The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.
Eric Lubat, Pierre-Emmanuel Hladik, Yoann Mateu
et al.
Supply chains involve geographically distributed manufacturing and assembly sites that must be coordinated under strict timing and resource constraints. While many existing approaches rely on Colored Petri Nets to model material flows, this work focuses on the temporal feasibility of supply chain processes. We propose a modular modelling approach based on Product Time Petri Nets (PTPNs), where each subsystem is represented independently and the global behaviour emerges through synchronised transition labels. A key feature of the model is the explicit representation of the supply chain manager as a critical shared and mobile resource, whose availability directly impacts system feasibility. We analyse how timing constraints and managerial capacity influence the system behaviour, identifying configurations that lead to successful executions, timeouts, or timelocks induced by incompatible timing constraints. This approach enables systematic what-if analysis of supply chain coordination policies and demonstrates the relevance of PTPNs for modelling and analysing synchronised timed systems.
Based on economic theories and integrated with machine learning technology, this study explores a collaborative Supply Chain Management and Financial Supply Chain Management (SCM - FSCM) model to solve issues like efficiency loss, financing constraints, and risk transmission. We combine Transaction Cost and Information Asymmetry theories and use algorithms such as random forests to process multi-dimensional data and build a data-driven, three-dimensional (cost-efficiency-risk) analysis framework. We then apply an FSCM model of "core enterprise credit empowerment plus dynamic pledge financing." We use Long Short-Term Memory (LSTM) networks for demand forecasting and clustering/regression algorithms for benefit allocation. The study also combines Game Theory and reinforcement learning to optimize the inventory-procurement mechanism and uses eXtreme Gradient Boosting (XGBoost) for credit assessment to enable rapid monetization of inventory. Verified with 20 core and 100 supporting enterprises, the results show a 30\% increase in inventory turnover, an 18\%-22\% decrease in SME financing costs, a stable order fulfillment rate above 95\%, and excellent model performance (demand forecasting error <= 8\%, credit assessment accuracy >= 90\%). This SCM-FSCM model effectively reduces operating costs, alleviates financing constraints, and supports high-quality supply chain development.
Xavier R. Advincula, Christoph Schran, Angelos Michaelides
Water behaves very differently at surfaces and under extreme confinement, but the boundary between these two regimes has remained unclear. Despite evidence that interfacial effects persist under sub-nanometre confinement, the molecular-scale behaviour and its evolution with slit width remain unclear. Here, we use machine-learning molecular dynamics with first-principles accuracy to probe water at graphene surfaces across slit widths ranging from the open-interface limit to angstrom-scale confinement. We find that water undergoes a sharp structural transition: when three or more water layers fit between the walls, the structure of the graphene-water interface is effectively indistinguishable from that in an open system, with density layering, hydrogen bonding, and orientational ordering retaining interfacial character. Below this threshold, however, angstrom-scale confinement strongly reorganises the liquid, producing enhanced ordering, a restructured hydrogen-bond network, and modified orientational motifs. These results establish a molecular-level picture that clearly separates interfacial behaviour from genuine nanoconfinement and provide guidance for predicting and controlling the structure of water in nanoscale solid-liquid environments.
Supply chain networks (SCN) form the structural backbone of any society. They constitute the societal metabolism that literally produces everything for everybody by coordinating practically every single person on the planet. SCNs are by no means static but undergo permanent change through the entry and exit of firms and the re-arrangement of supply relations. Here we use a unique dataset to explore the temporal evolution of firms and their supplier-buyer relations of a national SCN. Monthly reported value added tax data from Hungary from 2014 to 2022 allows us to reconstruct the entire economy with 711,248 companies and 38,644,400 connections, covering practically every re-structuring event of an entire economy at firm-level resolution. We find that per year about 25\% of firms exit the SCN while 28\% new ones enter. On average, 55\% of all supply-links present in one year will not be present in the next. We report the half-life time of supply-links to be 13 months. New links attach super-preferentially to firms with a probability, $p(i)\propto k_i^{1.08}$, with $k_i$ firm $i$'s number of supply-connections. We calibrate a simple statistical network generation model that reproduces the stylized characteristics of the dominant Hungarian SCN. The model not only reproduces local network features such as in- and out-degree distributions, assortativity and clustering structure, but also captures realistic systemic risk profiles. We discuss the present model in how rewiring dynamics of the economy is essential for quantifying its resilience and to estimate shock propagation.
Water adopts many different crystal structures in its solid form. These provide insight into potential structures of water even in its liquid phase, and they can be used to calibrate pair potentials used for simulation of water. In crowded biological environments, water may behave more like ice than bulk water. The different ice structures have different dielectric properties. This brief primer is intended to facilitate further research.
We introduce an interactive LLM-based framework designed to enhance the autonomy and robustness of domestic robots, targeting embodied intelligence. Our approach reduces reliance on large-scale data and incorporates a robot-agnostic pipeline that embodies an LLM. Our framework, InteLiPlan, ensures that the LLM's decision-making capabilities are effectively aligned with robotic functions, enhancing operational robustness and adaptability, while our human-in-the-loop mechanism allows for real-time human intervention when user instruction is required. We evaluate our method in both simulation and on the real robot platforms, including a Toyota Human Support Robot and an ANYmal D robot with a Unitree Z1 arm. Our method achieves a 95% success rate in the `fetch me' task completion with failure recovery, highlighting its capability in both failure reasoning and task planning. InteLiPlan achieves comparable performance to state-of-the-art LLM-based robotics planners, while using only real-time onboard computing. Project website: https://kimtienly.github.io/InteLiPlan.
This study presents two models to optimize pressure management in water distribution networks. The first model forecasts pressure at distribution points and compares predictions with actual data to detect anomalies such as leaks and blockages. Early detection allows for timely interventions, minimizing economic losses and ensuring system sustainability. The second model estimates the necessary inlet pressure based on the influence of various distribution points, ensuring consistent water supply while reducing waste and optimizing resource management. Both models utilize modern machine learning algorithms to enhance the prediction process. The methodology includes the CNN-EMD model, which analyzes historical data collected every 15 minutes over two months to predict future pressures. The Empirical Mode Decomposition (EMD) method identifies fluctuations and anomalies, improving prediction accuracy. The second model combines CNN, EMD, and LSTM techniques to forecast required inlet pressure, emphasizing the impact of distribution points. Results show that the CNN-EMD and CNN-EMD-LSTM models enhance pressure management capabilities, with the first model achieving an anomaly detection accuracy of 85% to 95% and the second model predicting inlet pressure with an average accuracy of 93%. This enables flexible system adjustments and identifies critical factors affecting inlet pressure. In conclusion, advanced machine learning models like CNN-EMD and LSTM significantly improve pressure management in water distribution networks, facilitating early issue identification, ensuring efficient water supply, and optimizing resource management for future generations.
This study uses data from domestic electricity smart meters to estimate annual electricity bills for a whole year. We develop a method for back-filling data smart meter for up to six missing months for users who have less than one year of smart meter data, ensuring reliable estimates of annual consumption. We identify five distinct electricity consumption user profiles for homes based on day, night, and peak usage patterns, highlighting the economic advantages of Time-of-Use (ToU) tariffs over fixed tariffs for most users, especially those with higher nighttime consumption. Ultimately, the results of this study empowers consumers to manage their energy use effectively and to make informed choices regarding electricity tariff plans.
Sulyab Thottungal Valapu, Aritri Saha, Bhaskar Krishnamachari
et al.
In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for high-risk transactions that cross blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.
Tonni Agustiono Kurniawan, Ayesha Mohyuddin, Joan Cecilia C. Casila
et al.
