Can there be a genetic marker for Post-Traumatic Stress Disorder outside the Diagnostic Interview within the Scope of Forensic Psychiatry? / Travma Sonrasi Stres Bozuklugunun Adli Psikiyatri Kapsaminda Tani Görüsmesi Disinda Genetik Belirteci Olabilir mi?

OBJECTIVE: This study was conducted to investigate the role of two candidate polymorphisms to improve the diagnosis of Post-Traumatic StressDisorder (PTSD) in forensic psychiatry settings. METHODS: Individuals who applied to our unit with PTSD symptoms following a traffic accident were included. The control group consisted of people who had experienced a similar accident without any symptoms. Sociodemographic data-form, Hamilton Depression Rating Scale and Anxiety Sensitivity Index-3 (ASI 3) were applied to the patients and controls, and the frequencies of the rs8042149 polymorphic allele of the RORA gene and the rs717947 polymorphic allele (4p15) were investigated. RESULTS: A total of 103 people were included (54 case, 49 control). The rates of polymorphisms were not different between the groups. Higher education levels were associated with lower PTSD incidence while higher scores in the Social Subscale of ASI strongly predicted the occurrence of PTSD. CONCLUSION: The polymorphisms assessed did not help to differentiate the groups in the current sample. The potential of the Social Subscale of ASI-3 in predicting the occurrence of PTSD following a trauma should be evaluated in a longitudinal design.

Mild Traumatic Brain Injury by the Glasgow Coma Scale Score and Early CT-Brain Findings in Acute Alcohol Intoxication Patients: A Prospective Observational Study.

Introduction Traumatic brain injuries (TBI) in recent years have proved to be a significant public health problem, with potentially life-changing consequences for the individual and their family. Alcohol consumption is a regular, well-documented problem among persons sustaining TBI due to road traffic accidents and accidental falls. The primary objective of this study was to find out the correlation between the Glasgow Coma Scale (GCS) score and CT brain findings among mild TBI patients under acute alcohol intoxication and determine if early CT-brain is indicated in this group. Methods A prospective observational study was conducted involving 111 alcohol-intoxicated patients with mild head injuries admitted to the surgical wards of Thanjavur Medical College Hospital over a period of three months. The Glasgow Coma Scale was used to assess the patient's neurological status and determine the severity of the brain injury. A semi-structured CT-brain findings chart and a severity of alcohol intoxication objective-based scoring system were developed and validated by experts. Descriptive statistics tools such as frequency, percentage, and mean were used, along with inferential statistics tools like the Chi-squared test, Fisher exact test, and Pearson's correlation coefficient test. Results The study findings showed that the comparison of GCS with early CT-brain was significant at a p-value of 0.012, and a negative correlation (r=-0.253) was found between GCS and CT-brain findings. A comparison of CT-brain findings with the severity of alcohol intoxication was non-significant at a p-value of 0.433. Conclusions Early CT-brain in intoxicated mild TBI patients may have a positive impact on early diagnosis and management, even in centers with limited resources catering to low-income population groups. The results of our short-term study show that early CT-Brain picks up lesions and helps initiate early management while it is up to the attending physician to keep in mind an adverse cost-benefit ratio in overuse of hospital resources and misdiagnosis leading to undertreatment causing long-term sequelae and morbidity before prescribing early CT-brain in this cohort of patients.

Evaluation of thermal effects on urban road spatial structure: A case study of Xuzhou, China.

