Search Results (2,209)

The decarbonization of maritime and inland waterway transport requires implementation pathways that go beyond fuel substitution and address energy, water, safety, infrastructure, and lifecycle constraints. This study proposes a sustainable technical pathway for hydrogen implementation in small-scale maritime and inland waterway vessels, using Colombia as a territorial case study. The methodology integrates technological surveillance, national energy-transition assessment, sectoral and territorial analysis, hydrogen pathway selection, water-resource management, safety and regulatory review, lifecycle criteria, and progressive validation under Technology Readiness Level principles. The results identify compressed gaseous hydrogen combined with Proton Exchange Membrane fuel cells and hybrid battery support as the most feasible short-term configuration for small vessels due to its modularity, operational flexibility, and compatibility with decentralized applications. The framework also shows that hydrogen production must be designed as a coupled water–energy–hydrogen system, prioritizing treated wastewater, rainwater, desalinated water, or other non-potable sources to avoid pressure on community and agricultural water demand. Laboratory and prototype validation demonstrated a progressive route from didactic hydrogen systems to small-vessel maquettes and scaled prototypes. The proposed pathway provides an implementation-oriented framework for safe, sustainable, and territorially adapted hydrogen deployment in small maritime systems. Full article

To address the stringent demands of intelligent connected vehicles for high bandwidth, low latency, and highly reliable communication, this paper systematically summarizes the semantic communication technology of the Internet of Vehicles (IoV) based on information “meaning” transmission, covering basic theory, key technologies, application practice and challenge and trends. First, the paper expounds the knowledge driven and task oriented paradigm characteristics of semantic communication and its efficiency advantages in the IoV. Second, in terms of key technologies, semantic extraction achieves efficient feature compression through multimodal fusion and Generative Artificial Intelligence (GAI); semantic coding employs hierarchical codebooks and adaptive strategies to optimize transmission efficiency; semantic transmission leverages deep reinforcement learning for the joint scheduling of resources such as spectrum and power; and semantic decoding utilizes reconstruction networks and GAI to enhance resilience against impairments. Application practices demonstrate that semantic communication can significantly compress image data transmission volume for autonomous driving collaborative perception while maintaining high-fidelity reconstruction under adverse channel conditions. It significantly reduces the communication load and improves the system utility in vehicle-to-infrastructure coordination and in-vehicle service. Despite facing technical challenges such as semantic consistency, dynamic adaptability, and security trustworthiness, future semantic communication will evolve towards deep integration with distributed collaborative knowledge networks, lightweight real-time decision-making agents, and integrated “communication, sensing, and computing” architectures, positioning itself as a key enabling technology for empowering Sixth Generation mobile communication (6G) of intelligent vehicular networks. Full article

Post-disaster scenarios such as tsunamis require rapid terrain assessment that cannot wait for the next satellite synthetic aperture radar (SAR) revisit, yet a readily deployable system remains lacking. We present an off-the-shelf K-band drone-borne dual circular polarimetric (DCP) SAR and a processing pipeline for on-demand terrain classification. Compared with fully polarimetric (FP) SAR, DCP requires only a single transmit polarization and two receive channels, providing a wider swath than FP for the same acquisition, while still separating odd-bounce and even-bounce scattering mechanisms, which dual linear polarimetric modes with the same channel count provide with greater ambiguity due to their sensitivity to target orientation angle. To compensate for platform motion, we implemented RTK global navigation satellite system (GNSS) guided time-domain backprojection (TDBP) with phase gradient autofocus (PGA), yielding an 11.98 dB improvement in peak amplitude. We then applied single-target wire calibration to correct a measured 8.91 dB inter-channel complex gain difference between co-polarization and cross-polarization. As a result, H/α decomposition of the calibrated DCP data classifies canonical reflectors, artificial structures, gravel roads, vegetation, and a pond surface. These field experiments extend compact polarimetric H/α decomposition to drone-borne SAR data for terrain discrimination, establishing a practical pathway toward rapid post-disaster terrain assessment. Full article

