Hasil untuk "Risk in industry. Risk management"

Kexin Wang, Yunxuan Bao, Xiangyi Wei et al.

Extreme pavement temperature (ETs) threatens road infrastructure through deformation, accidents, and black ice. This study analyzed 2015–2018 minute-resolution data from seven highway stations in China's Huaihe River Basin and proposed an analysis–modelling–mapping framework that combines spatiotemporal characterization of ETs, deep learning and spatial interpolation. Three forecasting challenges emerged: significant data imbalance (ETs/not-ETs ratio < 0.32; hot/cold extremes ratio < 0.83); spatial heterogeneity non-correlated with geographical positions; and divergent durations (hot: < 6 h, occasionally < 2 h in September; cold: > 12 h, even exceeding 24 h in January). Specifically, a multi-scale temporal fusion forecasting network (MSTF-ROAD) featuring multi-scale temporal feature pyramids, dual-pooling mechanisms (Avg–Max pooling), real-time attention and conventional events downsampling was proposed. Evaluations demonstrated MSTF-ROAD's superiority with 0.67 °C MAE, capturing >93% cold extremes and >74% hot extremes, effective trends/inflection tracking. The dynamic geospatial hazard maps with lightweight ETs prediction architectures attained 100-m spatial resolution and enabled minute-level temporal synchronization. The primary innovation of the MSTF-ROAD framework lies in its theory-guided lightweight model architecture, which collectively overcomes the critical challenges of data imbalance and spatiotemporal heterogeneity, achieving a balanced and accurate prediction of both hot and cold extremes. This interdisciplinary methodology directly supports Sustainable Development Goals 9 and 13.

Po Yun, Yifei Xu, Xiaodi Huang et al.

Effective forecasting the financial distress of energy enterprises can promote risk management and financial sustainability. Previous studies lack discussion of this topic in the emerging energy industry and ignore the sample imbalance problem and information screening. Therefore, the main work of this study is constructing a multi-dimensional evaluation system that integrates financial and non-financial indicators and then designing a hybrid forecasting model of Lasso-CCSM-VNWOA-GBDT. The model integrates the least absolute shrinkage and selection operator (Lasso) regression, cluster centroids (CC) algorithm, synthetic minority over-sampling technique (SMOTE), whale optimization algorithm improved by von Neumann topology (VNWOA), and gradient boosting decision tree (GBDT). The results show that, (1) the multi-dimensional evaluation system can comprehensively assess financial distress. In particular, the non-financial indicators of audit opinions, government subsidies, and number of R&D personnel are important identified variables. (2) The Lasso regression and CCSM algorithms are superior in indicator screening and sample balancing operations, the proposed model presents the advantage of forecasting financial distress for all evaluation criteria. (3) Total liabilities, operating income, and government subsidies are the top three marginal contributions to the forecasting of financial distress. This conclusion can help new energy enterprises improve risk warnings and achieve financially healthy and sustainable development.

Huaizhong Yu, Jingxue Zhang, Yue Liu et al.

China has established a Testing Center on the Collaboration for the Study of Earthquake Prediction (CSEP) for standardized evaluation of current earthquake prediction methods. Some traditional methods such as Load-Unload Response Ratio (LURR), Pattern Information (PI), Relative Intensity (RI), Epidemic Type Aftershock Sequence (ETAS), and b-value have been installed. In addition, several new methods, such as Crustal Vibration (CV), State Vector (SV), Earthquake Occurrence Rate (EOR), and Seismic Modulation Ratio (SMR) have also been involved. Their prediction performance determined by the prediction periods, magnitude of detection earthquakes, and optimal critical regions can be evaluated using the Molchan-test, R-test, N-test and ROC-test. It has a potential application in earthquake prediction by developing a Comprehensive Probability (CP) model, in which the predictive performance of each method is regarded as the weights and integrated using the Bayesian formulas. As a retrospective example, we applied the R-test to evaluate the prediction power of the LURR method by using the seismic data over the last 50 years in the north-south seismic belt of China. Results show that the predictions of LURR outperform the null hypothesis, and the optimal prediction performance for Ms > 6.0 earthquakes with a 2-year time window is derived from various model tests.

