María Castrillo Melguizo, Jaime Iglesias Blanco, Álvaro López García
The environmental impact of electricity consumption is commonly assessed through its carbon footprint (CF), while water-related impacts are often overlooked despite the strong interdependence between energy and water systems. This is particularly relevant for electricity-intensive activities such as data center (DC) operations, where both carbon emissions and water use occur largely off-site through electricity consumption. In this work, we present Wattnet, an open-source tool that jointly assesses the CF and water footprint (WF) of electricity consumption across Europe with high temporal resolution. Wattnet implements an electricity flow-tracing methodology that accounts for local generation mixes, as well as for cross-border electricity imports and exports at a 15-minute resolution. Operational and life-cycle impact factors are used to quantify and compare local (generation-based) and global (consumption-based) footprints for multiple European regions during 2024. The results demonstrate that neglecting electricity flows and temporal variability can lead to significant misestimations of both CF and WF, particularly in countries with high levels of electricity trade or hydropower dependence. Furthermore, the joint analysis reveals trade-offs between decarbonisation and water use, highlighting the prominent role of reservoir-based hydropower in increasing WF even in low-carbon systems. Wattnet facilitates informed decision-making for workload scheduling and energy-aware operation of DCs, while also enhancing transparency regarding the environmental impacts of electricity consumption for end users and policymakers.
Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation.
Automated semantic understanding of dense point clouds is a prerequisite for Scan-to-BIM pipelines, digital twin construction, and as-built verification--core tasks in the digital transformation of the construction industry. Yet for industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: TLS acquisitions of water treatment plants, chiller halls, and pumping stations exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks (e.g., S3DIS or ScanNet) cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset comprising 612 million expertly labelled points at 6 mm resolution from 13 water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest and most demanding testbed for industrial 3D scene understanding to date. We further establish the first industrial cross-paradigm benchmark, evaluating nine representative methods across fully supervised, weakly supervised, unsupervised, and foundation model settings under a unified benchmark protocol. The best supervised method achieves 55.74% mIoU, whereas zero-shot Point-SAM reaches only 15.79%--a 39.95 percentage-point gap that quantifies the unresolved domain-transfer challenge for industrial TLS data. Systematic analysis reveals that this gap originates from a dual crisis: statistical rarity (215:1 imbalance, 3.5x more severe than S3DIS) and geometric ambiguity (tail-class points share cylindrical primitives with head-class pipes) that frequency-based re-weighting alone cannot resolve. Industrial3D, along with benchmark code and pre-trained models, will be publicly available at https://github.com/pointcloudyc/Industrial3D.
A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most of this work focuses on widely-used general purpose programming languages like Python due to their widespread usage training data. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, remains underexplored. This research aims to utilize and integrate LLMs in the industrial development process, solving real-life programming tasks (e.g., generating a movement routine for a robotic arm) and accelerating the development cycles of manufacturing systems.
Abstract Water resource recovery facilities (WRRFs) face growing pressures to balance compliance, sustainability, and cost while adapting to evolving treatment needs. To support research, development, and deployment (RD&D) of innovative technological solutions, we developed an open-access benchmarking framework comprised of 18 plant-wide simulation models. Implemented in QSDsan, the framework is validated against GPS-X™ simulations while capturing distinct system behaviors, treatment performance, energy demand, and operational costs across diverse designs. It offers a rigorous and transparent foundation for comparative technology evaluations, guiding RD&D decision-making and advancing sustainable water management.
Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benchmark for graph-based supply chain analysis. By leveraging advanced GNN methodologies, we enhance the accuracy of forecasting models, uncover latent dependencies, and address temporal complexities inherent in supply chain operations. Comparative analyses demonstrate that GNN-based models significantly outperform traditional approaches, including Multilayer Perceptrons (MLPs) and Graph Convolutional Networks (GCNs), particularly in single-node demand forecasting tasks. The integration of graph representation learning with temporal data highlights GNNs' potential to revolutionize predictive capabilities for inventory management, production scheduling, and logistics optimization. This work underscores the pivotal role of forecasting in supply chain management and provides a robust framework for advancing research and applications in this domain.
