Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

The characteristics of e-scooter accidents reported by police in Türkiye.

OBJECTIVES: Shared e-scooter service has been offered by various operators in Türkiye since 2019. The use of private and shared e-scooters is increasing, especially in large cities. This raises social concerns about the dangers e-scooters pose regarding traffic safety and injuries requiring medical attention. This study aims to investigate the accidents related to e-scooters in Türkiye to determine the contributing factors and accident characteristics. METHODS: In this study, accident reports (collision reports) for 780 e-scooter collisions that occurred in 2021 in Türkiye were examined, and 771 accidents were included. Accident data were obtained from the Traffic Department of the Ministry of Interior, General Directorate of Security. Descriptive statistics of the factors affecting e-scooter accidents are presented to determine the relationship and differences; chi-square tests, independent samples t-tests, one-way analysis of variance, and binary logistic regression methods were used. RESULTS: Male e-scooter riders are involved in crashes and injured approximately 4 times more often than female riders. The average age for men injured in e-scooter accidents is 30.4, and the mean age for women is 27.2. For both males and females, most injuries occurred in the 15 to 20 age group. Riders under the age of 18 constitute a significant proportion of the accidents (32.5%). Most e-scooter accidents occur on Mondays and during the month of August. Most accidents occurred between 12:00 p.m. and 1:59 p.m. (15.7%) and between 4:00 p.m. and 5:59 p.m. (15.7%), mainly during the daytime. About half of the accidents occurred at intersections. In 10.5% of accidents, the accident occurred at a pedestrian crossing. Approximately one-fifth of the accidents involved falls, and the most common type of collision was a side collision (44.2%). The binary logistic regression model showed that multivehicle accidents occur more often at intersections and during busy traffic hours. Single-vehicle accidents are more common on concrete roads and stone block roads. CONCLUSIONS: Deaths and injuries caused by road traffic accidents are a public health problem in Türkiye and constitute a significant health burden. If necessary precautions are not taken, this burden is likely to increase. We hope that the findings from this study will help reduce e-scooter accidents in Türkiye.

Bayesian hybrid gamma-GPD model for extreme traffic conflict threshold determination in the peak over threshold approach.

Extreme value theory (EVT) models have been frequently utilized to estimate crash risk from traffic conflicts with the peak over threshold commonly used to identify conflict extremes. However, a common problem for the peak over threshold method is the selection of a suitable threshold to distinguish general and extreme conflicts. Subjective and arbitrary selection of the threshold in peak over threshold method can result in bias and unstable estimation results. The primary objective of the study is to propose a hybrid modelling approach for the threshold determination in peak over threshold method. The hybrid model consists of a joint gamma distribution and generalized Pareto distribution (GPD). The gamma distribution is used to fit general conflicts while the GPD is used to fit extreme conflicts. Specially, discontinued, continued and differentiable gamma-GPD models are developed with the threshold being treated as a model parameter. Traffic conflict data collected from three signalized intersections in the city of Surrey, British Columbia were used for the study. The modified time to collision (MTTC) was employed as conflict indicator. The Bayesian approach was employed to estimate the threshold as well as other hybrid gamma-GPD model parameters. The results show that the discontinued gamma-GPD model is superior to the continued and differentiable gamma-GPD models for determining the threshold in terms of crash estimation accuracy and model fit. The crash estimates using the threshold determined by the hybrid gamma-GPD model outperform those estimated based on the traditional quantile plots method, indicating that the superiority of the proposed threshold determination approach based on gamma-GPD hybrid model. The proposed hybrid gamma-GPD model could determine the threshold parameter in peak over threshold method for traffic conflicts extremes automatically in an objective and quantitative way. It contributes to existing peak over threshold method for producing reliable crash estimation.

Evaluating the quality of sediments in streams draining contrasting land-use areas in Osogbo metropolis, southwestern Nigeria.

