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)

A comprehensive evaluation of the frog effect on drivers in mountain highway tunnels - The effect of low-volume intermittent information.

OBJECTIVE: The driver's inability to fully absorb and react to operational cues while driving is like boiling a frog in warm water. With intermittent, low-volume information, drivers can underreact by ignoring these minor but continuous changes. This paper aims to provide an opportunity to test the effects of intermittently occurring low-volume information on drivers. METHODS: A real vehicle test with naturalistic driving was used to collect driving speed data from 40 drivers on a highway tunnel section in Chongqing, China, where nine tunnels are located. Drivers were classified into three categories according to the degree of compliance of their driving speed with the speed limit required by traffic signs, and drivers were analyzed in terms of their sensitivity to traffic signs and their reaction to driving maneuvers. RESULTS: Conservative drivers are the most absorbent of low-volume intermittent information, and the cumulative effect of the frog effect does not exceed 2.00 km; eager drivers tend to ignore this information, and the cumulative effect of the frog effect reaches 2.91 km; and the general type of driver is in the middle of these two types of drivers, and the frog effect gradually penetrates the driving speed in a weakly increasing manner, up to a maximum of 9.8 km. CONCLUSION: At the beginning of a journey, drivers are most sensitive to traffic signs, and low-volume intermittent information can also play a role in guiding driving operations effectively at this time. However, as the driving distance increases, the effect of the frog effect on different types of drivers gradually increases, even exceeding the effect caused by the black-and-white hole effect, especially when driving in tunnel groups. Considering the driving characteristics of different types of drivers to improve the deployment of low-volume intermittent information and reduce the distance of the frog effect can effectively improve driving safety.

Analysis of dynamic evolution characteristics and driving factors of regional synergistic effect of reducing carbon and saving water: the case of Yangtze River Delta Urban Agglomerations, China.

Carbon emissions and water consumption are both important factors affecting sustainable development. Therefore, it is necessary to put them in the same research framework and investigate the synergy. In this study, the dynamic evolution characteristics of the synergistic effect of reducing carbon and saving water (RCSW) were analyzed. Then, taking the Yangtze River Delta Urban Agglomerations (YRDUA) as the research object, the influencing factors and specific paths of the synergistic effect were clarified. The results showed that the low-carbon emission efficiency (LCEE) had a stable synergy with the intensive utilization efficiency of water resources (IUEWR) in the YRDUA. Government financial expenditure, actual use of foreign capital, and population density were the most significant driving forces for the synergistic effect of RCSW, with q values of 0.561, 0.363, and 0.240, respectively. In addition, most of the interactions of the driving factors were nonlinear enhancement and double-factor enhancement.

Drivers and collaborative governance of public health emergency response in the context of digital city.

Introduction: With the frequent occurrence of public health events, the government inevitably makes many mistakes in emergency management. In modern emergency management, it is particularly important to promote the diversification of emergency management subjects and improve the government's emergency management ability. Methods: In order to make up for the deficiency of government's participation in public health emergency management, this paper analyzes the driving factors and driving effects of enterprises' participation in public health emergency response under the background of digital city. A fully explained structural model is used to analyze the relationship between the different drivers. In addition, the spatial and temporal distribution characteristics of public health events were analyzed through spatial auto-correlation. On this basis, the government cooperative governance strategy is discussed. Results and discussion: The results show that in the context of digital cities, there are 14 driving factors for enterprises to participate in public health emergency response. The most important factors are the company's own development needs, relative technical advantages and so on. The driving efficiency is mainly concentrated in three aspects: psychology, resources and structure. Public health events have periodicity in time distribution and regional differences in spatial distribution. The significance of this study is to help the government improve the emergency management ability from different angles.

CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture.

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver's distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model's feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model's accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

Rapid nanomolar detection of cocaine in biofluids by electrochemical aptamer-based sensor with low-temperature effect for drugged driving screening.

