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.

Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of "logic regression" method.

Aim: Traffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting "Logic Regression" as a novel approach in the traffic field. Method: A case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014-2016. For each of the pedestrians, passengers, and drivers' datasets, logic regression with "logit" link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure. Results: regarding pedestrians, "The occurrence of the accident outside a city in a situation where there was insufficient light" (OR = 6.87, P-value<0.001) and "the age over 65 years" (OR = 2.97, P-value<0.001) increased the chance of DATAS. "Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road" combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, "Accidents happening in outside the city or overturn of the vehicle" combination (OR = 8.55, P-value<0.001), and "accidents happening on defective roads" (OR = 2.18, P-value<0.001) increased the odds of DATAS; When "driver was not injured or the vehicle was two-wheeled", chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for "drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle" (OR = 4.03, P-value<0.001). "Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area" (OR = 6.04, P-value<0.001), as well "the accident which occurred outside the city or on defective roads, and the drivers were male" had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001). Conclusion: By focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network.

As an important part of the unmanned driving system, the detection and recognition of traffic sign need to have the characteristics of excellent recognition accuracy, fast execution speed and easy deployment. Researchers have applied the techniques of machine learning, deep learning and image processing to traffic sign recognition successfully. Considering the hardware conditions of the terminal equipment in the unmanned driving system, in this research work, the goal was to achieve a convolutional neural network (CNN) architecture that is lightweight and easily implemented for an embedded application and with excellent recognition accuracy and execution speed. As a classical CNN architecture, LeNet-5 network model was chosen to be improved, including image preprocessing, improving spatial pool convolutional neural network, optimizing neurons, optimizing activation function, etc. The test experiment of the improved network architecture was carried out on German Traffic Sign Recognition Benchmark (GTSRB) database. The experimental results show that the improved network architecture can obtain higher recognition accuracy in a short interference time, and the algorithm loss is significantly reduced with the progress of training. At the same time, compared with other lightweight network models, this network architecture gives a good recognition result, with a recognition accuracy of 97.53%. The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system.

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.

Refractive error and abnormal stereopsis association with road traffic accidents in Saudi Arabian truck drivers.

PURPOSE: To investigate the visual functions of truck drivers of the Kingdom of Saudi Arabia (KSA) in the region of Qassim and Dammam and to see if is there any association between these visual functions and self-reported road traffic accidents (RTA). METHODS: It is a cross-sectional, descriptive study. LogMAR visual acuity, refractive error, color vision, stereopsis, and confrontation visual fields were measured in 300 truck drivers in the Qassim and Dammam regions of KSA. Driving-related history and incidence of RTA from the past 3 years, systemic history, and general eye compliance history were collected through a self-reporting questionnaire. RESULTS: Among 300 truck drivers examined, 54 (18.4%) subjects have a refractive error, 14 (4.7%) subjects have color vision deficiency, 37 (12.2%) subjects have abnormal stereo acuity, and none of them have confrontation visual field defect. RTA was reported in 25 (8.3%) subjects. The current study has found RTA is significantly associated with refractive error (P = 0.01) and abnormal stereopsis (P < 0.01). Systemic history revealed that 11% of the subjects had diabetes mellitus. CONCLUSION: The current study is the first to report on the visual functions of KSA truck drivers. Hence, the current study has found a significant association between visual functions and RTA among truck drivers, we recommend a comprehensive examination need to be part of issuing driver's licenses in KSA. More studies with larger samples from different regions of KSA are needed to extrapolate these findings.

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.

Home and School Pollutant Exposure, Respiratory Outcomes, and Influence of Historical Redlining.

BACKGROUND: The discriminatory and racist policy of historical redlining in the United States (U.S.) during the 1930s played a role in perpetuating contemporary environmental health disparities. OBJECTIVE: Our objectives were to determine associations between home and school pollutant exposure (fine particulate matter (PM2.5), nitrogen dioxide (NO2)) and respiratory outcomes (Composite Asthma Severity Index (CASI), lung function) among school-aged children with asthma and examine whether associations differed between children who resided and/or attended school in historically redlined compared to non-redlined neighborhoods. METHODS: Children ages 6 to 17 with moderate-to-severe asthma (N=240) from 9 U.S. cities were included. Combined home and school exposure to PM2.5 and NO2 was calculated based on geospatially assessed monthly averaged outdoor pollutant concentrations. Repeated measures of CASI and lung function were collected. RESULTS: Overall, 37.5% of children resided and/or attended schools in historically redlined neighborhoods. Children in historically redlined neighborhoods had greater exposure to NO2 (median: 15.4 vs 12.1 ppb) and closer distance to a highway (median: 0.86 vs 1.23 km), compared to those in non-redlined neighborhoods (p<0.01). Overall, PM2.5 was not associated with asthma severity or lung function. However, among children in redlined neighborhoods, higher PM2.5 was associated with worse asthma severity (p<0.005). No association was observed between pollutants and lung function or asthma severity among children in non-redlined neighborhoods (p>0.005). CONCLUSIONS: Our findings highlight the significance of historical redlining and current environmental health disparities among school-aged children with asthma, specifically, the environmental injustice of PM2.5 exposure and its associations with respiratory health.

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.

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.

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.

Implementation of the new acts on driving under the influence of alcohol and clinical outcomes for patients severely injured in road traffic crashes.

OBJECTIVE: Driving under the influence (DUI) of alcohol is a major risk factor for fatal road traffic injuries (RTIs) worldwide. This study aimed to investigate the relationship between the implementation of new acts on DUI of alcohol and the clinical outcomes of patients with severe RTIs in Korea. METHODS: This is a community-based cross-sectional study using a nationwide severe trauma registry in Korea. In 2018, 2 acts with the Yoon Chang-Ho Act (Yoon's Act) were passed to strengthen the punishment for drunk driving fatal RTIs (first Yoon's act) and lower the blood alcohol concentration limit to restrict driver's licenses (second Yoon's act). The first Yoon's act was implemented on December 18, 2018, and the second Yoon's act was implemented on June 25, 2019. The study periods were categorized as pre-Act-1, pre-Act-2, Act-1, and Act-2 according to the application of Yoon's Act, and the study outcome was in-hospital mortality. Multivariable logistic regression analysis was conducted to estimate the relationship of the new acts and in-hospital mortality. RESULTS: Among a total of 20,376 patients with severe RTIs and 7,928 patients (drivers) with RTIs (hereafter drivers), the in-hospital mortality rates were 20.8% and 17.0%, and alcohol-related RTIs accounted for 9.7% and 8.1%, respectively. Severe RTIs tended to increase with each period (25.5 cases/day, 24.5 cases/day, 26.8 cases/day, and 30.4 cases/day, P for trend <.01). In-hospital mortality significantly decreased during the Act-2 period compared to the pre-Act-2 period for all patients with severe RTIs (adjusted odds ratio = 0.54, 95% confidence interval 0.43-0.67) and drivers with RTIs (adjusted odds ratio = 0.50, 95% confidence interval 0.34-0.73). CONCLUSIONS: Implementation of the new acts on DUI of alcohol was associated with lower odds for in-hospital mortality for patients with severe RTIs. Further studies are needed to evaluate the long-term impact of the new acts on reducing alcohol-related RTIs.

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.