Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent, high-impact events from noisy and unstructured inputs - a setting where general-purpose models struggle without task-specific adaptation. We introduce an end-to-end framework that trains LLMs to produce calibrated probabilistic forecasts using realized disruption outcomes as supervision. The resulting model substantially outperforms strong baselines - including GPT-5 - on accuracy, calibration, and precision. We also show that training induces more structured and reliable probabilistic reasoning without explicit prompting. These results suggest a general pathway for training domain-specific forecasting models that produce decision-ready signals. To support transparency we open-source the evaluation dataset used in this study. Dataset: https://huggingface.co/datasets/LightningRodLabs/supply-chain-predictions
Morteza Rahimpour, Majid Rahimzadegan, Taha B. M. J. Ouarda
et al.
Abstract Accurate estimation of daily precipitation is essential for effective water resource management and climate risk assessment. Satellite precipitation products (SPPs) offer valuable spatial coverage but remain limited by uncertainties, particularly in arid and semi-arid regions. To address these challenges, this study develops a robust merging framework that integrates four SPPs with auxiliary topographic and meteorological data using ensemble machine learning models (EMLMs). Within this framework, we introduce for the first time the Multiple Linear Regression–based Sine Cosine Algorithm (MLR-SCA), designed to improve merging performance relative to the widely used Bayesian Model Averaging (BMA). Daily precipitation observations from 80 synoptic stations across Iran (2014–2022) were employed for training and validation. Results demonstrate that the proposed MLR-SCA significantly outperforms BMA, increasing the correlation coefficient (CC) by 132%, reducing RMSE by 34% and MAE by 19%, and achieving substantial improvements in KGE (+ 1142%), POD (+ 40%), and CSI (+ 47%), while reducing FAR (–24%) and BIAS (–7%). Although merging slightly reduced categorical event-detection skill in some cases, the EMLM framework consistently produced more accurate, stable, and reliable precipitation estimates across diverse climatic zones. Compared with existing approaches, the proposed framework offers three main advantages: (1) stronger performance across arid, semi-arid, and semi-humid climates; (2) improved detection of extreme precipitation events, which are often underestimated by raw SPPs; and (3) greater robustness through the simultaneous integration of multiple SPPs and auxiliary datasets. These findings highlight the potential of the EMLM–MLR-SCA framework to support operational hydrology, water resource planning, and climate adaptation in data-scarce regions.
Ekaterina Khmel, Anatoly Hurynovich, Volha Holubava
et al.
ABSTRACTEnsuring the efficient operation of water supply facilities in the agro‐industrial sector is critical for sustainable resource management. The article streamlines the grouping of works performed during the operation of water supply facilities in six stages, differing in purpose, duration, and frequency of work. A process approach has been applied to manage the operation of water supply facilities, ensuring competent planning and timely implementation of the operation stages, setting clear boundaries for interaction with specialized organizations, and reducing the cost of operating water supply facilities, which together can improve the reliability and increase their useful life. Four organizational models of operation management have been developed, reflecting different degrees of interaction between the owners of water supply facilities and specialized organizations during the implementation of operation stages. The strengths, limitations, and practical applicability of each model were assessed through SWOT analysis. The features, prospects, and possibilities of using the developed organizational models and limiting factors are considered. Developed organizational models reflect the features of interaction between the owners of water supply facilities and specialized organizations during the performance of operation stages. The findings suggest that a hybrid approach, involving both internal management and external specialized support, can optimize reliability and cost‐effectiveness. The proposed models provide a structured framework for improving the longevity and efficiency of water supply infrastructure.
US-China trade tensions, the COVID-19 pandemic, and the Russia-Ukraine conflict have disrupted and reshaped global supply chains. Existing studies caution that these tensions may not meaningfully reduce U.S. dependence on China-linked supply chains. This study examines the drivers of this unmet reallocation under overlapping geopolitical and public health disruptions. It investigates how these shocks jointly reconfigured bilateral trade and global value chain (GVC) participation and positioning among the U.S., China, and major trading partners during 2016-2023. Using monthly bilateral trade data across all sectors and multi-regional input-output tables for GVC decomposition, we combine a multi-period event-study with structural analysis to evaluate trade-flow disruptions and shifts in participation and functional positioning within GVCs. We find that China's exports remained robust, expanded across global markets, and sustained a rise in GVC participation, becoming more embedded in upstream segments through increased intermediate shipments to Asia and Europe. Meanwhile, U.S. imports increasingly shifted toward "China+1" partners, especially ASEAN, whose trade structures remain closely tied to Chinese upstream supply chains. These strengthening triangular relationships reveal how global reallocation and GVCs have evolved around the U.S. and China across successive shocks. Based on the evidence, we propose a supply chain resilience framework defined by three interacting dimensions: the level of GVC participation, the functional position within the value chain, and a country's capacity to re-couple in the post-shock landscape, conditioned by market diversification, economic complexity, and institutional capability. These findings carry significant implications for trade policy and industrial strategy in an era of geopolitical and geoeconomic fragmentation.
Courtney Miller, William Enck, Yasemin Acar
et al.
Supply chain security has become a very important vector to consider when defending against adversary attacks. Due to this, more and more developers are keen on improving their supply chains to make them more robust against future threats. On August 29, 2024 researchers from the Secure Software Supply Chain Center (S3C2) gathered 14 practitioners from 10 government agencies to discuss the state of supply chain security. The goal of the summit is to share insights between companies and developers alike to foster new collaborations and ideas moving forward. Through this meeting, participants were questions on best practices and thoughts how to improve things for the future. In this paper we summarize the responses and discussions of the summit.
Supply chains' increasing globalization and complexity have recently produced unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these concerns include the advanced field of forward stress testing, where threats and predicted impacts to the supply chain are evaluated to harden the system against the most damaging scenarios. Such approaches are limited by the almost endless number of potential threat scenarios and cannot capture residual risk. In contrast to forward stress testing, this paper develops a reverse stress testing (RST) methodology that allows to predict which changes, with probabilistic certainty, across the supply chain network are most likely to cause a specified level of disruption at a specific entity in the network. The methodology was applied to the case of copper wire imports into the USA, a simple good which may have significant implications for national security. Results show that Canada, Chile, and Mexico are predicted to consistently be sources of disruptions at multiple loss levels. Other countries (e.g., Papua New Guinea) may contribute to small disruptions but be less important for the catastrophic losses of concern for decision makers. Other countries' disruptions would be catastrophic (e.g., Chile). The proposed methodology is the first case of reverse stress testing application in complex multilayered supply chains and can be used to address both risk and resilience.
In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the achievement of comprehensive visibility between organisations or countries due to privacy, security, and regulatory concerns. Moreover, most existing research studies focused on individual firm- or product-level networks, overlooking the multifaceted interactions among diverse entities that characterise real-world supply chains, thus limiting a holistic understanding of supply chain dynamics. To address these challenges, we propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility through relationship prediction in supply chain knowledge graphs. FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange, ensuring compliance with privacy regulations and maintaining data security. GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections and provide comprehensive insights into supply chain networks. Experimental results validate the effectiveness of the proposed approach, demonstrating its ability to accurately predict relationships within country-level supply chain knowledge graphs. This enhanced visibility supports actionable insights, facilitates proactive risk management, and contributes to the development of resilient and adaptive supply chain strategies, ensuring that supply chains are better equipped to navigate the complexities of the global economy.
Short-term water demand forecasting (StWDF) is the foundation stone in the derivation of an optimal plan for controlling water supply systems. Deep learning (DL) approaches provide the most accurate solutions for this purpose. However, they suffer from complexity problem due to the massive number of parameters, in addition to the high forecasting error at the extreme points. In this work, an effective method to alleviate the error at these points is proposed. It is based on extending the data by inserting virtual data within the actual data to relieve the nonlinearity around them. To our knowledge, this is the first work that considers the problem related to the extreme points. Moreover, the water demand forecasting model proposed in this work is a novel DL model with relatively low complexity. The basic model uses the gated recurrent unit (GRU) to handle the sequential relationship in the historical demand data, while an unsupervised classification method, K-means, is introduced for the creation of new features to enhance the prediction accuracy with less number of parameters. Real data obtained from two different water plants in China are used to train and verify the model proposed. The prediction results and the comparison with the state-of-the-art illustrate that the method proposed reduces the complexity of the model six times of what achieved in the literature while conserving the same accuracy. Furthermore, it is found that extending the data set significantly reduces the error by about 30%. However, it increases the training time.
