resolution. POSEIDON-3C design is based on POSEIDON-3B, its predecessor on-board JASON-3. It is also a dual frequency radar altimeter operating in C and Ku bands, but with some improvements to enhance its performance. Even though it is a Low Resolution Mode altimeter, its performances over open ocean, inland waters and coastal zones are indeed excellent.This paper first describes the POSEIDON-3C design, and its modes with a focus on its new features and its Digital Elevation Model that drives its open-loop tracking mode. Then we assess the in-flight performances of the altimeter from an instrumental point of view. For that purpose, special and routine calibrations have been realized. They show the good performances and stability of the radar. In-flight assessments give thus confidence to ensure excellent altimeter measurement stability throughout the mission duration.

Maintaining the optimal microclimate in broiler houses is crucial for bird productivity, yet enabling efficient temperature control remains a significant challenge. This study developed and validated a computational fluid dynamics (CFD) model to predict temporal changes in indoor air temperature in response to variable ventilation operations in a commercial broiler house. The model accurately simulated air velocity and airflow distribution for different numbers of tunnel fans in operation, with air-velocity errors ranging from -0.22 to 0.32 m s-1. The predicted airflow rates through inlets and cooling pads showed good agreement with measured values with an accuracy of up to 108.1%. Additionally, the CFD model effectively predicted temperature dynamics, accounting for chicken heat production and ventilation effect. The model successfully predicted the longitudinal temperature gradients and their variations during ventilation cycles, validating its reliability through comparison with experimental data. The study also explored different variable inlet configurations to mitigate the temperature gradient. The variable inlet adjustment showed the potential to relieve the high temperatures but may reduce overall ventilation efficiency or intensify temperature gradients, which confirms the importance of optimising ventilation strategies. This CFD model provides a valuable tool for evaluating and improving ventilation systems and contributes to enhanced indoor microclimates and productivity in poultry houses.

This study explores the comparative performance of FinBERT, GPT-4, and Logistic Regression for sentiment analysis and stock index prediction using the NGX All-Share Index dataset. By leveraging advanced language models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment score, and predict market price movements. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed both FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was computationally demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of these approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 showing promise for future exploration.

Most available studies on the mechanical properties of apples lack the comprehensive results needed for the construction and validation of FEM models. Researchers typically focus on either the flesh and epidermis or the whole fruit, often overlooking the maturity stage of the examined apples. They usually report only firmness or the starch index as indicators of maturity. Furthermore, many studies use store-bought apples, which is impractical for industrial applications since this fruit have already undergone various treatments before reaching the shelf. This article aims to determine the mechanical properties of apples necessary for constructing FEM models that are both adequate and useful for the industry. The new Polish apple variety, Chopin, was selected as the research material. The study was conducted for three stages of apple maturity: development, ripening, and senescence. Mechanical properties of the flesh and skin were determined as material data for FEM models. Force-displacement curves and pressure-force functions were examined for future model validation. Using micro-computed tomography, the bruise volumes of fruit subjected to 20%, 50%, and 80% of the destructive force were determined. Significant differences were found between apples in the senescence stage and those in the development and ripening stages. Results of Micro-Ct and the results of modified and real compression tests of whole fruit have allowed us to formulate the research hypothesis regarding the influence of flesh cracking (characterized by local drops of force), influencing the bruise visibility and detection.

Background: Self-esteem is a psychological term that, since its emergence in 1890 with William James, has been widely studied. Furthermore, this construct has been examined in different areas of psychology, including the clinical field, where its relationship with mental health and low levels of depression has been demonstrated. In the realm of positive psychology, its connection to resilience and life satisfaction has also been established. Objective: To evaluate validity based on internal structure from the perspective of Classical Test Theory (CTT) and Item Response Theory (IRT), to obtain evidence of validity based on the relationship with other variables and to estimate the reliability of the Rosenberg Self-Esteem Scale (RSES). Method: To this end, 584 military personnel from the three ranks of the Spanish Army (officers, non-commissioned officers, and professional soldiers and sailors) were evaluated, of which 511 were men (87.70%) and 72 were women (12.30%), with an age range of 18 to 66 years (M = 33.17, SD = 7.38). Results: The results indicate that the unidimensional structure of the RSES shows adequate fit indices (CFI = .94, TLI = .91, SRMR = .05), while the difficulty and discrimination parameters exhibited favorable values. Additionally, an acceptable reliability value was found (ɷ = .81; α = .80). Conclusions: Therefore, it is concluded that the RSE-JGA presents adequate psychometric properties of validity and reliability, making it a useful and relevant instrument for measuring self-esteem in military personnel of the Spanish Army.

This is a methodological contribution to reference mode development in system dynamics model conceptualization. Existing behavior mode taxonomies-developed for various modeling phases-provide no comprehensive method for mode classification based on an exhaustive set of mutually exclusive modes, and derivation of questions the simulation model should answer. Novice modelers need such guidance. The article proposes a conceptual framework for systemic behavior with a hierarchical taxonomy of elementary, composite, and overlaid modes. A structured method identifies the intervals of each respective mode, orders them into episodes and then condenses the variables’ episodes to systemic episodes. Each transition then gives rise to a fundamental question for the model under development. Three classical cases illustrate the application and contrast the proposed taxonomy to others in the extant literature. The article then discusses some validation criteria referring to reference modes and their link to the dynamic hypothesis, current limitations, and questions for further research.

The microfluidic industry recognizes the lack of sensitive and standardized testing methods as one of the major hurdles to overcome before fulfilling its revolutionary promises. Highly ranked upon the list of things in need of testing methods are the internal dimensions of microfluidic structures after chip assembly. This work, performed under the MFMET project, aims to address this challenge by presenting and comparing multiple protocols for measuring the dimensions of internal microfluidic channels of fully assembled transparent chips. The protocols include optical profilometry, optical microscopy and tiled digital imagery. Several standardized chip designs out of two different materials commonly used in microfluidics (D263©bio and TOPAS© COC) were measured by the different protocols. A consistency analysis through normalized error statistics revealed optical profilometry as the preferred method due to its low uncertainty compared to the other protocols and its higher consistency with nominal geometry values. All protocols showed difficulty with vertical depth measurements of internal structures. Associated possible directions of future research are considered including further study of the influence of multiple refractive surfaces on optical profilometry measurements and further investigation of confocal microscopy as a potential measurement method showing promising preliminary results. The influence of the different materials appears to be minimal on optical profilometry, but other materials and method would merit looking into before such an observation might be generalized. In conclusion, the results of this work directly address the current lack of measurement methods for internal microfluidic structures by providing a comprehensive comparison of different protocols, ultimately suggesting a preferred option for immediate application within the microfluidic industry. Additionally, this work offers valuable directions for future research, serving as an initial step in overcoming a significant challenge that impedes the microfluidic industry from realizing its revolutionary potential.

Despite the increasing prevalence of pet ownership in Chinese societies, standardized tools to assess grief from pet loss remain lacking. Research predominantly focuses on Western populations, creating a gap in understanding pet bereavement in Chinese cultural settings. This study aimed to adapt and validate the Pet Bereavement Questionnaire (PBQ-C) for a Chinese context to create a culturally appropriate assessment tool. A total of 245 participants with companion animal loss experiences were recruited through the university of the research team. They were invited to complete an online survey including the Chinese PBQ, the Depression subscale of the Depression Anxiety Stress Scales (DASS-21), and the Inventory of Complicated Grief (ICG). Both Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to examine the psychometric properties of the PBQ-C and the findings supported a three-factor structure—grief, anger, and guilt—aligned with the original PBQ, with three items were reassigned to different factors. Despite these adjustments, the PBQ-C showed strong internal consistency, split-half reliability, and concurrent validity. The validated PBQ-C provides a culturally sensitive tool for assessing pet bereavement in Chinese society that can promote research and counselling support for this under-researched and under-recognized type of loss of human-animal relationships.

In ocean engineering, accurate and efficient numerical simulations are crucial. Leveraging axisymmetry vastly enhances the efficiency of a simulation, reducing a three-dimensional scenario to a two-dimensional simulation. Exploiting axisymmetry enables a significant reduction in computational demand while maintaining simulation fidelity. In this paper, axisymmetry is leveraged to perform a large ensemble of simulations, in order to evaluate and maximise the accuracy and efficiency of a Computational Fluid Dynamics (CFD) simulation. In particular, the free decay motion and wave radiation from a heaving semi-submerged sphere are simulated, using the Reynolds Averaged Navier Stokes (RANS) solver, interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering.

Well-annotated gene and genomic sequences serve as a foundation for making inferences in molecular biology and evolution, and can directly impact public health. The first SARS-CoV-2 genome was submitted to GenBank and used to develop the two successful vaccines. Conserved protein domains are often chosen as targets for developing antiviral medicines or vaccines. Mutation and substitution patterns provide crucial information not only on functional motifs and genome/protein interactions but also for characterizing phylogenetic relationships among viral strains. These patterns, together with the collection time of viral samples, serve as the basis for addressing the question of when and where the host-switching event occurred. Unfortunately, viral genomic sequences submitted to GenBank undergo little quality control, and critical information in the annotation is frequently changed without being recorded. Researchers often have no choice but to hold blind faith in the accuracy of the sequences. There have been reports of incorrect genome annotation but no report that casts doubt on the genomic sequences themselves because it seems theoretically impossible to identify genomic sequences that may not be authentic. This paper takes an innovative approach to show that some SARS-CoV-2 genomes submitted to GenBank cannot be possibly authentic. Specifically, some SARS-CoV-2 genomic sequences deposited in GenBank with collection time in 2023 and 2024, isolated from saliva, nasopharyngeal, sewage, and stool are identical to the reference genome of SARS-CoV-2 (NC_045512). The probability for such occurrence is effectively 0. I also compile SARS-CoV-2 genomes with changed sample collection time. One may led astray in bioinformatic analysis without being aware of errors in sequences and sequence annotation.

