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)

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.

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.

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.

Road traffic noise exposure and its impact on health: evidence from animal and human studies-chronic stress, inflammation, and oxidative stress as key components of the complex downstream pathway underlying noise-induced non-auditory health effects.

In heavily urbanized world saturated with environmental pollutants, road traffic noise stands out as a significant factor contributing to widespread public health issues. It contributes in the development of a diverse range of non-communicable diseases, such as cardiovascular diseases, metabolic dysregulation, cognitive impairment, and neurodegenerative disorders. Although the exact mechanisms behind these non-auditory health effects remain unclear, the noise reaction model centres on the stress response to noise. When exposed to noise, the body activates the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, leading to the secretion of stress hormones like catecholamines and cortisol. Prolonged exposure to noise-induced stress results in chronic inflammation and oxidative stress. This review underscores the role of inflammation and oxidative stress in the progression of noise-induced vascular dysfunction, disruption of the circadian rhythm, accelerated aging, neuroinflammation, and changes in microbiome. Additionally, our focus is on understanding the interconnected nature of these health outcomes: These interconnected factors create a cascade effect, contributing to the accumulation of multiple risk factors that ultimately lead to severe adverse health effects.

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.

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.

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.

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.

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.

Exploring the effects of two-segment loop tunnels on termite food transport efficiency: a simulation study.

This study explores the food transport efficiency (E) in a termite tunnel consisting of a main tunnel and a 2-segment loop tunnel through a model simulation. Simulated termites navigate between the main and loop tunnels through branching nodes (a, b, c, d) with associated probabilities (P1, P2, P3, P4). The loop tunnel locations (δ) are considered: near the nest (δ = 1), at the center of the main tunnel (δ = 2), and close to the food site (δ = 3). The results reveal that for δ = 1, paths such as a → d → b → c and c → d → b → a exhibited high E values. Conversely, for δ = 2, P3 and P4 demonstrate elevated E values ranging from 0.4 to 0.6. For δ = 3, paths like c → d or c → b display high E values, emphasizing the significance of in-loop separation tunnels (characterized by P3 and P4) in alleviating traffic congestion. Partial rank correlation validates that P1 and P2 minimally influence E, while P3 and P4 significantly negatively impact E, regardless of δ. However, for δ = 2, the influence of P3 and P4 is notably reduced due to the positional symmetry of the loop tunnel. In the discussion, we address model limitations and propose strategies to overcome them. Additionally, we outline potential experimental validations to ensure a comprehensive understanding of the dynamics governing termite food transport within tunnels.

Subjective memory concerns and car collisions: A cross-sectional cohort study among older Japanese drivers.

Background: A previous study suggested older drivers with subjective memory concerns (SMC) had increased odds of experiencing car collisions, but whether SMC in different contexts and the number of SMC applicable items change this association is unknown. The aim of this study was to examine the association between SMC and car collisions among older drivers in Japan. Methods: This cross-sectional study was conducted using data from a Japanese community-based cohort study. Participants were community-dwelling older adults aged ≥60 years. SMC was assessed using five questions: 1) "Do you feel you have more problems with memory than most?" 2) "Do you have any difficulty with your memory?" 3) "Do you forget where you have left things more than you used to?" 4) "Do you forget the names of close friends or relatives?" and 5) "Do other people find you forgetful?" Participants were asked about their experiences with car collisions during the previous two years. Results: A total of 13,137 older drivers (72.1 ± 5.5 years old, and 43.6 % female) were analyzed. Cochran-Armitage trend test showed that as the number of SMC applicable items increased, the percentage of the experiences of car collisions significantly increased (6.8 %-15.8 %, P < 0.001). Logistic regression models showed each SMC question was associated with an increased odds ratio (OR) of car collisions (OR 1.26 to 1.71, all P < 0.001) after adjusting for confounding factors. As the number of SMC applicable items increased, the OR of car collisions significantly increased (OR 1.19 to 2.28, all P < 0.05, P for trend <0.001). Conclusions: This cross-sectional study among community-dwelling older drivers in Japan suggested each SMC question and the number of applicable items were associated with car collisions. SMC may be a sign of increased risk of traffic incidents for older drivers.