Tiago Fonseca, Sara Ferreira

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts on road safety outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review aimed to identify hazardous driving behaviors, the internal and external factors contributing to these behaviors, and their consequences for traffic safety. Inclusion criteria targeted original research published in English between 2009 and 2024 specifically focused on truck driver behavior and road safety outcomes. Systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore yielded 104 studies meeting these criteria. The synthesis revealed prevalent risky behaviors—such as speeding, fatigue-related impairments, distracted driving, and substance use—driven by internal factors (e.g., health conditions, psychological stress) and external pressures (e.g., occupational demands, regulatory constraints). These behaviors were consistently associated with increased crash risk. Nonetheless, limitations including the exclusion of non-English studies, reliance on self-reported data, and lack of standardized metrics constrained cross-study comparability and generalizability. Effective interventions identified include fatigue management programs, driver monitoring technologies, and positive safety climates. Findings underscore the urgent need for evidence-based, multifaceted strategies to enhance truck driver safety and inform policy, industry practices, and future research.

Yanxuan Li, Vatcharapol Sukhotu

In recent years, the rapid advancement of digital technologies and the growing demand for sustainability have driven unprecedented transformations in the automotive industry, particularly toward electric vehicles (EVs) and renewable energy. The EV supply chain, a complex global network, has become increasingly vulnerable to globalization and frequent “black swan” events. The purpose of this study, grounded in organizational information processing theory, aims to systematically examine the role of digital capability in strengthening supply chain resilience (SCR) through improved risk management effectiveness. Specifically, it explores the multidimensional nature of digital capability, clarifies its distinct impact on SCR, and addresses existing research gaps in this domain. To achieve this, this study develops a theoretical framework and validates it using survey data collected from 249 EV supply chain enterprises in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) is employed to empirically test the proposed relationships. The findings provide valuable theoretical insights and actionable guidance for EV manufacturers seeking to leverage digital transformation to mitigate risks effectively and enhance supply chain resilience. However, as the study focuses on Chinese EV supply chain enterprises, caution is needed when generalizing the findings to other regions. Future research could extend this investigation to different markets, such as to Europe and the United States, to explore potential variations.

Zejing HE, Liping FANG, Chuanping LIU et al.

With the rapid development of industry, the issue of combined antibiotic-heavy metal contamination in farmland has become increasingly severe, threatening agricultural product safety and human health. However, current understanding of the environmental behaviors (migration, transformation, uptake, and accumulation) of combined antibiotic-heavy metal contamination in soil-crop systems and their toxic effects on soil organisms and crops remains incomplete, hindering the formulation of governance strategies for such contamination in farmland soils. This review reports on the current status of combined antibiotic-heavy metal contamination in Chinese farmland and the environmental fate and toxic effects in soil-crop systems. Existing studies show that combined contamination is observed in farmland soils, primarily sourced from livestock manure application, sewage irrigation, and pesticide use. Antibiotics and heavy metals can form stable complexes, whose environmental behaviors are influenced by soil organic matter, pH, and ion competition; after plant uptake, they mainly accumulate in roots and transfer to aboveground parts, with leafy vegetables exhibiting significantly higher enrichment capacity than legumes. Combined contamination exacerbates the spread of antibiotic resistance genes and heavy metal resistance genes through co-resistance mechanisms. Its toxicological effects are also more complex, presenting synergistic, antagonistic, or additive effects, leading to reduced soil microbial diversity, dysfunction of soil animal physiology, and oxidative stress damage in crops, thereby affecting crop growth and quality. However, current research has only reported short-term results for a few types of heavy metals, antibiotics, and crops, with unclear differences in outcomes under different doses and combinations. Future research is recommended in the aspects as follow: (1) Focusing on risk thresholds and precise management of combined antibiotic-heavy metal contamination, clarify safety thresholds through dynamic assessment models, develop traceability technologies for agricultural inputs and matching systems for pollution remediation, and achieve safe utilization of contaminated arable land; (2) Strengthening mechanistic studies on combined contamination under different combinations, exploring the environmental fate and toxic effect mechanisms at cellular, molecular, and genetic levels for different complexes formed by antibiotics and heavy metals, investigating the influencing mechanisms of environmental factors, advancing understanding of co-resistance gene transmission patterns, and cultivating and screening co-resistant crop varieties—thereby providing theoretical support for precise risk management of combined antibiotic-heavy metal contamination in farmland and promoting green agricultural development.

