Search Results (13,262)

In the connected and autonomous vehicle (CAV) environment, vehicles with different levels of automation are being deployed on public roads. Most research focuses on traffic flow simulation for a single vehicle type, while there are few studies on the interactions of mixed traffic involving CAVs, autonomous vehicles (AVs), and human-driven vehicles (HDVs). To fill this gap, this study investigates the traffic performance of heterogeneous traffic on multi-lane ring roads and highways with on-ramps. Leveraging the Python and SUMO simulation platform, the JAD strategy is introduced to optimize the dynamic interactions within heterogeneous traffic flow. Various scenarios with different proportions of CAVs, AVs, and HDVs were simulated to assess their impact on traffic efficiency, dynamics, safety, and environmental factors. The findings indicate that traffic efficiency, stability, and environmental impact improve as the share of HDVs declines and the proportion of CAVs and AVs rises. In scenarios with more HDVs, the improvements are minimal. Traffic safety gradually improves as the proportion of CAVs and AVs increases, with significant improvements observed when CAVs account for 40% of vehicles on ring roads and 50% on highways. This study advances the understanding of complex interactions in mixed traffic scenarios and their implications for traffic management. Full article

The objectives of this work were as follows: (i) the evaluation of the prevalence of detection of genetic material of Coxiella burnetii in the bulk tank milk of sheep and goat farms in Greece and (ii) the investigation of variables related to the management applied in farms as possible predictors for this. The presence of C. burnetii genetic material was studied in the bulk tank milk of 325 sheep and 119 goat farms throughout the country. For qualitative and quantitative identification of the genetic material of the pathogen, a commercially available real-time PCR was used. In total, 45 parameters were assessed for potential association with the detection of the pathogen: these referred to the management system, infrastructure, health management, animals, production characteristics, and human resources on the farms. Genetic material of the pathogen was detected in bulk tank milk samples from nine sheep (2.8%) and six goat (5.0%) farms. Genetic material was at significantly higher median concentrations in samples from goat farms than from sheep farms, 1,078,096 (min: 181,121, max: 2,331,386) versus 15,728 (min: 507, max: 505,852) GE mL⁻¹, respectively. For sheep farms, the intensive or semi-intensive management system applied in farms (p = 0.003), and for goat farms, the intensive or semi-intensive management system applied in farms (p = 0.0007) and the smaller number of annual veterinary visits to farms (p = 0.044) emerged as significant predictors. Among sheep farms managed under the intensive or semi-intensive system, the lack of accessory barns on farms (p = 0.024) emerged as a significant predictor; no significant predictor could be found among goat farms under such management systems. There was no significant difference in production outcomes between farms in which C. burnetii was or was not detected in the bulk tank milk; also, there was no association between the detection of C. burnetii and the annual incidence rate of cases of abortion on the farms. The results suggest that the risk of transfer of C. burnetii to dairy products from sheep and goat milk appears to be small, but not negligible, which indicates that the pasteurization of milk from small ruminants must be carried out consistently and correctly to ensure the safety of the product. Full article

The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. In this paper, classifier models such as LS, KNN, Random Forest, Extra Trees, and XGBoost, which have proven effective in classification tasks, are used to analyze incident reports parsed with natural language processing (NLP) techniques, to uncover hidden patterns and prevent future incidents. Metrics such as precision, recall, F1-score and accuracy are used to assess the degree of correctness of the predictive models. The adjustment of hyperparameters is obtained with Grid Search and Bayesian Optimization. KNN had the best predictive rating, followed by Random Forest and Extra Trees. The results indicate that the use of machine learning tools to classify incidents and accidents helps to identify their root cause, improving situational decision-making. Full article

Enavogliflozin, a sodium–glucose cotransporter 2 inhibitor, was approved in 2022 by the Korean Ministry of Food and Drug Safety as a therapeutic agent for type 2 diabetes mellitus and has been investigated for expanded therapeutic efficacy in diabetic retinopathy and cardioprotection. In this study, we developed and validated an analytical method to precisely detect enavogliflozin in mouse plasma, employing liquid–liquid extraction combined with liquid chromatography–tandem mass spectrometry. Overall, the analytical method, covering a range of 5–3000 ng/mL, is reliable for investigating the time-concentration profiles of enavogliflozin, demonstrating acceptable accuracy, precision, extraction recovery, and minimal matrix effects without stability concerns as evidenced by assessments of post-treatment stability, freeze–thaw stability, and short-term stability of enavogliflozin. Pharmacokinetic profiles and all pharmacokinetic parameters of enavogliflozin in mice did not differ between fed and fasted states after oral administration of enavogliflozin (1 mg/kg). Additionally, no differences in the pharmacokinetic profiles of enavogliflozin were observed among single, 7-day repeated, and 14-day repeated oral administrations at 1 mg/kg. In the tissue distribution study, enavogliflozin showed the highest distribution in the kidneys, followed by the large intestine, stomach, small intestine, liver, heart, lungs, spleen, and testes after oral administration at both 1 and 3 mg/kg doses. Dose proportionality in tissue distribution was observed except for the kidneys. In conclusion, enavogliflozin can be administered without concern for pharmacokinetic changes, regardless of single or multiple dosing and whether in fed or fasted states. Furthermore, the tissue distribution profile may offer valuable insights into the therapeutic potential of this drug. Full article