The attendant effects of urbanization on the environment and human health are evaluable by measuring the potentially harmful element (PHE) concentrations in environmental media such as stream sediments. To evaluate the effect of urbanization in Osogbo Metropolis, the quality of stream sediments from a densely-populated area with commercial/industrial activities was contrasted with sediments from a sparsely-populated area with minimal anthropogenic input.Forty samples were obtained: 29 from Okoko stream draining a Residential/Commercial Area (RCA, n = 14) and an Industrial Area (IA, n = 15), and 11 from Omu stream draining a sparsely-populated area (SPA). The samples were air-dried, sieved to < 75 micron fraction, and analysed for PHEs using inductively-coupled plasma atomic emission spectrometry (ICP-AES). Index of geoaccumulation (Igeo), pollution index (PI), ecological risk factor (Er) and index (ERI) were used for assessment. Inter-elemental relationships and source identification were done using Pearson's correlation matrix and principal component analysis (PCA).PHE concentrations in the stream sediments were RCA: Zn > Pb > Cu > Cr > Sr > Ni > Co, IA: Zn > Cr > Ni > Co > Pb > Cu > Sr and SPA: Zn > Co > Cr > Cu > Sr > Ni > Pb. Igeo calculations revealed moderate-heavy contamination of Cu, Pb and Zn in parts of RCA, moderate-heavy contamination of Zn in IA while SPA had moderate contamination of Co and Zn. PI values revealed that stream sediments of RCA are extremely polluted, while those of IA and SPA are moderately and slightly polluted, respectively.The pollution of the stream sediments in RCA and IA is adduced to anthropogenic activities like vehicular traffic, automobile repairs/painting, blacksmithing/welding and metal scraping. In SPA however, the contamination resulted from the application of herbicides/fertilizers for agricultural purposes.

Long-term exposure to air pollution, road traffic noise and greenness, and incidence of myocardial infarction in women.

BACKGROUND: Emerging evidence shows that long-term exposure to air pollution, road traffic noise, and greenness can each be associated with cardiovascular disease, but only few studies combined these exposures. In this study, we assessed associations of multiple environmental exposures and incidence of myocardial infarction using annual time-varying predictors. MATERIALS AND METHODS: In a population-based cohort of 20,407 women in Sweden, we estimated a five-year moving average of residential exposure to air pollution (PM2.5, PM10 and NO2), road traffic noise (Lden), and greenness (normalized difference vegetation index, NDVI in 500 m buffers), from 1998 to 2017 based on annually varying exposures and address history. We used adjusted time-varying Cox proportional hazards regressions to estimate hazard ratios (HR) and 95 % confidence intervals (95 % CI) of myocardial infarction per interquartile range (IQR). Furthermore, we investigated interactions between the exposures and explored potential vulnerable subgroups. RESULTS: In multi-exposure models, long-term exposure to greenness was inversely associated with incidence of myocardial infarction (HR 0.89; 95 % CI 0.80, 0.99 per IQR NDVI increase). Stronger associations were observed in some subgroups, e.g. among women with low attained education and in overweight (BMI ≥ 25 kg/m2) compared to their counterparts. For air pollution, we observed a tendency of an increased risk of myocardial infarction in relation to PM2.5 (HR 1.07; 95 % CI 0.93, 1.23) and the association appeared stronger in women with low attained education (HR 1.30; 95 % CI 1.06, 1.58). No associations were observed for PM10, NO2 or road traffic noise. Furthermore, there were no clear interaction patterns between the exposures. CONCLUSION: Over a 20-year follow-up period, in multi-exposure models, we found an inverse association between residential greenness and risk of myocardial infarction among women. Furthermore, we observed an increased risk of myocardial infarction in relation to PM2.5 among women with low attained education. Road traffic noise was not associated with myocardial infarction.

Endoplasmic reticulum exit sites are segregated for secretion based on cargo size.

TANGO1, TANGO1-Short, and cTAGE5 form stable complexes at the endoplasmic reticulum exit sites (ERES) to preferably export bulky cargoes. Their C-terminal proline-rich domain (PRD) binds Sec23A and affects COPII assembly. The PRD in TANGO1-Short was replaced with light-responsive domains to control its binding to Sec23A in U2OS cells (human osteosarcoma). TANGO1-ShortΔPRD was dispersed in the ER membrane but relocated rapidly, reversibly, to pre-existing ERES by binding to Sec23A upon light activation. Prolonged binding between the two, concentrated ERES in the juxtanuclear region, blocked cargo export and relocated ERGIC53 into the ER, minimally impacting the Golgi complex organization. Bulky collagen VII and endogenous collagen I were collected at less than 47% of the stalled ERES, whereas small cargo molecules were retained uniformly at almost all the ERES. We suggest that ERES are segregated to handle cargoes based on their size, permitting cells to traffic them simultaneously for optimal secretion.