Urban heat islands (UHI) are important environmental issue in cities where urban spatial structure has been proven to play an important role in alleviating UHI effects. The relationship between land surface temperature and urban spatial structures has been explored, providing strong support for their cooling effects. Urban roads are the skeleton of urban spatial structures, with obvious spatial structure characteristics; however, research on the relationship between roads and the thermal environment has been mostly focused at the micro and meso level, lacking exploration at the macro spatial structure scale. Xuzhou-a typical average-sized city in China-was selected as the research object and the road system as the carrier. The thermal environmental effects of road elements such as their structural attributes, geometric attributes and unique construction attributes were quantitatively analyzed using geographically weighted regression analysis. The results revealed that 1) the contribution of roads in the study area to the UHI effect is relatively stable; therefore, this area should become an important cooling space to decompose UHI patch connectivity and thus decrease the UHI effect. 2) the self-organizing structural characteristics of urban roads affect their thermal environments where in the straightness of the road structure and road thermal environment showed a clear overall negative correlation And 3) the length and width of the road segments had negative and positive effects on the thermal environment, respectively. The green coverage of the roads has a global negative effect on the thermal environment, but shows obvious spatial non-stationarity. Therefore, green measures must be implemented in different regions. The results here provide a quantitative basis for urban road system planning and urban form management and control that incorporates thermal environment improvements, as well as a reference for the study of urban thermal environments under different spatial forms and planning control systems in other countries and regions.

Information density threshold of urban road traffic signs based on visual comfort.

The content and density of traffic signs directly affect the operation of urban road traffic and drivers. To overcome the limitations of quantitative research on the density threshold of traffic signs on urban roads, a real vehicle experiment was conducted to record the psychological characteristics of drivers. Four psychological parameters of drivers-pupil area, fixation intensity, heart rate change rate, and heart rate variability-were explored. Subsequently, principal component analysis was used to present a new index, S, divided into 5 grade scales, to represent the driving visual comfort level. The information entropy theory was applied to quantify the amount of information on road traffic signs that are included in driving tests, and a regression relationship between the traffic sign information and comfort index S was established. The visual psychological load thresholds for different comfort levels were -2.289≤S < -1.526 for very comfortable, 1.526≤S < -0.763 for relatively comfortable, -0.763≤S ≤ 0.763 for comfortable, 0.763

Emissions of gaseous nitrous acid (HONO) from diesel trucks: Insights from real-driving emission experiments.

Nitrous acid (HONO) serves as a substantial contributor in the atmospheric chemistry of hydroxyl radicals (·OH). Despite its significance, the primary sources of atmospheric HONO, particularly diesel truck emissions, have not been thoroughly examined. This study investigated the factors influencing HONO emissions via on-road exhaust emission tests using a self-developed portable measurement system. The findings show that the HONO emissions measured during on-road testing are higher than those measured during chassis dynamometer testing, highlighting the need for on-road tests to capture HONO emissions. Emission standards and truck types greatly influence HONO emission factors (EFs), with stricter regulations leading to lower emissions. The average fuel consumption-based EFs for light-duty diesel trucks ranged from 0.93 g/kg for China III to 0.08 g/kg for China VI. For medium-duty diesel trucks, the EFs decrease from 1.43 g/kg for China III to 0.19 g/kg for China V. Moreover, the vehicle-specific power demonstrated a stronger correlation with HONO emissions. This research showed that HONO emissions were significantly higher without or before the optimal operation of the SCR device, and the device notably reduced HONO emissions. Future research should focus on the impact of various exhaust after-treatment systems and explore HONO conversion mechanisms.

An improvement of the conceptual system of the sequential events model of road crashes (i-MOSES).

