This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method leverages DRL methodology not only to execute precise and detrimental impacts on targeted infrastructure but also to evade detection by contemporary AI-driven defence systems. By developing and implementing tailored policies, the attackers ensure their hostile actions blend seamlessly with normal operational patterns, circumventing integrated security measures. Our research reveals the robustness of this attack strategy, shedding light on the potential for DRL models to be manipulated for adversarial purposes. Our research has been validated through testing and analysis in an industry-level setup. For reproducibility and further study, all related materials, including datasets and documentation, are publicly accessible.
Abstract Reliable estimation of groundwater recharge is essential for sustainable water resource management, as it underpins assessments of water availability, wellhead protection, contaminant transport, groundwater–surface water interactions, urbanization impacts, and aquifer vulnerability—yet national-scale assessments are particularly challenging in geologically complex regions like Ethiopia. This study presents a 250-m distributed recharge estimation by integrating three distinct methods—WetSpass-M, Baseflow Separation (BFS), and Chloride Mass Balance (CMB)—each offering unique insights due to differences in conceptual frameworks, assumptions, and input data requirements. The results revealed average annual groundwater recharge estimates of 73 mm (81 billion cubic meters) from WetSpass-M, 85 mm (51 billion cubic meters) from BFS, and 65 mm (72 billion cubic meters) from the CMB method across Ethiopia—each associated with a 95% confidence interval of ± 14 mm (16 BCM) for WetSpass-M, ± 13 mm (8 BCM) for BFS, and ± 13 mm (14 BCM) for CMB—derived through Monte Carlo-based uncertainty simulations. Spatial and temporal patterns of recharge were found to be strongly controlled by rainfall distribution, a conclusion further supported by the Cramér’s V correlation analysis. Hence, high rainfall basins (Abbay, Baro, and Tekeze) showed higher estimates—135–172 mm by WetsSpass-M and 139–182 mm by BFS; as well as rainy months (June–September)—10.5–18 mm by WetSpass-M and 3–14.2 mm by BFS. Interestingly, rainfall distribution had a dominant influence on recharge dynamics, outweighing soil texture effects—fine-grained soils exhibited higher recharge than coarse-textured ones due to preferential rainfall patterns. As Ethiopia’s first national-scale study combining multi-method approaches, high-resolution spatiotemporal analysis, and comprehensive datasets, this work significantly improves recharge estimation accuracy.
The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.
I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.
Rina Davila Severiano, Constance Crozier, Mark O Malley
Demand flexibility can offset some of the variability introduced on the supply-side by variable renewable generation. However, most efforts (e.g. control of residential vehicle charging) focus on short durations -- typically on the scale of minutes to hours. This paper investigates whether a fully electrified supply chain (transport and manufacturing) could provide demand flexibility over longer durations, exploiting the latency that typically exists between the processing of raw material to the delivery of finished product. Using a case study of the cement industry along the East Coast of the United States, we demonstrate that electrified supply chains could shift gigawatt-hours (GWh) of electricity demand for durations of more than a week, largely following wind power variability. Furthermore, we show that this occurs using low levels of carbon taxing (below $50/tn), at which battery storage is not economically viable. A sensitivity analysis shows potential to provide flexibility in all considered cost scenarios, although where the flexibility comes from can change (e.g. transport vs manufacturing). We show that today's cost of electrified heavy goods vehicles are the most significant parameter -- with substantially lower costs yielding a more demand-flexible supply chain.
Takashi Yamamoto, Hiroaki Yaguchi, Shohei Kato
et al.
A single service robot can present two distinct agencies: its onboard autonomy and an operator-mediated agency, yet users experience them through one physical body. We formalize this dual-agency structure as a User-Robot-Operator triad in an autonomous remote-control setting that integrates teleoperation with autonomous execution and human-in-the-loop remote assistance. Prior to the recent surge of language-based and multimodal interfaces, we developed and evaluated an early-stage prototype in 2020 that combined natural-language text chat with a sketch-based interface enabling freehand on-image annotation over the robot's live camera view to support remote intervention. We evaluated three modes - remote control via teleoperation, autonomous control, and autonomous remote control (a hybrid mode representing different levels of autonomy) - in controlled fetch-and-carry mobile manipulation tasks using a domestic mobile manipulator, the Human Support Robot (HSR), on a World Robot Summit 2020 rule-compliant test field. The results show systematic mode-dependent differences in user-rated affinity and perceived security, indicating that switching or blending agency within one robot measurably shapes human impressions in Human-Robot Interaction (HRI). These findings provide empirical guidance for designing human-in-the-loop mobile manipulation in domestic physical tasks.
Javed Mallick, Saeed Alqadhi, Majed Alsubih
et al.
