Safety Decisions Research Articles

Carefree cuteness: the effect of exposure to cuteness on risk seeking

Purpose Cuteness has grown to be a global phenomenon fueled by the explosive usage of social media. Cute stimuli are ubiquitous, but few have explored their effects on consumer decision-making; direct evidence is particularly lacking in the area of risky choices. In this research, the authors theorize and demonstrate the unintended effects of cuteness exposure on people’s risk preference. Design/methodology/approach Across five experimental studies situated in various risk contexts, including health, financial and safety, the authors demonstrate that exposure to cuteness makes consumers more risk-seeking due to the reduction of situational conscientiousness. Study 1 used an experimental lab study with a real circumstance to test the effect on the risk associated with food consumption. Study 2 used a classical gambling experiment to test the effect on financial risk. Studies 3a and 3b used a mass shooting news article to test the effect on safety risk. Lastly, study 4 tested the mediating role of low conscientiousness with the classical gambling experiment. Findings The findings show that exposure to cuteness makes people more likely to take risks in various domains (e.g. food consumption, safety and financial decisions). Research limitations/implications This study tested the effect of cuteness on risk-seeking with a limited number of domains of risk. In addition, the authors tested the effect with visual cuteness stimuli, while individuals may perceive cuteness through other senses, such as sound. Practical implications The findings have implications for business owners and marketers when deciding whether and how to use cuteness to promote their products and brands, as well as to avoid potential repercussions. For example, a marketer for a new extreme sports company could use videos or images of cute animals participating in sports on the company’s social media channels to expand its market share. In addition, findings from this research would make consumers more attentive when facing cute appeals as they gain a better understanding of how exposure to cuteness could impact their own decision-making. Originality/value To the best of the authors’ knowledge, this research is the first to demonstrate that exposure to subtle, cute environmental cues has a robust effect on consumers’ risk preferences across various domains, regardless of age and gender.

Behavior Safety Decision-Making Based on Deep Deterministic Policy Gradient and Its Verification Method

As an emerging mode of transportation, autonomous vehicles are increasingly attracting widespread attention. To address the issues of the traditional reinforcement learning algorithm, which only considers discrete actions within the system and cannot ensure the safety of decision-making, this paper proposes a behavior decision-making method based on the deep deterministic policy gradient. Firstly, to enable autonomous vehicles to drive as close to the center of the road as possible while sensitively avoiding surrounding obstacles, the reward function for reinforcement learning is constructed by comprehensively considering road boundaries and nearby vehicles. We account for the symmetry of the road by calculating the distances between the vehicle and both the left and right road boundaries, ensuring that the vehicle remains centered within the road. Secondly, to ensure the safety of decision-making, the safety constraints in autonomous driving scenarios are described using probabilistic computation tree logic, and the scenario is modeled as a stochastic hybrid automaton. Finally, the model is verified by the statistical model checker UPPAAL. The above method enables autonomous vehicles not only to independently acquire driving skills across diverse driving environments but also significantly enhances their obstacle avoidance capabilities, thereby ensuring driving safety.

Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis

This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation framework designed for autonomous vehicles. Identifying emergent behaviours that enhance safety and efficiency, verifying the efficacy of ABM in AV decision-making, and investigating the function of hardware acceleration to enable practical application in ADASs are some of the major achievements. According to the simulation results, ABM can greatly improve AV performance, providing a practical and scalable means of enhancing safety in future transportation systems.

Lateral Transpsoas Interbody Fusion.

The lateral transpsoas approach to lumbar interbody fusion has gained widespread adoption for a variety of indications. This approach to the interbody space allows for a favorable fusion environment, disc and neuroforaminal height restoration, and powerful alignment correction. Despite its minimally invasive nature, this procedure carries unique risks, the most severe of which include bowel injury, major vascular injury, and lumbosacral plexopathy. This poses a marked learning curve and requires rigorous attention to detail in technique. In this review, we provide a detailed description of our approach to preoperative imaging, patient positioning, and surgical technique, with an emphasis on patient safety and evidence-based decision-making. A brief description of intraoperative neuromonitoring techniques follows. The lateral transpsoas approach to interbody fusion has demonstrated reliable outcomes in regard to fusion rates, pain and function, and deformity correction, all across a widespread variety of lumbar spine pathologies. Here, we depict techniques, pearls, and pitfalls that are critical for any surgeon considering whether to add this technique to their practice.

