Search Results (502)

Driving style recognition plays a crucial role in improving the operational safety, fuel efficiency, and intelligent control of commercial vehicles. Under real-world driving conditions, Controller Area Network (CAN) bus data from commercial vehicles simultaneously contain rapid transient variations induced by pedal and braking operations, as well as long-term behavioral trends reflecting driving habits, exhibiting pronounced multi-temporal characteristics. In addition, such data are typically affected by high noise levels, high dimensionality, and highly variable operating conditions, which makes it difficult for methods relying on single-scale features or handcrafted rules difficult to maintain robust and stable performance in complex scenarios. To address these challenges, this paper proposes a driving style classification network, termed the Multi-Scale Convolution and Efficient Channel Attention Network (MSCA-Net). By employing parallel convolutional branches with different temporal receptive fields, the proposed network is able to capture fast driver responses, local temporal dependencies, and long-term behavioral evolution, enabling unified modeling of cross-scale temporal patterns in driving behavior. Meanwhile, the Efficient Channel Attention mechanism adaptively emphasizes CAN signal channels that are highly relevant to driving style discrimination, thereby enhancing the discriminative capability and robustness of the learned feature representations. Experiments conducted on real-world multi-dimensional CAN time-series data collected from commercial vehicles demonstrate that the proposed MSCA-Net achieves improved classification performance in driving style recognition. Furthermore, the potential application of the recognized driving styles in adaptive Automated Manual Transmission shift strategy adjustment is discussed, providing a feasible engineering pathway toward behavior-aware intelligent control of commercial vehicle powertrains. Full article

Driven by cleaner energy demands, environmental regulations, and technological advances, coal science is rapidly evolving, creating the need to understand its transition and transformation within the global energy research landscape. Building upon earlier national- and topic-specific bibliometric studies, this study presents a comprehensive long-term global bibliometric analysis of coal research (1975–2024), based on 272,370 Web of Science records, applying the Cross-Disciplinary Publication Index (CDPI), the Technology–Economic Linkage Model (TELM), VOSviewer, and Excel to assess research growth, structural shifts, and interdisciplinary integration. Results show that coal research is dominated by articles (74%) with publication output peaking at ~19,500 in 2024, reflecting fluctuations in global coal prices due to energy transition market dynamics. CDPI results highlight Energy & Fuels (0.83), Chemical Engineering (0.80), Environmental Sciences (0.77), Materials Science (0.74), and Geosciences (0.66), showing coal’s central role across technology, environment, and geological research domains and revealing a clear shift toward sustainability-oriented and advanced material applications. China leads output (122,130 publications), with strong contributions from the China University of Mining and Technology and the Chinese Academy of Sciences, while the USA, Australia, and Europe maintain strong international collaboration networks. The evolution of coal research can be divided into three major phases: conventional mining, coal preparation, combustion, and coalbed methane commercialization (1975–2004; ~64,000 publications); integrated gasification combined cycle (IGCC) and carbon capture and storage (CCS) technologies (2005–2014; ~58,707 publications); and a recent phase dominated by by-product valorization, carbon capture utilization and storage (CCUS), and digital technologies (AI, IoT, ML) (2015–2024; ~146,174 publications). Contemporary coal research spans three interconnected domains: energy supply (≈36% of global electricity generation and ~15 Gt CO₂ emissions), resource and geoscience applications (including large-scale fly ash utilization and critical element recovery), and environmental and health impacts related to greenhouse gas and pollutant emissions. The findings demonstrate that coal science is transitioning from a conventional fossil fuel-centered discipline toward an integrated, interdisciplinary energy research field, emphasizing emission reduction, resource efficiency, digitalization, and circular economy applications, thereby extending prior bibliometric studies through unprecedented temporal coverage, global scope, and the combined application of CDPI and TELM frameworks, providing critical insights for future energy strategies and policy development. Full article

