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Harsh driving events such as harsh brakings (HBs) and harsh accelerations (HAs) are promising Surrogate Safety Measures, already extensively utilised in road safety research. However, their occurrence relative to normal driving conditions has not been the explicit target of research, as they are typically used as inputs for crash prediction. The present study addresses this research gap by investigating factors influencing HB and HA occurrence using real-time naturalistic driving telematics data recorded from smartphones, traffic data and road geometry & network characteristics data. These multisource data were matched in order to capture the specific circumstances under which HBs and HAs occur. The utilized telematics dataset included trips from 314 anonymous drivers in an urban arterial of Athens, Greece. Subsequently, Synthetic Minority Oversampling TEchnique (SMOTE) was applied due to class imbalance and then binary classification was conducted to detect factors leading to HB and HA occurrence. Imbalanced Machine Learning (ML) XGBoost algorithms predicted over 75% of HBs and over 84% of HAs for the test dataset, indicating suitability for real-time monitoring. The algorithms were also augmented with SHapley Additive exPlanation (SHAP) values, aiming to increase outcome explainability. Results reveal strong nonlinear effects on harsh event occurrence, with individual speed and traffic flow parameters showing the highest influence, followed by exposure parameters such as segment length and pass count. Network characteristics such as number of lanes, and speed limit had limited influence on HA and HB occurrence, as did behaviors such as mobile phone engagement and speeding.

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Autism comes from the Greek word auto, which means "self." Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication. Dental treatment in patients with ASD can be challenging due to their behavior. Therefore, this review discusses preventive treatment techniques for pediatric patients with ASD at the dental office, as the prevalence of children with autism is growing. Thus, dentists would face more patients with autism in their daily practice. Regarding treatment protocols, they would require specialized attention in dental management. Information was searched in the following databases: PubMed, SciELO, Redalyc, Elsevier, and the International Association of Paediatric Dentistry (IAPD). The descriptors used were: Pediatric Dentistry, Autism, ASD, Autism Spectrum Disorder, and Management of the autistic patient.

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Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle-pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle-pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: 'driving risk index' and 'driving risk gradient,' facilitates the delineation of driving spaces for identifying safety-critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle-pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle-pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle-pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle-pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle-pedestrian interaction risks for safe and efficient vehicle navigation through an intersection.

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A major safety hazard for e-bike riders crossing an intersection is encountering heavy vehicles turning right in the same direction, which often results in severe casualties. Recently, some cities in China have implemented right-turn safety improvement treatments (i.e., right-turn yielding rules and right-turn warning facilities) at intersections to reduce the occurrence of such accidents. However, the risk perception and behavior of e-bike riders and heavy vehicle drivers dynamically change during the right-turn interaction process, and the safety effects of different right-turn safety measures remain unclear. This study aims to investigate the safety effect of right-turn safety measures on E-Bike-Heavy Vehicle (EB-HV) right-turn conflicts at signalized intersections. The right-turn conflicts and potential influencing factors are extracted from aerial video data, including characteristics of right-turn warning facilities, characteristics and behavior of e-bike riders and heavy vehicle drivers, environmental factors, and traffic-related factors. Moreover, traffic conflict indicators such as the Time to Collision (TTC), Post Encroachment Time (PET), and Jerk are selected and calculated. Multinomial and binary logit models are used to estimate and analyze the EB-HV right-turn conflict severity and drivers yielding behavior. The results reveal that: (a) right-turn warning facilities can decrease the probability of slight and severe EB-HV right-turn conflicts, while the presence of law enforcement cameras could prompt heavy vehicle drivers to comply with the yielding rules and adopt more cautious behavior; (b) increased heavy vehicle speed and acceleration before turning right have strong correlation to illegitimate yielding behavior of the driver and higher EB-HV right-turn conflict severity; and (c) aggressive behavior of e-bike rider increases the severe conflict probability, especially at intersections without right-turn warning facilities. Based on the study findings, several practical implications are suggested to reduce the risk of EB-HV right-turn conflicts, enhance the effectiveness of right-turn safety measures, and improve crossing safety for e-bike riders.