Rural greywater management is a critical global challenge due to the lack of centralized treatment in dispersed communities. This study aimed to characterize the pollution characteristics and bacterial community structure of samples from four greywater collection tanks in eastern China using high-throughput sequencing and absolute quantification of the 16S rRNA gene. Pollution characteristics showed spatial heterogeneity: chemical oxygen demand ranged from 19.8 to 272.5 mg/L, total nitrogen from 8.6 to 16.4 mg/L, and dissolved oxygen from 1.3 to 5.3 mg/L. Dissolved greenhouse gases also varied, with N₂O reaching 103.6 ppmv and CH₄ up to 50.4 ppmv. Based on the estimated absolute abundance of 16S rRNA gene copies, we found that the bacterial communities were dominated by Pseudomonadota, Actinomycetota, Bacteroidota, and Bacillota. Key genera such as Acinetobacter, Pseudomonas, and unclassified Enterobacteriaceae were positively correlated with nitrate, suggesting their potential association with denitrification and potential N₂O production. The methanotrophic genus Methyloparacoccus was enriched in a tank with high dissolved organic carbon. Co-occurrence network analysis revealed that core taxa like unclassified Paracoccaceae and Limnohabitans function as module hubs, maintaining community stability. These findings reveal associations between bacterial taxa, pollutant transformation, and greenhouse gas emissions in rural greywater and provide fundamental insights to support the development of low-carbon, resource-oriented treatment technologies. Full article

This study examines the influence of green market orientation (GMO) and green entrepreneurial orientation (GEO) on business competitiveness in increasingly demanding environmental contexts. Drawing on the literature on strategic management and sustainability, this research analyzes how environmentally oriented market and entrepreneurial capabilities contribute to strengthening firms’ competitive positioning. Using a quantitative approach, data were collected from 200 companies in the agri-food and manufacturing sectors, and the data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that both GMO (β = 0.534; p < 0.001) and GEO (β = 0.395; p < 0.001) have a significant influence on competitiveness, with GMO showing a stronger impact. These findings suggest that firms integrating environmental market demands and fostering innovation, proactiveness, and risk-taking with an ecological focus achieve enhanced competitive performance. This study concludes that green-oriented strategic capabilities constitute key drivers of sustainable competitive advantages and represent a relevant pathway for organizational strengthening in dynamic and environmentally regulated markets. Full article

The accurate and sensitive detection of toxin contamination remains a pressing challenge for food safety, environmental integrity, and public health, because conventional analytical methods suffer from high costs, poor field stability, and inadequate sensitivity for trace-level emerging contaminants. In this review, we provide a comprehensive overview of biosensor technologies for toxin detection, with a dedicated focus on nucleic acid aptamers and SELEX (Systematic Evolution of Ligands by Exponential Enrichment) technology. We systematically categorize nine SELEX variants developed for toxin detection, covering target-immobilized, library-immobilized, non-immobilized, cell-based, and high-throughput platforms, with an emphasis on their selection principles, applicability, and limitations. This review discusses computationally assisted aptamer discovery (e.g., AI-based sequence generation and molecular docking) as well as experimental post-SELEX optimization strategies such as cyclization, multivalent assembly, and structure-switching design. We then discuss key challenges and future perspectives, highlighting the shift from method-oriented to demand-oriented aptamer development through integrated SELEX strategies and AI-assisted design. Overall, this review covers mainstream SELEX technologies, aptamer selection, computational design, experimental optimization, and sensor integration to serve as a reference for next-generation toxin detection applications. Full article

Driven by the demand for high-quality housing, fourth-generation residential buildings—known internationally as “Vertical Forest” and in China as “Urban Forest Garden”—have developed rapidly. Initially built in mild southern regions, they have recently expanded to colder northern areas, with over 50 projects underway in provinces such as Shanxi, Hebei, Shaanxi, and Gansu. Several cities have introduced design standards and incentives, and the China Association for Standardization of Engineering Construction has issued the “Design Standards for Urban Forest Garden Housing.” However, in cold regions, where winters are long and cold and summers are short and hot, there is a lack of systematic quantitative research on how balcony design affects building carbon reduction, energy efficiency, and indoor thermal comfort. To address this research gap, this paper poses the following research questions: (1) In fourth-generation residential buildings in cold regions, how do different combinations of balcony orientations affect annual energy consumption and indoor thermal comfort? (2) Which balcony configurations offer the best balance between carbon reduction, energy efficiency, and thermal comfort? Based on statistical analysis of terrace configurations from more than 40 projects, 12 typical configuration models were identified. Using Ladybug and Honeybee tools on the Grasshopper platform, building energy consumption and indoor thermal comfort were simulated. Multi-objective trade-off analysis was performed using the Pareto front method. In this study, indoor thermal comfort was evaluated using the PMV (Predicted Mean Vote) index. PMV is an index proposed by Professor Fanger that comprehensively reflects human thermal sensation, taking into account air temperature, humidity, wind speed, mean radiant temperature, human metabolic rate, and clothing thermal resistance. Its typical range is −3 (cold) to +3 (hot); in this study, the comfort zone was defined as −1 ≤ PMV ≤ 1. Key findings: (1) The southwest + south terrace configuration shows the highest annual energy consumption, exceeding the lowest (northwest + west) by 2.7%, indicating that south-facing terraces are less favorable for carbon reduction. (2) The best thermal comfort is achieved with east, west, and south orientations. Compared to the least comfortable combination (southwest + northwest), the difference in PMV comfort percentage reaches 2.4%. (3) The Pareto front reveals that beyond a certain comfort level, energy consumption increases sharply. The west + south and east + south combinations yield the highest thermal comfort (49.4%) while maintaining relatively low energy consumption (17.98 kWh/m²). Therefore, in cold regions, fourth-generation residential designs should prioritize terrace combinations integrating south-facing and side-facing orientations and avoid pure corner configurations to balance winter solar gain and summer shading. Full article

This study examines whether stablecoin and Bitcoin transaction volumes in Brazil are associated with domestic macroeconomic conditions. Using monthly data from August 2019 to December 2025, the analysis compares the association of domestic economic activity, proxied by the IBCBr, and the BRLUSD exchange rate with the transaction volumes of stablecoins and Bitcoin reported in the open data records of the Receita Federal do Brasil, the Brazilian Tax Administration. The empirical strategy distinguishes between long-run relationships in log levels and short-run dynamics in log differences, applies Johansen cointegration and ARDL bounds testing, and estimates an error correction model for stablecoins. Global crypto market controls are used as complementary measures to assess the contrast between the two assets. The results show that stablecoin transaction volume is positively and significantly associated with Brazilian economic activity in both long-run and short-run specifications and that this association is not explained by global stablecoin activity. The exchange rate is associated with stablecoin volume mainly through a structural long-run channel rather than immediate monthly effects. In contrast, Bitcoin transaction volume does not exhibit a robust association with domestic economic activity and is instead more strongly associated with global Bitcoin volume. The findings suggest that stablecoins may act as a domestically embedded macro-financial instrument in Brazil. This finding reflects transactional demand, liquidity management, or demand for dollar-linked assets, whereas Bitcoin behaves as a more globally oriented and comparatively detached digital asset. This distinction has important implications for policy, as stablecoins may have stronger implications for monetary transmission, digital dollarisation, and financial intermediation than Bitcoin-focused analyses indicate. Full article

Text entry remains a bottleneck for productivity-oriented Virtual Reality (VR), especially in scenarios where optical hand tracking is unstable because of self-occlusion, poor lighting, or out-of-view interaction. We present SlideRing, a dual-thumb wearable text-entry method that senses thumb-to-finger micro-gestures with two miniature Inertial Measurement Units (IMUs). SlideRing defines a 30-command interaction space from two hands, three target fingers, and five gesture types, then maps these commands to a full alphabetic keyboard through two complementary strategies: an ergonomic layout optimized for low movement cost and a QWERTY-compatible layout optimized for learnability. To decode subtle inertial signals, we design a dual-stream recognition model with a Statistical Feature Encoder, a Temporal Feature Encoder, and a context-aware gating module for joint finger–action classification. In offline evaluation, the model reaches 96.5% target-finger accuracy and 94.2% action-type accuracy. In a five-day text-entry study, the ergonomic layout improves from 7.43 to 15.75 words per minute (WPM), while the QWERTY-compatible layout improves from 10.55 to 15.25 WPM. The ergonomic layout reduces physical demand, whereas the QWERTY-compatible layout lowers initial mental load. These results suggest that IMU-based thumb-to-finger input has the potential to provide robust, low-visual-demand text entry for constrained VR environments. Full article

Purpose—The flexspline of a harmonic drive constitutes a thin-walled structure with discontinuous gear rim and cylinder configuration, where cyclic stresses induce stress concentration, followed by crack initiation, propagation, and ultimately fatigue failure. This paper reviews advancements in understanding its mechanical properties and fatigue failure mechanisms, aiming to establish a foundation for enhancing operational longevity and guiding future research. Design/Methodology/Approach—The study integrates meshing theory, tooth shape parameters, cylinder stress influencers, and assembly/meshing stress considerations. Theoretical analysis, finite element simulations, and experimental methods are employed to examine stress patterns and fatigue dynamics. Structural parameters and tooth profiles are systematically analyzed for their impact on stress distribution and fatigue life. Findings—Flexspline fatigue failure arises from tooth root stress concentration and cylinder bending stress accumulation. The double-circular-arc tooth profile boosts load capacity by 35% relative to the involute profile, yet demands high-precision machining to preserve meshing performance. Increasing cylinder length mitigates stress concentration but reduces torsional stiffness, while optimized root fillet radii can lower the stress concentration coefficient by 28%. Assembly interference and meshing contact stress accelerate crack initiation, as validated by transient dynamics simulations. Surface strengthening processes (e.g., shot peening) enhance fatigue life by up to 66% through residual compressive stress regulation. Originality/Value—This paper synthesizes multi-scale research on flexspline design, structural optimization, and fatigue mechanisms, proposing novel approaches such as “manufacturability-oriented optimization” and digital twin-driven monitoring. By linking dynamic loads, material properties, and geometric parameters, it bridges theoretical gaps and provides actionable insights for high-precision harmonic drives in robotics and aerospace, advancing reliability in precision transmission systems. Full article

The increasing integration of renewable energy resources in microgrids requires effective frameworks for energy management, system monitoring, and operational assessment. This study presents a simulation-based digital twin-oriented framework for a small-scale renewable energy microgrid with integrated energy management control. The framework consists of a solar photovoltaic (PV) system, a lithium-ion battery energy storage system, and a variable load implemented in a MATLAB/Simulink 2024b environment. Mathematical models are developed to represent PV generation, battery state-of-charge (SOC) dynamics, and load variations, while a rule-based energy management strategy is used to regulate power flow between generation, storage, and demand. An interactive dashboard is incorporated to provide dynamic visualization within the simulation environment of the system operation and key performance indicators. Simulation results show that the controller successfully maintains the battery SOC within the safe operating range of 30–90% and eliminates SOC constraint violations. Compared with uncontrolled operation, renewable energy utilization increases from 67.4% to 92.8%, overall system efficiency improves from 79.6% to 91.3%, and system reliability increases from 93.1% to 99.2%. The Loss of Power Supply Probability (LPSP) decreases from 0.069 to 0.008, while RMS power imbalance is reduced by 50.0%. Battery and converter losses decrease by 41.7% and 43%, respectively. These results demonstrate the effectiveness of the proposed framework in improving energy utilization, reliability, and operational stability while providing a foundation for future digital twin-enabled microgrid optimization and decision support applications. Full article

A problem was formulated, and methods and algorithms for identifying, recognizing, and classifying micro-objects were developed using problem-oriented image processing systems utilizing the characteristics of image structural components, dynamic models, and neural networks. The micro-objects studied were pollen grains and unicellular microorganisms, the application of which is in demand in palynology, environmental protection, ecology, and medicine. Models, algorithms, and software were developed based on the statistical, dynamic, morphometric, textural, and brightness characteristics of point image signals. The main contribution of the work is a hybrid model combining Daubechies wavelet functions (orders 4 and 8) with convolutional neural networks, which provides an average pollen grain recognition and classification accuracy of up to 97.7% and an overall classification accuracy of 98.2%. For comparative evaluation, the YOLO11m model was used as an independent baseline deep learning model, achieving 88.6% overall accuracy on a validation set of 280 images. The software includes tools for Gaussian filtering, median filtering, Sobel and Canny operators, and threshold correction of defective points with hard and soft control rules. The software package includes modules for training a convolutional neural network. The study was conducted on real-world databases of micro-object images for crop breeding and environmental pollution assessment. Full article