We report a comprehensive spectroscopic, microscopic, and device-level investigation of the ambient-driven degradation of PTQ10:IDIC bulk-heterojunction organic solar cells (BHJ-OSCs), up to 500 h. The power conversion efficiency dropped from 9.51% to 7.69% (≈19% relative loss), primarily due to a decrease in short-circuit current density (J_SC 15.93 to 13.82 mA cm⁻²), while the open-circuit voltage remained largely stable (0.92 to 0.90 V). Atomic force microscopy reveals surface smoothing upon ageing, with the root-mean-square roughness decreasing from 4.29 to 3.45 nm, and the UV–vis absorption spectra show negligible changes, indicating preserved bulk light-harvesting capability. In contrast, X-ray photoelectron spectroscopy indicates pronounced surface compositional evolution, with a decrease in oxygen (5.18 to 3.18%) and a substantial increase in fluorine content (3.23 to 7.23%), consistent with fluorine-rich surface segregation or reorientation. Ultraviolet photoelectron spectroscopy further reveals a 0.48 eV reduction in surface work function, indicative of surface dipole modification and near-surface electronic reorganization. Collectively, these results demonstrate that ambient ageing primarily impacts interfacial chemistry and morphology rather than bulk optoelectronic properties, highlighting interfacial engineering and encapsulation as effective strategies for improving long-term device stability. Full article

In recent years, renewable energy production has expanded rapidly, becoming an essential component of the global energy transition. However, the inherent variability and unpredictability of renewable generation require technologies that can provide grid stability and operational flexibility. Battery Energy Storage Systems (BESS) play a central role in addressing this challenge, but their long-term effectiveness depends on a thorough understanding of their degradation mechanisms. This work aims to model and predict the capacity and power degradation of a real-world BESS operating in the electricity market, bridging the gap between laboratory-based ageing studies and field applications. Several degradation indicators, such as available capacity evolution, DC efficiency evolution, conductivity loss, and loss of lithium inventory, were evaluated to determine which models best describe the system’s ageing behaviour. Some estimations were found inaccurate and subsequently excluded, while the remaining analyses enabled a detailed characterisation of BESS performance over time. Using operational data collected between November 2022 and October 2024, results indicate a linear capacity degradation reaching 4.57% over 23 months (490 equivalent cycles), from approximately 9600 kWh to 9150 kWh, with a Mean Absolute Percentage Error (MAPE) of 0.2%. DC efficiency exhibited a two-phase evolution, with an initial rise followed by a slow reduction trend. These findings confirm that ageing effects can be effectively evaluated using operational data, enabling reliable lifetime forecasting for BESS assets. Full article

Premature skid resistance deterioration is a critical issue limiting the long-term performance of thin asphalt overlays. To elucidate the meso-scale degradation mechanisms, this study employs the Discrete Element Method (DEM) implemented in Particle Flow Code (PFC) to compare a conventional Stone Matrix Asphalt (SMA-10) mixture with an optimized skeleton-reinforced design, termed Optimized Gradation-10. The optimized gradation was developed by introducing supplementary sieve sizes of 5.6, 6.7, and 8.0 mm within the critical range of 4.75–9.5 mm following the V–S gradation framework. Cross-sectional images of actual mixtures were vectorized using Python (Version: 3.11.5) and MATLAB (Version: R2024a) to reconstruct irregular aggregate clump models that accurately capture particle morphology and spatial arrangement. Meso-scale parameters were calibrated and validated through uniaxial compression tests, and the evolution of contact number, contact force, and stress transmission was analyzed under 2.3 × 10⁵ wheel load cycles. Compared with SMA-10, the optimized mixture increased effective aggregate contacts by 41.8%, enhanced stress transfer efficiency by 19.8%, and reduced rut depth by 10%. These findings confirm that synergistic gradation optimization through supplementary sieves and the V–S method markedly improves structural stability and deformation resistance, providing a meso-mechanical foundation for prolonging skid resistance in thin overlays. Full article

