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).

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.

Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems.

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs.

Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data.

Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.

Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment.

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human-machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers' responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers' gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

A game-theoretic approach for modelling pedestrian-vehicle conflict resolutions in uncontrolled traffic environments.

The interactions of motorised vehicles with pedestrians have always been a concern in traffic safety. The major threat to pedestrians comes from the high level of interactions imposed in uncontrolled traffic environments, where road users have to compete over the right of way. In the absence of traffic management and control systems in such traffic environments, road users have to negotiate the right of way while avoiding conflict. Furthermore, the high level of movement freedom and agility of pedestrians, as one of the interactive parties, can lead to exposing unpredictable behaviour on the road. Traffic interactions in uncontrolled mixed traffic environments will become more challenging by fully/partially automated driving systems' deployment, where the intentions and decisions of interacting agents must be predicted/detected to avoid conflict and improve traffic safety and efficiency. This study aims to formulate a game-theoretic approach to model pedestrian interactions with passenger cars and light vehicles (two-wheel and three-wheel vehicles) in uncontrolled traffic settings. The proposed models employ the most influencing factors in the road user's decision and choice of strategy to predict their movements and conflict resolution strategies in traffic interactions. The models are applied to two data sets of video recordings collected in a shared space in Hamburg and a mid-block crossing area in Surat, India, including the interactions of pedestrians with passenger cars and light vehicles, respectively. The models are calibrated using the identified conflicts between users and their conflict resolution strategies in the data sets. The proposed models indicate satisfactory performances considering the stochastic behaviour of road users - particularly in the mid-block crossing area in India - and have the potential to be used as a behavioural model for automated driving systems.

The effect of human-machine interface modality, specificity, and timing on driver performance and behavior while using vehicle automation.

The effectiveness of the human-machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers' takeover time, performance, and eye glance behavior. Drivers' behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, or other eye glance metrics (e.g., on road glance duration). Future work should disentangle HMI design further to determine exactly which aspects of design yield between safety critical behavior.

Greater prosociality toward other human drivers than autonomous vehicles: Human drivers' discriminatory behavior in mixed traffic.

The development of autonomous vehicles (AVs) has rapidly evolved in recent years, aiming to gradually replace humans in driving tasks. However, road traffic is a complex environment involving numerous social interactions. As new road users, AVs may encounter different interactive situations from those of human drivers. This study therefore investigates whether human drivers show distinct degrees of prosociality toward AVs or other human drivers and whether AV behavioral patterns exert a relevant influence. Sixty-two drivers participated in the driving simulation experiment and interacted with other human drivers and different kinds of AVs (conservative, human-like, aggressive). The results show that human drivers are more willing to yield to other human drivers than to all kinds of AVs. Their braking reaction time is longer when yielding to AVs and their distance to AVs is shorter when choosing not to yield. AVs of different behavioral patterns do not significantly differ in yielding rate, but the braking reaction time of human-like AVs is longer than conservative AVs and shorter than aggressive AVs. These findings suggest that human drivers show more prosocial behaviors toward other human drivers than toward AVs. And human drivers' yielding behavior changes as the behavioral patterns of AVs changes. Accordingly, this study improves the understanding of how human drivers interact with nonliving road users such as AVs and how the former accept AVs with different driving styles on the road.

Analyzing relationships between latent topics in autonomous vehicle crash narratives and crash severity using natural language processing techniques and explainable XGBoost.

Safety is one of the most essential considerations when evaluating the performance of autonomous vehicles (AVs). Real-world AV data, including trajectory, detection, and crash data, are becoming increasingly popular as they provide possibilities for a realistic evaluation of AVs' performance. While substantial research was conducted to estimate general crash patterns utilizing structured AV crash data, a comprehensive exploration of AV crash narratives remains limited. These narratives contain latent information about AV crashes that can further the understanding of AV safety. Therefore, this study utilizes the Structural Topic Model (STM), a natural language processing technique, to extract latent topics from unstructured AV crash narratives while incorporating crash metadata (i.e., the severity and year of crashes). In total, 15 topics are identified and are further divided into behavior-related, party-related, location-related, and general topics. Using these topics, AV crashes can be systematically described and clustered. Results from the STM suggest that AVs' abilities to interact with vulnerable road users (VRUs) and react to lane-change behavior need to be further improved. Moreover, an XGBoost model is developed to investigate the relationships between the topics and crash severity. The model significantly outperforms existing studies in terms of accuracy, suggesting that the extracted topics are closely related to crash severity. Results from interpreting the model indicate that topics containing information about crash severity and VRUs have significant impacts on the model's output, which are suggested to be included in future AV crash reporting.

The abundance and pathogenicity of microbes in automobile air conditioning filters across the typical cities of China and Europe.

Bioaerosols are widely distributed in urban air and can be transmitted across the atmosphere, biosphere, and anthroposphere, resulting in infectious diseases. Automobile air conditioning (AAC) filters can trap airborne microbes. In this study, AAC filters were used to investigate the abundance and pathogenicity of airborne microorganisms in typical Chinese and European cities. Culturable bacteria and fungi concentrations were determined using microbial culturing. High-throughput sequencing was employed to analyze microbial community structures. The levels of culturable bioaerosols in Chinese and European cities exhibited disparities (Analysis of Variance, P < 0.01). The most dominant pathogenic bacteria and fungi were similar in Chinese (Mycobacterium: 18.2-18.9 %; Cladosporium: 23.0-30.2 %) and European cities (Mycobacterium: 15.4-37.7 %; Cladosporium: 18.1-29.3 %). Bartonella, Bordetella, Alternaria, and Aspergillus were also widely identified. BugBase analysis showed that microbiomes in China exhibited higher abundances of mobile genetic elements (MGEs) and biofilm formation capacity than those in Europe, indicating higher health risks. Through co-occurrence network analysis, heavy metals such as zinc were found to correlate with microorganism abundance; most bacteria were inversely associated, while fungi exhibited greater tolerance, indicating that heavy metals affect the growth and reproduction of bioaerosol microorganisms. This study elucidates the influence of social and environmental factors on shaping microbial community structures, offering practical insights for preventing and controlling regional bioaerosol pollution.

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.

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.

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.

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.

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.

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.