This research developed an optimization model within a circular supply chain framework incorporating factors such as carbon emissions, social sustainability, and warehouse capacity limitations. The model adopted a modified Economic Order Quantity (EOQ) approach, with a comprehensive cost assessment that included production cost, remanufacturing cost, storage cost, disposal cost, and penalty cost for emissions, all formulated within a Mixed Integer Nonlinear Programming (MINLP) structure. To address the complex nonlinear problem, the metaheuristic Chinese Pangolin Optimizer (CPO) algorithm was applied, as it effectively balanced solution exploration and exploitation. The simulation results indicated the optimal combination of production lot size, remanufacturing, and the share of reusable goods, achieving the minimum total system cost. The sensitivity analysis showed the significant influence of production and remanufacturing costs, emissions, and the rate of product returns on system efficiency. Overall, this research demonstrated more credible, cost-efficient, and sustainable inventory control approaches in a circular supply chain by considering warehouse constraints and applying the CPO.
Mohaddeseh Eydi Gabrabad, Ziaeddin Bonyadi, Mojtaba Davoudi
et al.
Abstract The ubiquitous presence of MPs in water bodies presents an escalating concern, as these minuscule plastic particles could ultimately reach humans via the drinking water supply. This study explores the efficacy and underlying mechanisms of removing PE and PVC MPs using Abelmoschus esculentus seeds (commonly known as okra), a natural and environmentally benign coagulant. Through experiments conducted under varying conditions—such as pH level, coagulant dosage, MP concentration, and EC—using the standard method and a Jar test apparatus, the sedimentation rate was assessed. ZP analysis revealed that charge neutralization and bridging cause pivotal in enhancing the removal efficiency of MPs. FESEM and FTIR analyses corroborated the formation of new bonds during the interaction between the MPs and the okra seed-based coagulant. The findings indicate that the optimal parameters for PVC removal were a coagulant dosage of 70 mg/L, a pH of 10, and an MP concentration of 20 mg/L, achieving a removal efficiency of 80.11%. Conversely, for PE, the maximum removal efficiency of 64.76% was realized at a coagulant dosage of 70 mg/L, a pH of 3, and an MP concentration of 20 mg/L. Abelmoschus esculentus seeds offer a practical and eco-friendly option, potentially substituting chemical coagulants, to efficiently eliminate MPs from aquatic environments.
Shan-e-hyder Soomro, Abdul Razzaque Soomro, Sahar Batool
et al.
Abstract Globally there is already a lot of pressure on water resources because of climate change, economic development, as well as an increasing global populace. Many rivers originate in the mountains, where snowfall fluctuations and the global climate’s inherent unpredictability affect the hydrological processes. Climate change sensitivity has been recognized in recent years and would affect hydropower, such as humidity, cloudiness, and precipitation, that are considered; global warming emerges as one of the most important contributors to climate change. The Yangtze River supports rich biodiversity and provides important ecosystem services for human survival and development. In addition, climate changes, particularly short-term and long-term precipitation and temperature fluctuations, influence the snow regime and the hydrological development of river flow response at the basin and sub-basin scales. More precise this review focused to understand the hydropower potential, freshwater fisheries, and hydrological response of snow dynamics in snow-dominated basins.
Abstract Although many desalination plants have been built in the countries around the Persian Gulf (PG) over the past decade, there exist crucial water demands in this region. Considering the limited water exchange between PG and the open seas, effluents more than the natural capacity of the PG will increase the sea-water salinity continuously. This excess salinity, in addition to threatening the marine ecosystems, endangers the water supply for many population centers. This study provides a comprehensive numerical analysis of the impact of existing and new desalination plants on the PG’s salinity. In addition, the water residence time and pollutant extension have been investigated in the PG. There exist several concerns, especially in recent years about the probable threat of desalination growth in semi-enclosed seas such as PG. The effect of desalination plants on the mean salinity of PG is one the questions investigated in this research. Results demonstrate that the water residence times in the southern and northwestern regions are more than five years. This time is reduced to nearly 26 to 45 months in the eastern regions near the Strait of Hormuz. Generally, the desalination plants have a negligible effect on the salinity of PG in comparison with the climate conditions such as evaporation and water exchanges. Based on the results, a 50% increase in effluent discharge of existing desalination plants increases the average salinity of the PG by only 0.01 psu. However, the annual volume of net evaporation (that exits the clean water directly) is nearly 36 times more than the effluent discharge from the existing desalination plants. Furthermore, this value is almost 0.2% of the amount of water that enters the PG through the Strait of Hormuz. In spite of these findings, the regional effects can be significant in some parts of the PG. For example, the southern and western coasts of PG are generally more vulnerable to pollution than other areas. The main reason is the shallow water depth in these areas and the water recirculation pattern. Some sensitive local areas have been also addressed in this study. Among the studied regions, the coastlines at the northwest of PG and at the north the Qeshm Island are two susceptible areas. The findings of this study underscore the importance of considering a new integrated viewpoint in developing desalination plants within PG.
Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.
Matthew Thiesse, Stephen T. Wilson, Jack Fannon
et al.
The PocketWATCH facility is a unique multi-purpose test bed designed to replicate the conditions of large water Cherenkov detectors. Housed at the University of Sheffield, the facility consists of a light-tight 2000L ultrapure water tank with purification and temperature control systems. Water temperature, resistivity, and UV attenuation in the tank are monitored and shown to be stable over time. The system is also shown to be compatible with a solution of 0.2% gadolinium sulfate, allowing further utility in testing equipment bound for the next generation neutrino and nucleon decay water Cherenkov particle detectors. The relevant water quality parameters are shown to be stable whilst running in Gd-mode, thereby providing a suitable test bed for hardware development in a realistic, ex situ environment.
Mengfei Chen, Mohamed Kharbeche, Mohamed Haouari
et al.
How to ensure accessibility to food and nutrition while food supply chains suffer from demand and supply uncertainties caused by disruptive forces such as the COVID-19 pandemic and natural disasters is an emerging and critical issue. Unstable access to food influences the level of nutrition that weakens the health and well-being of citizens. Therefore, a food accessibility evaluation index is proposed in this work to quantify how well nutrition needs are met. The proposed index is then embedded in a stochastic multi-objective mixed-integer optimization problem to determine the optimal supply chain design to maximize food accessibility and minimize cost. Considering uncertainty in demand and supply, the multi-objective problem is solved in a two-phase simulation-optimization framework in which Green Field Analysis is applied to determine the long-term, tactical decisions such as supply chain configuration, and then Monte Carlo simulation is performed iteratively to determine the short-term supply chain operations by solving a stochastic programming problem. A case study is conducted on the beef supply chain in Qatar. Pareto efficient solutions are validated in discrete event simulation to evaluate the performance of the designed supply chain in various realistic scenarios and provide recommendations for different decision-makers.
Organizations increasingly need to reassess their supply chain strategies in the rapidly modernizing world towards sustainability. This is particularly true in the United States, where supply chains are very extensive and consume a large number of resources. This research paper discusses how AI can support decision-making for sustainable supply chains with a special focus on carbon footprints. These AI technologies, including machine learning, predictive analytics, and optimization algorithms, will enable companies to be more efficient, reduce emissions, and display regulatory and consumer demands for sustainability, among other aspects. The paper reviews challenges and opportunities regarding implementing AI-driven solutions to promote sustainable supply chain practices in the USA.
Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.
Mohamed O. Amin, Entesar Al-Hetlani, Ariana R. Antonangelo
et al.