Groundwater is a critical strategic resource supporting agricultural production and ecological security in the transboundary river basins of Northeast China. However, intensified climate variability and rapid agricultural expansion over the past two decades have imposed increasing pressure on regional groundwater systems. In this study, we integrated GRACE-derived terrestrial water storage anomalies, GLDAS land surface data, meteorological datasets, land-use information, and agricultural statistics to construct a comprehensive assessment framework consisting of groundwater storage anomalies (ΔGWS), the GRACE Groundwater Drought Index (GGDI), and sustainability indicators—REL (Reliability), RES (Resilience), VUL (Vulnerability), and SI (Sustainability Index). By integrating GRACE-derived groundwater dynamics with sustainability indicators (REL, RES, VUL, and SI), enabling a basin-scale, long-term assessment of groundwater sustainability across Northeast China’s transboundary basins, and clarifying the relative roles of climatic variability and intensive human water use. We systematically examined the spatiotemporal evolution of groundwater conditions in the Heilongjiang, Suifen, Tumen, and Yalu River basins from 2002 to 2022, and quantified the relative roles of climatic and anthropogenic drivers. The results indicate that groundwater storage exhibited pronounced seasonal fluctuations alongside a persistent downward trend, with GGDI remaining predominantly negative after 2018, reflecting the development of structural groundwater drought. The SI declined markedly from 0.32 to 0.06, and areas with extremely low sustainability accounted for more than 90% of the study region in recent years. MIC-based dependence analysis showed that sown area (MIC = 0.98) and nighttime light intensity (MIC = 0.92) were the dominant drivers of groundwater degradation, exerting far greater influence than precipitation or potential evapotranspiration. These patterns highlight that policy-driven agricultural expansion and increased irrigation demand have surpassed natural recharge capacity, becoming the fundamental cause of long-term groundwater depletion. This study underscores the urgency of promoting agricultural green transformation, optimizing crop planting structures, improving irrigation efficiency, and enhancing ecological conservation to rebuild groundwater resilience. Moreover, coordinated cross-border groundwater monitoring and management will be essential for ensuring the sustainable use of water resources in Northeast Asia’s transboundary river basins. Full article

The integration of 3D printing and artificial intelligence is transforming healthcare management by driving innovations in personalized care, supply chain operations, and clinical workflows. This review offers a comprehensive overview and in-depth analysis of recent (2018–2025) applications where AI technologies enhance 3D printing within healthcare. We explore how AI-powered design and optimization facilitate the creation of patient-specific medical devices, implants, and even bioprinted tissues, while intelligent process control increases both quality and efficiency. Additionally, we examine regulatory and ethical considerations, including the evolution of frameworks for AI-enabled devices, as well as challenges in data governance, validation, and equitable access. The review takes a global perspective, presenting real-world case studies that showcase both successful implementations and ongoing challenges. We also discuss various perspectives and controversies, such as the balance between innovation and safety in autonomous AI design, and highlight areas where further research is needed. In contrast to previous narrative reviews that focus solely on clinical applications or technical aspects, this review uniquely evaluates the combined impact of AI and 3D printing on healthcare management—including cost-effectiveness, governance, decision-making processes, and point-of-care manufacturing. This work is particularly valuable for hospital administrators, clinical operations leaders, health policymakers, and biomedical innovation teams seeking to understand the broader implications of AI-enhanced 3D printing in healthcare management. Nevertheless, despite promising advancements, the field is constrained by heterogeneous evidence, a lack of standardized evaluation metrics, and insufficient long-term outcome data, which together limit the ability to fully assess the sustained impact of AI-integrated 3D printing in healthcare environments. Full article

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures—including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)—in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the “black box” issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland ecosystems. Full article