Abstract Water quality assessment is a critical issue in the Aseer region of Saudi Arabia, where environmental and anthropogenic factors pose a major challenge to both drinking water and irrigation systems. The aim of this study was to carry out a detailed assessment of the water resources in the region, focussing on the most important aspects affecting water quality. The main objectives were to calculate various water quality indices for drinking and irrigation purposes, to develop an automated system using convolutional neural networks (CNN) to predict these indices and to increase the transparency of these models using explainable artificial intelligence (XAI) methods. Methodologically, the study used CNN algorithms optimised by Bayesian techniques for the prediction of eight water quality indices, coupled with SHAPley Additive exPlanations (SHAP) analysis under XAI to interpret the complex decision-making processes of these models. This dual approach enabled a comprehensive and insightful assessment of water quality. Using a robust dataset from the Aseer region, eight water quality indices were calculated, revealing significant variations and highlighting areas of concern. In this study, the entropy weight-based DWQI averaged 77.90 with a high standard deviation (std) of 39.08, reflecting considerable variability. The automated CNN models demonstrated robust performance in predicting water quality indices, with high accuracy (R2 = 0.959 in training and 0.945 in testing) for sodium percentage (Na%). However, the Magnesium Hazard (MH) index showed lower accuracy, suggesting possible overfitting and the need for further optimisation. SHAP analysis highlighted chloride, sulphate, and total dissolved solids as key contributors to the WQI, while sodium and calcium were significant for the sodium adsorption ratio. These insights enhance understanding of parameter influence on water quality assessments. This study introduces an advanced computational approach integrating CNN and XAI techniques, improving water quality evaluation and supporting informed environmental management in the Aseer region.
Identifying and prioritizing barriers to people's participation (PPBs) in is a prerequisite for implementing participatory soil and water conservation projects (SWCPs). Comparison evaluation of the local community and experts perspectives on the PPBs has rarely been investigated. Therefore, in the current study the level of agreement on the PPBs importance from the perspectives two groups were examined. For this purpose, Dastgerd, Asadli and Emarat watersheds, eastern Iran, with different socio-economic conditions were selected. In the current study the 13 important PPBs in implementation of SWCPs were identifying, which can be used as a model in future studies of other watersheds. Then the indicators were prioritized using Friedman Test. Finally, the two-sample Kolmogorov–Smirnov Test was also used to examine the agreement of the two views on the importance of the items. The results of PPBs prioritization based on 215 local people and 51expert’s viewpoints showed that lake of participatory guidelines, expert oriented decision-making process and lack of incentives economic in implementation of SWCPs are the most important PPBs. The results of two-samples Kolmogorov-Smirnov test show that the opinions of people and experts regarding the importance and role of 65% PPBs have a significant difference. The disagreement between the opinions of the two groups is a barrier to achieving the goals of participatory SWCPs. Also, removing barriers related to economic-executive factors has a high effect on increasing the level of participation and encouraging voluntary participate in SWCPs.
Enovwo E. Odjegba, Abayomi O. Bankole, Olumide O. Ajulo
et al.
Abstract Efficient groundwater monitoring is crucial for public health, especially in self-supplied communities like Ayetoro. This study comprehensively assessed groundwater contamination and corrosivity in fifteen water sources (11 shallow hand-dug wells and 4 drilled wells) within the Abeokuta Formation, Nigeria. The assessment utilized physicochemical, ionic, and metal analyses, along with pollution assessment metrics; Contamination factor ( $$C_{f}$$ ), Degree of Contamination ( $$C_{deg}$$ ), Geo-accumulation Index ( $$I_{geo}$$ ), Enrichment Factor ( $$EF$$ ), and the Langelier Saturation Index ( $$LSI$$ ) to measure contamination, enrichment, and water stability. Results showed slightly acidic pH, attributable to geogenic carbonate weathering and ion exchange. All samples exceeded the World Health Organization cadmium (Cd) acceptable threshold (0.003 mg/L), ranging 0.0032–0.0219 mg/L. Pollution index analyses confirmed significant to extremely severe Cd contamination ( $$C_{f}$$ and $$EF$$ values notably high, including $$EF$$ up to 80.4) across the section of Ayetoro, which can be linked to agricultural land use patterns, fertilizer inputs, and geogenic enrichment through the carbonate-rich lithology of the Abeokuta Formation. Furthermore, all sites recorded negative $$LSI$$ (− 5.3 to − 2.7), indicating corrosive water that can damage distribution infrastructure. These findings provide critical, nuanced insight into the extent of combined geogenic and anthropogenic groundwater contamination, particularly concerning metal enrichment and corrosivity. They underscore an urgent need for targeted water resource management strategies, including establishing site-specific Cd monitoring thresholds and implementing remediation approaches such as phytoremediation using metal-accumulating plants, protection of aquifer recharge zones from agricultural runoff, and deployment of low-cost household filtration systems, to safeguard public health and ensure a sustainable groundwater supply in Ayetoro and similar vulnerable regions.
Water supply for domestic and industrial purposes, Environmental sciences
Lutendo D. Rambau, Paul T. Mativenga, Annlizé L. Marnewick
Abstract Driven by population growth, industrialization, and climate change, drinking water has become more scarce. The linear economy approach of extracting, using, and disposing of water has worsened the scarcity of this limited resource. To facilitate the transition to a circular economy, water circularity indicators are needed. Previous research on water circularity was limited to developing indicators that covered one or two stages of the water supply chain or considered the entire water system as an integrated entity. This study sets a new agenda and approach by developing a water circularity indicator dashboard to assess circularity at each stage of the water supply chain and across waste hierarchy options. This dashboard was used in a case study in South Africa to demonstrate a practical way of measuring water circularity at each stage of the water life cycle. The results indicate gaps in practicing or reporting reusing, recycling and reclaiming across the water supply chain. The dashboard approach is an effective tool that will help water practitioners, stakeholders, and decision‐makers monitor, identify gaps and opportunities, and maximize circularity at each stage of the water supply chain.
ABSTRACT The present study was conducted out in the Upper Kebir Sub-basin [North East (NE) Algeria] aiming to evaluate the status and spatial–seasonal variability of water quality for domestic purposes using hydrogeochemical parameters and water quality indices. Surface water samples were collected from 27 selected sites, in 2020–2021 during both the wet and dry seasons and were analysed for 22 parameters. The findings were compared with WHO standards and the water quality indices were calculated. The analysis revealed that the basin was mainly polluted and showed significant seasonal and spatial variations in water quality, considerably influenced by climatic conditions (surface runoff) and human activities (urban sewage and agricultural activities). The results reveal significant spatio-temporal fluctuations and highlight areas likely to be affected by anthropogenic activities.
The main aim of the paper is to create a trust and transparency in the food supply chain system, ensuring food safety for everyone with the help of Blockchain Technology. Food supply chain is the process of tracing a crop from the farmer or producer to the buyer. With the advent of blockchain, providing a safe and fraud-free environment for the provision of numerous agricultural necessities has become much easier. Because of the globalization of trade, the present supply chain market today includes various companies involving integration of data, complex transactions and distribution. Information tamper resistance, supply-demand relationships, and traceable oversight are all difficulties that arise as a result of this. Blockchain is a distributed ledger technology that can provide information that is resistant to tampering. This strategy can eliminate the need for a centralized trusted authority, intermediaries, and business histories, allowing for increased production and security while maintaining the highest levels of integrity, liability, and safety. In order to have an integrity and transparency in food supply chain in the agricultural sector, a framework is proposed here based on block chain and IoT.
Sophie Hall, Laura Guerrini, Florian Dörfler
et al.
The vast majority of products we use daily are supplied to us through complex global supply chains that transform raw materials into finished goods and distribute them to end consumers. This paper proposes a modeling methodology for dynamic competitive supply chains based on game theory and model predictive control. We model each manufacturer in the supply chain as a rational utility maximizing agent that selects their actions by finding an open-loop generalized Nash equilibrium of a multi-stage game. To react to competitors and the state of the market, every agent re-plans their actions in a receding horizon manner based on estimates of market and supplier parameters thereby creating an approximate closed-loop equilibrium policy. We demonstrate through numerical simulations that this modeling approach is computationally tractable and generates economically interpretable behaviors in a variety of settings such as demand spikes, supply shocks, and information asymmetry.
Mateusz Wyrembek, George Baryannis, Alexandra Brintrup
The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine learning for developing supply chain risk intervention models, and demonstrate its use with a case study in supply chain risk management in the maritime engineering sector. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions, allowing "what-if" scenario planning. We therefore propose different machine learning developmental pathways for for predicting risk, and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning.
Recent pandemics have highlighted vulnerabilities in our global economic systems, especially supply chains. Possible future pandemic raises a dilemma for businesses owners between short-term profitability and long-term supply chain resilience planning. In this study, we propose a novel agent-based simulation model integrating extended Susceptible-Infected-Recovered (SIR) epidemiological model and supply and demand economic model to evaluate supply chain resilience strategies during pandemics. Using this model, we explore a range of supply chain resilience strategies under pandemic scenarios using in silico experiments. We find that a balanced approach to supply chain resilience performs better in both pandemic and non-pandemic times compared to extreme strategies, highlighting the importance of preparedness in the form of a better supply chain resilience. However, our analysis shows that the exact supply chain resilience strategy is hard to obtain for each firm and is relatively sensitive to the exact profile of the pandemic and economic state at the beginning of the pandemic. As such, we used a machine learning model that uses the agent-based simulation to estimate a near-optimal supply chain resilience strategy for a firm. The proposed model offers insights for policymakers and businesses to enhance supply chain resilience in the face of future pandemics, contributing to understanding the trade-offs between short-term gains and long-term sustainability in supply chain management before and during pandemics.
