Search Results (638)

Background: Extracorporeal cardiopulmonary resuscitation (ECPR) can improve outcomes following refractory out-of-hospital cardiac arrest (OHCA); however, access is constrained by geography and resources. This study compared two strategies against the current system in Nara Prefecture, Japan: a two-stage hospital model using chest-pain network hospitals as ECPR-initiation sites, and a prehospital ECPR model using physician-staffed ambulances from two extracorporeal membrane oxygenation (ECMO)-ready hospitals. Methods: A geographic information system (GIS)-based simulation was conducted using emergency medical service (EMS) records of witnessed cardiac-origin OHCA cases (2017–2022). Isochrone analyses estimated areas reachable within a 60 min arrest-to-ECMO target. In the two-stage hospital model, patients located within a 15 min transport radius from chest-pain network hospitals were considered geographically covered. In the prehospital ECPR model, a physician-staffed ambulance was assumed to reach arrest sites within a 25 min travel-time radius from ECMO-ready hospitals. The study outcome was geographic coverage, defined as the proportion of cases within each service area; the two strategies were compared using McNemar’s test for paired proportions. Results: Among 1476 included cases, the coverage rate was as follows: current system, 28.7%; two-stage hospital model, 65.2%; prehospital model, 70.4% (p < 0.001). Certain eastern and southern mountainous regions remained outside both coverage areas. Conclusions: Using real-world EMS data, a mobility-focused prehospital ECPR strategy provided broader potential geographic access without requiring additional fixed hospital infrastructure than expanding hospital-based initiation sites. Optimization of prehospital deployment may represent a geographically feasible approach to expanding ECPR access in mixed urban–rural regions, though operational feasibility and cost-effectiveness require further evaluation. Full article

This study investigates the radiological characteristics of drinking water sources managed by the Bilbao Bizkaia Water Consortium (CABB). To this end, the radiological monitoring parameters established by current regulations, as well as those applied by other international organizations, are reviewed and analyzed. In addition, commercially available continuous monitoring equipment is assessed in terms of its suitability for drinking water applications. To identify optimal deployment locations, a comprehensive evaluation of CABB water infrastructure is conducted, with the aim of ensuring radiological safety across the Bizkaia region. Furthermore, an economic assessment is carried out to estimate the potential cost of water supply under abnormal contamination scenarios. Full article

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems. Full article

The urban tree canopy is an important resource that spans public and private property and whose form and quantity change over short distances. Although remote sensing and deep learning approaches have been used to map urban tree canopy, the high cost of commercial imagery and the technical complexity of model development have limited their adoption by urban forestry practitioners. We developed a structured and reproducible deep learning workflow optimized for freely available USDA National Agriculture Imagery Program (NAIP) imagery. The workflow incorporates a reproducible U-Net segmentation model for canopy delineation and a YOLOv9e object detection model for individual tree identification, enabling complementary estimation of the canopy extent and individual tree locations. Across two neighborhoods in Los Angeles, the optimized U-Net achieved a Dice coefficient of 0.824 for canopy segmentation, while YOLOv9e reached an F1-score of 0.687 for individual tree detection on a held-out test set with 17,466 annotated trees. A data sufficiency experiment showed that model performance stabilizes when approximately 130 trees are annotated per 320 × 320 pixel (px) tile, corresponding to about 25,379 training and 2641 validation labels, providing a practical target for annotation effort. Additional experiments demonstrate a structured workflow for spatial sampling, training data requirements, and the use of model inferences to estimate tree canopy extent and individual tree locations. The workflow also shows encouraging evidence of transferability to previously unseen urban areas without retraining. By relying solely on NAIP-optimized approaches, this new workflow bridges the gap between complex deep learning techniques and the practical needs of urban foresters; empowers local stakeholders to create accurate, affordable, and timely urban tree inventories; and fosters data-driven decision-making for the sustainable management of urban green infrastructure. Full article

A Quantum Key Distribution (QKD)-assisted Physical Layer Security (PLS) scheme for Multiple-Input Multiple-Output (MIMO) wireless links is proposed and numerically evaluated. The framework utilizes high-rate quantum keys to generate unitary precoding matrices for channel estimation preamble encryption, alongside a constellation-based encryption methodology for the data payload. Integration of the QKD is facilitated by a practical Key Management System (KMS) that orchestrates key synchronization and ensures seamless interoperability with the QKD infrastructure. By securing both the preamble and payload portions of the transmission frame, the proposed scheme prevents unauthorized entities from acquiring critical knowledge of transceiver functionalities. Furthermore, the framework leverages high-entropy QKD-derived keys to reseed a pseudo-random number generator (PRNG), providing a symmetric-key encryption layer that enhances data confidentiality. Numerical evaluation results obtained within a simulated residential wireless environment demonstrate that the proposed architecture yields enhanced security at the cost of a minor degradation in reception performance, driven by a small noise amplification penalty and a marginal elevation in the peak-to-average power ratio (PAPR). Full article

This paper assesses the role of green hydrogen and green ammonia in the low-carbon reconstruction of Ukraine’s energy sector. The country, severely affected by war, has more than 70% of its energy infrastructure damaged or destroyed, which calls for novel solutions for not only reconstructing but also rethinking Ukraine’s energy sector shaped by the Soviet-era planning. In this context, decentralized and renewable energy solutions appear to be one of the best options to achieve this goal. This study combines four novel and mutually reinforcing methods: a Scopus-based literature review of highly cited green hydrogen publications, natural language processing (NLP) and bibliometric network analysis of Ukraine-related hydrogen research, a SWOT assessment, and a geospatial hydrogen production cost model (GEOH2). The novelty of this research lies in this integrated Ukraine-specific framework, which links research trends, wartime reconstruction constraints, hub-level policy choices, and financing risk-sensitive cost modeling. Therefore, the quantitative part of GEOH2 estimates the levelized cost of green hydrogen, while ammonia is treated as a downstream screening-level conversion and export pathway rather than as a full plant-level ammonia model. Our results show that Ukrainian green hydrogen research is concentrated on renewable-energy strategy, wind and solar electrolysis, water and desalination constraints, gas grid blending, underground storage, ammonia derivatives, and decentralized energy systems. The GEOH2 results indicate that southern Ukraine has strong physical potential for competitive green hydrogen production under de-risked financing, while war risk financing can make even resource-rich areas economically unattractive. Odesa and Dnipro emerge as important export-oriented and industrial hubs, whereas Zakarpattia remains strategically relevant as a safer western corridor linked to European markets. Our findings demonstrate that Ukraine’s hydrogen and ammonia development needs to follow a phased pathway: domestic renewable build-out and grid repair, pilot electrolysis projects and screening-level ammonia conversion pathways, targeted de-risking and insurance mechanisms, and only then broader export corridor development. This pathway can support decarbonization, energy security, industrial modernization, and Ukraine’s long-term integration into European clean energy value chains. Full article

China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to 130 chemical enterprises across three cities in Jiangsu Province using 2020–2024 panel data. The mean idle rate reached 78.1%, with no significant inter-city differences (H = 0.96, p = 0.619), attributable to both production underutilization and systematic over-estimation of emission ceilings inherent in the design-capacity-based permit methodology. Ward hierarchical clustering revealed three emission behavioral patterns, Persistent Surplus (n = 74, IR = 0.95), Declining Surplus (n = 32, IR = 0.69), and Growing Surplus (n = 19, IR = 0.59), exhibiting distinct idle rate levels and temporal trajectories. Cluster differentiation was significantly associated only with production-side emission characteristics, while enterprise economic variables showed no significant effects. The estimated tradeable emission surplus reached 668.3 t/a, though its realization faces transaction cost barriers including the lack of standardized transfer mechanisms and formal VOC trading infrastructure. A quadrant-based strategy matrix integrating idle rate levels with temporal trends is proposed for differentiated permit management. Full article

