This study aims to examine the influence of the work environment and occupational health and safety (OHS) on employee performance, with job satisfaction acting as a mediating variable among technical service personnel at PT PLN (Persero) UP3 Samarinda. The research background stems from the critical importance of creating a conducive work environment and implementing an optimal OHS system to improve human resource performance in the electricity sector, which is classified as a high-risk industry. A well-structured work environment fosters comfort and motivation among employees, while effective OHS implementation reduces the likelihood of workplace accidents and enhances the sense of security, ultimately driving productivity. The research adopts a quantitative approach with a causal design. Data were collected through a Likert-scale questionnaire administered to 237 respondents and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to examine the relationships between variables comprehensively. The results demonstrate that both the work environment and OHS have a significant and positive effect on job satisfaction. Furthermore, job satisfaction significantly influences employee performance and serves as a mediating factor in the relationship between the work environment, OHS, and performance outcomes. These findings indicate that employees who perceive a safe and supportive work environment tend to show higher satisfaction levels, which translates into better performance. The study concludes that enhancing work environment quality and implementing effective safety programs have both direct and indirect positive impacts on employee performance through job satisfaction. Managerial implications include upgrading workplace facilities and infrastructure, optimizing safety training programs, and strengthening job satisfaction through structured reward systems and clear career development plans. This aligns with modern human resource management practices that emphasize employee well-being as a foundation for sustainable productivity.
Flood susceptibility mapping in large and heterogeneous basins requires methods capable of representing spatial variability that conventional basin-wide models often overlook. This study develops a sub-basin aggregation framework for the Upper Chao Phraya River Basin, Thailand, integrating localized susceptibility modelling into a unified basin-scale product. Thirteen flood conditioning factors were initially selected and objectively weighted using Shannon’s Entropy (SE), which reduced to 6–9 distinct hydrological drivers in the basin and each sub-basin. Nine machine learning algorithms (RF, KNN, SVM, DT, LR, ANN, NB, CART, and MLP) and a Stacking ensemble were applied to each basin, followed by three aggregation strategies: (1) SE-based sub-basin aggregation, (2) Stacking-based aggregation, and (3) best ML-based sub-basin aggregation. Results show that aggregated models outperform single basin-wide models, with the best ML-based aggregation achieving the highest accuracy (AUC = 0.973). Meanwhile, the SE-based aggregation produced the most balanced susceptibility map (44.3% Very Low; 15.7% Very High), highlighting a trade-off between predictive performance and spatial realism. The framework effectively captures sub-basin heterogeneity—for instance, Curvature was dominant in the Wang sub-basin, whereas Elevation prevailed elsewhere. Overall, the proposed aggregation strategy offers a scalable and transferable approach for large-scale modelling and supports data-driven flood management in complex river systems.
David Lefutso, Abiodun A. Ogundeji, Gideon Danso-Abbeam
et al.
Flood risk in South Africa remains a problem due to climate change, rapid urbanisation and persistent disparities in the region and low-income urban households are disproportionately impacted because of poor access to affordable flood insurance. This paper constructs the Integrated Market Flood Risk Insurance Framework (IMFRIF) based on a qualitative, desk-based research design consisting of contextual policy analysis, systematic literature review and analytical synthesis through systems thinking. The policy and document analysis reviewed the legislation on national disaster management, insurance and industry reports to determine institutional and market limitations on the provision of flood insurance. A PRISMA-ScR systematic literature review filtered 312 records on Scopus, Web of Science, and Google Scholar, which led to the identification of 47 peer-reviewed articles and 15 policy and comparative case studies. Thematic analysis led to the identification of six prevailing clusters of barriers based on the influence on insurance uptake, which included affordability and product design, trust and risk perception, data and risk assessment gaps, regulatory capacity, multi-stakeholder coordination, and community engagement. The results of these studies were used to design the IMFRIF, a system incorporating 9 major stakeholder groups and 5 interdependent system components into a single market-based design. The framework provides a systematic foundation to the resolution of systemic exclusion of flood insurance, but specifically acknowledges the implementation limitations regarding data availability, regulation capacity, fiscal sustainability and communal level of trust. The IMFRIF is placed as a progressive and responsive system that offers a point of future empirical confirmation and policy implementation to promote inclusive disaster risk financing in South Africa and comparable low- and middle-income contexts.
Disasters and engineering, Cities. Urban geography
Industrial explosion accidents often cause severe casualties, property damage, and environmental impacts, posing major challenges to process safety and emergency management. This study constructs an industrial-explosion eventic graph grounded in a domain-specific ontology and implements a Retrieval-augmented Generation (RAG) Q&A system powered by large language models (LLM) to support emergency decision-making. We designed an accident-emergency ontology that systematically captured accident characteristics and response workflows. A zero-shot information-extraction framework automatically identifies events from historical reports, and template-based matching extracts inter-event relations. Uncertainty modeling is introduced to ensure accurate knowledge representation. A semantic-similarity-driven knowledge-fusion method improves event abstraction and consistency, and the resulting graph is stored in Neo4j for efficient querying and analysis. By integrating the eventic graph with RAG, we created a Q&A system that significantly outperforms baseline models and traditional reasoning methods. A case study of the 8·12 Tianjin Port explosion demonstrates the framework’s ability to represent accident evolution patterns and causal chains. This integrated approach provides a practical tool for accident investigation, risk assessment, and emergency decision-making, contributing to improved safety management in industrial processes.
Abel Villa-Mancera, Eunice Vargas-Tizatl, José Manuel Robles-Robles
et al.
<i>Toxoplasma gondii</i> and <i>Neospora caninum</i> are intracellular protozoan parasites that cause reproductive failure and production losses in ruminants. Considering the limited information on the epidemiology of these infections in goats in different climate regions, this study aimed to estimate the seroprevalence and potential risk factors associated with parasitic infections in Mexico. Blood samples were collected from 627 goats in dry and temperate climates in two different states. The levels of <i>T. gondii</i> and <i>N. caninum</i> IgG antibodies were determined using commercially available ELISA kits. The prevalence of <i>T. gondii</i> in the dry and temperate climate, dry climate alone, and temperate climate alone were 52.0%, 57.1%, and 48%, respectively. The prevalence of <i>N. caninum</i> in the dry and temperate climate, dry climate alone, and temperate climate alone were 15.5%, 19.0%, and 12.7%, respectively. Using animal characteristics and farm management information obtained from a questionnaire and remotely sensed climate data, bivariate logistic regression analysis was performed to identify risk factors associated with parasite infections. Significant differences in the seroprevalence of <i>T. gondii</i> in goats were observed between sexes in the temperate climate. The history of abortion was the most significant risk factor for <i>T. gondii</i> in the dry climate. Factors such as goat age and history of abortion were significantly associated with high seropositivity of <i>N. caninum</i> in the dry climate. Sex and the presence of cats were identified as significant factors for <i>T. gondii</i> in regions with a dry and temperate climate. Abortion and climate regions were common risk factors for these infections in the dry and temperate climate regions. The results indicate that regionally adapted monitoring and control programmes may be developed to reduce the prevalence of these two parasites and reduce production losses in the livestock industry.
The aim of the study was to develop an enterprise risk management framework for Ethiopian commercial banks. This approach
is undertaken to enhance the risk management systems and practices and foster the soundness and stability of the Ethiopian banking
system. The study employed a multi-stage mixed methods research design that includes content analysis, survey study and Delphi techniques. The study established an enterprise risk management framework that comprises seventy-one constructs and seven factors.
The factors include Vision, mission, core values and strategy, Risk management environment, Risk management function, Risk Management tools and process, Risk appetite and tolerance limit, Alignment and integration, and Enhanced value.
Zhanjiang is located at the southernmost tip of mainland China, surrounded by the sea on three sides, with mariculture being crucial to local economic growth and fishermen's income. In the context of the rural revitalization strategy, marine disasters such as storm surges, red tides and sea waves pose particularly severe challenges to the industry. This study analyzes marine disaster data in Zhanjiang City from 2014 to 2023, combined with the analysis of first-hand information obtained from on-site research, to further clarify the specific impact of marine disasters on the aquaculture industry. The results found that marine disasters caused significant economic losses to the farmers, and the existing disaster management measures were insufficient in the accuracy of early warning, response speed and technical aspects. Therefore, this article suggests strengthening the monitoring and early warning system to improve the efficiency and accuracy of early warning; encouraging governments, research institutions, enterprises, and social organizations to collaborate and build a diversified disaster management network; utilizing modern technology to enhance the disaster resistance of aquaculture infrastructure; and promoting the insurance system and cultivating professional talents to enhance the risk management capabilities of farmers. These measures can not only significantly reduce the negative impact of marine disasters but also effectively promote the sustainable development of mariculture in Zhanjiang City, continuously release new momentum of the “blue engine” in thriving towards the sea, and contribute to the rural revitalization of Zhanjiang City.