Azmine Toushik Wasi, Enjamamul Haque Eram, Sabrina Afroz Mitu
et al.
Industry 5.0 marks a new phase in industrial evolution, emphasizing human-centricity, sustainability, and resilience through the integration of advanced technologies. Within this evolving landscape, Generative AI (GenAI) and autonomous systems are not only transforming industrial processes but also emerging as pivotal geopolitical instruments. We examine strategic implications of GenAI in Industry 5.0, arguing that these technologies have become national assets central to sovereignty, access, and global influence. As countries compete for AI supremacy, growing disparities in talent, computational infrastructure, and data access are reshaping global power hierarchies and accelerating the fragmentation of the digital economy. The human-centric ethos of Industry 5.0, anchored in collaboration between humans and intelligent systems, increasingly conflicts with the autonomy and opacity of GenAI, raising urgent governance challenges related to meaningful human control, dual-use risks, and accountability. We analyze how these dynamics influence defense strategies, industrial competitiveness, and supply chain resilience, including the geopolitical weaponization of export controls and the rise of data sovereignty. Our contribution synthesizes technological, economic, and ethical perspectives to propose a comprehensive framework for navigating the intersection of GenAI and geopolitics. We call for governance models that balance national autonomy with international coordination while safeguarding human-centric values in an increasingly AI-driven world.
The rise of Large Language Models (LLMs) has led to the widespread deployment of LLM-based systems across diverse domains. As these systems proliferate, understanding the risks associated with their complex supply chains is increasingly important. LLM-based systems are not standalone as they rely on interconnected supply chains involving pretrained models, third-party libraries, datasets, and infrastructure. Yet, most risk assessments narrowly focus on model or data level, overlooking broader supply chain vulnerabilities. While recent studies have begun to address LLM supply chain risks, there remains a lack of benchmarks for systematic research. To address this gap, we introduce the first comprehensive dataset for analyzing and benchmarking LLM supply chain security. We collect 3,859 real-world LLM applications and perform interdependency analysis, identifying 109,211 models, 2,474 datasets, and 9,862 libraries. We extract model fine-tuning paths, dataset reuse, and library reliance, mapping the ecosystem's structure. To evaluate security, we gather 1,555 risk-related issues-50 for applications, 325 for models, 18 for datasets, and 1,229 for libraries from public vulnerability databases. Using this dataset, we empirically analyze component dependencies and risks. Our findings reveal deeply nested dependencies in LLM applications and significant vulnerabilities across the supply chain, underscoring the need for comprehensive security analysis. We conclude with practical recommendations to guide researchers and developers toward safer, more trustworthy LLM-enabled systems.
Abstract Ammonia (NH3) decomposition offers a pathway for water purification and green hydrogen production, yet conventional catalysts often suffer from poor stability due to agglomeration. This study presents a novel (FeCoNiCuMn)O high-entropy ceramic (HEC) catalyst synthesized via fast-moving bed pyrolysis (FMBP), which prevents aggregation and enhances catalytic performance. The HEC catalyst, applied as an anode in electrochemical oxidation (EO), demonstrated a uniform spinel (AB2O4) structure confirmed by XRD, XRF, and ICP-OES. Electronic structure characterization using UPS and LEIPS revealed a bandgap of 4.722 eV, with EVBM and ECBM values facilitating redox reactions. Under 9 V and 50 mA/cm² current density, the HEC electrode achieved 99% ammonia decomposition within 90 min and retained over 90% efficiency after four cycles. Surface analysis by XPS and HAXPES indicated oxidation state variations, confirming catalyst activity and stability. Gas chromatography identified H2, N2, and O2 as the main products, with ~64.7% Faradaic efficiency for H2, classifying it as green hydrogen. This dual-function approach highlights the (FeCoNiCuMn)O HEC anode as a promising and sustainable solution for wastewater treatment and hydrogen production.
Md. Shafiquzzaman, Husnain Haider, Mohammad Alresheedi
et al.
Abstract This study evaluates biomass production, wastewater treatment efficiency, and membrane fouling behavior in Algal Membrane Photobioreactors (AMPBRs) operated under varying conditions. Six lab-scale AMPBRs were operated continuously under different organic loading rates (OLRs) and hydraulic retention times (HRTs), with a constant flux of 100 L/m2/day and a 12-h light/dark cycle. Performance was assessed in terms of biomass yield, contaminant removal efficiency, and membrane fouling characteristics. Principal Component Analysis (PCA) was employed to identify the most critical parameters influencing AMPBR performance and efficiency. Experimental results showed that the highest biomass production rate (40 mg/L d) occurred under lower OLR conditions. BOD and COD removal efficiencies exceeded 85–95%, while total nitrogen (TN) and total phosphorus (TP) removal rates ranged from 55 to 60% and 20 to 35%, respectively, under different OLR and HRT conditions. Five principal components (PCs) with eigenvalues higher than ‘1’ were extracted. PC1 reflected variability associated with influent organic content, photosynthetic activity, and membrane fouling rates. PC2 was influenced by HRT, effluent organic content, and phosphorus levels, while PC3 represented nutrient variability in the effluent. Factor loading analysis revealed that OLR and HRT strongly influenced biomass production. In contrast, organic matter (BOD and COD) removal was largely independent of these parameters. TN removal was primarily driven by algal assimilation at lower OLR, but shifted toward nitrification and denitrification under higher OLR conditions. TP removal was significantly affected by HRT, with minimal dependence on OLR. Membrane fouling rates increased at higher OLRs due to elevated production of extracellular polymeric substances (EPS).
Duriangkang Reservoir is located in the Duriangkang River Basin, Batam Island. The main problem of Duriangkang Reservoir is that industrial wastewater from the Batamindo Industrial Area has been heavily polluted with COD levels (134.61 mg/liter) exceeding the Wastewater Quality Standards for Class II (80 mg/liter), domestic wastewater from domestic activities in the Duriangkang Watershed has also been heavily polluted with BOD5 levels (58.22 mg/liter) exceeding the Wastewater Quality Standards for Class II (50 mg/liter). This study aims to examine the effect of industrial wastewater and domestic wastewater on the carrying capacity of Duriangkang Reservoir. The main function of Duriangkang Reservoir is as a water resource to support 78% of the drinking water supply needs of Batam Island with a processing capacity of 3000 liters/second.
In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.
A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of supply chains using data-driven techniques. Existing methods have been successful for predicting relationships but struggle to extract the context in which these relationships are embedded - such as the products being supplied or locations they are supplied from. Lack of context prevents practitioners from distinguishing transactional relations from established supply chain relations, hindering accurate estimations of risk. In this work, we develop a new Generative Artificial Intelligence (Gen AI) enhanced machine learning framework that leverages pre-trained language models as embedding models combined with machine learning models to predict supply chain relationships within knowledge graphs. By integrating Generative AI techniques, our approach captures the nuanced semantic relationships between entities, thereby improving supply chain visibility and facilitating more precise risk management. Using data from a real case study, we show that GenAI-enhanced link prediction surpasses all benchmarks, and demonstrate how GenAI models can be explored and effectively used in supply chain risk management.