Abstract In this work, a series of hypercrosslinked polymers of intrinsic microporosity (HCP-PIMs), namely nitro-triptycene (TRIP-NO2), amino-triptycene (TRIP-NH2), sulfonated-triptycene (TRIP-SO3H) and hydrocarbon-triptycene (TRIP-HC), are employed for the adsorption of organic dyes from wastewater. The materials show the efficient removal of cationic (malachite green, MG) and anionic (methyl orange, MO) dyes. The adsorption parameters herein investigated include the initial pH, the adsorbate concentration and the contact time, with the aim to elucidate their effect on the adsorption process. Furthermore, the adsorption kinetic and isotherms are studied, and the findings suggest the results fit well with pseudo-second-order kinetics and Langmuir model. The reported maximum adsorption capacity is competitive for all the tested polymers. More specifically, TRIP-SO3H and TRIP-HC exhibit adsorptions of ~ 303 and ~ 270 mg g−1 for MG and MO, respectively. The selectivity toward cationic and anionic dyes is assessed by mixing the two dyes, and showing that TRIP-HC completely removes both species, whereas TRIP-NO2, TRIP-NH2 and TRIP-SO3H show an enhanced selectivity toward the cationic MG, compared to the anionic MO. The effect of the type of water is assessed by performing ultrasonic-assisted adsorption experiments, using TRIP-SO3H and TRIP-HC in the presence of either tap or seawater. The presence of competing ions and their concentrations is evaluated by ICP-MS. Our study shows that tap water does not have a detrimental effect on the adsorption of both polymers, whereas, in the presence of seawater, the performance of TRIP-HC toward MO proved to be more stable than MG with TRIP-SO3H, which is probably due to a larger concentration of competing ions. Comparison between ultrasonic-assisted and magnetic stirring adsorption demonstrates that the former exhibits a greater efficiency. This seems due to a more rapid mass transfer, driven by the formation of high velocity micro-jets, acoustic microstreaming and shock waves, at the polymer surface. Reusability studies show a good stability up to five adsorption–desorption cycles.
Farmers in remote, arid areas, far from available water sources, need affordable water solutions for household and livestock use. In this study, the water needs and potential for rainwater harvesting (RWH) in the Kysylsu River Basin are estimated at different altitudes. The mean annual net rainfall depth varied from 545 to 900 mm. With an average roof area of 550 m2, 211 m3 to 344 m3 rainwater can be harvested for dry and wet years respectively. Based on the estimated water demand and rainfall deficit, the average household need was estimated to be 100 m3. For a low-cost water storage solution, we tested different types of waterproof materials to replace smaller 12-18 m3 concrete tanks commonly used in the region. Soil pits lined with 0.3 mm double layer polyethene (0.6 mm in total) was the best solution for 5 m3 volume tanks in terms of the costs and durability and 0.8 mm double layer polyethene (1.6 mm in total) was ideal for sealing 10 m3 volume tanks. This study demonstrates the efficiency of the system for improved livelihood of the families through rain harvesting.
Industrial demand response (IDR) plays an important role in promoting the utilization of renewable energy (RE) in power systems. However, it will lead to power adjustments on the supply side, which is also a non-negligible factor in affecting RE utilization. To comprehensively analyze this impact while enhancing RE utilization, this paper proposes a power demand-supply cooperative response (PDSCR) strategy based on both day-ahead and intraday time scales. The day-ahead PDSCR determines a long-term scheme for responding to the predictable trends in RE supply. However, this long-term scheme may not be suitable when uncertain RE fluctuations occur on an intraday basis. Regarding intraday PDSCR, we formulate a profit-driven cooperation approach to address the issue of RE fluctuations. In this context, unreasonable profit distributions on the demand-supply side would lead to the conflict of interests and diminish the effectiveness of cooperative responses. To mitigate this issue, we derive multi-individual profit distribution marginal solutions (MIPDMSs) based on satisfactory profit distributions, which can also maximize cooperative profits. Case studies are conducted on an modified IEEE 24-bus system and an actual power system in China. The results verify the effectiveness of the proposed strategy for enhancing RE utilization, via optimizing the coordination of IDR flexibility with generation resources.
Quantum computing, with its transformative computational potential, is gaining prominence in the technological landscape. As a new and exotic technology, quantum computers involve innumerable Intellectual Property (IP) in the form of fabrication recipe, control electronics and software techniques, to name a few. Furthermore, complexity of quantum systems necessitates extensive involvement of third party tools, equipment and services which could risk the IPs and the Quality of Service and enable other attack surfaces. This paper is a first attempt to explore the quantum computing ecosystem, from the fabrication of quantum processors to the development of specialized software tools and hardware components, from a security perspective. By investigating the publicly disclosed information from industry front runners like IBM, Google, Honeywell and more, we piece together various components of quantum computing supply chain. We also uncover some potential vulnerabilities and attack models and suggest defenses. We highlight the need to scrutinize the quantum computing supply chain further through the lens of security.