Saro Lee, Liadira Kusuma Widya, Jungsub Lee
et al.
Radon (Rn-222) is a naturally occurring radioactive gas that poses significant lung cancer risks when accumulated indoors, making accurate predictions of its spatial distribution crucial for public health. This study developed a high-resolution radon potential map for Jeollabuk-do, South Korea, using deep learning algorithms. A multivariate spatial database was compiled by integrating geological, geochemical, topographical, soil, and land-use variables. Fourteen input variables, including lithology, distance to faults, barium, potassium oxide, magnesium oxide, zinc, zirconium, wind exposition index, LS-factor (slope length and steepness), surface soil texture, deep soil texture, topography, effective soil thickness, and land use were used. Deep learning models, specifically Convolutional Neural Networks and Long Short-Term Memory networks, were implemented within a GIS framework to generate a predictive radon potential map by modeling relationships between the input variables and indoor radon concentrations, thereby identifying high-risk areas. The resulting radon potential map, produced at a 10 m spatial resolution, was validated using the receiver operating characteristic–area under the curve, achieving an accuracy of approximately 85%. The findings of this study provide a robust foundation for enhancing indoor air quality management and radiation protection strategies.
Daniel Gonzalez Cortes, Enrique Onieva, Iker Pastor Lopez
et al.
While Machine Learning significantly boosts the performance of predictive models, its efficacy varies across different data dimensions. It is essential to cluster time series data of similar characteristics, particularly in the financial sector. However, clustering financial time series data poses considerable challenges due to the market’s inherent complexity and multidimensionality. To address these issues, our study introduces a novel clustering framework that leverages autoencoders for a compressed yet informative representation of financial time series. We rigorously evaluate our approach through multiple dimensionality reduction and clustering algorithms, applying it to key financial indices, including IBEX-35, CAC-40, DAX-30, S&P 500, and FTSE 100. Our findings consistently demonstrate that incorporating autoencoders significantly enhances the granularity and quality of clustering, effectively isolating distinct categories of financial time series. Our findings carry significant ramifications for the financial industry. By refining clustering methodologies, we set the stage for increasingly accurate financial predictive models, offering valuable insights for optimizing investment strategies and enhancing risk management.
Verster Jaco, Roux Pieter, Magweregwede Fleckson
et al.
In recent years, transport-related accidents, notably those involving trackless mobile machinery (TMM), have consistently ranked among the top three causes of fatalities and injuries in the South African mining industry (SAMI) [1]. These accidents arise from a combination of mechanical and technical malfunctions, environmental factors, and human or machine operator errors. Remarkably, these incidents persist despite the existence of specific regulations, standards, and codes of practice for transportation and machinery. This paper introduces a digital twin framework for TMM, which employs a systems engineering approach combined with software tools and computational analysis. This framework aims to enhance the current regulations by offering a continuous, quantitative risk assessment. By modelling and detecting non-conformance and adverse vehicle interaction events, the framework provides a quantitative risk analysis that complements the prevailing qualitative methods reliant on historical data and operational experience. A case study conducted at the CSIR main campus in Pretoria showcases the potential of the TMM Digital Twin.
– This paper aims to develop a credit-risk model in which firms face rollover risk, and the markets for defaulted assets are segmented due to entry costs. The paper shows that reducing the entry costs in this economy may decrease the total surplus of the economy. This outcome can arise because when market barriers are lifted, the gap between the liquidation prices across the markets will shrink, but then the market that would experience a price drop may face more bankruptcies because the rollover risk will increase in that market. The paper describes under which condition such an intervention policy improves or hurts the total surplus.
This paper aims to investigate how organization capital influences different forms of corporate risk. It also explores how the relationship between organization capital and risks varies in the cross-section of firms.
To test the hypothesis, this study employs the ordinary least squares (OLS) regression model using a large sample of the United States (US) data over the 1981–2019 period. It also uses an instrumental variable approach and an errors-in-variables panel regression approach to mitigate endogeneity problems.
The empirical results show that organization capital is positively related to both idiosyncratic risk and total risk but negatively related to systematic risk. The cross-sectional analysis shows that the positive relationship between organization capital and idiosyncratic risk is significantly more pronounced for the subsample of firms with high information asymmetry and human capital. Moreover, the negative relationship between organization capital and systematic risk is significantly more pronounced for firms with greater efficiency and firms facing higher industry- and economy-wide risks.
