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Arguably, SiC technology is the most rapidly expanding IC manufacturing technology driven mostly by the aggressive roadmap for battery electric vehicle penetration and also industrial high-voltage/high-power applications. This paper provides a comprehensive overview of the state of the art of SiC technology focusing on the challenges starting from the difficult and lengthy SiC substrate growth all the way to the complex MOSFET assembly processes. We focus on the differentiation from the established Si manufacturing processes and provide a comprehensive list of references as well as a brief description of our own research into the key manufacturing processes in this technology. We also present a SiC technology and product roadmap. [ABSTRACT FROM AUTHOR]

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety. [ABSTRACT FROM AUTHOR]

Driving is integral to many people's daily existence, but aggressive driving behavior increases the risk of road traffic collisions. Young drivers are more prone to aggressive driving and danger perception impairments. A driver's physiological state (e.g., fatigue, anger, or stress) can negatively affect their driving performance. This is especially true for young drivers who have limited driving experience. This research focuses on examining the connection between emotional arousal and aggressive driving behavior in young drivers, using predictive analysis based on electrodermal activity (EDA) data through neural networks. The study involved 20 participants aged 18 to 30, who completed 84 driving sessions. During these sessions, their EDA signals and driving behaviors, including acceleration and braking, were monitored using an Empatica E4 wristband and a telematics device. This study conducted two key analyses using neural networks. The first analysis used a comprehensive set of EDA features to predict emotional arousal, achieving an accuracy of 65%. The second analysis concentrated on predicting aggressive driving behaviors by leveraging the top 10 most significant EDA features identified from the arousal prediction model. Initially, the arousal prediction was performed using the complete set of EDA features, from which feature importance was assessed. The top 10 features with the highest importance were then selected to predict aggressive driving behaviors. Another aggressive driving behavior prediction with a refined set of difference features, representing the changes from baseline EDA values, was also utilized in this analysis to enhance the prediction of aggressive driving events. Despite moderate accuracy, these findings suggest that EDA data, particularly difference features, can be valuable in predicting emotional states and aggressive driving, with future research needed to incorporate additional physiological measures for enhanced predictive performance. [ABSTRACT FROM AUTHOR]

Most existing two-lane traffic flow lattice models fail to fully consider the interactions between drivers' aggressive lane-changing behaviors and their desire for smooth driving, as well as their combined effects on traffic dynamics. To fill this research gap, under symmetric lane-changing rules, this paper proposes a novel two-lane lattice model that incorporates these two factors as co-influencers. Based on linear and nonlinear stability analyses, we derive the linear stability conditions of the new model, along with the density wave equation and its solutions describing traffic congestion propagation near critical points. Numerical simulations validate the theoretical findings. The results indicate that in the two-lane framework, enhancing either drivers' lane-changing aggressiveness or introducing the desire for smooth driving alone can somewhat improve traffic flow stability. However, when considering their synergistic effects, traffic flow stability is enhanced more significantly, and the traffic congestion is suppressed more effectively. [ABSTRACT FROM AUTHOR]

Anger impairs a driver's control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers' self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman's correlation coefficient analysis validated the DPV-MFD's effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset's effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Autophagy, an intrinsic catabolic mechanism that eliminates misfolded proteins, dysfunctional organelles, and lipid droplets, plays a vital function in energy balance and cytoplasmic quality control, in addition to maintaining cellular homeostasis. Liver cancer such as hepatocellular carcinoma (HCC) is one of the most common causes of cancer deaths globally and shows resistance to several anticancer drugs. Despite the rising incidence and poor prognosis of malignant HCC, the underlying molecular mechanisms driving this aggressive cancer remain unclear. Several natural compounds, such as phytochemicals of dietary and non-dietary origin, affect hepatocarcinogenesis signaling pathways in vitro and in vivo, which may help prevent and treat HCC cells. Current HCC cells treatments include chemotherapy, radiation, and surgery. However, these standard therapies have substantial side effects, and combination therapy enhances side effects for an acceptable therapeutic benefit. Therefore, there is a need to develop treatment strategies for HCC cells that are more efficacious and have fewer adverse effects. Multiple genetic and epigenetic factors are responsible for the HCC cells to become resistant to standard treatment. Autophagy contributes to maintain cellular homeostasis, which activates autophagy for biosynthesis and mitochondrial regulation and recycling. Therefore, modifying autophagic signaling would present a promising opportunity to identify novel therapies to treat HCC cells resistant to current standard treatments. This comprehensive review illustrates how natural compounds demonstrate their anti-hepatocellular carcinoma function through autophagy. [ABSTRACT FROM AUTHOR]

This study aims to examine the differences in drivers' reaction time (RTs) while driving on horizontal curves and straight roadway segments, among different driver classes, and in different driving environments to better understand human driver behavior in typical car-following situations. Therefore, behavioral measures were extracted from naturalistic car-following trajectories to estimate the RT. The RT was estimated for two stimulus–response pairs, namely, the speed–gap and relative speed–acceleration pairs, by using the cross-classification method. The RT was estimated separately for each driver and aggregated based on location and based on driver class. The results reveal that drivers' RTs on curves are consistently higher than their RTs on straight segments, and this difference is statistically significant. The comparison between normal drivers and aggressive drivers indicates that regardless of the location, aggressive drivers have a significantly longer RT than normal drivers, as aggressive drivers can accept closer gaps and higher relative speed. Also, cautious drivers have a longer RT compared with normal drivers; however, the difference is not significant in most cases. Furthermore, cautious and normal drivers have longer RTs on curves compared with their RTs on straight segments. Additionally, the RT on rural horizontal curves is longer than the RT on urban curves, yet the differences are insignificant. [ABSTRACT FROM AUTHOR]

