Safety Decisions Research Articles

The Role of the Medial Prefrontal Cortex in Spatial Margin of Safety Calculations.

Naturalistic observations show that animals pre-empt danger by moving to locations that increase their success in avoiding future threats. To test this in humans, we created a spatial margin of safety (MOS) decision task that quantifies pre-emptive avoidance by measuring the distance subjects place themselves to safety when facing different threats whose attack locations vary in predictability. Behavioral results show that human participants place themselves closer to safe locations when facing threats that attack in spatial locations with more outliers. Using both univariate and multivariate pattern analysis (MVPA) on fMRI data collected during a 2 h session on participants of both sexes, we demonstrate a dissociable role for the vmPFC in MOS-related decision-making. MVPA results revealed that the posterior vmPFC encoded for more unpredictable threats with univariate analyses showing a functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings converge in showing that during pre-emptive danger, the anterior vmPFC may provide a safety signal, possibly via foreseeable outcomes, while the posterior vmPFC drives unpredictable danger signals.

Employees are not the weakest link: an occupational safety view of information security

PurposeI adapt the Integrated Model of Workplace Safety (Christian et al., 2009) to information security and highlight the need to understand additional factors that influence security compliance and additional security outcomes that need to be studied (i.e. security participation).Research limitations/implicationsThis model argues that distal factors in four major categories (employee characteristics, job characteristics, workgroup characteristics and organizational characteristics) influence two proximal factors (security motivation and security knowledge) and the security event itself, which together influence two important outcomes (security compliance and security participation).Practical implicationsSafety is a systems design issue, not an employee compliance issue. When employees make poor safety decisions, it is not the employee who is at fault; instead, the system is at fault because it induced the employee to make a poor decision and enabled the decision to have negative consequences.Social implicationsSecurity compliance is as much a workgroup issue as an individual issue.Originality/valueI believe that by reframing information security from a compliance issue to a systems design issue, we can dramatically improve security.

Prediction of 10,000-hour rate of civil aviation incidents based on grey Markov model

The 10,000-hour rate of civil aviation incidents is an important index parameter to measure flight safety. Predicting the development trend of the 10,000-hour rate of civil aviation incidents plays an important role in aviation accident prevention and safety decision-making. Many complex factors influence the occurrence of civil aviation incidents, so the 10,000-hour rate of civil aviation incidents changes randomly and volatilely. This study proposed the idea of prediction by combining the grey GM (1, 1) model and the Markov model. Specifically, the grey GM (1, 1) prediction model was constructed using the statistical data on the 10,000-hour rate of civil aviation incidents in China during 2005–2020. On this basis, a grey Markov prediction model was established. The prediction of the 10,000-hour rate of incidents in 2021 based on the two models showed that the grey Markov model displayed higher prediction accuracy than the grey GM (1, 1) model and conformed to the change laws of the 10,000-hour rate data of civil aviation incidents better. Moreover, the grey Markov model could effectively improve the accuracy of the grey prediction model, compensate for its deficiencies, and facilitate the mastery of the change laws of civil aviation incidents, providing a reliable basis for aviation safety management and incident prevention.

Barriers and facilitators to adopting safe farm-machine related behaviors: A focus group study exploring older farmers’ perspectives

Background: Tractors and quad bikes pose a significant risk of fatal injuries among farmers, particularly affecting older farmers. This study aimed to explore the barriers and facilitators to the adoption of machine related safety behaviors among older farmers in Irish farm settings. Method: Four focus groups were conducted via Zoom in February 2021. Nineteen Irish farmers from four farm types participated. The discussions were audio-recorded, transcribed verbatim, and analyzed using an inductive, reflexive thematic analysis approach. The themes identified were then mapped to the COM-B (Capability-Opportunity-Motivation) model, providing a systematic theoretical basis for designing a future intervention to reduce machine-related accidents. Results: The analysis identified five inductive themes that encompassed both barriers and facilitators in farm safety practices: (1) Capability to manage competing responsibilities; (2) Characteristics of the farm and its work environment; (3) Availability and affordability of resources; (4) Prevailing sociocultural opportunities; and (5) Perceived likelihood and cost-benefit analysis in safety decision-making. These themes captured the complex interplay of capability, opportunity, and motivation in farmers’ decision-making processes. The study also revealed limitations in existing interventions, such as voluntary guidelines and educational methods, in effectively addressing these barriers. Conclusions: Farmers’ abilities (capability), prevailing sociocultural factors, resource availability (opportunity), and their perceived consequences and benefits (motivation) affect how safely they work with machines. The study emphasizes the need for comprehensive, theory-driven approaches that consider the interplay of capability, opportunity, and motivational factors that may support or impede machine safety. Understanding the challenges faced by Irish farmers highlights potential strategies for safety intervention, and these strategies should be co-designed with farmers and attentive to the local context. Practical applications: The study provides a template for understanding farmers’ perspectives using the COM-B model. The findings can inform the development of theoretically informed intervention strategies based on the Behavior Change Wheel framework.

Next generation risk assessment for occupational chemical safety – A real world example with sodium-2-hydroxyethane sulfonate

Next Generation Risk Assessment (NGRA) is an exposure-led approach to safety assessment that uses New Approach Methodologies (NAMs). Application of NGRA has been largely restricted to assessments of consumer use of cosmetics and is not currently implemented in occupational safety assessments, e.g. under EU REACH. By contrast, a large proportion of regulatory worker safety assessments are underpinned by toxicological studies using experimental animals. Consequently, occupational safety assessment represents an area that would benefit from increasing application of NGRA to safety decision making. Here, a workflow for conducting NGRA under an occupational safety context was developed, which is illustrated with a case study chemical; sodium 2-hydroxyethane sulphonate (sodium isethionate or SI). Exposures were estimated using a standard occupational exposure model following a comprehensive life cycle assessment of SI and considering factory-specific data. Outputs of this model were then used to estimate internal exposures using a Physiologically Based Kinetic (PBK) model, which was constructed with SI specific Absorption, Distribution, Metabolism and Excretion (ADME) data. PBK modelling indicated a worst-case plasma maximum concentration (Cmax) of 0.8 μM across the SI life cycle. SI bioactivity was assessed in a battery of NAMs relevant to systemic, reproductive, and developmental toxicity; a cell stress panel, high throughput transcriptomics in three cell lines (HepG2, HepaRG and MCF-7 cells), pharmacological profiling and specific assays relating to developmental toxicity (Reprotracker and devTOX quickPredict). Points of Departure (PoDs) for SI ranged from 104 to 5044 µM. Cmax values obtained from PBK modelling of occupational exposures to SI were compared with PoDs from the bioactivity assays to derive Bioactivity Exposure Ratios (BERs) which demonstrated the safety for workers exposed to SI under current levels of factory specific risk management. In summary, the tiered and iterative workflow developed here represents an opportunity for integrating non animal approaches for a large subset of substances for which systemic worker safety assessment is required. Such an approach could be followed to ensure that animal testing is only conducted as a “last resort” e.g. under EU REACH.

Research on Autonomous Vehicle Obstacle Avoidance Path Planning with Consideration of Social Ethics

Self-driving car research can effectively reduce the occurrence of traffic accidents, but when encountering sudden people or obstacles cutting into the lane, how to reduce the damage hazard to traffic participants and make ethical decisions is the key point that the development of self-driving technology must break through. When faced with sudden traffic participants, self-driving vehicles need to make ethical decisions between ramming into the traffic participants or other obstacles. Therefore, in this paper, we propose a model decision planning method based on multi-objective evaluation function path evaluation of local path planning. This method addresses the ethical model disagreement problem of self-driving vehicles encountering traffic participants and other obstacles. The aim is to ensure the safety of the lives of the traffic participants and achieve the vehicle’s reasonable ethical decision planning. Firstly, when anticipating traffic participants and other obstacles, the vehicle’s planning intention decisions are obtained through fuzzy algorithms. Different sets of curves for various positions are generated based on dynamic programming algorithms. These curves are then fitted using B-spline curves, incorporating obstacle collision costs, and classifying obstacles into different types with varying cost weights. Secondly, factors such as path length and average path curvature are considered for path total cost calculations. Finally, a local path that avoids traffic participants is obtained. This path is then tracked using a pure pursuit algorithm. The proposed algorithm’s effectiveness is verified through simulation experiments and comparative analyses conducted on the MATLAB platform. In conclusion, this research promotes a safer and more sustainable transport system in line with the principles of sustainable development by addressing the challenges associated with safety and ethical decision making in self-driving cars.

Drinking water QMRA and decision-making: Sensitivity of risk to common independence assumptions about model inputs

When assessing risk posed by waterborne pathogens in drinking water, it is common to use Monte Carlo simulations in Quantitative Microbial Risk Assessment (QMRA). This method accounts for the variables that affect risk and their different values in a given system. A common underlying assumption in such analyses is that all random variables are independent (i.e., one is not associated in any way with another). Although the independence assumption simplifies the analysis, it is not always correct. For example, treatment efficiency can depend on microbial concentrations if changes in microbial concentrations either affect treatment themselves or are associated with water quality changes that affect treatment (e.g., during/after climate shocks like extreme precipitation events or wildfires). Notably, the effects of erroneous assumptions of independence in QMRA have not been widely discussed. Due to the implications of drinking water safety decisions on public health protection, it is critical that risk models accurately reflect the context being studied to meaningfully support decision-making. This work illustrates how dependence between pathogen concentration and either treatment efficiency or water consumption can impact risk estimates using hypothetical scenarios of relevance to drinking water QMRA. It is shown that the mean and variance of risk estimates can change substantially with different degrees of correlation. Data from a water supply system in Calgary, Canada are also used to illustrate the effect of dependence on risk. Recognizing the difficulty of obtaining data to empirically assess dependence, a framework to guide evaluation of the effect of dependence is presented to enhance support for decision making. This work emphasizes the importance of acknowledging and discussing assumptions implicit to models.

Forecasting and uncertainty analysis of tailings dam system safety based on data mining techniques

Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters.

Introducing Point-of-Care PCR technology in general practice: Ambiguities, experiences, and perceptions among health care professionals.

In this paper we present findings from a qualitative ethnographic study investigating the experiences and perceptions of general practitioners and other practice staff when introducing a new point of care diagnostic test technology (point of care polymerase chain reaction (POC PCR)) in general practice in Denmark. The ethnographic study was conducted in five general practice clinics, involving observations in four of the clinics and interviews with general practitioners and practice staff in all five clinics. Following an initial analytic phase in which barriers and facilitators in the implementation process of the Point-of-Care test were identified, we developed theoretically informed themes, drawing upon Hartmut Rosa's social theory of technological acceleration. These themes included ambiguous experiences and perceptions of: (i) diagnostic specification and inflation embedded in diagnostic practices; (ii) empowerment and erosion of professional judgment; (iii) strategies of security and insecurity in communication; (iv) the interdependence between professional autonomy and economic structures associated with organizational power; and (v) subjective and organizational time. We discuss how diagnostic technologies simultaneously contribute to and disrupt treatment safety, efficiency, and medical decision-making. Using Rosa's sociological concepts of alienation and resonance, this article furthermore explores how these ambiguous dynamics are experienced in general practice settings. It also examines the implications of navigating a heterogeneous socio-technical and medical landscape and what it means to be a health professional in a contemporary general practice environment that is increasingly shaped by diagnostic technologies.

"We are pleading for the government to do more": Road user perspectives on the magnitude, contributing factors, and potential solutions to road traffic injuries and deaths in Ghana.