Abstract This paper investigates the role of digitalization in enhancing wastewater treatment processes, emphasizing its potential to optimize resource utilization, reduce energy consumption, and improve water quality. By examining the implementation of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), the study demonstrates how these tools enable real-time monitoring, predictive maintenance, and intelligent decision-making in wastewater treatment operations. The paper provides a comparative analysis based on key performance indicators (MAPE, RMSE, R 2 ) to evaluate the effectiveness of these digital solutions. Additionally, it discusses the benefits and challenges associated with integrating digital tools in wastewater treatment plants (WWTPs), including cost, complexity, and data security concerns. The study also addresses the impact of digitalization on carbon neutrality goals, highlighting how data-driven approaches can enhance resource allocation and management. By offering insights into current practices and future directions, this paper aims to contribute to the advancement of sustainable wastewater treatment and support the achievement of UN SDG#6, ensuring clean water and sanitation for all.
Water supply for domestic and industrial purposes, Environmental sciences
Zlata Tabachová, Christian Diem, András Borsos
et al.
Realistic credit risk assessment, the estimation of losses from counterparty's failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system's overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators' future systemic risk assessments.
Niki Soleimani Amiri, Sina Ebrahimi, Mahdi Emadi
et al.
Leachate production and management is a challenging environmental issue in municipal landfills and depots in Iran. Leachate contains toxic materials, heavy metals, and organic and microbial pollutants on a significant scale. Its uncontrolled entrance into the surface, groundwater, and soils can also substantially inverse impacts on human health and natural habitats. In Mazandaran province, during the last decades, depots and landfilling of municipal and industrial waste have led to environmental degradation in its eco-sensitive natural zones and brought a series of health, social, and security challenges to the region. Due to the region's high precipitation rate and landfills with no cover, these places practically convert into an extensive resource for leachate production. To diminish the environmental impacts, a lot of work has been done in recent years to develop a sort of leakage gathering system and treatment plants in these landfills, based primarily on an overall estimation. In this study, a calculating computer model has been developed for leakage production based on regional climate conditions and the characteristics of municipal waste. This model is different from the HELP model, which is commonly used for sanitary landfills and is specifically developed for the waste depots of the Mazandaran province. In this model, hydrological methods, which are based on the water balance in the landfill sites, were used for the calculation. The developed model was uploaded as an online service for public use. By referring to the internet address provided, the developed model in the landfill part and the leachate section, the amount of produced leachate for the landfill site of Mazandaran province can be calculated. Also, the leachate volume of the Babol Anjilsi landfill has been calculated as a case study. As a result of this study, the lowest and highest amount of the production leachate for hot and dry months of the year (June and July) and for wet and rainy months (October) was about 63.39 and 260.07 cubic meters per day, respectively.
Technology, Water supply for domestic and industrial purposes
In the present research, three data-driven models including M5P, REP tree, and random forest were used to estimate daily reference evapotranspiration. The abilities of these three models to estimate reference evapotranspiration were studied in single and combined modes. To this end, the daily meteorological data of five synoptic stations in Kerman province in the period from 2000 to 2020 were used. A combination of meteorological variables, using sensitivity analysis versus the reference evapotranspiration values obtained from FAO-Penman-Monteith, was considered as input for each of the mentioned models. Finally, the accuracy of the mentioned models and empirical methods in estimating the evapotranspiration of the reference plant were compared using statistical indicators, and the superior model was selected. The results of validation data showed that the M5P model in the form of individually (RMSE = 0.083 and NS = 0.998 in Bam station) and the weighted averaging in the form of the ensemble (RMSE = 0.155 and NS = 0.994 in Bam and Sirjan stations) in all stations had better results for estimating evapotranspiration rates than other methods. In general, tree models, especially M5P, had better results in estimating daily evapotranspiration than empirical models.
Environmental sciences, Water supply for domestic and industrial purposes
Abstract Permeable reactive barriers (PRBs) containing metallic iron (Fe0) as reactive materials are currently considered as an established technology for groundwater remediation. Fe0 PRBs have been introduced by a field demonstration based on the fortuitous observation that aqueous trichloroethylenes are eliminated in Fe0-based sampling vessels. Since then, Fe0 has been tested and used for treating various biological (e.g. bacteria, viruses) and chemical (organic and inorganic) contaminants from polluted waters. There is a broad consensus on the view that “reactivity loss” and “permeability loss” are the two main problems hampering the design of sustainable Fe0 systems. However, the view that Fe0 is a reducing agent (electron donor) under environmental conditions should be regarded as a distortion of Corrosion Science. This is because it has been long established that aqueous iron corrosion is a spontaneous process and results in the Fe0 surface being shielded by an oxide scale. The multi-layered oxide scale acts as a conduction barrier for electrons from Fe0. Accordingly, “reactivity loss”, defined as reduced electron transfer to contaminants, must be revisited. On the other hand, because “stoichiometric” ratios were considered while designing the first generation of Fe0 PRBs (Fe0 as reductant), “permeability loss” should also be revisited. The aim of this communication is to clarify this issue and reconcile a proven efficient technology with its scientific roots (i.e. corrosion science).
The present research, by problematizing the water crisis in Iran and Kurdistan province, has tried to analyze the situation of institutional and public water transfer disputes as a solution to this crisis. In this regard, the question of whether the transfer of water in the province has provided an opportunity for its development or if it has led to development bottlenecks, analyzing the debates and controversies of two institutional perspectives in the form of experts and managers of the regional water company of the province has been raised. Kurdistan and civil and environmental activists are concerned about the transfer of water from the west to the east of the province. Therefore, the sustainable development approach, which seeks the connection and interaction of the three spheres of society, economy and environment, has formed the theoretical basis of this research. The methodology is based on theoretical and experimental goals and position, while institutional ethnography is based on the experiences, knowledge, views of the interviewees [31 semi-structured interviews], and the review of statistics and documents related to the discussion of water, and its leading crisis has been water transfer in Kurdistan province. According to the analysis of 6 secondary categories, the results show that managers and experts consider climate change and natural obstacles to water exploitation as the basis of many conditions and obstacles to sustainable management of water resources, while activists focus on the weakness of sustainable management of water resources. They also emphasize, managers and institutional experts highlight the lack of integrated water management and the lack of funds as the basis of institutional weakness and the indifference of the academic elite and provincial representatives to the water issue, and the lack of bargaining power in allocating water to the province. They consider political issues and lack of public participation as the reason for this. And finally, it can be concluded that water transfer is not considered an opportunity but a bottleneck and challenge for the province's development.
Technology, Water supply for domestic and industrial purposes
Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. To the best of our knowledge, this paper is the first work studying the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, allocating the record searching primitives to the chunks with maximized parallelization ratio is challenging. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. The experimental results indicate that the time overhead can be reduced by up to 85.1% with affordable storage overhead.
Piergiorgio Ladisa, Henrik Plate, Matias Martinez
et al.
The widespread dependency on open-source software makes it a fruitful target for malicious actors, as demonstrated by recurring attacks. The complexity of today's open-source supply chains results in a significant attack surface, giving attackers numerous opportunities to reach the goal of injecting malicious code into open-source artifacts that is then downloaded and executed by victims. This work proposes a general taxonomy for attacks on open-source supply chains, independent of specific programming languages or ecosystems, and covering all supply chain stages from code contributions to package distribution. Taking the form of an attack tree, it covers 107 unique vectors, linked to 94 real-world incidents, and mapped to 33 mitigating safeguards. User surveys conducted with 17 domain experts and 134 software developers positively validated the correctness, comprehensiveness and comprehensibility of the taxonomy, as well as its suitability for various use-cases. Survey participants also assessed the utility and costs of the identified safeguards, and whether they are used.