This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

This study investigates seismic risk and potential impacts of future earthquakes in the Sunda Strait region, known for its susceptibility to significant seismic events due to the subduction of the Indo-Australian Plate beneath the Eurasian Plate. The aim is to assess the likelihood of major earthquakes, estimate their impact, and propose strategies to mitigate associated risks. The research uses historical seismic data and probabilistic models to forecast earthquakes with magnitudes ranging from 6.0 to 8.2 Mw. The Gutenberg-Richter model helps project potential earthquake occurrences and their impacts. The findings suggest that the probability of a major earthquake could occur as early as 2026–2027, with a more significant event estimated to likely occur around 2031. Economic estimates for a 7.8–8.2 Mw earthquake suggest potential damage of up to USD 1.255 billion with significant loss of life. The study identifies key vulnerabilities, such as inadequate building foundations and ineffective disaster management infrastructure, which could worsen the impact of future seismic events. In conclusion, the research highlights the urgent need for comprehensive seismic risk mitigation strategies. Recommendations include reinforcing infrastructure to comply with seismic standards, implementing advanced early warning systems, and enhancing public education on earthquake preparedness. Additionally, government policies must address these issues by increasing funding for disaster management, enforcing building regulations, and incorporating traditional knowledge into construction practices. These measures are essential to reducing future earthquake impacts and improving community resilience.

While extensive research has explored interconnectedness, volatility spillovers, and risk transmission across financial systems, the comparative dynamics between Islamic and conventional banks during crises, particularly in specific regions such as Saudi Arabia, are underexplored. This study investigates risk transmissions and contagion among banks operating in Islamic and conventional modes in the Kingdom of Saudi Arabia. Daily banking stock data spanning November 2018 to November 2023, encompassing two major crises—COVID-19 and the Russian-Ukraine war—were analyzed. Using the frequency TVP-VAR approach, the study reveals that average total connectedness for both banking groups exceeds 50%, with short-run risk transmission dominating over long-term effects. Graphical visualizations highlight time-varying connectedness, driven predominantly by short-run spillovers, with similar patterns observed in both Islamic and conventional banking networks. The main contribution of this paper is the insight that long-term investment strategies are crucial for mitigating potential risks in the Saudi banking system, given its limited diversification opportunities.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

In the process of forest recreation value development, there are some characteristics, such as large amount of investment capital, long financing recovery cycle and high potential risks, which lead to limited capital source and prominent financing risks. To achieve sustainable development, forest recreational value development enterprises must solve the financing dilemma, therefore, it is very urgent to identify the financing risk factors. The research constructed financing risk evaluation index system through WSR (Wuli-Shili-Renli) methodology (from affair law, matter principle and human art dimensions), taking S National Forest Park at Fujian Province as a case study, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method were used for empirical analysis. The results showed that for the first level indicators, operational risk should be paid close attention to, followed by political risk and environmental risk. Among the secondary level indicators, policy changes, financing availability and market demand need attention, which are consistent with the result of field survey. Based on that, countermeasures were put forward such as the multiple collaborative linkage and effective internal control; reduction on operating costs and broaden financing channels; encouragement diversification of investment entities and improvement of financial and credit support; strengthening government credit supervision, optimizing financing risk evaluation, and building a smart tourism financing information platform, to reduce and control financing risks, then promote the development of forest recreation value projects.