Cocaine is one of the most abused illicit drugs, and its abuse damages the central nervous system and can even lead directly to death. Therefore, the development of simple, rapid and highly sensitive detection methods is crucial for the prevention and control of drug abuse, traffic accidents and crime. In this work, an electrochemical aptamer-based (EAB) sensor based on the low-temperature enhancement effect was developed for the direct determination of cocaine in bio-samples. The signal gain of the sensor at 10 °C was greatly improved compared to room temperature, owing to the improved affinity between the aptamer and the target. Additionally, the electroactive area of the gold electrode used to fabricate the EAB sensor was increased 20 times by a simple electrochemical roughening method. The porous electrode possesses more efficient electron transfer and better antifouling properties after roughening. These improvements enabled the sensor to achieve rapid detection of cocaine in complex bio-samples. The low detection limits (LOD) of cocaine in undiluted urine, 50% serum and 50% saliva were 70 nM, 30 nM and 10 nM, respectively, which are below the concentration threshold in drugged driving screening. The aptasensor was simple to construct and reusable, which offers potential for drugged driving screening in the real world.

Chinese adaptation of the reckless driving behaviour scale: Testing its psychometric properties and links with safe driving climate among family and peers.

Purpose: The present study aimed to revise the Reckless Driving Behaviour Scale (RDBS) and examined its reliability and validity among young Chinese drivers. Methods: The RDBS, the Safe Driving Climate among Friends Scale (SDCaF), the Family Climate for Road Safety Scale (FCRSS) and a social desirability scale were administrated to 560 young drivers. Exploratory factor analysis (EFA, n = 250) and confirmatory factor analysis (CFA, n = 250) were conducted to examine the factorial structure of the RDBS. Results: The Chinese version of the RDBS has 18 items that are divided into 4 factors: distraction, substance use, extreme behaviour and positioning. Both the results of EFA and CFA confirmed its factorial structure. The reliability of the RDBS was acceptable and the concurrent validity of the scale was supported by its significant associations with the SDCaF and FCRSS factors. Finally, drivers who had violation involvement scored higher on all four factors than their peers who did not have violation involvement, providing evidence for its known-group validity. Conclusion: The revised RDBS has similar structure with the original version and its reliability and validity were satisfactory. It is an effective tool to measure the reckless driving behaviour of young drivers in China and interventions that incorporated joint efforts of family and peers should be developed.

Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected to capture within-group and between-group characteristics of the stages. Besides, this study also examines the prediction performance of conflict stage identification using specific driving behaviour characteristics associated with each stage. Results reveal variations in dominant driving characteristics and predictive importance across stages. Heterogeneity exists within stages, with differences among clusters. Drivers slow down during in-conflict, with decreasing speed reduction as stages progress. Reaction time increases in post-conflict. Insufficient space gaps contribute to rear-end conflicts in the in-conflict stage. Furthermore, the prediction performance of conflict stage identification, based on the specific driving behaviour characteristics associated with each stage, is commendable. This study enhances understanding and prediction of conflict stage identification in rear-end conflicts.Practitioner summary: This study explores driving behaviour in rear-end conflict stages using trajectory data. It identifies pre-, in-, and post-conflict stages via time-to-collision indicator and assesses within-group and between-group characteristics. Besides, prediction performance for conflict stage identification based on these characteristics is commendable. This research enhances understanding and prediction of rear-end conflicts.

The relationship between alcohol availability and drink-driving policies and admissions to substance use disorder treatment during pregnancy.

