Search Results (1,044)

Background/Objectives: Unlike rhabdomyolysis and neuroleptic malignant syndrome (NMS), massive asymptomatic creatine kinase elevation (MACKE) represents a condition commonly detected during routine screening in patients receiving antipsychotic drugs. In particular, current evidence indicates a greater incidence of this condition in patients without signs of NMS, rhabdomyolysis, or other causes of CK increase during exposure to second-generation antipsychotics (SGAs) than first-generation antipsychotics (FGAs) with a variable onset and duration. Although its pathophysiology is still not fully elucidated, MACKE has usually been recognized as a self-limiting condition, but drug discontinuation might also be required to successfully revert it. Overall, knowledge in this field is mainly extrapolated from adult data, while similar evidence in youths is still limited. As clinicians might often deal with MACKE, its understanding needs to be expanded to avoid misdiagnosis, potentially leading to wasteful healthcare spending and unfavorable patient outcomes. Methods: By reporting the first case of MACKE in an adolescent receiving an SGA, namely paliperidone, we also aimed to provide a comprehensive overview of this medical condition. Conclusions: Making a MACKE diagnosis is essential since its relevant clinical and economic implications are mainly related to unnecessary closer laboratory monitoring or therapeutic changes (e.g., drug discontinuation or switch to another medication). Full article

Autism Spectrum Disorder (ASD) presents challenges in early screening due to its varied nature and sophisticated early signs. From a machine-learning (ML) perspective, the primary challenges include the need for large, diverse datasets, managing the variability in ASD symptoms, providing easy-to-understand models, and ensuring ASD predictive models that can be employed across different populations. Interpretable or explainable classification algorithms, like rule-based or decision tree, play a crucial role in dealing with some of these issues by offering classification models that can be exploited by clinicians. These models offer transparency in decision-making, allowing clinicians to understand reasons behind diagnostic decisions, which is critical for trust and adoption in medical settings. In addition, interpretable classification algorithms facilitate the identification of important behavioural features and patterns associated with ASD, enabling more accurate and explainable diagnoses. However, there is a scarcity of review papers focusing on interpretable classifiers for ASD detection from a behavioural perspective. Thereby this research aimed to conduct a recent review on rule-based classification research works in order to provide added value by consolidating current research, identifying gaps, and guiding future studies. Our research would enhance the understanding of these techniques, based on data used to generate models and obtain performance by trying to highlight early detection and intervention ways for ASD. Integrating advanced AI methods like deep learning with rule-based classifiers can improve model interpretability, exploration, and accuracy in ASD-detection applications. While this hybrid approach has feature selection relevant features that can be detected in an efficient manner, rule-based classifiers can provide clinicians with transparent explanations for model decisions. This hybrid approach is critical in clinical applications like ASD, where model content is as crucial as achieving high classification accuracy. Full article

A group signature scheme allows a user to sign a message anonymously on behalf of a group and provides accountability by using an opening authority who can “open” a signature and reveal the signer’s identity. Group signature schemes have been widely used in privacy-preserving applications, including anonymous attestation and anonymous authentication. Fully dynamic group signature schemes allow new members to join the group and existing members to be revoked if needed. Symmetric-key based group signature schemes are post-quantum group signatures whose security rely on the security of symmetric-key primitives, and cryptographic hash functions. In this paper, we design a symmetric-key based fully dynamic group signature scheme, called DGMT, that redesigns DGM (Buser et al. ESORICS 2019) and removes its two important shortcomings that limit its application in practice: (i) interaction with the group manager for signature verification, and (ii) the need for storing and managing an unacceptably large amount of data by the group manager. We prove security of DGMT (unforgeability, anonymity, and traceability) and give a full implementation of the system. Compared to all known post-quantum group signature schemes with the same security level, DGMT has the shortest signature size. We also analyze DGM signature revocation approach and show that despite its conceptual novelty, it has significant hidden costs that makes it much more costly than using the traditional revocation list approach. Full article

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices. Full article

Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque. Full article

Leptospirosis is a bacterial zoonotic disease that spreads through contaminated soil and water or directly from infected animals through urine. Although animal-to-human transmission is low, humans are most susceptible to contracting leptospirosis from these contaminated sources. This makes leptospirosis a public health concern, and therefore it is important to control these bacteria from spreading into the environment. A survey targeting Los Angeles County communities, in which a 2021 leptospirosis outbreak occurred, was sent out via groups on the online platforms Instagram and Facebook to gather dog and owner demographics. With 92 (90.2%) respondents having a primary veterinarian, it could not be determined what caused certain owners to have a greater vaccination rate than those who did not (n = 10; 9.8%). Overall, 69 respondents (68%), regardless of whether they had a primary veterinarian or not, reported not knowing of canine leptospirosis and 79 (77%) not knowing the signs to look for or that it is zoonotic. These data help provide a basis in terms of the status of dog owners’ knowledge of leptospirosis and how to begin to inform dog owners better about preventatives for this disease. Full article

Many information-theoretic quantities have corresponding representations in terms of sets. Many of these information quantities do not have a fixed sign—for example, the co-information can be both positive and negative. In previous work, we presented a signed measure space for entropy where the smallest sets (called atoms) all have fixed signs. In the present work, we demonstrate that these atoms have natural algebraic behaviour which can be expressed in terms of ideals (characterised here as upper sets), and we show that this behaviour allows us to make bounding arguments and describe many fixed-sign information quantity expressions. As an application, we give an algebraic proof that the only completely synergistic system of three finite variables X, Y and $Z=f(X,Y)$ is the XOR gate. Full article

In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV. Full article

Background and Objectives: Acute exacerbation of idiopathic pulmonary fibrosis (IPF-AE) often results in severe respiratory distress requiring treatment in the intensive care unit and has a high mortality rate. Identifying prognostic markers and assessing disease severity are crucial for clinicians to gain detailed insights. The mean platelet volume-to-platelet count ratio (MPR) is an inflammatory marker commonly used in malignancies. This study aimed to evaluate MPR and other factors affecting mortality in patients with IPF-AE who were monitored in the intensive care unit (ICU). Materials and Methods: This retrospective study was conducted on patients monitored in the ICU for IPF-AE between 2017 and 2023. Demographic characteristics, vital signs, laboratory and imaging findings, and administered treatments were reviewed. MPR was calculated by dividing the mean platelet volume by the platelet count. The primary endpoint was defined as 1-month in-hospital mortality. Results: A total of 59 patients monitored in the ICU for IPF-AE were included in the study. The mean age of the patients was 62.75 years, and 81.4% of the participants were male. During the 30-day follow-up period, 62.7% of the patients died. The need for invasive mechanical ventilation (IMV) was significantly associated with increased mortality (p < 0.001). The optimal cutoff value for MPR was determined to be 0.033, with a sensitivity of 83.7% and specificity of 63.64%, indicating its predictive value for mortality (AUC: 0.764; 95% CI: 0.635–0.864; p < 0.001). Conclusions: In this study, the need for IMV emerged as a critical parameter in predicting mortality in patients with IPF-AE. Additionally, the use of the MPR as a prognostic biomarker may offer a novel approach in the management of IPF patients. These findings could contribute to the development of strategies aimed at early intervention in IPF patients. Further studies with larger sample sizes are needed to validate these results. This study has demonstrated that MPR is a significant prognostic biomarker for predicting mortality in patients with IPF-AE who are managed in the intensive care unit. The potential use of MPR as a biomarker in clinical decision-making may provide new approaches to the management of IPF patients. Additionally, the need for IMV in IPF-AE emerges as a critical parameter for predicting mortality. These findings may contribute to the development of early intervention strategies for IPF patients. Further studies with larger cohorts are needed to validate these results. Full article

Bacterial infection is frequently observed in disease outbreaks of aquatic animals, making it of significance to isolate and identify the bacterial pathogens. In this study, diseased individuals of Crassostrea gigas were sampled from the nearshore area in Zhanjiang, Guangdong in May 2023. Culturable bacteria were isolated from the diseased tissue and a pathogenic strain labeled as H27 was screened through a hemolysis test and bacterial challenge experiments. Morphological characterization, 16S rRNA gene sequence-based molecular identification and biochemical tests showed that strain H27 belonged to the genus of Pseudoalteromonas, a dominant genus in the diseased tissue of C. gigas revealed by bacterial community structure analysis. The clinical signs originally observed in naturally diseased C. gigas were reproduced in strain H27-challenged adults, both with the red mantle and adductor. Histopathological analysis was further performed on the diseased tissues of the latter, which showed a significantly increased accumulation of pigment granules in the cytoplasm of the diseased mantle as well as enlarged muscle fiber distances in the diseased adductor. In addition, strain H27 was re-isolated from tissues of the moribund C. gigas after bacterial challenge, indicating the fulfillment of Koch’s postulate. Our results help to enrich the knowledge of C. gigas diseases, possibly contributing to disease prevention and control. Full article

Out-of-hospital cardiac arrest (OHCA) is a major public health burden due to its high mortality rate, sudden nature, and long-term impact on survivors. Consequently, there is a crucial need to create prediction models to better understand patient trajectories and assist clinicians and families in making informed decisions. We studied 107 adult OHCA patients admitted at an academic Emergency Department (ED) from 2018–2023. Blood samples and ocular ultrasounds were acquired at 1, 6, and 24 h after return of spontaneous circulation (ROSC). Six classes of clinical and novel variables were used: (1) Vital signs after ROSC, (2) pre-hospital and ED data, (3) hospital admission data, (4) ocular ultrasound parameters, (5) plasma protein biomarkers and (6) sex steroid hormones. A base model was built using 1 h variables in classes 1–3, reasoning these are available in most EDs. Extending from the base model, we evaluated 26 distinct neural network models for prediction of neurological outcome by the cerebral performance category (CPC) score. The top-performing model consisted of all variables at 1 h resulting in an AUROC score of 0.946. We determined a parsimonious set of variables that optimally predicts CPC score. Our research emphasizes the added value of incorporating ocular ultrasound, plasma biomarkers, sex hormones in the development of more robust predictive models for neurological outcome after OHCA. Full article

Cybersecurity, complimenting authentication, has become the backbone of the Internet of Things. In the authentication process, the word authentication is of the utmost importance, as it is the door through which both Mr. Right Guy and Mr. Wrong Guy can pass. It is the key to opening the most important and secure accounts worldwide. When authentication is complete, surely there will be passwords. Passwords are a brain-confusing option for the user to choose when making an account during the registration/sign-up process. Providing reliable, effective, and privacy-preserving authentication for individuals in mobile networks is challenging due to user mobility, many attack vectors, and resource-constrained devices. This review paper explores the transformation and modern mobile authentication schemes, categorizing them into password, graphical, behavioral, keystroke, biometric, touchscreen, color, and gaze-based methodologies. It aims to examine the strengths and limitations focused on challenges like security and usability. Standard datasets and performance evaluation measures are also discussed. Finally, research gaps and future directions in this essential and emerging area of research are discussed. Full article

This paper explores the possibility of counteracting the crisis of culture and institutions by investing in the identity values of the user-actor within digital spaces built for the purpose. The strategy is applied to the analysis of three Catalan cloisters (Spain), with a focus on the representation of the cloister of Sant Cugat (Barcelona). Heuristic picklocks are found in the semantic richness proposed by Marius Schneider exclusively on the verbal level. The authors interpret the contents and transcribe them into graphic signs and digital denotations of sounds and colors. They organize proprietary ontologies, or syntagmatic lines, to be entrusted to the management of computer algorithms. The syncretic culture that characterized the medieval era allowed the ability to mediate science and faith to be entrusted to the mind of the praying monk alone in every canonical hour. The hypothesis that a careful direction has programmed the ways in which to orient souls to “navigate by sight” urges the authors to find the criteria that advanced statistics imitates to make automatic data processing “Intelligent”. In step with the times and in line with the most recent directions for the Safeguarding of Heritage, the musical, astral, and narrative rhythms feared by Schneider are used to inform representative models, to increase not only the visual perception of the user (XR Extended Reality) but also to solicit new analogies and illuminating associations. The results return a vision of the culture of the time suitable for shortening the distances between present and past, attracting the visitor and, with him, the resources necessary to protect and enhance the spaces of the Romanesque era. The methodology goes beyond the contingent aspect by encouraging the ‘remediation’ of contents with the help of machine learning. Full article

The use of precast concrete pipes for water and sewage transportation systems is a very important element of a country’s infrastructure. The main aim of this study was to investigate the effects of concrete’s compressive strength and reinforcement levels on the mechanical performance of spun-cast full-scale precast concrete pipes in the local construction industries of developing countries. A test matrix was adopted using a full 3² factorial design. The studied concrete’s compressive strength was 20, 30, and 40 MPa, and reinforcement levels were 60%, 80%, and 100%, representing low, medium, and high levels, respectively. The medium level of reinforcement represented the reinforcement requirement of ASTM C76 in concrete pipes. A total of eighteen full-scale pipes of 450 mm diameter were cast in an industrial precast pipe unit using a spin-casting technique and were tested under a three-edge bearing load. The experimental results showed that the crack load and ultimate load of the tested pipes increased with higher levels of concrete strength and reinforcement levels. For example, an approximately 35% increase in the 0.30 mm crack load was observed when the concrete strength increased from 20 MPa to 30 MPa for all tested levels of reinforcement. Similarly, around a 19% increase in ultimate load was observed for pipes with 80% reinforcement compared to identical pipes with 60% reinforcement. It was found that the pipe class, as per ASTM C76, is highly dependent on the concrete strength and reinforcement levels. All of the pipes exhibited the development of flexural cracks at critical locations (crown, invert, and springlines). Moreover, concrete pipes cast with low-level strength and reinforcement also showed signs of crushing at the crown location near to the pipe failure. The analysis of variance (ANOVA) results showed that the main factors (compressive strength and reinforcement levels) were significantly affected by the cracking loads of precast pipes. No significant effect of the interaction of factors was observed on the crack load response. However, interaction factors, along with main factors, have significant effects on the ultimate load capacity of the concrete pipes, as indicated by the F-value, p-value, and Pareto charts. This study made an effort to illustrate and optimize the mechanical performance of pipes cast with various concrete strengths and reinforcement levels to facilitate the efficient use of materials for more resilient pipe infrastructure. Moreover, the exact optimization of concrete strength and reinforcement level for the desired pipe class will make the pipe design economical, leading to an increased profit margin for local spin-cast pipe fabricators without compromising the pipe’s quality. Full article

We measured the spatiotemporal dynamics of habitat quality (HQ) in Sicily in two different reference years, 2018 and 2050, assuming a business-as-usual scenario. To estimate HQ and related vulnerability, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Habitat Quality model and data on land use/land cover provided by the Esri Land Cover 2050 project. We also implemented a Coarse–Filter approach to validate the reliability of HQ measures and detect biodiversity hotspots that require priority conservation. Further, we used spatial statistic tools for identifying clusters or hotspot/coldspot areas and uncovering spatial autocorrelation in HQ values. Finally, we implemented a geographically weighted regression (GWR) model for explaining local variations in the effects on HQ estimates. The findings reveal that HQ in Sicily varies across space and time. The highest HQ values occur in protected areas and forests. In 2018, the average HQ value was higher than it was in 2050. On average, HQ decreased from 0.29 in 2018 to 0.25 in 2050. This slight decline was mainly due to an increase in crop and urbanized areas at the expense of forests, grasslands, and bare lands. We found the existence of a positive spatial autocorrelation in HQ, demonstrating that areas with higher or lower HQ tend to be clustered, and that clusters come into contact randomly more often in 2050 than in 2018, as the overall spatial autocorrelation moved from 0.28 in 2018 to 1.30 in 2050. The estimated GWR model revealed the sign and the significance effect of population density, compass exposure, average temperature, and patch richness on HQ at a local level, and that such effects vary either in space and time or in significance level. Across all variables, the spatial extent of significant effects intensifies, signaling stronger localized influences in 2050. The overall findings of the study provide useful insights for making informed decisions about conservation and land planning and management in Sicily. Full article