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OBJECTIVES: Daily, approximately 3,400 traffic-related deaths occur globally, with over 90% concentrated in low and middle-income countries (LMICs). Notably, Rwanda has one of the highest road traffic death rates in the world (29.7 per 100,000 people) and is the first low-income country to implement a national Automated Speed Enforcement (ASE) policy. The primary goal of this study is to evaluate the effectiveness of ASE cameras in reducing the primary outcome of road traffic deaths and secondary outcomes of serious injury crashes and fatal crashes. METHODS: The study used data on road traffic deaths, and serious injury and fatal crashes collected by the Rwanda National Police between 2010 and 2022. Interrupted time series (ITS) models were fit to quantify the association between ASE and change in road traffic crash outcomes, adjusted for COVID-19-related variables (such as the start of the pandemic, the closure of schools and bars), along with exposure variables (such as GDP and population), and other concurrent road safety measures (such as road safety campaigns). RESULTS: The ITS models show that the implementation of ASE cameras significantly reduced road traffic deaths, serious injury crashes, and fatal crashes at the provincial level. For instance, the implementation of ASE cameras in the whole of Rwanda in April 2021 was significantly associated with a 0.14 (95% CI [0.072, 0.212]) reduction in monthly death incidence, equating to a 38.16% monthly decrease compared to the period before their installation (January 2010-March 2021). CONCLUSION: This study emphasizes the significant association of ASE in Rwanda with improved road traffic crash outcomes, a result that may inform road safety policy in other LMICs. Rwanda has become the first low-income country to implement nationwide scaling of ASE in Africa, paving the way for the generation of valuable evidence on speed-related interventions. In addition to new knowledge generation, African road safety research efforts like this one are opportunities to grow academic and law enforcement cooperations while improving data systems and sources for future research benefits.

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This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicle dynamics of differing dimensionality and fidelity, and suitable for dynamic, real-world environments. The framework incorporates a generic vehicle movement model, accommodating a spectrum of scenarios with varying degrees of complexity and dimensionality, facilitating the estimation of future vehicle trajectory evolution. It establishes a generic mathematical criterion to denote potential collisions, characterized by the spatial overlap between a vehicle and any other entity. A collision risk is present if the collision criterion is met at any non-negative estimated future time point, with the minimum threshold representing the remaining time to collision. The framework's proficiency spans from conventional one-dimensional (1D) SSMs to extended multi-dimensional, high-fidelity SSMs. Its validity is corroborated through simulation experiments that assess the precision of the framework when linearization is performed on the vehicle movement model. The outcomes showcase remarkable accuracy in estimating vehicle trajectory evolution and the time remaining before potential collisions occur, comparing to high-accuracy numerical integration solutions. The necessity of higher-dimensional and higher-fidelity SSMs is highlighted through a comparison of conventional 1D SSMs and extended three-dimensional (3D) SSMs. The results showed that using 1D SSMs over 3D SSMs could be off by 300% for Time-to-Collision (TTC) values larger than 1.5 s and about 20% for TTC values below 1.5 s. In other words, conventional 1D SSMs can yield highly inaccurate and unreliable results when assessing collision proximity and substantially misjudge the count of conflicts with predefined threshold (e.g., below 1.5s). Furthermore, the framework's practical application is demonstrated through a case study that actively evaluates all potential conflicts, underscoring its effectiveness in dynamic, real-world traffic situations.

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BACKGROUND: Due to the effect of coronavirus disease 2019 (COVID-19) outbreak on the continuation, schedule, and efficiency of radiation therapy, this study aimed to investigate the reliability and validity of the COVID-19 Safety Measures (CSM) questionnaire at the radiation therapy center. MATERIALS AND METHODS: In this analytical cross-sectional study, which all personnel of the radiation therapy center (20 people) participated, the validity and reliability of the 16-item CSM questionnaire were investigated. Cultural adaptation, face validity, content validity, test-retest reliability, and internal consistency were evaluated. For face and content validity, impact score, content validity ratio, and content validity index (CVR and CVI) were calculated, respectively. Also, internal consistency and stability reliability were calculated with Kuder-Richardson (KR20) alpha and Pearson correlation coefficient and intraclass correlation (ICC), respectively. Data analysis was performed using Statistical Package for the Social Sciences (SPSS) 24 with a significant level of 5%. RESULTS: Out of 20 employees, 70% (14 people) were female, 75% (15 people) were married and the mean age (SD) was 32.4 (6.35) years. Scale-based Kuder-Richardson alpha, S-CVI, ICC, and confidence interval were 0.79, 0.97, 0.68, and 0.38-0.89, respectively. CONCLUSION: The validity and reliability of the 16-item CSM questionnaire were confirmed. Therefore, the application of this scale is recommended.