Forensic Assessment of Alcohol Intoxication in Cases of Fatal Road Traffic Accidents in Lithuania.

Background: There is strong evidence that alcohol consumption is a significant risk factor for fatal road traffic accidents. It is estimated that the number of alcohol-related road accidents remains high in the past few years in Lithuania. This study aims to examine the prevalence of alcohol in blood samples collected from the autopsy results of road traffic accident victims. Materials and methods: A retrospective study of 136 road traffic accident victims was performed in State Forensic Medicine Service of Lithuania in the period of 2013 to 2023. We analyzed blood alcohol concentration (BAC) in relation to sex, age, road user type, place and time of the day at death. Results: 31% of the victims were under influence of alcohol at the time of death, with mean BAC 1.99 ± 0.92‰. The mean BAC was 2.16 ± 0.8‰ in male and 1.18 ± 1.12‰ in female group. By the type of road users, 23% of the pedestrians (mean BAC 2.45 ± 0.71‰), 32% of car drivers (mean BAC 2.13 ± 0.75‰), 41% of vehicle passengers (mean BAC of 1.73 ± 1.19‰), 37% of the motorcycle riders (mean BAC of 1.28 ± 0.53‰), 37% of the cyclists (mean BAC of 1.15 ± 0.75‰) were found to be intoxicated during the time of accident. Highest mean blood alcohol concentration was found during the night time hours (9 p. m. - 5 a. m.) 2.28 ± 0.91, comparing to in afternoon hours (12 p. m. - 5 p. m.) 1.49 ± 0.99, evening hours (5 p. m. - 9 p. m.) 2.10 ± 0.73 and morning hours (5 a. m. - 12 p. m.) 1.94 ± 1.00. The mean BAC in road traffic accidents during summer was 1.48 ± 0.71‰, spring 2.25 ± 0.76‰, autumn 2.12 ± 1‰, winter 2.42 ± 1‰. Conclusions: Alcohol consumption by road users is a significant contributing factor in road traffic accidents and their outcomes in Lithuania.

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.

Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection.

With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management.

Cooperative Motion Optimization Based on Risk Degree under Automatic Driving Environment.

Appropriate traffic cooperation at intersections plays a crucial part in modern intelligent transportation systems. To enhance traffic efficiency at intersections, this paper establishes a cooperative motion optimization strategy that adjusts the trajectories of autonomous vehicles (AVs) based on risk degree. Initially, AVs are presumed to select any exit lanes, thereby optimizing spatial resources. Trajectories are generated for each possible lane. Subsequently, a motion optimization algorithm predicated on risk degree is introduced, which takes into account the trajectories and motion states of AVs. The risk degree serves to prevent collisions between conflicting AVs. A cooperative motion optimization strategy is then formulated, incorporating car-following behavior, traffic signals, and conflict resolution as constraints. Specifically, the movement of all vehicles at the intersection is modified to achieve safer and more efficient traffic flow. The strategy is validated through a simulation using SUMO. The results indicate a 20.51% and 11.59% improvement in traffic efficiency in two typical scenarios when compared to a First-Come-First-Serve approach. Moreover, numerical experiments reveal significant enhancements in the stability of optimized AV acceleration.

Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving.

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.

Research on a Recognition Algorithm for Traffic Signs in Foggy Environments Based on Image Defogging and Transformer.

