Search Results (1,650)

The continuous detection of emerging pollutants (EPs) in water poses potential threats to aquatic environmental safety and human health, and their efficient removal is a frontier in environmental engineering research. This review systematically summarizes research progress from 2005 to 2025 on the application of activated carbon (AC) and its composites for removing EPs from water and analyzes the development trends in this field using bibliometric methods. The results indicate that research has evolved from the traditional use of AC for adsorption to the design of novel materials through physical and chemical modifications, as well as composites with metal oxides, carbon-based nanomaterials, and other functional components, achieving high adsorption capacity, selective recognition, and catalytic degradation capabilities. Although AC-based materials demonstrate considerable potential, their large-scale application still faces challenges such as cost control, adaptability to complex water matrices, material regeneration, and potential environmental risks. Future research should focus on precise material design, process integration, and comprehensive life-cycle sustainability assessment to advance this technology toward highly efficient, economical, and safe solutions, thereby providing practical strategies for safeguarding water resources. Full article

Contemporary design of medical and therapeutic facilities increasingly recognizes that healing outcomes are influenced not only by functional performance but also by spatial experience, sensory perception, and atmospheric qualities of the built environment. Within this context, courtyards represent a recurrent architectural element in healthcare settings, historically associated with access to nature, daylight, ventilation, and social interaction. Beyond their utilitarian role, courtyards can operate as multisensory environments that support psychological regulation, emotional restoration, and physical well-being. This study investigates the therapeutic courtyard as a spatial component situated at the intersection of functional requirements and phenomenological experience in healing architecture. Rather than aiming to demonstrate universal applicability, the research seeks to identify and structure the key functional and phenomenological attributes that contribute to the restorative potential of courtyard spaces in healthcare environments. The study combines a structured literature review with an exploratory, perception-oriented survey based on conceptual courtyard scenarios. The research explicitly focuses on key spatial parameters of therapeutic courtyard design, including access to daylight, ventilation and microclimate, contact with nature, accessibility and orientation, social integration, opportunities for activity and recreation, possibilities for isolation and safety, adaptability and multifunctionality, as well as aesthetic and symbolic qualities. By translating theoretical insights into practical design considerations, the study contributes to the development of human-centered strategies for contemporary healing architecture. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

Lithium thermal batteries are primary reserve batteries utilizing solid molten salt electrolytes. They are regarded as ideal power sources for high-reliability applications due to their high power density, rapid activation, long shelf life, wide operating temperature range, and excellent environmental adaptability. However, existing electrode systems are limited by insufficient conductivity and the use of high-impedance MgO binders. This results in sluggish electrode reaction kinetics and incomplete material conversion during high-temperature discharge, causing actual discharge capacities to fall far below theoretical values. To address this, FeS₂-CoS₂ multi-component composite cathode materials were synthesized via a high-temperature solid-phase method. Furthermore, two distinct MgO binders were systematically investigated: flake-like MgO (MgO-F) with a sheet-stacking structure and spherical MgO (MgO-S) with a low-tortuosity granular structure. Results indicate that while MgO-F offers superior electrolyte retention via physical confinement, its high tortuosity limits ionic conduction. In contrast, MgO-S facilitates the construction of a wettability-enhanced continuous ionic network, which effectively reduces interfacial impedance and enhances system conductivity. This regulation promoted Li⁺ migration and accelerated interfacial reaction kinetics. This study provides a feasible pathway for improving the electrochemical performance of lithium thermal batteries through morphology-oriented MgO binder regulation. Full article

This study investigates peak daylight exposure in zenithally lit museum rooms at 51° latitude through an experimental campaign using a 1:20 physical mock-up of a 12 × 12 × 6 m exhibition gallery space. Nine configurations of shading and light-transmitting elements (CSaLTE) were tested under real clear-sky conditions between June and October. To ensure a valid comparative analysis, indoor vertical illuminance (E_v) was measured at 15 min intervals and subsequently interpolated and normalised to a unified equinox-day solar geometry (06:00–18:00). This hybrid empirical-computational methodology allows for a direct performance comparison across different geometric arrangements regardless of their specific measurement dates. The results demonstrate that while traditional annual metrics are the standard, short-term illuminance peaks pose a severe and underexplored threat to conservation safety. Even the most light-attenuating diffusing-roof configurations produced short-term illuminance peaks and cumulative clear-sky exposures that are comparable in magnitude to commonly cited annual limits for highly light-sensitive materials, with several configurations recording extreme spikes surpassing the sensor’s 20,000 lx saturation limit. Stable, low-illuminance distributions were observed only in selected diffusing-roof arrangements (M05–M07), whereas direct-glazing systems (M01–M04) produced unsafe exposure patterns with high temporal variability and poor visual adaptation conditions. The study concludes that passive roof geometries alone are insufficient to ensure conservation-level safety without additional active filtering or adaptive control strategies, providing an experimentally grounded framework for designing zenithal daylighting systems in museum environments. The results are intended for relative peak-risk comparison under controlled clear-sky conditions rather than direct generalisation to whole-room annual conservation safety. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

