Risk Assessment In Industry Research Articles

Evaluating applicability domain of acute toxicity QSAR models for military and industrial chemical risk assessment

Quantitative Structure-Activity Relationship (QSAR) models can be used to predict the risk of novel and emergent chemicals causing adverse health outcomes, avoidance of which is crucial for military operations. While QSAR modeling approaches have been proposed for military and industry risk assessment, the applicability of peer-reviewed tissue-specific QSAR models in military and industrial contexts remain largely unexplored, particularly with respect to specific organ toxicity. We investigated the applicability domain (AD) of acute and sub-chronic tissue-specific QSAR models to evaluate the coverage of military- and industrial-relevant chemicals. Our analysis reveals that military-relevant compounds occupy a similar chemical space as industrial compounds. However, published models for acute target organ toxicity had minimal coverage of the military and industrial chemicals. The published Collaborative Acute Toxicity Modeling Suite (CATMoS) acute oral toxicity model was the notable exception, as it covers a broad range of military and industrial chemicals. Our study underscores the urgent need for development of novel tissue-specific QSAR models, or modification of existing models, to improve chemical risk prediction in both industrial and military applications.

An integrated industrial risk assessment methodology for accident safety, chronic health, and chemical exposure

An integrated industrial risk assessment methodology for accident safety, chronic health, and chemical exposure

Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments

This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.

Dynamic industrial risks assessment for enhanced safety in the oil and gas industry: a methodological innovations and applications

In recent years, continued and accelerated growth in the oil and gas sector has significantly contributed to catastrophic accidents in various regions of Algeria, especially those with high population densities. Therefore, assessing the risks associated with these industries is crucial to prevent such disasters. This study introduces a methodology integrating several Quantitative Risk Assessment techniques, such as Failure Modes and Effects Analysis, Fault Tree Analysis, Event Tree Analysis, and the Bow-Tie method, coupled with simulation tools (ALOHA and FLUENT). This integration created a Dynamic Industrial Risk Assessment Methodology (DIRAM), a comprehensive approach to assessing industrial risk consequences in the oil and gas industries. The study demonstrates DIRAM's effectiveness in understanding the dynamics of catastrophic events. It provides a visual representation of CO2 plume and heat dispersion after a Boiling Liquid Expanding Vapor Explosion (BLEVE), emphasising the importance of spatial-temporal awareness in risk assessment and emergency response. DIRAM offers a valuable basis for preventing and managing potential risks and reinforcing safety measures at hydrocarbon plants.

“Fly down”: the impact of new accounting standards on the airline industry risk assessment

“Fly down”: the impact of new accounting standards on the airline industry risk assessment

RISK-ORIENTED APPROACH TO THE ASSESSMENT OF WORKING CONDITIONS OF WORKERS WITH INCREASED THERMAL EXPOSURE

Problem statement. The issue of studying the working conditions of workers with increased heat exposure is considered. Diseases provoked by harmful working conditions have a rather long incubation period, so their manifestation is not always easy to recognize and there are difficulties in establishing the root cause of occupational disease. The purpose of the article. Development and implementation of risk assessment methodology based on the joint action of harmful and hazardous industrial factors and regularities of their intensity decrease with distance from the source. Methodology. The impact of occupational and heterogeneous industrial factors on the employee's health, taking into account the time of action and the impact of their joint action in quantitative form, can be fairly objectively assessed on the basis of the Weber-Fechner law through the calculation of the integral index of potential risk. Structural analysis was used to determine the structure and causes of occupational diseases. Correlation analysis was carried out to find out the nature of relationships between the levels of influence of factors of the industrial environment to assess the conditions and nature of labor at the workplaces of workers, at whose workplaces the increased thermal stress was installed. Probabilistic-statistical methods were used for data processing - to determine the algorithm of transformation of environmental parameters into an indicator of occupational risk for workers with harmful working conditions. Development of a model for determining the zones of transformation of occupational risk through mutual influence on the industrial risk of the processing of research results needed the application of formalization methods. A software system for the formation of aggregated for multivariate analysis of information was used, which made it possible to build a three-dimensional model of changes in the values of the indicator of occupational risk in the space between the workplaces of employees located in the welding department. Scientific novelty. The applied risk-oriented approach using the methods of occupational and industrial risk assessment for electric welders' workplaces allows to take into account the harm to the worker's organism from the factors of the industrial environment. The established regularities allow to determine the zones of transformation of occupational risk through mutual influence into production risk, which is the justification of measures to reduce injuries and occupational diseases. Practical value. On the example of labor conditions at the workplaces of electric welders the methodology of risk assessment is developed and tested taking into account the joint action of harmful and hazardous industrial factors and regularities of their intensity decrease with distance from the source. The given risk-oriented approach allows to fulfill all the requirements and recommendations set forth in ISO 45001: its application to the working conditions of workers of hot professions (welders) allows to carry out a quantitative assessment of occupational and industrial risk at their workplaces, in particular for preparation for certification under ISO 45001. Conclusions. An effective tool for theoretical analysis of pollution zones formed during an extreme situation at gas stations has been created. The results of computational experiments are presented. The methodology of risk assessment based on the fundamental Weber-Fechner law for objective assessment of the impact of occupational and heterogeneous industrial factors on the employee's health in quantitative form through the calculation of the integral index (potential risk), which will allow to assess the working conditions at workplaces and objectively determine the class of working conditions, has been developed and implemented.

Manual material handling risk assessment in furniture industry: REBA method

The Turkish furniture industry is a significant player in the global market and ranks eleventh in the worldwide in terms of exports. Despite its positive economic impact, the industry faces challenges such as a high number of occupational injuries and illnesses, especially musculoskeletal disorders (MSDs). The objective of this study is to conduct an ergonomic risk assessment using the Rapid Entire Body Assessment (REBA) method in a furniture Turkish manufacturing facility. A total of twenty-four tasks were assessed using the REBA method, revealing that 41.7% of the tasks were found to pose either a high or very high ergonomic risk. Tasks such as carrying sofa frames and cutting fabrics were found to be particularly risky due to prolonged and strenuous postures involved. Engineering and managerial control measures were recommended to mitigate these risks, including automation, workstation redesign, and employee training. While the REBA method provided valuable insights, its subjective nature highlights the need for more objective assessment methods, such as electromyography (EMG). Future research should investigate these alternative methods to improve the reliability of ergonomic assessments. In conclusion, this study highlights the significant ergonomic risks in the Turkish furniture industry and emphasizes the importance of implementing targeted interventions to safeguard employee health and improve overall productivity. By addressing these risks, companies can create safer working environments and enhance their competitiveness in the global market.

The ARIMA model approach for the biofilm-forming capacity prediction of Listeria monocytogenes recovered from carcasses

The present study aimed to predict the biofilm-formation ability of L. monocytogenes isolates obtained from cattle carcasses via the ARIMA model at different temperature parameters. The identification of L. monocytogenes obtained from carcass samples collected from slaughterhouses was determined by PCR. The biofilm-forming abilities of isolates were phenotypically determined by calculating the OD value and categorizing the ability via the microplate test. The presence of some virulence genes related to biofilm was revealed by QPCR to support the biofilm profile genotypically. Biofilm-formation of the isolates was evaluated at different temperature parameters (37 °C, 22 °C, 4 °C and − 20 °C). Estimated OD values were obtained with the ARIMA model by dividing them into eight different estimation groups. The prediction performance was determined by performance measurement metrics (ME, MAE, MSE, RMSE, MPE and MAPE). One week of incubation showed all isolates strongly formed biofilm at all controlled temperatures except − 20 °C. In terms of the metrics examined, the 3 days to 7 days forecast group has a reasonable prediction accuracy based on OD values occurring at 37 °C, 22 °C, and 4 °C. It was concluded that measurements at 22 °C had lower prediction accuracy compared to predictions from other temperatures. Overall, the best OD prediction accuracy belonged to the data obtained from biofilm formation at -20 °C. For all temperatures studied, especially after the 3 days to 7 days forecast group, there was a significant decrease in the error metrics and the forecast accuracy increased. When evaluating the best prediction group, the lowest RMSE at 37 °C (0.055), 22 °C (0.027) and 4 °C (0.024) belonged to the 15 days to 21 days group. For the OD predictions obtained at -20 °C, the 15 days to 21 days prediction group had also good performance (0.011) and the lowest RMSE belongs to the 7 days to 15 days group (0.007). In conclusion, this study will guide in using indicator parameters to evaluate biofilm forming ability to predict optimum temperature-time. The ARIMA models integrated with this study can be useful tools for industrial application and risk assessment studies using different parameters such as pH, NaCl concentration, and especially temperature applied during food processing and storage on the biofilm-formation ability of L. monocytogenes.