Background: The circulation of vehicles, motorized or not, is a risky activity that can lead to a traffic accident in which all road users can be affected. Road accidents generate high personal, labor, health and economic costs, as well as civil, administrative and even criminal responsibilities. Therefore, it is necessary to carry out a correct investigation of these road accidents. This paper reviews one of the models used for this investigation, the sequential events model for road crashes called MOSES. This model simplifies into a single sequential analysis the actions and conditions that have generated the occurrence and correlation of events that have led to a collision between two bodies, at least one of which is a vehicle, with harmful consequences for the environment, people and things. Methods: Analyzing the road accidents that occurred in the city of Badajoz between 2018 and 2022, this work proposes a new position of the sequential events in road accidents. This new position is present in more than fifty percent of the analyzed road accidents. How this new position can improve the description of traffic accidents is tested by analyzing an actual traffic accident recorded in the city of Badajoz between a motorcycle and a car. Results: The new position has been called Trust Position (TP) and is located between the Real Perception Position (RPP) and the Decision Enforcement Position (DEP) in the sequential events model for road crashes (i-MOSES). Furthermore, in this improvement of the MOSES model (i-MOSES), the reaction time (RT) is analyzed in more depth with the PIEV (Perception, Intellection, Emotion and Volition) theory, establishing that between RPP and TP are present the phases of perception and intellection, and between TP and DEP are present the phases of emotion and volition. Conclusions: This analysis shows how the proposed i-MOSES model allows for a deeper and more effective analysis of the causes that generated the traffic accident and all its circumstances. Moreover, it provides conclusions closer to the reality of how the accident actually happened and why it could have happened, ultimately leading to preventive measures to avoid future accidents.

A Case Report on a Syndesmotic Ankle Injury Treated With Arthrex TightRope.

Syndesmotic ankle injuries, often referred to as "high ankle sprains," pose intricate challenges in orthopedic practice, particularly among athletes engaged in high-impact sports. Conventional treatments have encompassed conservative approaches and the use of syndesmotic screws, each beset by inherent limitations. The Arthrex TightRope system has emerged as a pioneering alternative, heralded for its capacity to facilitate physiologic micromotion, eliminate the necessity for hardware removal, and expedite early rehabilitation. This case report delineates the management of a 29-year-old male professional soccer player who suffered a trimalleolar ankle fracture compounded by a severe syndesmotic injury subsequent to a road traffic accident. The patient underwent a comprehensive treatment involving open reduction and internal fixation (ORIF) of all three malleoli, complemented by syndesmotic stabilization employing the Arthrex TightRope system. Post-operative care encompassed a regimen of gradual weight-bearing and methodical rehabilitation. At the one-year follow-up, the patient demonstrated excellent ankle joint function devoid of pain or complications related to hardware, underscoring the efficacy of managing syndesmotic and malleolar fractures successfully. This case underscores the potential advantages of integrating traditional ORIF techniques with contemporary syndesmotic fixation strategies like the TightRope system for complex ankle fractures, advocating for further research to refine their optimal utilization in clinical settings.

LESA-Net: Semantic segmentation of multi-type road point clouds in complex agroforestry environment.

Point-cloud semantic segmentation is a visual task essential for agricultural robots to comprehend natural agroforestry environments. However, owing to the extremely large amount of point-cloud data in agroforestry environments, learning effective features for semantic segmentation from large-scale point clouds is challenging. Therefore, to address this issue and achieve accurate semantic segmentation of different types of road-surface point clouds in large-scale agroforestry environments, this study proposes a point-cloud semantic segmentation network framework based on double-distance self-attention. First, a point-cloud local feature enhancement module is proposed. This module primarily extends the receptive field and enhances the generalizability of multidimensional features by incorporating reflection intensity information and a spatial feature-encoding block that is enhanced with contextual semantic information. Second, we introduce a dual-distance attention pooling (DDAPS) block based on the self-attention mechanism. This block initially learns the feature representation of the local neighborhood of each point through the self-attention mechanism. Then, it uses the DDAPS block to aggregate more discriminative local neighborhood point features. Finally, extensive experimental results on large-scale point-cloud datasets, SemanticKITTI and RELLIS-3D, demonstrate that our algorithm outperforms similar algorithms in large-scale agroforestry environments.

Descriptive analysis of road traffic crashes encountered by Tanzanian motorcycle taxi drivers trained in first aid.