The efficient and accurate identification of traffic signs is crucial to the safety and reliability of active driving assistance and driverless vehicles. However, the accurate detection of traffic signs under extreme cases remains challenging. Aiming at the problems of missing detection and false detection in traffic sign recognition in fog traffic scenes, this paper proposes a recognition algorithm for traffic signs based on pix2pixHD+YOLOv5-T. Firstly, the defogging model is generated by training the pix2pixHD network to meet the advanced visual task. Secondly, in order to better match the defogging algorithm with the target detection algorithm, the algorithm YOLOv5-Transformer is proposed by introducing a transformer module into the backbone of YOLOv5. Finally, the defogging algorithm pix2pixHD is combined with the improved YOLOv5 detection algorithm to complete the recognition of traffic signs in foggy environments. Comparative experiments proved that the traffic sign recognition algorithm proposed in this paper can effectively reduce the impact of a foggy environment on traffic sign recognition. Compared with the YOLOv5-T and YOLOv5 algorithms in moderate fog environments, the overall improvement of this algorithm is achieved. The precision of traffic sign recognition of the algorithm in the fog traffic scene reached 78.5%, the recall rate was 72.2%, and mAP@0.5 was 82.8%.

Coral reef fish density at a tourist destination responded rapidly to COVID-19 restrictions.

Throughout the world, anthropogenic pressure on natural ecosystems is intensifying, notably through urbanisation, economic development, and tourism. Coral reefs have become exposed to stressors related to tourism. To reveal the impact of human activities on fish communities, we used COVID-19-related social restrictions in 2021. In French Polynesia, from February to December 2021, there was a series of restrictions on local activities and international tourism. We assessed the response of fish populations in terms of changes in the species richness and density of fish in the lagoon of Bora-Bora (French Polynesia). We selected sites with varying human pressures-some dedicated to tourism activities, others affected by boat traffic, and control sites with little human presence. Underwater visual surveys demonstrated that fish density and richness differed spatially and temporally. They were lowest on sites affected by boat traffic regardless of pandemic-related restrictions, and when activities were authorised; they were highest during lockdowns. Adult fish density increased threefold on sites usually affected by boat traffic during lockdowns and increased 2.7-fold on eco-tourism sites during international travel bans. Human activities are major drivers of fish density and species richness spatially across the lagoon of Bora-Bora but also temporally across pandemic-related restrictions, with dynamic responses to different restrictions. These results highlight the opportunity provided by pauses in human activities to assess their impact on the environment and confirm the need for sustainable lagoon management in Bora-Bora and similar coral reef settings affected by tourism and boat traffic.

A multi-information fusion anomaly detection model based on convolutional neural networks and AutoEncoder.

Network traffic anomaly detection, as an effective analysis method for network security, can identify differentiated traffic information and provide secure operation in complex and changing network environments. To avoid information loss caused when handling traffic data while improving the detection performance of traffic feature information, this paper proposes a multi-information fusion model based on a convolutional neural network and AutoEncoder. The model uses a convolutional neural network to extract features directly from the raw traffic data, and a AutoEncoder to encode the statistical features extracted from the raw traffic data, which are used to supplement the information loss due to cropping. These two features are combined to form a new integrated feature for network traffic, which has the load information from the original traffic data and the global information of the original traffic data obtained from the statistical features, thus providing a complete representation of the information contained in the network traffic and improving the detection performance of the model. The experiments show that the classification accuracy of network traffic anomaly detection using this model outperforms that of classical machine learning methods.

Becoming Healthier without Paying More? Experimental Evidence from the Impact of Multiple Traffic Lights on Chinese College Students.

The prevalence of overweight and obesity among Chinese residents has become a pressing public health concern. The UK Multiple Traffic Light labeling system, known for its user-friendly design, has demonstrated success in promoting healthier food choices. This paper presents novel findings from a randomized controlled experiment assessing the impact of traffic light labeling on Chinese consumers' food choices. Results indicate that the label significantly reduces the intake of calories, fat, carbohydrates, and sodium without increasing the economic costs of food choices. This study contributes empirical evidence to the effectiveness of traffic light labeling in China, with implications for the country's approach to front-of-pack nutrition labeling.

Road hazard: a systematic review of traffic injuries following recreational cannabis legalization.

