This investigation meticulously examines how growth opportunities, firm size, leverage, and liquidity affect the hedging decisions of mining companies listed on the Indonesia Stock Exchange from 2017 to 2022. Out of an initial population of 62 mining enterprises, a purposive sampling method distilled the focus to 14 representative firms, with the collected data subjected to rigorous analysis via SPSS. The research uncovers that growth opportunities do not significantly alter hedging decisions, whereas firm size demonstrates a significant positive association with the likelihood of engaging in hedging practices. In contrast, leverage and liquidity, as indicated by the current ratio, do not show a substantial impact on hedging behavior. This study seeks to illuminate the various determinants shaping hedging strategies within the mining sector, offering crucial insights that could inform future research and enhance the understanding of risk management approaches in this particular industry.
Business records management, Economics as a science
[Objective] In order to promote the digital empowerment of the urban rail transit industry, it is necessary to construct a Smart Fire Protection IoT (Internet of Things) System for Shanghai Rail Transit, aiming to strengthen the perception and awareness of the operation status and intelligent analysis of fire safety, while enhancing the capability of risk identification, forecasting and early warning. [Method] By collecting information such as the operation indicators of important equipment in the fire protection-related systems, incident alerts, and business data, a Smart Fire Protection IoT System for Shanghai Rail Transit is designed, in which, the IoT technology is used to collect and transmit various data of fire protection business; the big data technology is utilized to build a unified technical architecture for storing, analyzing, and managing the collected data; and the digital twin technology is applied to transform the processed data into practical applications, achieving a visual presentation of fire protection management as the result. [Result & Conclusion] Through the construction of the Smart Fire Protection IoT System for Shanghai Rail Transit, the businesses and data of various lines and stations in the metro network are integrated on one platform, and the workflows for daily fire safety management is thereby streamlined, digitized, and standardized. By using technologies such as cloud computing and big data, a deep integration of informatization and fire protection is achieved, significantly improving the fire protection management level of Shanghai Rail Transit.
Giane Carla Kopper Müller, Maria Pilar Serbent, Thiago Caique Alves
et al.
Brazil is the second largest tobacco producer. We investigated the pesticides used by tobacco farmers, their occurrence in the drinking water resources of Chapadão do Lageado (Santa Catarina, Brazil), and the relationship between tobacco cultivation and farmers' health. A liquid chromatography-tandem mass spectrometry method was used to quantify pesticide residues in water samples from rivers and wells. Both the handling of pesticides and the handling of the tobacco plant have negative consequences, even if preventive measures are taken. Of the 107 tobacco farmers surveyed, 91.6% reported symptoms related to green leaf disease and 19.6% reported symptoms related to pesticide handling. About 40% of the well water samples contained residues of imidacloprid, sulfentrazone, thiamethoxam and iprodione. In the river water samples, more than 70% had residues of the same pesticides detected in the well water, plus clomazone. Traditional tobacco cultivation and post-harvest management endanger human and environmental health. The harmful effects of exposure to tobacco leaves compound health problems. In areas where tobacco cultivation is a major industry, critical thinking is needed on policies, approaches and tools to address these complex and alarming public health risk situations.
Léa Charton, François Séverac, Yves Hansmann
et al.
Abstract Respiratory tract infections (RTIs) are common in general practice, and represent the main cause of inappropriate antibiotic prescribing, contributing to antimicrobial resistance. This prospective study aims to identify factors associated with inappropriate antibiotic prescribing for RTIs in general practice (GP). The study was conducted in France with the assistance of 15 GP trainees in France, in collaboration with 25 general practitioners. Data were collected from randomly selected GP consultations using ICPC-2 coding, with specific grids for RTI-related visits. Antibiotic prescribing was considered appropriate if it adhered to established guidelines. Of 807 consultations, 173 involved RTIs. Antibiotics were prescribed in 35% of cases, and management was appropriate in 73% of these cases. Sinusitis and bronchitis were more likely to result in inappropriate antibiotic prescriptions. Factors associated with inappropriate prescribing included a light clinical examination (as opposed to systematic), patient considered “at risk”, repeated consultations, and diagnostic uncertainty. Providing a clear diagnostic explanation to patients reduced the risk of inappropriate prescribing. Physicians who received visits from pharmaceutical industry representatives were more likely to prescribe antibiotics inappropriately. This study highlights the complexity of clinical reasoning underlying this practice. Improving the thoroughness of clinical examination, enhancing patient communication, and maintaining independence from pharmaceutical promotion may help optimize antibiotic use. Additionally, rapid diagnostic tests and prescribing software can help reduce uncertainty.
Oil and gas operations are categorized as high-risk because they involve numerous equipment and complex processes. Asset integrity management (AIM) aims to mitigate the risk of failure resulting from degradation with corrosion as the primary cause and to maintain equipment safety and functionality. The risk-based inspection (RBI) methodology is one of the AIM processes that considers risks in decision-making to prioritize inspection and maintenance. This paper provides a comprehensive review of risk-based studies in the context of AIM activities. Risk-based AIM is categorized and reviewed based on risk analysis methods, including quantitative, qualitative, semi-quantitative, probabilistic, deterministic, hybrid probabilistic-deterministic, and dynamic or traditional risk. Most research areas used in case studies focus on pipeline applications. Analysis tools for risk assessment and control applied in risk-based AIM, including the evolution of tools from traditional to machine learning approaches, are examined. The current trends and future research opportunities for applying risk-based AIM are also discussed. This study offers risk assessment models for researchers and oil and gas industry practitioners that fit their specific requirements.
Chloé Touzet, Henry Papadatos, Malcolm Murray
et al.
Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks. Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling. We conceptualize AI risk modeling as the tight integration of (i) scenario building$-$causal mapping from hazards to harms$-$and (ii) risk estimation$-$quantifying the likelihood and severity of each pathway. We review classical techniques such as Fault and Event Tree Analyses, FMEA/FMECA, STPA and Bayesian networks, and show how they can be adapted to advanced AI. A survey of emerging academic and industry efforts reveals fragmentation: capability benchmarks, safety cases, and partial quantitative studies are valuable but insufficient when divorced from comprehensive causal scenarios. Comparing the nuclear, aviation, cybersecurity, financial, and submarine domains, we observe that every sector combines deterministic guarantees for unacceptable events with probabilistic assessments of the broader risk landscape. We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed to supply deterministic evidence where current models are the result of opaque end-to-end optimization procedures rather than specified by hand. In one potential governance-ready framework, developers conduct iterative risk modeling and regulators compare the results with predefined societal risk tolerance thresholds. The paper provides both a methodological blueprint and opens a discussion on the best way to embed sound risk modeling at the heart of advanced-AI risk management.
We introduce a new paradigm for risk sharing that generalizes earlier models based on discrete agents and extends them to allow for sharing risk within a continuum of agents. Agents are represented by points of a measure space and have potentially heterogeneous risk preferences modeled by risk measures on a separable probability space. We derive the dual representation of the value function using a Strassen-type theorem for the weak-star topology and provide a characterization of the acceptance set using Aumann integration. These results are illustrated by explicit formulas when risk preferences are within the family of entropic and expected shortfall risk measures, and applications to Pareto efficiency in large markets.
This report presents a comprehensive evaluation of three Value-at-Risk (VaR) modeling approaches: Historical Simulation (HS), GARCH with Normal approximation (GARCH-N), and GARCH with Filtered Historical Simulation (FHS), using both in-sample and multi-day forecasting frameworks. We compute daily 5 percent VaR estimates using each method and assess their accuracy via empirical breach frequencies and visual breach indicators. Our findings reveal severe miscalibration in the HS and GARCH-N models, with empirical breach rates far exceeding theoretical levels. In contrast, the FHS method consistently aligns with theoretical expectations and exhibits desirable statistical and visual behavior. We further simulate 5-day cumulative returns under both GARCH-N and GARCH-FHS frameworks to compute multi-period VaR and Expected Shortfall. Results show that GARCH-N underestimates tail risk due to its reliance on the Gaussian assumption, whereas GARCH-FHS provides more robust and conservative tail estimates. Overall, the study demonstrates that the GARCH-FHS model offers superior performance in capturing fat-tailed risks and provides more reliable short-term risk forecasts.
We propose a new class of monetary risk measures for assessing financial and ESG risk. The construction is based on classical shortfall risk measures with loss function replaced by a multi-attribute utility function. We present an extensive theoretical analysis of these risk measures, showing specifically how properties of the utility function translate into properties of the associated risk measure. We furthermore discuss how these multi-attribute risk measures can be used to compute minimum risk portfolios and show in a numerical study that accounting for ESG risk in optimal portfolio choice has a significant influence on the composition of portfolios.
Risk matrices are widely used across a range of fields and have found increasing utility in warning decision practices globally. However, their application in this context presents challenges, which range from potentially perverse warning outcomes to a lack of objective verification (i.e., evaluation) methods. This paper introduces a coherent framework for generating multi-level warnings from risk matrices to address these challenges. The proposed framework is general, is based on probabilistic forecasts of hazard severity or impact and is compatible with the Common Alerting Protocol (CAP). Moreover, it includes a family of consistent scoring functions for objectively evaluating the predictive performance of risk matrix assessments and the warnings they produce. These scoring functions enable the ranking of forecasters or warning systems and the tracking of system improvements by rewarding accurate probabilistic forecasts and compliance with warning service directives. A synthetic experiment demonstrates the efficacy of these scoring functions, while the framework is illustrated through warnings for heavy rainfall based on operational ensemble prediction system forecasts for Tropical Cyclone Jasper (Queensland, Australia, 2023). This work establishes a robust foundation for enhancing the reliability and verifiability of risk-based warning systems.
AbstractThe aim of this paper is to empirically study corporate social responsibility management in the insurance industry, which has received increased attention in recent years. For this purpose, we use data from LSEG (former REFINITIV) over a period of 11 years (2010–2020) taking into account companies from the United States, Europe, China, and Japan, and analyze the determinants and the value of corporate social responsibility management. Our results show that larger insurers exhibit significantly better corporate social responsibility management. Moreover, focusing on the recent past we find an indication of the value‐relevance of a holistic corporate social responsibility management. Hence, our findings reveal that it might be economically rational for insurance companies to become more sustainable. Therefore, it might be that the insurance market is able to move towards a more sustainable direction on its own, finally, reducing the importance of regulatory interventions in this regard.
Gonzalo Torralba-Carnerero, Manuel García-Nieto, Juan Manuel Ramón-Jerónimo
et al.
<i>Introduction</i>: The aerospace industry has been significantly disrupted by recent economic downturns, underscoring the need for robust supply chain management. This is especially important given the complexity of aircraft manufacturing, the globalization of supply chains, and the requirement to meet stringent regulatory standards. While outsourcing is widely adopted to improve cost competitiveness, it also introduces risks, such as compromised product quality, inefficiency, and delays. <i>Methods</i>: This study explores how aerospace firms manage outsourcing relationships using control mechanisms. Data were gathered through seven semi-structured interviews with supply chain managers from contracting and supplier firms focusing on both formal and informal controls in supplier selection and relationship management. <i>Results</i>: Supplier selection is primarily guided by trust, past performance, and delivery reliability. Firms employ formal controls, such as KPIs and certifications, alongside informal practices, including embedding internal staff within supplier operations. This dual approach ensures quality, mitigates risks, and maintains compliance with regulatory standards. <i>Conclusions</i>: This study concludes that combining formal and informal controls is vital for balancing outsourcing efficiency with risk mitigation, offering valuable insights into supply chain management practices in regulated industries like aerospace.
Transportation and communication, Management. Industrial management
In light of escalating urbanization trends and climate change impacts worldwide, the susceptibility of urban power grids to natural disasters has become an overarching global concern. Prior research has predominantly concentrated on singular calamities while often disregarding cumulative repercussions from multiple concurrent events affecting power grid resilience. This investigation presents an exhaustive framework for assessing grid vulnerabilities by quantifying diverse impacts from potential natural disaster scenarios and delineating adaptive pathways for evaluating inadvertent occurrences. The framework amalgamates an extensive array of metrics— including probability assessments, system state evaluations, trigger threshold analyses, responsiveness measurements, and adaptability adjustments— within a dynamic scenario-oriented model. The inquiry progresses through distinct stages: formulating an all-encompassing methodology for assessing vulnerabilities; assessing varied impacts stemming from different environmental perils; mapping out post-disaster evolutions; and executing a case analysis focusing on an urban power grid.Concentrating specifically on rainfall, snowfall, and freezing incidents, the case analysis uses locale-specific data to appraise grid susceptibilities while employing multi-criteria decision analysis (MCDA) to facilitate decision-making. During this deliberative process, optimal strategies are derived, and mitigative actions are recommended with the aim of diminishing power-grid vulnerabilities. This investigation underscores intricate risk dynamics within urban power grids while presenting a feasible framework for sustainable planning and effective emergency responses in confronting natural hazards.
Gustavo A. Espinoza Calderón, Gloria O. Bustamante Cárdenas
The Occupational Safety and Health Administration (OSHA) is a division of the U.S. Department of Labor. Its mission is to minimize health and safety hazards to workers in manufacturing industries. The focus was on its application in the food industry. Specifically in the manufacture of corn and wheat flour tortillas. These products have a high consumption in the North American country. The purpose of this study was to examine the mechanisms of the tortilla industry to adapt each activity to safety standards. In addition, to evaluate a measurable impact on the accidents that occurred and how they were corrected. The results show that OSHA standards enabled the design and management of industrial safety for the tortilla industry. This study identified three safety measures: personal protective equipment (PPE), chemical handling (SDS), and lockout/tagout (LOTO). Descriptive analyses were conducted to examine the impacts of the revised standard on tortilla worker safety. The findings indicate that nearly 24% of all injuries occur in this type of industry. It can be concluded that increasing workplace safety and compliance with legislation is currently a high priority in the food industry, although food safety is also of great importance. Both aspects can now go hand in hand thanks to the wide variety of safety solutions identified with low risk of contamination.
Chemical engineering, Computer engineering. Computer hardware
Oona Freudenthal, Marcos Da Silveira, Louis Deladiennee
Abstract Persistent and toxic chemicals remain a significant pollution concern, underscored by the European Union's Zero Pollution Action Plan. Daily exposure to complex chemical mixtures starts early and continues throughout life, for instance for consumer products such as toys, plasticware, furniture, and synthetic fibres. EU-funded research projects like COPHES/DEMOCOPHES, HBM4EU, and PARC have documented population exposure to these substances. The outcomes of such research initiatives have contributed to highlighting the adverse health impacts of Substances of Concern (SoCs), leading to several regulatory actions within the EU. SoCs include hazardous chemicals such as carcinogens, mutagens, endocrine disruptors, and “persistent, bioaccumulative, and mobile” (PBM) chemicals. The digital transformation in chemicals management has resulted in policies that mandate electronic submissions of chemical risk assessment-relevant data, and the creation of industry-specific databases like the Substances of Concern in Products (SCIP) database, established by the European Chemicals Agency (ECHA) under the revised Waste Framework Directive (WstFD). These databases describe SoCs and their link with products, offering a comprehensive view of chemical quantities, emission sources, exposure pathways, and other relevant data, contingent on robust data governance. Effective chemical risk assessment requires characterizing hazards, exposure sources and levels, and drawing conclusions concluding on potential risks, supported by a well-defined problem formulation and monitoring. This includes setting objectives and defining the scope of the risk assessment and decision-making, particularly regarding early warning signal detection for the purpose of public health protection. Successful risk assessment hinges on access to robust, traceable, accessible, and interoperable data across scientific disciplines and regulatory frameworks. This paper discusses the challenges of aggregating human health risk assessment-relevant chemical information from multiple sources, especially from the perspective of data fusion and reuse. It presents findings from a research project focused on utilizing chemicals datasets from various governmental and scientific sources. The study highlights the need for improved data presentation and availability to enhance usability for all stakeholders. Recommendations are made for the EU Commission, ECHA, industry, and academia to support harmonized data practices, increased transparency, and the development of sustainable chemical applications fostering safer market introductions. These recommendations can also be useful to other data providers that care about the reusability of the data they publish or manage.
The research aimed to identify and evaluate the risks associated with IT projects, particularly focusing on their impacts. Despite
numerous efforts, a significant number of software projects still fail to achieve success; however, these risks can be effectively
managed. This study outlines methodologies for examining how different risks influence software projects, using statistical analyses
and models to uncover causal relationships. A survey was also conducted to assess critical risk factors, highlighting three key factors that have the greatest influence. The findings suggest that addressing these factors can improve decision-making, thereby increasing the likelihood of project success
AbstractThe occurrence of forest fires is determined by fuel, climate and ignition sources at different temporal and spatial scales. However, most analyses are performed only on a single scale, and few comprehensive statistical analyses elaborate on the cross-scale interaction of fire drivers. Understanding the differential effects of socioeconomic and climatic factors on forest fire incidence across different administrative and climatic scales can provide new information for wildfire studies. In this study, we applied a cross-classified multilevel model with forest fire incidence data derived from Moderate Resolution Imaging Spectroradiometer products (MODIS 14A1) to explore the relationship between the spatial distribution of fire incidence and socioeconomic and climatic factors at different levels and to estimate the effects of these drivers on forest fire incidence. Our results showed that the density of impervious surfaces, density of cropland, mean monthly precipitation, mean monthly temperature, density of primary gross domestic product, annual monthly average relative humidity, and annual monthly average precipitation at the county level, prefecture-level city and climatic levels had significant multilevel associations with forest fire incidence in the Changbai Mountain area. These findings will effectively support the development of forest fire administrative policies for specific regions.
This paper makes a systematic visual analysis of cruise research literature collected in science network database from 1996 to 2019. The results show that: the overall number of published literatures on cruise research are growing; North American states, Europe, and Asia are the main regions of cruise research. The evolutionary of theme development of cruise research has three stages, and the current hot topics of cruise research can be summarized as cruise tourism, luxury cruises, cruise passengers, destination ports, environmental and biological conservation, and cruise diseases. Future research in the cruise field is in the areas of cruise supply chain, technology in cruise, children’s cruise experience, itinerary design, planning and optimization, brand reputation and luxury cruises, public transportation in destinations, environmental responsibility of passengers and corporate social responsibility, optimization of energy systems, climate change in relation to the cruise industry, the Chinese cruise market and risk management of cruise diseases.
Science, General. Including nature conservation, geographical distribution
Motivated by the results of static monetary or star-shaped risk measures, the paper investigates the representation theorems in the dynamic framework. We show that dynamic monetary risk measures can be represented as the lower envelope of a family of dynamic convex risk measures, and normalized dynamic star-shaped risk measures can be represented as the lower envelope of a family of normalized dynamic convex risk measures. The link between dynamic monetary risk measures and dynamic star-shaped risk measures are established. Besides, the sensitivity and time consistency problems are also studied. A specific normalized time consistent dynamic star-shaped risk measures induced by $ g $-expectations are illustrated and discussed in detail.