Introduction: In Dar es Salaam, Tanzania, death from road traffic crashes (RTC) occurs at roughly double the global rate. In this study, we sought to understand the locations and types of vehicles involved in RTC in Dar es Salaam encountered by a cohort of motorcycle taxi drivers previously trained in first aid. Methods: This was a quasi-non-randomized interventional study, cohort subtype, with three-month follow-up. Some 186 motorcycle taxi drivers were selected by convenience sampling from 16 heavily populated, central wards and trained in a basic hemorrhage control course. Participants reported the location and types of vehicles involved in RTCs they encountered and intervened upon through performing bleeding control interventions. Surveys were designed on KoboToolbox and administered via phone call at monthly intervals over a three-month period. The main outcome measures were the location of crash encounters and types of vehicles involved. Results: In all 62 unique participants (33.3 %) encountered and provided bleeding control interventions to 83 injured individuals following 69 RTC in at least 31 distinct city wards, despite training only having occurred in 16 wards. Eight crash locations were not recorded. Crashes in distant wards typically contained major roads. Most commonly, crashes involved a motorcycle without the involvement of another vehicle (n=20), followed by motorcycle vs. car/three-wheeled vehicle (n=15), motorcycle vs. bus/van (n=10), motorcycle vs. motorcycle (n=9), motorcycle vs. pedestrian (n=7), pedestrian vs. bus/van (n=2), pedestrian vs. car/three-wheeled vehicle (n=1), motorcycle vs. bicycle (n=1), multi vehicle (n=1), and other (n=3). Conclusions: Motorcycle taxi drivers trained in hemorrhage control frequently encounter and intervene upon RTC in wards where they are based as well as in distant locations, commonly in wards containing major roads. Expanding first aid training for motorcycle taxi drivers could improve timely access to emergency care for RTC victims. Since most crashes involved motorcycles, road safety training should be integrated into future courses.

Quantifying future carbon emissions uncertainties under stochastic modeling and Monte Carlo simulation: Insights for environmental policy consideration for the Belt and Road Initiative Region.

This study critically examines future carbon (CO2) emissions in the Belt & Road Initiative (BRI) region, considering factors such as energy consumption, economic growth, population growth, and population density. The objective of this study is to identify critical areas of higher emissions, which require policy intervention capable of strengthening sustainability in the BRI compact. A combined approach of stochastic modeling and Monte Carlo simulations was employed, utilizing panel data from 45 countries in the BRI region from 1990 to 2021. Results confirm that emissions are higher in all scenarios in direct proportion to electric power consumption, population growth, and Gross Domestic Product (GDP) growth. In scenarios with high emissions, a continuous and significant upward trend in CO2 emissions was observe. The medium emissions scenario exhibited a more moderated rise in emissions, suggesting a balance between economic development and environmental considerations. Critical areas for future environmental policy-making resides in electric power consumption, population growth, and GDP growth. The study strongly recommends for a shift from the current focus on road and railway infrastructure to renewable energy infrastructure, green innovations and efficient technology transfer to member countries. Without this, the BRI region is likely to face increased emissions, posing significant challenges to future sustainable development and global environmental sustainability.

Enriched traffic datasets for the city of Madrid: Integrating data from traffic sensors, the road infrastructure, calendar data and weather data.

The proliferation of urban areas and the concurrent increase in vehicular mobility have escalated the urgency for advanced traffic management solutions. This data article introduces two traffic datasets from Madrid, collected between June 2022 and February 2024, to address the challenges of traffic management in urban areas. The first dataset provides detailed traffic flow measurements (vehicles per hour) from urban sensors and road networks, enriched with weather data, calendar data and road infrastructure details from OpenStreetMap. This combination allows for an in-depth analysis of urban mobility. Through preprocessing, data quality is ensured by eliminating inconsistent sensor readings. The second dataset is enhanced for advanced predictive modelling. It includes time-based transformations and a tailored preprocessing pipeline that standardizes numeric data, applies one-hot encoding to categorical features, and uses ordinal encoding for specific features. In constructing the datasets, we initially employed the k-means algorithm to cluster data from multiple sensors, thereby highlighting the most representative ones. This clustering can be adapted or modified according to the user's needs, ensuring flexibility for various analyses and applications. This work underscores the importance of advanced datasets in urban planning and highlights the versatility of these resources for multiple practical applications. We highlight the relevance of the collected data for a variety of essential purposes, including traffic prediction, infrastructure planning, studies on the environmental impact of traffic, event planning, and conducting simulations. These datasets not only provide a solid foundation for academic research but also for designing and implementing more effective and sustainable traffic policies. Furthermore, all related datasets, source code, and documentation have been made publicly available, encouraging further research and practical applications in traffic management and urban planning.