Инструментальные и мотивационные ресурсы специалистов сферы защиты прав детей в условиях неопределенности профессиональной деятельности

<p style="text-align: justify;">The article presents the results of a study of the instrumental and motivational resources of 596 specialists of three professional groups (specialists of guardianship and guardianship authorities; the Office of the Commissioner for Children’s Rights; organizations for orphans and children left without parental care) from 87 subjects of the Russian Federation in conditions of experiencing situations of uncertainty in their professional activities. The research model combines the type and perceived level of uncertainty of professional activity, the resources used to overcome it. It is shown that the frequency of occurrence of the five types of situations of professional uncertainty identified in the study depends on the specifics of the activities of professional groups. Situations related to the consequences for child safety and professional decision-making are assessed as the most uncertain. The degree of experiencing uncertainty correlates with the level of instrumental and motivational resources. In groups experiencing relatively simple situations of uncertainty of professional activity, the level of controlled professional motivation and the need for support, especially emotional, are lower.</p>

Advancing Systemic Toxicity Risk Assessment: Evaluation of a NAM-based Toolbox Approach.

For many years, a method that allowed systemic toxicity safety assessments to be conducted without generating new animal test data, seemed out of reach. However, several different research groups and regulatory authorities are beginning to use a variety of in silico, in chemico and in vitro techniques to inform safety decisions. To manage this transition to animal-free safety assessments responsibly, it is important to ensure that the level of protection offered by a safety assessment based on new approach methodologies (NAMs), is at least as high as that provided by a safety assessment based on traditional animal studies. To this end, we have developed an evaluation strategy to assess both the level of protection and the utility offered by a NAM-based systemic safety 'toolbox'. The toolbox comprises physiologically-based kinetic (PBK) models to predict internal exposures, and bioactivity NAMs designed to give broad coverage across many different toxicity modes of action. The output of the toolbox is the calculation of a bioactivity: Exposure ratio (analogous to a margin of internal exposure), which can be used to inform decision-making. In this work we have expanded upon an initial pilot study of 10 chemicals with an additional 38 chemicals and 70 consumer exposure scenarios. We found that, for the majority of these (>90%), the NAM-based workflow is protective of human health, enabling us to make animal-free safety decisions for systemic toxicity and preventing unnecessary animal use. We have also identified critical areas for improvement to further increase our confidence in the robustness of the approach.

Advanced Monocular Outdoor Pose Estimation in Autonomous Systems: Leveraging Optical Flow, Depth Estimation, and Semantic Segmentation with Dynamic Object Removal.

Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures. Hence, developing alternative localization techniques that do not depend on external signals is essential, showing a critical need for robust, GPS-independent localization solutions adaptable to different applications, ranging from Earth-based autonomous vehicles to robotic missions on Mars. This paper addresses these challenges using Visual odometry (VO) to estimate a camera's pose by analyzing captured image sequences in GPS-denied areas tailored for autonomous vehicles (AVs), where safety and real-time decision-making are paramount. Extensive research has been dedicated to pose estimation using LiDAR or stereo cameras, which, despite their accuracy, are constrained by weight, cost, and complexity. In contrast, monocular vision is practical and cost-effective, making it a popular choice for drones, cars, and autonomous vehicles. However, robust and reliable monocular pose estimation models remain underexplored. This research aims to fill this gap by developing a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry systems with monocular cameras, especially for applications where deploying additional sensors is not feasible due to cost or physical constraints. This framework is designed to be adaptable across different vehicles and platforms, ensuring accurate and reliable pose estimation. We integrate advanced control theory to provide safety guarantees for motion control, ensuring that the AV can react safely to the imminent hazards and unknown trajectories of nearby traffic agents. The focus is on creating an AI-driven model(s) that meets the performance standards of multi-sensor systems while leveraging the inherent advantages of monocular vision. This research uses state-of-the-art machine learning techniques to advance visual odometry's technical capabilities and ensure its adaptability across different platforms, cameras, and environments. By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation. Experimental results on the KITTI odometry dataset demonstrate a significant improvement in pose estimation accuracy, offering a cost-effective and robust solution for real-world applications.