In drug development, drug-target affinity (DTA) prediction is a key indicator for assessing the drug’s efficacy and safety. Despite significant progress in deep learning-based affinity prediction approaches in recent years, there are still limitations in capturing the complex interactions between drugs and target receptors. To address this issue, a dynamic heterogeneous graph prediction model, DynHeter-DTA, is proposed in this paper, which fully leverages the complex relationships between drug–drug, protein–protein, and drug–protein interactions, allowing the model to adaptively learn the optimal graph structures. Specifically, (1) in the data processing layer, to better utilize the similarities and interactions between drugs and proteins, the model dynamically adjusts the connection strengths between drug–drug, protein–protein, and drug–protein pairs, constructing a variable heterogeneous graph structure, which significantly improves the model’s expressive power and generalization performance; (2) in the model design layer, considering that the quantity of protein nodes significantly exceeds that of drug nodes, an approach leveraging Graph Isomorphism Networks (GIN) and Self-Attention Graph Pooling (SAGPooling) is proposed to enhance prediction efficiency and accuracy. Comprehensive experiments on the Davis, KIBA, and Human public datasets demonstrate that DynHeter-DTA exceeds the performance of previous models in drug-target interaction forecasting, providing an innovative solution for drug-target affinity prediction. Full article

Background: Angiotensin-converting enzyme inhibitors, such as enalapril, are foundational in treating pediatric heart failure. However, they are often administered off-label to young children using extemporaneous formulations. This study, conducted as part of the EU-funded Labeling of Enalapril from Neonates up to Adolescents (LENA) project, aimed to evaluate the acceptability and palatability of an age-appropriate enalapril orodispersible minitablet (ODMT). These factors are critical for ensuring adherence, efficacy, and safety in pediatric patients. Methods: An 8-week trial was conducted in children with heart failure caused by dilated cardiomyopathy or congenital heart disease. Enalapril ODMTs (0.25 mg or 1.0 mg) were dose-titrated and administered to 38 children aged 0–6 months and 22 children aged 6 months to 6 years. This study aimed to assess its acceptability and palatability, key factors contributing to adherence, and therefore, efficacy and safety. Results: Across all 169 assessments in 38 children aged 0–6 months and 22 aged 6 months to 6 years, complete or partial swallowability was observed, and the acceptability rate was 100%. There were no cases of choking, inhalation/coughing, or spitting out. A favorable or neutral rating was observed in 96% of palatability assessments based on observations of facial expressions. Acceptability and palatability were higher in subjects aged 6 months–6 years than 0–6 months, with no significant influence from repeated administration. Conclusions: Enalapril ODMTs are widely accepted and well-tolerated among young children, including neonates, with heart failure. These findings suggest that ODMTs are a suitable and effective method for administering pediatric medicinal products. Full article

Typhoon-induced waves significantly threaten marine transportation and safety, often leading to catastrophic marine disasters. Accurate wave simulations are vital for effective disaster prevention. However, traditional studies have primarily focused on significant wave height (SWH) and heavily relied on resource-intensive numerical simulations while often neglecting wave spectra, which are essential for understanding the distribution of wave energy across various frequencies and directions. Addressing this gap, our study introduces an LSTM–Self Attention–Dense model that comprehensively simulates both SWH and wave frequency spectra. The model was rigorously trained and validated on three years of global typhoon data and exhibited accuracy in forecasting both SWH and wave spectra. Furthermore, our analysis identifies optimal input data windows and underscores wind speed and central pressure as critical predictive features. This novel approach not only enhances marine risk assessment but also offers a swift and efficient forecasting tool for managing extreme weather events, thereby contributing to the advancement of disaster management strategies. Full article