Remote sensing image road network detection based on channel attention mechanism.

Extracting and detecting road network consistency from high-resolution remote sensing images has been a hot and difficult problem in the computer vision. Although it has made significant progress, there is still a phenomenon of high training accuracy but unsatisfactory actual extraction and detection results. The attention mechanism is one of the efficient and practical mechanisms in deep learning. It improves the performance of deep learning by selectively focusing on a portion of all information while ignoring other visible information, while effectively utilizing computing resources. Numerous experiments have also confirmed that the attention mechanism is resource-saving and effective. Its plug and play feature brings great convenience to programmers. In order to provide better road network detection results and solve the above problem, this paper combines the channel attention mechanism with ResNet and proposes SE-ResNet and ECA-ResNet for remote sensing image road network detection, making networks extract and learn road network features and ignore some non-road network features. The experimental results show that on the Massachusetts roads (MR) and CHN6-CUG roads datasets, ECA-ResNet and SE-ResNet based on channel attention mechanism perform similar to LeNet7 and ResNet in terms of accuracy, loss, accuracy convergence, and loss convergence, and even increase a certain computational burden. However, their final road network detection results (including road network detection pixel count, precision, recall, accuracy, IOU, F1 score, and actual road network detection result) of the former are significantly better than those of the latter. The channel attention mechanism makes the deep neural network pay more attention to the extraction and learning of road network features, while ignoring the extraction and learning of some non-road network features, which improves the accuracy of containing road network samples and reduces the accuracy of not containing road network samples. Therefore, the performance of ECA-ResNet and SE-ResNet is similar to that of LeNet7 and ResNet in the accuracy, loss, accuracy convergence and loss convergence, but the final road network detection results of ECA-ResNet and SE-ResNet are significantly better than those of LeNet7 and ResNet. Therefore, the proposed ECA-ResNet and SE-ResNet have broad application prospects in road network detection, especially ECA-ResNet.

Optimization of Chinese film and television communication technology in Portuguese-speaking areas under the application of digital Internet of Things.

With the continuous change and progress of the world system, cultural communication has become a vital topic in today's society. This study aims to optimize the communication effect of Chinese film in Portuguese-speaking countries and promote the development of cultural communication in China through deep learning (DL) technology. First, this study utilizes DL technology to design film feature recognition and classification models to provide technical support for Chinese film communication. Second, by exploring the principle, development, and function of the digital Internet of Things, the study analyzes the evolution and spread of Chinese film in Portuguese-speaking countries and uses the Bayes algorithm to optimize the model. The results show that the calculation time of the designed model is shorter and the accuracy is higher, which offers an important reference for the effective communication of Chinese films in Portuguese-speaking countries. Hence, this study not only provides technical support for the improvement of the international communication effect of Chinese film but also contributes to the development of cultural communication in China.

Road crash dynamics in Malaysia: Analysis of trends and patterns.

Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014-2022.

Visual Navigation of Caged Chicken Coop Inspection Robot Based on Road Features.