Corrosion Rate Prediction for Underground Gas Pipelines Using A Levenberg-Marquardt Artificial Neural Network (ANN)

Abstract This study addresses the challenge of accurately predicting corrosion rates and estimating the remaining life of underground gas pipelines, which is complicated by the complex interaction of physical factors and environmental conditions. Traditional models are inadequate in capturing these variables, leading to less reliable predictions, which this study aims to address by developing a more accurate and optimized artificial neural network (ANN) model. This study focuses on predicting corrosion rates and estimating the remaining life of underground gas pipelines using ANNs implemented in MATLAB. It incorporates both physical factors, such as maximum corrosion depth and pipe thickness, and environmental variables such as moisture, soil resistivity, and chloride concentration. The analysis identified corrosion depth and wall thickness as significant contributors, influencing material integrity by 20% and 16%, respectively. The optimal ANN model, with a Levenberg-Marquardt structure and one hidden layer of 10 neurons, achieved superior accuracy, with an MSE of 0.038 and R² of 0.9998. The study addresses the challenge of accurately predicting corrosion rates and remaining life in underground gas pipelines by developing an optimised ANN model. Its contribution lies in creating a highly accurate prediction tool that outperforms traditional models and enables more informed decisions for pipeline maintenance and safety.

Integration of National Chemical Hazards Monitoring, Total Diet Study, and Human Biomonitoring Programmes for Food Safety Exposure Assessment in Singapore

Food safety and food security can impact the quality of human life, and these two aspects are interrelated alongside many influencing external factors. Global stressors such as climate change, recent pandemic, and geopolitical tensions have demonstrated tangible impacts on food security and safety. Food and food system innovation is a key strategy towards feeding the world in a more sustainable and climate-resilient manner. This paper highlights the use of a science-based risk assessment and management in Singapore’s food safety system, specifically in the integration of exposure assessment approaches to support evidence-based food safety risk analysis and decision-making. The use of complementary top-down and bottom-up exposure assessment approaches through the market monitoring programme, total diet study and human biomonitoring forms a comprehensive integrated exposure assessment strategy which can ultimately inform policy and measures in ensuring and securing a supply of safe food. The discussion on such application for chemical food safety in Singapore offers additional insights into the synergistic inter-relationships contributing to the exposure assessment associated with chemicals in food.

Beyond one million years: The intrinsic radiation hazard of high-level nuclear wastes

Abstract This paper highlights the absence of quantitative estimates regarding the intrinsic radiation hazard of high-level nuclear wastes, namely, spent fuel (SF) and vitrified high-level wastes (VHLW), for periods exceeding one million years. Using available data, conducting scoping calculations of radiation doses, and comparing the results to radiation protection guidelines and natural background radiation, this paper shows that high-level wastes cannot be safely handled or left unprotected essentially indefinitely. By quantitatively evaluating the dose rates of unshielded SF and VHLW, this study identifies critical new insights, such as the roles of the Np-237 decay chain; the eventual, long-term dominance of the U-238 decay chain; and the interplay of three actinide decay chains, including the significant role of Bi-214. These findings fill a gap in the literature and emphasize the need for more detailed investigations in this as-yet-unexplored research area, which has a direct bearing on technical and societal decision-making for both waste disposal safety and the choice of the back end of the nuclear fuel cycle.

Robust Deep Reinforcement Learning in Autonomous Car Path Planning

The rapid advancements in autonomous vehicle technology have emphasized the need for adaptable and dependable control systems. Deep Reinforcement Learning (DRL) has emerged as a key method to address the complexity involved in autonomous driving. This research explores how DRL can enhance path planning and control mechanisms in self-driving vehicles, with a particular focus on its ability to handle dynamic traffic environments. The challenges in training DRL models, including generalization, safety, and real-time decision-making, are analyzed. The paper also suggests potential research directions for improving DRL's application in autonomous driving, offering insights into its strengths and limitations in the current technological landscape

Knowledge, attitudes and practices regarding medication splitting and crushing among the general public in Jordan: a cross-sectional study

BackgroundSplitting or crushing medications are used for dosage administration when a certain dose is not easily accessible. Understanding the splitting or crushing of medications is essential to guarantee safe medication...

Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers

Highway traffic officers (HTOs) are often exposed to life-threatening workplace incidents while performing their duties. However, scant research has been undertaken to address these safety concerns. This research explores case study data from highway incident reports (held by National Highways, a UK government company) and employs deep neural network (DNN) in unearthing patterns which inform safety decision makers on pertinent safety challenges confronting HTOs. A mixed philosophical stance of positivism and interpretivism was adopted to synthesise the findings made. A four-phase sequential method was implemented to evaluate the validity of the research viz.: (i) architectural design; (ii) data exploration; (iii) predictive modelling; and (iv) performance evaluation. The DNN model’s predictive performance is benchmarked against three other machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes (NB). The DNN model outperformed the other three models. Findings from the data exploration also show that most work operations undertaken by HTOs have a medium risk level with night shifts posing the greatest risk challenges. Carriageways and traffic management enclosures had the highest incident occurrence. This is the first study to uncover such hidden patterns and predict risk levels using a database specifically for HTOs. This study presents evidence-based information for proactive risk management for HTOs.

A Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain Areas

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

Hybrid WARIMA-WANN algorithm for data prediction in bridge health monitoring system

Application of data-driven algorithm to model and predict the future trend of the structural health monitoring (SHM) data serves as important references for the future safety evaluation and decision-making of the bridges. An innovative hybrid WARIMA-WANN algorithm combining the Wavelet model, the autoregressive moving average (ARIMA) model and the artificial neural network (ANN) model is proposed to predict massive monitoring data in the bridge SHM system. The WPT serves as the decomposition function for the linear and nonlinear components of the hybrid model. The separated linear/nonlinear components are individually predicted with the ARIMA/ANN models and then combined to obtain the prediction values of the future data. The hybrid method is applied to predict the deflection values of a single-tower cable-stayed bridge. Multiple parameters in individual models are discussed and selected to increase the credibility of the prediction results for the example bridge. The results show that the proposed hybrid algorithm successfully captures the linear and nonlinear coupling relationships inside the SHM data from the frequency perspective of view and shows the best prediction performance when compared with the other four existing models. Different prediction steps obviously influence prediction performance due to the periodic nature of the bridge SHM data.

Mode 2 critical systems practice for complex safety decisions: Reflections from New Zealand's dairy industry

AbstractPoor decision‐making contributes to disasters in complex industrial systems. Improving group decision‐making practices for safety‐critical interventions is crucial yet hindered by a fragmented safety science literature. Our study applies Jackson's critical systems practice (CSP) to a complex safety problem at a New Zealand dairy manufacturing facility. Reflective practice, employed in the study and writing this article, underlines CSP's usefulness in navigating complex emergent safety challenges. Key insights include clarifying cognitive complexity amongst participants early in an intervention, focusing more sharply on crucial safety outcomes by using critical facilitation and enhancing CSP's ‘Check’ stage using reflective practice. We conclude by calling on scholars to prioritise the application of CSP in real‐world contexts to deepen our understanding of systemic praxis and generate actionable improvements.

An Immersive VR Training Scenario for Safe Methanol Bunkering in the Maritime Industry

Abstract The maritime industry, pivotal in global commerce, faces critical challenges in mitigating environmental impacts, especially greenhouse gas emissions. The International Maritime Organization (IMO) targets a substantial reduction in emissions by 2050, necessitating innovative approaches to fuel usage. Methanol, recognized for its lower carbon intensity, emerges as a viable alternative fuel. However, its flammability and toxicity introduce complex safety and operational demands. To address these challenges, we have developed a state-of-the-art Virtual Reality (VR) training program specifically for methanol bunkering operations. This training, utilizing Unity 3D and Meta Quest 2, offers a highly interactive and realistic experience. Trainees engage in a comprehensive simulation that covers the entire bunkering process, including safety checks, equipment handling, and emergency response protocols. The VR environment allows for dynamic adjustments by trainers, enhancing the realism and educational value of the scenario. Our program also includes an assessment component that evaluates the trainee’s performance across various metrics such as safety protocol adherence, decision-making, and situational awareness. The anticipated outcome of this VR training initiative is to significantly improve the preparedness of maritime personnel in handling alternative fuels, thereby supporting the industry’s transition towards sustainable practices and contributing to broader environmental protection goals.

