Search Results (462)

Energy transition, encompassing the development of renewable energy sources and associated power transmission grids, may significantly impact landscape visual resources, particularly those legally protected. Large-scale energy transitions require a mandatory visual impact assessment procedure, which utilises proximity and visibility analyses to comply with legal regulations and achieve minimal visual impact. While design stage proximity provides full compliance with the given country’s legal acts, the following visual impact analysis is more about demonstrating the low visual impact of design variants. Notably, at the energy infrastructure planning stage, the information on visual landscape resources remains insufficient; hence, avoiding conflicts is particularly challenging. To address this issue, a geoinformatic framework for Visual Landscape Absorption Capacity (VLAC) is proposed to support the sustainable planning of energy infrastructure right before the visual impact assessment. The framework involves identifying sensitive and valuable vantage points across the analysed landscape and determining the dimensions of energy infrastructure to be developed in a sustainable way regarding visual landscape resources. This paper presents a case study from Roztocze National Park in Poland, a protected area under significant pressure from solar farms and accompanying power transmission lines development. The results provide a critical assessment of the existing transmission lines (110 kV) and solar farms in relation to landscape visual resources, while also identifying three key areas where further infrastructure development can occur without landscape resource degradation. The framework geocomputation is based on digital elevation models, enabling easy replication in other locations to support the decision-making process and facilitate sustainable energy facility planning, thereby minimising potential conflicts with landscape resources. Full article

Improving the efficiency and range of hydrogen-powered electric vehicles (HPEVs) is essential for their global adoption, especially in developing countries with limited resources. This study systematically evaluates regenerative braking and suspension systems in HPEVs and proposes a deployment-focused framework tailored to the needs of developing nations. A comprehensive search was performed across multiple databases to identify relevant studies. The selected studies are screened, assessed for quality, and analyzed based on predefined criteria. The data is synthesized and interpreted to identify patterns, gaps, and conclusions. The findings show that regeneration systems, such as regenerative braking and regenerative suspension, are the most effective energy recovery systems in most electric and hydrogen-powered vehicles. Although the regenerative braking system (RBS) offers higher energy efficiency gains that enhance cost-effectiveness despite its high initial investment, the regenerative suspension system (RSS) involves increased complexity. Still, it offers comparatively efficient energy recovery, particularly in developing countries with patchy road infrastructure. The gaps highlighted in this review will aid researchers and vehicle manufacturers in designing, optimizing, developing, and commercializing HPEVs for deployment in developing countries. Full article

This paper develops a bilevel game-theoretic model to analyze the strategic effects of tariffs and subsidies in a global supply chain involving a manufacturer and a third-party logistics (3PL) provider. The government, acting as a Stackelberg leader, sets fiscal instruments to maximize national welfare, while downstream supply chain participants respond by optimizing production, pricing, and logistics outsourcing decisions. The model is evaluated under three coordination structures—centralized, decentralized, and alliance-based—to examine how decision alignment influences policy effectiveness. Simulation results show that while tariffs negatively impact supply chain efficiency and profitability, well-designed subsidies can partially or fully offset these effects, particularly under centralized coordination. The model further reveals that policy outcomes are highly sensitive to the strategic power structure within the supply chain. This study advances the literature by integrating endogenous government behavior with logistics coordination and supply chain decision-making within a unified bilevel optimization framework. The findings offer actionable insights for both policymakers and global supply chain managers in designing robust fiscal and coordination strategies. Full article

In recent years, solar energy has emerged as one of the most advanced renewable energy sources, with its production capacity steadily growing. To maximize output and efficiency, choosing the right configuration for a specific location for these installations is crucial. This study uniquely integrates detailed multi-variant fixed-tilt PV system simulations with comprehensive economic evaluation under temperate climate conditions, addressing site-specific spatial constraints and grid integration considerations that have rarely been combined in previous works. In this paper, an energy and economic efficiency analysis for a photovoltaic power plant, located in central Poland, designed in eight variants (10°, 15°, 20°, 25°, 30° PV module inclination angle for a south orientation and 10°, 20°, 30° for an east–west orientation) for a limited building area of approximately 300,000 m² was conducted. In PVSyst computer simulations, PVGIS-SARAH2 solar radiation data were used together with the most common data for describing the Polish local solar climate, called Typical Meteorological Year data (TMY). The most energy-efficient variants were found to be 20° S and 30° S, configurations with the highest surface production coefficient (249.49 and 272.68 kWh/m²) and unit production efficiency values (1123 and 1132 kWh/kW, respectively). These findings highlight potential efficiency gains of up to approximately 9% in surface production coefficient and financial returns exceeding 450% ROI, demonstrating significant economic benefits. In economic terms, the 15° S variant achieved the highest values of financial parameters, such as the return on investment (ROI) (453.2%), the value of the average annual share of profits in total revenues (56.93%), the shortest expected payback period (8.7 years), the value of the levelized cost of energy production (LCOE) (0.1 EUR/kWh), and one of the lowest costs of building 1 MWp of a photovoltaic farm (664,272.7 EUR/MWp). Among the tested variants of photovoltaic farms with an east–west geographical orientation, the most advantageous choice is the 10° EW arrangement. The results provide valuable insights for policymakers and investors aiming to optimize photovoltaic deployment in temperate climates, supporting the broader transition to renewable energy and alignment with national energy policy goals. Full article

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity. Full article

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

Implementing energy-efficient solutions and developing energy complexes to decentralise power supply are key objectives for enhancing national security in Ukraine and Eastern Europe. This study compares the design, numerical, and experimental parameters of a channel-type jet-reaction turbine. A steam turbogenerator unit and a pilot industrial experimental test bench were developed to conduct full-scale testing of the unit. The article presents experimental data on the operation of a steam turbogenerator unit with a capacity of up to 475 kW, based on a channel-type steam jet-reaction turbine (JRT), and includes the validation of a computational fluid dynamics (CFD) model against the obtained results. For testing, a pilot-scale experimental facility and a turbogenerator were developed. The turbogenerator consists of two parallel-mounted JRTs operating on a single electric generator. During experimental testing, the system achieved an electrical output power of 404 kW at a turbine rotor speed of 25,000 rpm. Numerical modelling of the steam flow in the flow path of the jet-reaction turbine was performed using ANSYS CFX 25 R1 software. The geometry and mesh setup were described, boundary conditions were defined, and computational calculations were performed. The experimental results were compared with those obtained from numerical simulations. In particular, the discrepancy in the determination of the power and torque on the shaft of the jet-reaction turbine between the numerical and full-scale experimental results was 1.6%, and the discrepancy in determining the mass flow rate of steam at the turbine inlet was 1.34%. JRTs show strong potential for the development of energy-efficient, low-power turbogenerators. The research results confirm the feasibility of using such units for decentralised energy supply and recovering secondary energy resources. This contributes to improved energy security, reduces environmental impact, and supports sustainable development goals. Full article

The accurate early-stage estimation of environmental load (EL) and construction cost (CC) in road infrastructure projects remains a significant challenge, constrained by limited data and the complexity of construction activities. To address this, our study proposes a machine learning-based predictive framework utilizing artificial neural networks (ANNs) and deep neural networks (DNNs), enhanced by autoencoder-driven feature selection. A structured dataset of 150 completed national road projects in South Korea was compiled, covering both planning and design phases. The database focused on 19 high-impact sub-work types to reduce noise and improve prediction precision. A hybrid imputation approach—combining mean substitution with random forest regression—was applied to handle 4.47% missing data in the design-phase inputs, reducing variance by up to 5% and improving data stability. Dimensionality reduction via autoencoder retained 16 core variables, preserving 97% of explanatory power while minimizing redundancy. ANN models benefited from cross-validation and hyperparameter tuning, achieving consistent performance across training and validation sets without overfitting (MSE = 0.06, RMSE = 0.24). The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. DNN models, with their deeper architectures and dropout regularization, further improved performance—achieving 27.1% (EL) and 17.0% (CC) average error rates at the planning stage and 24.0% (EL) and 14.6% (CC) at the design stage. These results met all predefined accuracy thresholds, underscoring the DNN’s advantage in handling complex, high-variance data while the ANN excelled in structured cost prediction. Overall, the synergy between deep learning and autoencoder-based feature selection offers a scalable and data-informed approach for enhancing early-stage environmental and economic assessments in road infrastructure planning—supporting more sustainable and efficient project management. Full article

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

Naval ships are of paramount importance to national security, culture, and naval operations. A primary challenge for naval authorities is to balance the imperatives of maritime dominance with the operational demands of achieving sufficient, sustainable reliability. Shaft generators (SGs) are crucial to the energy conversion systems on naval ships, functioning as part of the main power systems on board and providing both propulsion and power for various operational loads. In this sense, the design of shaft generators is an engineering element that has a major impact on the overall ship performance. The design process will be conducted within the MATLAB/Simulink environment, a platform that facilitates the study of the dynamic behaviors of the system through simulation. The increasing demand for efficiency, reliability, and sustainability in the military, along with the impact of emerging technologies, will further underscore the significance of shaft generators. Analyses carried out in MATLAB/Simulink demonstrate that the selection of the most suitable power system for naval ships is dictated by the system requirements and operational demands. The main construction is such that this work is the first of its kind in the field of shaft generator research for naval ships. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Power cable dielectrics must be tested to ascertain their insulation integrity after their design and manufacture. In Southern Africa, power cables must undergo testing in accordance with the South African National Standard (SANS) 1339. The SANS 1339 provides a destructive diagnostic method to evaluate voltage breakdown strength and water tree growth. The shortfall is that there is no provision for the non-destructive determination of the residual strength and assessment of the condition of the power cables. It is possible that non-destructive tests are available. However, a question arises as to how they compare in effectiveness, which is the intention of this study. Accelerated aging at 500 Hz was conducted on the water-retardant cross-linked polyethene (TR-XLPE) power cable sample specimens, each 10 m long, according to SANS 1339. Non-destructive diagnostic tests (Tan δ, IRC, and RVM) were conducted on accelerated-aged and unaged cable samples. The comparative results of the accelerated-aged and unaged XPLE power cable samples, when applying non-destructive diagnostic techniques, show consistency and reveal the extent of degradation in the tested cable samples. This study demonstrates that non-destructive diagnostic methods can be used to assess the extent of XLPE power cable insulation aging. Full article