Background: Malnutrition screening in hospitalized children is essential, and the STAMP is a val-idated tool for this purpose. The study aimed to modify STAMP for a Saudi context and assess the sensitivity and specificity of both the original and modified tools. Method: A cross-sectional study was conducted among 307 hospitalized children, where both the original and Saudi-modified STAMP were applied. Anthropometric measurements were also recorded, and statistical analysis was used to assess the tools' validity. Results: The Saudi-modified STAMP identified a higher percentage of children at high risk of malnutrition compared to the original STAMP (91.6% vs. 62.9%). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and agreement of the Saudi-modified STAMP compared to the original were 94.3%, 13.2%, 64.8%, 57.7%, 0.654, and 0.089, respectively. Conclusion: The Saudi-modified STAMP showed excellent sensitivity and varied negative predictive value, indicating its potential effectiveness in screening for malnutrition among hospitalized children compared to the original STAMP and using an-thropometric measurements as references.

The Phubbing Scale (PS) is an instrument used to measure the frequency and extent of the behaviour of ignoring someone you are with and giving attention to your mobile phone instead. However, there is insufficient evidence about the psychometric adequacy of the Spanish version of the instrument. The main goal of this research was to analyse the psychometric properties of PS in a representative sample of Spanish adolescents and young adults. A total of 1351 participants comprised the sample. (42,78% females, age range = 12-21). The goodness-of-fit indices for the two-factor model were good. The McDonald’s Omega coefficient for the total score was 0.787. The phenomenon of phubbing was found to have statistically significant correlations with emotional well-being, other mental health indicators, and with Problematic Internet Use (PIU); with the sole exception of the hyperactivity subscale of the SDQ This study provided validity evidence for the Spanish version of the Phubbing Scale (PS), suggesting that PS is a reliable tool for quantifying phubbing in Spanish adolescents.

Imbalanced classes can cause machine learning models to classify positive class instances poorly, and the models require hyperparameter sets to optimal values. This study aims to develop random forest (RF) and support vector machine (SVM) models that use the optimal hyperparameters obtained through 5-fold cross-validation data. Both models were trained using optimal hyperparameter pairs that fit two data scenarios, the original and oversampling training sets, to produce the benchmark and best models. The model's performance was evaluated using six metrics, including training and testing data. The acquired optimal RF hyperparameter pair was (500, 10) for the minimum number of instances and tree depth level. In contrast, the acquired optimal SVM hyperparameter pair was (0.001, 500) for the gamma and constant values. The benchmark model performed approximately 98% on Accuracy, Precision, Recall, and F1 score metrics but failed to deliver any performance on Mathew's Correlation Coefficient (MC) and Area under Curve (AUC) metrics. The best RF and SVM perform less than both benchmark models in four famous metrics. Both best models have performance improvements of approximately 6% and 11% for the MCC and AUC metrics, respectively. The best RF performance was slightly better than the best SVM performance.

Sodium valproate is a established antiepileptic drug of first choice, primarily used to treat epilepsy, but also bipolar disorder and prevent migraine headaches. Valproate increases concentrations of gamma-aminobutyric acid in the brain, due to inhibition of enzymes responsible for the catabolism of gamma-aminobutyric acid. Main goal of this research consisted in the optimization and development for a new sensitive spectrophotometric quantitative analysis method of sodium valproate from tablets in the Visible field. Following the quantitative color reaction of sodium valproate with diazonium salt of alpha naphthylamine in alkaline medium, a light yellow azo dye was formed and spectrophotometrically analyzed at the wavelength of 386 nanometers. The amount of pure sodium valproate found on pharmaceutical extended-release tablet was 492.578 miligrams compared to the official declared pure amount of 500 miligrams on prolonged-release tablet. Amount found was assigned to a percentage content of 98.5156 percent pure sodium valproate in a pharmaceutical tablet. Mean average percentage error (deviation) from the officially stated value was about 1.4844 percent, which was included within normal limits, below five percent maximum limit allowed by European and Romanian Pharmacopoeias rules. Spectrophotometric method was subjected to the statistical validation procedure and was successfully validated.

The drinking water temperature is expected to increase in the Netherlands due to climate change and the installation of district heating networks as part of the energy transition. To determine effective measures to prevent undesirable temperature increase of the drinking water a model is developed. This model describes the temperature in the drinking water distribution network as a result of the transfer of heat from the climate, above and underground heat sources through the soil. The model consists of two coupled applications. The extended soil temperature model (STM+) describes the soil temperatures, in a two-dimensional finite element method that includes a drinking water pipe and two hot water pipes, coupled to a micro meteorology model. The extended water temperature model (WTM+) describes the drinking water temperature as a function of the surrounding soil temperature (the boundary temperature, resulting from the STM+), thermal sphere of influence where the drinking water temperature influences the soil temperature, and the hydraulics in the drinking water network. Both models are validated with field measurements. This paper describes the WTM+. Previous models did not consider the cooling effect of the drinking water on the surrounding soil, which led to an overestimation of the boundary temperature, and to an overestimation of how quickly the drinking water temperature reaches this boundary temperature. The field measurements show the improved accuracy of the WTM+ when considering one to two times the radius of the drinking water pipe as the thermal sphere of influence around the pipe.

This study focuses on calibrating the linear Drucker-Prager yield criterion for polypropylene under multiaxial loading conditions, using the Arcana machine to achieve a wide range of triaxiality states. The Drucker-Prager criterion has been adapted for polymers, composites, and metals due to its ability to account for the influence of hydrostatic pressure on yield stress. This study employs the Arcana machine, which allows for testing flat plate samples under various angles, thereby simulating different stress states from pure shear to biaxial tension. Additionally, uniaxial compression tests were conducted to extend the range of triaxiality. The material used in this study is Sabic 83MF10, a polypropylene polymer. Samples were prepared by injection molding and cut to specific dimensions. Tests were performed using a universal testing machine with the Arcana handle, and the results were analyzed to determine the yield strength, pressure, and triaxiality for each sample. The results showed a linear relationship between von Mises stress and hydrostatic pressure, with a friction angle (β) of 20.65° and material cohesion (d) of 27.81. The numerical simulations in Abaqus confirmed the validity of the Drucker-Prager model, accurately reflecting the moment of yielding for the tested samples.

A rapid and simple high-performance liquid chromatography (HPLC) method using extraction with zinc sulfate has been developed for the determination of Casiopeina III-ia (Cas III-ia) and validated over the linear range 10-120 g/mL in 200 L of rabbit blood. Previously we have reported a HPLC method to quantify CasIII-ia in plasma, but in distribution studies in total blood [20], the Cas III-ia concentrations were higher in total blood that in plasma [19]. The analysis was performed on a Symmetry C (18) (5 microm) column. The mobile phase was Methanol-sodium phosphate buffer (pH 6.5; 0.01 M) (40:60 v/v) was kept at a flow-rate of 0.8 ml/min. The analyses were performed at room temperature. The column effluent was monitored at 262 nm. Acetaminophen was used as internal standard. The results showed that the assay is sensitive at 10 g/mL. Maximum intra-day coefficient of variation was 5.10%. The average recovery obtained in blood was 94.51%. The applicability of this method for distribution in vitro and pharmacokinetic studies in rabbits was demonstrated. Conclusions: the present assay is rapid, simple, precise, and accurate. The pharmacokinetic study was carried out in rabbits and the following pharmacokinetic parameters were obtained: (kel) = 0.0150 min-1, half-life time (T1/2) = 53.92 min, apparent volume of distribution (Vd) = 202.81 mL, clearance (Cl) = 2.08 mL /mi and area under the curve (AUC)=23163.85 µg/mL.min. Contributing to the preclinical characterization of the Cas III-ia.

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results demonstrate that these control strategies can effectively reduce vibrations and impacts, significantly enhancing ride comfort, which is crucial for reducing driver fatigue and improving driving safety.

PET imaging of bacterial infection could potentially provide added benefit for patient care by non-invasive means. [68Ga]Ga-desferrioxamine B – a radiolabelled siderophore – shows specific uptake by human-pathogenic bacteria like Staphylococcus aureus or Pseudomonas aeruginosa as well as sufficient serum stability for clinical application. Here, we provide data for automated production of [68Ga]Ga-desferrioxamine B on two different cassette based synthesis modules (Modular-Lab PharmTracer and GRP 3V) using commercially available cassettes together with an approved 68Ge/68Ga radionuclide generator. Quality control including determination of radiochemical purity as well as a system suitability test was set up via RP-HPLC on a C18 column. The two described production processes, using an acetic acid/acetate buffer system with ascorbic acid as a radiation scavenger for radiolabelling, yield ready-to-use formulations with sufficient activity yield. Batch data showed radiochemical purity of &gt;95 % as determined by RP-HPLC and ITLC as well as excellent stability over 2 hours post production. Specifications for routine production were set up and validated with four master batches for each synthesis module. Based on this study, an academic clinical trial for imaging of bacterial infection was initiated. Both described synthesis methods enable reproducible, automated in-house production of [68Ga]Ga-desferrioxamine B for clinical application.

The origin and climate impact of Arctic aerosols, like the Arctic Haze, are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study we present a homogenised data set from sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, from 2004 – 2023. Due to polar day and polar night it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that winter months are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn months. The Arctic Haze is not characterised by large particles and can not be distinguished from large aerosols in winter months. With autocorrelation analysis we found out, that AOD events usually occur with duration of several hours and are therefore caused by large-scale processes and may have a periodicity of several days. Local processes can be neglected in Ny-Ålesund but long-range transport plays a major role for the Arctic aerosol budget. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions or wildfires on the Northern Hemisphere but did not find one single cause, which clearly determines the Arctic AOD.

This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. The proposed methodology employs a novel virtual test environment that records not only basic flight metrics but also complex mission performance metrics, such as the video quality from a target. A group of Belgian Defence drone pilots were trained using this simulator system, yielding several practical results. These include a human performance model linking human factors to pilot performance, an AI co-pilot providing real-time flight performance guidance, a tool for generating optimal flight trajectories, a mission planning tool for ideal pilot assignment, and a method for iterative training improvement based on quantitative input. The training results with real pilots demonstrate the methodology’s effectiveness in evaluating pilot performance for complex military missions, suggesting its potential as a valuable addition to new pilot training programs.

