Search Results (6,348)

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and application by enabling tailored control over design, pore geometry, materials, and integration. This perspective work examines the opportunities and challenges associated with utilizing additive manufacturing as an enabling battery manufacturing technology. Recent advances in the additive fabrication of electrodes, electrolytes, separators, and integrated devices are examined, exhibiting the potential to acheive electrochemical performance, design adaptability, and sustainability. At the same time, key challenges—including materials formulation, reproducibility, economic feasibility, and regulatory uncertainty—are discussed as limiting factors that must be addressed for achieving the expected results. Rather than being viewed as a replacement for conventional gigafactory-scale production, additive manufacturing is positioned as a complementary fabrication technique that can deliver value in niche, distributed, and application-specific contexts. This work concludes by outlining research and policy priorities that could accelerate the maturation of 3D-printed batteries, stressing the importance of hybrid manufacturing, multifunctional printable materials, circular economy integration, and carefully phased timelines for deployment. Moreover, by enabling customized form factors, improved device–user interfaces, and seamless integration into smart, automated environments, additive manufacturing has the potential to significantly enhance user experience across emerging battery applications. In this context, this perspective provides a grounded assessment of how additive fabrication methods may contribute to next-generation battery technologies that not only improve electrochemical performance but also enhance user interaction, reliability, and seamless integration within automated and control-driven systems. Full article

Deaf and hearing-impaired individuals rely on sign language, a visual communication system using hand shapes, facial expressions, and body gestures. Sign languages vary by region. For example, Arabic Sign Language (ArSL) is notably different from American Sign Language (ASL). This project focuses on creating an Arabic Sign Language Recognition (ArSLR) System tailored for healthcare, aiming to bridge communication gaps resulting from a lack of sign-proficient professionals and limited region-specific technological solutions. Our research addresses limitations in sign language recognition systems by introducing a novel framework centered on ResNet50ViT, a hybrid architecture that synergistically combines ResNet50’s robust local feature extraction with the global contextual modeling of Vision Transformers (ViT). We also explored a tailored Vision Transformer variant (SignViT) for Arabic Sign Language as a comparative model. Our main contribution is the ResNet50ViT model, which significantly outperforms existing approaches, specifically targeting the challenges of capturing sequential hand movements, which traditional CNN-based methods struggle with. We utilized an extensive dataset incorporating both static (36 signs) and dynamic (92 signs) medical signs. Through targeted preprocessing techniques and optimization strategies, we achieved significant performance improvements over conventional approaches. In our experiments, the proposed ResNet50-ViT achieved a remarkable 99.86% accuracy on the ArSL dataset, setting a new state-of-the-art, demonstrating the effectiveness of integrating ResNet50’s hierarchical local feature extraction with Vision Transformer’s global contextual modeling. For comparison, a fine-tuned Vision Transformer (SignViT) attained 98.03% accuracy, confirming the strength of transformer-based approaches but underscoring the clear performance gain enabled by our hybrid architecture. We expect that RAFID will help deaf patients communicate better with healthcare providers without needing human interpreters. Full article

Carbon dioxide (CO₂), the primary anthropogenic greenhouse gas, drives significant and potentially irreversible impacts on ecosystems, biodiversity, and human health. Achieving the Paris Agreement target of limiting global warming to well below 2 °C, ideally 1.5 °C, requires rapid and substantial global emission reductions. While recent decades have seen advances in clean energy technologies, carbon capture, utilization, and storage (CCUS) remain essential for deep decarbonization. Despite proven technical readiness, large-scale carbon capture and storage (CCS) deployment has lagged initial targets. This review evaluates CCS technologies and their contributions to net-zero objectives, with emphasis on sector-specific applications. We found that, in the iron and steel industry, post-combustion CCS and oxy-combustion demonstrate potential to achieve the highest CO₂ capture efficiencies, whereas cement decarbonization is best supported by oxy-fuel combustion, calcium looping, and emerging direct capture methods. For petrochemical and refining operations, oxy-combustion, post-combustion, and chemical looping offer effective process integration and energy efficiency gains. Direct air capture (DAC) stands out for its siting flexibility, low land-use conflict, and ability to remove atmospheric CO₂, but it’s hindered by high costs (~$100–1000/t CO₂). Conversely, post-combustion capture is more cost-effective (~$47–76/t CO₂) and compatible with existing infrastructure. CCUS could deliver ~8% of required emission reductions for net-zero by 2050, equivalent to ~6 Gt CO₂ annually. Scaling deployment will require overcoming challenges through material innovations aided by artificial intelligence (AI) and machine learning, improving capture efficiency, integrating CCS with renewable hybrid systems, and establishing strong, coordinated policy frameworks. Full article

