Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and control, it also increases vulnerability to cyber-physical disruptions. Operators must distinguish physical incidents, such as faults or line disturbances, from malicious actions, such as false data injection or unauthorized command execution. This chapter investigates this problem using the well-known MSU/ORNL Power System Attack Dataset. The proposed method combines machine learning with genetic-algorithm-based feature selection. The objective is twofold: to classify attack and natural events accurately, and to determine whether a reduced set of physically informative PMU/IED measurements can support reliable detection. Several baseline models are evaluated, including logistic regression, RBF-SVM, XGBoost, Random Forest, and Extra Trees. The results show that tree-based ensemble models are the most effective for the considered dataset, with Extra Trees providing the strongest full-feature baseline. After feature selection, the GA + Extra Trees model reduces the clean PMU feature space from 112 attributes to an average of 27.4 attributes over five runs, while increasing macro-F1 from 0.9118 to 0.9212 and ROC-AUC from 0.9791 to 0.9837. These results indicate that many synchronized electrical measurements are redundant. A compact subset of phasor-based features can still provide accurate and interpretable anomaly detection in smart grids.

[This corrects the article DOI: 10.1371/journal.pone.0315011.].

The Kashmir issue is a long-standing international dispute with significant regional and global implications for contemporary relations, marked by episodic tensions, sovereignty questions, and humanitarian concerns. Despite decades of negotiations, sustained United Nations engagement, and bilateral dialogue, the issue remains unresolved, while the continued involvement of international regulatory systems reflects ongoing efforts to support regional stability and constructive dialogue. By revisiting Halford Mackinder’s Heartland theory, the paper highlights the strategic significance of Kashmir within the broader Asian geographical context and security dynamics. Located at the intersection of South Asia, Central Asia, and China’s western edge, Kashmir represents a crucial focal point for India and Pakistan, a condition that further contributes to the persistence and complexity of competing claims. Methodologically, the study uses a qualitative research design that combines analysis with contextual assessment. The methodological approach includes a review of classical geopolitical theory and an examination of Kashmir's strategic location between South and Central Asia. The findings highlight the enduring significance of Kashmir not only as a geographic and strategic pivot, but also as a region that exemplifies the complex interactions between regional actors and broader international dynamics. By integrating classical geopolitical insights with an understanding of contemporary strategic considerations, the paper provides a comprehensive perspective on why Kashmir continues to shape security, diplomacy, and strategic planning in Asia.

Die Salmonellose stellt aufgrund ihres hohen Potenzials für lebensmittelbedingte Infektionen und ihrer langfristigen Persistenz in landwirtschaflichen Betrieben nach wie vor ein großes zoonotisches Problem in der modernen Masthähnchenproduktion dar. Ziel dieser Studie war es, Salmonella spp.-Serotypen aus kommerziellen Masthähnchenbeständen in Bosnien und Herzegowina während der Primärproduktionsphase zu isolieren und zu identifzieren sowie das Vorkommen von Serotypen zu untersuchen, die für die öffentliche Gesundheit relevant sind. Die Studie umfasste eine detaillierte Analyse von 1810 Proben, die drei Wochen vor der geplanten Schlachtung entnommen wurden. Es wurden zwei sich ergänzende Probenahmemethoden verwendet: Überschuhe (n = 1200) und Kotproben (n = 610). Die Isolierung und Identifzierung im Labor erfolgte streng nach der standardisierten horizontalen Methode ISO 6579-1:2017. Das Verfahren umfasste eine nicht-selektive Anreicherung in gepuffertem Peptonwasser bei 37 ± 1 °C, gefolgt von einer selektiven Anreicherung auf modifziertem halbfestem Rappaport-Vassiliadis-Agar (MSRV) bei 41,5 ± 1 °C. Die Isolierung erfolgte auf selektiven XLD- und BG-Agar-Medien. Die endgültige Bestätigung der Isolate erfolgte mittels biochemischer Testreihen und des kommerziellen API-20E- Systems. Die Serotypisierung erfolgte durch Agglutination auf Objektträgern unter Verwendung spezifscher Ound H-Antiseren gemäß dem White-Kauffmann-Le-Minor-Schema. Von der Gesamtzahl der Proben wurden in 36 Fällen (1,98 %) Salmonella spp. isoliert. Die Ergebnisse zeigten, dass Abstriche von Überschuhen eine sensitivere Methode darstellten (2,3 %) als Kotproben (1,5 %). Der vorherrschende Serotyp war S. Enteritidis (80,5 %), gefolgt von S. Typhimurium (16,6 %) und S. Senfenberg (2,8 %). Zusätzlich wurden 14 Isolate von Wildtieren (Wildente, Taube, Chinchilla und Steinadler) analysiert, wobei Serotypen wie S. Seremban und S. Reading nachgewiesen wurden. Die Dominanz von S. Enteritidis deutet auf ein ernstes Risiko für die öffentliche Gesundheit hin, da dieser Serotyp die häufgste Ursache für lebensmittelbedingte Infektionen beim Menschen ist. Die Ergebnisse unterstreichen die Notwendigkeit einer kontinuierlichen Überwachung und einer strengeren Umsetzung von Biosicherheitsmaßnahmen in landwirtschaflichen Betrieben.

Secure multiparty computation (MPC) enables privacy-preserving data analysis across distributed computing parties, but its practicality remains limited by the cost and inaccuracy of evaluating complex nonlinear functions. Although secure multiplications are efficiently supported by Beaver multiplication triples-a cornerstone technique in MPC-existing protocols rely on polynomial approximations to evaluate more sophisticated functions, often incurring significant computational overhead and numerical imprecision. We present Decor, a framework that generalizes the core principle behind the Beaver triples to a broad class of nonlinear functions, enabling the construction of efficient MPC primitives. Decor delegates costly nonlinear operations to computations involving only random values, which can be executed in an offline preprocessing phase by a trusted dealer without access to private data. This design enables efficient and accurate evaluation of diverse functions, including trigonometric, hyperbolic, exponential, and sigmoid functions, and introduces a new general-purpose function approximation method for MPC based on Fourier series. Our experiments show that Decor achieves orders-ofmagnitude improvements in accuracy while maintaining comparable or faster runtimes than existing approaches, leading to substantial utility gains for downstream applications such as implicit neural representation of images and logistic model estimation in genome-wide association studies. By demonstrating how randomized preprocessing can yield enhanced MPC primitives, Decor establishes a new framework for practical, privacy-preserving computation.

This study aimed to provide a more in-depth analysis of the fruit quality of three table grape varieties: 'Moldova', 'Lasta', and 'Italia', cultivated in the Žepče area (Bosnia and Herzegovina). The findings from the comparative analysis indicated substantial variations in grape quality among the studied varieties. 'Moldova' grapes exhibited significantly higher total soluble solids and pH values than those of the other table grape varieties. 'Moldova' also had higher total phenolic and flavonoid contents in the grape skin relative to 'Italia' and 'Lasta'. On the other hand, 'Italia' variety showed the highest titratable acidity, followed by 'Lasta' and 'Moldova'. Total phenolic and flavonoid contents were highly positively correlated with the antioxidant capacities of all analyzed grape samples, suggesting that phenolic compounds contribute significantly to the antioxidant properties of grapes. Study results also indicated that all heavy metal levels tested in grapes were below the threshold limits, which was expected considering that the experimental soil was not contaminated with the heavy metals being assessed. Overall, the results from the study have shown that all grape varieties studied hereby displayed a satisfactory level of quality based on key chemical parameters, and that the experimental area is quite favorable for their cultivation.

ABSTRACT Guano samples were collected from nine bat roosts across Bosnia and Herzegovina (caves, tunnels and an abandoned underground quarry) between August 2021 and November 2022. Samples originated from colonies of both sedentary and migratory species. Concentrations of potentially toxic elements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) were analysed using flame atomic absorption spectrometry (FAAS). A composite PTI (based on normalised Cd, Cu, Pb and Zn) was calculated to support spatial screening, and KDE mapping was used to visualise the clustering of higher-index sites. Two sites showed evidence of direct anthropogenic pressure, while several others may have been indirectly influenced. Sites associated with anthropogenic activities were compared with those outside apparent pollution sources. The highest concentrations were observed for Fe, Zn, Mn and Cu. Marked interspecific differences at the same site suggest that migratory behaviour and roosting habits may modulate exposure to pollutants. This pilot study provides baseline data for Bosnia and Herzegovina and supports the use of bat guano as a non-invasive biomonitoring matrix for PTE contamination.

The semiconductor industry is foundational to modern technology, yet its complex global multi-relational firm network remains poorly understood, posing challenges to scientists, firms, and policymakers. Traditional analysis relies on proprietary databases that are often expensive, incomplete, and slowly updated, limiting their ability to capture rapidly evolving dependencies. Here, we demonstrate that a novel, generalizable methodology combining Large Language Models (LLMs) with open web data can reconstruct this network and its structural dynamics at scale. We identify and classify supply-chain, partnership, and ownership links from 170 million semiconductor firm webpages, yielding a temporal network of over 1,300 linked firms. We validate link-extraction quality (Precision: 0.884; F1-score: 0.784), network overlap and complementarity with a proprietary database, and consistency with aggregate economic data. Our network reveals a temporary 9% decline in edges during the 2022 chip shortage, rapid increases in the centrality of AI supply-chain bottleneck firms such as NVIDIA, and geographic realignment of interfirm relations amid geopolitical turbulence. This generalizable framework overcomes barriers to transparency and provides essential, up-to-date maps for assessing resilience and informing policy across strategically relevant sectors.

<p><i>For decades, the just-in-time (JIT) paradigm has defined operational efficiency by minimizing inventories and reducing working capital requirements. However, recent global disruptions have exposed its structural vulnerabilities, prompting renewed interest in just-in-case (JIC) strategies that prioritize resilience through redundancy, inventory buffering, and supplier diversification. At the same time, rapid advances in digital technologies are reshaping how firms manage this transition. This paper examines the interplay between transition dynamics, digitalization, and financial implications as European companies move from JIT to JIC. Building on a structured review of the literature, we develop a conceptual framework that positions digitalization not merely as an enabler but as a mediating force that redefines the efficiency-resilience trade-off. Digital tools such as real-time data analytics, platform integration, and predictive systems alter inventory strategies, risk management practices, and capital allocation decisions. Drawing on illustrative case evidence, we show that digitalization can both mitigate and amplify the financial burdens of JIC adoption, depending on firms&rsquo; capabilities and resource endowments. The paper contributes by integrating fragmented insights across supply chain management, digital transformation, and corporate finance, offering a coherent perspective on strategic adaptation under uncertainty. It also provides actionable implications for firms and policymakers navigating an increasingly volatile and digitalized economic landscape.</i></p>

Clinical pharmacists enhance safe and high-quality patient care through effective interprofessional collaboration. This study aimed to evaluate pharmacotherapy counseling services provided by clinical pharmacists, assess physician acceptance of recommendations, and determine their impact on patients and the healthcare system. A retrospective observational study was conducted at the University Hospital’s Pharmacotherapy Counseling Unit over a 15-month period. Pharmacotherapy plans of 61 ambulatory patients were analyzed, and therapy modifications were classified according to PCNE V9.1. After clinical pharmacist intervention, the median (IQR) number of prescribed medications significantly decreased from 7.5 (8) to 3 (9) ( p < 0.05) and drug-related problems (DRPs) from 2 (4) to 0 (4) ( p < 0.05). Among patients aged ≥65 years ( n = 22), potentially inappropriate medications were significantly reduced ( p < 0.05). Most DRPs were related to inappropriate drug selection. This study demonstrates the positive impact of clinical pharmacists in improving pharmacotherapy quality in ambulatory care in Bosnia and Herzegovina.

Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learning framework for next-day prediction of urban thermal conditions and for heat risk assessment. The study was carried out in Sarajevo by using MODIS land surface temperature data and Open-Meteo forecast data. We tested several models, including convolutional models and spatiotemporal models. Among them, ConvLSTM with a mixed loss function gave the best results. The obtained values were MAE = 0.2293, RMSE = 0.3089, and R2 = 0.8877. The experiments also showed that results can be improved by using longer temporal series and additional meteorological variables. Since the framework was implemented on a GPU and trained with mixed precision, the execution time was reduced. Based on the predicted temperature fields, it was also possible to combine hazard information with exposure and vulnerability data in order to generate city heat risk maps. The proposed framework can be used as a practical basis for city heat analysis.

We develop a framework in which Yukawa hierarchies arise from powers of fully anarchic spurions transforming in higher representations of the flavor symmetry group $SU(2)^{n_2}\times SU(3)^{n_3}$. The core mechanism is the progressive lifting of Yukawa ranks through successive outer products of composite doublets and triplets. We formulate the general construction in detail and build explicit models realizing it. We then investigate whether renormalizable scalar potentials for higher $SU(2)$ representations can dynamically generate anarchic spurions with non-vanishing composites. The framework predicts distinctive patterns in flavor-changing neutral currents and potentially observable stochastic gravitational-wave backgrounds.

The dark web hosts a dynamic ecosystem of cybercrime forums and marketplaces that adapt to law enforcement pressure, technological change, and economic incentives. Prior research has extracted cyber threat intelligence from these platforms using static snapshots, with limited attention to how discussions evolve over time. In this study, we conduct a longitudinal analysis of 25,065 websites in the dark web using 11,403,638 HTML snapshots (approximately $\mathbf{1 2 4 5. 3 8 ~ G B}$) collected over six years. We develop a longitudinal topic-modeling framework combining domain-specific embeddings, density-based clustering and temporal aggregation to measure topic prevalence and lifecycle at the website level. Our analysis identifies 55 thematic clusters. We find that $\approx 75 \%$ of total discussion volume is concentrated in a small set of persistent core topics, while short-lived themes account for $\approx 3 \%$ of activity. The median topic lifespan is 75 months, indicating gradual thematic evolution rather than abrupt replacement.