Olga Murujew, Kristell Le Corre, Andrea Wilson
et al.
Reactive media present an alternative to gravel in constructed wetlands and have the potential to sustainably and efficiently remove phosphorus from wastewater. In this study, a full-scale steel slag wetland has been operated for its whole lifecycle at which 1.39 mg P/g media were retained. During its lifecycle, this wetland met strict consents below 0.5 mg P/L for the first 6 months and was operated for 266 and 353 days before the effluent phosphorus concentration rose above the typical consents of 1 and 2 mg P/L, respectively. A detailed analysis of the system demonstrated that the performance was directly associated with the release of materials from the media into the water which in turn affected other critical parameters such as pH. Further analysis of the media suggested that greater understanding was needed concerning the role of carbonates and in particular calcite if steel slag is to be effectively managed for use on constructed wetlands. Importantly, controlled release of calcium oxide from the media surface is required by managing the concerns of pH and vanadium release.
HIGHLIGHTS
First long-term full-scale study of a BOF steel slag media wetland for P removal.;
Phosphorus retention capacity of 1.39 mg P/g media after 782 days of operation.;
Low effluent phosphorus (<2 mg/L) achieved for up to 1 year of operation.;
High phosphorus removal efficiencies were associated with elevated pH (>9).;
Precipitation of calcite, Mg and Fe minerals likely to influence P removal mechanisms.;
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
Planned water management is inevitable in India's path towards sustainable development. Rapid urbanization and population growth have resulted in significant amounts of wastewater being generated and severe water pollution-related issues in the country. Given the scarcity of freshwater resources and the extent of pollution, it is essential to implement and maximize safe wastewater reuse. The primary impediment to reaching this goal is the enormous disparity between treatment capacity and wastewater generation. Only effective water usage and wastewater reuse practices will help to meet future water demand. Enforcing zero liquid discharge and stringent regulations to ensure treated wastewater quality are necessary to optimally reuse industrial wastewater. The release of untreated industrial wastewater into municipal sewerage system must be prevented. Selection of the appropriate treatment units, operation and maintenance, and real-time monitoring of wastewater quality are the key measures in ensuring the treated wastewater quality. Moreover, the current wastewater treatment methods need to be modified or upgraded to remove the toxic and emerging contaminants too. This chapter discusses the role of domestic and industrial effluents in water pollution, the challenges encountered, and regulations and policies related to water management. Examples of wastewater reuse and zero liquid discharge are also included in the chapter.
The results of the Igor Sikorsky Kyiv Polytechnic Institute the Department of Bioenergetics, Bioinformatics and Ecobiotechnology researches in two directions: 1 - research into the processes of physical and chemical purification of industrial wastewater of a number of industrial enterprises from antibiotics, heavy metal ions, synthetic surfactants, etc. pollutants, and the development of local cleaning technologies before discharging pre-treated wastewater into the city's drainage network; 2 – study of processes of biological wastewater treatment of industrial enterprises with deep removal of nitrogen and phosphorus compounds using immobilized microorganisms. The greatest decrease in the COD index was observed in the processes of coagulation and settling of wastewater of a pharmaceutical enterprise. The effects of purification according to the COD indicator in the case of using ferrous sulfate III were 76.0% and 72.2% for the initial values of COD of untreated wastewater, respectively, 90 and 120 mg/dm3. For biological purification, microorganisms immobilized on VIA carriers were used in oxygen conditions created in model bioreactors - anaerobic, anoxic, aerobic. To increase the biomass, we used nylon textured thread according to TU 6-06-S116-87 with a fiber diameter of 1.5-2.5 mm and a microfiber diameter of 100 μm. The specific surface area was 4000-5000 m2/m3. The average concentration of biomass in bioreactors by dry matter, g/dm3, was: in anaerobic I – 30; in anaerobic II – 24; in anoxic I - 16.8; in anoxic II - 5.4; in aerobic - 3.2. Biological treatment of industrial wastewater using immobilized microorganisms made it possible to obtain high pollutant removal effects. The indicators of purified wastewater were: COD - 50-80 mg/dm3; BODfull - 15-20 mg/dm3; suspended substances - up to 15 mg/dm3; compounds of nitrogen and phosphorus - within the norm for discharge into natural water bodies. The developed technologies were implemented at industrial enterprises, the savings in energy costs amounted to 40-45%, the high quality of purified water was ensured in accordance with the current standards for discharge into the city drainage system and into natural reservoirs.