Authors - Wonseong Kim Abstract - Geopolitical risk now tends to surface in information environments well before it shows up as physical disruption or moves in market prices, and often before any policy response. Current geopolitical risk indices track the salience of news, and a separate body of work on disinformation detection looks for signals of manipulation. What neither line of work does is connect manipulated discourse to the channels through which food, energy, supply chain, sanctions, and macro-financial risks are actually transmitted. To address this gap, the paper develops a DISARM-informed large language model framework for narrative-based monitoring of disinformation-driven geopolitical risks. The framework is organised as a four-layer architecture that brings together observable manipulation signals, the classification of narrative function, mapping onto risk domains, and the construction of indicators. From these layers it derives interpretable indicators that capture manipulated risk discourse, gaps in framing, concentration of narratives, and transmission across domains. Our central claim is a methodological one: once observable manipulation, narrative function, and risk-domain mapping are represented together, unstructured multilingual media can be turned into auditable early-warning signals. We intend the result as a decision-support instrument for sustainable security monitoring, not as a means of attribution or causal estimation.
Authors - Nassour Annour Saad, Mahamat Issa Hassan, Marayi Choroma, Mahamat Atteib Ibrahim Doutoum, Djaury Dadjia Abstract - Pediatric type 1 diabetes (T1D) remains a major public health priority, especially in low-resource settings where presenting diabetic ketoacidosis is still common. This systematic review (2020–2025), conducted under PRISMA 2020 and complemented by TRIPOD/TRIPOD+AIinspired criteria for predictive models, synthesizes AI/ML work on early screening and risk stratification in children. Islet autoantibodies and genetic risk scores improve discrimination, but the literature shows substantial AUC variability depending on sample size, calibration, and validation design (single split versus repeated or family-level validation). Ensemble models often outperform classical approaches with multimodal data. We emphasize external validation, class-imbalance handling, and reproducible pipelines. The main gap remains the absence of models simultaneously integrating autoantibodies, HLA/GRS, and C-peptide.
Authors - Imane Bari, Abdellatif Aziki, Zineb Alaoui Abstract - This study analyses the relationship between digital operational risk management and the financial performance of industrial firms in the Agadir region of Morocco and investigates the role of artificial intelligence in risk governance. Using a quantitative survey of 50 industrial firms and linear regression with principal component analysis, the results show that a structured digital risk management framework, covering identification, assessment, and mitigation of threats, is positively and significantly associated with financial performance (R = 58.7%, F = 3.518, p < 0.05). Several constraints are identified, including skill shortages, limited technological resources, and insufficient digital governance culture. The study further shows that AI-based tools, through automated anomaly detection and predictive analysis, strengthen the effectiveness of risk management frameworks. These findings support the integration of AI as a technical component of operational risk governance in industrial settings.
Authors - Libero Nigro, Franco Cicirelli Abstract - This paper builds on the Hartigan-Wong (HW) algorithm for unsupervised clustering. Although basic HW comes with an intrinsic high computational cost, it is known to be a better solution than K-Means, because it is less likely to get stuck around a sub-optimal solution of the data space. The paper, in particular, proposes a variant of HW, named Evolutionary HW (E-HW), which embodies genetic concepts and favors the achievement of more accurate clustering. E-HW depends on the use of a population of candidate solutions (centroid configurations), preliminarily created. E-HW is fed by a solution extracted from the population, which gets refined (crossed) and possibly replaced (mutated) following the basic HW operations. New generations of the population then come into existence. E-HW can be repeated a certain number of times, after that, experimental results highlight that the population favors the emergence of a solution close to the optimal one. To smooth out the computational burden, many operations of E-HW are implemented in parallel Java, so as to exploit the computing benefits of modern multi-core machines. The paper demonstrates the effectiveness of E-HW by using a collection of benchmark datasets, and the clustering results are compared with those achieved by competitor algorithms.
Authors - Ilgar G. Aliyev, Konul Gafarbayli, Firangiz Mammadrzayeva Abstract - Modern parallel gas pipeline systems require intelligent and operationally reliable emergency-management mechanisms capable of distinguishing real leakage events from normal technological transients under real-time operating conditions. Although IoT- and SCADA-based monitoring technologies are widely used in modern gas transmission infrastructures, most existing systems primarily rely on threshold-based supervision or empirical data-driven methods, which often lack physical interpretability and analytical decision-making capability. This paper proposes an intelligent IoT-driven emergency-management and analytical decision-making framework for parallel gas pipelines based on the integration of digital monitoring technologies with analytical gas-dynamic modeling. The proposed cyber-physical architecture combines wireless pressure sensors, SCADA-assisted supervisory control, synchronized shut-off valves, and analytical decision algorithms to ensure real-time identification, localization, and mitigation of emergency operating modes. Analytical criteria are developed for distinguishing emergency and technological pressure variations, estimating emergency detection time, localizing the leakage coordinate, and determining the optimal activation time of interconnecting pipeline valves. The proposed framework enables rapid isolation of damaged pipeline sections while ensuring adaptive gas redistribution through intact parallel lines. Unlike conventional monitoring-based approaches, the developed methodology transforms emergency control into an analytically justified intelligent supervision mechanism capable of minimizing gas losses, preventing cascade disturbances, and improving operational sustainability. The integration of IoT-based sensing with analytical decision-making additionally improves compatibility with Industry 4.0 and digital twin concepts for future smart gas transmission infrastructures.
Authors - Kirill Kalichkin, Tatiana Gritskevich Abstract - The study is devoted to the analysis of vibration agnostics problems as a method of preventive control in the design of wheel sets of railway locomotives. The study examines vibration agnostics as a preventative control method for designing individual mechanisms and components of railway locomotive wheel sets designed for long-term, safe operation. Currently, the main issue with the mechanical drives of wheel-motor unit assemblies and motor-anchor bearing assemblies in railway locomotive wheel sets remains increased vibration during highfrequency operation. The authors analyze the prediction of potential defects using digital twins, the goal of which is to enable engineers to accurately predict solutions when similar defect signs are detected during operation of different digital twin scenarios. This enables the development of preventative measures to prevent accidents at the early stages of wheel set defect development.