OBJECTIVE: Pregnancy-specific alcohol policies are widely adopted yet have limited effectiveness and established risks. It is unknown whether general population alcohol policies are effective during pregnancy. This study investigated associations between general population policies and alcohol treatment admission rates for pregnant people specifically. METHOD: Data are from the Treatment Episodes Data Set: Admissions and state-level policy data for 1992-2019 (n=1,331 state-years). The primary outcome was treatment admissions where alcohol was the primary substance, and the secondary outcome included admissions where alcohol was any substance. There were five policy predictors: 1) Government spirits monopoly, 2) Ban on Sunday sales, 3) Grocery store sales, 4) Gas station sales, and 5) Blood alcohol concentration (BAC) laws. Covariates included poverty, unemployment, per capita cigarette consumption, state and year fixed effects, and state-specific time trends. RESULTS: In models with alcohol as the primary substance, prohibiting spirits sales in grocery stores (vs. allowing heavy beer and spirits) had lower treatment admission rates [IRR=0.88, 95% CI: 0.78-0.99, p=0.028]. States with BAC laws at 0.10% (vs. no law) had higher treatment admission rates [IRR=1.24, 95% CI: 1.08-1.43, p=0.003]. When alcohol was any substance, prohibiting spirits sales in grocery stores (vs. allowing heavy beer and spirits) was again associated with lower treatment admission rates [IRR=0.89, 95% CI: 0.80-0.98, p=0.021], but there was no association for BAC laws. CONCLUSIONS: Restrictions on grocery store spirits sales and BAC laws were associated with lower and higher alcohol treatment admission rates among pregnant people, respectively, suggesting general population alcohol policies are relevant for pregnant people's treatment utilization.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Assessing the impact of stricter drink driving policy on alcohol consumption in a population of Hanoi, Vietnam using wastewater analysis.

INTRODUCTION: On 1 January 2020, Vietnam introduced a new law with harsher fines and penalties for driving under the influence of alcohol. Reports of empty beer restaurants following this implementation suggested the new law has the potential to reduce population-level alcohol consumption. This pilot study aims to quantify short-term changes in alcohol consumption levels after the implementation of the new law and assess whether it could lead to a reduction in total alcohol consumption in the population. METHODS: Wastewater samples were collected from two sites along a sewage canal in Hanoi during two periods: Period 1 (15 December 2018 to 14 January 2019) and Period 2 (15 December 2019 to 14 January 2020). Ethyl sulfate, a specific metabolite of alcohol, was quantified to monitor the trend of alcohol consumption. Both interrupted time series and controlled interrupted time series approaches were utilised, with Period 1 and Period 2 serving as the control and intervention periods, respectively. RESULTS: Our analysis indicated that the implementation of the new law did not result in an immediate and significant reduction in alcohol consumption at the population level. Meanwhile, there was no significant difference in alcohol consumption between weekdays and weekends both before and after the implementation of the new law. DISCUSSION AND CONCLUSIONS: Long-term monitoring is needed to assess the impact of stricter DUI policy on alcohol consumption in the urban areas of Vietnam.

The impact of a lung-protective ventilation mode using transpulmonary driving pressure titrated positive end-expiratory pressure on the prognosis of patients with acute respiratory distress syndrome.