Measuring exercise intensity for safety and to inform prescription in acute burn survivors, is challenging. This study aimed to assess the validity of adult patient end-of-workout rating of session perceived exertion (sRPE); and calculated training load (TL) (sRPE × session duration) as measures of exercise intensity. Secondly, the study aimed to compare clinician and patient perception of exercise effort during physiotherapist-led sessions. Repeated RPE data were collected every 5-min during two resistance exercise sessions completed by 25 burns patients. Physiological (heart rate [HR], blood lactate [BLa]) and perceptual measures (sRPE, ratings of pain, fatigue, delayed onset muscle soreness, sleep quality and stress) were also captured. Adjusted, multivariable linear regression models were used to determine the associations between sRPE and TL and significant predictor variables. Paired t-tests were performed to compare clinician and participant sRPE. Results: Average RPE calculated from 5-min repeats, after adjustment for age and %TBSA, was significantly associated with sRPE, F(1, 45) = 100.82, (p < 0.001, adjusted R² = 0.64) and TL, F(1, 45) = 33.66, (p < 0.001, adjusted R² = 0.39). No significant differences between patient and clinician sRPE were apparent (p = 0.948). Thus, one-off reporting of sRPE and calculated TL may be appropriate markers to monitor exercise intensity and aid prescription in individuals with burn injuries, regardless of patient and burn characteristics or time since burn. There was also no difference between patient and clinician’s perceptions of exercise effort. Full article

This study evaluates the use of biochar as a sustainable substitute to peat in the soilless cultivation of rocket salad (Eruca vesicaria (L.) Cav.). Biochar was added to a peat-based substrate at concentrations of 0% (control), 5%, 10%, 20%, 40%, and 70% v/v to assess its effects on seed germination, plant growth, mineral content, and nitrate accumulation. The results show that biochar concentrations up to 40% v/v maintained germination rates above 80%, similar to the control, while higher concentrations (70% v/v) drastically reduced germination to 29% and entirely compromised plant development and growth. A moderate biochar concentration (20%) had a positive effect on fresh weight and leaf area, while maintaining comparable levels of nutrient uptake, chlorophyll, and flavonols. In addition, biochar-enriched substrates (≥20% v/v) reduced nitrate accumulation in leaves by 26–30%, addressing a critical quality and safety concern. A high biochar content (≥40% v/v) altered the substrate’s physicochemical properties, including pH, porosity, and electrical conductivity, negatively affecting plant growth (a 38% reduction in plant growth and 42% in leaf area) and increasing heavy metal concentrations, such as that of zinc (~30%). These findings suggest that incorporating up to 20% v/v biochar in soilless substrates offers a sustainable alternative to peat, supporting rocket salad performance and improving leaf nitrate quality, without compromising yield or safety. Full article

Background: Despite a large number of published studies, the effect of vitamin D3 supplementation on mortality in hospitalized patients, as well as the recommended dose and duration of therapy, is unclear. In our retrospective study, we aimed to investigate the impact of vitamin D deficiency and moderately high-dose vitamin D3 supplementation on mortality and disease outcomes in patients with COVID-19 infection. Methods: We analyzed data from 148 COVID-19-infected hospitalized patients in two different departments, Internal Medicine and Oncology, at Semmelweis University. The severity of COVID-19 and the treatment used were the same except at one of the departments, where patients received circa 90,000 IU of vitamin D3. We compared in-hospital mortality rates between the groups. In a subgroup analysis, we evaluated the efficacy and safety of vitamin D3 supplementation by assessing 25(OH)D and 1,25(OH)₂D concentrations on days 0, 4, and 8. Results: As a result of the supplementation, the deficiency was resolved in 4 days in deficient patients, and none of the 25(OH)D or 1,25(OH)₂D concentrations exceeded the normal range. Mortality was significantly lower and decreased 67% in the group receiving vitamin D3 supplementation, regardless of baseline 25(OH)D concentrations. Conclusions: The supplemental dosage, 3 × 30,000 IU of vitamin D3, is effective and safe and may reduce mortality in COVID-19 infection. Full article

The European Food Safety Authority (EFSA) monitors the presence and concentration of contaminants in food to mitigate health risks. EU legislation sets maximum levels of heavy metals in foods, including cadmium (Cd), lead (Pb), and total Hg (THg) in seafood, due to their toxicity. In the framework of official control, between 2014 and 2023, 5854 seafood samples were collected and 4300 analyses for THg, 3338 for Cd, and 2171 for Pb were performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and cold vapor atomic absorption spectrometry (CVAAS). The aim was to assess the proportion of contaminated foods in the dataset, the concentration of contaminants, and the potential health risks associated with their intake. Of the total samples analyzed, 142 (2.43%) were found to be non-compliant (n.c.). Concentrations exceeding the limits for Cd were primarily detected in cephalopods (n = 17), mainly squids. In contrast, Hg levels exceeded the limits in marine fish (n = 118), notably in swordfish (11.30% of n.c. samples among those analyzed for this species), sharks (6.48%), and tuna species (3.11%). Regarding Pb, only a single bivalve sample was found to exceed the maximum limits. A preliminary assessment of weekly exposure to Hg through swordfish consumption raised concerns about the frequent intake of marine top predators, particularly for vulnerable people. Full article