PURPOSE: Acute cannabis use is associated with impaired driving performance and increased risk of motor vehicle crashes. Following the Canadian Cannabis Act's implementation, it is essential to understand how recreational cannabis legalization impacts traffic injuries, with a particular emphasis on Canadian emergency departments. This study aims to assess the impact of recreational cannabis legalization on traffic-related emergency department visits and hospitalizations in the broader context of North America. METHODS: A systematic review was conducted according to best practices and reported using PRISMA 2020 guidelines. The protocol was registered on July 5, 2022 (PROSPERO CRD42022342126). MEDLINE(R) ALL (OvidSP), Embase (OvidSP), CINAHL (EBSCOHost), and Scopus were searched without language or date restrictions up to October 12, 2023. Studies were included if they examined cannabis-related traffic-injury emergency department visits and hospitalizations before and after recreational cannabis legalization. The risk of bias was assessed. Meta-analysis was not possible due to heterogeneity. RESULTS: Seven studies were eligible for the analysis. All studies were conducted between 2019 and 2023 in Canada and the United States. We found mixed results regarding the impact of recreational cannabis legalization on emergency department visits for traffic injuries. Four of the studies included reported increases in traffic injuries after legalization, while the remaining three studies found no significant change. There was a moderate overall risk of bias among the studies included. CONCLUSIONS: This systematic review highlights the complexity of assessing the impact of recreational cannabis legalization on traffic injuries. Our findings show a varied impact on emergency department visits and hospitalizations across North America. This underlines the importance of Canadian emergency physicians staying informed about regional cannabis policies. Training on identifying and treating cannabis-related impairments should be incorporated into standard protocols to enhance response effectiveness and patient safety in light of evolving cannabis legislation.

RéSUMé: OBJECTIF: La consommation aiguë de cannabis est associée à une conduite avec facultés affaiblies et à un risque accru d'accidents de la route. À la suite de la mise en œuvre de la Loi canadienne sur le cannabis, il est essentiel de comprendre l'incidence de la légalisation du cannabis à des fins récréatives sur les blessures de la route, en mettant l'accent sur les services d'urgence canadiens. Cette étude vise à évaluer l'impact de la légalisation du cannabis à des fins récréatives sur les visites et les hospitalisations aux urgences liées à la circulation dans le contexte plus large de l'Amérique du Nord. MéTHODES: Une revue systématique a été menée selon les meilleures pratiques et a été rapportée en utilisant les directives PRISMA 2020. Le protocole a été enregistré le 5 juillet 2022 (PROSPERO CRD42022342126). MEDLINE(R) ALL (OvidSP), Embase (OvidSP), CINAHL (EBSCOHost) et Scopus ont été fouillés sans restriction de langue ou de date jusqu'au 12 octobre 2023. Des études ont été incluses si elles examinaient les visites aux urgences et les hospitalisations avant et après la légalisation du cannabis à des fins récréatives. Le risque de biais a été évalué. La méta-analyse n'était pas possible en raison de l'hétérogénéité. RéSULTATS: Sept études étaient admissibles à l'analyse. Toutes les études ont été menées entre 2019 et 2023 au Canada et aux États-Unis. Nous avons trouvé des résultats mitigés concernant l'impact de la légalisation du cannabis récréatif sur les visites aux urgences pour les blessures de la route. Quatre des études incluaient une augmentation des accidents de la route après la légalisation, tandis que les trois autres études n'ont révélé aucun changement significatif. Le risque global de biais était modéré parmi les études incluses. CONCLUSIONS: Cet examen systématique met en évidence la complexité de l'évaluation de l'impact de la légalisation du cannabis récréatif sur les blessures de la route. Nos résultats montrent un impact varié sur les visites aux urgences et les hospitalisations en Amérique du Nord. Cela souligne l'importance pour les médecins d'urgence canadiens de se tenir informés des politiques régionales sur le cannabis. La formation sur l'identification et le traitement des déficiences liées au cannabis devrait être intégrée aux protocoles normalisés afin d'améliorer l'efficacité de l'intervention et la sécurité des patients à la lumière de l'évolution de la législation sur le cannabis.

Application of soil magnetometry and geochemical methods to investigate soil contamination with antimony.