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Aims: Hand hygiene (HH) is an essential practice to evade the transmission of germs and minimize community-acquired infections. This study assesses the knowledge, attitude and practice (KAP) of HH and other health and safety measures before, during, and after the COVID-19 pandemic. in university students in the United Arab Emirates (UAE). Methods: A cross-sectional questionnaire study was conducted between December 2022 and March 2023, targeting university students from all disciplines and study levels. A 44-item questionnaire was used which included student demographics, knowledge, attitude, and practice of HH, as well as the anticipated risk of COVID-19 morbidity and mortality. Participants consented before commencing the questionnaire, and the collected data were analysed using the student's t-test and ANOVA test, as required. Results: A total of 378 responses were received nationwide, with a valid response rate of 98%. The HH knowledge revealed an average score of 62%, which was significantly higher in students with moderate family income. Additionally, the average attitude score was 74.7%, as measured on the Likert scale, and the score lacked any correlation with the other variables. HH practice showed an average score of 86.8%, which was correlated with the students' gender and field of study. Conclusions: This study showed a moderate level of knowledge, a good attitude, and good practice around HH and other safety measures among the UAE's university students. Socioeconomic status, gender, and field of study influenced the study outcomes. This study highlights the need for effective awareness campaigns to reinforce students' health and safety, especially for male and non-health science students, in order to protect against communicable diseases.

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Pesticides are chemical constituents used to prevent or control pests, including insects, rodents, fungi, weeds, and other unwanted organisms. Despite their advantages in crop production and disease management, the use of pesticides poses significant hazards to the environment and public health. Pesticide elements have now perpetually entered our atmosphere and subsequently contaminated water, food, and soil, leading to health threats ranging from acute to chronic toxicities. Pesticides can cause acute toxicity if a high dose is inhaled, ingested, or comes into contact with the skin or eyes, while prolonged or recurrent exposure to pesticides leads to chronic toxicity. Pesticides produce different types of toxicity, for instance, neurotoxicity, mutagenicity, carcinogenicity, teratogenicity, and endocrine disruption. The toxicity of a pesticide formulation may depend on the specific active ingredient and the presence of synergistic or inert compounds that can enhance or modify its toxicity. Safety concerns are the need of the hour to control contemporary pesticide-induced health hazards. The effectiveness and implementation of the current legislature in providing ample protection for human health and the environment are key concerns. This review explored a comprehensive summary of pesticides regarding their updated impacts on human health and advanced safety concerns with legislation. Implementing regulations, proper training, and education can help mitigate the negative impacts of pesticide use and promote safer and more sustainable agricultural practices.

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Urban inter-tunnel weaving (UIW) sections are characterized by short lengths and frequent lane-changing behaviors in the area, commonly used for fast through traffic. These features increase the likelihood of collisions, however, collision risk assessment in this area has been inadequate. The aim of this study was to evaluate the potential collision risk of urban inter-tunnel weaving (UIW) sections in mixed lane-changing traffic conditions in morning rush hours, utilizing surrogate safety measures. The investigation involved the collection of trajectory data via an unmanned aerial vehicle (UAV). Time to collision (TTC) and extended time to collision (ETTC) were chosen as surrogate safety indicators. The estimation of collision risk was conducted using Extreme Value Theory (EVT) by means ofsurrogate safety indicators. It was found that the threshold of TTC and ETTC in this area was 1.25 s. Furthermore, a comprehensive evaluation of collision risks associated with various vehicle types was performed, revealing an inverse relationship between thecollisions riskof vehicles in mixed traffic and their size. It was worth noting that while heavy vehicles exhibit a lower collision risk, they resulted in the highest energy loss and inflicted greater harm in the event of a collision. By an examination of the distribution features pertaining to conflict types during the operation of heavy vehicles, it showed that the highest likelihood of conflict with heavy vehicles occurred when adjacent lanes are involved. Consequently, the implementation of assisted driving technology for heavy vehicles was imperative in order to mitigate the risk associated with side collisions.

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Conflict-based road safety assessments may provide a deeper insight into the processes leading to crashes compared to assessments solely based on field crash data. The evaluation of road safety is conducted on specific road segments using different surrogate measure of safety indicators, such as temporal, spatial, and kinematic proximity measures, depending on the relevant context and applicability of these measures. Therefore, this study endeavored to develop a methodology by adopting safety measures such as post encroachment time (PET) and conflicting speeds of through vehicles for crossing maneuvers and time to collision (TTC) for rear-end collisions at five unsignalized intersections in urban mixed traffic conditions. Critical conflicts are calculated by calculating a speed variable known as the critical speed, which is based on the braking distance. A study found that the motorized two wheeler (MTW) categories involve the highest proportion of critical conflict with right-turning vehicles, followed by cars, autos, and light commercial vehicle (LCVs). Furthermore, crossing conflicts were modeled as a function using the generalized linear regression approach. The findings revealed that the most significant factors were traffic volume and vehicular composition in a conflicting stream. The unsupervised classification technique k-mean clustering was used to determine the defined severity level threshold for rear-end maneuvers. The result observed was that a TTC threshold of less than 1.15 s was identified as high-risk vehicular interaction. Additional investigation indicated that presence of certain moving vehicle categories, including MTWs and cars, led to a higher proportion of critical crossing conflicts. The conceptualized safety framework can be applied to evaluate safety at unsignalized intersections in the mixed traffic scenarios.

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Merging areas serve as the potential bottlenecks for continuous traffic flow on freeways. Traffic incidents in freeway merging areas are closely related to decision-making errors of human drivers, for which the autonomous vehicles (AVs) technologies are expected to help enhance the safety performance. However, evaluating the safety impact of AVs is challenging in practice due to the lack of real-world driving and incident data. Despite the increasing number of simulation-based AV studies, most relied on single traffic/vehicle driving simulators, which exhibit limitations such as inaccurate description of AV behavior using pre-defined driving models, limited testing modules, and a lack of high-fidelity traffic scenarios. To this end, this study addresses these challenges by customizing different types of car-following models for AVs on freeway and developing a software-in-the-loop co-simulation platform for safety performance evaluation. Specifically, the environmental perception module is integrated in PreScan, the decision-making and control model for AVs is designed by Matlab, and the traffic flow environment is established by Vissim. Such a co-simulation platform is supposed to be able to reproduce the mixed traffic with AVs to a large extent. By taking a real freeway merging scenario as an example, comprehensive experiments were conducted by introducing a single AV and multiple AVs on the mainline of freeway, respectively. The single AV experiment investigated the performance of different car-following models microscopically in the case of merging conflict. The safety and comfort of AVs were examined in terms of TTC and jerk, respectively. The multiple AVs experiment examined the safety impact of AVs on mixed traffic of freeway merging areas macroscopically using the developed risk assessment model. The results show that AVs could bring significant benefits to freeway safety, as traffic conflicts and risks are substantially reduced with incremental market penetration rates.

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Despite recent measures on accident prevention, road collisions, mainly on London's "A" roads, persist as accident sources, endangering vulnerable users in particular. Analysing evidence from London's A-Roads unveils issues concerns and trends. This study utilises extensive data to target factors magnifying accidents: speed, traffic, vulnerable interactions. Stats 19 and transport data including volumes, types, speeds, and congestion parameters are all analysed alongside the collision data. The descriptive statistics have been employed to understand nature of data in the first instance. This has supported the process to cleanse the data outliers or periods where were subjected to incidents and interventions. Predictive model development is conducted to analyse and forecast accident frequency using ARIMA and SARIMAX models forecasted accident rates and interventions. ARIMA yielded higher accuracy. Method of analysis resulted in a statistically reliable formulation of the main factors, enabling use of this method for similar cities across the world. Formulated analysis revealed key contributors as population density, weather, and time of the day. The analysis of data supported identification of strategies emerging as infrastructure improvements, traffic control measures and severity and vulnerable users affected in particular. The analysis reveals distinct exhibits of causation, leading to focused recommendations on infrastructure enhancements, traffic control measures, and the impact on severity and vulnerable users, deviating from prior research findings. Insights aid safer London roads, have global predictive and mitigation value.

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AORN has identified safety risks unique to the perioperative setting and has developed ergonomic safety measures to help prevent musculoskeletal injuries and disorders. Little is known about adherence to these safety measures or the perceived barriers and facilitators to adherence. This study used a cross-sectional survey to determine the prevalence of pain and occurrence of musculoskeletal injuries and disorders. We asked perioperative staff members about their perceived barriers and facilitators to adherence with safety measures. A total of 155 perioperative nurses in one health system completed the online survey (55% response rate). Most (93%) had experienced at least one musculoskeletal injury or disorder or related pain. Years worked as a perioperative nurse and having neck pain were associated with safety measure adherence. The most reported barrier to safety measure adherence was inadequate staffing. Study findings highlight the need for increased attention to the physical workload demands in the perioperative setting.

