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Effective waste management is crucial for sustainable industrial operations. This paper introduces a state-of-the-art digital tool designed for the circular economy. Primarily it pinpoints and quantifies symbiotic possibilities between industries with liquid waste streams, emphasising the most lucrative inter-industry connections. In practice, the tool takes in data such as waste stream volumes, material concentrations within these streams, market prices of materials, and industries’ raw material consumption rates. Utilising these, its algorithm identifies and assesses the most profitable material exchanges among the specified industries. This assessment considers the market value of materials and the costs associated with recovering those materials from liquid waste streams. One of the major challenges, the estimation of recovery costs, is addressed using an innovative Sherwood plot analysis. This analysis draws a correlation between a material’s recovery cost and its concentration within a liquid medium. The tool’s output provides a detailed list of potential transactions complemented by illustrative graphs that detail mass flows, profit margins, and environmental advantages for each industry. Collectively, these details offer insights both for individual industries and the industrial ecosystem as a whole. One of the tool’s most significant revelations is its ability to uncover potential “bridges” linking industrial waste streams to resource needs, unearthing previously unnoticed economic and ecological gains. By calculating economic and environmental benefits of “waste” reuse, this tool offers a compelling rationale for the adoption of industrial symbiosis. Ultimately, it uncovers the transformative potential of aligning industrial activities with a balance that fosters both economic growth and ecological responsibility.

This study advances the discourse on the transition from a linear to a circular water paradigm, within which water is reused and resources such as nutrients and energy can be recovered. The research provides an empirical evidence from demonstrative cases, identifying the technological, economic, socio-cultural, and regulatory factors that facilitate or impede the broader adoption of circular solutions in the water sector. It proposes an integrated system approach, which encompasses a comprehensive set of enabling instruments, including (a) the demonstration of the sustainability of circular water technologies at a system level, thereby providing a robust proof of concept; (b) a shift from a conventional financial cost-benefit approach to a business model predicated on circular value chains, underscoring the economic feasibility of these solutions; (c) the enhancement of social acceptance through active stakeholder engagement, thereby fostering a supportive community for these transformative changes; and (d) the adaptation of the regulatory framework to incentivise circular water solutions, such as the establishment of dedicated end-of-waste criteria to facilitate market access for recovered resources. The study concludes that a concerted effort is required to reconceptualise our water systems as circular systems, and to legitimise the role of circular water within our society and economy. HIGHLIGHTS Water-embedded resources can be transformed into valuable and high-quality products such as reclaimed water, energy, and/or recovered nutrients.; A comprehensive package of technological, economic, socio-cultural, and regulatory support instruments is needed for the wider uptake of circular water solutions.; The circular water paradigm requires to rethink the system with its current legal and social norms and practices.;

The world grapples with immediate crises like COVID-19, Russia's invasion of Ukraine, floods, droughts and wildfires. However, a longer-term crisis looms due to humanity's overstepping of planetary boundaries and its disruptive consequences. Growing awareness of the potential collapse of societies due to planetary boundary violations has prompted increased attention in the scientific literature. In the water sector, where infrastructure built today might persist during a future collapse, we must therefore ask ourselves how a (basic) level of water supply can be maintained in a collapsing society. This paper explores this question and proposes research directions to address it in the short to medium term. Despite the seeming remoteness of a societal collapse scenario, it is imperative to incorporate it urgently into water infrastructure research and planning. HIGHLIGHTS Environmentally induced societal collapse is an ignored risk for the water sector.; Research into potential consequences and strategies for deep adaptation are needed.; A four-stranded approach for this research is proposed, mirroring a prior one for research on worst-case climate change and systemic risks.;

The planning and management of decentralized technologies in water systems is one of the promising, yet overlooked, domains where artificial intelligence (AI) can be successfully applied. In this study, we develop and deploy a reinforcement learning (RL)-based ‘smart planning agent’ capable of designing alternative decentralized water systems under demanding operational contexts. The agent's aim is to identify optimal water infrastructure configurations (i.e., proposed decisions on water management options and interventions) for different conditions with regard to climate, occupancy and water technology availability in a demanding, off-grid setting, i.e., a water system with high requirements of independence from centralized infrastructure. The agent is coupled with a source-to-tap water cycle simulation model capable of assessing and stress-testing the proposed configurations under different conditions. The approach is demonstrated in the case of a military camp deployed abroad for peacekeeping operations. The agent is tasked with selecting optimal interventions from an array of real-world camp water management technologies and evaluating their efficiency under highly variable, operational conditions explored through simulation. The results show that RL can be a useful addition to the arsenal of decision support systems (DSS) for distributed water system planning and management, especially under challenging, highly variable conditions. HIGHLIGHTS We present a design framework coupling a water cycle simulation model with a reinforcement learning (RL) agent to support the planning of distributed water infrastructure.; The framework is demonstrated in an off-grid camp and is tested against a highly variable environment with multiple external drivers, including climate, occupancy and technology availability.; The results show that RL agents can be a useful planning aid for water infrastructures under deep uncertainty.;

Abstract A significant amount of electricity in numerous regions worldwide is used for lighting roads, squares, and other public spaces. Renewable energy can contribute notably to electricity usage for public lighting. This paper focuses on the day‐ahead scheduling of a hybrid renewable energy system (HRES) exploiting solar–wind energy potential to meet the electrical energy needs of public lighting. The studied HRES provides electricity for a Wi‐Fi hotspot and a charging hotspot for the end users and has an energy storage system that ensures a reliable electricity supply without interruptions. The day‐ahead scheduling of the studied HRES is based on electricity generation forecasting by using a deep‐learning approach. Particularly, the long short‐term memory model is utilized, considering the fact that it is able to perceive long‐term dependencies among the time series. Moreover, the model's performance is investigated through the determination of diverse inputs: (a) historical data, (b) weather predictions, and (c) historical data with weather predictions. Multiple scenarios of energy consumption are assumed and applied to optimize the day‐ahead scheduling. A new recommendation method is proposed and applied for day‐ahead scheduling, utilizing the power forecasts to achieve optimum operation and energy savings. The results point out that the utilization of the proposed recommendation method controls loads when a shortage in power generation and battery capacity is forecasted for the day ahead, leading to significant energy savings and minimizing the power demand from the grid.

Although the Circular Economy (CE) has made remarkable technological progress by offering a wide range of alternative engineering solutions, an obstacle for its large-scale commercialization is nested in the adoption of those business and financial models that accurately depict the value generated from resource recovery. Recovering a resource from a waste matrix conserves natural reserves in situ by reducing demand for virgin resources, as well as conserving environmental carrying capacities by reducing waste discharges. The standard business model for resource recovery is Industrial Symbiosis (IS), where industries organize in clusters and allocate the process of waste matrices to achieve the recovery of a valuable resource at an optimal cost. Our work develops a coherent microeconomic architecture of Chemical Leasing (Ch.L.) contracts within the analytical framework of the Sherwood Plot (SP) for recovering a Value-Added Compound (VAC) from a wastewater matrix. The SP depicts the relationship between the VAC’s dilution in the wastewater matrix and its cost of recovery. ChL is engineered on the SP as a financial contract, motivating industrial synergies for delivering the VAC at the target dilution level at the market’s minimum cost and with mutual profits. In this context, we develop a ChL market typology where information completeness on which industry is most cost-efficient in recovering a VAC at every dilution level determines market dominance via a Kullback–Leibler Divergence (DKL) metric. In turn, we model how payoffs are allocated between industries via three ChL contract pricing systems, their profitability limits, and their fitting potential by market type. Finally, we discuss the emerging applications of ChL financial engineering in relation to three vital pillars of resource recovery and natural capital conservation.

Establishing cooperation in transboundary rivers is challenging especially with the weak or non-existent river basin institutions. A nexus-based approach is developed to explore cooperation opportunities in transboundary river basins while considering system operation and coordination under uncertain hydrologic river regimes. The proposed approach is applied to the Nile river basin with a special focus on the Grand Ethiopian Renaissance Dam (GERD), assuming two possible governance positions: with or without cooperation. A cooperation mechanism is developed to allocate additional releases from the GERD when necessary, while a unilateral position assumes that the GERD is operated to maximize hydropower generation regardless of downstream users' needs. The GERD operation modes were analysed considering operation of downstream reservoirs and varying demands in Egypt. Results show that average basin-wide hydropower generation is likely to increase by about 547 GWh/year (1%) if cooperation is adopted when compared to the unilateral position. In Sudan, hydropower generation and water supply are expected to enhance in the unilateral position and would improve further with cooperation. Furthermore, elevated low flows by the GERD are likely to improve the WFE nexus outcomes in Egypt under full cooperation governance scenario with a small reduction in GERD hydropower generation (2,000 GWh/year (19%)). HIGHLIGHTS Water-Food-Energy Nexus framework is applied to explore cooperation opportunities in shared rivers.; Cooperation is likely to increase total average hydropower generation compared to the unilateral mode.; Downstream drought-related risks could be reduced with negligible impacts on upstream objectives if countries agree to share the risk.; A high level of coordination among countries is required to achieve the cooperation benefits.;

A circular, nature-based solution has been implemented in the drought-prone zone of the Mediterranean Island of Mykonos (Greece) to demonstrate how a residential rainwater collection system can be upgraded to enable optimal use of low-cost rainwater while making use of the natural services provided by the subsurface. The overall purpose is to develop a novel distributed intervention for rainwater harvesting, storage and recovery, by collecting excess water during the winter season and reusing it in the summer, thus increasing its management efficiency. The functioning of the entire system is flexible and adaptable, as it can be adjusted according to specific environmental conditions (rainfall, temperature) for each hydrological year. The configuration is highly replicable as it can be implemented in similar water scarce areas of the Mediterranean region and beyond and scalable as it can be applied to various scales (e.g. residential, touristic, municipal) of water demanding systems. [ABSTRACT FROM AUTHOR]

Abstract Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY.

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The identification and assessment of the cyber-physical-threat landscape that surrounds water systems in the digital era is governed by complex socio-technical dynamics and uncertainties that exceed the boundaries of traditional risk assessment. This work provides a remedy for those challenges by incorporating socio-technical modelling to account for the adaptive balance between goal-driven behaviours and available skills of adversaries, exploitable vulnerabilities of assets and utility’s security posture, as well as an uncertainty-aware multi-scenario analysis to assess the risk level of any utility against cyber-physical threats. The proposed risk assessment framework, underpinned by a dedicated modelling chain, deploys a modular sequence of processes for (a) the estimation of vulnerability-induced probabilities and attack characteristics of the threat landscape under a spectrum of adversaries, (b) its formulation to a representative set of stochastically generated threat scenarios, (c) the combined cyber-physical stress-testing of the system against the generated scenarios and (d) the inference of the system’s risk level at system and asset level. The proposed framework is demonstrated by exploring different configurations of a synthetic utility case study that investigate the effects and efficiency that different cyber-security practices and design traits can have over the modification of the risk level of the utility at various dimensions.