Deepfake technology, driven by advances in artificial intelligence (AI) and deep learning (DL), has become one of the foremost threats to digital trust and the authenticity of information. Despite the rapid development of deepfake detection methods, the dynamic evolution of generative models continues to outpace current mitigation efforts. This highlights the pressing need for more effective and proactive deepfake prevention strategy. This study introduces a comprehensive and multifaceted deepfake prevention framework that leverages both technical and non-technical countermeasures and involves collaboration among key stakeholders in a unified structure. The proposed framework has four modules: trusted content assurance, detection and monitoring, awareness and human-in-the-loop verification, and policy, governance, and regulation. The framework uses a combination of hybrid watermarking and embedding techniques, as well as cryptographic digital signature algorithms (DSAs) and blockchain technologies, to make sure that the media is authentic, traceable, and cannot be denied. Comparative experiments were conducted in this research using both classical and post-quantum DSAs to evaluate their efficiency, resource consumption, and gas costs in blockchain operations. The results revealed that the Falcon-512 algorithm outperformed other post-quantum algorithms while consuming fewer resources and lowering gas costs, making it a preferable option for real-time, quantum-resilient deepfake prevention. The framework also employed AI-based detection models and human oversight to enhance detection accuracy and robustness. Overall, this research offers a novel, multifaceted, and governance-aware strategy for deepfake prevention. The proposed approach significantly contributes to mitigating deepfake threats and offers a practical foundation for secure and transparent digital media ecosystems. Full article

The growing complexity of modern building systems requires advanced monitoring frameworks to improve fault detection, energy efficiency, and operational resilience. Digital Twin (DT) technology, which integrates real-time data with virtual models of physical systems, has emerged as a promising enabler for predictive diagnostics. Despite growing interest, key challenges remain, including the neglect of short- and long-term forecasting across different scenarios, insufficiently robust data preparation, and the rare validation of models on multi-zone buildings over extended test periods. To address these gaps, this study presents a comprehensive DT-enabled framework for predictive monitoring and anomaly detection, validated in a multi-zone educational building in Rhode Island, USA, using a full year of operational data for validation. The proposed framework integrates a robust data processing pipeline and a comparative analysis of machine learning models, including LSTM, RNN, GRU, ANN, XGBoost, and RF, to forecast short-term (1 h) and long-term (24 h) indoor temperature variations. The LSTM model consistently outperformed other methods, achieving R² > 0.98 and RMSE < 0.55 °C for all tested rooms. For real-time anomaly detection, we applied the hybrid LSTM–Interquartile Range (IQR) method on one-step-ahead residuals, which successfully identified anomalous deviations from expected patterns. The model’s predictions remained within a ±1 °C error margin for over 90% of the test data, providing reliable forecasting up to 16 h ahead. This study contributes a validated, generalizable DT methodology that addresses key research gaps, offering practical tools for predictive maintenance and operational optimization in complex building environments. Full article

Peer-to-peer (P2P) streaming networks are widely used for large-scale multimedia delivery, but they continue to face challenges related to reliability, security, and scalability. To address these issues, we propose a hybrid framework that integrates distributed software-defined networking (DSDN) with blockchain to provide a more reliable and secure P2P streaming environment. Based on this proposal, we simulated three scenarios using NS-3: P2P with blockchain only, P2P with DSDN only, and P2P with the combined DSDN–blockchain model. Network performance was evaluated through three key metrics: throughput, latency, and energy consumption. Furthermore, the hybrid model’s security was validated under simulated attack scenarios by integrating Intrusion Detection System and Access Control List (ACL) modules within the DSDN controller and an encryption module for data-in-transit. The experiments were conducted under varying numbers of nodes to assess scalability and consistency. Across all network sizes, the hybrid model consistently outperformed the single-technology scenarios. At 50 nodes, for example, the hybrid approach achieved 8–9 percent higher throughput, 5–6 percent lower latency, and 7–21 percent better energy efficiency compared to blockchain or DSDN alone. Overall, the findings demonstrate that combining DSDN and blockchain yields a P2P streaming network with enhanced performance, making the integration highly beneficial for future multimedia streaming applications. Full article

Under the drive of the “dual carbon” goals, the insufficient frequency regulation capability of nuclear power as a baseload source and the dynamic demand of integrating a high proportion of renewable energy into the grid have increasingly highlighted conflicts. The inherent minute-level regulation inertia of nuclear power units struggles to cope with second-level frequency fluctuations in the grid, leading to an increased risk of system instability. There is an urgent need for energy storage technologies to fill the millisecond-level power support gap for nuclear power frequency regulation. This paper, focusing on nuclear power frequency regulation scenarios, constructs a “Technology–Economy–Policy” multidimensional energy storage evaluation system for the first time. Through a systematic analysis of 11 key indicators, such as response time and safety, the paper selects energy storage technologies suitable for nuclear power frequency regulation scenarios and proposes a hybrid energy storage optimization strategy. The research provides a systematic evaluation framework and empirical support for the selection of energy storage for nuclear power frequency regulation, with significant practical value in improving grid dynamic stability and promoting the construction of new power systems under the “dual carbon” goals. Full article

This study analyzes clay sources, ceramic paste recipes, and technological choices in Neolithic pottery from west Lithuania, where local hunter–fisher–gatherer groups encountered incoming communities of the Globular Amphora (GAC) and Corded Ware cultures (CWC) during the fourth to third millennium BCE. Thirty sherds from coastal Šventoji and the inland Biržulis region were analyzed by optical microscopy and SEM–EDS, revealing that most ceramic pastes comprise variegated hydromicaceous clay with quartz and feldspar. In Narva Culture pottery, vessels from the Biržulis region (Daktariškė 5) are dominated by fine-grained clay, whereas Šventoji examples are more variegated and diatom-bearing; both assemblages show organic inclusions (mussel shell, bone, charred plant material) and very low firing temperatures (<650 °C). GAC exhibits cross-site coherence, characterized by crushed, deformed, cataclastic muscovite granite in fine lacustrine clay and low firing temperatures (~650–750 °C). CWC from Daktariškė 5 geochemically clusters with Narva and hybrid-type pottery, while CWC at Šventoji aligns with GAC; both show low firing temperatures (~650–750 °C). Ceramic pastes contain argillaceous clasts partly diffused or intertwined with the main matrix; only a few show traits typical of grog. All pottery was made from local Quaternary glacial sediments, with cultural traditions and environmental context shaping clay selection and manipulation. Full article

In the context of the growing adoption of alternative gas separation processes, combined with the interest in hydrogen as a fuel and energy carrier, the use of membrane technology in H₂/CH₄ purification is analyzed in this work, focusing on the techno-economic aspects. In particular, the separation and economic performance of three Pd–Ag/Si-CHA membrane plants are simulated, aiming to achieve high degrees of purity and recovery paired with cost-effective configurations. A single Pd–Ag membrane stage operating at 20 atm and 350 °C can theoretically guarantee a CH₄ concentration of 95%, while a completely pure H₂ stream leaves the plant as a permeate product. The choice of a less selective Si-CHA membrane allows a temperature reduction but implies the use of more stages to achieve the desired CH₄ target. In addition, H₂ purity does not exceed 98%. A two-stage hybrid process, in which the retentate gas leaving the Pd–Ag membrane is cooled and fed to the Si-CHA unit, is also a cost-effective solution, as feed pressure can be reduced to 10 atm with significant compression cost savings. All the configurations are able to provide positive values of economic potential (EP); however, the single Pd–Ag membrane plant is the best option since it guarantees the highest EP, net profit and net present value (NPV). Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

With the global energy structure transitioning towards clean and low-carbon alternatives, electrochemical energy storage technologies have emerged as pivotal enablers for achieving efficient renewable energy utilization and carbon neutrality objectives. However, conventional electrode materials remain constrained by inherent limitations, including low specific surface area, sluggish ion diffusion kinetics, and insufficient mechanical stability, which fundamentally hinder the synergistic fulfillment of high energy density, superior power density, and prolonged cycling durability. Three-dimensional printing technology offers a revolutionary paradigm for designing and fabricating carbon-based electrochemical energy storage devices. By enabling precise control over both the microstructural architecture and macro-scale morphology of electrode materials, this additive manufacturing approach significantly enhances energy/power densities, as well as cycling stability. Specifically, 3D printing facilitates biomimetic topological designs (e.g., hierarchical porous networks, vertically aligned ion channels) and functional hybridization strategies (e.g., carbon/metal oxide hybrids, carbon/biomass-derived composites), thereby achieving synergistic optimization of charge transfer kinetics and mechanical endurance. This review systematically summarizes recent advancements in 3D-printed carbon-based electrodes across major energy storage systems, including supercapacitors, lithium-ion batteries, and metal–air batteries. Particular emphasis is placed on the design principles of carbon-based inks, multiscale structural engineering strategies, and process optimization methodologies. Furthermore, we prospect future research directions focusing on smart 4D printing-enabled dynamic regulation, multi-material integrated systems, and artificial intelligence-guided design frameworks to bridge the gap between laboratory prototypes and industrial-scale applications. Through multidisciplinary convergence spanning materials science, advanced manufacturing, and device engineering, 3D-printed carbon electrodes are poised to catalyze the development of next-generation high-performance, customizable energy storage systems. Full article

Structure-Modulated Long-Period Fiber Gratings (SM-LPFGs) represent an advancement in fiber optic sensor technology, moving beyond traditional photosensitivity-based fabrication to achieve enhanced performance through the direct physical modification of the geometry of the fiber. This review provides a comprehensive analysis of the primary fabrication techniques enabling this approach, including CO₂ laser inscription, femtosecond laser micromachining, electric-arc discharge, chemical etching, and fusion tapering. The central focus of this work is the elucidation of the definitive structure–performance relationship, systematically detailing how engineered geometries such as helical profiles, micro-tapers, and asymmetric grooves unlock novel sensing capabilities. We demonstrate how these specific structures are strategically designed to induce circular birefringence for torsion measurement, enhance evanescent field interaction for ultra-sensitive refractive index detection, and create localized stress concentrations for high-resolution strain and vector bending sensing. Furthermore, the review surveys the practical implementation of these sensors in critical application domains, including structural health monitoring, biomedical diagnostics, and environmental sensing. Finally, we conclude by summarizing key achievements and identifying promising future research directions, such as the development of hybrid fabrication processes, the integration of machine learning for advanced signal demodulation, and the path towards industrial-scale production. Full article

Biomass has long been a major source of energy for residential heating and, in recent decades, has regained attention as a renewable alternative to fossil fuels. This review explores the current state and prospects of domestic biomass-based heating technologies, including biomass-fired boilers, local space heaters, and hybrid systems that integrate biomass with complementary renewable energy sources to deliver heat, electricity, and cooling. The review was conducted to identify key trends, performance data, and innovations in conversion technologies, fuel types, and efficiency enhancement strategies. The analysis highlights that biomass is increasingly recognized as a viable energy carrier for energy-efficient, passive, and nearly zero-energy buildings, particularly in cold climates where heating demand remains high. The analysis of the available studies shows that modern biomass-fired systems can achieve high energy performance while reducing environmental impact through advanced combustion control, optimized heat recovery, and integration with low-temperature heating networks. Overall, the findings demonstrate that biomass-based technologies, when designed and sourced efficiently and sustainably, can play a significant role in decarbonizing the residential heating sector and advancing nearly zero-energy building concepts. Full article

Unmanned aerial underwater vehicles (UAUVs) will play significant roles in several complex application scenarios including observation of mesoscale ocean phenomena, monitoring of offshore platforms, ocean protection, and maritime rescue. These innovative vehicles can be used in the air and underwater and can easily enter and exit water. This review systematically analyzes the research progress, design challenges, and future prospects of UAUVs, emphasizing their potential to revolutionize integrated cross-domain collaboration. We classify UAUVs into five categories—rotary-wing, fixed-wing, folding-wing, hybrid-wing, and flapping-wing—based on propulsion configurations, and critically evaluate their prototypes, highlighting technological milestones and functional limitations. Unlike prior reviews focused solely on technical developments, this study advocates for a paradigm shift from a technology-push to a market-pull and technology-push interactive development model. Combining the design of UAUV with solutions to technical challenges and specific application requirements is crucial for practical deployment. By synthesizing historical context, current advancements, and future developments, this review not only provides possible strategies for design challenges but also lays a roadmap for UAUV commercialization. Full article