The pursuit of sustainable ammonia production has accelerated the development of electrocatalytic pathways capable of operating under ambient conditions with renewable electricity. Recent studies have revealed that the efficiency and selectivity of both electrochemical nitrogen reduction reaction (eNRR) and nitrate reduction reaction (eNO₃RR) are not governed solely by catalyst composition, but by the synergistic interplay among electrolyte identity, interfacial solvation structure, and catalyst architecture. Hydrated cations such as Li⁺ profoundly reshape the electric double layer, polarize interfacial water, and lower activation barriers for key proton–electron transfer steps, thereby redefining the electrolyte as an active promoter. Parallel advances in structural engineering, including alloying, heteroatom doping, controlled defect formation, and nanoscale morphological control, have enabled the optimization of intermediate adsorption energies while simultaneously suppressing competing hydrogen evolution. In addition, the emergence of metal–organic-framework (MOF)-derived single-atom catalysts has demonstrated that atomically dispersed transition-metal centers anchored within dynamically adaptable matrices can deliver exceptional Faradaic efficiencies, high turnover rates, and long-term operational durability. These developments highlight a unified strategy in which electrolyte–catalyst coupling, rational structural modification, and atomic-scale design principles converge to enable predictable and high-performance ammonia electrosynthesis. This review integrates mechanistic insights across these domains and outlines future directions for translating molecular-level understanding into scalable technologies for green ammonia production. Full article

Phosphogypsum (PG) is a byproduct of wet-process phosphoric acid production and contains soluble phosphorus (P), fluorine (F), and other harmful impurities in addition to calcium sulfate. Its acidic leachate enriched with P and F poses long-term risks to soil and surrounding water bodies. Owing to the incorporation of soluble P and F within calcium sulfate crystal interlayers, these contaminants are gradually released during storage, making it difficult to achieve an economically efficient and environmentally benign treatment of PG at an industrial scale. In this study, a low-cost and sustainable process for the effective and long-term immobilization of soluble P and F in PG was developed using sulfuric acid-activated red mud (RM), an industrial waste rich in Fe and Al. After pulping PG with water, activated RM was added, followed by pH adjustment with Ca(OH)₂, leading to the in situ formation of amorphous calcium aluminate and calcium ferrite polymers with strong adsorption affinity toward soluble P and F. The immobilization mechanism and phase evolution were systematically investigated using inductively coupled plasma optical emission spectroscopy (ICP-OES, PS-6PLASMA SPECTROVAC, BAIRD, USA), on a Rigaku Miniflex diffractometer (Rigaku Corporation, Tokyo, Japan), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and zeta potential analysis. The leachate of PG treated with activated RM and Ca(OH)₂ contained P < 0.5 mg/L and F < 10 mg/L at pH 8.5–9.0, meeting environmental requirements (pH = 6–9, P ≤ 0.5 mg/L, F ≤ 10 mg/L). Moreover, the immobilized P and F exhibited enhanced stability during long-term stacking, indicating the formation of durable immobilization products. This study demonstrates an effective “treating waste with waste” strategy for the large-scale, environmentally safe utilization of phosphogypsum. Full article

This study introduces a mathematical framework for analyzing unsteady gas filtration in porous media with pressure-dependent porosity variations. The physical process is formulated as a nonlinear parabolic boundary value problem that captures the coupled interaction between pressure evolution and porosity changes during gas production. To solve the equation, a numerical strategy is developed by integrating the Alternating Direction Implicit (ADI) scheme with quasi-linearization iterations, employing finite difference discretization on a two-dimensional spatial grid. Extensive computational experiments are performed to investigate the influence of key reservoir parameters—including porosity coefficient, permeability, gas viscosity, and well production rate—on the spatiotemporal behavior of pressure and porosity during long-term extraction. The results indicate significant porosity variations near the wellbore driven by local pressure depletion, reflecting strong sensitivity of the system to formation properties. The validated numerical model provides valuable quantitative insights for optimizing reservoir management and improving production forecasting in gas field development. Overall, the proposed methodology serves as a practical tool for oil and gas engineers to assess long-term reservoir performance under diverse operational conditions and to design efficient extraction strategies that incorporate pressure-dependent formation property changes. Full article

Phacoemulsification is performed within a highly dynamic intraocular environment, in which fluid exchange, pressure regulation, and tissue biomechanics interact continuously. Although modern cataract surgery is considered safe and efficient, disruption of this delicate intraoperative microenvironment remains a major source of complications. Among fluidics-related events, post-occlusion break surge represents one of the most critical destabilizing factors of the anterior chamber. A surge occurs when the sudden release of an occluded aspiration port generates an abrupt pressure–volume imbalance that cannot be immediately compensated by infusion, leading to a transient collapse of the intraocular environment. This narrative review integrates current experimental and clinical evidence on the pathophysiology, quantification, and technological control of surge, framing it as a model of environmentally driven intraoperative stress. The evolution of phacoemulsification fluidics, from gravity-based systems to active, adaptive, and predictive platforms, is analyzed in relation to their ability to preserve a stable and physiologic intraocular environment. Comparative data from contemporary devices are reviewed, highlighting differences in surge volume, recovery time, and pressure restitution. Special emphasis is placed on the impact of surge on the microenvironments of both the anterior and posterior segments, including endothelial stress, capsular instability, vitreoretinal traction, and macular perfusion. Emerging strategies such as handpiece-integrated pressure sensors, predictive fluidics algorithms, intraoperative imaging, and artificial intelligence are reshaping environmental control during surgery. Despite substantial technological progress, the complete elimination of surge remains an unmet need. Continued innovation, standardized biomechanical models, and robust clinical validation will be essential to further protect the intraoperative intraocular environment and improve long-term visual outcomes. Full article

Rapid motorization in Dhaka has worsened congestion, motivating the launch of Mass Rapid Transit (MRT) as a potential solution. However, metro adoption depends not just on infrastructure but on commuter perceptions, intentions, and actual behavior. To track the dynamic evolution of commuter adoption over time, the study employs a unique three-stage Bayesian framework—Pre-MRT Stated Preference (SP), Post-MRT SP, and Post-MRT Revealed Preference (RP) for MRT line-6. Bayesian logistic regression with Markov Chain Monte Carlo (MCMC) estimation captures posterior distributions and parameter uncertainty, offering insights into the shifting determinants of MRT adoption. The pre-MRT SP model (pseudo R² = 0.0668) identified affordability as an incentive but highlighted concerns around safety and reliability. Post-MRT, the SP model (pseudo R² = 0.186) found that socio-demographic factors, including gender and employment, strongly influenced preferences, while the RP model (pseudo R² = 0.502) showed that actual behavior was most influenced by proximity to stations, education, and security perceptions. Overall, the findings reveal that expectations and actual behavior often diverge, with adoption maturing over time. The evidence indicates that commuter adoption evolves with system maturity, requiring policies that first build affordability and integration, then strengthen safety and reliability, and ultimately enhance accessibility and long-term efficiency. Full article

This study investigates the dynamics of electricity consumption in Brazil over the past two decades, with a focus on the persistent slowdown in consumption growth observed since 2013. Using segmented regression and interrupted time series (ITS) modeling, the research identifies statistically significant structural breakpoints in national and regional electricity demand. The main novelty of this study lies in the integrated use of segmented regression, ITS, and seasonal SARIMA models to systematically characterize asymmetric and phase-dependent demand behavior rather than to produce short-term forecasts. Seasonal Autoregressive Integrated Moving Average (SARIMA) models reveal that monthly seasonality plays a dominant role in electricity consumption dynamics, with seasonal specifications consistently outperforming non-seasonal alternatives. The results show that Brazil’s electricity demand evolution is best explained by three distinct phases: (i) a stagnation of industrial demand associated with deindustrialization prior to 2013; (ii) an abrupt contraction in commercial and residential demand during the 2014–2016 economic crisis; and (iii) a permanently lower growth trajectory driven by energy efficiency policies under the Brazilian National Electric Energy Conservation Program (PROCEL) and the expansion of solar distributed generation. The findings demonstrate that policy and structural interventions exert gradual, cumulative effects on electricity consumption rather than immediate shifts, providing critical insights for long-term energy planning and policy design in emerging economies. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article