Search Results (1,119)

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

Long-range underwater gliders (LRUGs) have emerged as essential platforms for sustained and autonomous observation in deep and remote marine environments. This paper provides a comprehensive review of their developmental status, performance characteristics, and application progress. Emphasis is placed on two critical enabling technologies that fundamentally determine endurance: lightweight, pressure-resistant hull structures and high-efficiency buoyancy-driven propulsion systems. First, the role of carbon fiber composite pressure hulls in enhancing energy capacity and structural integrity is examined, with attention to material selection, fabrication methods, compressibility compatibility, and antifouling resistance. Second, the evolution of buoyancy control systems is analyzed, covering the transition to hybrid active–passive architectures, rapid-response actuators based on smart materials, thermohaline energy harvesting, and energy recovery mechanisms. Based on this analysis, the paper identifies four key technical challenges and proposes strategic research directions, including the development of ultralight, high-strength structural materials; integrated multi-mechanism antifouling technologies; energy-optimized coordinated buoyancy systems; and thermally adaptive glider platforms. Achieving a system architecture with ultra-long endurance, enhanced energy efficiency, and robust environmental adaptability is anticipated to be a foundational enabler for future long-duration missions and globally distributed underwater glider networks. Full article

In the context of global climate governance and the green transition, digital infrastructure serves as a critical enabler of resource allocation in the digital economy, offering strategic value in tackling synergistic pollution and carbon reduction challenges. Using panel data from 280 prefecture-level cities, this study employs a multiperiod difference-in-differences (DID) approach, leveraging smart city pilot policies as a quasinatural experiment, to assess how digital infrastructure affects urban synergistic pollution-carbon mitigation (SPCM). The empirical results show that digital infrastructure increases the urban SPCM index by 1.5%, indicating statistically significant effects. Compared with energy and income effects, digital infrastructure can influence this synergistic effect through indirect channels such as the energy effect, economic agglomeration effect, and income effect, with the economic agglomeration effect accounting for a larger share of the total effect. Additionally, fixed-asset investment has a nonlinear moderating effect on this relationship, with diminishing marginal returns on emission reduction when investment exceeds a threshold. Heterogeneity tests reveal greater impacts in eastern, nonresource-based, and environmentally regulated cities. This study expands the theory of collaborative environmental governance from the perspective of new infrastructure, providing a theoretical foundation for establishing a long-term digital technology-driven mechanism for SPCM. Full article

The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power systems. This paper presents a comprehensive review of generator technologies used in wind turbine applications, ranging from conventional synchronous and asynchronous machines to advanced concepts such as low-speed direct-drive (DD) generators, axial-flux topologies, and superconducting generators utilizing low-temperature superconductors (LTS) and high-temperature superconductors (HTS). The advantages and limitations of each design are discussed in the context of efficiency, weight, reliability, scalability, and suitability for offshore deployment. Special attention is given to HTS-based generator systems, which offer superior power density and reduced losses, along with challenges related to cryogenic cooling and materials engineering. Furthermore, the paper analyzes selected modern generator designs to provide references for enhancing the performance of grid-synchronized hybrid microgrids integrating solar PV, wind, battery energy storage, and HTS-enhanced generators. This review serves as a valuable resource for researchers and engineers developing next-generation wind energy technologies with improved efficiency and integration potential. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart grid architecture. SMRs offer compact, low-carbon, and reliable baseload power suitable for urban environments, while PV and storage enhance system flexibility and renewable integration. Six energy mix scenarios are evaluated using a lifecycle-based cost model that incorporates both capital expenditures (CAPEX) and cumulative carbon costs over a 25-year horizon. The modeling results demonstrate that hybrid SMR–renewable systems—particularly those with high nuclear shares—can reduce lifecycle CO₂ emissions by over 90%, while maintaining long-term economic viability under carbon pricing assumptions. Scenario C, which combines 50% SMR, 40% PV, and 10% battery, emerges as a balanced configuration offering deep decarbonization with moderate investment levels. The proposed framework highlights key trade-offs between emissions and capital cost and seeking resilient and scalable pathways to support the global clean energy transition and net-zero commitments. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

The objective of this research was to conduct a comprehensive review of rainbow trout (Oncorhynchus mykiss) culture in recirculating aquaculture systems (RAS), identify knowledge gaps, and propose strategies oriented towards intelligent and sustainable aquaculture. A systematic review and bibliometric analysis of 387 articles published between 1941 and 2025 in the Scopus database was carried out. Since 2011, there has been a sustained growth in scientific production, with the United States, Denmark, Finland, and Germany standing out as the main contributors. The journals with the highest number of publications were Aquacultural Engineering, Aquaculture, and Aquaculture Research. The conceptual analysis revealed the following three thematic clusters: experimental studies on physiology and metabolism; research focused on nutrition, growth, and yield; and technological developments for water treatment in RAS. This evolution reflects a transition from basic approaches to applied technologies oriented towards sustainability. There was also evidence of a thematic transition toward molecular tools such as proteomics, transcriptomics, and real-time PCR. However, there is still limited integration of smart technologies such as the IoT. It is recommended to incorporate self-calibrating multi-parametric sensors, machine learning models, and autonomous systems for environmental regulation in real time. Full article