Assessment of Different Methods for Enhancing Progressive Collapse Resistance of Irregular Reinforced Concrete Buildings Using Pushdown Analysis

Ensuring structures meet rigorous structural requirements is paramount in mitigating progressive collapse risk. In this comprehensive investigation, we scrutinize the impact of four distinct mitigation techniques on the propensity for progressive collapse in a six-story building featuring irregular structural attributes. The study adheres to the concrete building construction code ACI 318-14 and evaluates methods that include: (a) reinforcing the reinforced concrete (RC) slab with high-performance fiber-reinforced cementitious composites (HPFRCCs), (b) enhancing the RC slab with carbon fiber-reinforced polymers (CFRPs), (c) incorporating steel plate shear wall (SPSW) within specific columns, and (d) introducing an innovative approach named as the steel belt strip (SBS). In the context of 10 independent column loss scenarios conducted on the first floor, the nonlinear dynamic analysis reveals that HPFRCC effectively reduces vertical displacement under the removed column by up to 99.89% in certain scenarios. Meanwhile, the use of CFRP layers leads to reductions of up to 95% in vertical displacement, but with variations in effectiveness across scenarios. Notably, the SBS technique demonstrates remarkable potential by reducing vertical displacement by 97%, 89%, and 25.9% in different scenarios. This reduction, in conjunction with the mitigation of axial load on adjacent columns, makes the SBS a standout performer. Moreover, pushdown analysis indicates that, with the employment of these mitigation methods, the maximum loading factor can be increased up to 2.14 times in specific scenarios, significantly enhancing the structure’s resistance to progressive collapse. This pioneering research not only bolsters the resilience of irregular RC buildings but also holds profound implications for industry standards, risk assessment, and construction technology innovation.

Benzalkonium chloride disinfectant residues stimulate biofilm formation and increase survival of Vibrio bacterial pathogens.

Vibrio spp. are opportunistic human and animal pathogens found ubiquitously in marine environments. Globally, there is a predicted rise in the prevalence of Vibrio spp. due to increasing ocean temperatures, which carries significant implications for public health and the seafood industry. Consequently, there is an urgent need for enhanced strategies to control Vibrio spp. and prevent contamination, particularly in aquaculture and seafood processing facilities. Presently, these industries employ various disinfectants, including benzalkonium chloride (BAC), as part of their management strategies. While higher concentrations of BAC may be effective against these pathogens, inadequate rinsing post-disinfection could result in residual concentrations of BAC in the surrounding environment. This study aimed to investigate the adaptation and survival of Vibrio spp. exposed to varying concentrations of BAC residues. Results revealed that Vibrio bacteria, when exposed, exhibited a phenotypic adaptation characterized by an increase in biofilm biomass. Importantly, this effect was found to be strain-specific rather than species-specific. Exposure to BAC residues induced physiological changes in Vibrio biofilms, leading to an increase in the number of injured and alive cells within the biofilm. The exact nature of the "injured" bacteria remains unclear, but it is postulated that BAC might heighten the risk of viable but non-culturable (VBNC) bacteria development. These VBNC bacteria pose a significant threat, especially since they cannot be detected using the standard culture-based methods commonly employed for microbiological risk assessment in aquaculture and seafood industries. The undetected presence of VBNC bacteria could result in recurrent contamination events and subsequent disease outbreaks. This study provides evidence regarding the role of c-di-GMP signaling pathways in Vibrio adaptation mechanisms and suggests that c-di-GMP mediated repression is a potential avenue for further research. The findings underscore that the misuse and overuse of BAC may increase the risk of biofilm development and bacterial survival within the seafood processing chain.

A method of risk assessment for static ignitions

This paper presents a method of risk assessment for static ignitions that can be widely used overseas. It describes how the developed risk assessment method identifies static ignition hazards and analyses static ignition risks. This method has been developed on the basis of the flow of procedures in IEC/ISO Guide 51, lessons learned from incidents over 50 years and the survey of the situation of electrostatic risk assessment in Japanese industry, and has been maintained as an implementation guide to assist safety managers since 2008. The method includes a process/operation review; hazard identification for explosive atmosphere formation, charging, electrostatic induction and electrostatic discharges; static ignition risk estimation and evaluation; and risk reduction countermeasures. Tools are also provided to aid implementation: a risk assessment sheet to follow the method and record the results, and 30 check items to identify static ignition hazards. The method with the guide has been adopted by many safety managers in Japan.

Foodborne Viruses and Somatic Coliphages Occurrence in Fresh Produce at Retail from Northern Mexico.