The speed and accuracy of navigation road extraction and driving stability affect the inspection accuracy of cage chicken coop inspection robots. In this paper, a new grayscale factor (4B-3R-2G) was proposed to achieve fast and accurate road extraction, and a navigation line fitting algorithm based on the road boundary features was proposed to improve the stability of the algorithm. The proposed grayscale factor achieved 92.918% segmentation accuracy, and the speed was six times faster than the deep learning model. The experimental results showed that at the speed of 0.348 m/s, the maximum deviation of the visual navigation was 4 cm, the average deviation was 1.561 cm, the maximum acceleration was 1.122 m/s2, and the average acceleration was 0.292 m/s2, with the detection number and accuracy increased by 21.125% and 1.228%, respectively. Compared with inertial navigation, visual navigation can significantly improve the navigation accuracy and stability of the inspection robot and lead to better inspection effects. The visual navigation system proposed in this paper has better driving stability, higher inspection efficiency, better inspection effect, and lower operating costs, which is of great significance to promote the automation process of large-scale cage chicken breeding and realize rapid and accurate monitoring.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes.

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety.

Analysis of Road Roughness and Driver Comfort in 'Long-Haul' Road Transportation Using Random Forest Approach.

Global trade depends on long-haul transportation, yet comfort for drivers on lengthy trips is sometimes neglected. Rough roads have a major negative influence on driver comfort and increase the risk of weariness, distracted driving, and accidents. Using Random Forest regression, a machine learning technique well-suited to examining big datasets and nonlinear relationships, this study examines the relationship between road roughness and driver comfort. Using the MIRANDA mobile application, data were gathered from 1,048,576 rows, including vehicle acceleration and values for the International Roughness Index (IRI). The Support Vector Regression (SVR) and XGBoost models were used for comparative analysis. Random Forest was preferred because of its ability to be deployed in real time and use less memory, even if XGBoost performed better in terms of training time and prediction accuracy. The findings showed a significant relationship between driver discomfort and road roughness, with rougher roads resulting in increased vertical acceleration and lower comfort levels (Road Roughness: SD-0.73; Driver's Comfort: Mean-10.01, SD-0.64). This study highlights how crucial it is to provide smooth surfaces and road maintenance in order to increase road safety, lessen driver weariness, and promote long-haul driver welfare. These results offer information to transportation authorities and policymakers to help them make data-driven decisions that enhance the efficiency of transportation and road conditions.

Comparative Study of Lightweight Target Detection Methods for Unmanned Aerial Vehicle-Based Road Distress Survey.

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy.

[Impact of road infrastructure on ecological networks in the Guangdong-Hong Kong-Macao Greater Bay Area, China]. / 粤港澳大湾区道路基础设施对生态网络的影响.

The construction of road infrastructure has resulted in the degradation of wildlife habitat and the decrease of ecological network connectivity and stability. Studying the impacts of road infrastructure on wildlife life and migration is significant for regional wildlife conservation and ecological network optimization. We assessed the impacts of road infrastructure on habitat suitability using the MaxEnt model based on wildlife occurrence point data in the Guangdong-Hong Kong-Macao Greater Bay Area. We constructed the ecological networks and identified ecological breakpoints using the minimum cumulative resistance model, and compared the ecological network connectivity of different scenarios with the landscape connectivity index and graph theory index. The results showed that railway and motorway significantly affected habitat suitability, causing a decrease in wildlife habitat suitability. Affected by road infrastructure, the fragmentation of ecological sources intensified, the resistance of ecological corridors increased, and the ecological network connectivity and stability significantly decreased. A total of 536 ecological breakpoints were identified, which were concentrated in the area adjacent to ecological sources. The results would provide important scientific references for wildlife habitat conservation and ecological restoration in the Guangdong-Hong Kong-Macao Greater Bay Area.

Auditing learner driver information about floodwaters: An environmental scan of government issued resources in Australia.

INTRODUCTION: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. METHODS: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted. RESULTS: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. DISCUSSION: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. CONCLUSIONS: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. PRACTICAL APPLICATIONS: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive.