Henk J. van Waarde, Jeremy Coulson, Alberto Padoan
This paper studies the problem of verifying dissipativity of linear time-invariant (LTI) systems using input-output data. We leverage behavioral systems theory to express dissipativity in terms of quadratic difference forms (QDFs), allowing the study of general dynamic quadratic supply rates. We work under the assumptions that the data-generating system is controllable, and an upper bound is given on its lag. As our main results, we provide sufficient conditions for the data to be informative for dissipativity. We also show that for a specific class of static supply rates, these conditions are both necessary and sufficient. For the latter supply rates, it turns out that certification of dissipativity is only possible from data that enable unique system identification. As auxiliary results, we highlight some properties of QDFs, such as upper bounds on the degree of storage functions.
Abstract Irrigation water quality is a cornerstone of agricultural sustainability, yet conventional assessments remain constrained by narrow hydrochemical indices that rarely capture the complex interactions among soils, crops, and climate. In an era of climate uncertainty, water scarcity, and technological acceleration, rethinking irrigation water quality is essential to sustain productive and resilient agroecosystems. This state-of-the-art review synthesizes conceptual, technological, and policy-level advances across three interlinked domains. First, it critically revisits classical hydrochemical frameworks salinity, sodicity, toxic ions, and microbiological contamination while identifying their limitations in predicting long-term soil degradation and crop responses. Second, it explores how agroecosystem intelligence, through the integration of remote sensing, geographic information systems, and artificial intelligence, enables predictive and spatially explicit water-quality monitoring. Third, it situates irrigation water quality within the soil–water–crop nexus, linking water composition to soil health, nutrient cycling, and food security within the broader context of the Sustainable Development Goals (SDGs). This review follows a structured literature-analysis approach based on peer reviewed articles retrieved from Scopus, Web of Science, and Google Scholar (2000–2025) using keywords such as “irrigation water quality,” “soil water crop nexus,” and “smart water management.” Studies were screened for relevance, originality, and methodological rigor to ensure balanced coverage. The synthesis highlights persistent knowledge gaps, including the need to integrate water-quality indices with system-level models, evaluate trade-offs between productivity and environmental risk, and design governance frameworks for equitable access to safe irrigation water. Ultimately, this article advocates a paradigm shift from static assessments of water suitability toward intelligent, integrated, and adaptive frameworks that enable climate-resilient irrigation and sustainable futures.
Water supply for domestic and industrial purposes, Environmental sciences
The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry by improving decision-making, predictive analytics, and operational efficiency. This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory management, supplier relationship management, and logistics optimization. By leveraging advanced data analytics and real-time insights, LLMs enable organizations to optimize resources, reduce costs, and improve responsiveness to market changes. Key findings highlight the benefits of integrating LLMs with emerging technologies such as IoT, blockchain, and robotics, which together create smarter and more autonomous supply chains. Ethical considerations, including bias mitigation and data protection, are taken into account to ensure fair and transparent AI practices. In addition, the paper discusses the need to educate the workforce on how to manage new AI-driven processes and the long-term strategic benefits of adopting LLMs. Strategic recommendations for SCM professionals include investing in high-quality data management, promoting cross-functional collaboration, and aligning LLM initiatives with overall business goals. The findings highlight the potential of LLMs to drive innovation, sustainability, and competitive advantage in the ever-changing supply chain management landscape.
Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services, and other systems that contribute to the output of modern AI systems. This gap is particularly problematic when AI systems are used in critical applications, such as the food supply, healthcare, utilities, law, insurance, and transport. We survey the current state of AI risk assessment and management, with a focus on the supply chain of AI and risks relating to the behavior and outputs of the AI system. We then present a proposed taxonomy specifically for categorizing AI supply chain entities. This taxonomy helps stakeholders, especially those without extensive AI expertise, to "consider the right questions" and systematically inventory dependencies across their organization's AI systems. Our contribution bridges a gap between the current state of AI governance and the urgent need for actionable risk assessment and management of AI use in critical applications.
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.
Ismail Abd-Elaty, Alban Kuriqi, Lorenzo Pugliese
et al.
Abstract Fresh groundwater in arid and highly populated regions is limited. In coastal aquifers, the deterioration of fresh groundwater is accelerated by saltwater intrusion, primarily occurring through lateral encroachment and vertical movements in the proximity of discharging wells. Coastal regions have high salinity due to saline intrusion, where many abstraction wells are turned off by this high salinity, which leads to increased freshwater supply costs. This study investigates the performance of new approach using the shoreline subsurface dams (SSDs) for mitigating the saline water wedge in coastal aquifers, where the dams are installed at the shoreline (distance from shoreline = 0). Specifically, the current study's novelty is testing the effectiveness of SSDs by different relative heights ranging from 0.05 to 0.50 in the test case (Henry problem) and from 0.09 to 0.53 relative to the aquifer thickness in the field scale aquifer (Biscayne aquifer, Florida, USA). The results showed an exponential increase in salt repulsion for increasing SSDs height, reaching a maximum of + 0.70%, + 1.80%, + 3.25%, + 5.80%, + 10.45%, and + 18.40% for the dam height to aquifer thickness ratios of 0.09, 0.18, 0.26, 0.35, 0.44 and 0.53, respectively, in the field scale case. The SSDs increase the freshwater storage at the coastal zones where the low salinity occurs and reduces the freshwater supply cost. Despite the positive impact of height on repulsion, important factors such as economics, construction aspects, geographical suitability, and environmental impacts must be considered for real applications. This is crucial to develop feasible solutions applicable globally under the growing pressure of sea level rise.
Defar Getahun Gizachew, Edo Begna Jiru, Tsigab Tekle’Ab
et al.
Abstract The co-precipitation approach is used in the current study to create an environmentally friendly Ag/Fe/ZnO nanocomposites utilizing an aqueous leaf extract of Embelia schimperia. The synthesized nanocomposite was characterized using Fourier-transform infrared, UV, X-ray, UV–vis, DLS, TGA, and SEM to determine its functional group, structure, bandgap energy, size distribution, a mass of loss, and energy gain or loss, and morphological structure, respectively. The bioactive components of Embelia schimperia, synthesized Ag/Fe/ZnO NCs and degradation of Amoxicillin via photocatalyst were assessed. The response surface methodology of central composite design (CCD) was used to examine and optimize the effects of three independent variables on the degradation of Amoxicillin under visible light. According to the experimental findings, the maximum photocatalytic degradation efficiency was achieved at green synthesized Ag/Fe/ZnO NCs dosage of 100 mg, a concentration of Amoxicillin of 30 mg/L and a radiation time of 180 min. Their findings show that Embelia schimperia extract-derived Ag/Fe/ZnO nanocomposites is a promising alternative for degradation of pharmaceuticals contamination of wastewater via photocatalytic under the given conditions.
Abstract The hyperconcentrated fluid flow occurs as a result of heavy rainfall, during which a large amount of sediments from the upstream basin is washed away and suddenly increases the flow concentration of the alluvial channels. The stresses exerted by this type of fluid on the bed and body of the stream/river and related structures such as dam lead to the failure them and cause many human and financial losses. One of the important topics in the simulation of dam break caused by non-Newtonian fluid flow is the modeling of frictional stresses. In this research, after collecting several relationships to model the coefficient of friction loss of non-Newtonian fluid, a two-dimensional model was developed based on the numerical solution of shallow water equations in curvilinear coordinates to simulate hyperconcentrated flow. The results of the validation of the model were presented by comparing the measurement data of the suddenly complete dam break caused by the non-Newtonian fluid flow in the form of graphs, which all emphasize the accuracy of the developed model. It was also shown that for a suddenly complete dam break, with an increase in fluid volume concentration from 13.8 to 36.4%, the flow depth at the failure site increases by 18.8%. Next, asymmetric two-dimensional partial dam break of non-Newtonian fluid was simulated and compared with the results of Newtonian fluid. The results showed that the maximum flow velocity in the center of the fracture wall for the non-Newtonian fluid with a concentration of 32.2% is less than half of the maximum velocity of the Newtonian fluid.