Foodborne disease outbreaks linked to consumption of vegetables have been often attributed to human enteric viruses, such as Norovirus (NoV), Hepatitis A virus (HAV), and Rotavirus (RoV). Information about the occurrence of these viruses is scarce in many fresh-producing countries. Viral contamination detection of indicators, such as somatic coliphages, could indirectly reflect the presence of viral pathogens, being a valuable tool for better viral risk assessment in food industry. This study aimed to establish the occurrence and correlation of foodborne viruses and somatic coliphages in leafy greens in northern Mexico. A total of 320 vegetable samples were collected, resulting in 80 composite rinses, 40 of lettuce and 40 of parsley. Somatic coliphages were determined using the EPA 1602 method, while foodborne viruses (HAV, RoV, NoV GI, and GII) were determined by qPCR. The occurrence of RoV was 22.5% (9/40, mean 2.11 log gc/g) in lettuce and 20% (8/40, mean 1.91 log gc/g) in parsley. NoV and HAV were not detected in any samples. Somatic coliphages were present in all lettuce and parsley samples, with mean levels of 1.85 log PFU/100ml and 2.28 log PFU/100ml, respectively. Spearman analysis established the correlation of somatic coliphages and genomic copies of RoV, resulting in an r2 value of -0.026 in lettuce and 0.349 in parsley. Although NoV or HAV were undetected in the samples, the presence of RoV is a matter of concern as leafy greens are usually eaten raw, which poses a potential risk of infection.

Modeling the Investment Efficiency and Risk Assessment of Great Health Industry in the Context of Digital Economy

Abstract With the development of the economy and society, people pay more and more attention to physical health. In order to make the big health enterprises in the long term at the level of smooth development, it is necessary to carry out an in-depth study on the investment efficiency and potential risk of the big health industry. This study constructs an investment efficiency evaluation method based on the DEA model. Firstly, the comprehensive efficiency is decomposed through the CCR model to further obtain the output results. Then, the effectiveness of enterprise investment is evaluated. The changes in the investment efficiency of the big health industry and other sample decision-making units are analyzed through the DEA-Malmquist model to output the trend of the overall investment efficiency. Logistic regression, support vector machine, and random forest models are used to assess the risk of the large health industry, respectively, and several classifiers are trained. When predicting the final sample, the voting or mean value method is used to count the effect of classification. The overall mean value of big health enterprises hovered between 0.96 and 0.98 in five years, indicating that the comprehensive investment efficiency of the big health industry is relatively stable. The average AUC value of the random forest model is 0.635, which is 0.028 higher than the average AUC value of the support vector machine; thus, it is concluded that there is no great fluctuation in the investment efficiency of the big health industry under the background of the digital economy, and the random forest model is more suitable for the risk assessment of the big health industry.

A New Methodology for Validation of the Ergonomics Risk Assessment in Industry

In order to carry out an ergonomic assessment of the entire working situation in the industrial field, we have created and implemented an ergonomic risk assessment methodology—Ergonomics Risk Assessment Methodology in Industry (ERAI)—which takes into account a number of aspects that have a major impact on the physical condition and health of workers as well as on their efficiency. This study was conducted on 18 assembly line workers. ERAI identifies the level of exposure of the neck, trunk, shoulders/arms, wrists/hands and feet and can be used using printed forms, but it is preferred to use software that implements this method, thus avoiding errors. For this purpose, we have developed the ERAI software application that allows the management of the entire evaluation project. The main activities, the sub-activities carried out by the worker, together with the anthropometric characteristics are entered into the application, evaluating the posture of each part of the body, the effort exerted, the physical condition, etc. ERAI highlights the possible problems related to the physical condition of workers, e.g., there are three workers with a weaker physical condition, and the score for them is between 258 and 282, which is very high compared to the score of the other workers, which varies between 43 and 141. The results obtained with ERAI provide a correct diagnosis, facilitating effective ergonomic interventions to reduce the level of exposure.

Vehicle leasing credit risk assessment modeling by applying extended logistic regression

In vehicle leasing industry which presents a great business opportunity, information completed by applicants was assessed and judged by leasing associates manually in most cases; therefore, assessment results would be affected by their personal experience of leasing associates and decisions would be further affected accordingly. There are few researches on applicant credit risk assessment due to not easy to obtain of vehicle leasing data. Further, the difficulty in vehicle leasing risk assessment is increased due to class imbalance problems in vehicle leasing data. In order to address such issue, a research on credit risk assessment in vehicle leasing industry was conducted in this study. The great disparity in the ratio of high risk and low risk data was addressed by applying synthetic minority over-sampling technique (SMOTE). Then, classification effect of risk assessment model was improved by applying logistic regression in a two-phase manner. In the section of empirical analysis, the feasibility and effectiveness of the approach proposed in this study was validated by using data of actual vehicle leasing application cases provided by a financial institution in Taiwan. It is found that the proposed approach provided a simple yet effective way to build a credit risk assessment model for companies that provide vehicle leasing.