In 2023, Sonatype reported a 200\% increase in software supply chain attacks, including major build infrastructure attacks. To secure the software supply chain, practitioners can follow security framework guidance like the Supply-chain Levels for Software Artifacts (SLSA). However, recent surveys and industry summits have shown that despite growing interest, the adoption of SLSA is not widespread. To understand adoption challenges, \textit{the goal of this study is to aid framework authors and practitioners in improving the adoption and development of Supply-Chain Levels for Software Artifacts (SLSA) through a qualitative study of SLSA-related issues on GitHub}. We analyzed 1,523 SLSA-related issues extracted from 233 GitHub repositories. We conducted a topic-guided thematic analysis, leveraging the Latent Dirichlet Allocation (LDA) unsupervised machine learning algorithm, to explore the challenges of adopting SLSA and the strategies for overcoming these challenges. We identified four significant challenges and five suggested adoption strategies. The two main challenges reported are complex implementation and unclear communication, highlighting the difficulties in implementing and understanding the SLSA process across diverse ecosystems. The suggested strategies include streamlining provenance generation processes, improving the SLSA verification process, and providing specific and detailed documentation. Our findings indicate that some strategies can help mitigate multiple challenges, and some challenges need future research and tool enhancement.
The Danube-Black Sea hydrographic space, spanning 6,500 km² or 19.2% of Moldova, includes the river basins of Ialpug, Cogâlnic, Sarata, and Hadjider. This region faces considerable challenges due to its limited water resources and its primarily agrarian and rural character. This study examines the unique aspects of water use in Danube-Black Sea Hydrographical Space, assessing its role in Moldova’s overall water supply and the factors affecting its efficiency. The research employs statistical, deductive, comparative, and cartographic methods, utilizing data from the Moldovan Water Agency, Inspectorate of Environmental Protection, National Bureau of Statistics, and Moldova Water-Canal Association, in addition to watershed management plans and pertinent analytical studies. Danube-Black Sea Hydrographical Space contribution to Republic of Moldova’s water supply is minimal, with over 85% of water sourced from groundwater due to poor surface water quality. Water use in the region is largely agricultural (58%), 30% allocated for domestic purposes and a small 3% for industrial uses. While there has been an increase in water use for technological and domestic needs, agricultural water use shows fluctuating trends. The region’s strained water resources call for improved management strategies to address its hydrological characteristics and the impacts of climate change, ensuring sustainable water use and supporting local economic and environmental needs.
The primary goal of the study was to compare the quality of domestic water supply services in Kolfe Keranyo Sub City and Bole Sub City using data from surveys, interviews, and observations of 286 sample households. Survey responses were analyzed using qualitative and quantitative methods were used. The researcher emphasized to comparing the extent to which essential facilities/factors/determine the water service and comparing the major water service problem and its effects on households of the two sub cities. The city's water supply service is unable to meet its needs using the available resources.The finding shows only 60.8 percent of the demand is satisfied in the two sub cities. The main cause for inadequate and equitable, water supply service were, due to frequent interruptions caused by limited sources, inappropriate organizational system, lacks of trained manpower, limited financial sources and poor management of technical activities that hinders even distribution. The central part of Bole sub city relatively obtains proper water service due to government influence, existence of well to do people, Ambassadors and Government officials, where as Kolfe Keranyo Sub City has been experiencing poor water service, from which large number of households are suffering. The situation is serious for populations living in peripheral areas and for the (low income) poor section of the community to buy water from water vendors. The researcher recommend that AAWSA should regulate public stand points, detect and improve leakage controlling mechanisms, change the existing tariff structure, encourage using water container, community awareness on how to manage water, good governance in the water sector, coordination and cooperation among stakeholders, provision of water supply service to those who lack private title on land ownership.
Damir B. Krklješ, Goran V. Kitić, Csaba M. Petes
et al.
Due to the global water crisis there is a strong need for real-time water quality monitoring with high temporal and spatial resolution. This paper presents an economical multiparameter water quality monitoring system for continuous monitoring of fresh waters. It is based on a sensor node that integrates turbidity, temperature, and conductivity sensors, a miniature eighteen-channel spectrophotometer, and a sensor for the detection of thermotolerant coliforms, which is a major novelty of the system. Due to the influence of water impurities on the measurement of thermotolerant coliforms, a heuristic method has been developed to mitigate this effect. Moreover, the sensor is low power and with an integrated Long Range Wide Area Network module, it comprises a system that is wireless sensor network ready and can send data to a dedicated server. In addition, the system is submersible, capable of long-term field operation, and significantly cheaper in comparison to existing solutions. The purpose of the system is to give early warning of incidental pollution situations, thus enabling authorities to take action regarding further prevention of such occasions.