The aim of the study was an assessment of the pollution level and identification of the antimony sources in soils in areas subjected to industrial anthropopressure from: transport, metallurgy and electrical waste recycling. The combination of soil magnetometry, chemical analyzes using atomic spectrometry (ICP-OES and ICP-MS), Sb fractionation analysis, statistical analysis (Pearson's correlation matrix, factor analysis) as well as Geoaccumulation Index, Pollution Load Index, and Sb/As factor allowed not only the assessment of soil contamination degree, but also comprehensive identification of different Sb sources. The results indicate that the soil in the vicinity of the studied objects was characterized by high values of magnetic susceptibility and thus, high contents of potentially toxic elements. The most polluted area was in the vicinity of electrical waste processing plants. Research has shown that the impact of road traffic and wearing off brake blocks, i.e. traffic anthropopression in general, has little effect on the surrounding soil in terms of antimony content. Large amounts of Pb, Zn, As and Cd were found in the soil collected in the vicinity of the heap after the processing of zinc-lead ores, the average antimony (11.31 mg kg-1) content was lower in the vicinity of the heap than in the area around the electrical and electronic waste processing plant, but still very high. Antimony in the studied soils was demobilized and associated mainly with the residual fraction.

Attitudes and experiences of traditional Korean medicine practitioners in cases of traffic accidents: A qualitative study.

Objective: The surge in vehicles has escalated traffic volume, leading to an upswing in traffic accidents and subsequent disorders. Complex symptoms often characterize post-traumatic syndrome from these accidents. Traditional Korean medicine (TKM), increasingly used in car insurance, forms a substantial part of treatment costs. However, the current system lacks explicit fee guidelines and approval criteria for non-reimbursable TKM procedures, relying heavily on practitioners' judgment without robust evidence-based decision-making. This scenario raises concerns about treatment appropriateness and transparency. We aim to explore physicians' perspectives on utilizing TKM in emergency medicine, their participation sentiments, and their session selection process post-traffic accident. Methods: We collected TKM practitioners' opinions regarding their role in clinical environment and involvement in treating patients after traffic accidents. The need for comprehensive and standardized protocols for the diagnosis, treatment, management, and prognosis of patients with post-traumatic syndrome is evident. Additionally, improvements that facilitate rational decision-making by medical consumers and protect the treatment rights of healthcare providers are necessary. Results has emphasized the importance of evidence-based decision-making, establishing appropriate fee structures and detailed criteria for non-reimbursable TKM-based procedures, and enhancing regulations for the reliability and transparency of TKM-based treatments in the context of car insurance. Results and conclusions: The perspective of healthcare providers directly involved in TKM-based treatments must be considered to maintain a sustainable vehicular insurance system, transcending administrative policy discourse. We highlighted the challenges and potential solutions for improving the effectiveness and appropriateness of TKM-based treatments in the context of car insurance.

p­Phenylenediamine antioxidants in PM2.5: New markers for traffic in positive matrix factorization source apportionment.

The extensive utilization of rubber-related products can lead to a substantial release of p-phenylenediamine (PPD) antioxidants into the environment. In recent years, studies mainly focus on the pollution characteristics and health risks of PM2.5-bound PPDs. This study presents long-time scale data of PPDs and N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) in PM2.5 and proposes the innovative use of PPDs as new markers for vehicular emissions in the Positive Matrix Factorization (PMF) source apportionment. The results indicate that PPDs and 6PPD-Q were detectable in 100 % of the winter PM2.5 samples, and the concentration ranges of PPDs and 6PPD-Q are 15.6-2.92 × 103 pg·m-3 and 3.90-27.4 pg·m-3, respectively, in which 6PPD and DNPD are the main compounds. Moreover, a competitive formation mechanism between sulfate, nitrate, ammonium (SNA) and 6PPD-Q was observed. The source apportionment results show that the incorporation of PPDs in PMF reduced the contribution of traffic source to PM2.5 from 13.5 % to 9.5 %. In the traffic source factor profiles, the load of IPPD, CPPD, DPPD, DNPD and 6PPD reaches 91.8 %, 91.6 %, 92.9 %, 80.6 % and 87.2 %, respectively. It`s amazing that traditional markers of traffic source, which often overlap with coal burning and industrial sources, over-estimated the contribution of vehicles by one third or more. The discovery of PPDs as specific markers for vehicular emissions holds significant utility, particularly considering the growing proportion of new energy vehicles in the future. The results may prove more accurate policy implications for pollution control. SYNOPSIS: PPDs are excellent indicators of vehicle emissions, and PMF without PPDs over-estimated the contribution of traffic source to PM2.5.