The concept of Digital Twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber-physical systems. Despite the various foundations, standards, and platforms in Systems Engineering, there are ongoing challenges with Verification and Validation methodology. This study aims to establish a generic framework that addresses the various aspects of Digital Twinning. The multifaceted nature of the problem requires raising the abstraction level in both the Real (Actual) and Virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the Quintuple Helix model with the Problem and Operational Domains of a Real (Actual) Twin, the Solution and Implementation Domains of a Virtual Twin, and the Execution Domain as the bridge that links them. Verification and Validation dimensions follow the Meta Object Facility abstraction layers (Instance, Model, Meta-model, and Meta-meta-model) mapping over five Helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios.

High consequence pathogens such as Ebola, Marburg and Lassa viruses are handled in maximum containment Biosafety Level 4 (BSL-4) laboratories. Genetic material is often isolated from such viruses and subsequently removed from BSL-4 for a multitude of downstream analyses using readily accessible technologies and equipment available at lower biosafety level laboratories. However, it is essential to ensure that these materials are free of viable viruses before removal from BSL-4 to guarantee sample safety. This study details the in-house procedure used for validating the inactivation of Ebola, Marburg and Lassa virus cultures after incubation with AVL lysis buffer (Qiagen) and ethanol. This study’s findings showed no viable virus was detectable when high-titer cultures of Ebola, Marburg, and Lassa viruses were incubated with AVL lysis buffer for 10 minutes, followed by an equal volume of 95% ethanol for 3 minutes, using a method with a sensitivity of ≤0.8 log10 TCID50 as the limit of detection.

The Hyper-Torus Universe Model (HTUM) proposes a novel framework to unify quantum mechanics, cosmology, and consciousness. This paper introduces HTUM's core concept: a higher-dimensional hyper-torus encompassing all possible states of existence. HTUM suggests that the universe functions as a quantum system where all outcomes are inherently connected, with consciousness playing a significant role in actualizing reality. The model introduces several key innovations, including the universe's 4-dimensional toroidal structure (4DTS), providing a new geometric interpretation of spacetime. HTUM presents dark matter and dark energy as nonlinear probabilistic phenomena within this toroidal framework, offering a unique perspective on their nature and interactions. The model proposes a mechanism for wave function collapse through universal self-observation, bridging quantum mechanics and gravity. It introduces a Topological Vacuum Energy Modulator (TVEM) function, offering a potential resolution to the cosmological constant problem. Furthermore, HTUM integrates consciousness as a fundamental aspect of the universe, influencing quantum state actualization and potentially resolving the hard problem of consciousness. The model explores the implications of a timeless singularity and the emergence of time as a product of causal relationships within the toroidal structure. This framework addresses critical challenges in modern physics, including the nature of quantum entanglement, the origin and fate of the universe, and the relationship between mind and matter. The paper discusses the mathematical formulation of HTUM, its implications for quantum mechanics and cosmology, and its potential to bridge the gap between science and philosophy. We present detailed predictions for the cosmic microwave background (CMB) power spectrum, gravitational wave signatures, and large-scale structure formation, demonstrating how HTUM differs from the standard ΛCDM model. Our analysis reveals distinctive oscillatory patterns in the relative difference between HTUM and ΛCDM predictions, with deviations reaching up to ±20% at certain scales. The paper also explores the unification of fundamental forces within the HTUM framework, proposing novel approaches to particle physics and quantum field theory. It introduces the concept of unified mathematical operations, suggesting a more holistic approach to mathematical thinking that aligns with the model's interconnected view of the universe. Additional testable predictions and proposed experimental approaches are discussed, including advanced space-based tests and laboratory experiments probing quantum gravity effects. These specific, quantifiable predictions offer multiple avenues for empirical validation, highlighting HTUM's potential to be distinguished from ΛCDM and other alternative cosmological models. HTUM represents a significant new perspective in our understanding of the universe, inviting further research and exploration into the nature of reality, consciousness, and the fundamental structure of the cosmos. By providing a comprehensive framework that unifies various aspects of physics, cosmology, and consciousness studies, HTUM offers a promising path toward a more complete understanding of our universe.

The use of low cost sensors has dramatically increased in recent years in all the engineering sectors. In the field of buildings and automotive, low cost sensors open very interesting perspectives, because they allow to monitor temperature and humidity distributions together with air quality, in a widespread and punctual way and allow control of all energy parameters. The main issue still open remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems have been set to measure Particulate air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity and GPS position. These systems have been calibrated by the comparison with standard and certified sensors used by the regional authority of Emilia-Romagna region (ARPAE, Italy) for characterising the air-quality. The innovation of this approach lies in both the in-field comparison of low-cost and high quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors performance is given, with a focus on effects of time-granularity, relative humidity, mass conversion from particle counts and size detection response. The results show that the low-cost sensors measurements of air temperature, relative humidity and particle number concentration are in a good agreement with high quality sensors measurements, with a strong impact of relative humidity on performance indicators. Overall, a good quality and consistency of the data among the sensors has been achieved.

For quantitative determination of honokiol (HNK) and metformin hydrochloride (M-HCl) by HPLC from formulations and from dissolution studies, a QbD approach using three factors - three levels design was implemented to optimize the chromatographic conditions. To study the impact of each variable on the peak area of both compounds and retention time of last compound eluted, the 3D response surface plots were developed. HNK and M-HCl were separated on a Thermoscientific ODS Hypersyl TM (250 x 4.6 mm, 5 μm) column mantained at 30 C, using 0.02 M acetate buffer (pH = 3) / methanol (15 / 85, v / v) as mobile phase with a flow rate of 1 mL/min. The injected volume was 20 L, and the detection wavelengths were 256 nm for HNK and 236 nm for M-HCl. The validation of proposed method showed a linearity range of 4 – 1200 μg/mL for M-HCl (r2 = 0.9992) and 1 – 300 μg/mL for HNK (r2 = 0.9998), good precision (RSD &lt; 2% for both HNK and M-HCl, in intra- and inter-day studies) and accuracy (mean recovery = 100.09% in the range 99.80 – 100.43% for M-HCl and mean recovery = 100.12% in the range 99.70 – 100.38% for HNK). The environmentally friendly nature of the method was confirmed by the evaluation of the method’s greenness. The proposed method was applied for the determination of HNK and M-HCl from oral dosage forms and in dissolution studies.

Long-distance Real-Time Kinematic (RTK) positioning is crucial for applications in remote areas, such as maritime environments. Achieving 2-3 cm accuracy with RTK requires successful ambiguity resolution, which involves two main two steps: identifying the best integer ambiguity candidate and confirming its validity. While previous research has largely concentrated on the first step, including the development of Cascading Ambiguity Resolution methods, and reducing tropospheric delay, studies on the validation of ambiguity for long-distance RTK are limited. This study conducts a thorough examination of ambiguity validation for long-distance RTK, focusing on two prevalent methods: the theoretical success rate and the R-ratio test. The results reveal several key insights. Firstly, the six commonly used bounds for the theoretical success rate are not accurate reflection of the actual success rate, making them unsuitable for long-distance RTK applications. Secondly, the R-ratio test proves to be dependable when the threshold is set above 1.7, assuming there is a minimum observation period of one minute and at least ten satellites are visible. However, the probability of successfully resolving ambiguities with the R-ratio test does not surpass 50%. Additionally, if ambiguity resolution is not achieved within 20 minutes, simply prolonging the observation time is generally unproductive. To improve the performance of ambiguity resolution in practical situations that require extended observation times, this research proposes a novel ambiguity validation method. This new approach is based the duration for which an integer ambiguity resolution candidate maintaining the best status. This method aims to provide a reliable means of validating ambiguities in case that the R-ratio test fails.

Background : The quantification of electromyographic activity using surface electrodes is inval-uable for understanding gait disorders in patients with central nervous system lesions. We propose to evaluate a commercially available low-cost system compared to a reference system in partic-ipants with stroke-related movement disorders in functional situations. Methods : Three hemiparetic participants performed three functional tasks: two treadmill walks at different speeds and a sit-to-stand test. The vastus lateralis and gastrocnemius medialis muscles were equipped with two EMG sensors. The comparison between the two EMG systems was based on 883 identified cycles. Spearman’s correlation coefficients (SC), linear correlation coefficients (LCC), and cross-correlation coefficients (CCC) were calculated. Results : The main results indicate good to very good similarity of the EMG signals collected from the two tested sEMG systems. In the comfortable walking condition, an SC of 0.894±0.091 and an LCC of 0.909±0.094 were noted. In the fast walking condition, an SC of 0.918±0.064 and an LCC of 0.935±0.056 were observed. For the 1-minute sit-to-stand test an SC of 0.880±0.058 and an LCC of 0.881±0.065 were noted. Conclusions : This study demonstrates good to very good similarity between the two sEMG systems, enabling the analysis of muscle activity during functional tasks.

Barrett’s esophagus (BE) is a known precursor to Esophageal Adenocarcinoma (EAC). Guidelines recommend BE screening in populations with multiple risk factors, for which non-endoscopic esophageal cell collection with biomarker testing is considered an acceptable alternative to esophagogastroduodenoscopy (EGD). This study presents analytical validation of EsoGuard® (EG), a two gene DNA methylation biomarker assay, in esophageal cell samples collected with the 510(k)-cleared, swallowable EsoCheck® (EC) device. EG is a Next Generation Sequencing (NGS) assay that evaluates methylated Vimentin (VIM) and Cyclin A1 (CCNA1) genes, clinically validated biomarkers for detection of BE and EAC. The studies were conducted according to standards of College of American Pathology (CAP), Clinical Laboratory Improvement Amendments (CLIA), and New York (NY) state requirements for analytical validation of molecular assays. Comparison to Sanger sequencing showed EG was 100% accurate at all 31 CpG sites evaluated by the assay. Analytical sensitivity, specificity and accuracy of the assay was 89%, 100% and 96% respectively. Intra- and inter-assay precision was 100%. Limit of detection (LOD) was 1 in 400 methylated cells and reference range was 84%. In summary, EsoGuard is a robust DNA methylation test to detect BE with high accuracy, repeatability, and reproducibility in samples collected using the EsoCheck device.

In a world where physical activity and social participation are fundamental pillars for a full, healthy, and quality life, confidence in walking emerges as a fundamental aspect to be assessed, especially in older adults. The purpose of this study was therefore to develop a Portuguese (Euro-pean) version, linguistically and psychometrically adapted, of the Ambulatory Self-Confidence Questionnaire (ASCQ); A translation method was used followed by an assessment of its validity and reliability. The Portuguese version was completed by 173 older adults. To assess reliability was used Cronbach’s Alpha and Intraclass Correlation Coefficients (ICC). For sociodemographic and clinical characterization, as well as for the questionnaire scoring was used descriptive statistical analysis. Pearson's correlation (r), Student´s t-test and ANOVA were used to analyse the criterion and con-struction validity; The Portuguese interactions of ASCQ was effectively translated and adjusted, revealing exceptional internal consistency and test-retest reliability, as reflected in Cronbach's alpha and ICC values of 0.95. No Floor Effect was observed, however, a Celling Effect was identified (3.5%). Criterion and construct validity were verified because all the correlations established were statistically significant; The adaptation of the ASCQ to Portuguese culture is adequate, making it valid for use within the Portuguese population of Portugal.

As split learning (SL) becomes popular for addressing data privacy and resource limitation issues in edge model training process, it is crucial to pre-validate whether the extensive use of wireless and computing resources in SL can achieve the expected training latency and convergence. While digital twin network (DTN) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for SL tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreport resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 13.44% and convergence estimation accuracy by 116.74% compared to traditional equation-based methods.

This study presents a fit-for-purpose evaluation of commercially available portable sensor systems, measuring PM, NO2 and/or BC, through extensive lab- and field benchmarking. The main aim of the study is to identify sensor systems that can be used for mobile monitoring by citizens to assess dynamic exposure in ambient air. After an initial literature study and market search resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked in laboratory and real-word conditions. We evaluated comparability to reference analyzers, sensor precision and sensitivity to temperature, humidity and O3. Moreover, we evaluated if accuracy can be improved by the lab and field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM and BC, but maturity of the tested NO2 sensors is still low and additional efforts are needed in terms of signal noise and calibration. The horizontal accuracy of the build-in GPS was generally good achieving a &lt;10m horizontal accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and exposome impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

This paper proposes a methodology for the development and its Verification and Validation(V&amp;V) of safety-rated software. The development methodology is based on the structured analysis and structured design methodology, which has been proven in the field of safety software development during long period. The suggested the development and the V&amp;V methodology additionally focuses on the quality attributes 3C+T (completeness, Correctness, Consistency and traceability), which are important criteria in IEC international standards. The software development life cycle is compiled from IEC international standardization, which are of the requirements analysis phase, design phase, implementation phase, testing phase, installation/commissioning phase, and operation &amp; maintenance phases. The activities of the development process method were defined from requirements analysis to implementation, and the activities of the verification and validation were defined to include the scope of the developer's activities and then to include the test phase, installation/commissioning phase, and operation phases additionally. In other words, testing phase performed by the developer was defined as informal testing, and testing performed by the verifier according to test criteria, test plans, and test procedures was defined as formal testing. According to proposed methodology, the requirements phase defines the sequence of Entity Relationship Diagram (ERD)-Context Diagram-Data Flow Diagram (DFD), functional decomposition process and defines operational scenario. In the design phase, module design such as task analysis and task allocation were defined. In the implementation phase, we listed the coding guidelines that must be followed for safety-related system. The test phase defined the test criteria, test plan, test procedure, and test result report activities. In the installation/commissioning and operation phases, recommendations for improving reliability, quality, and safety were proposed for deliverables such as user documentation and technical documentation.

Escherichia coli O157:H7, a Shiga toxin-producing E. coli (STEC) is an important pathogen related to foodborne disease that is responsible for a growing number of outbreaks worldwide and has been detected in processed meats, dairy, and fresh vegetables. Although culturing is the gold standard method for detection of this bacteria, molecular methods based on nucleic acid amplification techniques such as PCR are becoming more common because of their rapidity, sensitivity, and specificity. However, to ensure reliable results among the several alternative PCR protocols (e.g., commercial kits and reference methods), different measurement assurance tools, including validated methods, reference materials, and proficiency tests among others, are required. Herein, we present a digital PCR method validation for E. coli O157:H7 detection and quantification using seven gene sequences; this method quantified nucleic acids from different E. coli serotypes, with a detection range between 6.6 to 7,900 copies/µL and a repeatability standard deviation over the concentration range of between 1% to 13.6%. The relative standard uncertainty was 3.5%–14.6%, and the detection limit was 0.27 copies/µL. Subsequently two batches of a candidate reference material based on E. coli O157:H7 genomic DNA were then produced and characterized for evaluation of copy number concentration with the validated dPCR method, with assigned values of 164,770 ± 9,251 and 172 ± 9 copies/μL. Thus, this study demonstrated the development a validated method and reference material for dPCR and qPCR detection of E. coli O157:H7, a key STEC responsible for food poisoning.

This study aimed to translate and validate the Genomic Nursing Concept Inventory (GNCI) in Greek, analyze the genomic knowledge of nurses in Crete, Greece, and explore potential factors that could affect it. This cross-sectional study included 324 nurses from public hospitals and primary care units between July and December 2022. The GNCI is a 31-item questionnaire that evaluates genomic nursing concepts, but it has not been translated and validated in Greek. The translation process included independent forward translations, reconciliation, and back-translation. To evaluate internal consistency, Cronbach’s alpha was used, and Confirmatory Factor Analysis (CFA) was conducted to determine the fit of the model. Multiple linear regression analysis was performed to examine the associations between the demographic variables and GNCI scores. The internal consistency of the GNCI was acceptable (Cronbach’s alpha= 0.622). The CFA indicated that the one-factor model had an adequate fit (SRMR = 0.076, CD = 0.677, CFI = 0.744). The mean GNCI score was moderate (0.35±0.14). Regression analysis suggested that only gender could affect GNCI scores, with female nurses scoring higher than male nurses (p&gt;0.001). In conclusion, the Greek version of the GNCI is a reliable tool for assessing genomic knowledge among Greek nurses.

Poor therapeutic outcomes have been attributed to inter-individual and interethnic variability in cytochrome P450 (CYP450)-dependent metabolism and altered drug retention associated with P-glycoprotein (P-gp). An individualized pharmacotherapeutic approach would benefit the genetically diverse South African population. The study aim was to develop a validated, targeted, analytical method to quantify seven probe drugs and their metabolites in dried blood spots (DBS) using the Mitra™ volumetric absorptive micro-sampling device for blood collection. An Agilent liquid chromatography (LC) system coupled to a Sciex 4000 QTRAP tandem mass spectrometer (MS) was used for method optimization and validation. Targeted LC-MS/MS methods were validated according to ICH guidelines. The validated LC-MS/MS method met the required bioanalytical standards for specificity, sensitivity, linearity, accuracy, precision, carry-over, and stability. This study successfully validated the use of DBS, collected with the Mitra™ microsampling device, to measure expected probe drug and metabolite concentrations using the “Geneva phenotyping cocktail” for the purpose of simultaneous phenotyping of in vivo CYP450 metabolic activity of the CYP1A2, -2B6, -2C9, -2C19, -2D6, and -3A4 enzymes, and P-gp transport activity. However, statistical analysis using Bland-Altman plots showed that not all analytes exhibited linear distribution pharmacokinetics between DBS and plasma, which influenced the predicted vs real plasma concentrations based on measurements in a DBS matrix. These findings were attributed to the blood-to-plasma concentration ratio, the physicochemical properties and stability of the analytes, and the extraction efficiency of the Mitra™ sampler. More research is required before DBS sampling can be substituted for conventional plasma sampling

The Dual-Frequency Precipitation Radar (DPR) on the Core GPM satellite provides spaceborne 3-dimensional observations of the precipitation field and surface rainfall rate with quasi global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and Drop Size Distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR derived products version 07 from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground reference. Four matching techniques between satellite estimates and ground level observations were tested, and best results were found for the so-called optimal comparison approach. Overall, GPM DPR products capture well the variability of the observed DSD at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw are observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, lowest errors were found for radar reflectivity factor and Dm, and highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested and it was found a substantial overestimation of stratiform cases compared to disdrometer observations.

In the United States, several land use and land cover (LULC) data sets are available based on Landsat. These data sets often fail to accurately represent the features on the ground despite having several advantages over other data. Detailed mapping of complex heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences between scenes, low spectral depth of imagery, landscape heterogeneity, and computational limitations. Machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study are to examine and evaluate bagging (Random Forest; RF), Boosting (Gradient Boosting Machines [GBM] and Extreme Gradient Boosting [XGB]), and stacking ensemble models on sentinel 2A fusion data on NAIP imagery. We also compared the accuracy based on random cross-validation (80% / 20% split) without accounting for spatial autocorrelation and target-oriented validation (10 clusters) accounting for spatial structures of the training data set. To make the LULC map of a portion of Tom Green and Irion counties in central Texas, we used a time series of sentinel data and NAIP orthoimagery. We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. The random and target-oriented validation results show that autocorrelation in the training data offers over-optimistic results. In our case, the overestimation ranged from 2 % to 3.5 %. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base-learner. The main contribution of this research is that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use land cover classification using Geographic Object-Based Image Analysis (GEOBIA).

The increasing prevalence of Metabolic Syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of Metabolic Syndrome, thereby addressing a significant concern in public health.