Reza Roshanpour, Mohammadreza Parsanejad, Mir Saman Pishvaee

Achieving Sustainable Supply Chain Management (SSCM) has become a strategic priority for organizations integrating environmental, social, and economic objectives. Interpretive Structural Modeling (ISM) is a powerful methodology for analyzing interdependencies among SSCM drivers by structuring them into a multilevel hierarchical framework. However, traditional ISM relies on subjective expert judgments, making it prone to inconsistencies and biases. To address these challenges, this study proposes a novel Hybrid AI-ISM methodology, integrating Genetic Algorithms (GA) to optimize the reachability matrix by minimizing transitivity violations and reciprocal inconsistencies. The research adopts a structured, expert-driven approach, engaging 25 domain specialists from executive, production, and supply chain management sectors within the steel industry. By leveraging AI-driven optimization, the proposed framework enhances accuracy, objectivity, and scalability, refining hierarchical structuring for effective SSCM decision-making. This study contributes to the SSCM literature by introducing a bias-resistant, computationally enhanced ISM framework, facilitating granular decision-making across organizational levels. Findings reveal notable differences in expert perspectives across professional roles. Executives emphasize strategic sustainability initiatives, particularly the Adoption of Renewable Energy, while production managers prioritize operational aspects, including Waste Minimization and Supply Chain Flexibility. Conversely, supply chain managers focus on stakeholder engagement and risk mitigation, highlighting Community Engagement and Supply Chain Disruption Management as critical drivers. The study provides actionable insights for policymakers, industry leaders, and supply chain professionals seeking to drive sustainable transformations in global supply chains.

Akinwale T. Ogunrinde, Paul Adigun, Xue Xian et al.

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Hong-zhou Ai, Dong Han, Xin-zhi Wang et al.

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety. The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light. This often results in a high false alarm rate in complex air transportation environments. The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information. This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model. Dual-wavelength optical sensors, flue gas analyzers, and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy. The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective, which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN (recurrent neural network) and CNN (convolutional neural network). Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information, respectively, which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism. Finally, the output results of the two models are fused through the gate mechanism. The research results show that, compared with the traditional single-feature detection technology, the multi-technology collaborative fire detection method can better capture fire information. Compared with the traditional deep learning model, the multivariate fire prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.

Syed Imran Ali, Shaine Mohammadali Lalji, Saud Hashmi et al.

Asphaltene precipitation and its subsequent deposition always remain a major concern for Oil industry. Formulation of a comprehensive and reliable risk management system for asphaltene prone wells and fields is a challenging task because of the influence of diverse factors. In this study, a decision support system is developed for the asphaltene risk assessment in wells and fields. Since, the data present in the literature is scarce and not consistent, therefore, a hypothetical data of fields and their wells was considered to conduct the study comprehensively. Three hypothetical fields namely; Field A, Field B and Field C were assumed and in each field ten wells were taken into consideration. A decision support system for assessing the risk of asphaltene prone wells was developed using one of the popular and powerful multi-criteria decision making technique i.e. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) coupled with Analytic Hierarchy process (AHP). The risk of wells was evaluated using three criteria namely; Detection, Severity and Controls. These criteria were further sub-divided into sub-criteria and their data was assumed. The assumed data was transformed into Triangular fuzzy numbers for calculations. According to the final outcomes, Field A was proved be the most risky field followed by Field B and in the last comes Field C. The outcomes were further validated by other method namely; Fuzzy Complex Proportional Assessment (COPRAS) and all TOPSIS outcomes were found in good relationship with Fuzzy COPRAS. The proposed methodology proposed in this study will be landmark in risk ranking of asphaltene prone wells and fields.

Jiahui Dong, Ruiqing Niu, Bingquan Li et al.

AbstractInterferometric Synthetic Aperture Radar (InSAR) is an important method for acquiring surface deformation. Considering the difficulty of the identification work, the identification of landslides needs to be combined with the context of the pregnant disaster and the precipitating conditions. To identify potential landslides, we applied spatial and temporal filtering to the InSAR results, which consists of rainfall and landslide susceptibility mapping. In this paper, taking the Badong Ecological Barrier Zone of the Three Gorges reservoir area as the study area, the deformation aggregation areas in the study area were obtained by applying Small Baseline Subset InSAR (SBAS-InSAR) technology and spatial statistical analysis. We screened deformation aggregation areas by combining the susceptibility map and the correlation analysis of rainfall and deformation. Field verification and investigation were conducted on the suspected deformation areas, and 11 landslides were found to have signs of deformation, two of them are newly discovered landslides. In addition, we selected one of the landslides, the Songjiawuchang landslide, and compared the InSAR results with the GPS accumulated displacement to verify the reliability of the results. This research demonstrated the feasibility of combining InSAR results with spatial susceptibility maps and monthly rainfall factors for landslides identification methods.Key policy highlightsApplied Spatial filtering and temporal filtering on the InSAR results.Evaluated in conjunction with geological hazards themselves.Landslide identification accuracy significantly improved.

