Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors.

This study focuses on the spatiotemporal distribution, urban-rural variations, and driving factors of ammonia Vertical Column Densities (VCDs) in China's Yangtze River Delta region (YRD) from 2008 to 2020. Utilizing data from the Infrared Atmospheric Sounding Interferometer (IASI), Generalized Additive Models (GAM), and the GEOS-Chem chemical transport model, we observed a significant increase of NH3 VCDs in the YRD between 2014 and 2020. The spatial distribution analysis revealed higher NH3 concentrations in the northern part of the YRD region, primarily due to lower precipitation, alkaline soil, and intensive agricultural activities. NH3 VCDs in the YRD region increased significantly (65.18%) from 2008 to 2020. The highest growth rate occurs in the summer, with an annual average growth rate of 7.2% during the period from 2014 to 2020. Agricultural emissions dominated NH3 VCDs during spring and summer, with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones. Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH3 VCDs. The rise in NH3 VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO2, as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model. The concentration changes of acidic gases contribute to over 80% of the interannual variations in NH3 VCDs. This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases. Effective emission control is urgent to mitigate environmental hazards and secondary particulate matter, especially in the northern YRD.

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.

OBJECTIVE: In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety. METHODS: On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model. RESULTS: In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%. CONCLUSIONS: The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

Anticipated buffer time - An evasive surrogate safety indicator for risk assessment of unsignalized intersections under heterogeneous traffic and aggressive driving conditions.

Risk assessment of unsignalized intersections is particularly challenging when confronted with a combination of factors such as heavy traffic, diverse vehicle types, lane indiscipline, aggressive driving, and evasive manoeuvres. Understanding how people drive in these situations is crucial for accurately assessing the risks at unsignalized intersections. This study introduces a novel surrogate safety indicator, i.e. Anticipated Buffer Time (ABT), designed to account for these various factors. Additionally, three new indicators derived from ABT are introduced, namely ABT Negation Ratio, ABT Extremity Ratio, and ABT Progression Ratio. A risk assessment measure, denoted as UnSigRisk Score, is formulated using these three indicators for unsignalized intersections. Three intersections in Ahmedabad, India, were selected for the study due to their manifestation of these challenging conditions. Spearman Rank Correlation Coefficient was estimated to find out how well can UnSigRisk Score measure is able to quantify evasive behaviour. The results indicate that this score proficiently measures evasive behaviour, exhibiting coefficients exceeding 0.6 in all cases-significantly outperforming the current evasive indicators, Yaw Rate Ratio and Jerk. The proposed risk assessment score could serve as a practical tool for transportation authorities, enabling them to identify the most vulnerable intersections and allocate resources for targeted safety interventions wisely. The study unequivocally demonstrates that the use of ABT paves the way for a thorough examination of safety at unsignalized intersections, regardless of driving behaviour and traffic conditions.

Usefulness of driver's eye movement measurement to detect potential risks under combined conditions of taking second-generation antihistamines and calling tasks.

BACKGROUND: Concerns persist regarding the potential reduction in driving performance due to taking second-generation antihistamines or performing hands-free calling. Previous studies have indicated a potential risk to driving performance under an emergency event when these two factors are combined, whereas a non-emergency event was operated effectively. Currently, there is a lack of a discriminative index capable of detecting the potential risks of driving performance impairment. This study aims to investigate the relationship between driving performance and eye movements under combined conditions of taking second-generation antihistamines and a calling task, and to assess the usefulness of eye movement measurements as a discriminative index for detecting potential risks of driving performance impairment. METHODS: Participants engaged in a simulated driving task, which included a calling task, both under taking or not taking second-generation antihistamines. Driving performance and eye movements were monitored during both emergency and non-emergency events, assessing their correlation between driving performance and eye movements. The study further evaluated the usefulness of eye movement as a discriminative index for potential driving impairment risk through receiver operating characteristic (ROC) analysis. RESULTS: In the case of a non-emergency event, no correlation was observed between driving performance and eye movement under the combined conditions. Conversely, a correlation was observed during an emergency event. The ROC analysis, conducted to assess the discriminative index capability of eye movements in detecting the potential risk of driving performance impairment, demonstrated a high discriminative power, with an area under the curve of 0.833. CONCLUSIONS: The findings of this study show the correlation between driving performance and eye movements under the concurrent influence of second-generation antihistamines and a calling task, suggesting the usefulness of eye movement measurement as a discriminant index for detecting potential risks of driving performance impairment.

A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions.

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

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)

Alcohol use disorder and fitness to drive: Discrepancies between health professionals' evaluations and objective measures of alcohol use and cognitive functioning.

Aims: In this study, we investigated if health professionals' evaluations of driving ability corresponded with measures of severity of alcohol use and measures of cognitive functions necessary for safely driving a car. Methods: A total of 90 participants from a multicentre study were included. Participants were categorised into three groups: (1) the group judged fit to drive (FIT); (2) the group judged not fit to drive (UNFIT); and (3) the group who had lost their driver's licence due to legal sanctions (LEGAL). The participants' AUDIT scores, earlier treatment episodes and results from neuropsychological tests of reaction time, attention and visuospatial ability were included in the analyses. Results: We found a significant difference in the severity of alcohol use disorder (AUD) and visuospatial abilities between the FIT and UNFIT groups. Half of the UNFIT group had at least mild visuospatial difficulties, compared to only a quarter in the FIT group. There were no group differences in reaction time or attentional measures. The LEGAL group had more severe AUD than the other groups. Conclusion: The FIT group did not perform differently from the UNFIT group on attention and reaction time measures. The UNFIT group had more visuospatial impairments, but even half of this group had normal scores. It is uncertain whether the differences between the two groups are of practical significance. The quality of health professionals' evaluations may be questioned, and the results highlight the need for more reliable and valid criteria for doing fitness to drive evaluations.

Structure and assembly process of skin fungal communities among bat species in northern China.

Background: The skin fungal communities of animals play a crucial role in maintaining host health and defending against pathogens. Because fungal infections can affect the skin microbiota of bats, gaining a comprehensive understanding of the characteristics of healthy bat skin fungal communities and the ecological processes driving them provides valuable insights into the interactions between pathogens and fungi. Methods: We used Kruskal-Wallis tests and Permutational Multivariate Analysis of Variance (PERMANOVA) to clarify differences in skin fungal community structure among bat species. A Generalized Linear Model (GLM) based on a quasi-Poisson distribution and partial distance-based redundancy analysis (db-RDA) was performed to assess the influence of variables on skin fungal communities. Using community construction models to explore the ecological processes driving fungal community changes, t-tests and Wilcoxon tests were used to compare the alpha diversity and species abundance differences between the fungal structure on bat species' skin and the environmental fungal pool. Results: We found significant differences in the composition and diversity of skin fungal communities among bat species influenced by temperature, sampling site, and body mass index. Trophic modes and skin fungal community complexity also varied among bat species. Null model and neutral model analysis demonstrated that deterministic processes dominated the assembly of skin fungal communities, with homogeneous selection as the predominant process. Skin fungal communities on bat species were impacted by the environmental fungal reservoir, and actively selected certain amplicon sequence variants (ASVs) from the environmental reservoir to adhere to the skin. Conclusion: In this study, we revealed the structure and the ecological process driving the skin fungal community across bat species in northern China. Overall, these results broaden our knowledge of skin fungal communities among bat species, which may be beneficial to potential strategies for the protection of bats in China.

The Key Descriptors for Predicting the Exciton Binding Energy of Organic Photovoltaic Materials.

Exciton binding energy (Eb) is a key parameter to determine the mechanism and performance of organic optoelectronic devices. Small Eb benefits to reduce the interfacial energy offset and the energy loss of organic solar cells. However, quantum-chemical calculations of the Eb in solid state with considering electronic polarization effects are extremely time-consuming. Furthermore, current studies lack critical descriptors. Here, we use data-driven machine learning (ML) to accelerate the computation and identify the key descriptors most relevant to the solid-state Eb. The results verify two key descriptors associated with molecular and aggregation-state properties for efficient prediction of the solid-state Eb. Moreover, a very high accuracy is achieved by using the extreme gradient boosting algorithm, with the Pearson's correlation coefficient of 0.92. Finally, we use this ML model to predict the Eb of thin films, which is difficult to achieve using the current quantum-chemical calculations due to the large structural disorder. Remarkably, the predicted thin-film Eb values are fully consistent with the results of temperature-dependent photoluminescence spectra. Therefore, our work provides an accurate and efficient approach to predict the solid-state Eb and would be helpful to accelerate the exploitation of novel promising organic photovoltaic materials.

An Ultrasensitive Bi2O2Se/In2S3 Photodetector with Low Detection Limit and Fast Response toward High-Precision Unmanned Driving.

The machine vision utilized in unmanned driving systems must possess the ability to accurately perceive scenes under low-light illumination conditions. To achieve this, photodetectors with low detection limits and a fast response are essential. Current systems rely on avalanche diodes or lidars, which come with the drawbacks of increased energy consumption and complexity. Here, we present an ultrasensitive photodetector based on a two-dimensional (2D) Bi2O2Se/In2S3 heterostructure, incorporating a homotype unilateral depletion band design. This innovative architecture effectively modulates the transport of both free and photoexcited carriers, suppressing the dark current and facilitating the rapid and efficient separation of photocarriers. Owing to these features, this device exhibits a responsivity of 144 A/W, a specific detectivity of 1.2 × 1014 Jones, and a light on/off ratio of 1.1 × 105. These metrics rank among the top values reported for state-of-the-art 2D devices. Moreover, this device also demonstrates a fast response time of 170/296 µs and a low noise equivalent power of 0.57 fW/Hz1/2, attributes that endow it with ultraweak light imaging capabilities. Furthermore, we have successfully integrated this device into an unmanned driving system, providing a perspective on the design and fabrication of future optoelectronic devices.

Analysis of changes and driving forces of landscape pattern vulnerability at Qianping Reservoir in Central China.

Reservoir projects often have significant impacts on ecosystems. The resulting environmental problems hinder the ecologically sustainable development of project areas. Research focusing on landscape pattern vulnerability could shed light on ecological restoration in disturbed sites. However, few studies have specifically examined reservoir areas in this context. This study investigates the spatial distribution characteristics, change rules, spatial autocorrelation, and driving forces of landscape pattern vulnerability in the Qianping Reservoir area (3859.16 hm2) from 2000 to 2020 using land use data. The findings reveal several key points: (1) Over the study period, cultivated land, grassland, and forest land are the key landscape types, covering more than 90% of the area. Cultivated land decreased by 481.57 hm2 as other land use types expanded. (2) Vulnerability remained stable in the first decade but sharply increased from 2010 to 2020, showing a trend of spatial aggregation. Reservoir construction and simultaneous ecological restoration efforts led to shifts in vulnerability zones across the landscape. (3) Spatial distribution of landscape pattern vulnerability shows a positive correlation, which strengthened by 2020 compared to earlier years. (4) Man-made factors, particularly land use changes, significantly influence landscape pattern vulnerability, with their impact growing over time. These findings not only provide a scientific basis for ecological restoration and landscape reconstruction in the Qianping Reservoir area but also offer insights applicable to similar environments. Overall, this study enhances theoretical understanding of reservoir landscape pattern vulnerability and contributes valuable perspectives on ecological restoration strategies for reservoir areas.

Ventilatory pressure parameters impact the association between acute gastrointestinal injury and all-cause mortality in mechanically ventilated patients.

Acute gastrointestinal injury (AGI) is common in mechanically ventilated (MV) patients, but the potential association between ventilatory pressure parameters and AGI grade and their impact on mortality remains unclear. This study aimed to explore the association between ventilatory pressure parameters and AGI grade, and their interaction on all-cause mortality in MV patients. This study was a secondary analysis of a multicenter, prospective, observational study that enrolled adult patients with an expected duration of mechanical ventilation ≥ 48 h from 14 general intensive care units in Zhejiang Province between March and August 2014. The AGI grade was assessed daily on the basis of gastrointestinal symptoms, intra-abdominal pressures, and feeding intolerance in the first week of admission to the ICU. This study included 331 patients (69.2% men; mean age, 64.6 ± 18.9 years). Multivariate regression analysis showed that plateau pressure (Pplat) (OR 1.044, 95% CI 1.009-1.081, P = 0.013), serum creatinine (OR 1.003, 95% CI 1.001-1.006, P = 0.042) and APACHE II score (OR 1.035, 95% CI 1.021-1.072, P = 0.045) were independently associated with global AGI grade III/IV within 7 days of ICU admission. Moreover, global AGI grade (HR 2.228, 95% CI 1.561-3.182, P < 0.001), serum creatinine (HR 1.002, 95% CI 1.001-1.003, P = 0.012) and APACHE II score (HR 1.039, 95% CI 1.015-1.063, P = 0.001) were independently associated with 60-day mortality. In addition, there were significant (Pint ≤ 0.028) interactions of Pplat and DP with AGI grade in relation to 60-days mortality, whereas no interaction (Pint = 0.061) between PEEP and AGI grade on 60-days mortality was observed. In the presence of Pplat ≥ 19 cmH2O, the patients with AGI grade III/IV had 60-day mortality rate of 72.2%, significantly higher than those with AGI grade I/II (48.7%, P = 0.018), whereas there were no significant differences (27.9% vs. 33.7%, P = 0.39) in 60-days mortality between AGI grade I/II and III/IV among the patients with Pplat < 19 cmH2O. In comparison with Pplat, DP had a similar interaction (Pint = 0.028) with AGI grade on 60-day mortality. Ventilatory pressure parameters (Pplat and DP) are independent risk factors of AGI grade III/IV. Pplat and DP interact with AGI grade on 60-days mortality, highlighting the importance of optimizing ventilatory pressure parameters to improve gastrointestinal function and survival outcomes of MV patients.Trial registration: ChiCTR-OCS-13003824.

Heterogeneous evolution and driving forces of multiple hazardous air pollutants and GHGs emissions from China's primary aluminum industry.

The booming of China's primary aluminum industry (PAI) brought substantial emissions of hazardous air pollutants (HAPs) and greenhouse gases (GHGs). By using life cycle assessment and bottom-up method, a comprehensive emission inventory for multiple typical HAPs and GHGs from China's PAI during 1990-2021 was developed and explored for the first time. Our results show that spatial-temporal emissions trends of HAPs and GHGs from PAI in China diverse significantly. The conventional atmospheric pollutants (including SO2, NOx and particulate matter (PM)), fluoride and per fluorinated compound (PFCs) had been effectively suppressed since 2007 due to the implementation of various environmental policies; while, emissions of CO, VOCs, CH4, heavy metals and CO2 had increased at different rates unexpectedly. From the spatial distribution perspective, Henan, Shanxi, Guizhou, Guangxi and Shandong dominated the emissions of PAI in China, but with consumption expansion and environmental constrains, PAI plants start to expand to northwest and southwest areas where are richer in sufficient and cheaper power resources, thus bring significant emission increasing there, particular for conventional atmospheric pollutants in northwest and CO and VOCs in southwest China. By underlying driving forces of PAI emissions, results show that end-of-pipe control measures at various stages have played different roles to reduce emissions of the concerned species at each period, but its reduction effect diminished gradually. Future reduction should seek underlying changes in production technology and energy system. Under constrains of environmental regulation and resource endowment, promoting circular economic development for PAI would be a key strategy to reduce HAPs and GHGs emissions simultaneously in PAI.

Eutrophication constrains driving forces of dissolved organic carbon biodegradation in metropolitan lake systems.

Dissolved organic carbon (DOC) components can be highly variable in aquatic ecosystems, and play a pivotal role in the global carbon cycles. To comprehend potential effects of nutrient enrichment on portion of DOC biodegradability (%BDOC), we conducted an extensive investigation on 26 urban lakes in a major metropolitan area in subtropical China in a small gradient of trophic levels from mesotrophic to light and middle eutrophic. In addition to field measurements on lake ambient conditions and laboratory analysis of DOC characteristics, we conducted a 28-day temperature-controlled incubation experiment, in which %BDOC of lake waters was determined. In the mesotrophic waters, %BDOC ranged from 0.6 to 41.4 % (11.2 ± 8.9 %). The %BDOC levels spanned from 5.2 to 20.2 % (10.7 ± 4.0 %) in the light eutrophic waters, and the %BDOC ranged from 2.7 to 35.0 % (13.7 ± 8.4 %) in the middle eutrophic waters. We found a significant change in DOC chemical composition across the study lakes characterized by shifting of trophic levels. Although the experiment found significant changes in the factors that can influence %BDOC, a significant difference was not observed in %BDOC among the three trophic levels. The %BDOC was primarily influenced by the inherent DOC concentration and aromaticity, with eutrophication leading to the varied driving factors of %BDOC in lake systems. We show that most of the lake water DOC was stable. The findings indicate the intricate interplay between biological metabolism and nutrient availability governing %BDOC dynamics in urban lake ecosystems.

Attitudes toward driving after cannabis use: a systematic review.

BACKGROUND: Driving after cannabis use (DACU) is associated with increased risk of motor vehicle collisions. As cannabis legalization expands, DACU is emerging as a major public safety concern. Attitudes have a significant impact on behavioural decision making. As such, understanding the degree to which people have favorable or unfavorable evaluations of DACU is an important first step for informing prevention efforts. This systematic review summarizes existing evidence on attitudes toward DACU, their association with actual or intended DACU, and changes in attitudes following legalization of recreational cannabis. METHODS: Four electronic databases (MEDLINE, EMBASE, PsycINFO, and TRID) were searched for studies that reported attitudes or changes in attitudes toward DACU published between their inception dates and February 26 2024. A total of 1,099 records were retrieved. Studies were analyzed using an inductive thematic synthesis approach. RESULTS: Seventy studies from seven countries originating predominantly from the United States and Canada met inclusion criteria. Thematic analysis identified six themes. (I) Attitudes toward the safety and acceptability of DACU are mixed; participants in 35 studies predominantly expressed negative attitudes toward DACU (e.g., DACU is dangerous, affects driving ability, and increases crash risk). However, 20 studies reported opposing views. (II) Attitudes toward DACU vary by age, sex/gender, and cannabis use frequency; youth, men, and frequent cannabis users tended to view DACU more favorably than older participants, women, and occasional or non-users. (III) Attitudes toward DACU are associated with past DACU and intention to DACU. (IV) DACU is viewed more favorably than driving after drinking alcohol. (V) The relationship between legal status of recreational cannabis and attitudes toward DACU is unclear. (VI) Perceived risk of apprehension for DACU is low to moderate. CONCLUSIONS: This review found that perceptions of DACU are primarily negative but mixed. Findings suggest that attitudes toward DACU are important targets for interventions to reduce this behaviour.

Spatiotemporal profiles and underlying mechanisms of the antibiotic resistome in two water-diversion lakes.

Human-induced interventions have altered the local characteristics of the lake ecosystems through changes in hydraulic exchange, which in turn impacts the ecological processes of antibiotic resistance genes (ARGs) in the lakes. However, the current understanding of the spatiotemporal patterns and driving factors of ARGs in water-diversion lakes is still seriously insufficient. In the present study, we investigated antibiotic resistome in the main regulation and storage hubs, namely Nansi Lake and Dongping Lake, of the eastern part of the South-to-North Water Diversion project in Shandong Province (China) using a metagenomic-based approach. A total of 653 ARG subtypes belonging to 25 ARG types were detected with a total abundance of 0.125-0.390 copies/cell, with the dominance of bacitracin, multidrug, and macrolide-lincosamide streptogramin resistance genes. The ARG compositions were sensitive to seasonal variation and also interfered by artificial regulation structures along the way. Human pathogenic bacteria such as Acinetobacter calcoaceticus, Acinetobacter lwoffii, Klebsiella pneumoniae, along with the multidrug resistance genes they carried, were the focus of risk control in the two studied lakes, especially in summer. Plasmids were the key mobile genetic elements (MGEs) driving the horizontal gene transfer of ARGs, especially multidrug and sulfonamide resistance genes. The null model revealed that stochastic process was the main driver of ecological drift for ARGs in the lakes. The partial least squares structural equation model further determined that seasonal changes of pH and temperature drove a shift in the bacterial community, which in turn shaped the profile of ARGs by altering the composition of MGEs, antibacterial biocide- and metal-resistance genes (BMGs), and virulence factor genes (VFGs). Our results highlighted the importance of seasonal factors in determining the water transfer period. These findings can aid in a deeper understanding of the spatiotemporal variations of ARGs in lakes and their driving factors, offering a scientific basis for antibiotic resistance management.

Mobile phone conversation during nighttime driving: Effects on driving behavior.

OBJECTIVES: This study aims to investigate the impact of mobile phone use (specifically, conversation), considering various use modes, on driving behavior at night. Mobile phone use is a source of driver distraction and has been associated with increased accident risk. Driving at night also entails higher accident risk and severity compared to daytime driving. Several studies have investigated the impact of mobile phone use on driving behavior; however, only a few have explored the differences between the different use modes. Most present studies involved daytime driving, although mobile phone use at night is equally if not more prevalent. METHOD: A driving simulator experiment was designed in which 55 participants drove under nighttime simulator conditions, in different road environments (urban and rural) and under different types of distraction: no distraction, handheld, wired earphone, and speaker mode. The drives were performed during late afternoon and evening hours to resemble nighttime conditions both in the simulator and in the actual environment. Participants also completed a questionnaire for collection of different types of data. RESULTS: Results highlight the effect of mobile phone use on driving behavior, through specific indicators. Mobile phone use resulted in reduced 85th percentile driving speed and 85th percentile acceleration and increased reaction time and lateral deviation. However, safer stopping distance was observed in rural roads. Parameters relative to mobile phone use familiarity and exposure were found to mitigate mobile phone use effects. CONCLUSIONS: Mobile phones affect driving behavior at night in a similar manner to that noted in several different studies considering daytime driving. The hands-free regulation should be revisited, because driver distraction also occurred under this particular use mode. Further research is required considering mobile phone use familiarity and exposure and effects of mobile phone use, because the latter is reduced with an increase in the former. Stopping distance, an understudied but more immediate surrogate measure of road safety, was increased with mobile phone use, mainly as a result of the risk compensation behavior that drivers adopt, indicating that more research is required in this field.

Enhancing highway Loop Safety Level through proactive risk-based assessment of geometric configuration using lateral acceleration.

OBJECTIVE: Loop ramps are complex due to their combination of horizontal curves and vertical alignments. Analyzing driving behavior and measuring safety levels can provide insights for designers, helping to improve the performance and alignment of design assumptions with actual driving behavior on loops. Therefore, the primary objective of this research is to explore the safety, performance and geometric configuration of the main body and general shape of free-flow loop ramps in the free-following mode. METHODS: The study uses data from a UAV to investigate vehicle demand behavior. Maximum lateral acceleration (ay,i) in loops is used as a Surrogate Safety Measure (SSM), along with a new parameter, the a/b ratio, to determine the general shape of loop bodies. The study presents the Loop Safety Level (LSL), an approach for proactive risk analysis and ranking that relies on threshold lateral acceleration (at), 85th percentile maximum lateral acceleration (ay,max,85%), and crash analysis. RESULTS: A higher LSL value points to a more critical safety concern regarding the loop's shape in relation to lateral acceleration caused by driving behaviors. Comparing crash statistics with lateral acceleration results enables the LSL to provide appropriate safety ratings and diagnose loop segment safety. A prediction model for maximum lateral acceleration, based on loop geometry, demonstrates a good fit (R2=0.88) between observed and predicted data. CONCLUSIONS: The study enhances understanding of safety considerations in geometric configuration and general shape enhancement of loops during the design process.

A tidal volume of 7 mL/kg PBW or higher may be safe for COVID-19 patients.

PURPOSE: The novel coronavirus disease (COVID-19) has revived the debate on the optimal tidal volume during acute respiratory distress syndrome (ARDS). Some experts recommend 6 mL/kg of predicted body weight (PBW) for all patients, while others suggest 7-9 mL/kg PBW for those with compliance >50 mL/cmH2O. We investigated whether a tidal volume ≥ 7 ml/kg PBW may be safe in COVID-19 patients, particularly those with compliance >50 mL/cmH2O. MATERIALS AND METHODS: This secondary analysis of a multicenter study compares the Intensive Care Unit (ICU) mortality among 600 patients ventilated with <7 or ≥ 7 mL/kg PBW. Compliance was categorized as <40, 40-50, or > 50 mL/cmH2O. RESULTS: 346 patients were ventilated with <7 (6.2 ± 0.5) mL/kg PBW and 254 with ≥7 (7.9 ± 0.9) mL/kg PBW. ICU mortality was 33 % and 29 % in the two groups (p = 0.272). At multivariable regression analysis, tidal volume ≥ 7 mL/kg PBW was associated with lower ICU mortality in the overall population (odds ratio: 0.62 [95 %-confidence interval: 0.40-0.95]) and in each compliance category. CONCLUSIONS: A tidal volume ≥ 7 (up to 9) mL/kg PBW was associated with lower ICU mortality in these COVID-19 patients, including those with compliance <40 mL/cmH2O. This finding should be interpreted cautiously due to the retrospective study design. TRIAL REGISTRATION: ClinicalTrails.govNCT04388670.

A combined trade-off strategy of battery degradation, charge retention, and driveability for electric vehicles.

Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability.