Under the combined effects of climate change and human activities, the evolution of hydrological drought in river basins has become complex. Using monthly runoff data from six major Chinese rivers, a coupled machine learning-hydrological model was applied to simulate runoff changes, and the standardized runoff index (SRI) was used to quantify the contributions of climate and human factors to hydrological drought. The results show that: (1) the coupled model outperformed single models, especially in environmentally complex basins. (2) On a monthly scale, human activities were the primary driver of hydrological drought in the Upper Yangtze River Basin from January to March, May, September, and October. Climate change dominated the monthly drought evolution in the source regions of the Yellow River, Upper Pearl River, Middle-Upper Songhua River, Upper Huaihe River, and the source region of Lancang River in April, June–August, and October. (3) Seasonally, both factors influenced the Upper Yangtze, while climate change dominated other basins (except the Lancang in spring and summer). (4) Overall, climate change was the main driver in most basins, while human activity dominated in the Lancang River Basin.
We introduce $\textbf{Slippage-at-Risk (SaR)}$, a quantitative framework for measuring liquidity risk in perpetual futures exchanges. Unlike backward-looking metrics such as Value-at-Risk computed on historical returns or realized deficit distributions, SaR provides a \emph{forward-looking} assessment of liquidation execution risk derived from current order book microstructure. The framework comprises three complementary metrics: $SaR(α)$, the cross-sectional slippage quantile; $ESaR(α)$, the expected slippage in the distributional tail; and $TSaR(α)$, the aggregate dollar-denominated tail slippage. We extend the base framework with a \emph{concentration adjustment} that penalizes fragile liquidity structures where a small number of market makers dominate quote provision. Drawing on recent work by Chitra et al. (2025) on autodeleveraging mechanisms and insurance fund optimization, we establish a direct mapping from SaR metrics to optimal capital requirements. Empirical analysis using Hyperliquid order book data, including the October 10, 2025 liquidation cascade, demonstrates SaR's predictive validity as a leading indicator of systemic stress. We conclude with practical implementation guidance and discuss philosophical implications for risk management in decentralized financial systems.
Systematic investment strategies are exposed to a subtle but pervasive vulnerability: the progressive erosion of their effectiveness as market regimes change. Traditional risk measures, designed to capture volatility or drawdowns, overlook this form of structural fragility. This article introduces a quantitative framework for assessing the durability of systematic strategies through minimum regime performance (MRP), defined as the lowest realized risk-adjusted return across distinct historical regimes. MRP serves as a lower bound on a strategy's robustness, capturing how performance deteriorates when underlying relationships weaken or competitive pressures compress alpha. Applied to a broad universe of established factor strategies, the measure reveals a consistent trade-off between efficiency and resilience -- strategies with higher long-term Sharpe ratios do not always exhibit higher MRPs. By translating the persistence of investment efficacy into a measurable quantity, the framework provides investors with a practical diagnostic for identifying and managing strategy-decay risk, a novel dimension of portfolio fragility that complements traditional measures of market and liquidity risk.
The rapid adoption of Industry 4.0 technologies has expanded the industrial Internet of Things (IoT) ecosystem, creating new opportunities for automation, efficiency, and data-driven innovation. However, this transformation has also intensified cybersecurity risks, as interconnected devices and real-time data exchanges increase the attack surface of industrial systems. This paper proposes a Business Intelligence (BI)-driven framework for managing IoT security risks in Industry 4.0 environments. The framework integrates advanced analytics, threat intelligence, and predictive modeling to support proactive decision-making and incident response. By leveraging BI tools, organizations can correlate data across operational technology (OT) and information technology (IT) layers to identify vulnerabilities, assess risk exposure, and optimize mitigation strategies. The study highlights how data visualization, automated reporting, and real-time dashboards enhance situational awareness and resilience against cyber threats. Through this approach, industrial enterprises can transform cybersecurity management from a reactive process into a strategic capability that aligns with business performance and operational excellence. The framework also underscores the importance of continuous monitoring, governance, and collaboration among stakeholders to sustain security in the evolving Industry 4.0 landscape.
