Prior studies have highlighted the critical role of safety within high-hazard sectors like oil and gas operations. Improving process industry safety is a consequence of analyzing process safety performance indicators. Data gathered from a survey is used in this paper to rank process safety indicators (metrics) according to the Fuzzy Best-Worst Method (FBWM).
The study's structured methodology leverages the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines for generating an aggregate collection of indicators. Expert perspectives from Iranian and some Western countries are used to quantify the level of importance each indicator holds.
This study's results indicate that the importance of lagging indicators, including the rate of process failures due to insufficient staff skills and the number of unexpected process interruptions from faulty instrumentation or alarms, is consistent in both Iranian and Western process industries. Western experts identified the process safety incident severity rate's status as a critical lagging indicator; Iranian experts, however, found this metric comparatively unessential. Immunoinformatics approach Additionally, vital leading indicators, including thorough process safety training and capability, the intended performance of instruments and alarms, and the proper management of fatigue risks, are fundamental to enhancing safety standards in process industries. Iranian experts saw the work permit as a crucial leading indicator, whereas Western authorities prioritized the mitigation of fatigue risks.
Utilizing the methodology of this study, managers and safety professionals gain a substantial understanding of the most important process safety indicators, prompting a more strategic focus on these indicators.
The methodology used in the current study effectively highlights the most important process safety indicators, thus enabling managers and safety professionals to prioritize these crucial aspects.

Automated vehicles (AVs) represent a promising avenue for boosting the efficiency of traffic operations and minimizing harmful emissions. Highway safety can be dramatically improved and human error eliminated thanks to the potential of this technology. However, concerning autonomous vehicle safety, knowledge is limited by the restricted availability of crash data and the relatively infrequent occurrence of autonomous vehicles on the road. A comparative study of the collision-inducing factors in autonomous and traditional vehicles is presented in this research.
The study's aim was achieved through the application of a Markov Chain Monte Carlo (MCMC) process, resulting in a fitted Bayesian Network (BN). California road crash data covering the period of 2017 to 2020, involving autonomous vehicles and conventional cars, were the subject of the study's investigation. The California Department of Motor Vehicles supplied the crash data for autonomous vehicles, complemented by the Transportation Injury Mapping System database for conventional vehicle collisions. To correlate each autonomous vehicle collision with its equivalent conventional vehicle accident, a 50-foot buffer zone was implemented; the dataset comprised 127 autonomous vehicle collisions and 865 traditional vehicle collisions for the study.
A comparative analysis of the related characteristics indicates a 43% heightened probability of AV involvement in rear-end collisions. Autonomous vehicles are 16% and 27% less likely, respectively, to be involved in sideswipe/broadside collisions and other accident types (head-on, object impact, etc.), when measured against conventional vehicles. Autonomous vehicle rear-end collision risk increases at locations like signalized intersections and lanes with posted speed limits under 45 mph.
Autonomous vehicles exhibit improved road safety in various collision types, stemming from reduced human error, yet their current technological implementation requires further refinements in safety characteristics.
Autonomous vehicles, having shown to increase road safety by reducing collisions stemming from human error, are nevertheless in need of further enhancements to bolster their safety features.

Automated Driving Systems (ADSs) pose significant, as yet unaddressed, challenges to established safety assurance frameworks. These frameworks' design failed to account for, nor effectively accommodate, automated driving's reliance on driver intervention, and safety-critical systems deploying machine learning (ML) for operational adjustments weren't supported during service.
To analyze the safety assurance of adaptive ADS systems utilizing machine learning, an intensive qualitative interview study was conducted as part of a wider research project. Feedback from leading global experts, encompassing regulatory and industrial stakeholders, was sought with the intent of determining prevalent themes useful in developing a safety assurance framework for autonomous delivery systems, and assessing the support for and practicability of diverse safety assurance concepts for autonomous delivery systems.
Following the analysis of the interview data, ten central themes were identified. To assure safety throughout the operational lifecycle of ADSs, several crucial themes advocate for mandatory Safety Case development by ADS developers and the continuous maintenance of a Safety Management Plan by ADS operators. There existed strong backing for allowing in-service machine learning modifications within the framework of pre-approved system boundaries, however, the topic of mandated human supervision remained a subject of debate. With respect to every identified topic, there was a preference for developing reforms inside the existing regulatory environment, avoiding the necessity for a complete system transformation. The practical application of certain themes proved challenging, largely because regulators struggled to develop and maintain a sufficient level of understanding, ability, and capacity, and in clearly specifying and pre-approving the parameters within which in-service adjustments could be made without requiring further regulatory authorization.
Investigating the particular themes and research outcomes in more detail would contribute to the formulation of more effective policy reforms.
Subsequent examination of the particular themes and the associated findings would contribute substantially to the development of more well-reasoned reform initiatives.

The question of whether the advantages of micromobility vehicles, providing new transport options and perhaps reducing fuel emissions, outweigh the safety concerns remains uncertain and requires further investigation. HRS-4642 clinical trial A ten-fold increase in crash risk has been observed among e-scooter users compared to ordinary cyclists, according to reports. Despite today's advancements, the critical question of safety concerns remains unanswered: is it the vehicle, the human element, or the infrastructure that holds the key? The safety of new vehicles might not be the central problem; instead, the problematic combination of rider conduct and infrastructure that hasn't been planned for micromobility could be the real cause.
This study used field trials to evaluate e-scooters, Segways, and bicycles, focusing on whether these novel transportation methods create varying demands on longitudinal control, including braking maneuvers.
Across various vehicles, differences in acceleration and deceleration performance were identified, particularly in e-scooters and Segways, which exhibited a substantially lower braking efficiency than bicycles. Subsequently, bicycles are regarded as more stable, easier to navigate, and safer than the alternatives of Segways and e-scooters. We developed kinematic models for both acceleration and braking, which are capable of forecasting rider trajectories within active safety systems.
Analysis of the data from this study implies that, while newer micromobility solutions might not inherently be unsafe, modifications to user habits and/or the underlying infrastructure are likely required for improved safety. Cecum microbiota We examine the implications of our research for policymaking, safety system architecture, and traffic education programs, to guide the safe integration of micromobility within the existing transportation infrastructure.
This study's outcome indicates that, though new micromobility solutions are not inherently unsafe, alterations to user behavior and/or the supporting infrastructure are likely required to optimize safety. Furthermore, we examine the potential applications of our research in the development of policies, safety infrastructure, and traffic education programs to facilitate the seamless integration of micromobility into the transportation system.

Numerous previous studies have shown that drivers in various countries exhibit a tendency to yield insufficiently to pedestrians. This investigation explored four different strategies designed to elevate driver yielding rates at designated crosswalks on channelized right-turn lanes of signalized intersections.
Four driving gestures were scrutinized in field experiments conducted in Qatar, using a sample of 5419 drivers differentiated by gender (male and female). During the daytime and nighttime hours of weekends, the experiments were performed at three different locations, two being urban and one rural. To investigate yielding behavior, a logistic regression model analyzes the effects of pedestrian and driver demographics, gestures, approach speed, time of day, intersection location, vehicle type, and driver distractions.
Observations indicated that, in the case of the basic gesture, only 200% of drivers complied with pedestrian demands, however, the yielding rates for the hand, attempt, and vest-attempt gestures were markedly higher, specifically 1281%, 1959%, and 2460%, respectively. The findings unequivocally indicated that female subjects exhibited significantly higher yield rates than male subjects. Along these lines, the driver's probability of yielding the right of way multiplied twenty-eight times when the speed of approach was reduced when compared to a higher speed.