Enhancing attack resilience of cyber-physical systems through state dependency graph models

AbstractThis paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool’s output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model’s attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences.

Industrial and Environmental Disaster Risk Assessment for Hazardous Materials in Balikpapan City, East Kalimantan, Indonesia

Industrial disasters may cause massive public health problems, as they create large environmental damage and major loss of life. Balikpapan City has experienced a large oil spill which caused an industrial and environmental disaster: five fishermen died, various public health problems arose, and damage to environmental wildlife and ecosystems occurred. The objective of this study was to evaluate the industrial and environmental disaster risk related to hazardous material (hazmat) in Balikpapan. The research method involved the use of a semi-quantitative risk analysis with an originally organized checklist, a risk matrix, and a Geographic Information System (GIS) analysis. The results suggested that the risk score in Balikpapan’s City is 32 (MEDIUM; the dominant types of hazmat were flammables and corrosives). The major sectors contributing to the risk appeared to be the mining, energy, and oil and gas industries, with a medium risk (average risk score 33), while one clean water industry showed a low risk (risk score 24) using the checklist and risk matrix. According to the results from the GIS mapping, the areas t the highest risks appeared to be located within Balikpapan City coastal regions. The industries and the government of Balikpapan must be prepared for industrial and environmental disasters by educating competent major hazmat responders and ensuring a large spills response equipment, extensive environmental monitoring and measurement, procedures to deal with major fire and explosions and adequate disaster communication and coordination.

Chronicles of security risk assessment in process industries: Past, present and future perspectives

Security incidents around the world causes enormous loss in terms of life, economy, and environment. The 9/11 ′triggered the serious onset of research initiatives in the field of security with a more specific focus on the protection of high sensitive and hazardous facilities. It is well known that technology is advancing in a very rapid phase, proportionally increasing the security risk of the facilities. Conventional deterministic risk techniques widely found their applications in the earlier stage but owing to the increasing dynamic nature of the security risk, probabilistic dynamic risk assessment models were developed over time. In this article, the authors attempted to present the evolution of security risk concepts, their present status, and their future scope in a precise and consolidated form. The last 20 years of development in the security risk assessment concept involving deterministic and dynamic risk assessment models and applications in security risk assessment relevant to physical security are discussed in this article. The objective of the article is to outline the past, current and future directions of security risk highlighting their strength, weakness and limitations.

Hierarchical Analyst Domino Evaluation System (HADES)-a new domino effect accidents evaluating tool

This paper introduces a highly versatile domino evaluation system-Hierarchical Analyst Domino Evaluation System (HADES)-incorporating the most advanced industrial risk assessment system for Domino Effect Accidents (DEAs). HADES differs from traditional DEAs risk assessment methods that use empirical formulas extensively to calculate the probability of risk or damage. Instead, it takes the DEAs triggering mechanisms as the core and the industrial process as the backbone, upgrades the QRA framework with AHP idea, evaluates the accidents’ physical effects, and compares the target criteria database for forming accident sequence dynamically. The decommissioning of an offshore oil and gas facility in the North Sea was used as a case study to show how HADES works. In general, with approximately modifications, HADES can be widely used in many industries with little adaptation and modification.

An expertise-based consensus reaching process with probability-hesitant fuzzy preference relations and its application in risk assessment in food industry

This paper proposes a novel expertise-based consensus reaching process (CRP) for probability-hesitant fuzzy preference relations (PHFPRs). First, to identify each expert's comprehensive expertise level, an expertise identification function is constructed based on consistency, hesitancy, and discrimination indicators of PHFPRs. Then, we introduce an expected value based expertise induced ordered weighted averaging (E-IOWA) operator for the aggregation of individual PHFPRs. Here, experts’ weights are objectively and dynamically assigned according to their identified expertise levels at each round. To prompt a consensus, this paper presents an expertise-based feedback mechanism so as to provide highly personalized direction rules for experts’ preference adjusting. Finally, a case study of risk assessment in food industry and comparative analysis are conducted to prove the validity of the proposed CRP.