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1.
Ulus Travma Acil Cerrahi Derg ; 28(8): 1115-1121, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-35920420

RESUMO

BACKGROUND: Traffic accidents are among the most common causes of death. A small proportion of drownings are associated with traffic accidents. The roads in the Eastern Black Sea Region, where the study was conducted are fairly close to the seas, rivers, and ponds. This study aims to evaluate the cases who underwent autopsies after the traffic accident between 2009 and 2016 and who were found to have died as a result of drowning. METHODS: A retrospective examination was made of the autopsy reports in the period 2009-2016. RESULTS: As a result of the examination of forensic reports, from a total of 7124 autopsies performed in our center between 2009 and 2016, 41 (0.57%) were seen to be due to death in a traffic accident that resulted in drowning. Of the vehicles involved in the ac-cidents, 30 (73.2%) were retrieved from a river/stream, 7 (17.1%) from a lake, and 4 (9.7%) from the sea. In all 39 cases, the primary cause of death was determined as asphyxia related to drowning. Other reasons affecting death were traumatic intracranial bleeding in 7 (17.1%) cases, medulla spinalis injury in 4 (9.7%), and pulmonary injury in 2 (4.9%). CONCLUSION: It was determined in the study that the typical autopsy results of trauma and drowning after a traffic accident could coexist. Drowning alone could be the cause of death, even though there was a traumatic origin such as a traffic accident in such cases. It was revealed that chemical and microscopic examinations should be handled together with crime scene results and eyewitness statements in addition to traumatic results during the examination phase.


Assuntos
Afogamento , Acidentes , Acidentes de Trânsito , Automóveis , Causas de Morte , Humanos , Estudos Retrospectivos
2.
Artigo em Chinês | MEDLINE | ID: mdl-35785897

RESUMO

Objective: To investigate the current situation of occupational exposure to noise among noise workers in an automobile manufacturing enterprise in Tianjin, understand the impact of noise on workers' nervous system and hearing, and assess the risk of hearing loss among noise workers. Methods: In May 2021, 3516 workers in an automobile manufacturing enterprise were investigated by using a self-made questionnaire"Noise Workers Questionnaire" and cluster sampling method. The occupational noise hygiene survey and occupational hazards detection were carried out in their workplaces. They were divided into noise exposure group and non-noise exposure group according to whether they were exposed to noise or not. The general characteristics, hearing and nervous system symptoms of the two groups of workers were compared, and the risk of hearing loss was assessed. Results: There were 758 workers in the noise exposure group, aged (26±5) years old, with a working age of 3.0 (2.0, 6.0) years exposed to noise. 2758 workers in the non-noise exposure group, aged (25±6) years old, with a working age of 2.0 (1.0, 4.0) years. There were statistically significant differences in the distribution of workers'education level, working age and memory loss between the two groups (χ(2)=37.98, 38.70, 5.20, P<0.05). The workers in the noise exposure group showed a decreasing trend of insomnia, dreaminess, sweating and fatigue with the increase of working age (χ(2trend)=6.16, 7.99, P<0.05). The risk classification of binaural high-frequency hearing loss for workers in all noise positions until the age of 50 and 60 was negligible, the risk of occupational noise deafness was low for workers in stamping and welding noise positions until the age of 60. Conclusion: The occupational noise exposed to automobile manufacturing workers may cause certain harm to their nervous and auditory systems. Noise protection measures should be taken to reduce the risk of hearing loss and occupational noise deafness.


Assuntos
Surdez , Perda Auditiva Provocada por Ruído , Ruído Ocupacional , Doenças Profissionais , Adulto , Automóveis , Perda Auditiva Provocada por Ruído/diagnóstico , Perda Auditiva Provocada por Ruído/epidemiologia , Humanos , Ruído Ocupacional/prevenção & controle , Doenças Profissionais/diagnóstico , Medição de Risco , Adulto Jovem
3.
Comput Intell Neurosci ; 2022: 7606896, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35845904

RESUMO

Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection.


Assuntos
Algoritmos , Automóveis , Humanos , Aprendizado de Máquina
4.
J Environ Public Health ; 2022: 3010851, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35815254

RESUMO

The deterioration of the environment in the 21st century has made environmental issues one of the most severe tests for modern society. With this comes a change in energy structure from high-carbon to low-carbon direction, and electric vehicles are gradually developing into the darling of a city with low-carbon transportation and safe travel. This paper carries out a systematic analysis of landscape design and environmental protection in the development of new energy electric vehicle charging facilities in urban habitat. By categorizing the content and provisions of published domestic and international standards, new requirements for standardization are obtained, including barrier-free design, electromagnetic radiation, child safety protection, and urban landscape integration. Among them, ecological landscape public charging facilities can enhance the overall quality of urban environment. This paper analyzes the necessity of landscape design in charging facilities, explores the ecological concepts extended by macroscopic landscape design principles and the problems of public charging facilities, and proposes a design and evaluation method of ecologically landscaped public charging facilities based on hierarchical analysis and neural networks. The hierarchical analysis method is introduced to establish a landscape design assessment index system, and then a neural network is introduced to describe the characteristics of electric vehicle charging, and the landscape design assessment learning samples are trained to establish a landscape design assessment model. Finally, a comparison experiment is conducted with other landscape design assessment methods using specific examples, and the results show that the proposed method has more obvious advantages in ecological landscape public charging facility design assessment with high accuracy, faster landscape design assessment, charging efficiency, and environmental protection.


Assuntos
Automóveis/classificação , Planejamento de Cidades , Conservação dos Recursos Naturais , Eletricidade , Carbono , Criança , Cidades , Planejamento de Cidades/normas , Planejamento de Cidades/tendências , Humanos , Redes Neurais de Computação , População Urbana/classificação , População Urbana/estatística & dados numéricos
5.
BMC Public Health ; 22(1): 1424, 2022 07 26.
Artigo em Inglês | MEDLINE | ID: mdl-35883078

RESUMO

BACKGROUND: This study aims to test the effectiveness of an awareness-raising model designed based on the theory of planned behaviour regarding helmet use for motorcycle taxi drivers. METHODS: This quasi-experimental study took place in the cities of Parakou (intervention group) and Porto Novo (control group). Over a three-month period, a package of awareness-raising activities, based on the theory of planned behaviour, have been implemented in the intervention area. Data relate to knowledge, attitudes and practices regarding helmet use was collected prospectively before the intervention, at the end, and 6 months later. Stata 15 was used for data analysis. Chi-square or Fisher, Student's or Kruskal-Wallis tests was carried out. The difference-in-difference method was used to determine the specific effect of the awareness activities. RESULTS: After the intervention, there was an improvement in the total score in both groups compared to baseline. The total score increased by 0.2 (0.06-0.3) in the experimental group when the number of sessions attended increased by one (p = 0.005). The difference-in-difference estimator measured among subjects who attended at least one awareness session, controlling for socio-demographic variables, showed a significantly higher difference in the total score of subjects in the experimental group compared to those in the control group both at the end of the interactive sessions and 6 months later. CONCLUSION: This model improves the helmet-wearing behaviour of motorbike taxi drivers in the experimental area. It could be adapted and applied to other socio-professional groups and other types of users.


Assuntos
Acidentes de Trânsito , Dispositivos de Proteção da Cabeça , Acidentes de Trânsito/prevenção & controle , Automóveis , Benin , Humanos , Motocicletas
6.
Artigo em Inglês | MEDLINE | ID: mdl-35897491

RESUMO

Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed with normal cars. In order to mitigate the influence of cruising for parking on normal cars, the simulation framework based on VISSIM was proposed for reproducing the cruising vehicles and normal traffic flows. The car-following model of cruising vehicles was calibrated by the GPS and video data. The scenarios under different cruising ratios were analyzed to evaluate the influence of cruising for parking on traffic efficiency and emissions. Finally, the layout optimization changing the parking locations and positions of entrance-exit gates were discussed to mitigate the negative effect. The results indicated that cruising for parking deteriorates the traffic congestion and emissions on the road sections, intersections and network. The closer distances the intersections and sections are to the parking lot, the greater the negative impact is. But the negative effect after the 30% proportion on traffic performance only illustrates the slight deterioration, because the carrying capacity of the network is reached. The research results provide a quantitative method for the hidden contribution of cruising for parking on traffic congestion and emissions.


Assuntos
Automóveis , Modelos Teóricos , Emissões de Veículos , Simulação por Computador , Emissões de Veículos/análise
7.
Sensors (Basel) ; 22(14)2022 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-35890757

RESUMO

Besides the failures that cause accidents, there are the ones responsible for preventing the car's motion capacity. These failures cause inconveniences to the passengers and expose them to the dangers of the road. Although modern vehicles are equipped with a failure detection system, it does not provide an online approach to the drivers. Third-party devices and skilled labor are necessary to manage the data for failure characterization. One of the most common failures in engines is called misfire, and it happens when the spark is weak or inexistent, compromising the whole set. In this work, two algorithms are compared, based on Wavelet Multiresolution Analysis (WMA) and another using an approach performing signal analysis based on Chaos using the density of maxima (SAC-DM) to identify misfare in a combustion engine of a working automotive vehicle. Experimental tests were carried out in a car to validate the techniques for the engine without failure, with failure in one piston, and with two failed pistons. The results made it possible to obtain the failure diagnosis for 100% of the cases for both WMA and SAC-DM methods, but a shorter time window when using the last one.


Assuntos
Automóveis , Smartphone , Algoritmos
8.
Sensors (Basel) ; 22(15)2022 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-35898103

RESUMO

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder-decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder-decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.


Assuntos
Automóveis , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Análise Espectral Raman , Tempo (Meteorologia)
9.
PLoS One ; 17(7): e0271532, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35862304

RESUMO

As automated driving technology continues to develop, studies are being conducted to develop various scenarios for assessing the functional safety, failure safety, and mobility of automated vehicles (AVs). As the commercialization of AVs progresses, it is necessary to develop a set of test scenarios for new car assessment programs (NCAPs), so as to provide information on the safety and reliability of AVs to consumers. To provide valuable information regarding newly emerged AVs to consumers who are willing to purchase them, it is necessary to derive specific and well-defined test scenarios based on the safety-in-use. Accordingly, to apply NCAPs to AVs, this study established test scenarios targeting freeways where AVs were expected to be commercialized. To this end, based on freeway traffic accident data and opinions of traffic safety and AV experts, we derived possible dangerous situations when an AV is maintaining a lane on a freeway. Functional scenarios were defined based on the derived dangerous situations. The priority of the defined functional scenarios was set using the analytic hierarchy process (AHP). Accordingly, this study presents a logical and concrete scenario construction methodology for deriving the ranges and values of test parameters for functional scenarios.


Assuntos
Condução de Veículo , Automóveis , Acidentes de Trânsito/prevenção & controle , Veículos Autônomos , Reprodutibilidade dos Testes
10.
Artigo em Chinês | MEDLINE | ID: mdl-35680586

RESUMO

Objective: To learn about the noise exposure and health status of workers and analyze factors that may affect the health outcomes of workers in an auto manufacturing enterprise in Tianjin City. Methods: In September 2020, occupational hygiene survey, noise exposure level detection and occupational health examination data collection were carried out in an auto parts manufacturing enterprise. Chi square test and unconditional logistic regression analysis were used to analyze the health effects of noise exposure and hearing loss of 361 noise exposure workers. Results: The rates of over-standard noise exposure, hearing loss and hypertension were 69.39% (34/49) , 33.24% (120/361) and 11.36% (41/361) , respectively. There were upward trends on age and noise-working years for hearing loss and hypertension rates (χ(2)=-5.95, -6.16, -2.81, -2.74, P<0.05) . Unconditional logistic regression analysis showed that age>35 years old, noise exposure length of service >10 years and noise L(EX, 8 h)>85 dB (A) were risk factors for hearing loss (OR=3.57, 95%CI: 1.09, 11.75; OR=4.05, 95%CI: 1.97, 8.25; OR=1.75, 95%CI: 1.00, 3.05; P=0.036, 0.001, 0.047) . Conclusion: This company has a high rate of job noise exceeding the standard, and noise-exposed workers have more serious hearing loss. Age, noise exposure and high noise exposure are risk factors for hearing loss.


Assuntos
Surdez , Perda Auditiva Provocada por Ruído , Hipertensão , Ruído Ocupacional , Doenças Profissionais , Exposição Ocupacional , Adulto , Automóveis , Perda Auditiva Provocada por Ruído/epidemiologia , Perda Auditiva Provocada por Ruído/etiologia , Humanos , Hipertensão/complicações , Ruído Ocupacional/efeitos adversos , Doenças Profissionais/complicações , Exposição Ocupacional/efeitos adversos , Exposição Ocupacional/análise
11.
Waste Manag ; 148: 71-82, 2022 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-35667238

RESUMO

The pyrolysis of passenger-car-waste-tires (PCWT) has recently attracted widespread attention because it is a highly effective disposal method. However, a comprehensive understanding of real tire pyrolytic processes is limited owing to the complicated PCWT pyrolysis reaction system, particularly regarding the reaction mechanism. This study investigated the PCWT pyrolytic processes using a thermogravimetric analyzer coupled with mass spectrometry and analyzed all the pyrolytic products using pyrolysis-gas chromatography coupled with mass spectrometry. The composition and distribution of the PCWT pyrolytic products were investigated under a kinetic regime to eliminate other influences on the intrinsic reaction. The pyrolytic products mainly consisted of chain and cyclic alkenes, and monocylic aromatics. Importantly, an integral pyrolytic mechanism network for the PCWT was established based on the pyrolysis of single rubbers (natural, styrene butadiene, and butadiene rubbers). The reaction routes for the main products were determined according to the mechanism. Moreover, a kinetic study of the PCWT pyrolysis revealed the activation energy for this complicated reaction system.


Assuntos
Butadienos , Pirólise , Automóveis , Cinética , Borracha
12.
Sensors (Basel) ; 22(12)2022 Jun 09.
Artigo em Inglês | MEDLINE | ID: mdl-35746164

RESUMO

Motivated by the conflict between travelers' habitual choice behavior and traffic information search behavior, in this paper, a behavioral experiment under different types of traffic information (i.e., per-trip traffic information and en-route traffic information) was designed to obtain data regarding car commuters' daily route choices. Based on the observed data, participants' route choices, habit strength, response time, and information search behaviors were analyzed. It is concluded that, in the beginning, the traffic information had a great influence on the habit participants' route choices, let them think more, and made most of them switch from habit route to the best route (as recommended by traffic information); however, as time went on, the impact of traffic information declined, and several features of habits, such as automatically responding and repeated behavior, would reappear in some participants' decision-making. Meanwhile, the different way of traffic information search behaviors (i.e., in active performance or in passive reception) could cause different information compliance ratios. These results would help to understand the interrelationship between car commuters' daily route choice behaviors and traffic information search behaviors in short-term and in long-term, respectively, and provide an interesting starting point for the development of practical traffic information issuing strategies to enhance the impact of traffic information to alleviate traffic congestion during morning commuting.


Assuntos
Automóveis , Meios de Transporte , Hábitos , Humanos , Inquéritos e Questionários , Meios de Transporte/métodos
13.
Sensors (Basel) ; 22(12)2022 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-35746220

RESUMO

Road segmentation has been one of the leading research areas in the realm of autonomous driving cars due to the possible benefits autonomous vehicles can offer. Significant reduction of crashes, greater independence for the people with disabilities, and reduced traffic congestion on the roads are some of the vivid examples of them. Considering the importance of self-driving cars, it is vital to develop models that can accurately segment drivable regions of roads. The recent advances in the area of deep learning have presented effective methods and techniques to tackle road segmentation tasks effectively. However, the results of most of them are not satisfactory for implementing them into practice. To tackle this issue, in this paper, we propose a novel model, dubbed as TA-Unet, that is able to produce quality drivable road region segmentation maps. The proposed model incorporates a triplet attention module into the encoding stage of the U-Net network to compute attention weights through the triplet branch structure. Additionally, to overcome the class-imbalance problem, we experiment on different loss functions, and confirm that using a mixed loss function leads to a boost in performance. To validate the performance and efficiency of the proposed method, we adopt the publicly available UAS dataset, and compare its results to the framework of the dataset and also to four state-of-the-art segmentation models. Extensive experiments demonstrate that the proposed TA-Unet outperforms baseline methods both in terms of pixel accuracy and mIoU, with 98.74% and 97.41%, respectively. Finally, the proposed method yields clearer segmentation maps on different sample sets compared to other baseline methods.


Assuntos
Condução de Veículo , Atenção , Automóveis , Veículos Autônomos , Humanos , Processamento de Imagem Assistida por Computador , Análise Espectral Raman
14.
Sensors (Basel) ; 22(12)2022 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-35746234

RESUMO

The train horn sound is an active audible warning signal used for warning commuters and railway employees of the oncoming train(s), assuring a smooth operation and traffic safety, especially at barrier-free crossings. This work studies deep learning-based approaches to develop a system providing the early detection of train arrival based on the recognition of train horn sounds from the traffic soundscape. A custom dataset of train horn sounds, car horn sounds, and traffic noises is developed to conduct experiments and analysis. We propose a novel two-stream end-to-end CNN model (i.e., THD-RawNet), which combines two approaches of feature extraction from raw audio waveforms, for audio classification in train horn detection (THD). Besides a stream with a sequential one-dimensional CNN (1D-CNN) as in existing sound classification works, we propose to utilize multiple 1D-CNN branches to process raw waves in different temporal resolutions to extract an image-like representation for the 2D-CNN classification part. Our experiment results and comparative analysis have proved the effectiveness of the proposed two-stream network and the method of combining features extracted in multiple temporal resolutions. The THD-RawNet obtained better accuracies and robustness compared to those of baseline models trained on either raw audio or handcrafted features, in which at the input size of one second the network yielded an accuracy of 95.11% for testing data in normal traffic conditions and remained above a 93% accuracy for the considerable noisy condition of-10 dB SNR. The proposed THD system can be integrated into the smart railway crossing systems, private cars, and self-driving cars to improve railway transit safety.


Assuntos
Automóveis , Ruído , Humanos
15.
Sci Rep ; 12(1): 10044, 2022 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-35710859

RESUMO

Why do some people tend to drive faster than others? Personality characteristics such as the evaluation of risk to oneself or to others, impulsivity, adherence to norms, but also other personal factors such as gender, age, or driving experience all may play a role in determining how fast people drive. Since driving speed is a critical factor underlying accident prevalence, identifying the psychological metrics to predict individual driving speed is an important step that could aid in accident prevention. To investigate this issue, here, we used an immersive virtual reality driving simulation to analyze average driving speed. A total of 124 participants first took a comprehensive set of personality and background questionnaires and a behavioral risk-taking measure. In the virtual reality experiment, participants were required to navigate a difficult driving course in a minimally-restricted, non-urban setting in order to provide baseline results for speed selection. Importantly, we found that sensation seeking and gender significantly predicted the average driving speed, and that sensation seeking and age were able to predict the maximum driving speed.


Assuntos
Condução de Veículo , Realidade Virtual , Condução de Veículo/psicologia , Automóveis , Benchmarking , Simulação por Computador , Humanos
16.
Environ Sci Technol ; 56(13): 9593-9603, 2022 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-35735988

RESUMO

This research investigates carbon footprint impacts for full fleet electrification of Swedish passenger car travel in combination with different charging conditions, including electric road system (ERS) that enables dynamic on-road charging. The research applies a prospective life cycle analysis framework for estimating carbon footprints of vehicles, fuels, and infrastructure. The framework includes vehicle stock turnover modeling of fleet electrification and modeling of optimal battery capacity for different charging conditions based on Swedish real-world driving patterns. All new car sales are assumed to be electric after 2030 following phase-out policies for gasoline and diesel cars. Implementing ERS on selected high-traffic roads could yield significant avoided emissions in battery manufacturing compared to the additional emissions in ERS construction. ERS combined with stationary charging could enable additional reductions in the cumulative carbon footprint of about 12-24 million tons of CO2 over 30 years (2030-2060) compared to an electrified fleet only relying on stationary charging. The range depends on uncertainty in emission abatement in global manufacturing, where the lower is based on Paris Agreement compliance and the higher on current climate policies. A large share of the reduction could be achieved even if only a small share of the cars adopts the optimized battery capacities.


Assuntos
Automóveis , Emissões de Veículos , Gasolina , Veículos Automotores , Estudos Prospectivos , Emissões de Veículos/análise , Emissões de Veículos/prevenção & controle
17.
J Hazard Mater ; 437: 129362, 2022 09 05.
Artigo em Inglês | MEDLINE | ID: mdl-35716575

RESUMO

Tire particles are generated by the abrasion of tire treads on roads and are major contributors to microplastics in soil environments. Contamination by tire wear particles worsens annually as the use of personal mobilities increases. Tire particles (112-541 µm) were obtained from three types of personal mobility tires (bicycle, car, and electric scooter) and exposed to plants (Vigna radiata) and springtails (Folsomia candida) for 28 d to assess the toxicity of each tire-particle type. The laboratory-generated tire particles exhibit adverse effects depending on the origin of the tire or test species. Particles from bicycle or electric-scooter tires changed the soil's bulk density and water holding capacity and adversely affected plant growth. Car tire particles had leached various organic compounds and induced detrimental effects on springtails (adult and offspring growth). We concluded that laboratory-generated tire particles (frow new tires) can affect the soil environment by changing soil properties and leaching chemicals; thus, causing adverse effects on soil organisms. Since this study found tire particle toxicity on soil organisms, it would be possible to compare the various contamination levels in areas near road soil and other clean soils.


Assuntos
Automóveis , Solo , Ciclismo , Microplásticos , Plásticos
18.
Math Biosci Eng ; 19(7): 6680-6698, 2022 04 29.
Artigo em Inglês | MEDLINE | ID: mdl-35730277

RESUMO

The public's awareness of pollution in cities is growing. The decrease of carbon dioxide emissions from the use of fossil-fuel-powered cars stands out among the different viable alternatives. To this purpose, more sustainable options, such as carsharing fleets, could be used to replace private automobiles and other services such as taxis. This type of vehicle, which is usually electric, is becoming more common in cities, providing a green mobility option. In this research, we use multi-agent simulations to examine the efficiency of the current taxi fleet in Valencia. After that, we evaluate various carsharing fleet arrangements. Our findings demonstrate the possibility for a mix of the two types of fleets to meet present demand while also improving the city's sustainability.


Assuntos
Automóveis , Cidades
19.
Chemosphere ; 305: 135481, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35753424

RESUMO

PM10 emissions generated from the brake wear of passenger car per braking event during three test driving cycles (WLTP, LACT, and WLTP-Brake) were studied using a finite element analysis (FEA) approach in combination with the relationship among the mass emitted rate of airborne particles versus local contact pressure and sliding speed. In addition, PM10 emissions were measured per braking event during the WLTP-Brake cycle on a brake dynamometer using an electrical low-pressure impactor (ELPI+) to validate the proposed FEA approach. The simulated and experimental results for WLTP-Brake illustrated that the proposed simulation approach has the potential to predict PM10 from brake wear per braking event, with an R2 value of 0.93. The FEA results of three test driving cycles showed that there was a gradient rise in pad wear on both sides from the inner to outer radii. The simulated PM10 emission factors during the WLTP, LACT, and WLTP-Brake were 7.9 mg km-1 veh-1, 9.8 mg km-1 veh-1, and 6.4 mg km-1 veh-1, respectively. Among three test driving cycles, the ratio of PM10 to total brake wear mass per braking event was the largest for the LACT, followed by WLTP and WLTP-Brake. From a practical application perspective, reducing the frequency of high-speed braking may be an effective way to decrease the generation of PM10 emissions.


Assuntos
Poluentes Atmosféricos , Emissões de Veículos , Poluentes Atmosféricos/análise , Automóveis , Monitoramento Ambiental/métodos , Tamanho da Partícula , Material Particulado/análise , Emissões de Veículos/análise
20.
Sci Rep ; 12(1): 9388, 2022 06 07.
Artigo em Inglês | MEDLINE | ID: mdl-35672424

RESUMO

Changes in the online car-hailing industry have brought new challenges to government governance. Effectively enhancing governance efficiency has become the focus of academic research. Based on the technical governance perspective, this paper introduces the consortium blockchain to construct an evolutionary game model between the online car-hailing platform and the government under blockchain technology. By solving the replicated dynamic equations and the Jacobian matrix, the influences of the change in initial conditions and decision parameters on the evolutionary stability results are revealed, and numerical experiments are carried out by using the Python programming language. This paper claims that the system presents three evolutionary stable results and a periodic stochastic state when the key parameters are located in different thresholds. The additional cost of the platform's negative regulation and the government's punishment intensity have a positive effect on the evolution of the system to the ideal state (active regulation, active governance). Platform technology R&D cost and government innovation input have a negative effect on the evolution of the system to the ideal state. Therefore, using blockchain to increase the additional cost of the platform's negative regulation, appropriately increasing the government's punishment intensity, reasonably controlling the government's innovation input to the platform, and reducing the technology R&D cost of the platform will help the system evolve into an ideal state. This paper provides useful references to implement effective governance and the innovative and healthy development of the online car-hailing industry.


Assuntos
Blockchain , Automóveis , Governo , Punição , Tecnologia
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