Background: The incidence of traffic accidents in Iran is significantly higher than the global average (more than 17000 deaths a year, mostly young adults). The aim of this study was to determine the risk factors of high-risk drivers based on their observable driving behaviors, police records, and car condition in Fars province (Iran).Methods: In this case-control study, an interviewer interviewed a random sample (about 1 in 5 of the drivers) of all drivers who were referred to Shiraz traffic accident court from March 21, 2021, to June 21, 2021, due to being involved in a traffic accident. Based on the police report, we interviewed at-fault drivers who caused injurious or fatal traffic accidents as the case group (200 at-fault drivers) and those not-at-fault drivers as the control group (200 not-at-fault drivers). No matching was done. Data were collected using a researcher-made questionnaire filled out through face-to-face interviews with the drivers. Using univariate and multivariate logistic regression, the statistical analyses were conducted in R 4.0.2 software.Results: In this study, a total of 400 drivers were interviewed, of whom 367 (91.8%) were male. The mean ages of the case and control groups were 32.35 years (SD=9.84) and 31.75 (SD=10.33) years, respectively (P>0.05). In addition, 102 (51.0%) and 95 (47.5%) drivers were married in the case and control groups, respectively. Based on the results of a multiple logistic regression model, statistically significant associations were observed between the risk of being the at-fault driver in an injurious or fatal traffic accident and receiving a traffic ticket due to speedingovertaking (ORspeeding overtaking/no ticket=3.38, 95%CI: 1.75, 6.49, P<0.001), self-reported high-speed driving (ORyes/no=2.41, 95%CI: 1.25, 4.63, P=0.008), and having a history of car accident within the recent two years (ORyes/no=1.87, 95%CI: 1.11, 3.14, P=0.017).Conclusion: Our study suggested that among several potential factors, recently receiving traffic tickets due to speeding or overtaking, self-reported speed driving, and recent car accidents may effectively be used to screen out high-risk drivers who will probably cause injurious or fatal car accidents in the future. Intervention measures for the defined high-risk drivers including closer supervision and training programs may reduce the risk of fatal accidents in Iran.

Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, which arise from the abnormal excessive or synchronous neuronal activity in the brain. Understanding the complex pathobiology of epilepsy is crucial for developing more effective diagnostic tools and targeted therapeutic interventions. This comprehensive review examines the current understanding of the molecular, cellular, and genetic mechanisms underlying the epileptic condition. The key pathological mechanisms discussed include neuronal hyperexcitability, imbalances in excitatory and inhibitory neurotransmitter systems, structural and functional changes in the brain, altered ion channel function and ion homeostasis, neuroinflammation, and the influence of genetic factors and epigenetic modifications. Delving into the molecular and cellular underpinnings, the review explores the impact of genetic mutations on ion channels and neurotransmitter receptors, the dysregulation of gene expression and epigenetic alterations, mitochondrial dysfunction and oxidative stress, as well as the role of synaptic plasticity and network reorganization in the pathogenesis of epilepsy. The clinical implications of these pathobiological insights are also discussed, highlighting the potential for novel biomarkers, diagnostic approaches, and targeted therapeutic strategies. The review underscores the importance of personalized medicine and precision treatment, as the heterogeneity of epilepsy necessitates tailored management strategies based on individual patient characteristics. Finally, the article explores ongoing research and future directions in the field, including the identification of novel therapeutic targets and the emergence of innovative technologies to better understand and manage this complex neurological disorder.

This study uses Real driving cycles of a city bus and a standard driving cycle “ WLTP” to implement a full comparison for energy demand and fuel consumption for different propulsion systems (i. e., Diesel ICE, Fuel cell and Electric engines). These results were obtained by simulating each propulsion system in MATLAB, SIMULINK and EES. To better understand the comparison, a life cycle assessment is conducted using “ GREET” and “ GHGenius” software, which represents a clear demonstration of side effects and emissions of each engine on the environment. The results show that for “ WLTP” cycle the bus needs 2423kJ energy for traveling each kilometer while the averaged amount of energy for traveling one kilometer of Real driving cycle reaches to 1708kJ, the difference is due to speed range difference and number of stop/start points. In both cycles inertia force consumes the most used energy portion of the bus. By computing total energy use of an electric bus we conclude, electric buses use almost 58% of electric energy for driving and the rest is lost. Then fuel cell and internal combustion engine buses have energy efficiency of 36% and 24% respectively. Concerning LCA analysis, it becomes apparent that unlike efficiency, electric buses are not environmentally benign as fuel cell buses. LCA analysis showed that fuel cell buses that use steam reforming hydrogen production process are a cleaner option than electric buses, for instance a fuel cell bus emit 90 (g/100km) CO, 140 (g/100km) SOx and 67 (g/100km) NOx while an electric bus emit 110 (g/100km), 220 (g/100km) and 99 (g/100km) respectively which in all cases the fuel cell bus is more environment friendly than the electric bus. Finally, since diesel buses produce the most emission, especially CO2, and consume the most energy in the total life cycle, they have no advantage for public transportation fleet.

Using a portable emissions measurement system, 16 gasoline passenger cars were tested on a fixed route consisting of different types of roads in Macao and the data were normalized with the vehicle-specific power bin method. The normalized HC, CO and NO X emission levels of the seven passenger car samples with model year older than 2000 were 3.19±5.04, 14.59±22.88, 2.57±2.12 g/km, respectively. The HC, CO and NO X emission levels of other newer samples were 0.02±0.02, 0.23±0.29 and 0.10±0.13 g/km, respectively. The scrappage of old passenger cars in Macao should be a high priority to control the total emissions of motor vehicles. Based on relative emission levels, a clear and similar pattern for gaseous pollutants and fuel consumption with driving conditions was identified. The emissions of HC, CO and NO X are best fitted to average speed with inverse functions. Fuel consumption is best fitted to average speed with a power function. Compared to the average driving conditions, the emission factors of HC, CO and NO X and fuel consumption of gasoline passenger cars during the rush hours on the Macau Peninsula will be increased by 61, 55, 45 and 90%, respectively. This situation will deteriorate by 2015 if no further transportation management strategies are implemented in Macao. To save energy and mitigate the air pollutant emissions in the urban area, improved traffic planning and travel demand management are also necessary.

Each driving cycle designed to monitor fuel consumption, vehicle emissions, and the specific driving patterns of a region is created to facilitate better planning and enhancement of local standards. In this study, the Real driving Emissions (RDE) method, regarded as one of the most precise techniques for determining driving cycles, was employed. To gather the necessary data, GPS from mobile phones was utilized while traveling on city buses through the streets of Kermanshah. This data collection included recording speed, acceleration, duration of constant speed, deceleration, altitude, number of braking events, and the duration of stops along the route from origin to destination. The research employed the clustering of Real city data and the K-means method to derive the driving cycle for Kermanshah’s city buses. Analyzing the collected data reveals that bus drivers in Kermanshah tend to avoid rapid acceleration and high constant speeds, leading to reduced fuel consumption in the city bus cycle, indicating that this behavior is intentional and aimed at this goal.