The findings have important implications for investors and policymakers. For example, since organization capital increases idiosyncratic risk and total risk but reduces systematic risk, investors should take organization capital into account in portfolio formation and risk management. Moreover, the findings lend support to the argument on the recognition of intangible assets in financial statements. In particular, the study suggests that standard-setting bodies should consider corporate reporting frameworks to incorporate the disclosure of intangible assets into financial statements, particularly given the recent surge of corporate intangible assets and their critical impact on corporate risks.
To the best of the authors' knowledge, this is the first study to adopt a large sample to provide systematic evidence on the relationship between organization capital and a wide range of risks at the firm level. The authors show that the effect of organization capital on firm risks differs remarkably depending on the kind of firm risk a particular risk measure captures. This study thus makes an original contribution to resolving competing views on the effect of organization capital on firm risks.
Risk is inherent in all parts of life and brings consequences, but when it specifically emerges in supply chains, it is susceptible. Therefore, this study aims at identifying and assessing supply chain risks and developing criteria for managing these risks. Supply chain (SC) risks consist of complex, uncertain, and vague information, but risk assessment techniques in the literature have been unable to handle complexity, uncertainty, and vagueness. Therefore, this study presents a holistic approach to supply chain risk management. In this paper, neutrosophic (N) theory is merged with the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) to deal with complexity, uncertainty, and vagueness. Then the proposed methodology is practically implemented through a case study on the automotive industry. SC resilience, SC agility, and SC robustness were selected as criteria for managing supply chain risks and analyzed using N-AHP. Furthermore, seventeen risks were identified and assessed by using N-TOPSIS. Results suggest supply chain resilience is the most important criterion for managing supply chain risks. Moreover, supplier delivery delays, supplier quality problems, supplier communication failures, and forecasting errors are the most vulnerable risks that occur in supply chains of the automotive industry in Pakistan.
This study provides an evaluation of the effectiveness of the maize index insurance in reducing the risk exposure of small-scale farmers in Zimbabwe. Maize yields and rainfall data for the period 2010–2019 farming season were obtained from AGRITEXT and the NASA website. The Black-Scholes optional pricing framework was applied to estimate the prices of the maize index insurance. The mean root square loss (MRSL) was evaluated for the case where there is no insurance and where there is insurance. MRSL was compared for the two scenarios. The index insurance was found to be efficient in risk reduction as positive changes in MRSL were observed.
Scientific article is devoted to researching the possibility of developing a simulation model of an investment project for a travel agency. In recent years, the tourism industry in the world is experiencing rapid development. The development of the tourism industry requires new investments. The investor must assess the degree of risks, the likelihood of profit or loss. It is noted that the most important advantage of simulation modeling is that it makes it possible to study economic systems at the design stage. Due to this, simulation models can be used as a universal tool in making appropriate decisions under conditions of uncertainty and taking into account those factors that are difficult to predict and take into account, which is why simulation is so often used in the development of investment projects. When assessing the risks of investment projects, the collection of information requires significant financial expenses, is quite time-consuming, and sometimes impossible. The subject of the study is a set of the theoretical, methodological and organizational problems related to the management decisions regarding the investment of the projects in the field of tourism. The methodology is based on the stochastic simulation modeling of economic processes. The aim of the study is to develop a simulation model for an investment project in the tourism sector, which will allow the investor to appreciate the degree of the risk and the likelihood of return on the investment. The article reveals the features of computer simulation in the MathCAD system. It is proved that it is convenient to develop simulation models of investment projects in the MathCAD system. It has a powerful mathematical support, remains one of the systems in which the description of the solution of mathematical problems is given with the use of conventional mathematical formulas and symbols and does not require special training in programming. The simulation model of the investment project of the travel agency was developed in the MathCAD system. Statistical processing of the results of the experiments with the model has been carried out. The necessity of the detailed study of critical intervals of the histogram at the transition from loss-making to profitable NPV values has been substantiated. Based on the results of simulation experiments with the model, the errors of the model were estimated, the law of NPV random variables distribution was established. The developed simulation model allows the investor to estimate the risk factor, the probability of profit or loss from the investment, to assess the possible uncertainty of the results of their own decision to invest in the project. Conclusion: the simulation model of the investment project built and investigated in MathCAD system allows investor to estimate the risk factor, probability of profit or loss for choosing the optimal pricing policy and optimization of economic strategy of tourist agency.