The Fourth Industrial Revolution, particularly Artificial Intelligence (AI), has had a profound impact on society, raising concerns about its implications and ethical considerations. The emergence of text generative AI tools like ChatGPT has further intensified concerns regarding ethics, security, privacy, and copyright. This study aims to examine the perceptions of individuals in different information flow categorizations toward AI. The results reveal key themes in participant-supplied definitions of AI and the fourth industrial revolution, emphasizing the replication of human intelligence, machine learning, automation, and the integration of digital technologies. Participants expressed concerns about job replacement, privacy invasion, and inaccurate information provided by AI. However, they also recognized the benefits of AI, such as solving complex problems and increasing convenience. Views on government involvement in shaping the fourth industrial revolution varied, with some advocating for strict regulations and others favoring support and development. The anticipated changes brought by the fourth industrial revolution include automation, potential job impacts, increased social disconnect, and reliance on technology. Understanding these perceptions is crucial for effectively managing the challenges and opportunities associated with AI in the evolving digital landscape.
Prosper Kpiebaya, Abdul-Ganiyu Shaibu, Ebenezer Ebo Yahans Amuah
et al.
The need for quality water in Africa for agriculture cannot be overemphasized amidst the current global water crises. The focus of this study sought to evaluate the quality of groundwater for irrigation purposes while unearthing the emerging challenges in the study area. In total, 202 groundwater samples were collected, and several parameters were tested. The study employed QGIS and multi-criteria decision analysis to examine zones of suitable groundwater quality for agriculture. Findings from the study indicate that the primary water types were Na–HCO3 and Ca–HCO3. The overall accuracy (OV) of the land use land cover (LULC) map using the Random Forest (RF) algorithm was 94.5%. The analysis revealed that SpH influences GpH at p = 0.02891 (p < 0.05) and SOM and SOC influence GNO3 at p = 0.044 (p < 0.05). The overlay analysis spatially classified the groundwater in the study area into three categories of suitability with ariel coverage showing areas of good quality (1,534.34 km2), moderate quality (1,933.35 km2), and poor quality (1,815.21 km2). The results from this study uncovered that 72.33% of the samples were within the desirable limits and it can be concluded that the quality of groundwater in the area is acceptable for irrigation.
HIGHLIGHTS
This paper examines different surficial factors.;
This study presents several cases (quality versus LULC, slope, and soil).;
The paper examines the suitable areas for irrigation as per the available groundwater quality.;
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
Abstract The rational allocation of the water resources in drylands and the scientific evaluation of their values are important for the regional sustainable development. We estimated the water consumption of each ecosystem in Ejina Oases in the arid area in northwest China based on the water balance equation, then assessed their ecosystem service values (ESVs) using the value per unit area method and the value equivalent factor per unit area method considering the cost of water consumption, respectively, and calculated their water productivities from 1990 to 2015. With the implementation of the ecological water diversion project (EWDP) in 2000, the deciduous broadleaf forest dominated by Populus euphratica had an increasing trend, meanwhile, lakes had a changing process from shrinking and drying up to recovery and expansion from 1990 to 2015. The total water consumption in Ejina Oases decreased from 5.26*108 m3 in 1990 to 4.79*108 m3 in 2000, and then increased continually to 5.97*108 m3 in 2015. The water consumption of forest, grassland and cultivated land hardly changed, while the water consumption of water or wetland changed obviously. The total ESVs estimated using the value per unit area method in Ejina Oases decreased from 1052.6 to 787.3 million ¥ in the period of 1990–2000, then increased to 1500.6 million ¥ in 2015, while the ESVs estimated using the value equivalent factor per unit area method decreased from 6368.7 million ¥ in 1990 to 5892.0 million ¥ in 2000, then increased to 7139.9 million ¥ in 2015, and the latter method was more reasonable. The results showed that the EWDP started in 2000 led to the reallocation of water resources and transfer of ESVs among ecosystems, which had obvious performance in the ecological environment and social economy. This study provided a comprehensive view of water use and management, ecological restoration and socio-economic development in this key ecological function zone, and will help decision-makers to formulate the scheme of rational allocation and efficient utilization of water resources in the basin.