Huihua Chen, Wenjing Gong, Hujun Li et al.

The construction industry in China is characterized by higher safety risk, and construction workers' unsafe behaviors are one of the main causes of construction safety accidents, thus, designing scientific mechanisms that motivate and cultivate the construction workers to adopt safety behaviors becomes the key to the construction safety problem. Existing studies have examined some of the factors leading to workers' safety behavior (WSB) at the social, organizational, and individual levels, but ignore investigating the impact of co-workers' guanxi (CWG) on WSB. Thus, this research utilized exploratory factor analysis, confirmatory factor analysis, and structural equation modeling to examine the impact of CWG on WSB, and the mediating role of group identification (GI) in the relationship between CWG and WSB. Results show that CWG can directly or indirectly influence WSB, GI can exert a partial mediating effect on the relationship between CWG and GI. The research results enrich the research on c guanxi and causation of WSB, and provide a reference for project managers to carry out relationship-related safety management and industry regulations.

Wen Wang, Wei Wang, Wei Yuan et al.

In the development of deep resources, blasting fracturing technology is one of the most effective means to improve the permeability of otherwise low-permeability reservoirs, while the pressure rise time of shock wave and the charge structure are the key factors affecting the blasting effect. Thus, this paper first deduces a formula for the pressure rise time, based on which, the blast-induced damage evolution is numerically simulated, and the numerical result is consistent with the existing studies, which verifies the feasibility of the formula. In addition, the influence of decoupling coefficients (K) of different types of explosives (i.e. TNT, emulsion, and ANFO explosives) on the damage range of reservoir blasting is studied. It is found that under the blasting of different types of explosives, the damage evolution law of the reservoir is different, and appropriate explosives should be selected in combination with rock stratum parameters in actual construction. Finally, the increase in permeability and the drainage effect of the reservoir after blasting are quantified by numerical simulations. It is demonstrated the average permeability increment of the reservoir under the action of TNT explosive is the largest, which is 3.08 (K = 4), while that under the action of emulsion explosive and ANFO explosive are 1.49 (K = 2) and 1.17 (K = 3) respectively; and the changes in reservoir permeability and drainage volume are coherent with the range of damage; the greater the range of damage, the greater the reservoir permeability and the greater the drainage efficiency.

S. Ashwin Ram, Zareena Begum Irfan

India is one among the high risk countries susceptible to water stress and as a result of exponential population growth, industrialization and rapid urbanization the per capita water availability is fast diminishing and adding to this climate change is further expected to exacerbate the problem resulting in more frequent and prolonged drought. Water is considered to be a wicked problem and hence relying on a linear and reductionist approach may no longer seems relevant in solving such complex systems. This paper adopts a system thinking principle to understand various water management challenges across sectors. System thinking has its roots in mental models and has been evolving and increasingly being used to understand Complex Dynamic Systems. Based on a systematic review of literature, the present study has developed a series of Causal Loop Diagrams (CLDs) capturing key variables pertaining to water sector. CLDs are believed to create a broader and holistic understanding of the water management challenges by clearly exhibiting the relationship between the key variables. The proposed CLDs serve as a decision making tool to understand the challenges of integrated water resources management through the complex interactions of the variables between balancing and reinforcing loops. The CLDs highlights the existing water related challenges in India and proposes a pathway for sustainable management of water resources across agriculture, industry and domestic sectors. Though the CLD discussion in this paper is based on Indian scenario, it holds good for any developing countries context.