Urban green infrastructure (GI) is widely promoted for cooling, stormwater regulation, biodiversity support, and human health benefits; however, these benefits remain unevenly distributed across communities with different socio-economic status. Machine learning (ML) is increasingly used in GI planning and design through high-resolution mapping and demand and exposure modelling, enabling planners and landscape architects to explore scenarios and assess alternative solutions based on the benefits generated. However, ML risks generating or perpetuating spatial injustice through biassed training, opaque optimisation priorities, and epistemic exclusion of Indigenous and local knowledge when models are not developed and applied transparently and collaboratively. This perspective discusses recent GI and ML trends and debates to: (i) clarify how ML can support equity-oriented GI planning; (ii) identify technical and socio-economic risks; and (iii) outline ethical and governance pathways supportive of legitimate and accountable GI planning. We argue that ML should be treated as a component of socio-technical governance rather than a neutral technical tool and therefore should be applied through collaborative design and periodic re-evaluations. Full article

Hydrogen has become a strategic component of the energy transition and decarbonization. In China, province-level administrative regions have issued many hydrogen-related policies, but existing research has paid limited attention to how these policy texts are organized into coherent policy systems. This study evaluates the text-based structural coherence, design completeness, and textual coordination of China’s provincial hydrogen industry policies. Using 315 hydrogen industry policy documents issued across 31 province-level administrative regions, it constructs a “themes–instruments–text-based coherence” analytical framework that integrates the latent Dirichlet allocation topic model, content analysis, and the policy modeling consistency index model. The results show that China’s provincial hydrogen industry policy systems contain five themes: hydrogen industry development, technological innovation, policy support, industry regulation, and operational management. The thematic structure is strongly oriented toward industrial development and technological innovation. The policy instrument analysis indicates that capacity-building instruments dominate the instrument configuration, accounting for 55.87%, while instruments related to government demand, institutional supply, and safety regulation are less prominently represented. The PMC-based evaluation shows that the average text-level coherence score is 6.75, indicating a generally good level of structural coverage and design completeness as reflected in policy texts, but with regional and dimensional differences. Issuing levels constitute the weakest textual dimension, while thematic coverage and instrument configuration show room for improvement, suggesting less systematic textual representation of vertical linkage and uneven instrument complementarity. This study contributes to hydrogen policy research by shifting the analytical focus from individual policy documents to provincial policy systems and by clarifying how policy themes, instruments, administrative arrangements, governance levels, and temporal structures shape the structural coverage and design completeness of hydrogen industry policy at the textual design level. Full article

Background/Objectives: Long-term care (LTC) is becoming a strategic priority for health systems facing population ageing, multimorbidity, frailty, and increasing demand for coordinated medical and social support. Greece faces these pressures in a context of fragmented governance, limited formal LTC capacity, heavy reliance on family care, and quality oversight that remains largely compliance-oriented rather than performance-oriented. This policy review aims to translate international and national evidence into an operational framework for measuring and improving LTC quality in Greece. Methods: The review combined a structured search of peer-reviewed literature, international policy reports, statistical sources, and Greek legislative and regulatory texts with a pragmatic feasibility assessment of candidate indicators. Results: Evidence from OECD and EU systems suggests that mature LTC quality systems share four operational features: legally mandated reporting, standardised indicators, public transparency, and use of data for provider-level improvement. For Greece, the analysis identifies major gaps in legal reporting obligations, data interoperability, workforce monitoring, public reporting, and user-experience measurement. We propose a three-tier indicator framework covering safety, clinical care processes, workforce and staffing, person-centredness, access and equity, efficiency, governance, and digital readiness. Implementation should proceed through a five-year roadmap: foundation, scale-up, and consolidation. Provider-level dashboards and a National LTC Quality Observatory are recommended as key mechanisms for transforming data into continuous quality improvement. Conclusions: A phased, feasible, and legally anchored approach could strengthen patient safety, dignity, operational efficiency, and accountability in Greek LTC. Full article