Landslide susceptibility assessment and attribution analysis of triggering factors are essential for regional risk management. However, existing methods face challenges such as subjectivity in determining factor weights and insufficient capacity to reveal the complex nonlinear mechanisms and causal relationships of landslide occurrences. To address these issues, this study proposes a GeoDetector-based Weighted Information Value-Logistic Regression (WIV-LR) model for Yunnan Province, combined with the Geographical Convergent Cross Mapping (GCCM) method to explore the complex causal relationships between susceptibility and influencing factors. The results show that: (1) the WIV-LR model achieves high predictive accuracy (AUC = 0.886), effectively predicting landslide occurrences in Yunnan; (2) landslide susceptibility exhibits significant spatial heterogeneity, with very high and high susceptibility zones mainly distributed in western, central, and northeastern Yunnan, accounting for 41.14% of the total area; (3) GCCM reveals significant bidirectional causal relationships between elevation, slope, soil moisture, rainfall, and landslide susceptibility, while lithology and seismic magnitude show unidirectional causal relationships. Elevation, slope, and relief control the distribution of gravitational potential energy and serve as the main driving forces for landslides. This study provides a scientific basis for landslide risk assessment, targeted prevention, and disaster reduction planning in Yunnan and similar regions.
Popović Ivana, Radosavljević Katica, Pătărlăgeanu Simona Roxana
et al.
One of the biggest problems faced by the world business is the constant struggle to stay competitive in the market. The acquired knowledge and satisfied numerous technical and organizational conditions have resulted in the development of a whole range of modern management techniques whose meaningful application should enable the acquisition and presentation of relevant management models. Namely, in the light of these new circumstances, in addition to strategic and operational management, companies introduce controlling into the basic activities of corporate management. The aim of this article is to understand the importance of the controlling introduction, its role in the business process, its place in the organizational structure of the company, and the potential benefits that controlling brings. This study addresses a research gap in the literature by examining the implementation of financial controlling practices in the energy sector of a non-EU transition economy. While extensive research has been conducted in the Western European contexts, empirical studies focusing on Southeast European countries, particularly Serbia, remain limited. The article contributes to filling this gap by providing survey-based insights into managerial perceptions, controlling functions, and risk management tools in a transitional regulatory and institutional environment. The added value of this study lies in its empirical examination of financial controlling within a transition economy context, offering sector-specific insights from Serbia’s energy industry. Unlike existing studies focusing on developed markets, this article highlights managerial attitudes, applied tools, and risk-related practices in an underexplored regulatory environment. The aim of this work is to determine the influence of controlling in Serbia based on the results of practical research. Testing of statistical hypotheses proved the importance of controlling in the company. The value of this work is that it was observed that traditional management approaches, based on information obtained from conventional systems, are not sufficient and that they need to be upgraded with modern controlling techniques.
Phase change materials (PCMs) have been limited in their energy storage applications due to inherent defects, such as leakage and low thermal conductivity. Consequently, the development of highly efficient adsorptive PCMs has become a focal point of research. By constructing a three-dimensional (3D) porous aerogel support skeleton, a new composite PCM (CPCM) with a high loading rate (more tham 98%), leakage prevention, and multifunctional properties has been successfully developed. The system innovatively integrates a 3D MXene framework, constructed via the ice template method, which enhances the thermal conductivity from 0.374 W/(m·K) to 1.388 W/(m·K) and achieves 43 dB electromagnetic shielding efficiency in the Ku-band (12–18 GHz). Owing to the synergistic design, the material exhibits significant electro-thermal conversion, with a local temperature rise of 91 °C at an input voltage of 15 V. Additionally, its high energy storage density (latent heat value more than 175 J·g−1) and easy-to-shape characteristics offer potential for multi-scenario applications in electronic thermal safety management and smart energy storage systems.
Disasters and engineering, Risk in industry. Risk management
In today’s rapidly evolving organizations, talent management plays a critical role in driving sustainable growth. Talents, particularly those exhibiting leadership potential, are often seen as essential assets for organizational development. However, the presence of high employee’s leadership potential can also generate adverse emotional reactions from leaders, potentially leading to behaviors such as leader jealousy and leader ostracism. This study investigates the dark side of employee’s leadership potential by examining the mechanisms through which employee’s leadership potential influences leader ostracism, with leader jealousy acting as a mediator. Drawing on social comparison theory, we propose a theoretical model that includes organizational competitive climate and leader’s core self-evaluation as moderating factors. Using a three-wave survey of 672 leaders in the Chinese construction industry, hierarchical regression analysis was employed to test the hypotheses. The results show that employee’s leadership potential significantly increases both leader jealousy and leader ostracism, with leader jealousy serving as a mediator. Moreover, a high organizational competitive climate strengthens the relationship between employee’s leadership potential and leader jealousy, thereby enhancing the entire mediated effect. In contrast, high leader core self-evaluation weakens the relationship between employee’s leadership potential and leader jealousy, reducing the likelihood of leader ostracism and attenuating the mediated effect. This study provides both theoretical contributions and practical insights for organizations seeking to manage high-leadership potential employees while minimizing the risk of negative leadership behaviors.
The frequent occurrence of natural disasters has posed significant challenges to society, necessitating the urgent development of effective risk management strategies. From the early informal community-based risk sharing mechanisms to modern formal index insurance products, risk management tools have continuously evolved. Although index insurance provides an effective risk transfer mechanism in theory, it still faces the problems of basis risk and pricing in practice. At the same time, in the presence of informal community risk sharing mechanisms, the competitiveness of index insurance deserves further investigation. Here we propose a three-strategy evolutionary game model, which simultaneously examines the competitive relationship between formal index insurance purchasing (I), informal risk sharing strategies (S), and complete non-insurance (A). Furthermore, we introduce a method for calculating insurance company profits to aid in the optimal pricing of index insurance products. We find that basis risk and risk loss ratio have significant impacts on insurance adoption rate. Under scenarios with low basis risk and high loss ratios, index insurance is more popular; meanwhile, when the loss ratio is moderate, an informal risk sharing strategy is the preferred option. Conversely, when the loss ratio is low, individuals tend to forego any insurance. Furthermore, accurately assessing the degree of risk aversion and determining the appropriate ratio of risk sharing are crucial for predicting the future market sales of index insurance.
Patience Mbola, Davies V. Nkosi, Oyewale M. Morakinyo
The growing frequency and severity of disasters worldwide have highlighted the need for environmental health practitioners to be equipped with specialised training to respond effectively to evolving public health contexts. Disasters can have long-lasting impacts on the environment and environmental health services, necessitating prompt and effective responses. However, the current environmental health workforce faces challenges in acquiring the necessary competencies to address environmental health threats during disasters. This narrative review synthesises existing literature on disaster management education for environmental health professionals, exploring current training, advancements and emerging trends. The review follows Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and includes a total of 45 records that met inclusion criteria (compromising 15 peer-reviewed articles and 30 training records) published between 2002 and 2023. Findings highlight the expansion of environmental health degree programmes to include disaster management, better preparing newly qualified practitioners. However, a knowledge gap remains for previously qualified practitioners. High-income countries prioritising capacity building for environmental health practitioners in disaster management are better equipped to respond to and mitigate disasters.
Contribution: The review suggests that with proper basic training for disaster responders, more lives can be saved during and after disasters. It highlights the insufficiency of current training programmes and emphasises the need for advanced role-specific training for environmental health practitioners. The review emphasises the need for advanced role-specific training, community assessment skills and focused disaster response strategies to enhance environmental health practitioners’ ability to respond to disasters and improve public health resilience. Enhanced training, capacity building and collaboration are necessary to improve the competencies, skills and knowledge of environmental health practitioners in disaster risk management and public health emergencies.
This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced. Second, the process of text data preprocessing and feature extraction is described. Finally, the effectiveness and predictive performance of the model are validated through empirical research. The results show that the NLP-based financial risk detection model performs excellently in risk identification and prediction, providing effective risk management tools for financial institutions. This study offers valuable references for the field of financial risk management, utilizing advanced NLP techniques to improve the accuracy and efficiency of financial risk detection.
In the financial field, precise risk assessment tools are essential for decision-making. Recent studies have challenged the notion that traditional network loss functions like Mean Square Error (MSE) are adequate, especially under extreme risk conditions that can lead to significant losses during market upheavals. Transformers and Transformer-based models are now widely used in financial forecasting according to their outstanding performance in time-series-related predictions. However, these models typically lack sensitivity to extreme risks and often underestimate great financial losses. To address this problem, we introduce a novel loss function, the Loss-at-Risk, which incorporates Value at Risk (VaR) and Conditional Value at Risk (CVaR) into Transformer models. This integration allows Transformer models to recognize potential extreme losses and further improves their capability to handle high-stakes financial decisions. Moreover, we conduct a series of experiments with highly volatile financial datasets to demonstrate that our Loss-at-Risk function improves the Transformers' risk prediction and management capabilities without compromising their decision-making accuracy or efficiency. The results demonstrate that integrating risk-aware metrics during training enhances the Transformers' risk assessment capabilities while preserving their core strengths in decision-making and reasoning across diverse scenarios.
We introduce the concept of partial law invariance, generalizing the concepts of law invariance and probabilistic sophistication widely used in decision theory, as well as statistical and financial applications. This new concept is motivated by practical considerations of decision making under uncertainty, thus connecting the literature on decision theory and that on financial risk management. We fully characterize partially law-invariant coherent risk measures via a novel representation formula. Strong partial law invariance is defined to bridge the gap between the above characterization and the classic representation formula of Kusuoka. We propose a few classes of new risk measures, including partially law-invariant versions of the Expected Shortfall and the entropic risk measures, and illustrate their applications in risk assessment under different types of uncertainty. We provide a tractable optimization formula for computing a class of partially law-invariant coherent risk measures and give a numerical example.
Magdalena Kowacka, Dariusz Skorupka, Agnieszka Bekisz
et al.
The implementation of processes comprising the overall project management consists in the use of various tools, methods and techniques depending on the type of the project. The knowledge of the industry and the characteristics of construction projects make it possible to select those which, on the one hand, will not cause difficulties for the contractors and on the other, will, in fact, constitute a necessary complement to the technical skills of the project manager. Construction companies face situations that have a profound impact on the failure of projects. Such occurrences include a large number of simultaneously implemented projects, the appointment of a person who knows the project mainly from the implementation side as the project manager, a failure to perform risk analysis and procedures that become irrelevant when deadlines are approaching. After reviewing the available construction projects, analysing the literature, consulting experts and making observations, the authors determined that the majority of difficulties and failures result from omissions or errors that take place during the project planning and implementation stages. The following paper outlines the selected elements of project management, whose application in construction projects may significantly affect their final success and the results obtained. It also includes an example of the use of modern management methods, which certainly include risk management methods. A utilitarian tool addressing the effects of risk analysis is a contingency plan. Contingency may be translated as eventuality, possibility and sometimes also as coincidence.
– This paper shows a new methodology for evaluating the value and sensitivity of autocall knock-in type equity-linked securities. While the existing evaluation methods, Monte Carlo simulation and finite difference method, have limitations in underestimating the knock-in effect, which is one of the important characteristics of this type, this paper presents a precise joint probability formula for multiple autocall chances and knock-in events. Based on this, the calculation results obtained by utilizing numerical and Monte Carlo integration are presented and compared with those of existing models. The results of the proposed model show notable improvements in terms of accuracy and calculation time.
This paper introduces a novel approach to financial risk assessment by incorporating topological data analysis (TDA), specifically cohomology groups, into the evaluation of equities portfolios. The study aims to go beyond traditional risk measures like Value at Risk (VaR) and Conditional Value at Risk (CVaR), offering a more nuanced understanding of market complexities. Using last one year daily real-world closing price return data for three equities Apple, Microsoft and Google , we developed a new topological riskmeasure, termed Topological VaR Distance (TVaRD). Preliminary results indicate a significant change in the density of the point cloud representing the financial time series during stress conditions, suggesting that TVaRD may offer additional insights into portfolio risk and has the potential to complement existing risk management tools.
Offshore and marine renewable energy applications are governed by a number of uncertainties relevant to the design process and operational management of assets. Risk and reliability analysis methods can allow for systematic assessment of these uncertainties, supporting decisions integrating associated consequences in case of unexpected events. This paper focuses on the review and classification of such methods applied specifically within the offshore wind industry. The quite broad differentiation between qualitative and quantitative methods, as well as some which could belong to both groups depending on the way in which they are used, is further differentiated, based on the most commonly applied theories. Besides the traditional qualitative failure mode, tree, diagrammatic, and hazard analyses, more sophisticated and novel techniques, such as correlation failure mode analysis, threat matrix, or dynamic fault tree analysis, are coming to the fore. Similarly, the well-practised quantitative approaches of an analytical nature, such as the concept of limit states and first or second order reliability methods, and of a stochastic nature, such as Monte Carlo simulation, response surface, or importance sampling methods, are still common practice. Further, Bayesian approaches, reliability-based design optimisation tools, multivariate analyses, fuzzy set theory, and data pooling strategies are finding more and more use within the reliability assessment of offshore and marine renewable energy assets.
Abstract Construction industry has one of the highest rates of fatalities and injuries compared to other industries, despite technological advancements and implementations of occupational health and safety initiatives. In this paper, a systematic review has been conducted on the contemporary literature of safety risk management. The interface with system modeling has been investigated to identify correlations between the two, and opportunities for improving project performance metrics such as quality, productivity, and cost. Findings show that simulation and optimization technics have advanced in the past 20 years but there is room for improvement when it comes to modeling safety related risks. This review paper contributes to the literature of safety management by providing insight into dynamics of different simulation and optimization modeling techniques. Future research opportunities have been identified including the need for construction safety research on integrating multi-method modeling approaches.