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In recent times, the assessment of unsignalized intersection safety has received significant research attention because of the complex and diverse traffic movements and driving behaviour at such locations. However, priority traffic regulations are not well followed in comparison to the unsignalized junctions, which leads to more conflicts. Additionally, the severity of conflicts increases with continuous traffic manoeuvres, including right-turns and through traffic, combined with different driving behaviours. Several studies have compared crash-based analysis to proactive traffic safety measures. Current research outcomes imply that surrogate safety measures (SSMs) have the potential to elucidate the sequence of events that result in collisions, their underlying causes, and their outcomes. Therefore, to further understand the appropriateness of SSMs, further study is required based on heterogeneity in traffic along with driver behaviour that incorporates turning vehicle factors. This study presents an all-inclusive evaluation of the recent advancements in SSMs and their practical implementation, with a particular emphasis on unsignalized intersections in developing nations. The findings of this investigation would be helpful in identifying the appropriate safety indicators for evaluating traffic safety at unsignalized intersections.

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Introduction: Quarrying is a thriving occupation providing widespread employment opportunities to the poor indigenes of Zamfara State. The workers are more susceptible to various morbidities due to numerous hazards in their working environment. This study aims to assess the morbidity pattern, availability, and compliance with safety measures among quarry workers in Gusau metropolis, Zamfara State. Methodology: A cross-sectional study was conducted among 307 quarry workers in Gusau metropolis between July and August 2022. The respondents were selected by multi-stage sampling technique. Data was collected using an interviewer-administered questionnaire (ODK), an observer checklist, and clinical examination. Appropriate descriptive and inferential statistics were done to determine significant association (p<0.05). Results: The common morbidities among the respondents were respiratory (39.4%), ear (6.5%), eye (30.0%), and injuries (39.7%). Working hours per day and duration of work in the quarry were factors associated with respiratory morbidities. Respondents' level of compliance with safety measures were; no compliance (5.2%), low compliance (43.7%), moderate compliance (49.8%), and considerable compliance (1.3%). Conclusions: This study showed high morbidities and low compliance with safety measures among quarry workers in Gusau metropolis, Zamfara State.

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The exposure of farmers to pesticides due to inadequate safety measures is a concern in low-income countries in Africa and Asia. However, until now, there have been limited studies on the farmers' risk due to pesticide application to fruit crops. The knowledge of farmers' exposure related to pesticide use and their safety practices was studied among 100 banana farmers in three areas (Padampur, Jagatpur, and Thimura) of Chitwan district, Nepal. More than 75% of the farmers complained about problems related to insects. Most frequently used insecticides in the area were chlorpyrifos and cypermethrin. Ten percent (10%) of the applied pesticides were highly hazardous to humans, according to the World Health Organization hazard category, with skin rash being the most common acute symptom reported by 29% of the farmers. Banned organochlorine and organophosphate insecticides, such as endosulfan and triazophos, respectively, are still being used by farmers in the aforementioned areas. Spearman's correlation analysis revealed the lack of knowledge and safety practices among farmers leading to inadequate awareness related to the negative effects of pesticide use on human health and the environment. Therefore, government extension service can play a crucial role in improving banana farmers' knowledge of the toxic effects of pesticides as well as enforcing the Nepali language in the labeling of pesticide containers and packages.

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This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.

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Incident investigation reports provide information on defects related to the system safety and indications for improvements. Currently, the analysis of these reports relies heavily on expert' experience. The foreseeable work-load and lack of understanding about the importance of near misses have created a situation where severe accidents are rigorously investigated, and minor incidents are often omitted. Consequently, incident reports have not been fully analyzed to provide sufficient solutions. The aim of this research is to propose a framework that uses text mining and multilevel association rules to efficiently structure Chinese incident reports and identify important incident patterns, providing an analysis of trends, rectification strategies, and guidance for safety management. A case study of a construction company in China was conducted using two years of incident data dated 2018-2019, including accidents and near misses. To identify incident elements, a pattern extraction workflow involving TextRank, and domain pertinence was devised based on the linguistic and writing styles of Chinese reports. A concept hierarchy was applied to determine the taxonomic relationships within the risk factors. Multilevel association rule mining was adopted and proven to deliver more comprehensive pattern indications. Comparative and cross-analysis of patterns in different time periods revealed the severity and temporal features of incidents as well as the effectiveness of preventive and precautionary measures. The results also highlight the importance of learning from near miss events. Decision makers can formulate countermeasures and management policies based on these results to improve safety performance.

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