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler Wind Lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth in a polar sun-synchronous orbit at about 320 km altitude with a repeat cycle of 7 days, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. Exceeding scientific expectations, Aeolus, initially designed for a 3-year lifetime, provided global coverage of wind profiles for almost five years. In this long-term validation study, we assessed the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products by collecting radiosonde data from three stations in the Brazilian Amazon: Cruzeiro do Sul (7.62° S, 72.67° W), Porto Velho (8.76° S, 63.91° W), and Rio Branco (10.00° S, 67.80° W). Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data, launched daily at 12:00 UTC from October 2018 to March 2023. Considering all data collected during the mission period, Pearson’s coefficient (R) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy, revealing a strong correlation between Aeolus and radiosonde winds. No significant biases were detected, with values of -0.14 m/s for Rayleigh-clear and -0.40 m/s for Mie-cloud, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was -0.95 m/s, exceeding the mission requirements.

Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, the analysis covers the theoretical and practical aspects of sound attenuation in a turboengine testing stand. This paper presents a preliminary analysis and also an evaluation of a procedure for projecting a noise attenuator for industrial application and especially for turboengine test stands. While primarily focusing on the static acoustic behavior of the attenuator, considerations were also made regarding flow dynamics, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective in selecting the optimal design configuration. The study investigates different calculation methods for assessment of the noise reduction for linear and staggered baffles, applied on a scaled reduced model of attenuator. Thus, the critical parameters and projecting requirements necessary for effective noise reduction in high-performance turboengine testing environments will be evaluated in a down-scaled model. Key factors examined include the selection of design parameters and configurations from various options. Advanced computational methods, like analytic and finite element analysis (FEM) are utilized to predict the acoustic performance and identify potential design optimizations. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The findings indicate that a well-designed sound attenuator module can significantly reduce noise levels without compromising the operational performance of the turboengine inside a test cell.

Introduction: The image of the nurse is a source of concern because of its impact on recruitment to the profession, policy decisions about the profession, funding of nursing services, consumer use of nursing services and how the image of the nurse affects nursing practice. Aim: This study aims to assess the psychometric properties of the Italian version of the Nursing Attitudes Questionnaire (NAQ-IV). Methods: 564 persons between the years 2022 and 2023 with heterogeneous ages and occupations from Northern, Central and Southern Italy participated in the study. Cronbach's alpha, item versus total correlations, skewness and kurtosis were calculated; factor analysis was performed using principal axis factoring and the viramax rotation method. Results: The results showed a Cronbach's alpha of 0.89 on the scale and between 0.88 and 0.89 between the factors; all items tested for correlation between the item and the total and the criteria for variance in the responses. Factor analysis showed a 4-factor model explaining more than 59.91% of the variance, with the largest variance being explained by the 'Role & professionalism' factor (25.96%). Conclusion: These results allow for a discussion regarding the theoretical framework of the NAQ-IV and provide a reliable and valid instrument for the comparison of welfare, organisation of health services, role, professionalism, stereotypes, values, advocacy, motivation and perceived satisfaction with the figure of the nurse and nursing care.

The study conducts a comparative analysis of solar radiation estimation methods, assessing the efficacy of the Heliosat-2 algorithm against ground measurements across seven distinct countries: Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, it utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—spanning the years Jan 2022 to April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median value of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W.m⁻². Notably, the median root mean squared error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W.m⁻², compared to cloudy days where RMSE values lie between 75.3 and 180.2 W.m⁻². In terms of R² values, both satellites show a strong correlation between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviation.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean differences in SSTskin from the SD-1036 and SD-1037 measurements are different by ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, -0.234 K for Aqua and -0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

(1) E-governance is defined as the application of electronic means in the interaction between government and citizens and government and business, as well as in internal government operations to simplify and improve democratic, governmental, and business aspects of governance. Thus, e-governance is built from a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational one such as e-government. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy; (2) Methods: Based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out; (3) Results: After the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy; (4) Conclusions: This research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (&lt;0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

In the construction sector, the integration of biobased materials has emerged as a pivotal aspect of EU policies, aimed at promoting sustainable practices throughout the value chain to mitigate resource consumption and carbon emissions. This paper explores the development and validation activities of a bio-based façade system within the Basajaun H2020 project, focusing on enhancing the utilization of biobased components within building envelopes to replace conventional solutions with eco-friendly alternatives. This paper reports the methodologies employed to detect requirements and outline the testing protocols undertaken to validate the façade system design devised within the project focusing on the original façade components as the bio composite profile. Vision and opaque façade modules are prototyped and tested following curtain wall standards standard for performance and acoustic assessments, to showcase the efficacy of the developed solution. The conducted tests demonstrate the feasibility of integrating bio-based components into building envelopes, aligning with project expectations, and prevailing standards for curtain wall façade solutions. Notably, the designed façade system meets technical conditions and project objectives. Nevertheless, the research underscores the need for further refinements to facilitate solution industrialization and explore broader market applicability. Future efforts will focus on addressing these improvements and assessing the scalability of the solution.

Abstract: The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-45 spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

Biometric fingerprint identification hinges on the reliability of its sensors, however, calibrating and standardizing these sensors poses significant challenges, particularly in regard to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like shaped phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels and in the contactless fingerprint sensor setting, 3D printing and silicone filling provides the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

Background: Visual acuity (VA) assessments are crucial in ophthalmology but traditionally rely on in-clinic evaluations. The emergence of telemedicine has spurred interest in creating dependable self-administered VA tests for use beyond standard clinical environments. This study evaluates the practicality and validity of a self-administered Near VA card test against traditional Snellen and Rosenbaum Pocket Vision Screener (RPVS) methods for home monitoring and enhancing clinical workflow. Methods: In a cross-sectional study a near VA card (Hadassah Self-Visual Acuity Screener-HSVA) was developed with written and videotaped instructions for self-use. Patients with a minimal best corrected VA (BCVA) of 1.0 LogMAR in at least one eye were recruited from Ophthalmology and Optometry clinics. Outcomes included mean BCVA difference between the self-administered and those obtained by the examiner, and correlations among BCVA values obtained by Snellen, RPVS, HSVA and previous distance BCVA according to the patient electronic medical records. Results: A total of 275 participants (mean age: 42.5±19.4 years, range: 18-89 years, 47% female) were included. Test-retest reliability analysis for the HSVA demonstrated very good correlation and repeatability (n=38 patients.; Rs=1.0, P

A handheld NIR spectrophotometric method was developed, validated and applied for determination of tadalafil in tablets. The aim of our work was to develop analytical methods based on vibrational techniques and using low-cost portable equipment. Based on different chemometric modeling, we attempt to validate the method, that gave encouraging results from the principal component analysis (PCA), DD-SIMCA and PLS modeling. Then came optimization using an appropriate experiment plan. For validation, we used the total error approach with acceptance limits set at ±10% with a risk level of 5%. The method showed that it was possible to perform both qualitative and quantitative analysis of pharmaceutical products using low-cost portable NIR systems with chemometric tools. The developed approach enabled to cross the first step in implementing a NIR method for quality control of Tadalafil-based drugs in the DRC. Validation difficulties of PLS method resulted from the lack of information about inter-days serial variations of spectral responses. This would be interesting to extend the study to a larger calibration interval in order to correct uncertainties that may result from the variability observed under different conditions, and to verify robustness. These are the limitations of this work, but the results are nevertheless very encouraging.

To handle the challenges and uncertainties of the actual labor market, the concept of employability becomes increasingly relevant, especially for graduates facing the transition from university to work. Graduate employability is a complex concept comprising different dimensions that may trigger effective job search and career management. This study introduces a new multifaceted tool, labeled the Orientamentis Questionnaire, to assess graduate employability from an agentic perspective, testing its psychometric properties. The Orientamentis questionnaire measures 14 dimensions merged into four 4 areas, namely graduate psychological capital, graduate agentic capabilities, graduate career perspective, and graduate proactive strategies. The tool was administered to a sample of 1238 graduate students (66% females) examining its factorial structure and reliability. To test its criterion validity, associations with three occupational outcomes collected one year later were explored on a subsample of 273 participants (66% females). Results attested the factorial structure of the Orientamentis questionnaire and the reliability of all dimensions. Moreover, the four areas and most of the dimensions were differently related to at least one subsequent occupational outcome. The Orientamentis Questionnaire represents a sound and reliable tool providing a multifaceted profile of graduate employability strengths and weaknesses, that could be used in academic placement programs to foster graduates’ self-awareness and proactive job search.

Background: The adoption of self-care behaviors among patients with congestive heart failure (CHF) is essential for the management of their health condition. However, there is a lack of tools for estimating self-care in CHF patients. We aim to develop and validate the Hippocratic heart failure self-care scale (HHFSCS). Methods: the scale includes 22 items which are reviewed by a committee of experts. Individuals indicate the frequency at which they follow each self-behavior on a five-point Likert scale. Adult patients with CHF (n=250) from a General Hospital, in Athens, were participated in the study from June 2020 to March 2021. Reliability coefficients and Explanatory Factor Analysis (EFA), using a Varimax rotation and the Principal Components Method were used to assess the psychometric measurements. Results: The Cronbach’s alpha coefficient of the HHFSCS was 0.807. The exploratory factor analysis identified two domains that accounted for 88.44% of the variance of the scale items; however, each sub-scale could not be used as an independent scale. Finally, the test-retest showed a significant strong correlation (r=0.973, p&lt;0.001). Conclusions: The HHFSCS is a reliable and valid tool for assessing self-behaviors in CHF patients. Health professionals can use it in their clinical practice to improve the management of patient’s health conditions.

Wang et al. (Nature 623, 340—350; 2023)¹ used satellite observations and phenological signatures to estimate the extent of planted rubber (Hevea brasiliensis Müll.Arg.) and associated deforestation for eight Southeast Asian countries. Their map implies that planted rubber has caused more than 4 Mha of deforestation since 1993 — a value greater than previously assumed. Unfortunately, their approach was flawed. Our assessment indicates that the actual extent of planted rubber is approximately half that reported by Wang et al., while the extent of associated deforestation is approximately one quarter.

This study investigates the application of regression neural networks, in particular the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the effectiveness of the models and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R² of 0.9900. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.

This paper provides an overview of modelling and simulation methods for complex systems, including queuing and life support systems. To realize and capture every aspect of a system or situation, a model is used in place of the actual one. Using a model and simulation, we may create scenarios and make decisions. Computer experiments with the model can save costs, time, and effort once its validity has been shown. A model of the thing under study is created by computer simulation, which then runs the model on a digital computer and examines the results. It's interesting to note that a knowledge base of data includes information about predicted behaviour of the model as well as coordinated determination of external variables and parameters. Selecting the precise model creation technique is the first and most difficult task in simulation. A review of techniques used in simulation model execution (serial and parallel discrete-event simulation), simulation model analysis (calibration, validation, verification, goal-seeking), and conceptual, declarative, functional, constraint, and multimodal design are presented in this paper.

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested by generalizing the uniform shrinkage of ridge regression to the non-uniform shrinkage, which was shown to perform well under sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (the first version published on 2023-12-07 at https://cran.r-project.org/package=g.ridge) that implements both the ridge estimator and generalized ridge estimators. The package equips with the generalized cross-validation for automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with the normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on “glmnet”, and hence, it can be the most recommended estimator under sparse and high-dimensional models. We demonstrate the package using the intracerebral hemorrhage data.

In Nigeria, Cervical cancer (CC) and lack of Human Papillomavirus (HPV) awareness poses significant HPV infection risk among women. This study was conducted at Ibrahim Badamasi Babangida (IBB) University in Nigeria; focused on assessing HPV awareness and knowledge, particularly concerning its association with CC and oral contraceptive (OC) pills among students and staff. We adapted and validated a 20-item HPV knowledge scale and explored factors influencing knowledge, vaccination rates, awareness, and the impact of ethnicity on HPV knowledge. Also, due to the non-normality of the knowledge score, non-parametric tests were done. Data were summarized using median and interquartile range (IQR), while categorical data were presented in frequency and percentage distribution. We established the reliability of the 20-item knowledge scale with a strong Cronbach's alpha coefficient of 0.913, indicating internal consistency. However, it revealed a concerning lack of awareness and knowledge about HPV, only 34.8% of participants were aware of HPV infection, and only 25.0% knew about HPV vaccine. Additionally, ethnicity was identified as a significant factor affecting HPV knowledge. Considering these findings, this study emphasizes the need for targeted interventions to enhance HPV awareness and knowledge, particularly among specific ethnic groups. Despite the development and validation of a reliable knowledge scale, the study underscores the importance of educational initiatives to address this knowledge gap, ultimately reducing the risk of HPV infection and CC in Nigeria.

Background: Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression. Materials and Methods: The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved byscientific and ethical review committees of all participating centers. Sanger sequencing was employed to detect ANKRD36mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.Results: During our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10(8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML. Conclusions: The study confirms ANKRD36 as a novelgenomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

Hydroxyapatite and b-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, there is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing the two models as follows: ‘Model 1’, which predicts material properties from their fabrication conditions, and ‘Model 2’, which predicts the bone-formation rate from material properties and in vivo experimental conditions. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Electroencephalography (EEG) enabled earbuds represent a promising frontier in evaluating brain activity beyond traditional laboratory settings. However, these devices need more comprehensive characterization before widespread health related usage. A toolbox was developed to facilitate and expand the assessment of ear-EEG devices. The first component of this toolbox is a desktop application (“EaR-P Lab”) that integrates EEG validation paradigms. This application is designed to be used with EEG amplifiers compatible with the Lab Streaming Layer (LSL) protocol. The second element of the toolbox introduces an adaptation of the phantom testing concept, to the domain of ear-EEG by using 3D ear scans of the tested individuals, allowing controlled assessment of ear-EEG devices. The EEG paradigms were validated for scalp acquisitions and applied on ear-EEG measurements, under wet ear electrode conditions. The ear-EEG phantom was successful in measuring useful metrics in EEG system characterization, highlighting performance differences within the electrodes on the tested earpieces. This knowledge was factored in to select an optimal electrode reference which resulted in increased response power on the auditory steady-state response (ASSR). Through this work, an ear-EEG evaluation toolkit is made available with the intention to enable future and systematic assessment of novel ear-EEG devices from the hardware to the neural signals.

In this paper, for the Reynolds-averaged Navier-Stokes equations, a self-closed turbulence model without any adjustable parameter is formulated and a simple model is proposed. The validation rule for self-closed turbulence model is rigorously derived from the Reynolds-averaged Navier-Stokes equation. The rule is not effected by turbulence modelling on the Reynolds stresses. As an application, two dimensional turbulent boundary layers, and plate turbulent boundary layers have been formulated.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

This research deals with risk assessment for human life, man-made infrastructure, and agriculture in a landslide-prone area using GIS-based spatial methods. The study area landslide inventory map was prepared based on previous landslide information, aerial photograph analysis, and several field observations. A total of 550 landslides have been included with 182 debris flow and 368 soil slides. All included landslides were classified into two groups by random selection; half were used for model calibration and the rest were used for cross-validation. In the analysis, fourteen causative factors were vastly used, such as aspect, slope, curvature, elevation, topographic wetness index, forest timber diameter, forest type, forest crown density, forest age, land-use, geology, soil drainage, soil depth, and soil texture. Moreover, to identify the interaction between occurred landslides and causative factors, the affected pixels were divided into different sub-classes using a frequency ratio method. Based on the total dataset, three landslide susceptibility maps were constructed using Bayesian prediction, likelihood ratio, and fuzzy set method. By evaluating cross-validation and success rate curve, model susceptibility results were plotted with a receiver operating characteristic (ROC) curve and the area under the curve (AUC) was estimated. In addition, for risk assessment, each social data layer such as agriculture, house, industry, business, road, river, population intensity, monetary value, and vulnerability level was added based on the local standard and incident time and was converted into US dollars. During the analysis, each method hazard map was used with a specific group of thematic data layers. Subsequently, for preparing the probability table, study area total pixels and predictive landslide affected pixels were considered. Matching with the affected pixels, a standard of 5000 pixels was selected to run the final evaluation. Based on the results, the agricultural field showed the highest vulnerability and estimated risk of US $ 16.3 million. Further, the man-made infrastructure map showed a risk of US $ 31.3 million. The total estimated population casualties were 6.77, which was relatively similar to the published data.

Validation of the TROPOspheric Monitoring Instrument (TROPOMI) SIF product is a necessity to evaluate its feasibility in various applications. A few validation works have been conducted through direct comparison with in-situ SIF retrievals or cross-comparison with similar satel-lite-based SIF or vegetation index (VI) products. Nevertheless, the influence of the geolocation mismatch between the validation pixel and reference data on validation results was never con-sidered. This study, for the first time, quantifies the geolocation shift of the TROPOMI validation pixel based on a geometric location matching method and then the uncertainty caused by the ge-olocation mismatch. The results indicate that the geolocation shift of the TROPOMI pixel shows large temporal variations, with a standard deviation of 2.45 km and 1.96 km in the across- and along-track directions. The mean shifts are 0.07 km and 0.25 km in the across- and along-track directions respectively, indicating eastward shifts in the across-track direction and northward shifts in the along-track direction in general. More than half (42/54) of the time periods showed relative uncertainty larger than 5%, with the maximum even reaching 55.7%. Hence, the uncertainty caused by geolocation mismatch should be fully considered in the validation of satellite SIF products, especially over heterogeneous surfaces.

Cyberaggression is an important problem today; it can affect adolescents in different ways. Therefore, reliable and valid measures are necessary to better study the phenomenon. The aim of the present study was to generate validity and reliability evidence for a Spanish-language cyberaggression scale in a population of adolescents from northwestern Mexico. The results of this study contribute to the research and focus on cyberaggression in adolescents in Mexico. The measure in this article differs from other measures by detecting the different roles, including the bystander, contributing to the state of knowledge about cyberaggression, ratifying the presence of this actor that has not been sufficiently explored in the cyber context.

A picture is worth a thousand words. Many branches of Science have been historically benefited with plots and visual analyses (lately, image processing and deep learning) alongside with traditional number crunching. In Molecular Biophysics, one such problem is the structure validation problem in proteins which stands with a history of plot-tools being effectively serving the complex problem to its complete resolution. Spanning across six decades, validation of protein structures (from experimental to modeled) dates back to the legendary Ramachandran Plot (with its never ending growth and modern-day applications) to the relatively recent innovation of the Complementarity Plot (CP), establishing the dual nature of complementarity as the physical basis of both binding and folding of proteins. Lately, CP has been extended to serve as a trustworthy free energy predictor utilizing supervised learning in the form of a comprehensive web-server (EnCPdock: https://www.scinetmol.in/EnCPdock/) that can be directly used in the design of protein interfaces. The commentary recapitulate the history of structure validation with a special emphasis on plot tools, highlighting key features and important discoveries worth re-visiting.

Recent findings have demonstrated an increase in the prevalence of childhood obesity and overweight in Portugal, urging the need to study modifiable risk factors such as parental feeding practices. The Comprehensive Feeding Practices Questionnaire (CFPQ) is an important self-report measure assessing a broad range of responsive and non-responsive feeding practices. However, the CFPQ has not yet been validated in Portugal. Therefore, the present study aimed to test the validity of this measure with Portuguese parents of 2- to 8-year-old children. A sample of 409 parents completed a Portuguese-adapted version of the CFPQ and the already validated Child Feeding Questionnaire (CFQ). Confirmatory Factor Analysis (CFA), Exploratory Factor Analysis (EFA), and psychometric analysis were conducted. CFA demonstrated the original 12-factor structure did not fit the sample. EFA identified an 8-factor structure, comprising 29 items: Monitoring, Modeling, Environment, Involvement, Emotion Regulation, Restriction for Weight Control, Restriction for Health, and Pressure. Findings suggest that parental feeding practices are sensitive to parents’ background culture and children’s developmental period.

Deep learning is based on matrix computing with a large amount of hidden parameters that is not visible outside the computing module. Deep learning is a non-linear system and a linear approach is not possible. It is natural for people to visualize the algorithm and to follow some hidden parameters. In this paper, we propose a simple graphical programming of a nonlinear system based on drawing the simplest unit, such as a single neuron model. A more complex scheme of a classical neural network is obtained by commands copy-move-place of one neuron. The number of neurons and layers can be chosen arbitrarily. Once the scheme is complete, the implementation code is evaluated with symbolic parameters and nonlinear activation functions. This cannot be done manually. With the symbolic expression of outputs in terms of inputs and symbolic parameters, including symbolic activation pure functions, some other properties can be derived in closed form. This unique original approach can help scientists and developers and design numerical algorithms for machine learning and to understand how deep learning algorithms work.

Addressing nautical bottlenecks is crucial to optimize the utilization of inland waterways and maximize economic benefits of transports. To maximize economic benefits, a study was conducted to validate a Key Performance Indicator (KPI)-based framework. This framework offers a structured approach to assess the impact of resolved nautical bottlenecks on the economic benefits of inland waterway transport (IWT). To validate the applicability of the KPI framework, interviews with eleven experts were conducted. The goal was to prioritize each KPI based on their insights. The results of the interviews shed light on the relevance and coherence of both the individual KPIs and the overall KPI framework. The experts confirmed the importance of measures related to transportation efficiency, such as reduced transit times, increased vessel throughput, and enhanced reliability. The validated KPI-based framework serves as a valuable tool for policymakers, industry stakeholders, and researchers. It enables the assessment of the effects of resolving nautical bottlenecks in inland waterway systems. Future research should focus on quantifying the multifaceted impacts, making this framework even more useful for decision-making processes concerning investments in infrastructure upgrades and maintenance.

Background: Assessing body image and personalized rehabilitation in lower limb amputation is essential to measure impact on an individual’s quality of life and psychosocial wellbeing. The aim of this study was to develop a Spanish version of the Amputee Body Image Scale (ABIS-E) for Spanish-speaking lower limb amputee. Methods: A cross-sectional study was conducted including amputee patients in local in Madrid, Spain. Clinical outcomes about body image (Amputee Body Image Scale, ABIS), health-related quality of life (EuroQol-5D-5L), depression and anxiety (Hospital Anxiety and Depression Scale, HADS), pain (Brief Pain Inventory-Short Form, BPI-SF) and functional capacity (Prosthesis Evaluation Questionnaire, PEQ) were collected. Results: Seventy-three participants were assessed. Excellent test-retest reliability (ICC=0.847) and good internal consistency (Cronbach's alpha=0.753) were obtained. Three factors were extracted in the factor analyses. Convergent validity with Pearson's and Spearman's correlation coefficients were calculated for the depression and anxiety questionnaires (HADS) (values between .57 and .67), functional capacity (PEQ) (values between -.35 and .71) and quality of life (EQ-5D-5L) (values between -.37 and .61). Conclusions: The ABIS-E is a reliable and valid tool for measuring body image in the Spanish population with a lower limb amputation, potentially useful in primary care to identify psychosocial problems.

The Individual Work Performance Questionnaire (IWPQ) can be defined as the individual be-haviour capable of generating value and competitive advantage for the organization. It is a mul-ti-dimensional construct, consisting of task performance, contextual performance and counter-productive work behavior. Albeit important, a comprehensive measure of individual job per-formance is still lacking in the Italian context. This paper describes the validation of an Italian adaptation of the IWPQ. A back translation procedure was used. To investigate the psychometric properties of the 18-item Italian IWPQ, 1053 participants (502 men, 47.7%; 551 women, 52.3%) were enrolled; the age of participants ranged between 19 and 69 (Mage=34.6, SD= 11.8), and for the oc-cupation, the sample is classified in blue collar 32.5%, pink collar 33% and white collar 34.5%. Exploratory and confirmatory factor analyses and multigroup confirmatory factor analyses were performed to test the structural factor of the IWPQ. The results supported the goodness of the IWPQ in the Italian context and the scales were found consistent with the original structure. One item was omitted due to a low factor loading, resulting in a final 17-item structure. Multigroup confirmatory factor analysis showed that the factor solution was invariant across both gender and occupational category and found evidence of metric invariance, uniqueness and scalar invariance, and structural invariance. Convergent and discriminant validity were also tested and showed.

In this analysis, we assess the performance of GSMaP-GNRT6 data in capturing precipitation climatology and climate variability across China from 2001 to 2020. The evaluation involves comparing four precipitation indices of accumulated precipitation, the number of rainy days, rainstorm days, and precipitation maximum with the daily precipitation data from 2419 stations in China. The findings reveal that the GSMaP data effectively captures the overall spatial distribution of annual, summer, and monthly precipitation. However, it exhibits minor limitations in accurately depicting the spatial distribution of the number of rainy days from July to September and the precipitation maximum during wintertime in eastern China. While the GSMaP data depicts a coherent annual cycle consistent with the station observations, a general underestimation is observed. Notably, the GSMaP data presents a much smoother annual cycle in precipitation maximum. Regarding accumulated precipitation, the number of rainstorm days, and precipitation maximum, the GSMaP data shows an almost consistent interannual variation and increasing trends, aligning with station observations. However, it is noteworthy that the magnitude of the increasing trend based on the GSMaP data is greater, especially entering the early 21st century. Conversely, a significant discrepancy in the annual variation and an almost opposite changing trend are noticed in the number of rainy days between the GSMaP data and station observations.

Background. ADHD is a neurodevelopmental disorder that is accompanied by executive deficits. Objectives. To obtain evidence of the usefulness of the BRIEF-P and to analyse the possible ceiling and floor effect of its scores in the assessment of executive function in preschoolers with signs compatible with a possible diagnosis of ADHD. Methods. Seven articles were selected from 2538 potentially eligible articles from different databases. We included studies assessing samples of individuals with symptoms compatible with ADHD, with age range 2-6 years, published in English or Spanish, during the period 2012-2022. References published in: Science Direct, NCBI (PubMed), ProQuest Education Journals in the last decade. Risk of bias was assessed using the QUADAS-2 questionnaire. The main variables were: age and executive functioning. Conclusions. Executive deficits in early-life individuals with ADHD-compatible symptoms are more extensive than deficits in working memory. BRIEF-P is an instrument that facilitates obtaining a sensitive and discriminative executive profile. It should be used in combination with other neuropsychological performance tests, finding a floor effect in tests associated with warm executive functions and a ceiling effect in cold executive functions. This requires using a variety of tests to assess executive performance in preschoolers with symptoms compatible with ADHD and designing intervention proposals in line with them...

The integration of bioinformatics in drug discovery has revolutionized the field of pharmaceutical research. The use of computational tools and techniques has enabled researchers to analyze vast amounts of data, identify potential drug targets, and design new drugs with greater precision and efficiency. The integration of bioinformatics has also facilitated the development of personalized medicine, where drugs can be tailored to individual patients based on their genetic makeup. However, there are still challenges that need to be addressed, such as the need for more accurate predictive models and the ethical considerations surrounding the use of patient data. Overall, the integration of bioinformatics in drug discovery holds great promise for improving human health and advancing our understanding of disease mechanisms. In this mini-review, we discuss how Bioinformatics plays a crucial role in each step of drug discovery by providing tools and techniques to analyze large amounts of data generated from various sources, as well as the challenges and the opportunities offered by bioinformatics.

The study of coordination practices in health policy and, especially in emergency situations is a central aspect. The COVID-19 pandemic has highlighted the need for further research on such practices, especially those that are deployed in complex and unforeseen situations to respond quickly while minimising errors. These dialogic coordination practices (DP) have been identified through case studies but have not been validated. The purpose of this study is to develop and validate a DP questionnaire for healthcare teams. The development of the items was based on a literature review and the content validation was carried out by means of a Delphi study. A total of 10 experts assessed the clarity and appropriateness of the items and their corresponding measurement scales. Consensus was reached after the second round, with a percentage of agreement greater than or equal to 90% for all items. The validation of these items constitutes a methodological innovation that responds to the call in the literature to open up new avenues for comparative studies, the possibility of generalising the findings obtained and the possibility of bringing together different approaches to the problem of coordination, key in health policy.

Cognitive reserve is the adaptability of cognitive processes in the face of brain aging and pathology. This study aimed to validate the Arabic version of the Cognitive Reserve Index Questionnaire (CRIq) in a healthy Lebanese sample. CRIq assesses cognitive reserve through three domains: education, working activity, and leisure time. Statistical measures, including descriptive and regression analysis along with structural equation modeling, were utilized to investigate the convergent and discriminant validity of CRIq, incorporating fluid intelligence (Gf) and measures of cognitive function, long-term memory encoding and retrieval (Glr), and processing speed (Gs). Results from 174 participants revealed that the activities assessed by CRIq-Arabic were comparable to the original CRIq study, with slight cultural differences. The internal consistency of CRIq-Arabic was good (Cronbach's α = 0.88), indicating reliability. Convergent validity was confirmed, with moderate to high loadings on the cognitive reserve latent construct. Discriminant validity was supported as correlations between cognitive reserve variables and non-target constructs (Gf, Glr, Gs) were less than 1. The findings provide an initial psychometric validation of the CRIq-Arabic. Further research in clinical samples is needed to enhance its utility in neuropsychological practice.

Accurately glacier mapping is crucial for understanding climate change impacts, but existing efforts may be biased due to overlooking spatial autocorrelation during map validation. To address this, we compared several widely used machine learning algorithms as gradient boosting machines (GBM), k-nearest neighbor (KNN) and random forest (RF) with parametric logistic regression (GLM) and an unsupervised remote sensing-based method (NDSI) for mapping Peru's glacier regions in a thoughtful experimental setup. Spatial and non-spatial cross-validation methods were used to evaluate model’s performance and compared with a fully independent test set. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results when compared to independent test set, emphasizing the need to consider spatial autocorrelation when using machine learning for glacier mapping. K-nearest neighbors (KNN) was the overall best model across regions consistently demonstrating the highest performance followed by logistic regression (LR) and gradient boosting machines (GBM). Our novel validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a significant challenge to global wheat production. Common wheat landraces AUS27507 and AUS27894 displayed stripe rust resistance against several commercially prevailing Pst pathotypes. These genotypes were crossed with a stripe rust susceptible landrace AUS27229 to understand inheritance of resistance and to determine genomic location(s) of underlying gene(s). F3 generations of crosses AUS27507/AUS27229 and AUS27894/AUS27229 showed monogenic segregation for stripe rust resistance under greenhouse conditions. The absence of segregation for stripe rust response among the AUS27507/AUS27894-derived F3 population suggested that both genotypes carry the same gene. The stripe rust resistance gene carried by AUS27507 and AUS27894 was tentatively named YrAW4. Bulked segregant analysis placed YrAW4 in the long arm of chromosome 2B. The AUS27507/AUS27229 F3 population was enhanced to develop an F6 recombinant inbred line (RIL) population for detailed mapping of chromosome 2BL. DArT-based SSR, STS and KASP markers were employed to enrich the 2BL map. DArT-based STS markers sun481 and SNP marker IWB12294 flanked YrAW4 proximally (1.8 cM) and distally (1.2 cM), respectively. Deletion mapping placed sun481 in the deletion bin 2BL-5. All stripe rust resistance genes, previously located on chromosome 2BL, neither express infection type like YrAW4, nor are mapped in the deletion bin 2BL-5. Hence YrAW4 represented a new locus and was formally named Yr72. The robustness of IWB12294 marker in marker-assisted selection was demonstrated by amplification of and alternative allele to that linked Yr72 in 21 common wheat background.

Women's empowerment represents a process in which they are recognized as subjects of rights. Despite advances in women's rights, inequalities persist in various areas. This study aims to validate a psychometric scale of 31 items in seven factors: Participatory Empowerment, Temerity, External Influences, Independence, Social Satisfaction and Equality. The objective is to measure the empowerment of women in the university context. The research was carried out in a transversal and non-experimental manner, with the participants' voluntary collaboration, including the participation of 1,478 university students from four Higher Education Institutions in Zone 4 of Ecuador. The data was analyzed through inferential statistics, including exploratory and confirmatory factor analysis. The results indicated that the instrument for measuring empowerment can be made up of 24 items grouped into six factors, and this model showed a good fit.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

RANS simulations have been broadly used to investigate turbulence in the oceans and atmosphere. Within these environments there are a multitude of tracers undergoing reactions (eg. phytoplankton growth, chemical reactions). The distribution of these reactive tracers is strongly influenced by turbulent mixing. With a 50 member ensemble of two dimensional Rayleigh-Taylor induced turbulent mixing, we show that the dynamics of a reactive tracer growing according to Fisher’s equation are poorly captured by the mean. A fluctuation-dependent sink introduced by Reynolds averaging Fisher’s equation transfers concentration from the mean to the fluctuations. We compare the dynamics of the reactive tracer with those of a passive tracer. The reaction causes the concentration of reactive tracer to increase thereby increasing Fickian diffusion and allowing the reactive tracer to diffuse into turbulent structures that the passive tracer cannot reach. A positive feedback between turbulent mixing and fluctuation growth is identified. We show that eddy viscosity and diffusivity parameterizations fail to capture the bulk trends of the system and identify a need for negative eddy diffusivities. One must therefore be cautious when interpreting RANS results for reactive tracers.

Eustachian Tube Dysfunction is considered a common condition among ENT patients and requires careful history, clinical examination and appropriate investigations to obtain a diagnosis. The ETDQ-7 questionnaire is a useful tool to subjectively score severity of symptoms that are related to this pathology (Appendix A). This study aimed to adapt and validate the ETDQ-7 questionnaire in Greece to ensure correct and efficient use in the outpatient setting (Appendix B). The ETDQ-7 was translated into the Greek language following appropriate methodology. Data for the main study were collected from a sample of 75 Greek patients diagnosed with ETD as well as 25 patients that did not have ETD and who served as a control group. The participants completed the adapted ETDQ-7 questionnaire and underwent a clinical examination that was statistically correlated with their ETDQ-7 answers. Face and content validity were confirmed and the questionnaire was found easy to administer and to be completed by our patients. Test-retest reliability revealed similar internal consistency for the questions and good correlation between individual items and total score. Discriminative validity confirmed statistically significant difference between the two patient groups to ensure that the Greek version of ETDQ-7 is useful to confirm the diagnosis. The Greek version of the ETDQ-7 is safe and efficient to use among a variety of investigation methods for the diagnosis of ETD in the Greek speaking population.

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote sensing technology has demonstrated a great potential in monitoring soil moisture at a scale greater than 1 km. This capability can be applied to improve weather forecast accuracy, enhance water management for agriculture, and climate-related disasters. Despite the techniques increasing used worldwide, its accuracy still requires field validation in specific regions like Thailand. In this paper, we report on extensive in-situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand spanning the years 2021 to 2023. The aim was to validate SMAP surface soil moisture (SSM) Level 2 product over a period of two years. Using one month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physics properties. These soil properties were then used for estimating the correlation constants between SMAP and in-situ soil moistures using multiple linear regression. The results demonstrated R-squared values between 0.933 and 0.847. An upscaling approach of SMAP was proposed which showed a promising results when using 3-month average of all measurements in cropland together. The finding also suggest that the SMAP-Sentinel remote sensing technology exhibits significant potential for accurate soil moisture monitoring in diverse applications. Further validation efforts and research, particularly in terms of root zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology’s effectiveness and usefulness in the region.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of adf. Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses edf and the so-called dvm to effectively quantify deviations between distributions. By applying this metric, the study measures the consistency, reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behaviour under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.

Obstetric Violence refers to dehumanized or derogative treatment of women in their pregnancy, childbirth or postpartum periods and may be manifested through the attitudes of health professionals or the performance of unjustified or outdated practices without maternal consent. Currently, there is no tool validated in Spain to measure women’s perception of obstetric violence. The objective of this study was to carry out the cultural adaption and validation of an existing 14-item Obstetric Violence Scale to the Spanish context, and to evaluate its psychometric properties. The research was conducted in two phases: first, a methodological study designed to evaluate content validity, through assessments by 8 experts (calculating the Aiken V coefficient) and face validity in a sample of 20 women; second, a cross-sectional study to evaluate construct validity (through confirmatory factor analysis and Rasch analysis), divergent validity against a scale of birth satisfaction, known groups validity and reliability. In Phase 1, Aiken V values higher than 0.71 were obtained for all items. Phase 2 was conducted on a sample of 256 women and the fit values for the unidimensional model were RMSEA: 0.070 [95%CI: 0.059-0.105] and GFI: 0.982 [95%CI: 0.823-0.990]. The Rasch analysis indicated poor performance of item 2, which was removed. The Omega and Cronbach's Alpha coefficients were 0.863 and 0.860, respectively. A final 13-item version of the Obstetric Violence scale was produced, with total score ranging from 0 (no obstetric violence perception) to 52 (maximum obstetric violence perception). The Obstetric Violence Scale is a reliable and useful tool to measure women's perception of obstetric violence.

AlphaFold is a groundbreaking Deep Learning tool for protein structure prediction. It achieved remarkable accuracy in modeling the many 3D structures while taking as the input only the known amino acid sequence of proteins in question. Intriguingly though, in the early steps of each individual structure prediction procedure, AlphaFold is not respecting topological barriers that in real proteins result from the reciprocals impermeability of polypeptide chains. This study aims to investigate how this non-respecting of topological barriers affects AlphaFold predictions with respect to the topology of protein chains. We focus on such classes of proteins that during their natural folding form reproducibly the same knot type on their linear polypeptide chain as revealed by their crystallographic analysis. We use partially artificial test constructs in which the mutual non-permeability of polypeptide chains should not permit the formation of such knots during natural protein folding. We find that despite the formal impossibility that the protein folding process could produce such knots, AlphaFold predicts these proteins to be knotted. Our study underscores the necessity for cautious interpretation and further validation of topological features in protein structures predicted by AlphaFold.

As an established and widely available infrastructure, wireless local area networks (WLANs) have emerged as a viable option for indoor localization of both mobile and stationary users. In planning a mobile communications network and radio system design, the critical role of coverage prediction becomes evident, empowering network operators to optimize cellular networks and elevate the overall customer experience. Moreover, WLANs present several challenges that must be fulfilled when it comes to localization based on Wi-Fi signals to get a proper coverage prediction map. This paper presents a study based on application of the extra trees regression (ETR) for indoor localization by using coverage prediction maps. The aim of the proposed method is to accurately estimate a user’s position within a radio environment map (REM) area using collected received signal strength indicator (RSSI) values. The proposed scheme investigates several machine learning (ML) regression algorithms for localization, where the training dataset is obtained from the REM by using the nearest neighbors method. Parameter tuning is conducted to optimize the performance of the ETR scheme by using 10-fold cross-validation. In the numerical results, we first demonstrate the effectiveness of utilizing ML regression techniques for generating coverage maps, which enables accurate estimation of the Wi-Fi signal strength in indoor environments. Then, we showcase the superior performance of the proposed ETR-based method compared to several other ML schemes for indoor localization using the REM. ML algorithms, including decision tree regression and the ETR, are compared to evaluate the system model. Based on error metrics, the proposed ETR-based approach exhibits the best performance among the evaluated techniques. The combination of coverage map generation and localization using regression techniques offers a powerful approach for analyzing the radio frequency (RF) environment in indoor spaces.