Digital twin-driven safety management and decision support approach for port operations and logistics

Ports are of great significance in processing cargo containers and facilitating global marine logistics. Nevertheless, the susceptibility of the container shipping network for hazardous cargo is likely to intensify in the event of a significant disruption at a major port, such as adverse weather conditions, inadequate management practices, or unforeseen catastrophes. Such situations require the deployment of port protection emergency response and prevention in advance. This study proposes a digital twin (DT) model that employs extensive and trajectory data within containers to comprehensively analyze the occurrence of hazardous cargo failures within the port storage process. The virtual models of physical entities in the port are created through a data-driven approach, and the behavior of these entities in a port environment with big data is then simulated. A combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) algorithm is employed to provide predictions for the service layer of the DT system. The predicted correlation coefficients of temperature and humidity in the container reach 0.9855 and 0.9181, respectively. The developed system driven by DT models integrated with a CNN and the LSTM algorithm can more effectively assist the safety manager in achieving prevention in port operations. This study enables marine authorities and decision-makers to optimize emergency procedures, thereby reducing the probability of accidents in port operations and logistics.

Exploring client violence during home visits: a qualitative study of perceptions and experiences of Israeli nurses

BackgroundHome care provides an excellent opportunity for personalizing treatment as nurses see patients in their natural environment. Along with its many advantages, the home care environment carries unique risks, as nurses are usually alone, without the protection and security provided by primary care clinics. There are no accurate data in Israel on the scope and characteristics of client violence against nurses during home visits. We conducted a qualitative study to investigate the nature of client violence faced by Israeli nurses during home visits, to gain insights into their perceptions and experiences, and to contribute to the development of effective policies and strategies to combat client violence in the healthcare sector.MethodsTwenty-seven female nurses from primary care clinics, who were exposed to client violence during a home visit, were interviewed using a semi-structured interview guide. The interviews were transcribed and analyzed, and categories and themes were extracted.ResultsMost nurses interviewed experienced at least three incidents of client violence, the most common of which was verbal abuse. The nurses perceived that the location of the encounter between the nurse and the patient in the patient’s natural surroundings, rather than within the controlled boundaries of a clinic, contributes to the risk of violence. Violence affected the nurses’ professional decisions. The nurses reported that their organization had no established guidelines or instructions for safely conducting home visits, they were not provided with protective or security measures for emergencies, nor did they perceive that they had sufficient training to deal with client violence in clients’ homes.ConclusionsNurses encounter a range of challenges that make it difficult for them to deal with client violence during home visits, affecting their personal safety and professional decisions. Their ability to manage such situations is shaped by a complex interplay of personal and organizational factors and requires a range of strategies and resources to effectively address them.

A proof-of-concept multi-tiered Bayesian approach for the integration of physiochemical properties and toxicokinetic time-course data for Daphnia magna

The use of in silico and in vitro methods, commonly referred to as New Approach Methodologies (NAMs), has been proposed to support environmental (and human) chemical safety decisions, ensuring enhanced environmental protection. Toxicokinetic models developed for environmentally relevant species are fundamental to the deployment of a NAMs-based safety strategy, enabling the conversion between external and internal chemical concentrations, although they require historical toxicokinetic data and robust physical models to be considered a viable solution. Daphnia magna is a key model organism in ecotoxicology albeit with limited and scattered quantitative toxicokinetic data, as for most invertebrates, resulting in a lack of robust toxicokinetic models. Moreover, current D. magna models are chemical specific, which restricts their applicability domain. One aim of this study was to address the current data availability limitations by collecting toxicokinetic time-course data for D. magna covering a broad chemical space and assessing the dataset's uniqueness. The collated toxicokinetic dataset included 48 time-courses for 30 chemicals from 17 studies, which was developed into an R package named AquaTK, with 11 studies unique to our work when compared to existing databases. Subsequently, a proof-of-concept Bayesian analysis was developed to estimate the steady-state concentration ratio (internal concentration / external concentration) from the data at three different levels of precision given three different data availability scenarios for environmental risk assessment. Specifically, an atrazine case study illustrates the multi-level modelling approach providing improvements (uncertainty reductions) in predictions of ratios for increasing amounts of data availability. Our work provides a consistent and self-contained Bayesian framework that irrespective of the hierarchy or resolution of individual experiments, can utilise the available information to generate optimal predictions of steady-state concentration ratios in D. magna. This approach is paramount to supporting the implementation of a NAMs based environmental safety paradigm shift in environmental risk assessment.