Road traffic collisions disproportionately impact Ghana and other low- and middle-income countries. This study explored road user perspectives regarding the magnitude, contributing factors, and potential solutions to road traffic collisions, injuries, and deaths. We designed a qualitative study of 24 in-depth interviews with 14 vulnerable road users (pedestrians, occupants of powered 2- and 3-wheelers, cyclists) and ten non-vulnerable road users in four high-risk areas in November 2022. We used a mixed deductive (direct content analysis) and inductive (interpretive phenomenological analysis) approach. In the direct content analysis, a priori categories based on Haddon's Matrix covered human, vehicle, socioeconomic environment, and physical environment factors influencing road traffic collisions, along with corresponding solutions. We used inductive analysis to identify emerging themes. Participants described frequent and distressing experiences with collisions, and most often reported contributing factors, implementation gaps, and potential solutions within the human (road user) level domain of Haddon's Matrix. Implementation challenges included sporadic enforcement, reliance on road users' adherence to safety laws, and the low quality of the existing infrastructure. Participants expressed that they felt neglected and ignored by road safety decision-makers. This research emphasizes the need for community input for successful road safety policies in Ghana and other low- and middle-income countries, calling for greater governmental support an action to address this public health crisis. We recommend the government collaborates with communities to adapt existing interventions including speed calming, footbridges, and police enforcement, and introduces new measures that meet local needs.

Digital sailmate: enhancing safety through low-cost stability monitoring in artisanal fishing

Despite overall mortality decreasing, offshore fishing remains one of the riskiest work-based activities worldwide. For example, fishing communities in East Africa have a 43-fold higher rate of drowning than the general population. A lack of safety culture and knowledge around vessel stability contributes to this issue. Formal safety measures can be difficult to enforce, especially in small scale and subsistence fishing activities dominated by small artisanal boats. Digital technologies hold potential to effectively improve fishing safety. A digital safety device based on commonly held and relatively low-cost consumer products such as smartphones can provide increasing information to fishers enabling more informed safety decisions to be taken during vessel use. This paper proposes the algorithms for a prototype device to monitor stability of fishing vessels, with focus on the capabilities of low-fidelity data in stability assessment. The findings of experimental results at model and full scale are presented. The research indicates that an inclining test can be carried out with minimal training or knowledge base to allow an adequate stability assessment of a vessel before departure on a fishing trip. This baseline measure can then be used to track stability whilst underway as vessel motion is recorded and processed continually updating the stability assessment.

Formal Inconsistencies of Expertise Aggregation Techniques Commonly Employed in Engineering Teams

Engineering managers leverage the expertise of engineers in their teams to inform decisions. Engineers may convey their expertise in the form of opinions and/or judgements. Given a decision, it is common to elicit and aggregate the expertise from various engineers to capture a broader set of experiences and knowledge. Establishing an internally and externally consistent aggregation framework is therefore paramount to yield a meaningful aggregation, that is, to make sure that the expertise of each engineer is accounted for reasonably. However, we contend that most de facto aggregation techniques lack such consistency and lead to the inadequate use and aggregation of engineering expertise. In this paper, we investigate the consistency or lack thereof of various expertise aggregation techniques. We derive implications of such inconsistencies and provide recommendations about how they may be overcome. We illustrate our discussion using safety decisions in engineering as a notional case.

Enhancing Road Safety Decision-Making through Analysis of Youth Survey Data: A Descriptive Statistical Approach

Enhancing Road Safety Decision-Making through Analysis of Youth Survey Data: A Descriptive Statistical Approach

Data-driven automatic classification model for construction accident cases using natural language processing with hyperparameter tuning

The construction industry, while vital to societal progress, is marred by a high incidence of accidents and injuries. Manual classification of accident cases is intensive and susceptible to human bias. This study addresses this challenge by developing an automated accident case classification system for the construction industry using Natural Language Processing and machine learning techniques. This study was conducted using the following steps: (1) Establishment of dataset (2) Korean Natural Language Processing (3) Selection of machine learning models (4) Model evaluation. The models exhibited competitive performance, demonstrating high accuracy, precision, and recall rates across all classification tasks. XGBoost outperformed NB, SVM, and KNN for accident type, facility type, and work type with accuracy of 0.80, 0.56, and 0.67, respectively. The results also provided insights into the factors influencing accident classification. This study contributes to construction safety by providing a data-driven foundation for safety decision-making, resource allocation, and benchmarking.

Analysis of the explosion risk of low-concentration oxygen-bearing coal bed methane in the low-temperature environment

In the process of deep cooling and liquefaction of low-concentration (concentration <30 %) oxygen-bearing coal bed methan, there is a risk of explosion because the methane concentration in the ultra-low temperature environment is within the explosion limit. It is of great guiding significance to study the occurrence mechanism and accident-causing process of such accidents. Firstly, the experimental system of low-temperature methane explosion characteristics is constructed, and it is found that when ignition energy exceeds a certain range, the process of deep cooling and liquefaction of low-concentration oxygen-bearing coal bed methane will cause explosion. Then, the occurrence of the gas explosion accident in low-temperature environment is briefly described, and the causes of the accident are analyzed preliminarily, and the dynamic characteristics of methane explosion propagation under ultra-low temperature conditions are studied by Fluent numerical simulation, and it is obtained that ultra-low temperature conditions have a promotion effect on the explosion pressure. Finally, based on the analysis of catastrophe theory, the traditional accident causation cusp catastrophe model is improved, and the “experimental safety state" and the “internal causes of explosion-external cause of explosion" are taken as the state variables and control variables respectively, and the explosion risk analysis model for deep cooling and liquefaction of low-concentration oxygen-bearing coal bed methane based on the cusp catastrophe theory is constructed. Based on the accident case, the process of accidents caused by risk factors is described, and the evolution mechanism of such disasters and accidents is clarified. Finally, the hazard state evaluation method for liquefaction explosion of low-concentration oxygen-bearing coal bed methane is proposed based on the catastrophe theory, and this evaluation model is applied to evaluate and analyze the hazard state of accident case. The evaluation results are consistent with the actual hazard state, confirming the rationality and feasibility of this evaluation method. In this paper, the key hazards of such accidents are found out in detail and provides support for safety decision-making in the deep cooling and liquefaction process of low-concentration oxygen-bearing coal bed methane.

P001 An audit to assess whether patients who had a delayed or missed denosumab dose during the covid-19 pandemic came to any harm or suffered a subsequent fracture

Abstract Background/Aims Denosumab is licensed by NICE for the secondary prevention of osteoporosis in patients who are unable to comply, have an intolerance, or contraindication to bisphosphonates. With the outbreak of Covid-19, denosumab appointments were delayed. A delay of denosumab doses by more than 16 weeks is associated with an increased risk of vertebral fractures. This audit was carried out to see if the Pandemic caused a delayed (&amp;gt;1 month) or missed (&amp;gt;6 months) dose in patients receiving their denosumab injection and whether this delay/missed dose resulted in patients coming to any harm such as a subsequent fracture. Methods We included 166 patients of which 95% were female. We stratified data utilising the Oberoi consulting clinical audit services which provided a database of patients who were on six monthly repeat prescriptions for Denosumab. Retrospective data was collected including indications for denosumab, start date, duration, missed or delayed doses, reasons for delayed doses and whether a fracture of a pathological nature was sustained. We later reviewed if the missed or delayed doses occurred during the covid pandemic (2020-22) and subsequently, if the fracture occurred as a result of the pandemic. Results 34/166(21%) patients did not receive their Denosumab dose on time during Covid-19. 21/166(12.6%) patients had a delayed dose (6 pre-Covid and 15 post-Covid) and 13/166 (7.8%) patients missed a dose entirely (3 pre-Covid and 10 post-Covid) showing more delays/missed doses occurred during the pandemic. Reasons as to why patients did not receive timely doses include, clinical reasons (7/34 20.6%), patient safety decisions made by clinicians in patients’ best interest 12/34(35.3%) and patient choice related factors 15/34 (44.1%). Reasons for delays include not wanting repeat bloods, being on holiday, chest infection low Vitamin D levels, pre dose bloods delayed, patient had covid, and isolating. Whilst on Denosumab, 8/166(4.8%) patients sustained a fracture of which 6/8(75%) also experienced a delayed/missed dose during Covid-19. Conclusion Of the 34/166 patients that had a delayed/missed dose, eight suffered a fracture bringing the fracture risk to 23.5% which is an increase from baseline (6.6%) confirming that a missed Denosumab dose does increase fracture risk. We can conclude a greater proportion of patients endured a missed or delayed dose as a consequence of the Covid-19 pandemic. 8/166(4.8%) patients sustained a fracture of which six had a missed/delayed dose as a result of the pandemic. The conclusions drawn are in keeping with the MHRA4 alert, a missed dose of Denosumab does increase the risk of pathological treatment discontinuation fractures. This highlights to clinicians the significance of timely doses and uninterrupted treatment. Disclosure S. Rehman: None. S. Sultan: None.

A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage

Chinese cultural heritage architecture, a blend of traditional and modern construction methods, showcases the country's technological, artistic, and cultural advancements, yet its preservation faces challenges due to structural decay and aesthetic degeneration. Using Microtrans Maryland 4-1000, Dazu Rock Carvings, Nanchan Temple and Foguang Temple smokescreen were captured; surface images and ground photos were captured with smokescreen resolution of 5192 x 4153 pixels. The goal of the research is to use an Ensemble Ant Colony Fused Convolutional Capsule Neural Network (EAC-CCNN) to enhance fault analysis in Chinese cultural heritage structures images, then the combination of Augmented Reality (AR), and Building Information Modeling (BIM) to enhance the designing model for the safety management and decision making. The process entails gathering and annotating a variety of information, creating a hybrid EAC-CCNN model to investigate the architectural building's problem, training it, integrating it with BIM, doing on-site inspections, and utilizing AR-enhanced BIM models to analyze the flaws found. The findings demonstrate how this integrated method improves the precision of flaw diagnosis, fosters teamwork, and aids in the upkeep and preservation of cultural heritage assets. Metrics including accuracy and F1 score are used to evaluate the machine learning model recognizes and categorizes flaws in Chinese cultural heritage architecture. The defect identifying and safety management model of architectural designing outcome of the accuracy is 93.29% and F1 Score is 95.47%. During the training, validation, and testing phases, performance is evaluated by project goals. With the help of this method, machine learning models can be trained to identify patterns, identify flaws, and generate well-informed predictions in a variety of circumstances.

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

Aircraft Engine Remaining Useful Life Prediction using neural networks and real-life engine operational data

Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight operations safety and flight operators business decisions. In the article authors decided to present the concept of engine remaining useful life prediction. Proposed method was created as a result of the analysis of the real turbofan engine operational data collected for a few years which was used as an input data for the deep neural network, in order to train, validate and test machine learning algorithms. Two architectures of deep neural networks were created: multilayered deep convolutional neural networks and a long short-term memory network with regression output. Both neural networks were trained, validated and tested on the same engine data and with a various network training options. Results were compared with the neural network performance metrics and figures presenting network prediction convergence. To present how the real-life engine dataset differs the results from the simulated data, both datasets were validated on the same neural network architectures. The main purpose of this article was to present the idea and method of how the artificial neural networks could be used to predict aircraft remaining useful life indicator on the real-life engine operational data not the simulated one.

New approach methods in chemicals safety decision-making – Are we on the brink of transformative policy-making and regulatory change?

Decision-making on the use and management of chemicals in society is on the brink of a scientific and technological revolution. At the same time world politics is focusing more on chemicals, waste and pollution prevention, alongside climate change and biodiversity loss. To enable effective decision-making, policy makers and regulators will need to draw upon the best scientific evidence available on the real-life causation and consequences of adverse effects of chemical and waste exposures affecting humans, wildlife and the environment. New Approach Method (NAM) data from modern day multidisciplinary science and technology is becoming more available using cheminformatics, computational prediction algorithms using AI, transcriptomics, genomics, proteomics, mathematical modelling, epidemiology, biological monitoring, and clinical science. Current chemical regulation has been shaped by the animal models of the 20th century. NAMs and Next Generation Risk Assessment (NGRA) have the potential to better support innovations in chemicals and materials through science-informed decision making that is more species-relevant and protective of adverse outcomes; this will require future-proofed regulatory transformation. Capacity building and skills development in computational and in vitro NAMs will be key to this transformation.