Ran Cheng, Qin’ou Liang, Degen Lin

In many areas affected by climate change, agriculture is an industry sensitive to climate change. Many scholars have studied the relationship between climate factors and rice, but these studies did not quantitatively construct high temperature indicator and determine the threshold for high temperature hazard. In this paper, we constructed the index of high temperature intensity (HTI) based on the daily maximum temperature and high temperature days, and then used multiple regression analysis method to determine the hazard factor. Based on this, we can analyze the risk distribution of hazard factor for late rice in Zhejiang. The results showed that: (i) high temperature values and high temperature days above 35 °C during the growth period of late rice are the main meteorological factors for the decrease of late rice yield in Zhejiang Province. The high temperature intensity comprehensive index constructed in this paper can quantitatively evaluate the effect of high temperature on late rice yield; (ii) With the increase of the level of recurrence, the high temperature intensity spreads around Lishui City and Yunhe County, and the intensity of the hazard factor in central Zhejiang is higher than that in the surrounding area.

Anyway Katanha, Danny Simatele

This paper presents the literature reviewed on the evolution of the natural hazard mitigation perspective and an overview of its progression to date. The article uses information taken from diverse sources such as a globally accepted scientific databases Google Scholar (http://www.scholar.google.co.in), Scopus (http://www.scopus.com), Science Direct (http://www.sciencedirect.com), SpringerLink (http://www.springer.co.in) and Wiley (http://www.onlinelibrary.wiley.com); conference proceedings; theses; abstracts; and impact and non-indexed journals. It demonstrates how the actor–network theory (ANT) theoretical framework can be applicable to Muzarabani in Zimbabwe as a tool for analysing and elaborating hazard mitigation strategies. Actor–network theory is gradually becoming influential but is still a bone of contention, mainly because of its radical approach. Actor–network theory treats humans and non-humans as equal actors. In spite of its limitations, studies have shown that an ANT-grounded approach is useful in providing a framework for the comprehension of the complexities of daily life during natural hazard episodes and the dynamic role of Ziziphus mauritiana in the network in Muzarabani, Zimbabwe. The theory can demonstrate its importance in respect of how social results are produced as a result of linkages among diverse actors (human and non-human) in a network. The article argues that if ANT is used logically it is useful in examining eco-based natural hazard mitigation and resilience approaches in semi-arid regions.

D. V. Shamin

The article focuses on the identification, qualitative and quantitative risk assessment, make recommendations for non-insurance risk mitigation techniques, the development of the insurance program оbjects of the gas pipeline “South Stream”.

S. YU. Kuznetsov

Business intelligence is a new management tool of an innovative enterprise. Business intelligence as a continuous innovation in business analytics of the new intellectual enterprise can integrate all activities into an efficient and sustainable business organization. BI promotes the prosperity of the firm, through intelligent management of strategies, finance, marketing, business processes, staff and assets in a world of unlimited information.

Stephane Cartier, Cloe Valette, Hafida Mediene

Seismic vulnerability challenges sustainable urbanism. Antioch, Manosque and Oran, three Mediterranean cities destroyed by earthquakes, demonstrate how preservation of urban patrimony protects populations. The methodological pattern “urban seismic patrimonial strategies” cross investment and patrimonial care demonstrate natural hazards mitigation as a factor of urban policy. Urban patrimonial managers are unaware of seismic threat which obliges them to explain liabilities. Buildings evolution observation indicates urban phases. According urban policy reshape urban morphology and amplify social vulnerability.

Teodora Barbu, Georgeta Vintila

The diversity of national banking systems in the European banking system and the absence of consolidated supervision creates the premises for a series of interrogations whose essence is the same: Is it possible to discuss about a Pan European Banking System? The starting point in answering this question was the efforts to create a single banking market, which took place in 1973-1999, and the impact of integration on the European Banking Industry. Among the most representative aspects, it must be emphasized the necessity of consolidating banking supervision at an European level, considering that the International Banking Community studies the problematic of banking regulations at a global level. The two dimensions of the prudential and European bank supervision device – the geographic and the institutional – demand the creation of a structural reform in order to ensure the functioning of a Pan European system of banking supervision and regulations. The considerations on the Consolidation of European Banking Supervision draws into discussion the Financial Supervision Authority which has generalized as an applicable model in numerous European countries and has been mentioned as an alternative of Pan European banking supervision. In the process of the integration of the banking sector, the Basel II Accord represents an opportunity in reaching a convergence of national regulations and practices in matters of risk management, considering that these actions are in line with the preoccupations of realizing a Pan European banking system. Thus, the creation of Pan European banking system involves actions in more directions: legal, institutional, operational meant to ensure the consolidation of banking supervision.