OBJECTIVE: This study aimed to assess the impact of a lung-protective ventilation strategy utilizing transpulmonary driving pressure titrated positive end-expiratory pressure (PEEP) on the prognosis [mechanical ventilation duration, hospital stay, 28-day mortality rate and incidence of ventilator-associated pneumonia (VAP), survival outcome] of patients with Acute Respiratory Distress Syndrome (ARDS). METHODS: A total of 105 ARDS patients were randomly assigned to either the control group (n = 51) or the study group (n = 53). The control group received PEEP titration based on tidal volume [A tidal volume of 6 mL/kg, flow rate of 30-60 L/min, frequency of 16-20 breaths/min, constant flow rate, inspiratory-to-expiratory ratio of 1:1 to 1:1.5, and a plateau pressure ≤ 30-35 cmH2O. PEEP was adjusted to maintain oxygen saturation (SaO2) at or above 90%, taking into account blood pressure], while the study group received PEEP titration based on transpulmonary driving pressure (Esophageal pressure was measured as a surrogate for pleural pressure using an esophageal pressure measurement catheter connected to the ventilator. Tidal volume and PEEP were adjusted based on the observed end-inspiratory and end-expiratory transpulmonary pressures, aiming to maintain a transpulmonary driving pressure below 15 cmH2O during mechanical ventilation. Adjustments were made 2-4 times per day). Statistical analysis and comparison were conducted on lung function indicators [oxygenation index (OI), arterial oxygen tension (PaO2), arterial carbon dioxide tension (PaCO2)] as well as other measures such as heart rate, mean arterial pressure, and central venous pressure in two groups of patients after 48 h of mechanical ventilation. The 28-day mortality rate, duration of mechanical ventilation, length of hospital stay, and ventilator-associated pneumonia (VAP) incidence were compared between the two groups. A 60-day follow-up was performed to record the survival status of the patients. RESULTS: In the control group, the mean age was (55.55 ± 10.51) years, with 33 females and 18 males. The pre-ICU hospital stay was (32.56 ± 9.89) hours. The mean Acute Physiology and Chronic Health Evaluation (APACHE) II score was (19.08 ± 4.67), and the mean Murray Acute Lung Injury score was (4.31 ± 0.94). In the study group, the mean age was (57.33 ± 12.21) years, with 29 females and 25 males. The pre-ICU hospital stay was (33.42 ± 10.75) hours. The mean APACHE II score was (20.23 ± 5.00), and the mean Murray Acute Lung Injury score was (4.45 ± 0.88). They presented a homogeneous profile (all P > 0.05). Following intervention, significant improvements were observed in PaO2 and OI compared to pre-intervention values. The study group exhibited significantly higher PaO2 and OI compared to the control group, with statistically significant differences (all P < 0.05). After intervention, the study group exhibited a significant increase in PaCO2 (43.69 ± 6.71 mmHg) compared to pre-intervention levels (34.19 ± 5.39 mmHg). The study group's PaCO2 was higher than the control group (42.15 ± 7.25 mmHg), but the difference was not statistically significant (P > 0.05). There were no significant differences in hemodynamic indicators between the two groups post-intervention (all P > 0.05). The study group demonstrated significantly shorter mechanical ventilation duration and hospital stay, while 28-day mortality rate and incidence of ventilator-associated pneumonia (VAP) showed no significant differences. Kaplan-Meier survival analysis revealed a significantly better survival outcome in the study group at the 60-day follow-up (HR = 0.565, 95% CI: 0.320-0.999). CONCLUSION: Lung-protective mechanical ventilation using transpulmonary driving pressure titrated PEEP effectively improves lung function, reduces mechanical ventilation duration and hospital stay, and enhances survival outcomes in patients with ARDS. However, further study is needed to facilitate the wider adoption of this approach.

Once a driver, always a driver - Manual driving style persists in automated driving takeover.

As automated vehicles require human drivers to resume control in critical situations, predicting driver takeover behaviour could be beneficial for safe transitions of control. While previous research has explored predicting takeover behaviour in relation to driver state and traits, little work has examined the predictive value of manual driving style. We hypothesised that drivers' behaviour during manual driving is predictive of their takeover behaviour when resuming control from an automated vehicle. We assessed 38 drivers with varying experience in a high-fidelity driving simulator. After completing manual driving sessions to assess their driving style, participants performed an automated driving task, typically on a subsequent date. Measures of driving style from manual driving sessions, including headway and lane change speed, were found to be predictive of takeover behaviour. The level of driving experience was associated with the behavioural measures, but correlations between measures of manual driving style and takeover behaviour remained after controlling for driver experience. Our findings demonstrate that how drivers reclaim control from their automated vehicle is not an isolated phenomenon but is associated with manual driving behaviour and driving experience. Strategies to improve takeover safety and comfort could be based on driving style measures, for example by the automated vehicle adapting its behaviour to match a driver's driving style.

Lived experience of driving in individuals with functional neurological disorder.

BACKGROUND: Functional neurological disorder (FND) is a common neurological diagnosis that encapsulates a range of incapacitating clinical presentations. These include functional seizures, movement disorders, and sensory disturbances. Safe driving requires both cognitive skills and physical abilities, which may be impacted by FND symptoms. The primary objective of this study was to gain deeper insights into the challenges faced by people with FND when driving. METHODS: A qualitative study and interpretative phenomenological analysis were conducted. Individuals experiencing functional seizures and/or movement disorders completed both questionnaires and semi-structured interviews about FND symptoms, driving behavior, and crashes. RESULTS: A total of 26 patients with FND participated in this study. Based on the interviews, four key themes were identified: (1) driving difficulties experienced by individuals with FND; (2) strategies utilized by people with FND to overcome difficulties experienced while driving; (3) barriers preventing driving challenges being addressed in this population; and (4) crashes and perceived dangerous driving events experienced by individuals with FND. All participants reported that driving a car provoked FND symptoms and this affected their driving ability. FND sufferers reported using a number of strategies such as limiting how far they drive and relying on advanced driver assistance system features to help manage their associated symptoms, such as fatigue and/or pain. Several participants reported crashes and perceived dangerous driving events since developing FND. CONCLUSION: Individuals experiencing FND often employ self-regulation techniques, yet the extent to which these methods enhance driving safety remains uncertain. The variable nature of the disorder makes judging an individual's driving risk particularly difficult. The themes emerging from the interviews highlighted the need for further empirical research to inform guidelines and best practice when determining the impact of FND on an individual's driving safety .

Atmospheric reactive nitrogen conversion kicks off the co-directional and contra-directional effects on PM2.5-O3 pollution.

As the two important ambient air pollutants, particulate matter (PM2.5) and ozone (O3) can both originate from gas nitrogen oxides. In this study, applied by theoretical analysis and machine learning method, we examined the effects of atmospheric reactive nitrogen on PM2.5-O3 pollution, in which nitric oxide (NO), nitrogen dioxide (NO2), gaseous nitric acid (HNO3) and particle nitrate (pNO3-) conversion process has the co-directional and contra-directional effects on PM2.5-O3 pollution. Of which, HNO3 and SO2 are the co-directional driving factors resulting in PM2.5 and O3 growing or decreasing simultaneously; while NO, NO2, and temperature represent the contra-directional factors, which can promote the growth of one pollutant and reduce another one. Our findings suggest that designing the suitable co-controlling strategies for PM2.5-O3 sustainable reduction should target at driving factors by considering the contra-directional and co-directional effects under suitable sensitivity regions. For co-directional driving factors, the design of suitable mitigation strategies will jointly achieve effective reduction in PM2.5 and O3; while for contra-directional driving factors, it should be more patient, otherwise, it is possible to reduce one item but increase another one at the same time.

Rethinking real-time risk prediction from multi-step time series forecasting on highway car-following scenarios.

Driving risk prediction emerges as a pivotal technology within the driving safety domain, facilitating the formulation of targeted driving intervention strategies to enhance driving safety. The driving safety undergoes continuous evolution in response to the complexities of the traffic environment, representing a dynamic and ongoing serialization process. The evolutionary trend of this sequence offers valuable information pertinent to driving safety research. However, existing research on driving risk prediction has primarily concentrated on forecasting a single index, such as the driving safety level or the extreme value within a specified future timeframe. This approach often neglects the intrinsic properties that characterize the temporal evolution of driving safety. Leveraging the high-D natural driving dataset, this study employs the multi-step time series forecasting methodology to predict the risk evolution sequence throughout the car-following process, elucidates the benefits of the multi-step time series forecasting approach, and contrasts the predictive efficacy on driving safety levels across various temporal windows. The empirical findings demonstrate that the time series prediction model proficiently captures essential dynamics such as risk evolution trends, amplitudes, and turning points. Consequently, it provides predictions that are significantly more robust and comprehensive than those obtained from a single risk index. The TsLeNet proposed in this study integrates a 2D convolutional network architecture with a dual attention mechanism, adeptly capturing and synthesizing multiple features across time steps. This integration significantly enhances the prediction precision at each temporal interval. Comparative analyses with other mainstream models reveal that TsLeNet achieves the best performance in terms of prediction accuracy and efficiency. Concurrently, this research undertakes a comprehensive analysis of the temporal distribution of errors, the impact pattern of features on risk sequence, and the applicability of interaction features among surrounding vehicles. The adoption of multi-step time series forecasting approach not only offers a novel perspective for analyzing and exploring driving safety, but also furnishes the design and development of targeted driving intervention systems.

Evaluating the Effectiveness of the Driving Resumption Assessment in Brain Disorders: Insights From a Retrospective Observational Study.

Objectives For patients with brain disorders, regaining the ability to drive is crucial to their reintegration into society. Despite the existence of numerous assessment methods for determining the ability to resume driving, the most effective approach remains unclear. This study evaluated patients with brain disorders who had received support for driving resumption. We examined the factors influencing the acquisition of driving ability in this specific population. Methods This retrospective observational study was conducted from July 2019 to March 2022. Initially, a desk-based assessment was conducted using neuropsychological tests. Successful candidates subsequently underwent an on-road assessment at an affiliated driving school. Patients who passed both assessments were granted permission to resume driving. The participants were categorized into pass and fail groups based on their assessments, and a comparative analysis was conducted. Age, sex, type of brain disorder, functional independence measures (FIMs), assessments of higher cognitive skills, and physical function test results were evaluated. Results Forty-five patients (average age: 62±13 years) underwent evaluation. Logistic regression analysis for the desk-based assessment identified the Rey-Osterrieth complex figure test (ROCFT) (three-minute delayed recall) as the most influential factor (cutoff value: 21.5 points; sensitivity: 65%; specificity, 72.7%). In the on-road assessment, the 10-m walking test was significantly faster in the passing group than in the failing group (p<0.005). Conclusions We demonstrated that the ROCFT (three-minute delayed recall) was the most effective neuropsychological assessment tool for evaluating driving resumption. The assessment of walking speed may also be able to predict the resumption of driving in patients with brain disorders.

Impact of elevated CO2 on soil microbiota: A meta-analytical review of carbon and nitrogen metabolism.

In the face of 21st-century challenges driven by population growth and resource depletion, understanding the intricacies of climate change is crucial for environmental sustainability. This review systematically explores the interaction between rising atmospheric CO2 concentrations and soil microbial populations, with possible feedback effects on climate change and terrestrial carbon (C) cycling through a meta-analytical approach. Furthermore, it investigates the enzymatic activities related to carbon acquisition, gene expression patterns governing carbon and nitrogen metabolism, and metagenomic and meta-transcriptomic dynamics in response to elevated CO2 levels. The study reveals that elevated CO2 levels substantially influence soil microbial communities, increasing microbial biomass C and respiration rate by 15 % and upregulating genes involved in carbon and nitrogen metabolism by 12 %. Despite a 14 % increase in C-acquiring enzyme activity, there is a 5 % decrease in N-acquiring enzyme activity, indicating complex microbial responses to CO2 changes. Additionally, fungal marker ratios increase by 14 % compared to bacterial markers, indicating potential ecosystem changes. However, the current inadequacy of data on metagenomic and meta-transcriptomic processes underscores the need for further research. Understanding soil microbial feedback mechanisms is crucial for elucidating the role of rising CO2 levels in carbon sequestration and climate regulation. Consequently, future research should prioritize a comprehensive elucidation of soil microbial carbon cycling, greenhouse gas emission dynamics, and their underlying drivers.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013-2020.

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013-2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.