Traffic collisions are the seventh leading cause of death in Ecuador, with reckless driving being one of the main causes. Although there are statistical data on traffic crashes, there has not yet been a comprehensive investigation of the causes. Therefore, the main objective of this study is to investigate unsafe driving behavior using a modified version of the Spanish Driving Behavior Questionnaire (SDBQ) adapted for Ecuador. The 34-item SDBQ we used has four main dimensions: lapses, errors, violations, and aggressive driving. To apply the SDBQ, a stratified random probability sample of 470 drivers with valid driver's licenses aged 18–69 was used. Of the drivers, 68.8% were male, while 33.2% were female. We used a chi-square test and descriptive statistics to analyze the data for the SDBQ application items. Finally, four generalized linear Poisson models were used. The results show that taxi drivers have the highest scores on three of the four main dimensions of the SDBQ and male drivers are more likely than female drivers to cause traffic accidents. Drivers are also more likely to cause traffic accidents if they drive more hours per day. This research is the first of its kind to analyze driver behavior-based solutions in Ecuador to reduce traffic accidents. The error factor is the most critical outcome of dangerous behavior in the city of Cuenca. The SDBQ aims to foster a culture of safety and sustainability by promoting road safety measures through legislation and traffic regulations. [ABSTRACT FROM AUTHOR]

This article presents a model-based approach to assess the battery performance of a two-wheeler EV drive train system for various user driving patterns using the selected urban drive cycles. The battery pack is one of the most expensive parts of an EV, and its life is heavily dependent on its usage pattern. The impact of the user's driving behaviour on the performance parameters of the EV battery pack needs to be investigated. Thus, a two-wheeler EV drive train model was developed in MATLAB with a 5 kW motor, a 4.32 kWh battery, vehicle dynamics, and the power train control algorithms for in-depth analysis of battery performance. The validity of the developed model was tested against various state-of-the-art drive cycles for a duration of 3600 s. Numerous user driving behaviours, such as aggressive, moderate, and slow driving behaviours, were modelled with modified drive cycles, which were used to assess the two-wheeler battery pack performance. An optimum speed range, which ranges from 21 km/h to 34 km/h for different drive cycles, was identified, and these speed ranges minimised the battery energy consumption for the selected drive cycles with the modified drive cycle models. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess whether road risk, road hazard perception skills, and attitudes towards risky driving are significant psychological antecedents of different driving styles. The study sample consisted of 446 non-professional drivers (with an average age of 32.6 years) and 200 professional drivers (with an average age of 47.7 years) from Lithuania. The study questionnaire included demographic questions, a multidimensional driving style assessment, a Lithuanian version of a hazard prediction test, a risk perception scale, and a subjective evaluation of driving competenc3 (perceptual, motor, and safety driving skills), as well as an evaluation of attitudes towards risky driving. The results confirmed that cognitive factors, together with attitudes towards driving and demographic factors, are important for understanding the origins of different driving styles. Cognitive factors like hazard perception and risk perception skills were found to be significant predictors of anxious, careless, and angry driving styles, mainly for professional drivers. Attitudes towards risky driving together with demographic characteristics and cognitive factors were found to important in predicting anxious, careless, and angry driving styles among professional as well as non-professional drivers. The subjective evaluation of driving competence (driving skills) was found to be crucial in predicting all four driving styles, but only in the non-professional drivers sample. [ABSTRACT FROM AUTHOR]

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers' safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver's perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one's driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving. [ABSTRACT FROM AUTHOR]

The growing concerns over road safety and the increasing popularity of two-wheeled vehicles highlight the need to address aggressive driving behaviors in this context. Understanding and detecting such behaviors can significantly contribute to rider safety and accident prevention. The primary aim of this research is to develop an effective method for detecting aggressive driving patterns, specifically focusing on rapid turns and lane-change maneuvers using two-wheeled vehicles. To achieve this objective, we conducted a survey to establish criteria for aggressive driving. Subsequently, we collected data through a virtual simulator, implementing staged aggressive driving scenarios. The data underwent preprocessing, feature engineering, and deep learning model training for detection. The results of this study demonstrate the successful detection of aggressive driving patterns, including rapid turns and lane changes, using sensor data. The criterion for rapid turns is specified as a significant change in sensor values within 1 s. In the CNN-LSTM model for aggressive lane changes, the precision for normal driving is 0.97, and the overall accuracy for aggressive driving is 95%. Our approach, which relies on sensor technology rather than impractical camera systems, showcases the potential for enhancing rider safety in two-wheeled vehicles. In conclusion, this research provides valuable insights into the detection of aggressive driving patterns in two-wheeled vehicles. By leveraging sensor data and innovative methods, it offers promising implications for improving rider safety and accident prevention in the future. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs. [ABSTRACT FROM AUTHOR]

Trucking is an important production link in most open-pit mines, and its transportation cost accounts for more than 50% of the total production cost of open-pit mines. The quality of the driver's driving behavior plays a crucial role in the fine control of the production cost of transportation. Different from the previous evaluation studies of drivers' driving behavior in open-pit mines, which mainly took safety driving behavior index as a factor variable, this paper puts forward a comprehensive evaluation method of driving behavior of mining truck drivers, which takes both safety driving and transportation cost as factor variables. Taking the mining truck as the research object, firstly, a scientific and reasonable data collection scheme is established, and the data information characterizing the transport state of the mining truck is obtained through data collection and analysis. Secondly, the RKNN algorithm of time series prediction and the wavelet analysis method are used to achieve noise reduction and missing processing of the original data so as to obtain accurate sample data. Then, taking the principal component analysis method as the entry point, through constructing the principal component analysis theory model, the key index system representing safe driving behavior and transportation cost is established to realize the comprehensive evaluation of the driving behavior of mining truck drivers, and the evaluation system of "standard driving", "prudent driving" and "aggressive driving" of mining truck drivers is formulated. The results show that after noise reduction, the accuracy of mining car operation data can be improved by 7~12%, and the transportation cost can be reduced by about 5% after the driver's operation behavior is standardized. [ABSTRACT FROM AUTHOR]

Roundabout crashes are common worldwide but have received little attention. In particular, the investigation remains relatively understudied in distinct collision modes of roundabout crashes, including angle and non-angle crashes. This study investigates differences in factors affecting injury severity between angle and non-angle crashes and how these differences change over time. Random parameter logit models with heterogeneity in mean and variance were estimated using 2012–2019 Florida crash data. Variables considered for the modeling include temporal characteristics, environmental characteristics, road characteristics, spatial characteristics, vehicle characteristics, crash characteristics, and human characteristics. In addition, likelihood ratio tests were used to investigate the temporal instabilities of the models and differences in collision modes. The results showed that only a few variables demonstrated temporally stable effects for angle collisions (weekend and dark-lighted) and non-angle collisions (paved shoulders and cyclists), respectively. Unique influencing factors of injury severity were identified for different collision modes, such as dark-lighted, straight alignment, merge, lane departure, the disabled driver for angle collisions, and clear, fog, commercial vehicles, and aggressive driving for non-angle collisions. The results of the out-of-sample prediction simulations further demonstrate the difference in injury severity over time for angle and non-angle collisions. Overall results suggest that countermeasures can be implemented to reduce the injury severity of angle crashes/non-angle crashes based on the significant variables identified. This study may be used to improve roundabout safety by considering different collision modes. [ABSTRACT FROM AUTHOR]

Coastal is the coupling of socio-economic and fragile ecosystems area existing development and protection problems, with lots of reserve land resources (i.e., bottomland and tidal flats). Analyzing and predicting the carbon storage changes caused by land use/land cover (LULC) on the Jiangsu coast were critical for revealing the potential problems of land surface changes and sustainable development. Then, we utilized the single dynamic degree and transfer in/out contribution, exploring the characteristics of LULC change in the study area from 1980 to 2018. Using the InVEST model, PLUS-LEAS model, and PLUS-Markov chain module, we assessed the spatiotemporal of the study area at the county level to reveal the LULC change strategy and driving factor contribution, as well as the composition of LULC and carbon storage in 2036. The results show that the LULC structure in the study area significantly changed from 1980 to 2018, in which the tidal flat and high coverage grassland decreased by 552.84 km2 and 383.71 km2 while the reservoir ponds and urban residential land increased by 1210.69 km2 and 101.70 km2. The major driving factor of LULC change has shifted from a single-factor to multi-factor coupling, and the influence contribution of human activity increased by 6.73%, especially the population. The carbon storage of study areas showed a significant decrease trend during 1980–2010, followed by a slight increase during 2010–2018. High-density carbon storage was mainly distributed in Lianyungang and Nantong and presented a decreasing trend along the coastline extending inland. The dry land and reservoir ponds are the main composition of LULC types in 2036, and the carbon storage increased to 2.39 × 108 t. In addition, more than decades of LULC change will cover part or all of the land use change process and trends, especially high-covered grasslands, so we suggest a 10-year LULC change to analyze coastal areas with lots of tidal flats and bottomlands. Therefore, this study can provide reference and theoretical guidance for ecologically sustainable development and future LULC evolution in coastal cities. [ABSTRACT FROM AUTHOR]

A new microscopic traffic model is proposed that characterizes driver response according to reaction and sensitivity. Driver response in the intelligent driver (ID) model is based on a fixed acceleration exponent and so does not follow traffic physics. This inadequate characterization results in unrealistic traffic behavior. With the proposed model, drivers can be aggressive, sluggish, or typical. It is shown to be string stable, and for appropriate distance headway and velocity (speed), the traffic flow is smooth. Furthermore, the proposed model has better stability than the ID model because it is based on driver reaction and sensitivity, while the ID model is based on a fixed exponent. The ID and proposed models are evaluated on a circular road of length 1200 m with a platoon of 21 vehicles for 150 s. The results obtained show that the proposed model characterizes traffic more realistically than the ID model. [ABSTRACT FROM AUTHOR]

For those who cannot afford vehicles, motorcycles are a common mode of transportation in Pakistan. Although many motorcycle riders also drive vehicles, they continue to engage in dangerous behaviors such as speeding, weaving through traffic, and disobeying traffic laws, which can result in accidents. The purpose of this study was to investigate the relationship between prior motorcycle-riding experience, dangerous driving behaviors, and traffic accidents. A total of 623 drivers in Pakistan participated in a study in which questions on their demographics, involvement in accidents, and dangerous driving practices were posed. Two statistical models were employed to analyze the data and ascertain how motorcycle-riding experience affected dangerous driving behaviors and accidents. Drivers with past motorcycle-riding experience were found to be more likely to exhibit aggressive and risky driving behaviors, as validated by principal component analysis. Certain demographic characteristics were also linked to dangerous driving behaviors, and prior motorcycle experience was identified as a crucial factor in anticipating traffic collisions. The probability of a traffic accident increased by 67% for each unit rise in motorcycle-riding experience. To lower the incidence of accidents, the study suggests that the government and road safety regulatory authorities impose strict rules and regulations for motorcycle riders. [ABSTRACT FROM AUTHOR]

Driving behavior analysis has become crucial for traffic safety. In addition, more abundant driving data are needed to analyze driving behavior more comprehensively and thus improve traffic safety. This paper proposes an approach to image data extraction and driving behavior analysis that uses geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was performed to derive the relationship between aggressive driving behavior and road facilities. The results indicated that lane ratios, no lane markings, and straight lane markings are important features that affect aggressive driving behaviors. Several traffic improvements were proposed for specific intersections and roads to make drivers and pedestrians safer. [ABSTRACT FROM AUTHOR]

The epidemiology of invasive fungal infections (IFIs) is currently changing, driven by aggressive immunosuppressive therapy, leading to an expanded spectrum of patients at risk of IFIs. Aspergillosis is a leading cause of IFIs, which usually affects immunocompromised patients. There are a limited number of antifungal medications available for treating IFIs, and their effectiveness is often hindered by rising resistance rates and practical limitations. Consequently, new antifungals, especially those with novel mechanisms of action, are increasingly required. This study assessed the activity of four novel antifungal agents with different mechanisms of activity, namely, manogepix, rezafungin, ibrexafungerp, and olorofim, against 100 isolates of Aspergillus section Terrei, containing amphotericin-B (AmB)-wildtype/non-wildtype and azole-susceptible/-resistant strains, according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) method. In general, all tested agents showed potent and consistent activity against the tested isolates, exhibiting geometric mean (GM) and minimum effective concentration (MEC)/minimum inhibitory concentration (MIC) ranges, respectively, as follows: manogepix (0.048 mg/L, 0.032–0.5 mg/L), rezafungin (0.020 mg/L, 0.016–0.5 mg/L), ibrexafungerp (0.071 mg/L, 0.032–2 mg/L), and olorofim (0.008 mg/L, 0.008–0.032 mg/L). In terms of MIC90/MEC90, olorofim had the lowest values (0.008 mg/L), followed by rezafungin (0.032 mg/L), manogepix (0.125 mg/L), and ibrexafungerp (0.25 mg/L). All the antifungals tested demonstrated promising in vitro activity against Aspergillus section Terrei, including A. terreus as well as azole-resistant and AmB-non-wildtype cryptic species. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) incidence has been rising in recent years, with strong association between differential microRNA (miRNA) expression and neoplastic progression. Specifically, overexpression of miR-155-5p has been associated with promoting aggressive cancer in ccRCC and other cancers. In this study, we further investigate the role of this miRNA and one of its protein targets, Jade-1, to better understand the mechanism behind aggressive forms of ccRCC. Jade-1, a tumor suppressor, is stabilized by Von-Hippel Lindau (VHL), which is frequently mutated in ccRCC. Experiments featuring downregulation of miR-155-5p in two ccRCC cell lines (786-O and Caki-1) attenuated their oncogenic potential and led to increased levels of Jade-1. Conversely, knockdown experiments with an anti-Jade-1 shRNA in 786-O and Caki-1 cells showed increased metastatic potential through elevated proliferation, migration, and invasion rates. In a mouse xenograft model, downregulation of miR-155 decreased the rate of tumor implantation and proliferation. Direct interaction between miR-155-5p and Jade-1 was confirmed through a 3′UTR luciferase reporter assay. These findings further elucidate the mechanism of action of miR-155-5p in driving an aggressive phenotype in ccRCC through its role in regulating Jade-1. [ABSTRACT FROM AUTHOR]

Virtual testing requires hazardous scenarios to effectively test autonomous vehicles (AVs). Existing studies have obtained rarer events by sampling methods in a fixed scenario space. In reality, heterogeneous drivers behave differently when facing the same situation. To generate more realistic and efficient scenarios, we propose a two-stage heterogeneous driver model to change the number of dangerous scenarios in the scenario space. We trained the driver model using the HighD dataset, and generated scenarios through simulation. Simulations were conducted in 20 experimental groups with heterogeneous driver models and 5 control groups with the original driver model. The results show that, by adjusting the number and position of aggressive drivers, the percentage of dangerous scenarios was significantly higher compared to that of models not accounting for driver heterogeneity. To further verify the effectiveness of our method, we evaluated two driving strategies: car-following and cut-in scenarios. The results verify the effectiveness of our approach. Cumulatively, the results indicate that our approach could accelerate the testing of AVs. [ABSTRACT FROM AUTHOR]

Real-time monitoring of the battery thermal status is important to ensure the effectiveness of battery thermal management system (BTMS), which can effectively avoid thermal runaway. In the study of BTMS, driver behavior is one of the factors affecting the performance of the battery thermal status, and it is often neglected in battery temperature studies. Therefore, it is necessary to predict the dynamic heat generation of the battery in actual driving cycles. In this work, a thermal equivalent circuit model (TECM) and an artificial neural network (ANN) thermal model based on the driving data, which can predict the thermal behavior of the battery in real-world driving cycles, are proposed and established by MATLAB/Simulink tool. Driving behaviors analysis of different drivers are simulated by PI control as input, and battery temperature is used as output response. The results show that aggressive driving behavior leads to an increase in battery temperature of nearly 1.2 K per second, and the average prediction error of TECM model and ANN model is 0.13 K and 0.11 K, respectively. This indicates that both models can accurately estimate the real-time battery temperature. However, the computational speed of the ANN thermal model is only 0.2 s, which is more efficient for battery thermal management. [ABSTRACT FROM AUTHOR]

As the use of fossil energy sources in transport declines, new technologies, e.g., electric vehicles, are being introduced. One of the advantages of electric vehicles in urban driving is the possibility to charge their batteries with regenerative energy during braking. For this reason, electric cars usually have a longer range per charge in urban driving than in non-urban driving. This research experimentally examined the regenerative braking of a converted Renault Clio electric car at different regenerative braking settings in the range of 0–100%. An original research methodology was developed for road tests in urban driving. The driving cycle included aggressive driving with rapid acceleration and braking. The road test was conducted in second and third gears, which are the usual gears for driving an electric car in a city. The highest regenerative braking efficiencies were achieved at a 100% setting, which in some replications reached 24% of the total electrical energy consumed; however, the 100% setting was too high from the perspective of comfortable driving of the electric car and contributed to a too significant increase in the braking force at the initial stages of braking. [ABSTRACT FROM AUTHOR]

Loss PTEN function is one of the most common events driving aggressive prostate cancers and biochemically, PTEN is a lipid phosphatase which opposes the activation of the oncogenic PI3K-AKT signalling network. However, PTEN also has additional potential mechanisms of action, including protein phosphatase activity. Using a mutant enzyme, PTEN Y138L, which selectively lacks protein phosphatase activity, we characterised genetically modified mice lacking either the full function of PTEN in the prostate gland or only lacking protein phosphatase activity. The phenotypes of mice carrying a single allele of either wild-type Pten or PtenY138L in the prostate were similar, with common prostatic intraepithelial neoplasia (PIN) and similar gene expression profiles. However, the latter group, lacking PTEN protein phosphatase activity additionally showed lymphocyte infiltration around PIN and an increased immune cell gene expression signature. Prostate adenocarcinoma, elevated proliferation and AKT activation were only frequently observed when PTEN was fully deleted. We also identify a common gene expression signature of PTEN loss conserved in other studies (including Nkx3.1, Tnf and Cd44). We provide further insight into tumour development in the prostate driven by loss of PTEN function and show that PTEN protein phosphatase activity is not required for tumour suppression. [ABSTRACT FROM AUTHOR]

Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities. [ABSTRACT FROM AUTHOR]

Road traffic crashes are a leading cause of death for young people. Aberrant driver behaviors, such as drink driving, speeding, not wearing seatbelts, non-compliance with traffic rules and aggressive driving, are key contributors to these crashes. Gender and urban/rural differences are also risk factors. In Serbia, where this study was conducted, as well as in most European countries, younger people have the highest road crash and fatality risk. Thus, it is important to understand not only when these behaviors occur, but also the attitudes surrounding them. The latter will provide an avenue for intervention. To address this, a mixed design study was conducted, using a quantitative survey, focus groups and in-depth interviews to understand the attitudes and safety behaviors of young people (aged 16–25) in Serbia. Results across all methods showed that attitudes and perceptions regarding road safety differ across gender and location (urban/rural). Young drivers reported frequent engagement in alcohol-impaired driving, speeding, non-using seatbelts and using mobile phones while driving. Dominant attitudes underlying these behaviors related to lower perceived risk and a lack of perceived enforcements. These results show support for education campaigns in improving the risky behavior of young drivers. [ABSTRACT FROM AUTHOR]

The long and tiring discussion of who are the best drivers, men or women, is not answered in this article. This article, though, sheds some light on the actual differences that can be seen in how men and women drive. In this study, GPS-recorded driving dynamics data from 123 drivers, 48 women and 75 men, are analysed and drivers are categorised as aggressive, normal or gentle. A total of 10% of the drivers was categorised as aggressive, with an even distribution between the genders. For the gentle drivers, 11% of the drivers, the men dominated. The driving style investigation was extended to utilise machine learning, confirming the results from statistical tools. As driving style highly impacts a vehicle's fuel consumption, while switching over to battery electric vehicles it is important to investigate how the different driving styles impact battery utilisation. Two Li-ion battery cell types were tested utilising the same load cycle with three levels of current amplitude, to represent accelerations for the three drive categories. While one cell type was insensitive to the current amplitude, the highly energy-optimised cell proved to be sensitive to higher current amplitudes, corresponding to a more aggressive driving style. Thus, the amplitude of the dynamic current can for some cells be a factor that needs to be considered for lifetime predictions, while it can be neglected for other cells. [ABSTRACT FROM AUTHOR]

Both the EU and China are evaluating the feasibility of lowering the detection limit of particle number (PN) measurement to 10 nm in future legislations, making it necessary to better understand the sub-23 nm particle emission characteristics from state-of-the-art vehicles. In this study, solid PN emissions with a diameter larger than 10 nm and 23 nm (known as SPN10 and SPN23) were compared over the WLTC, RTS95, and a so-called "worst-case" real driving emission (RDE) cycle (highly dynamic/0 °C) using two certification-level particle number counters (PNCs) employing evaporation tube (ET) and catalytic stripper (CS) as volatile particle remover (VPR). The results show that SPN10 emissions were 31.7%, 27.8%, and 15.2% higher than SPN23 over the WLTC, RTS95, and laboratory RDE cycles. Sub-23 nm particles were almost not identified within the engine cold-start phase and tended to be a hot-running pollutant favored by aggressive driving styles (frequent accelerations and high engine loads), fuel-cut during decelerations, and long idles. Lower testing temperature delayed the light-off of catalyst and, therefore, significantly reduced the formation of sub-23 nm particles within the engine warm-up stage. Lowering the detection limit to 10 nm is deemed to provide more public health protection since it will guide manufacturers to pay more attention to vehicle hot-running emissions. [ABSTRACT FROM AUTHOR]

In developing countries, heavy-duty trucks play an important role in transportation for infrastructure construction. However, frequent truck accidents cause great losses. Previous studies have mainly focused on passenger drivers; to date, little has been done to assess the driving behavior of heavy truck drivers. The overall objective of this study is to classify driving styles at intersections, analyze the impacts of differing types of traffic control at intersections on driving styles, and identify potentially risky intersections. We selected 11 heavy-duty truck drivers and collected kinematic driving parameters (including driving speed and both lateral and longitudinal acceleration) from field experiments in Nanjing for our study. Our study on driving styles followed the following steps. First, we reduced data size and extracted data features on the basis of time windows in Python. Second, driving styles were classified into three driving styles: cautious, normal, and aggressive, based on the K-means clustering method, and the corresponding thresholds for each category were obtained. Kinematic driving parameters were used as driving style measurements. Third, according to classifications of driving style, the impacts of four different intersection traffic control types: two-phase signalized, multiphase signalized, stop, and yield intersections, on driving styles have been analyzed using the multinomial logit model. Moreover, based on the above analysis, potentially risky intersections were identified. The results suggest that different types of traffic control at intersections lead to variations in driving styles and have different influences on driving styles. In terms of accuracy, our method, which uses driving speed, both lateral and longitudinal acceleration, and jerk as features, performs better than traditional methods which only use speed and acceleration. The results of the study allow us to analyze the driving data of heavy-duty trucks and identify drivers who drive more aggressively during a trip. In addition, the results show that aggressive driving styles mostly occur at stop intersections and in the dilemma zones of signalized intersections. Therefore, early-warning interventions can be provided during a driver's trip by analyzing the different types of traffic control at intersections on the route in advance. Finally, the cumulative analysis of driving styles at intersections over multiple trips can be used to identify potentially high-risk intersections. It is possible to eliminate potential risks in these areas through measures such as early warnings and by improving traffic management control methods. [ABSTRACT FROM AUTHOR]

Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver's attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers' behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers. [ABSTRACT FROM AUTHOR]

Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle. [ABSTRACT FROM AUTHOR]

Monitoring driving behaviour is important in controlling driving risk, fuel consumption, and CO2 emissions. Recent advances in machine learning, which include several variants of convolutional neural networks (CNNs), and recurrent neural networks (RNNs), such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks, could be valuable for the development of objective and efficient computational tools in this direction. The main idea in this work is to complement data-driven classification of driving behaviour with rules derived from domain knowledge. In this light, we present a hybrid representation approach, which employs NN-based time-series encoding and rule-guided event detection. Histograms derived from the output of these two components are concatenated, normalized, and used to train a standard support vector machine (SVM). For the NN-based component, CNN-based, LSTM-based, and GRU-based variants are investigated. The CNN-based variant uses image-like representations of sensor measurements, whereas the RNN-based variants (LSTM and GRU) directly process sensor measurements in the form of time-series. Experimental evaluation on three datasets leads to the conclusion that the proposed approach outperforms a state-of-the-art camera-based approaches in distinguishing between normal and aggressive driving behaviour without using data derived from a camera. Moreover, it is demonstrated that both NN-guided time-series encoding and rule-guided event detection contribute to overall classification accuracy. [ABSTRACT FROM AUTHOR]

New models and methods have been designed to predict the influence of the user's environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers' activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users' activity. The driving style was predicted from the user's environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user's environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts. [ABSTRACT FROM AUTHOR]

The spread of the novel coronavirus COVID-19 resulted in unprecedented worldwide countermeasures such as lockdowns and suspensions of all retail, recreational, and religious activities for the majority of 2020. Nonetheless, no adequate scientific data have been provided thus far about the impact of COVID-19 on driving behavior and road safety, especially in Malaysia. This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. This was accomplished by comparing the driving behaviors of the same drivers in three periods: before COVID-19 lockdown, during COVID-19 lockdown, and after COVID-19 lockdown. Thirty people were previously recruited in 2019 to drive an instrumental vehicle on a 25 km route while recording their driving data such as speed, acceleration, deceleration, distance to vehicle ahead, and steering. The data acquisition system incorporated various sensors such as an OBDII reader, a lidar, two ultrasonic sensors, an IMU, and a GPS. The same individuals were contacted again in 2020 to drive the same vehicle on the same route in order to capture their driving behavior during the COVID-19 lockdown. Participants were approached once again in 2022 to repeat the procedure in order to capture their driving behavior after the COVID-19 lockdown. Such valuable and trustworthy data enable the assessment of changes in driving behavior throughout the three time periods. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers in particular being most affected by the traffic restrictions, driving significantly faster and performing more aggressive steering behaviors during the COVID-19 lockdown than any other time. Furthermore, the locations where the most speeding offenses were committed are highlighted in order to provide lawmakers with guidance on how to improve traffic safety in those areas, in addition to various recommendations on how to manage traffic during future lockdowns.

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F1-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance. [ABSTRACT FROM AUTHOR]

The present investigation addresses the topic of Urban Air Mobility with particular reference to the air-taxi service with electrified power systems. A new and detailed methodology is proposed for the simplified design and energy analysis of conventional, hybrid-electric, and full-electric power systems for this application. The original contributions to the scientific literature on UAM are the detailed modeling approach, the evaluation of CO2 emissions with a Well-to-Wing approach as a function of the electricity Emission Intensity factor, and the comparison with road vehicles performing the same route in different driving conditions. The comparison demonstrates the advantages of a full electric air-taxi with today's technology versus a hybrid-electric road taxi, especially in cases involving low emission intensity and unfavorable driving conditions (congested traffic, aggressive driving style, and high circuity factor values). In the case of 2035 technology, the comparison with a referenced fully electric road vehicle is detrimental to the air taxi but the values of Well-to-Wheel/Wing CO2 with the expected Emission Intensity of 90 g/kWe for the European Union are still quite low (67 g/km). The investigation also quantifies the negative effect of battery aging on the consumption of the air taxi and on the number of consecutive flights that can be performed without fully charging the battery. [ABSTRACT FROM AUTHOR]

Glioblastoma remains the deadliest form of brain cancer, largely because these tumors become resistant to standard of care treatment with radiation and chemotherapy. Intracellular production of reactive oxygen species (ROS) is necessary for chemo- and radiotherapy-induced cytotoxicity. Here, we assessed whether antioxidant catalase (CAT) affects glioma cell sensitivity to temozolomide and radiation. Using The Cancer Genome Atlas database, we found that CAT mRNA expression is upregulated in glioma tumor tissue compared with non-tumor tissue, and the level of expression negatively correlates with the overall survival of patients with high-grade glioma. In U251 glioma cells, CAT overexpression substantially decreased the basal level of hydrogen peroxide, enhanced anchorage-independent cell growth, and facilitated resistance to the chemotherapeutic drug temozolomide and ionizing radiation. Importantly, pharmacological inhibition of CAT activity reduced the proliferation of glioma cells isolated from patient biopsy samples. Moreover, U251 cells overexpressing CAT formed neurospheres in neurobasal medium, whereas control cells did not, suggesting that the radio- and chemoresistance conferred by CAT may be due in part to the enrichment of glioma stem cell populations. Finally, CAT overexpression significantly decreased survival in an orthotopic mouse model of glioma. These results demonstrate that CAT regulates chemo- and radioresistance in human glioma. [ABSTRACT FROM AUTHOR]

Public transport systems have a vital role in achieving sustainable mobility goals, diminishing reliance on private individual transport and improving overall public health. Despite that, transport operators are often in situations that require them to cope with complex working conditions that lead to negative emotions such as anger. The current study represents a segment of the permanent global research agenda that seeks to devise and test a psychometric scale for measuring driving anger in professional drivers. The present research is one of the first attempts to examine the factorial validity and the cross-cultural measurement equivalence of the broadly utilized Driving Anger Scale (DAS) in three culturally different countries within the Western Balkans region. The respondents (N = 1054) were taxi, bus and truck drivers between 19 and 75 years of age. The results pertaining to confirmatory factor analysis showed that there were adequate fit statistics for the specified six-dimensional measurement model of the DAS. The measurement invariance testing showed that the meaning and psychometric performance of driving anger and its facets are equivalent across countries and types of professional drivers. Furthermore, the results showed that driving anger facets had positive correlations with dysfunctional ways of expressing anger and negative correlations with the form of the prosocial anger expression. In addition, the results revealed that taxi drivers displayed considerably higher levels of anger while driving and aggressive driving than truck and bus drivers. Overall, this study replicates and extends the accumulated knowledge of previous investigations, suggesting that the original DAS remains a reliable and stable instrument for measuring driving anger in day-to-day driving conditions.

Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user's behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver's personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems. [ABSTRACT FROM AUTHOR]

The real driving emission (RDE) testing for certification of vehicles is performed in conditions that are well defined in legislation. For emissions inventories and for research, the influences of some extended driving conditions on emissions are an interesting issue. In the present work, some examples of RDE results from two common passenger cars with gasoline and diesel propulsion are given. The varying driving conditions were "winter/summer", "mild/aggressive", and "higher altitude/slop". The driving conditions: "winter", "aggressive", and "higher slope/altitude" generally require more energy, cause higher fuel consumption, and therefore, higher CO2-emissions. The condition of "winter driving", especially in the urban type of operation, may cause some longer phases with not enough warmed-up exhaust aftertreatment and consequently some increased gaseous emissions. The DPF eliminates the nanoparticles (PN) independently on the driving conditions. Nevertheless, the DPF regeneration has an influence on the CO2-normality of the trip. The CO2-normality primary tolerance range can also be exceeded with aggressive driving. The elaborated results confirm the usefulness of the existing legal limits for the driving conditions of RDE homologation tests. [ABSTRACT FROM AUTHOR]

The maintenance of port infrastructures presents difficulties due to their location: an aggressive environment or the variability of the waves can cause progressive deterioration. Maritime conditions make inspections difficult and, added to the lack of use of efficient tools for the management of assets, planning maintenance, important to ensure operability throughout the life cycle of port infrastructures, is generally not a priority. In view of these challenges, this research proposes a methodology for the creation of a port infrastructure asset management tool, generated based on the Design Science Research Method (DSRM), in line with Building Information Modeling (BIM) and digitization trends in the infrastructure sector. The proposal provides workflows and recommendations for the survey of port infrastructures from UAVs, the reconstruction of digital models by photogrammetry (due to scarce technical documentation), and the reconstruction of BIM models. Along with this, the bidirectional linking of traditional asset management spreadsheets with BIM models is proposed, by visual programming, allowing easy visualization of the status and maintenance requirements. This methodology was applied to a port infrastructure, where the methodology demonstrated the correct functionality of the asset management tool, which allows a constant up-dating of information regarding the structural state of the elements and the necessary maintenance activities. [ABSTRACT FROM AUTHOR]

The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that vehicle data sheets do not contain realistic consumption and range figures. Since the fear of low range is a significant obstacle to the acceptance of electric mobility, a reliable database can provide developers with additional insights and create confidence among vehicle users. This study presents a detailed, yet easy-to-implement and modular physical model for both passenger and commercial battery electric vehicles. The model takes consumption-relevant parameters, such as seasonal influences, terrain character, and driving behavior, into account. Without any a posteriori parameter adjustments, an excellent agreement with known field data and other experimental observations is achieved. This validation conveys much credibility to model predictions regarding the real-world impact on energy consumption and cruising range in standardized driving cycles. Some of the conclusions, almost impossible to obtain experimentally, are that winter conditions and a hilly terrain each reduce the range by 7–9%, and aggressive driving reduces the range by up to 20%. The quantitative results also reveal the important contributions of recuperation and rolling resistance towards the overall energy budget. [ABSTRACT FROM AUTHOR]

The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not practical. This is due to the "safe/aggressive" categorization being a state that describes a driver's conduct at a specific point in time rather than a continuous state or a human trait. Furthermore, due to the disparity in traffic laws and regulations between countries, what is considered aggressive behavior in one place may differ from what is considered aggressive behavior in another. As a result, adopting existing profiles is not ideal. The authors provide a unique approach to driver behavior profiling based on timeframe data segmentation. The profiling procedure consists of two main parts: row labeling and segment labeling. Row labeling assigns a safety score to each second of driving data based on criteria developed with the help of Malaysian traffic safety experts. Then, rows are accumulated to form timeframe segments. In segment labeling, generated timeframe segments are assigned a safety score using a set of criteria. The score assigned to the generated timeframe segment reflects the driver's behavior during that time period. Following that, the study adopts three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), to classify recorded driving data according to the established profiling procedure, and selects the most suitable one for a proposed recognition system. Various techniques were used to prevent the classification algorithms from overfitting. Using gathered naturalistic data, the validity of the modulated algorithms was assessed on various timeframe segments ranging from 1 to 10 s. Results showed that the CNN, which achieved an accuracy of 96.1%, outperformed the other two classification algorithms and was therefore recommended for the recognition system. In addition, recommendations were outlined on how the recognition system would assist in improving traffic safety.

Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption. [ABSTRACT FROM AUTHOR]

For many institutions, it is important to evaluate a given driving technique as safe or unsafe based on measurable vehicle movement parameters. The paper constitutes a part of studies aimed at establishing a method of parameter-based evaluation of drivers in various road conditions, in other words, to create a so-called 'driver profile'. The tests were carried out on a 650 km route, on four varying road types. Longitudinal and lateral acceleration values are used to evaluate the driving style. An analysis is presented of the impact of the type and shape of road on acceleration values. The results demonstrate that the same driver, when driving the same vehicle on an expressway, an inter-urban road or in urban traffic, will move with various acceleration values. A detailed analysis of acceleration values and distributions was conducted. Interesting conclusions were drawn after excluding the so-called 'smooth driving' sections, by acceleration ranges of −0.5 to 0.5 m/s2 from the analysis. This allowed for the evaluation of the structure of other longitudinal and lateral acceleration values. After this modification, the distributions showed specificity for the given road type, thereby allowing the road type used by the vehicle's driver to be recognized based solely on the distribution. [ABSTRACT FROM AUTHOR]

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver's characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios. [ABSTRACT FROM AUTHOR]

With the rapid and wide implementation of adaptive cruise control system (ACC), the testing and evaluation method becomes an important question. Based on the human driver behavior characteristics extracted from naturalistic driving studies (NDS), this paper proposed the testing and evaluation method for ACC systems, which considers safety and human-like at the same time. Firstly, usage scenarios of ACC systems are defined and test scenarios are extracted and categorized as safety test scenarios and human-like test scenarios according to the collision likelihood. Then, the characteristic of human driving behavior is analyzed in terms of time to collision and acceleration distribution extracted from NDS. According to the dynamic parameters distribution probability, the driving behavior is divided into safe, critical, and dangerous behavior regarding safety and aggressive and normal behavior regarding human-like according to different quantiles. Then, the baselines for evaluation are designed and the weights of different scenarios are determined according to exposure frequency, resulting in a comprehensive evaluation method. Finally, an ACC system is tested in the selected test scenarios and evaluated with the proposed method. The tested vehicle finally got a safety score of 0.9496 (full score: 1) and a human-like score as fail. The results revealed the tested vehicle has a remarkably different driving pattern to human drivers, which may lead to uncomfortable ride experience and user-distrust of the system. [ABSTRACT FROM AUTHOR]