This is the first study to report the groundwater (GW) quality indices for drinking and irrigation purposes in Chuadanga Municipality, Bangladesh. GW samples were collected from 32 locations across the municipality's nine administrative wards. The physical parameters, such as total dissolved solids (TDS): 383 ppm, pH: 7.4, and electrical conductivity (EC): 583 μs/cm were reported, respectively. Among the detected chemical components, arsenic (As): 0.16 mg/L, iron (Fe): 0.77 mg/L, and manganese (Mn): 0.75 mg/L were found beyond the standard values. Geospatial analysis and multivariate analyses, such as correlation coefficient analysis (CCA), cluster analysis (CA), and principal component analysis (PCA), were performed to determine the potential sources of contaminants in the GW of the selected study areas. The water quality index (WQI) and heavy metal pollution index (HPI) suggest that most samples are unsuitable for drinking and irrigation. Following the water quality indices, CCA indicates the significant impact of As and Mn on water quality. The CA and PCA suggest geogenic, anthropogenic, and natural processes are mainly responsible for releasing those contaminants in GW.
Many industrial sectors have been collecting big sensor data. With recent technologies for processing big data, companies can exploit this for automatic failure detection and prevention. We propose the first completely automated method for failure analysis, machine-learning fault trees from raw observational data with continuous variables. Our method scales well and is tested on a real-world, five-year dataset of domestic heater operations in The Netherlands, with 31 million unique heater-day readings, each containing 27 sensor and 11 failure variables. Our method builds on two previous procedures: the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning algorithm from Boolean data. C4.5 pre-processes each continuous variable: it learns an optimal numerical threshold which distinguishes between faulty and normal operation of the top-level system. These thresholds discretise the variables, thus allowing LIFT to learn fault trees which model the root failure mechanisms of the system and are explainable. We obtain fault trees for the 11 failure variables, and evaluate them in two ways: quantitatively, with a significance score, and qualitatively, with domain specialists. Some of the fault trees learnt have almost maximum significance (above 0.95), while others have medium-to-low significance (around 0.30), reflecting the difficulty of learning from big, noisy, real-world sensor data. The domain specialists confirm that the fault trees model meaningful relationships among the variables.
In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem consists of determining the optimal quantity of products to be produced and shipped across different warehouses over a given time horizon. In particular, we present a mathematical formulation of a two-echelon supply chain environment with stochastic and seasonal demand, which allows managing an arbitrary number of warehouses and product types. Through a rich set of numerical experiments, we compare the performance of different deep reinforcement learning algorithms under various supply chain structures, topologies, demands, capacities, and costs. The results of the experimental plan indicate that deep reinforcement learning algorithms outperform traditional inventory management strategies, such as the static (s, Q)-policy. Furthermore, this study provides detailed insight into the design and development of an open-source software library that provides a customizable environment for solving the supply chain inventory management problem using a wide range of data-driven approaches.
Zahra Nafrieh, Mahdi Sarai Tabrizi, Hossein Babazadeh
et al.
The importance of regulating the supply and demand regime shows the need for planning in the exploitation of surface water resources. The aim of this study was to compare the performance of two models of Bayesian network (BN) with a probabilistic approach and MLP neural network for flow prediction and selection of the best structural model. Monthly meteorological data including rainfall, monthly average temperature, evaporation, and the volume of water transferred from five hydrometric stations were introduced as input data to the models, and runoff to the dam was considered as predictable. Input data with different layouts were introduced to BN and MLP models. The results were obtained by comparing 17 selected models according to the index criteria: Nash-Sutcliffe coefficient (NS), mean square error (MSE), mean square error root (RMSE), and MEAN absolute prediction error (MAPE). The best model in BN model with 43.3% similarity and index criteria was estimated to be -3.98, 300, 17.3, and 0.06, respectively. The MLP model with 80% similarity and index criteria were introduced as -10.3, -8266, 23.9, and 122.3 in the best model, respectively. As a result, both models performed well in runoff estimation, but the BN model had much better accuracy in forecasting. Finally, a structural pattern with acceptable results in both MLP and BN models was identified.
Environmental sciences, Water supply for domestic and industrial purposes
Abstract This paper examines various features of energy and groundwater irrigation nexus in a highly irrigated ecosystem of north western India. The study is based both on primary and secondary sources of data. Electric tube-wells account for about 72 percent of the total tube-wells population and consume about 40 percent of the total electricity consumption. Power subsidies account approximately 46 percent of the total subsidies disbursed which stimulate the groundwater development. The area irrigated by means of tube-wells has enlarged from 22 to 58 percent. Rice and sugarcane crops are the key consumers of energy both in terms of average energy consumption as well as per hectare of cultivated land. The average use factor of tube-wells is about 7.5 times high during kharif than in rabi season. Farmers have yielded high economic productivity under all crops with the exception of rice than other states such as Uttar Pradesh, Bihar and Gujarat.
In this work, we analyze the spatio-temporal economic properties of multi-product supply chains. Specifically, we interpret the supply chain as a coordinated market in which stakeholders (suppliers, consumers, and providers of transportation, storage, and transformation services) bid into a market that is cleared by an independent entity to obtain allocations and prices. The proposed model provides a general graph representation of spatio-temporal product transport that helps capture geographical transport, time delays, and storage (temporal transport) in a unified and compact manner. This representation allows us to establish fundamental economic properties for the supply chain (revenue adequacy, cost recovery, and competitiveness) and to establish bounds for space-time prices. To illustrate the concepts, we consider a case study in which organic waste is used for producing biogas and electricity. Our market model shows that incentives for waste storage emerge from electricity demand dynamics and illustrates how space-time price dynamics for waste and derived products emerge from geographical transport and storage.
Vincenzo Botta, Laura Fusco, Attilio Mondelli
et al.
A major problem in blockchain-based supply chain management is the potential unreliability of digital twins when considering digital representations of physical goods. Indeed, the use of blockchain technology to trace goods is obviously ineffective if there is no strong correspondence between what is physically exchanged and the digital information that appears in blockchain transactions. In this work, we propose a model for strengthening the supply chain management of physical goods by leveraging blockchain technology along with a digital-twin verification feature. Our model can be instantiated in various scenarios and we have in particular considered the popular case of food traceability. In contrast to other models known in the literature that propose their own ad-hoc properties to assess the robustness of their supply chain management systems, in this work we use the formalism of secure computation, where processes are described through generic and natural ideal functionalities.
While in today’s Sponge Cities flood control works effectively, the sponge-based rainwater harvesting is associated with substantial challenges. In addition to water management, the pollution of collected stormwater counts as one of the major barriers for urban water augmentation. The aim of this communication is to outline how this constraint can be overcome and how the water service portfolio of the Sponge City can successfully undergo feasible expansion considering technical and also economic aspects. Innovative engineered solutions for a sponge-based rainwater harvesting are the key to an adaptive and flexible water supply infrastructure for Sponge Cities especially to preserve its manifold urban water and life quality services. The complementary water service, emerged from the Sponge City, can provide an imperative contribution to compensate the high capital investments and to cover the operation and maintenance costs. This enables a tremendous funding opportunities that can be invested for the preservation of the blue-green future city. Moreover, this would offer a feasible way of urban water service development over the negative impacts caused by climate change.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes