Search Results (7,641)

A prominent development in environmental engineering and water resource management is the growing adoption of the term “One Water,” which encompasses all categories of water. In practical terms, One Water Concept (OWC) suggests that some municipalities could benefit from combining their traditionally separate water supply and wastewater divisions into a single, unified department. This integration is believed to enable more strategic, efficient, and economically viable approaches to addressing future water challenges. The OWC promotes an integrated approach to water resource management, emphasizing the interconnectedness of water systems and the need for holistic governance. The focus of this paper is on an examination of the implementation of OWC in Greece based on an analysis of recent international case studies, and the identification of the methodological and epistemological challenges. Through critical engagement with current literature and policy frameworks, the study highlights the successes and obstacles in adopting OWC, offering insights into future directions for sustainable water management. The study identifies key challenges such as institutional fragmentation, insufficient reuse infrastructure, and fragmented policy frameworks, while also highlighting opportunities related to digital monitoring, stakeholder collaboration, and investment in green infrastructure. These findings underscore the need for coordinated strategies to advance the One Water approach in Greece. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

Oil and gas fields in subsidence-prone regions face multiple hazards that threaten the resilience of their infrastructure. This study presents an integrated risk mapping framework for the Yibal field in the Sultanate of Oman, utilizing remote sensing and geophysical data. Multi-temporal PS-InSAR analysis from 2010 to 2023 revealed cumulative surface deformation and tilt anomalies. Micro-seismic and fault proximity data assessed subsurface stress, while a flood risk map-based surface deformation-adjusted elevation captured hydrological susceptibility. All datasets were standardized into five risk zones (ranging from very low to very high) and combined through a weighted overlay analysis, with an emphasis on surface deformation and micro seismic factors. The resulting risk map highlights a central corridor of high vulnerability where subsidence, seismic activity, and drainage pathways converge, overlapping critical infrastructure. The results demonstrate that integrating geomechanical and hydrological factors yields a more accurate assessment of infrastructure risk than single-hazard approaches. This framework is adaptable to other petroleum fields, enhancing infrastructure protection (e.g., pipelines, flowlines, wells, and other oil and gas facilities), and supporting sustainable field management. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

This paper presents a PRISMA-guided systematic literature review (2015–2025) of 20 empirical studies on digital transformation in retail banking, examining how artificial intelligence (AI) strengthens cybersecurity, enables FinTech collaboration through interoperable APIs and open-banking infrastructures, and embeds data-driven decision-making across core functions. We searched major databases, applied predefined eligibility criteria, appraised study quality, and coded outcomes related to digital adoption, operational resilience, and customer experience. The synthesis indicates that AI-enabled controls and API-mediated partnerships are consistently associated with higher digital-maturity indicators, conditional on robust model-risk governance and prudent third-party/outsourcing management. Benefits span improved customer experience, efficiency, and inclusion; however, legacy systems, regulatory fragmentation, cyber threats, and organizational resistance remain binding constraints. We propose a unified framework linking technology choices, regulatory design, and organizational outcomes, and distill actionable guidance for policymakers (e.g., interoperable standards, proportional AI governance, sector-wide cyber resilience) and bank managers (sequencing AI use cases, risk controls, and partnership models). Future research should assess emerging technologies—including quantum-safe security and central bank digital currencies (CBDCs)—and their implications for digital-banking stability and trust. Full article

Technical constraints to be faced in microgrids have become more frequent with high renewable integration. In this context, Thermal Energy Storage (TES) has emerged as a promising solution to enable consumers’ flexibility to contribute to the solution of such operational issues. This paper examines the integration of the novel system ECHO-TES (a Thermal Energy Storage System developed within the European Project ECHO) in microgrids to address technical constraints, utilizing OpenDSS and Python simulations. Building on that, the Efficient Compact Modular Transaction Simulation System (ECHO-TSS) adds a layer of virtual automated transactions, coordinating multiple ECHO-TES assets to simulate not only energy flows and electricity consumption, but also the associated economic interactions. The study explores the critical role of TES in enhancing microgrid efficiency, flexibility, and sustainability, particularly when coupled with renewable energy sources. By analyzing diverse demand scenarios, the research aims to assess its impact on grid stability and management. The paper highlights the importance of advanced modeling tools like OpenDSS in simulating complex microgrid operations, including the dynamic behavior of TES systems. It also investigates demand-side management strategies and the potential of TES to mitigate challenges associated with renewable energy variability. The findings contribute to the development of robust, adaptive microgrid systems and support the global transition towards sustainable energy infrastructure. Full article

Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks. Full article

Sensors in plant and crop monitoring play a key role in improving agricultural efficiency by enabling the collection of data on environmental conditions, soil moisture, temperature, sunlight, and nutrient levels. Traditionally, wide-scale wireless sensor networks (WSNs) gather this information in real-time, supporting the optimization of cultivation processes and plant management. Our paper proposes a novel “plant-to-machine” interface, which uses a plant-based biosensor as a primary data source. This model allows for direct monitoring of the plant’s physiological parameters and environmental interactions via Electrical Impedance Spectroscopy (EIS), aiming to reduce the reliance on extensive sensor networks. We present simple data-gathering hardware, a non-invasive single-wire connection, and a machine learning-based framework that supports the automatic analysis and interpretation of collected data. This approach seeks to simplify monitoring infrastructure and decrease the cost of digitizing crop monitoring. Preliminary results demonstrate the feasibility of the proposed model in monitoring plant responses to sunlight exposure. Full article

Artificial Intelligence (AI) is a transformational force reshaping business processes, financial decision-making, and enabling firms to create, deliver and capture value more effectively. While large corporations in South Asian countries, particularly Bangladesh, India, Pakistan and Sri Lanka have started leveraging AI to drive Business Model Innovation (BMI), Small and Medium Enterprises (SMEs) continue to face significant challenges. These include limited infrastructure, poor bandwidth penetration, unreliable electricity, weak institutional capacity and governance immaturity, along with ethics and compliance concerns. These challenges hinder SMEs from fully exploiting AI-driven BMI and reduce their financial resilience and competitiveness in increasingly digital and globalised markets. This paper examines how South Asian countries are adopting AI technologies in SMEs by comparing patterns and variations in adoption, capability, ethics, risks, compliance, and financial outcomes. The paper proposes a tailored, actionable framework, called TRIAD (Target, Restructure, Integrate, Accelerate, and Democratise)-AI, designed to address technical, organisational and institutional challenges that shape AI-driven BMI across South Asian SMEs and to meet regional and global SME needs. The framework integrates the best practices from global AI leaders such as China, Estonia and Singapore, emphasising responsible AI adoption through robust ethics and compliance standards, and risk management, and offering practical guidance for South Asian SMEs. By adopting this framework, South Asian countries can gain a competitive advantage, enhance operational efficiency, support GDP growth across the region and ensure adherence to all relevant international AI standards for responsible, sustainable, and financially sound innovation. Full article

Solid waste management remains a critical challenge in rapidly urbanizing regions of the Global South, where limited infrastructure and informal disposal practices compromise environmental and public health. This study addresses the issue of illegal waste dumping in Dakar, Senegal, by integrating remote sensing, geographic information systems, and citizen science into a multi-criteria framework to identify areas most susceptible to dumping. Using Landsat 8 and Sentinel-2 imagery, indicators such as land surface temperature, vegetation, soil, and water indices were combined with demographic and infrastructural data. A citizen survey involving local university students provided social perception scores and criterion weights through the Analytic Hierarchy Process. The resulting susceptibility maps revealed that high and very high dumping probabilities are concentrated around the Mbeubeuss landfill and densely populated areas of Keur Massar, while Malika showed lower susceptibility. Sensitivity analysis confirmed the model’s robustness but highlighted the influence of thermal and social perception variables. The results show that 28–35% of the study area falls under high or very high susceptibility, with hotspots concentrated near wetlands, informal settlements, and poorly serviced road networks. The weighted model demonstrates stronger spatial coherence compared to the unweighted version, offering improved interpretability for waste monitoring. These findings provide actionable insights for the Société Nationale de Gestion Intégrée des Déchets (SONAGED) and for municipal planners to prioritize interventions in high-susceptibility zones. Rather than being entirely novel, this study builds on existing remote sensing, geographic information systems and citizen science approaches by integrating them within a multi-criteria framework specifically adapted to a West African context. Full article

Large-scale Low Earth Orbit (LEO) satellite constellations have become critical infrastructure for global communications, yet routing optimization remains challenging. Due to high-speed satellite mobility and limited local perception capabilities, traditional shortest-path algorithms struggle to adapt to dynamic topology changes and effectively handle random fluctuations in traffic loads and inter-satellite link states. Meanwhile, as constellation scales expand, centralized routing mechanisms face deployment difficulties due to high communication latency and computational complexity. To address these issues, this paper proposes a hierarchical load-balanced routing optimization algorithm based on geographic partitioning. The algorithm divides the constellation into multiple regions by latitude and longitude, establishing a hierarchical cooperative decision mechanism: the upper layer handles inter-region routing decisions while the lower layer manages intra-region routing optimization. Within regions, a load-aware K-shortest paths algorithm enables path diversification, achieving global coordination through cross-region information sharing and dynamic path selection, thereby reducing end-to-end routing latency while enhancing adaptability to dynamic environments and balancing routing performance with system scalability. In simulation scenarios with a Starlink-like architecture of 1512 satellites, experimental results demonstrate that compared to shortest-path routing, the algorithm reduces end-to-end latency by 14.1% and average satellite load by 15.9%. Under dynamic load scenarios with incrementally increasing user traffic, the algorithm maintains stable performance, validating its robustness under traffic fluctuations and link state variations. Full article

Urban greenways are essential ecological infrastructure connecting residents to nature and enhancing well-being. However, previous research has largely focused on the health benefits and related spatial patterns of greenways, while their roles and mechanisms in promoting pro-nature behaviors remain underexplored. Pro-nature behaviors are external manifestations of connectedness with nature, forming a gradient from visitation to usage habits and alignment with nature, thereby fostering sustainable human–nature relationships and enhancing urban well-being. At the deepest level, alignment with nature refers to residents’ deep engagement with natural environments, characterized by immersive perception and environmentally responsible behaviors that reflect an awareness of human–nature interdependence. This study systematically reviews existing literature to explore how urban greenways promote residents’ pro-nature behaviors. Grounded in the theory of connectedness with nature, this study develops a hierarchical framework linking network–place attributes to multilevel pro-nature behaviors (visitation, usage habits, and alignment with nature) to guide a systematic review of 88 articles retrieved from Web of Science and Scopus. Results show that visitation and usage habits are shaped mainly by greenway connectivity of built environment elements and internal features such as facilities, maintenance, and social factors, whereas alignment with nature is driven by ecological connectivity and habitat quality. The study argues that enduring pro-nature behaviors emerge when greenways integrate two complementary attributes: network coupling that links urban systems and ecological corridors, and composite place-based qualities that sustain human–nature interactions. These findings offer theoretical and practical insights for designing and managing urban greenways that combine ecological functionality with social well-being and promote sustainable urban development. Full article

Coastal areas are increasingly affected by erosion due to climate change and human interventions, threatening the stability of many shorelines. Understanding coastal dynamics is therefore crucial for developing effective conservation and management strategies. This study analyzes the evolution of the coastline between the Port of Valencia and the Port of Sagunto from 1957 to the present, one of the most anthropized littoral cells in the Eastern Mediterranean, where urban development, groyne fields, and major harbor structures strongly modify longshore transport. Using Geographic Information Systems (GIS), including QGIS and the DSAS extension, five shoreline change indicators (EPR, LRR, NSM, SCE, and WLR) were calculated based on coastlines extracted from orthophotos and satellite images. The analysis was conducted across five distinct zones and three temporal scales (long, medium, and short term) to capture spatial and temporal variations. The results reveal significant heterogeneity: the Arenas–Malvarrosa–Patacona area shows long-term accretion but recent erosion (LRR = +0.88 m/year; NSM = +58 m), Port Saplaya shows moderate erosion (LRR ≈ 0.27 m/year), Pobla de Farnals is undergoing strong erosion (LRR = −0.57 m/year; NSM = −44 m), Puzol appears recently stabilized (2015–2024; LRR ≈ +0.06 m/year) and Marjal dels Moros, historically stable, now exhibits a short-term retreat of −0.53 m/year. Overall, coastal evolution in the study area exhibits a clear pattern, being influenced by both natural processes and human actions: long-term accretion occurs exclusively in sectors located updrift of major infrastructures, while most remaining areas show persistent or recently accelerated erosion, reflecting the cumulative impact of sediment scarcity, coastal armoring and increasing storm intensity. The data provide valuable insights for medium- and long-term coastal planning and sustainable territorial management. Full article

Symmetric cryptographic schemes such as RSA and ECDSA (Elliptic Curve Digital Signature Algorithm), used for digital signatures in protocols like TLS, DTLS, and secure messaging, are computationally intensive. This makes them unsuitable for constrained environments, such as the Internet of Things (IoT) and the Internet of Vehicles (IoV). This study introduces a blockchain-based framework that utilizes the Ethereum network to store and verify public keys associated with digital certificates. By replacing signature decryption with blockchain-based public key verification, the solution significantly reduces cryptographic overhead and latency in V2V messages. It supports various certificate formats, including Public Key Infrastructure (PKI)/Certificate Authority (CA) certificates such as X.509 and L-ECQV, as well as self-signed certificates. Applications include secure communication protocols like Datagram Transport Layer Security (DTLS)/Transport Layer Security (TLS), V2V mutual authentication in V2X messaging, and lightweight certificate management within IoT ecosystems. Empirical results show that the DTLS handshake with this scheme is reduced from 12 s to less than 6 s. Additionally, it enables vehicles and IoT devices to perform one-time signature verification with minimal latency in V2V messaging, demonstrating significant performance improvements for high-density deployments involving mutual authentication between IoT devices and V2V communication. Full article

Sustainable urban water governance in rapidly transforming cities requires integrative decision-making frameworks capable of balancing social equity, economic efficiency, and environmental resilience. This study develops an Integrative Decision-Making Framework (IDMF) for optimizing urban water policy in Yerevan, Armenia, built upon AI- and GIS-assisted diagnostics and incorporating a Governance Readiness Index (GRI) together with spatial hotspot overlay analysis. The framework employs an AHP–TOPSIS multi-criteria structure to evaluate five policy alternatives—leakage reduction, demand-side management, decentralized reuse, green–blue infrastructure, and data-driven governance—based on normalized quantitative indicators across social, economic, and ecological domains. Results show that Leakage Reduction (A1) and Data-Driven Governance (A5) consistently rank as the top-performing strategies across both baseline and sensitivity scenarios, while equity-prioritized weightings enhance social outcomes without significantly compromising economic performance. The approach also demonstrates robustness under ±10–20% weight variations. Acknowledging limitations related to data availability and expert-based judgments, the study outlines the minimum governance and data-readiness conditions required for transferability. The IDMF thus advances decision-support science in urban water management by integrating governance feasibility with spatial diagnostics and provides adaptable guidance for mid-income cities facing institutional and environmental constraints. Full article