On accident causation models, safety training and virtual reality.

Inefficiency in real-time visualization and user interaction in traditional accident causation models (ACMs) necessitates the development of a dynamic ACM that can foster real-time hazard identification, accident prevention and interactive safety training. A virtual reality-based accident causation model (VR-ACM) may serve such a purpose. In this study, we performed a comprehensive literature review on different ACMs and safety training practices. The limitations of the existing models and practices are identified. A VR-ACM model is proposed comprising three modules: VR-based modelling and simulation, accident causation and safety training. Several research issues for VR-ACM are highlighted. An experimental study with 22 crane operators is presented, showing the applicability of the proposed model. The proposed VR-ACM serves as a medium for analysis of potential underlying causes of accidents, the three-dimensional perspective of visual analysis, real-time user interactions and real-time judgement and decision-making.

COVID-19 outbreak: A data-driven optimization model for allocation of patients.

COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.

A real-time video surveillance system for traffic pre-events detection.

In this study, a conceptual framework is proposed for the development of a video surveillance-based system for improving road safety. Based on the framework, a set of algorithms are developed which are capable of detecting various traffic pre-events from traffic videos, such as speed violation, one-way traffic, overtaking, illegal parking, and wrong drop-off location of passengers. After detecting the pre-events, an alarm will be automatically generated in the control room which helps to take precautionary measures to avoid any potential mishap on road, thereby, improving the road safety. In previous studies, a single system can handle either one or two pre-events. Whereas, in our present study, five anomalies can be detected in a single system using five different algorithms. Our study further contributes to the detection of "wrong drop-off location of passengers". The effectiveness of the developed algorithms is demonstrated over 132 traffic videos acquired from an integrated plant in India. Some additional comparative studies for overtaking and illegal parking are done using two benchmark datasets, namely 'CamSeq01' and 'ISLab-PVD'. Through an extensive study, it can be concluded that our developed algorithms are superior to some state-of-the-art algorithms in the detection of pre-events on road.

Text-document clustering-based cause and effect analysis methodology for steel plant incident data.

The purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause-effect diagram is usually prepared by using experts' knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.

A preliminary analysis of incident investigation reports of an integrated steel plant: some reflection.

Large integrated steel plants employ an effective safety management system and gather a significant amount of safety-related data. This research intends to explore and visualize the rich database to find out the key factors responsible for the occurrences of incidents. The study was carried out on the data in the form of investigation reports collected from a steel plant in India. The data were processed and analysed using some of the quality management tools like Pareto chart, control chart, Ishikawa diagram, etc. Analyses showed that causes of incidents differ depending on the activities performed in a department. For example, fire/explosion and process-related incidents are more common in the departments associated with coke-making and blast furnace. Similar kind of factors were obtained, and recommendations were provided for their mitigation. Finally, the limitations of the study were discussed, and the scope of the research works was identified.

Modelling safety of gantry crane operations using Petri nets.

Being a powerful tool in modelling industrial and service operations, Petri net (PN) has been extremely used in different domains, but its application in safety study is limited. In this study, we model the gantry crane operations used for industrial activities using generalized stochastic PNs. The complete cycle of operations of the gantry crane is split into three parts namely inspection and loading, movement of load, and unloading of load. PN models are developed for all three parts and the whole system as well. The developed PN models have captured the safety issues through reachability tree. The hazardous states are identified and how they ultimately lead to some unwanted accidents is demonstrated. The possibility of falling of load and failure of hook, sling, attachment and hoist rope are identified. Possible suggestions based on the study are presented for redesign of the system. For example, mechanical stoppage of operations in case of loosely connected load, and warning system for use of wrong buttons is tested using modified models.

Assessment of work compatibility across employees' demographics: a case study.

'Work compatibility' (WC) is a multi-dimensional diagnostic tool for measuring human performance that affects safety performance of work force. There are a dearth of literature on the use of WC in industrial applications. In this study, the status of WC and its components across employees' demographics such as age, experience, designation and location of work were examined in a steel plant in India. Data on 119 employees collected using Demand-Energizer Instrument was analysed. The results revealed that supervisors perceive higher energizers, higher demands and low WC as compared to workers. Older and high experience employees perceive higher energizers, lower demands and high WC as compared to younger and less experienced employees. All employee groups perceive higher demand for physical environment and physical task content. The problematic work groups identified are less experienced employees and workers in 'allied sections'. The outcomes of the study help the management in three ways to improve human performance at work places: (i) it provides useful information about the work factors to be considered for intervention design, (ii) it identifies the work groups to be targeted while preparing intervention strategies and (iii) it can be used as a leading indicator of human performance.

L-plastin Ser5 phosphorylation in breast cancer cells and in vitro is mediated by RSK downstream of the ERK/MAPK pathway.

Deregulated cell migration and invasion are hallmarks of metastatic cancer cells. Phosphorylation on residue Ser5 of the actin-bundling protein L-plastin activates L-plastin and has been reported to be crucial for invasion and metastasis. Here, we investigate signal transduction leading to L-plastin Ser5 phosphorylation using 4 human breast cancer cell lines. Whole-genome microarray analysis comparing cell lines with different invasive capacities and corresponding variations in L-plastin Ser5 phosphorylation level revealed that genes of the ERK/MAPK pathway are differentially expressed. It is noteworthy that in vitro kinase assays showed that ERK/MAPK pathway downstream ribosomal protein S6 kinases α-1 (RSK1) and α-3 (RSK2) are able to directly phosphorylate L-plastin on Ser5. Small interfering RNA- or short hairpin RNA-mediated knockdown and activation/inhibition studies followed by immunoblot analysis and computational modeling confirmed that ribosomal S6 kinase (RSK) is an essential activator of L-plastin. Migration and invasion assays showed that RSK knockdown led to a decrease of up to 30% of migration and invasion of MDA-MB-435S cells. Although the presence of L-plastin was not necessary for migration/invasion of these cells, immunofluorescence assays illustrated RSK-dependent recruitment of Ser5-phosphorylated L-plastin to migratory structures. Altogether, we provide evidence that the ERK/MAPK pathway is involved in L-plastin Ser5 phosphorylation in breast cancer cells with RSK1 and RSK2 kinases able to directly phosphorylate L-plastin residue Ser5.

Modeling uncertainty in risk assessment: an integrated approach with fuzzy set theory and Monte Carlo simulation.

Modeling uncertainty during risk assessment is a vital component for effective decision making. Unfortunately, most of the risk assessment studies suffer from uncertainty analysis. The development of tools and techniques for capturing uncertainty in risk assessment is ongoing and there has been a substantial growth in this respect in health risk assessment. In this study, the cross-disciplinary approaches for uncertainty analyses are identified and a modified approach suitable for industrial safety risk assessment is proposed using fuzzy set theory and Monte Carlo simulation. The proposed method is applied to a benzene extraction unit (BEU) of a chemical plant. The case study results show that the proposed method provides better measure of uncertainty than the existing methods as unlike traditional risk analysis method this approach takes into account both variability and uncertainty of information into risk calculation, and instead of a single risk value this approach provides interval value of risk values for a given percentile of risk. The implications of these results in terms of risk control and regulatory compliances are also discussed.

Design for worksystem safety using employees' perception about safety.

In this paper, the predictors of work injuries based on Leamon's Man-Machine model are identified in a sociotechnical framework. Several hypotheses are developed and tested to describe the accident/injury phenomena in mining worksystems. Possible designs for improving work-system's safety are specified using scaled Mahalanobis distance (MD). A case control study design is adopted. Five variables namely, age, negative-affectivity, physical-hazards, job-dissatisfaction, and safety-practice are emerged as significant contributors to work injuries for the mines studied. Two most interesting findings obtained through this study are (i) 36% of cases (injured employees) (MD < 1) are unlucky to meet an accident and (ii) 40% of the controls (non-injured employees) (MD > 1) are lucky to be able to avoid an accident. The most probable reason for the former case is the organizational ineffectiveness while that for the latter may be risky adventures of employees which are due to lack of education, awareness, and appropriate training. Based on the MD values for cases and controls, possible design guidelines are suggested. The study categorically identifies the accident situations where engineering control, education and training, and other organizational safety measures are to be adopted.

Injury count model for quantification of risk of occupational injury.

Reduction of risk of occupational injuries is one of the most challenging problems faced by industry. Assessing and comparing risks involved in different jobs is one of the important steps towards reducing injury risk. In this study, a comprehensive scheme is given for assessing and comparing injury risks with the development of injury count model, injury risk model and derived statistics. The hazards present in a work system and the nature of the job carried out by workers are perceived as important drivers of injury potential of a work system. A loglinear model is used to quantify injury counts and the event-tree approach with joint, marginal and conditional probabilities is used to quantify injury risk. A case study was carried out in an underground coal mine. Finally a number of indices are proposed for the case study mine to capture risk of injury in different jobs. The findings of this study will help in designing injury intervention strategies for the mine studied. The job-wise risk profiles will be used to prioritise the jobs for redesign. The absolute indices can be applied for benchmarking job-wise risks and the relative indices can be used for comparing job-wise risks across work systems.

Injury severity assessment for underground coalmine workers.

An injury severity model is proposed for assessment of injury incidents in industrial settings. A classification scheme for injury incidents considering interactions is also developed. The injury severity model considers injury potential in the form of unsafe conditions and analyzes its transfer to actual injury of varying severity. A case study was conducted in an underground coalmine of eastern India. An observed reduction in risk realization is explained through the model. Presence of interactions is found to be the most significant incident attribute of injury occurrences. The classification scheme and the results obtained from this study will help in improving accident/injury investigation reporting and devising preventive measures for reducing injury severity.

A methodology for overall consequence modeling in chemical industry.

Risk assessment in chemical process industry is a very important issue for safeguarding human and the ecosystem from damages caused to them. Consequence assessment is an integral part of risk assessment. However, the commonly used consequence estimation methods involve time-consuming complex mathematical models and simple assimilation of losses without considering all the consequence factors. This lead to the deterioration of quality of estimated risk value. So, the consequence modeling has to be performed in detail considering all major losses with optimal time to improve the decisive value of risk. The losses can be broadly categorized into production loss, assets loss, human health and safety loss, and environment loss. In this paper, a conceptual framework is developed to assess the overall consequence considering all the important components of major losses. Secondly, a methodology is developed for the calculation of all the major losses, which are normalized to yield the overall consequence. Finally, as an illustration, the proposed methodology is applied to a case study plant involving benzene extraction. The case study result using the proposed consequence assessment scheme is compared with that from the existing methodologies.

Development of environmental consequence index (ECI) using fuzzy composite programming.

Estimation of environmental consequences of hazardous substances in chemical industries is a very difficult task owing to (i) diversity in the types of hazards and their effects, (ii) location, and (ii) uncertainty in input information. Several indices have been developed over the years to estimate the environmental consequences. In this paper, a critical literature review was done on the existing environmental indices to identify their applications and limitations. The existing indices lack in consideration of all environmental consequence factors such as material hazard factors, dispersion factors, environmental effects, and their uncertainty. A new methodology is proposed for the development of environmental consequence index (ECI), which can overcome the stated limitations. Moreover, the recently developed fuzzy composite programming (FCP) is used to take care of the uncertainty in estimation. ECI is applied to benzene extraction unit (BEU) of a petrochemical industry situated in eastern part of India. The ECI for all the eight sections of BEU are estimated and ranked. The results are compared with well-established indices such as Dow fire and explosion index, safety weight hazard index (SWeHI), and environmental accident index (EAI). The proposed ECI may outperform other indices based on its detailed consideration of the factors and performed equally to Dow F&E index, and EAI in most of the cases for the present application.

The synergic role of sociotechnical and personal characteristics on work injuries in mines.

Occupational injuries in mines are attributed to many factors. In this study, an attempt was made to identify the various factors related to work injuries in mines and to estimate their effects on work injuries to mine workers. An accident path model was developed to estimate the pattern and strength of relationships amongst the personal and sociotechnical variables in accident/injury occurrences. The input data for the model were the correlation matrix of 18 variables, which were collected from the case study mines. The case study results showed that there are sequential interactions amongst the sociotechnical and personal factors leading to accidents/injuries in mines. Amongst the latent endogenous constructs, job dissatisfaction and safe work behaviour show a significant positive and negative direct relationship with work injury, respectively. However, the construct safety environment has a significant negative indirect relationship with work injury. The safety environment is negatively affected by work hazards and positively affected by social support. The safety environment also shows a significant negative relationship with job stress and job dissatisfaction. However, negative personality has no significant direct or indirect effect on work injury, but it has a significant negative relationship with safe work behaviour. The endogenous construct negative personality is positively influenced by job stress and negatively influenced by social support.

Risk-based maintenance--techniques and applications.

Plant and equipment, however well designed, will not remain safe or reliable if it is not maintained. The general objective of the maintenance process is to make use of the knowledge of failures and accidents to achieve the possible safety with the lowest possible cost. The concept of risk-based maintenance was developed to inspect the high-risk components usually with greater frequency and thoroughness and to maintain in a greater manner, to achieve tolerable risk criteria. Risk-based maintenance methodology provides a tool for maintenance planning and decision making to reduce the probability of failure of equipment and the consequences of failure. In this paper, the risk analysis and risk-based maintenance methodologies were identified and classified into suitable classes. The factors affecting the quality of risk analysis were identified and analyzed. The applications, input data and output data were studied to understand their functioning and efficiency. The review showed that there is no unique way to perform risk analysis and risk-based maintenance. The use of suitable techniques and methodologies, careful investigation during the risk analysis phase, and its detailed and structured results are necessary to make proper risk-based maintenance decisions.

An epidemiological study of injury in mines: implications for safety promotion.

The role of various factors in coal mine-related injuries was investigated using a case-control design. The study setting was two neighbouring underground coal mines in India. Cases comprised mine workers (n = 150) who had sustained a prior mine-related injury from a population of 1000 underground workers. Controls were selected from those mineworkers with no history of a prior mine-related injury using frequency matching (n = 150) from the same source population. Data were collected from the cases and controls using a structured survey questionnaire. Based on the responses of the participants, each factor was grouped into three categories. High-low plots and Chi-square tests were conducted to explore the differences between the cases and controls. Bivariate logistic regression was run to estimate the crude odds of injuries, while multivariate logistic regression estimated the adjusted odds of injuries to the workers for the various variable categories. High-low plots and the Chi-square test clearly revealed that the cases and controls significantly differed in their responses for the variables studied. Accident-involved workers take more risks, are negatively affected, job dissatisfied, feel more production pressure, job stress, work hazards and are less job involved and are more dissatisfied with safety environment and social climate of the mines compared to the controls. The multivariate odds of injuries to high risk taking, negatively affected and job dissatisfied workers are 1.21, 9.34 and 2.00 times more compared to their lowest counterparts. Similarly, workers satisfied with the overall safety practice and safety equipment availability and maintenance are 1.5 and 3.12 times less likely to be injured than the workers with little or no satisfaction with the above factors. It is therefore concluded that negative affectivity and job dissatisfaction are the two major personal level factors that contribute more towards accident/injury in the mines studied. Identification and elimination/reduction of negative attitudes are of utmost importance.

Determinants of work injuries in mines - an application of structural equation modelling.

In spite of stringent regulations and much attention towards reducing risks in the physical environment, the mining industry continues to be associated with high levels of accidents, injuries and illnesses. Only engineering solutions to accident prevention are inappropriate unless coupled with focused attention to the attitudes and behaviours of the mineworkers in coping with the inherent physical, technical and situational risks. The present study identified these various risk factors and analysed their influences on work injury in a causal framework. Data were collected from an underground coalmine of India. The pattern and strength of relationships of 16 causal factors with work injuries were assessed through structural equation modelling. The case study results showed that negatively personified individuals are of major concern for safety improvement in the mine studied. They not only fail to avoid work injuries, they are unable to extend safe work behaviours in their work. The variable safety environment is negatively affected by personality, whereas social support has a positive relationship with safety environment. The variable job hazards appeared to have a significant relationship with job involvement, which has a negative relationship with work injury. Elimination of negative behaviours must be focused and committed by the mine safety management. Long term planning through (i) identification of negative individuals, (ii) proper councelling of adverse effects of negative behaviours, and (iii) special training with psychological treatment is highly required. Identification may begin while recruiting new workers through interview. Proper allocation of jobs (right person for right job) may be a judicial solution to this end.