Large language models (LLMs) are increasingly deployed in high-stakes domains, where rare but severe failures can result in irreversible harm. However, prevailing evaluation benchmarks often reduce complex social risk to mean-centered scalar scores, thereby obscuring distributional structure, cross-dimensional interactions, and worst-case behavior. This paper introduces Social Harm Analysis via Risk Profiles (SHARP), a framework for multidimensional, distribution-aware evaluation of social harm. SHARP models harm as a multivariate random variable and integrates explicit decomposition into bias, fairness, ethics, and epistemic reliability with a union-of-failures aggregation reparameterized as additive cumulative log-risk. The framework further employs risk-sensitive distributional statistics, with Conditional Value at Risk (CVaR95) as a primary metric, to characterize worst-case model behavior. Application of SHARP to eleven frontier LLMs, evaluated on a fixed corpus of n=901 socially sensitive prompts, reveals that models with similar average risk can exhibit more than twofold differences in tail exposure and volatility. Across models, dimension-wise marginal tail behavior varies systematically across harm dimensions, with bias exhibiting the strongest tail severities, epistemic and fairness risks occupying intermediate regimes, and ethical misalignment consistently lower; together, these patterns reveal heterogeneous, model-dependent failure structures that scalar benchmarks conflate. These findings indicate that responsible evaluation and governance of LLMs require moving beyond scalar averages toward multidimensional, tail-sensitive risk profiling.
Adele Ravagnani, Mattia Chiappari, Andrea Flori
et al.
Shorting for hedging exposes to risk when the market dynamics is uncertain. Managing uncertainty and risk exposure is key in portfolio management practice. This paper develops a robust framework for dynamic minimum-variance hedging that explicitly accounts for forecast uncertainty in volatility estimation to achieve empirical stability and reduced turnover, further improving other standard performance metrics. The approach combines high-frequency realized variance and covariance measures, autoregressive models for multi-step volatility forecasting, and a box-uncertainty robust optimization scheme. We derive a closed-form solution for the robust hedge ratio, which adjusts the standard minimum-variance hedge by incorporating variance forecast uncertainty. Using a diversified sample of equity, bond, and commodity ETFs over 2016-2024, we show that robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges. While overall variance reduction is comparable, the robust approach improves downside protection and risk-adjusted performance, particularly when transaction costs are considered. Bootstrap evidence supports the statistical significance of these gains.
K. Thornber, D. Verner-Jeffreys, S. Hinchliffe
et al.
Abstract Antimicrobial resistance (AMR) is a growing threat to global public health, and the overuse of antibiotics in animals has been identified as a major risk factor. With high levels of international trade and direct connectivity to the aquatic environment, shrimp aquaculture may play a role in global AMR dissemination. The vast majority of shrimp production occurs in low‐ and middle‐income countries, where antibiotic quality and usage is widely unregulated, and where the integration of aquaculture with family livelihoods offers many opportunities for human, animal and environmental bacteria to come into close contact. Furthermore, in shrimp growing areas, untreated waste is often directly eliminated into local water sources. These risks are very different to many other major internationally‐traded aquaculture commodities, such as salmon, which is produced in higher income countries where there are greater levels of regulation and well‐established management practices. Assessing the true scale of the risk of AMR dissemination in the shrimp industry is a considerable challenge, not least because obtaining reliable data on antibiotic usage is very difficult. Combating the risks associated with AMR dissemination is also challenging due to the increasing trend towards intensification and its associated disease burden, and because many farmers currently have no alternatives to antibiotics for preventing crop failure. In this review, we critically assess the potential risks the shrimp industry poses to AMR dissemination. We also discuss some of the possible risk mitigation strategies that could be considered by the shrimp industry as it strives for a more sustainable future in production.
This study aims to identify risks arising in housing construction projects, particularly those related to budget overruns, and to develop mitigation strategies to ensure that projects run according to plan in terms of cost, time, and resources. The methods used are the Project Management Life Cycle (PMLC) to map the project stages from initiation to closure, and the House of Risk (HoR) to identify risk events and risk agents. The novelty of this study lies in the development of difficulty analysis in HoR, which focuses on three main aspects —cost, time, and resources — that have not been explicitly described in previous studies. The results show that the project experienced a cost increase from IDR 150 million to IDR 165 million due to dominant risks, including misinterpretation of design drawings, fluctuations in material prices, and work delays. Through HoR analysis, priority risks were successfully identified, and mitigation strategies were developed in the form of team training to improve design understanding, control of material usage, and the implementation of contingency planning. This research contributes academically by modifying the HoR method with difficulty analysis based on cost, time, and resources, and integrating it with PMLC. It also provides practical guidance to contractors and project managers on anticipating and managing risks more effectively, thereby improving cost efficiency, timeliness, and resource optimization in housing construction projects.
With the rapid development of AI Generated Content (AIGC) technology, its application in the financial field is gradually deepening, bringing new impetus to financial innovation. However, the application of AIGC technology also comes with potential risks, such as data security, algorithm bias, and market manipulation. This article explores the innovative models of AIGC technology in the financial industry based on its core characteristics, including intelligent investment advisory, risk assessment, quantitative trading, financial marketing, and analyzes the risk management challenges it brings. Finally, this article proposes strategies such as improving the regulatory system, optimizing algorithm transparency, and strengthening data governance to promote the healthy development of AIGC technology in the financial sector.
Despite more than 30 years of construction of the Three Gorges Project, comprehensive studies on the spatiotemporal evolution of landslides in the Three Gorges Reservoir area are still limited. This study aims to analyze changes in landslide susceptibility from 1991 to 2020 and identify the key factors driving these changes. We constructed 24 datasets based on 4860 landslide events, 13 static factors, and 7 dynamic factors, covering 12 time periods for analysis. We applied two machine learning models—Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost)—along with the isolation Forest (iForest) algorithm. The iForest-LightGBM model achieved the highest accuracy and demonstrated efficient training performance. Temporal analysis showed that high-susceptibility areas expanded along the Yangtze River, peaking in 2020, with notable anomalies from 2001 to 2010, followed by stabilization between 2011 and 2020. Using the SHapley Additive exPlanation (SHAP) algorithm, we quantified the importance of the influencing factors over time. This study establishes a multi-temporal evaluation framework for landslide susceptibility and introduces a method for quantitatively analyzing the evolution of influencing factors. The findings provide valuable insights into landslide risk management in the Three Gorges Reservoir area and contribute to understanding the spatial evolution of landslides in dynamic environments.
The poultry industry is an infant but rapidly growing sector in Ethiopia. Although poultry farming is one of the Ethiopian government’s developmental initiatives, the sector is facing various challenges, particularly due to infectious diseases. Among infectious diseases, helminthiasis is one of the challenges affecting poultry production. A cross-sectional study was conducted from April to June 2023 to determine the occurrence and associated risk factors of nematode infections in intensively managed commercial poultry farming in Bishoftu, Ethiopia. Representative pooled fecal samples were collected from 60 poultry farms and examined for the presence of worm eggs by using the flotation technique. Gastrointestinal nematode eggs were identified based on their morphological characteristics. Coprological analysis results reveal that out of 60 poultry farms screened, 19 (31.7%) were tested positive. The most identified worms were Ascaridia galli 11 (18.3%), followed by Heterakis gallinarum 3 (5%), Trichostrongylus tenuis 3 (5%), Syngamus trachea 2 (3.3%), and Capillaria species 1 (1.7%). Production types, management practices, and proximity to other farms were found to significantly (p<0.05) influence the occurrence of worm infections. The prevalence of worm infections was significantly lower (6.06%, p<0.05) in the farms using footbaths as compared to farms not utilizing footbaths (62.96%). Similarly, significantly higher (p<0.05) prevalence was observed in the farms that did not apply wet cleaning (76.2%) and chemical disinfection (66.7%) as compared to those using wet cleaning (7.7%) or chemical disinfection (16.7%) during farm downtime. However, there was no significant difference (p>0.05) between age groups and poultry farm scale. This study strongly suggests that gastrointestinal nematode infections pose a significant challenge to poultry production. Therefore, implementing effective worm control strategies, such as regular deworming, implementing proper farm hygiene practices, and strict biosecurity measures, is strongly recommended.
Joshua Dimasaka, Christian Geiß, Robert Muir-Wood
et al.
In the aftermath of disasters, many institutions worldwide face challenges in continually monitoring changes in disaster risk, limiting the ability of key decision-makers to assess progress towards the UN Sendai Framework for Disaster Risk Reduction 2015-2030. While numerous efforts have substantially advanced the large-scale modeling of hazard and exposure through Earth observation and data-driven methods, progress remains limited in modeling another equally important yet challenging element of the risk equation: physical vulnerability. To address this gap, we introduce Graph Categorical Structured Variational Autoencoder (GraphCSVAE), a novel probabilistic data-driven framework for modeling physical vulnerability by integrating deep learning, graph representation, and categorical probabilistic inference, using time-series satellite-derived datasets and prior expert belief systems. We introduce a weakly supervised first-order transition matrix that reflects the changes in the spatiotemporal distribution of physical vulnerability in two disaster-stricken and socioeconomically disadvantaged areas: (1) the cyclone-impacted coastal Khurushkul community in Bangladesh and (2) the mudslide-affected city of Freetown in Sierra Leone. Our work reveals post-disaster regional dynamics in physical vulnerability, offering valuable insights into localized spatiotemporal auditing and sustainable strategies for post-disaster risk reduction.
Sabrina Aufiero, Silvia Bartolucci, Fabio Caccioli
et al.
This work explores the formation and propagation of systemic risks across traditional finance (TradFi) and decentralized finance (DeFi), offering a comparative framework that bridges these two increasingly interconnected ecosystems. We propose a conceptual model for systemic risk formation in TradFi, grounded in well-established mechanisms such as leverage cycles, liquidity crises, and interconnected institutional exposures. Extending this analysis to DeFi, we identify unique structural and technological characteristics - such as composability, smart contract vulnerabilities, and algorithm-driven mechanisms - that shape the emergence and transmission of risks within decentralized systems. Through a conceptual mapping, we highlight risks with similar foundations (e.g., trading vulnerabilities, liquidity shocks), while emphasizing how these risks manifest and propagate differently due to the contrasting architectures of TradFi and DeFi. Furthermore, we introduce the concept of crosstagion, a bidirectional process where instability in DeFi can spill over into TradFi, and vice versa. We illustrate how disruptions such as liquidity crises, regulatory actions, or political developments can cascade across these systems, leveraging their growing interdependence. By analyzing this mutual dynamics, we highlight the importance of understanding systemic risks not only within TradFi and DeFi individually, but also at their intersection. Our findings contribute to the evolving discourse on risk management in a hybrid financial ecosystem, offering insights for policymakers, regulators, and financial stakeholders navigating this complex landscape.
The investment in solar energy projects is the main emphasis of green financing. To a further degree, this encourages the renewable energy industry to embrace green financial practices. This method has emerged as an important theoretical framework. On top of that, it is an essential instrument for attaining sustainable development. Solar energy projects under green finance are analyzed for their investment benefits, risk management, and related investment strategies. By analyzing green finance policies and tools, the thesis is going to be able to identify risk, evaluate it, and propose ways to respond. Improving economic, ecological, and societal advantages is our primary goal in introducing a new research paradigm and a new point of view. Green finance innovation is another area we want to encourage. In particular, we look at how the green financing system came to be. Our long-term objective is to combine solar energy projects with green financing.
In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management.
The construction industry, characterized by its intricate network of stakeholders and diverse workforce, grapples with the challenge of managing information effectively. This study delves into this issue, recognizing the universal importance of safeguarding data, particularly amid rising concerns around unauthorized access and breaches. Aiming to harness the potential of blockchain technology to address these challenges, this study used hypothetical biographical and safety data of construction workers securely stored on a Hyperledger Fabric blockchain. Developed within the Amazon Web Services (AWS) cloud platform, this blockchain infrastructure emerged as a robust solution for enhancing data security and privacy. Anchored in the core principles of data security, the model emerges as a potent defender against the vulnerabilities of traditional data management systems. Beyond its immediate implications, this study exemplifies the marriage of blockchain technology and the construction sector, and its potential for reshaping workforce management, especially in high-risk projects and optimizing risk assessment, resource allocation, and safety measures to mitigate work-related injuries. Practical validation through transaction testing using Hyperledger Explorer validates the model’s feasibility and operational effectiveness, thus serving as a blueprint for the industry’s data management. Ultimately, this research not only showcases the promise of blockchain technology in addressing construction data security challenges but also underscores its practical applicability through comprehensive testing, thus heralding a new era of data management that harmonizes security and efficiency for stakeholders’ benefit.
Vishwas Dohale, Priyanka Verma, A. Gunasekaran
et al.
PurposeThis study prioritizes the supply chain risks (SCRs) and determines risk mitigation strategies (RMSs) for the Indian apparel industry to mitigate the shock of the COVID-19 pandemic disruption.Design/methodology/approachInitially, 23 SCRs within the apparel industry are identified through an extant literature review. Further, a fuzzy analytical hierarchy process (FAHP) is utilized to prioritize the SCRs considering the epidemic situations to understand the criticality of SCRs and determine appropriate RMSs to mitigate the shock of SCRs during COVID-19.FindingsThis study prioritized and ranked the SCRs within the Indian apparel industry based on their severity during the COVID-19 disruption. Results indicate that the demand uncertainty and pandemic disruption risks are the most critical. Based on the SCRs, the present work evaluated and suggested the flexibility and postponement mitigation strategies for the case under study.Research limitations/implicationsThis study has novel implications to the existing literature on supply chain risk management in the form of the FAHP framework. Supply chain practitioners from the other industrial sectors can extend the proposed FAHP framework to assess the SCRs and identify suitable mitigation strategies. The results aid the practitioners working in an apparel industry to benchmark and deploy the proposed RMSs in their firm.Originality/valueThe present study is a unique and earlier attempt to develop a quantitative framework using FAHP to evaluate and determine the risk mitigation strategy for managing the SCRs during the coronavirus epidemic.
Nadezhda V. Nikiforova, Nina V. Zaitseva, Svetlana V. Kleyn
According to the World Health Organization (WHO), 24% of the global burden of diseases and 23% of all deaths associate with environmental factors. The presence of chemical impurities in the air can have an adverse effect on the health of the population. A connection has been established between the increased content of chemical impurities in the air and the development of such pathologies as diseases of the respiratory system, circulatory system, the formation of malformations, etc. Many countries implement projects to improve air quality. For the purpose of targeted development of management decisions aimed at minimizing the adverse impact of atmospheric air on the health of the population, the study of the morbidity of the population associated with the impact of priority atmospheric air hazard factors is relevant. The research goal is to characterize the morbidity of the population associated with air quality (on the example of Krasnoyarsk Krai) to identify the priority factors of atmospheric air that form the greatest contribution to the associated morbidity. The qualitative characteristics of the atmosphere and the level of morbidity of the population in Krasnoyarsk Krai are estimated on the basis of official data of federal and industry statistics. The analysis of the primary morbidity of the population, designated by the WHO as an indication of the effect of environmental factors, has been carried out. The authors calculate the number of third-party cases of diseases associated with the quality of the atmosphere and determine the priority risk factors. High proportions of air samples that do not meet the standards for the content of heavy metals, benz(a)pyrene, xylenes (59% of samples) have been registered over the territory of Krasnoyarsk Krai. Since 2012, there has been an increase of 0.2%–6.4% in the indicators of primary morbidity, indicated by the WHO as an indicator of the effect of environmental factors and a significant increase in congenital anomalies (the growth rate is 93.2%). Up to 231 thousand third-party cases of diseases of the general population with diseases affecting the respiratory organs, circulatory system, hematopoietic organs, nervous system, eyes and their appendages determine the presence of aromatic hydrocarbons, nitrogen dioxide, hydroxybenzene (as well as its derivatives), benz(a)pyrene, nitrogen oxide, ammonia, dihydrosulfide and carbon disulfide, sulfur dioxide in the air in concentrations exceeding the standards. The most substantial number of diseases associated with air quality forms in the class of respiratory diseases (2020 – 587.4 cases per 100 thousand population, 64.5% of the total air-associated morbidity).
With climate change and urbanization, the rainstorm disaster has become one of the major climate risks in constructing the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Based on daily precipitation, socio-economic data and a comprehensive evaluation method, the characteristics and risks of rainstorm disaster in the GBA at county level are investigated in this paper. Considering the hazard, vulnerability and exposure, the risks of rainstorm disasters are high (Level 3) and very high (Level 4) in Zhuhai, Zhongshan, Dongguan, Jiangmen and Shenzhen. From 1990 to 2018, the frequency and intensity of rainstorms in the GBA have increased by 10 times and 2 mm per decade, respectively. Moreover, the population, gross domestic product, and built-up areas have increased significantly in the GBA, while the vegetation coverage has decreased. The combination of these changes results in an increase in the frequency of rainstorm disasters by two times from 2005 to 2018 compared with that from 1990 to 2004. The findings of this study can provide a positive reference for local governments to improve the pertinence of the strategies for rainstorm disaster prevention and mitigation.
The purpose of this article is to develop a theoretical disaster risk reduction model, creating a virtuous cycle of knowledge and action across the festival and events industry, based on occupational safety and health (OSH) strategic objectives, as informed by a systematic literature review. The main constructs of this conceptual article are explored through a systematic literature review. Sources include publications of key stakeholders in the festival and event industry, applicable global directives, strategic documents of relevant governmental and non-governmental organisations and academic publications. From the data gathered in the systematic literature review it can be surmised that sustainable development goals (SDGs) related research in tourism, festivals, events and OSH is lacking in quantity and there is room for these aspects to be addressed in future research to ensure that these fields of study make a more substantial contribution to disaster risk reduction in festival and event management. This article is limited to secondary data collected through a systematic literature review, supported by additional literature to inform a theoretical framework incorporating SDGs, disaster risk reduction and OSH strategic objectives for festivals and events. Sustainable development goals are aimed at achieving a sustainable future for all. The detrimental effect of OSH incidents can be counterproductive to achieving such goals and should therefore be closely monitored and managed. Festival and event managers should thus take cognisance of the importance of OSH through a plan of action, benchmarked against best practice, to allow for enhanced disaster risk management. This article investigates the concepts of sustainability, disaster risk reduction, OSH, events and festival management and combines the concepts in a unique manner.