The rapidly increasing availability of low-cost commercial UAVs poses significant security challenges for critical infrastructure and law enforcement agencies. This paper presents an integrated LiDAR-based detection and vision-based verification system for an autonomous drone-on-drone aerial interception system. To eliminate the threat of possible dangerous target drones, the interception UAVs presented in this paper use a net to capture them safely in the air. The system addresses the critical limitation of ground-based sensors, which provide insufficient precision for reliable net-based capture operations. Moving beyond simulation-only approaches, the core novelty of this work lies in the successful real-world integration of these sensors on a strictly constrained aerial platform in size, weight and power to achieve sub-meter terminal guidance precision. The developed system uses real-time point cloud processing, DBSCAN clustering, and Moving Horizon Estimation tracking for the detection and tracking of the target. Vision-based verification uses a custom-trained YOLO neural network and achieves over 90% detection rates. The evaluation demonstrates a detection accuracy of less than 0.4 m at ranges exceeding 40 m during dynamic interception scenarios using RTK-GNSS ground truth. The dual-sensor approach successfully completed multiple autonomous interception missions with target detection ranges of up to 60 m, validating the capability of the system for safe, autonomous civilian UAV interception. Full article

This paper examines the costs and benefits of decarbonisation policy in the Polish energy generation sector. Accordingly, the analysis focuses on the costs of transforming the national energy mix up to 2050, as well as the environmental benefits associated with reducing emissions from electricity and district heating generation. The study addresses the question of which energy production structures are optimal at different levels of global warming costs, given the uncertainty surrounding the magnitude of human impact on the climate. The results indicate that relatively low SCC justify only a limited optimal reduction in CO₂ emissions. Full decarbonisation of the Polish energy sector, corresponding to a 100% reduction in CO₂ emissions by 2050, becomes socially optimal only at an SCC of around €165/tCO₂. Simulations conducted for different EUA price levels allow for the construction of a MAC curve, which can be used to identify the economically optimal scope of decarbonisation policy. Due to its heavy reliance on coal and the high-emission starting point of its energy transition, Poland faces particularly high investment requirements. Achieving climate neutrality in the energy sector by 2050 is estimated to require approximately €228 billion in investment, including substantial expenditures on RES, the construction of nuclear power plants, and the development of energy storage infrastructure. Full article

Road infrastructure projects are frequently affected by cost overruns driven by volatility in critical construction inputs and by the uneven association between external market shocks and material price movements. However, existing studies still provide limited evidence on how comparative forecasting, temporal price-signal diagnostics and probabilistic simulation can be integrated into a contingency-oriented decision framework. This study examines how construction input price forecasting and probabilistic simulation can inform contingency estimation in a road infrastructure case study. The empirical application is based on a Peruvian bridge project and combines benchmark-oriented forecasting using Bi-GRU and Random Walk models, descriptive temporal diagnostics based on lead–lag assessment and rolling-correlation analysis, and Monte Carlo simulation. Monthly series for structural steel, construction steel, cement, and diesel were transformed into log-returns and evaluated under a strict chronological design, while oil, the exchange rate, and the consumer price index were incorporated as exogenous variables. The Random Walk model produced lower forecasting errors for most inputs, achieving lower RMSE values in seven of the eight input-period comparisons; Bi-GRU outperformed it only for diesel in the test subset, with a 7.24% lower RMSE. From a project cost-risk perspective, the P95 contingency was estimated at 3.92% under Bi-GRU and 3.96% under Random Walk, indicating a similar upper-percentile contingency envelope under both forecasting specifications. The findings support contingency as a confidence-based budgeting decision rather than a fixed percentage. Full article

Urban drought pressure is increasing the operational risk and cost of maintaining municipal green infrastructure. Irrigation is still widely managed through fixed routines and fragmented information. To address this challenge, the study develops an integrated operational analysis by combining water consumption records, maintenance data and a GIS inventory for twenty municipal green spaces. System characterisation and performance screening were carried out using hourly meter readings to distinguish typical scheduled irrigation peaks from non-standard consumption patterns. To move from monitoring to control, irrigation needs were estimated using evapotranspiration (ET₀) and a garden-coefficient logic adapted to urban planting conditions and compared with measured consumption. The comparison indicates a potential reduction of 29–61% through improved scheduling and system adjustment. Based on the diagnosis, technical intervention scenarios were defined and assessed using techno-economic metrics, including ground-cover redesign and Mediterranean-adapted planting strategies. To support implementation, options were organised into intervention priorities using a multicriteria tool that balances water savings, costs and feasibility under municipal operations. Coimbra, Portugal is used as a case study, and a pilot application in a city garden, supported by 797 user surveys, clarifies practical constraints for scaling beyond isolated pilots. Turf-free scenarios indicate a 53.4% reduction in water use and a 60.5% reduction in operational costs, with a payback period below three years. The results highlight the potential of data-driven irrigation management to support more resilient, cost-effective and water-efficient municipal green infrastructure across diverse urban contexts. Full article

The digital transformation of construction processes has highlighted the need for integrated and sustainable automation frameworks, particularly in public-sector infrastructure planning where cost estimation, documentation, and approval workflows remain fragmented. This study proposes OnlinePlan, a computational and system-level framework that operationalizes a regulation-compliant cost estimation process within an integrated digital platform. The framework integrates heterogeneous data sources, category-specific engineering models, and regulatory transformations into a structured workflow that combines the Standard Construction Cost Estimation System, the Construction Planning and Budget Documentation System, and the Highway Maintenance Budget Planning Information System, with interoperability to PlanNET. A real-world dataset of 74 projects is used to evaluate system performance against traditional workflows. The results demonstrate zero computational deviation (0.00%) and significant efficiency improvements, with total processing time reduced by approximately 75.7%. Statistical validation confirms strong significance (t = 35.09, p < 0.001) and an exceptionally large effect size (Cohen’s d = 7.85), indicating substantial practical impact. The findings reveal that the primary contribution of construction automation lies not only in computational acceleration but in the integration of estimation, documentation, and approval processes into a workflow-governed digital system. This study contributes a scalable and interpretable framework for sustainable construction automation, advancing ICT-enabled decision-making, resource efficiency, and institutional transparency in infrastructure management. These dimensions are explicitly interpreted as measurable indicators of sustainability in public-sector infrastructure management. The primary contribution lies in the integration of estimation, documentation, and approval workflows into a unified system, rather than in the formulation of new cost equations. Full article

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership. Full article

Small islands often face chronic water shortages due to limited storage capacity, seasonal variability, and growing demand from tourism and urbanization. Si Chang Island, Thailand, experiences severe dry-season water scarcity, requiring improved water supply planning. This study applies the EPANET hydraulic modelling tool to design and evaluate water distribution networks under two scenarios: (1) a surface water supply system from the Si Chang Reservoir, and (2) a groundwater-based system near the island’s football field. Using Darcy–Weisbach head loss calculations and demand estimates, we assessed flow velocity, pressure, and construction costs. Both systems met design criteria, but the reservoir-based option achieved better cost efficiency (2.81%) and reliable pressure (minimum 15.05 m) with an average velocity of 1.20 m/s. The system can supply approximately 130% of the estimated demand, corresponding to a surplus capacity of about 30%. The findings demonstrate how hydraulic modelling can guide infrastructure planning for small, data-limited islands. Integrating technical design with policy considerations enhances the reliability, cost-effectiveness, and resilience of water supply systems. The approach presented herein offers a practical framework for decision-makers addressing water scarcity challenges on small islands worldwide. Full article

The continuous increase in Renewable Energy Sources (RES) in Greece’s electricity system has led to growing energy curtailment due to limited grid capacity, especially in high-production regions. According to recent data, more than 200 GWh of clean energy was curtailed in a single quarter in 2024, highlighting the urgent need for effective storage solutions. Curtailment represents a growing system level challenge, but it also creates an opportunity to convert surplus renewable electricity into green hydrogen through electrolysis. This study quantifies the hydrogen production potential of curtailed RES electricity in four Greek regions, Peloponnese, Crete, Thrace, and Western Macedonia, and evaluates alternative storage pathways under harmonized techno-economic assumptions. A scenario-based framework is developed using regional RES capacity, curtailment estimates, electrolyzer efficiency, hydrogen conversion factors, and indicative storage cost ranges. The analysis compares pressurized tank storage, underground storage, and hybrid configurations, while also estimating avoided CO₂ emissions from the substitution of grey hydrogen. The results indicate substantial regional variation. The Peloponnese exhibits the highest annual hydrogen potential, followed by Crete, Thrace, and Western Macedonia, while each region presents different infrastructure constraints and deployment roles. Mainland regions with access to geological storage show lower indicative hydrogen costs than island systems, where storage and export constraints increase costs. The findings show that curtailed renewable electricity can function as a low-carbon feedstock for hydrogen production in Greece, supporting grid flexibility, regional decarbonization, and the gradual development of hydrogen hubs under differentiated regional strategies. Full article