Background/Objectives: The health sector is one of the most important sectors, and occupational accidents and occupational diseases that health workers are exposed to are more important compared to those in other sectors. Especially, the increase in health and safety problems faced by hospital workers necessitates the development of an occupational health and safety (OHS) management system model specific to the health sector. Existing health and safety management systems generally do not sufficiently take sectoral dynamics into account, and the adaptation of these standards to the sectors is left to the individual efforts of the users. This situation leads to management systems that lack a sector-specific approach and do not adopt a common language and methodology. Methods: The aim of this study was to create an OHS management system specific to the health sector. While developing the model, AHP and DEMATEL, which are multi-criteria decision-making methods that help people to make complex decisions, were used. Results: By applying these methods, important criteria for the proposed model were determined. The criteria, including “Improvement of the management system of the health institution”, “Determination of control measures”, “Assessment of risks specific to the health institution”, “Identifying hazards specific to the health institution” and “Determining its context”, have been determined as priority criteria and weighted using the AHP and DEMATEL methods. Conclusions: As a result of the findings of this research, designing a unique occupational health and safety (OHS) management system that takes into account the dynamics of the health sector will contribute to the prevention of occupational accidents and occupational diseases in the health sector. Full article

Background/Objectives: The rapid development of Large Language Models (LLMs) presents promising applications in healthcare, including patient education. In anesthesia, where patient anxiety is common due to misunderstandings and fears, LLMs could alleviate perioperative anxiety by providing accessible and accurate information. This study explores the potential of LLMs to enhance patient education on anesthetic and perioperative care, addressing time constraints faced by anesthetists. Methods: Three language models—ChatGPT-4, Claude 3, and Gemini—were evaluated using three common patient prompts. To minimize bias, incognito mode was used. Readability was assessed with the Flesch–Kincaid, Flesch Reading Ease, and Coleman–Liau indices. Response quality was rated for clarity, comprehension, and informativeness using the DISCERN score and Likert Scale. Results: Claude 3 required the highest reading level, delivering detailed responses but lacking citations. ChatGPT-4o offered accessible and concise answers but missed key details. Gemini provided reliable and comprehensive information and emphasized professional guidance but lacked citations. According to DISCERN and Likert scores, Gemini had the highest rank for reliability and patient friendliness. Conclusions: This study found that Gemini provided the most reliable information, followed by Claude 3, although no significant differences were observed. All models showed limitations in bias and lacked sufficient citations. While ChatGPT-4o was the most comprehensible, it lacked clinical depth. Further research is needed to balance simplicity with clinical accuracy, explore Artificial Intelligence (AI)–physician collaboration, and assess AI’s impact on patient safety and medical education. Full article

To ensure security and stable quality, deeper cybersecurity evaluations are essential for the development of safety features and functionalities in vehicles. Among these, the Autonomous emergency braking (AEB) system is the most relevant. This research presents a comprehensive Threat analysis and risk assessment (TARA) of the AEB system, emphasizing the identification, validation, and mitigation of major cybersecurity threats and risks. We systematically examine potential attack vectors by utilizing the STRIDE threat model. This approach enables a detailed analysis of each security threat associated with AEB systems, providing insights into how malicious actors could use the attack paths. The assessment aligns with ISO/SAE 21434, which offers a robust framework for risk management in automotive cybersecurity and IT security, ensuring a thorough evaluation of a system’s architecture. By assessing the AEB system’s architecture against these standards, we identify key components and communication pathways that may be particularly prone to cyberattacks. The results of this analysis highlight critical flaws within the AEB framework and propose corrective measures to enhance cybersecurity resilience. This article provides a structured methodology for assessing and mitigating automotive cybersecurity risks in compliance with industry standards, aiming to facilitate the safe implementation of AEB technology and ultimately improve overall vehicle security and safety. Full article

Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as intrusiveness and delayed reaction times. This research addresses these gaps by leveraging facial analysis and state-of-the-art machine learning techniques to develop a real-time, non-intrusive DDD system. A distinctive aspect of this research is its systematic assessment of various machine and deep learning algorithms across three pivotal public datasets, the NTHUDDD, YawDD, and UTA-RLDD, known for their widespread use in drowsiness detection studies. Our evaluation covered techniques including the K-Nearest Neighbors (KNNs), support vector machines (SVMs), convolutional neural networks (CNNs), and advanced computer vision (CV) models such as YOLOv5, YOLOv8, and Faster R-CNN. Notably, the KNNs classifier reported the highest accuracy of 98.89%, a precision of 99.27%, and an F1 score of 98.86% on the UTA-RLDD. Among the CV methods, YOLOv5 and YOLOv8 demonstrated exceptional performance, achieving 100% precision and recall with [email protected] values of 99.5% on the UTA-RLDD. In contrast, Faster R-CNN showed an accuracy of 81.0% and a precision of 63.4% on the same dataset. These results demonstrate the potential of our system to significantly enhance road safety by providing proactive alerts in real time. Full article