Optimal scheduling strategy of electric vehicle based on improved NSGA-III algorithm.

Aiming at the problem of load increase in distribution network and low satisfaction of vehicle owners caused by disorderly charging of electric vehicles, an optimal scheduling model of electric vehicles considering the comprehensive satisfaction of vehicle owners is proposed. In this model, the dynamic electricity price and charging and discharging state of electric vehicles are taken as decision variables, and the income of electric vehicle charging stations, the comprehensive satisfaction of vehicle owners considering economic benefits and the load fluctuation of electric vehicles are taken as optimization objectives. The improved NSGA-III algorithm (DJM-NSGA-III) based on dynamic opposition-based learning strategy, Jaya algorithm and Manhattan distance is used to solve the problems of low initial population quality, easy to fall into local optimal solution and ignoring potential optimal solution when NSGA-III algorithm is used to solve the multi-objective and high-dimensional scheduling model. The experimental results show that the proposed method can improve the owner's satisfaction while improving the income of the charging station, effectively alleviate the conflict of interest between the two, and maintain the safe and stable operation of the distribution network.

Enterovirga aerilata sp. nov. and Knoellia koreensis sp. nov., isolated from an automobile air conditioning system.

Two strictly aerobic and rod-shaped bacteria, labelled as DB1703T and DB2414ST, were obtained from an automobile air conditioning system. Strain DB1703T was Gram-stain-negative, while strain DB2414ST was Gram-stain-positive. Both strains were catalase-positive and oxidase-negative. Strains DB1703T and DB2414ST were able to grow at 18-42 °C. Strain DB1703T grew within a NaCl range of 0-3 % and a pH range of 6.0-8.0; while strain DB2414ST grew at 0-1 % and pH 6.5-8.5. The phylogenetic and 16S rRNA gene sequence analysis indicated that strains DB1703T and DB2414ST belonged to the genera Enterovirga and Knoellia, respectively. Strain DB1703T showed the closest phylogenetic similarity to Enterovirga rhinocerotis YIM 100770T (94.8 %), whereas strain DB2414ST was most closely related to Knoellia remsis ATCC BAA-1496T (97.7 %). The genome sizes of strains DB1703T and DB2414ST were 4 652 148 and 4 282 418 bp, respectively, with DNA G+C contents of 68.8 and 70.5 mol%, respectively. Chemotaxonomic data showed Q-10 as the sole ubiquinone in DB1703T and ML-8 (H4) in DB2414ST. The predominant cellular fatty acid in DB1703T was summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c), whereas iso-C16 : 0, C17 : 1 ω8c, and iso-C15 : 0 were dominant in DB2414ST. Overall, the polyphasic taxonomic comparisons showed that strains DB1703T and DB2414ST were distinct from their closest taxa and represent novel species within the genera Enterovirga and Knoellia, respectively. Accordingly, we propose the names Enterovirga aerilata sp. nov., with the type strain DB1703T (=KCTC 72724T=NBRC 114759T), and Knoellia koreensis sp. nov., with the type strain DB2414ST (=KCTC 49355T=NBRC 114620T).

Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.

OBJECTIVES: This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL. DESIGN: Population based, a cross sectional study. SETTING: Han, China. PARTICIPANTS: This study selected 3266 Han male workers from three automobile manufacturing industries. PRIMARY OUTCOME MEASURES: Information including personal life habits, occupational health test information and occupational exposure history were collected and predictive factors of NIHL were screened from these workers. BPNN and logistic regression models were constructed using these predictors. RESULTS: The input variables of BPNN model were 20, 16 and 21 important factors screened by univariate, stepwise and lasso-logistic regression. When the BPNN model was applied to the test set, it was found to have a sensitivity (TPR) of 83.33%, a specificity (TNR) of 85.92%, an accuracy (ACC) of 85.51%, a positive predictive value (PPV) of 52.85%, a negative predictive value of 96.46% and area under the receiver operating curve (AUC) is: 0.926 (95% CI: 0.891 to 0.961), which demonstrated the better overall properties than univariate-logistic regression modelling (AUC: 0.715) (95% CI: 0.652 to 0.777). The BPNN model has better predictive performance against NIHL than the stepwise-logistic and lasso-logistic regression model in terms of TPR, TNR, ACC, PPV and NPV (p<0.05); the area under the receiver operating characteristics curve of NIHL is also higher than that of the stepwise and lasso-logistic regression model (p<0.05). It was a relatively important factor in NIHL to find cumulative noise exposure, auditory system symptoms, age, listening to music or watching video with headphones, exposure to high temperature and noise exposure time in the trained BPNN model. CONCLUSIONS: The BPNN model was a valuable tool in dealing with the occupational risk prediction problem of NIHL. It can be used to predict the risk of an individual NIHL.

Research on the characteristics of EV interior sound quality and its dynamic active control system design and development un-der accelerated driving conditions.

In order to improve the interior sound quality of Electric Vehicles (EV), solve the problem of low sense of power and comfort of the interior sound as well as the large electromagnetic excitation order noise of motor and the sharp interior sound, this article designs a dynamic active sound control system for EV under accelerated driving conditions. Firstly, by comparing and analyzing the sound spectrum characteristics of fuel vehicle (FV) and EV during acceleration, a short-time Fourier transform (STFT) is adopted to extract and synthesize the engine sound. Secondly, the influence of the engine order composition and the energy distribution in the frequency domain on the sound quality of the vehicle is analyzed, and an active control system for sound quality is proposed. And the software and hardware development of the active control sound system is completed. Finally, through real-vehicle testing and verification, the sense of comfort and power of the EV interior sound has been greatly improved during acceleration, and the total value of interior sound can meet the requirement. The sound pressure level and loudness of interior sound have been increased, and the sharpness of the sound inside the vehicle has been improved, with a maximum reduction of 1.0acum.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Ghost roads and the destruction of Asia-Pacific tropical forests.

Roads are expanding at the fastest pace in human history. This is the case especially in biodiversity-rich tropical nations, where roads can result in forest loss and fragmentation, wildfires, illicit land invasions and negative societal effects1-5. Many roads are being constructed illegally or informally and do not appear on any existing road map6-10; the toll of such 'ghost roads' on ecosystems is poorly understood. Here we use around 7,000 h of effort by trained volunteers to map ghost roads across the tropical Asia-Pacific region, sampling 1.42 million plots, each 1 km2 in area. Our intensive sampling revealed a total of 1.37 million km of roads in our plots-from 3.0 to 6.6 times more roads than were found in leading datasets of roads globally. Across our study area, road building almost always preceded local forest loss, and road density was by far the strongest correlate11 of deforestation out of 38 potential biophysical and socioeconomic covariates. The relationship between road density and forest loss was nonlinear, with deforestation peaking soon after roads penetrate a landscape and then declining as roads multiply and remaining accessible forests largely disappear. Notably, after controlling for lower road density inside protected areas, we found that protected areas had only modest additional effects on preventing forest loss, implying that their most vital conservation function is limiting roads and road-related environmental disruption. Collectively, our findings suggest that burgeoning, poorly studied ghost roads are among the gravest of all direct threats to tropical forests.

How would autonomous vehicles behave in real-world crash scenarios?

Autonomous Vehicles (AVs) have the potential to revolutionize transportation systems by enhancing traffic safety. Safety testing is undoubtedly a critical step for enabling large-scale deployment of AVs. High-risk scenarios are particularly important as they pose significant challenges and provide valuable insights into the driving capabilities of AVs. This study presents a novel approach to assess the safety of AVs using in-depth crash data, with a particular focus on real-world crash scenarios. First, based on the high-definition video recording of the whole process prior to the crash occurrences, 453 real-world crashes involving 596 passenger cars from China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database were reconstructed. Pertinent static and dynamic elements needed for the construction of the testing scenarios were extracted. Subsequently, 596 testing scenarios were created via each passenger car's perspective within the simulation platform. Following this, each of the crash-involved passenger cars was replaced with Baidu Apollo, a famous automated driving system (ADS), for counterfactual simulation. Lastly, the safety performance of the AV was assessed using the simulation results. A logit model was utilized to identify the fifteen crucial scenario elements that have significant impacts on the test results. The findings demonstrated that the AV could avoid 363 real-world crashes, accounting for approximately 60.91% of the total, and effectively mitigated injuries in the remaining 233 unavoidable scenarios compared to a human driver. Moreover, the AV maintain a smoother speed in most of the scenarios. The common feature of these unavoidable scenarios is that the AV is in a passive state, and the crashes are not caused by the AV violating traffic rules, but rather caused by abnormal behavior exhibited by the human drivers. Additionally, seven specific scenarios have been identified wherein AVs are unable to avoid a crash. These findings demonstrate that, compared to human drivers, AVs can avoid crashes that are difficult for humans to avoid, thereby enhancing traffic safety.

European Technical Democracy? How Consumer Associations Did-or Did Not-Shape the 1980s Automotive Environmental Standards.

Did the 1980s automotive standards reflect the European Economic Community's move toward a "technical democracy" or a broader democratic deficit? In the early 1980s, Europe's automotive sector faced multiple challenges: the European Commission's desire to harmonize technical standards and achieve greater European integration, intense competition between manufacturers, and environmental issues like acid rain. Debates on reducing air pollution focused on unleaded petrol and catalytic converters. Two associations representing civil society in Brussels responded to the increase in environmental concerns with a 1982 joint campaign. Despite a rich historiography on pollutant emission standards, highlighting the strategies of governments and companies, no study has dealt with the role nongovernmental organizations played. Based on public and private archives, particularly those of the European Bureau of Consumers' Unions, this article argues the new regulations did not result from the EU's consultation with civil society organizations like consumer groups but rather with the automotive industry.

As Ceaseless as the Sea: How Modern Construction Machines Disrupted Canadian Senses and Sensibilities, 1870s-1940s.

Since the late nineteenth century, Canada has required modern construction machines for industrial growth. Thanks to their novelty and visibility, these machines entered the Canadian psyche, symbolizing hopes and fears about the relentless transformations of modernity. Metaphors depicting these machines as zoomorphic and monstruous reflected the environmental-technological infrastructures they built, which redefined nature through technologies like trains, ships, and automobiles. This article discusses how Anglo-Canadians, particularly Ontarians, interpreted technology, drawing parallels with the automobile's history. Both had a problematic coexistence with humans as equally empowering and oppressive mobile machines that were imposed on public spaces and constructed as necessary for progress. The builders used the machines' allure to present construction as an inclusive civic spectacle and foster public tolerance for their relentless disruptions. They accomplished this faster than the automobile industry came to dominate the streets, as evidenced by the celebration of "sidewalk superintendents," compared to the contentious reproach of "jaywalkers."

Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving.

BACKGROUND: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. OBJECTIVE: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. METHODS: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. RESULTS: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F2,68=4.3; P<.05 and F2,76=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F2,68=3.9; P<.05), while blood glucose phase affected it in real-world driving (F2,76=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F2,68=2.46; P=.09, blood glucose phase: F2,68=0.3; P=.74), nor in the real-world driving study (modality: F2,76=0.8; P=.47, blood glucose phase: F2,76=0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84). CONCLUSIONS: Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving. TRIAL REGISTRATION: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.

Simulated impact of lift car sizes on transport of critical care patients: Informing the design of the New Dunedin Hospital.

The New Dunedin Hospital (NDH) is New Zealand's largest health infrastructure build. Here we describe the use of a simple simulation-based hospital design exercise to inform the appropriate lift car size for critical care intrahospital transfers in the NDH. The intensive care unit (ICU) user group tested a series of entries and exits of simulated complex patient transfers in mocked-up lift cars of three different dimensions. Time taken to enter and exit the lift were recorded, reflecting the relative difficulty of transfer. Qualitative assessments were made of ease and perceived safety of transfer. These simulations demonstrated that recommended standard patient lift cars, often proposed for critical care transfers, could not physically accommodate all complex ICU transfers. A size of 1800 mm wide (W) × 3000 mm deep (D) had the physical capacity to permit all simulated ICU transfers, but with staff and patient risk. As lift car size increased to 2200 mm W × 3300 mm D, the simulation demonstrated reduced transfer times, smoother entry and exit, improved access to the head end of the bed, and reduced risk of disconnection or dislodgement of lines and airway support. The resultant clinical recommendations for the dimensions of a critical care lift car surpass current international health architecture guidelines and may help to inform future updates. The NDH project benefited from an objective assessment of risk, in language familiar to clinicians and healthcare architects. The outcome was an upsizing of the two ICU-capable lifts.

Identifying factors that influence electric vehicle charging station performance in expanding networks.

Charging infrastructure deployment has taken off in many cities with the rise of the number of electric vehicles on the road. Expansion of infrastructure is a matter of prioritisation of resources to optimise the infrastructure. This paper explores how to measure charging station performance, to address the challenges that policy makers face. These performance indicators are used in a regression model, based upon current utilisation of the network, to predict which charging stations perform best. The results show that a model based on available geographical data and performance metrics of the current network are best combined to predict infrastructure performance. The variability between public charging stations is however big, as frequent user characteristics do determine the performance to a large extent.

Valorization of pyrolysis oils recycled from waste car tires as potential collector in coal flotation: Production, characterization, and collecting mechanism.

The present research study investigates the performance of pyrolysis oils recycled from waste tires as a collector in coal flotation. Three different types of pyrolysis oils (namely, POT1, POT2, and POT3) were produced through a two-step pressure pyrolysis method followed by an oil rolling process. The characteristics of POTs were adjusted using various oil-modifying additives such as mineral salts and organic solvents. The chemical structure of POTs was explored by employing necessary instrumental analysis techniques, including microwave-assisted acid digestion (MAD), inductively coupled plasma atomic emission spectroscopy (ICP-AES), Fourier-transform infrared spectroscopy (FT-IR), and gas chromatography-mass spectrometry (GC-MS). The collecting performance of POTs in coal flotation was evaluated using an experimental design based on Response Surface Methodology (RSM), considering the ash content and yield of the final concentrate. The effect of the type and dosage of POTs was evaluated in conjunction with other important operating variables, including the dosage of frother, dosage of depressant, and the type of coal. Results of POTs characterization revealed that the pyrolysis oils were a complex composition of light and heavy hydrocarbon molecules, including naphthalene, biphenyl, acenaphthylene, fluorene, and pyrene. Statistical analysis of experimental results showed that among different POTs, POT1 exhibited remarkable superiority, achieving not only a 15% higher coal recovery but also a 12% lower ash content. The outstanding performance of POT1 was attributed to its unique composition, which includes a concentrated presence of carbon chains within the optimal range for efficient flotation. Additionally, the FT-IR spectra of POT1 reveal specific functional groups, including aromatic and aliphatic compounds, greatly enhancing its interaction with coal surfaces, as confirmed by contact angle measurement. This research provides valuable insights into the specific carbon chains and functional groups that contribute to the effectiveness of POT as a collector, facilitating the optimization of coal flotation processes and underscoring the environmental advantages of employing pyrolysis oils as sustainable alternatives in the mining industry.

Deciding when to cross in front of an autonomous vehicle: How child and adult pedestrians respond to eHMI timing and vehicle kinematics.

How autonomous vehicles (AVs) communicate their intentions to vulnerable road users (e.g., pedestrians) is a concern given the rapid growth and adoption of this technology. At present, little is known about how children respond to external Human Machine Interface (eHMI) signals from AVs. The current study examined how adults and children respond to the combination of explicit (eHMI signals) and implicit information (vehicle deceleration) to guide their road-crossing decisions. Children (8- to 12-year-olds) and adults made decisions about when to cross in front of a driverless car in an immersive virtual environment. The car sometimes stopped, either abruptly or gradually (manipulated within subjects), to allow participants to cross. When yielding, the car communicated its intent via a dome light that changed from red to green and varied in its timing onset (manipulated between subjects): early eHMI onset, late eHMI onset, or control (no eHMI). As expected, we found that both children and adults waited longer to enter the roadway when vehicles decelerated abruptly than gradually. However, adults responded to the early eHMI signal by crossing sooner when the cars decelerated either gradually or abruptly compared to the control condition. Children were heavily influenced by the late eHMI signal, crossing later when the eHMI signal appeared late and the vehicle decelerated either gradually or abruptly compared to the control condition. Unlike adults, children in the control condition behaved similarly to children in the early eHMI condition by crossing before the yielding vehicle came to a stop. Together, these findings suggest that early eHMI onset may lead to riskier behavior (initiating crossing well before a gradually decelerating vehicle comes to a stop), whereas late eHMI onset may lead to safer behavior (waiting for the eHMI signal to appear before initiating crossing). Without an eHMI signal, children show a concerning overreliance on gradual vehicle deceleration to judge yielding intent.

[Application and comparison of three occupational health risk assessment methods in an automobile manufacturing industry].

Objective: Three occupational health risk assessment methods were used to assess the occupational health risk of noise exposed posts in an automobile manufacturing enterprise. According to the results, the selection of risk assessment methods and risk management of such occupational noise enterprises were provided. Methods: Form April to November 2021, The occupational health field survey was carried out in an automobile manufacturing industry in Tianjin. The occupational health MES risk assessment method, occupational health risk index risk assessment method and Australian occupational hazard risk assessment method were used to evaluate the occupational health risk of noise-exposed posts in this enterprise, and the evaluation results of different methods were analyzed and compared. Results: The average value of L(Aeq, 8 h) in the four workshops of automobile manufacturing industry was 82.95 dB (A) , and the noise detection exceeding rate was 22.41% (26/116) . The LAeq, 8h and exceeding rate noise of welding workshop were higher than those of other workshops (χ(2)=23.56, 32.94, P<0.01) . The three occupational health risk assessment methods have the same risk assessment results for the four major workshops. The assembly and painting workshops are level 4 risk (possible risk) , and the stamping and welding workshops are level 3 risk (significant risk) . Conclusion: Occupational noise has certain potential hazards to workers in automobile manufacturing enterprises. Therefore, in the future work, corresponding organizational management measures should be taken to improve the working environment and reduce the actual exposure level of workers in order to protect the health of occupational workers.

Rib and sternum fracture risks for restrained occupants in frontal car crashes.

OBJECTIVE: Most car occupant fatalities occur in frontal crashes and the thorax is the most frequently injured body region. The objectives of the study were, firstly, to quantify the relation between risk factors (such as speed and occupant age) and rib and sternum fracture injury probability in frontal car crashes, and, secondly, to evaluate whether rib fracture occurrence can predict sternum fractures. METHODS: Weighted German data from 1999-2021 were used to create the injury risk curves to predict both, at least moderate and at least serious, rib and sternum fracture risks. A contingency table for rib and sternum fractures allowed the calculation of sensitivity, specificity, and precision, as well as testing for the association. RESULTS: Elderly occupants (≥65 years old) had increased rib and sternum fracture risk compared to mid aged occupants (18-64 years old). Besides occupant age, delta-V was always and sex sometimes a significant predictor for skeletal thoracic injury. Sternum fractures were more common than rib fractures and more likely to occur at any given delta-V. Sternum fractures occurred often in isolation. Female occupants were at higher risk than males to sustain at least moderate rib and sternum fractures together and sternum fractures in isolation. Rib and sternum fractures were associated, but low sensitivity and precision show that rib fractures do not predict sternum fractures well. CONCLUSIONS: Elderly and female occupants were at the highest risk and should be targeted by thoracic injury criteria and thresholds for frontal crash occupant protection. Rib and sternum fractures were not associated. Therefore, sternum fractures need to be predicted and evaluated separately from rib fractures.

Towards zero pollution vehicles by advanced fuels and exhaust aftertreatment technologies.

Vehicular emissions deteriorate air quality in urban areas notably. The aim of this study was to conduct an in-depth characterization of gaseous and particle emissions, and their potential to form secondary aerosol emissions, of the cars meeting the most recent emission Euro 6d standards, and to investigate the impact of fuel as well as engine and aftertreatment technologies on pollutants at warm and cold ambient temperatures. Studied vehicles were a diesel car with a diesel particulate filter (DPF), two gasoline cars (with and without a gasoline particulate filter (GPF)), and a car using compressed natural gas (CNG). The impact of fuel aromatic content was examined for the diesel car and the gasoline car without the GPF. The results showed that the utilization of exhaust particulate filter was important both in diesel and gasoline cars. The gasoline car without the GPF emitted relatively high concentrations of particles compared to the other technologies but the implementation of the GPF decreased particle emissions, and the potential to form secondary aerosols in atmospheric processes. The diesel car equipped with the DPF emitted low particle number concentrations except during the DPF regeneration events. Aromatic-free gasoline and diesel fuel efficiently reduced exhaust particles. Since the renewal of vehicle fleet is a relatively slow process, changing the fuel composition can be seen as a faster way to affect traffic emissions.

Recent tobacco smoking, restaurant and in-car secondhand smoke exposure are associated with depressive symptoms among young adults: a population-based cross-sectional analysis.

The purpose of this study was to evaluate the association between recent tobacco smoking, household secondhand smoke exposure, confined space secondhand smoke exposure and depressive symptoms in young adults after adjustments for each other. Data from NHANES 2013-2018 were extracted. A total of 4129 young adults age 18-35 years (mean age 26.11 ± 5.39 years, 2021 males and 2108 females) were included. Depressive symptoms were screened by PHQ-9. Recent tobacco smoking was assessed through question "smoked tobacco in the last 5 days?". Household secondhand smoke exposure was assessed through question "living with a smoker who smoked inside the house?". Confined space secondhand smoke exposure was assessed by SSEQ. Binary logistic regression models were performed to analyze the associations. Significant association were observed in recent tobacco smoking (OR = 1.593, 95% CI 1.318-1.926) and confined space secondhand smoke exposure (OR = 1.399, 95% CI 1.185-1.651), but not in household secondhand smoke exposure (P = 0.108). Among the different settings of confined space secondhand smoke exposure, restaurant (OR = 1.732, 95% CI 1.120-2.678) and in-car (OR = 1.350, 95% CI 1.102-1.652) exposure were significantly associated with depressive symptom after after fully adjustments.