Search Results (8,350)

Background/Objective: SeptiCyte RAPID is a transcriptional host response assay that discriminates between sepsis and non-infectious systemic inflammation (SIRS) with a one-hour turnaround time. The overall performance of this test in a cohort of 419 patients has recently been described [Balk et al., J Clin Med 2024, 13, 1194]. In this study, we present the results from a detailed stratification analysis in which SeptiCyte RAPID performance was evaluated in the same cohort across patient groups and subgroups encompassing different demographics, comorbidities and disease, sources and types of pathogens, interventional treatments, and clinically defined phenotypes. The aims were to identify variables that might affect the ability of SeptiCyte RAPID to discriminate between sepsis and SIRS and to determine if any patient subgroups appeared to present a diagnostic challenge for the test. Methods: (1) Subgroup analysis, with subgroups defined by individual demographic or clinical variables, using conventional statistical comparison tests. (2) Principal component analysis and k-means clustering analysis to investigate phenotypic subgroups defined by unique combinations of demographic and clinical variables. Results: No significant differences in SeptiCyte RAPID performance were observed between most groups and subgroups. One notable exception involved an enhanced SeptiCyte RAPID performance for a phenotypic subgroup defined by a combination of clinical variables suggesting a septic shock response. Conclusions: We conclude that for this patient cohort, SeptiCyte RAPID performance was largely unaffected by key variables associated with heterogeneity in patients suspected of sepsis. Full article

This paper addresses the challenge of objectively determining a website’s personality by developing a methodology based on automated quantitative analysis, thus avoiding the biases inherent in human surveys. Utilizing a database of 3000 websites, data extraction tools gather relevant data, which are then analyzed using Artificial Intelligence (AI) techniques, including machine learning (ML) and natural language processing. Four ML algorithms—K-means, Expectation Maximization, Hierarchical Agglomerative Clustering, and DBSCAN—are implemented to assess and classify website personality traits. Each algorithm’s strengths and weaknesses are evaluated in terms of data organization, cluster flexibility, and handling of outliers. A software tool is developed to facilitate the research process, from database creation and data extraction to ML application and results analysis. Experimental validation, conducted with identical training and testing datasets, achieves a success rate of up to 94% (with an Error of $\le 50\%$) in accurately identifying website personality, which is validated by subsequent surveys. The research highlights significant relationships between website attributes and personality traits, offering practical applications for website developers. For instance, developers can use these insights to design websites that align with business goals, enhance customer engagement, and foster brand loyalty. Additionally, the methodology can be applied to creating culturally resonant websites, thus supporting New Zealand’s cultural initiatives and promoting cross-cultural understanding. This research lays the groundwork for future studies and has broad applicability across various domains, demonstrating the potential for automated, unbiased website personality classification. Full article

Research by scientists dealing with sustainable development issues in the aviation industry security focuses on finding solutions that constitute the so-called ‘golden mean’ between appropriate efficiency and high levels of system safety and reliability (including human reliability). The features mentioned above have been repeatedly investigated in various studies, but always individually—to date, no one has proposed a solution indicating the balance point of all the abovementioned features. Here we propose a solution to this research gap: a model for assessing the configuration of airport security control systems. The model allows for the optimal configuration of airport security control systems. The multi-level model validation presented in the article was performed, among others, based on one of the airports in Poland, and showed that the correct configuration of the system can bring energy savings of 913,500 kWh/year in the case of large international airports. Additionally, the article discusses all solutions and modern technologies equipped with devices supporting the passenger and baggage screening process. Full article

Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure–property relationships. Full article

Let $f_n$ be the non-parametric kernel density estimator based on a kernel function K and a sequence of independent and identically distributed random vectors taking values in ${\mathbb{R}}^d$. With some mild conditions, we establish sharp moderate deviations for the kernel density estimator. This means that we provide an equivalent for the tail probabilities of this estimator. Full article

In this study, we propose a fire classification system using image clustering based on a federated learning (FL) structure. This system enables fire detection in various industries, including manufacturing. The accurate classification of fire, smoke, and normal conditions is an important element of fire prevention and response systems in industrial sites. The server in the proposed system extracts data features using a pretrained vision transformer model and clusters the data using the bisecting K-means algorithm to obtain weights. The clients utilize these weights to cluster local data with the K-means algorithm and measure the difference in data distribution using the Kullback–Leibler divergence. Experimental results show that the proposed model achieves nearly 99% accuracy on the server, and the clustering accuracy on the clients remains high. In addition, the normalized mutual information value remains above 0.6 and the silhouette score reaches 0.9 as the rounds progress, indicating improved clustering quality. This study shows that the accuracy of fire classification is enhanced by using FL and clustering techniques and has a high potential for real-time detection. Full article

Farmers today are using biochemical treatments to improve their crop yields. Commercialized organic biostimulants exist in the form of pyroligneous acid generated by burning agricultural waste products. During the 2023 growing season, we demonstrated that soil treatment with a commercial pyroligneous acid product, Coriphol™, manufactured by Corigin Solutions, Inc., stimulated plant growth and significantly improved yield with an optimal treatment dose of 2 gal. acre⁻¹. In the present work, we examined the effect of this treatment on soybean nutritional content using seed harvested from the 2023 season. Total mean seed protein content for untreated control plants was 32.26 ± 0.49% of dry mass and increased 10.8% to 35.64 ± 0.64% with treatment. This increase resulted in a net reduction in total free amino acid content, although levels of the essential dietary amino acid, lysine, were boosted 6-fold. Total lipid content was unaffected by treatment with mean levels of 21.61 ± 0.70% of dry mass noted. Treatment, however, reduced saturated fatty acid content by roughly 40%, and reduced the polyunsaturated content of linoleic acid in favor of the monounsaturated fatty acid, oleic acid. Finally, Coriphol™ treatment did not impact seed content of eight essential micronutrients including Na, Mg, K, Ca, Fe, Ni, Cu, and Mo, but did significantly boost Zn and Mn levels. Altogether, these results demonstrate that soil treatment with the growth stimulant Coriphol™ has the potential to improve the dietary nutritional value of soybean. Full article

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development. Full article

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management. Full article

Over the last decade, hardware-in-the-loop (HIL) simulation has been established as a safe, efficient, reliable, and flexible method for performing real-time simulation. Furthermore, in the automotive sector, the HIL system has been recommended in the ISO 26262 standard as a powerful platform for performing realistic simulation during system integration testing. As a result of performing HIL black-box tests, the results of executing test cases (TCs) are reported as pass/fail without determining the nature and root causes of the underlying failures. The conventional analysis process of the failed TCs relies on expert knowledge. The higher the number of failed TCs, the higher the cost of manual analysis in terms of time and effort. In light of the shortcomings of existing methodologies, this study presents a novel intelligent framework capable of analyzing failed TCs without the need for expert knowledge or code access. To this end, a convolutional auto-encoder-based deep-learning approach is proposed to extract representative features from the textual description of the failed TCs. Furthermore, k-means-based clustering is used to categorize the extracted features according to their respective failure classes. To illustrate the effectiveness and validate the performance of the proposed method, a virtual test drive with real-time HIL simulation is presented as a case study. The proposed model exhibits superior clustering performance compared to other standalone k-means algorithms, as demonstrated by the David Bouldin Index (DBI) and accuracy values of 0.5184 and 94.33%, respectively. Furthermore, the proposed model shows a significant advantage in terms of feature extraction and clustering performance compared to the current state-of-the-art fault-analysis method. The proposed approach not only supports the validation process and improves the safety and reliability of the systems but also reduces the costs of manual analysis in terms of time and effort. Full article

(1) Background: The kick of the ball in soccer is considered one of the most important technical gestures in soccer. Despite this, there is little evidence on ball-striking performance factors in base soccer. The main objectives of the present study were to identify the potential factors of long-ball kicking (LBK) performance and to determine the target training cut-off for LBK performance in young soccer players. (2) Methods: A cross-sectional observational study was conducted with 31 soccer players, with ages ranging from 12 to 18 years. Age, anthropometric data, sport experience, range of motion (ROM) and maximal isometric strength (MIS) of the lower limb were noted. Kick-of-the-ball performance was assessed by maximum ball displacement per kick. A k-mean cluster analysis determined two groups according to ball-kicking performance: low group (LPG-LBK) and high group (HPG-LBK). (3) Results: Differences were found between both groups in age, body mass, body mass index, leg length and knee flexion ROM (BF10 ≤ 6.33; δ ≥ 0.86 (moderate or higher)). Among the factors discussed above, age was the strongest predictor of ball-striking performance (odds ratio = 2.867; p = 0.003). The optimal cut-off for age predicting those players most likely to have a higher ball-striking performance was determined to be 13.5 years (p = 0.001; area under the curve = 85.3%). (4) Conclusions: Age over 13.5 increases the chances of a higher optimal ball-striking performance. The flexibility (knee flexion ROM) and strength (knee flexors) must be specifically trained in soccer players beginning at an early age. Full article

N-glycans have recently emerged as highly varied elements of Chlorella strains and products. Four years and eighty samples later, the increasing N-glycan diversity calls for a re-examination in the light of concepts of species designations and product authenticity. N-glycans of commercial products were analyzed by matrix-assisted time-of-flight mass spectrometry (MALDI-TOF MS) supported by chromatography on porous graphitic carbon with mass spectrometric detection. Although 36% of 172 products were labeled C. vulgaris, only 9% presented what could be taken as a C. vulgaris type N-glycan pattern. Respectively, 5 and 20% of the products matched with C. sorokiniana strains SAG 211-8k and SAG 211-34, which, however, carry entirely different structures. Furthermore, 41% presented with one of four frequently occurring glyco-types while 26% of the samples showed unique or rare N-glycan patterns. These glycan signatures thus profoundly challenge the stated species designations. By no means do we want to question the presumed health benefits of the products or the sincerity of manufacturers. We rather aim to raise awareness of the fascinating but also concerning diversity of microalgal N-glycans and suggest it as a means for defining product identity and taxonomic classifications. Full article

Background: Albuminuria and albumin excretion rate (AER) are important risk factors for chronic kidney disease (CKD) development. Despite the extensive evidence of the influence of sodium and potassium on cardiovascular health, the existing evidence regarding their impact on albuminuria and kidney disease is limited and inconsistent. Our study aimed to assess the correlation between urinary sodium and potassium excretion, and the sodium-to-potassium ratio (Na/K ratio) with impaired kidney function, particularly the AER and albuminuria. Materials and Methods: Data were collected from the Lithuanian NATRIJOD study. A total of 826 single 24-h urine samples from individuals aged 18 to 69 were collected and analyzed for their sodium and potassium levels, Na/K ratio, and AER. Albuminuria was defined as an AER exceeding 30 mg/24 h. Results: The participant mean age was 47.2 ± 12.1 years; 48.5% of the participants were male. The prevalence of albuminuria was 3%. Correlation analysis revealed a positive correlation between AER and urinary sodium excretion (r_s = 0.21; p < 0.001) and urinary potassium excretion (r_s = 0.28; p < 0.001). In univariate linear regression analysis, sodium and potassium excretion and the Na/K ratio were significant AER predictors with β coefficients of 0.028 (95% CI: 0.015; 0.041; p < 0.001), 0.040 (95% CI: 0.003; 0.077; p = 0.035), and 1.234 (95% CI: 0.210; 2.259; p = 0.018), respectively. In the multivariable model, only urinary sodium excretion remained significant, with a β coefficient of 0.028 (95% CI: 0.016; 0.041). Potential albuminuria predictive factors identified via univariate logistic regression included urinary sodium excretion (OR 1.00; 95% CI: 1:00; 1.01) and the Na/K ratio (OR 1.53; 95% CI: 1.11; 2.05). However, these factors became statistically insignificant in the multivariate model. Conclusions: Urinary sodium and potassium excretion and the Na/K ratio are significantly associated with kidney damage, considering the assessed 24-h albumin excretion rate and presence of albuminuria content. Full article

If toddlers are not promptly checked and rescued after falling from relatively high locations at homes, they are at risk of severe health complications. We present a toddler target extraction method and real-time falling alarm. The procedure is executed in two stages: In stage I, a GELAN-integrated YOLOv8 model is used to extract the body features. Based on this, a head capture technique is developed to obtain the head features. In stage II, the “safe zone” is calculated through Generalized Hough Transform (GHT). The spatial location is compared to the preceding stage’s two centers of mass points, K for the toddler’s body and H for the head. Position status detection is performed on the extracted data. We gathered 230 RGB-captured daily videos of toddlers aged 13 to 30 months playing and experiencing upside-down falls. We split 500 video clips (×30 FPS) from 200 videos into 8:2 training and validation sets. A test set of 100 clips (×30 FPS) was cut from another 30 videos. The experimental results suggested that the framework has higher precision and recall in detection, as well as improved mean average precision and F1 scores compared to YOLOv3, v5, v6, and v8. It meets the standard FPS requirement for surveillance cameras and has an accuracy of 96.33 percent. Full article

Electrochemically anodized TiO₂ nanotube arrays (NTAs) were used as a support material for the electrodeposition of zinc nanoparticles. The morphology, composition, and crystallinity of the materials were examined using scanning electron microscopy (SEM). Electrochemical impedance spectroscopy (EIS) was performed to evaluate the electrochemical properties of TiO₂ NTAs. Annealing post-anodization was shown to be effective in lowering the impedance of the TiO₂ NTAs (measured at 1 kHz frequency). Zinc nanohexagons (NHexs) with a mean diameter of ~300 nm and thickness of 10–20 nm were decorated on the surface of TiO₂ NTAs (with a pore diameter of ~80 nm and tube length of ~5 µm) via an electrodeposition process using a zinc-containing deep eutectic solvent. EIS and CV tests were performed to evaluate the functionality of zinc-decorated TiO₂ NTAs (Zn/TiO₂ NTAs) for glucose oxidation applications. The Zn/TiO₂ NTA electrocatalysts obtained at 40 °C demonstrated enhanced glucose sensitivity (160.8 μA mM⁻¹ cm⁻² and 4.38 μA mM⁻¹ cm⁻²) over zinc-based electrocatalysts reported previously. The Zn/TiO₂ NTA electrocatalysts developed in this work could be considered as a promising biocompatible electrocatalyst material for in vivo glucose oxidation applications. Full article