Nature-based health interventions (NBHIs) are increasingly used in the healthcare system to support people with anxiety, depression and/or stress, highlighting the need for systematic development and evaluation. This study aims to identify target group, professionals, mechanisms, and outcomes of NBHIs for people with mild to moderate anxiety, depression, and/or stress. A Delphi-based study was conducted to explore core components of NBHIs in healthcare settings. Thirteen vs. eleven researchers with expertise related to the target group responded in two rounds. Respondents rated statements on a 7-point Likert scale and prioritised core components regarding target group, professionals, mechanisms, and outcomes. A thematic analysis was applied to synthesise qualitative responses. Consensus was achieved on 12 of 21 items across the four domains. Highest agreement concerned core mechanisms (nature interaction, social community, and physical activity), outcome priorities (mental wellbeing and quality of life), and professional competencies. Greater variation was observed regarding group composition and team delivery. Analysis of qualitative expert responses highlighted four key themes: (1) Balancing Group Composition, (2) Adapting Competencies to Context, (3) Core Mechanisms for Change, and (4) Weighing Perspectives in Outcome Selection. By setting out guiding principles for a programme theory, the study lays the foundation for the design and implementation of context-adapted NBHIs. The study underscores the need to approach NBHIs as complex interventions, thus contributing to a paradigm shift towards a new era of a bio-psycho-social health perspective. Full article

Background and Objectives: Long COVID-19 (LC) is a multifaceted condition characterized by persistent fatigue and impaired health-related quality of life (HRQoL). Exercise intolerance and post-exertional symptom exacerbation (PESE) pose challenges for rehabilitation. This study aimed to evaluate the effects of a 12-week core-focused plank exercise program on fatigue and HRQoL in women with LC, using validated patient-reported measures. Materials and Methods: A pilot quasi-experimental design was implemented, with non-randomized group allocation. Thirty-nine women with LC were recruited from the Madrid Long COVID Association. Participants were assigned to either an intervention group (n = 20), which completed a supervised plank-based motor control program, or a control group (n = 19), which maintained usual activity. Fatigue was assessed using the Modified Fatigue Impact Scale (MFIS), and HRQoL was measured using the EQ-5D-5L and EQ Visual Analog Scale (EQ-VAS). Body composition was evaluated via bioelectrical impedance analysis. Results: The intervention group showed significant reductions after intervention in the MFIS total scores compared to the control group, particularly in the physical (21.26 ± 6.76 vs. 25.21 ± 6.06; p < 0.001) and psychosocial domains (4.51 ± 0.41 vs. 5.21 ± 0.38; p < 0.001), without triggering PESE. EQ-VAS scores improved significantly (63.94 ± 15.33 vs. 46.31 ± 14.74; p = 0.034). No significant changes were found in body composition parameters, suggesting that benefits were driven by neuromuscular adaptations rather than morphological changes. Conclusions: A core-focused, non-aerobic exercise program effectively reduced fatigue and improved perceived health status in women with LC. These findings support the use of motor control-based interventions as a safe and feasible strategy for LC rehabilitation, particularly in populations vulnerable to PESE, suggesting clinical applicability for the rehabilitation of women with LC. Further randomized trials are warranted to confirm these results and explore long-term outcomes. Full article

Recent progress in Reinforcement Learning (RL), especially deep RL, has created new possibilities for autonomous control in complex and uncertain environments. This study explores these possibilities through a practical approach, implementing an RL agent on a custom-built CubeSat. The CubeSat, equipped with a reaction wheel for active attitude control, serves as a physical testbed for validating RL-based strategies. To mimic space-like conditions, the CubeSat was placed on a custom air-bearing platform that allows near-frictionless rotation along a single axis, simulating microgravity. Unlike simulation-only research, this work showcases real-time hardware-level implementation of a Double Deep Q-Network (DDQN) controller. The DDQN agent receives real system state data and outputs control commands to orient the CubeSat via its reaction wheel. For comparison, a traditional PID controller was also tested under identical conditions. Both controllers were evaluated based on response time, accuracy, and resilience to disturbances. The DDQN outperformed the PID, showing better adaptability and control. This research demonstrates the successful integration of RL into real aerospace hardware, bridging the gap between theoretical algorithms and practical space applications through a hands-on CubeSat platform. Full article

Ectomycorrhizal (ECM) symbiosis is a fundamental mutualism crucial for forest eco-system health. Its establishment is governed by sophisticated molecular dialogue preceding physical colonization. This review synthesizes this pre-colonization crosstalk, beginning with reciprocal signal exchange where root exudates trigger fungal growth, and fungal lipochitooligosaccharides activate host symbiotic programming, often via the common symbiosis pathway. Successful colonization requires fungi to navigate plant immunity. They employ effectors, notably mycorrhiza-induced small secreted proteins (MiSSPs), to suppress defenses, e.g., by stabilizing jasmonate signaling repressors or inhibiting apoplastic proteases, establishing a localized “mycorrhiza-induced resistance.” Concurrent structural adaptations, including fungal hydrophobins, expansins, and cell wall-modifying enzymes like chitin deacetylase, facilitate adhesion and apoplastic penetration. While this sequential model integrates immune suppression with structural remodeling, current understanding is predominantly derived from a limited set of model systems. Significant knowledge gaps persist regarding species-specific determinants in non-model fungi and hosts, the influence of environmental variability and microbiome interactions, and methodological challenges in capturing early signaling in situ. This review’s main contributions are: providing a synthesized sequential model of molecular crosstalk; elucidating the dual fungal strategy of simultaneous immune suppression and structural remodeling; and identifying crucial knowledge gaps regarding non-model systems and species-specific determinants, establishing a research roadmap with implications for forest management and ecosystem sustainability. Full article

Understanding the dynamics of soil water and root systems is essential for managing and restoring ecosystems impacted by coal mining subsidence. However, existing research treats soil and plant responses separately, also with limited comparisons across different soil types, which hampers our understanding of their coupled effects. We examined the distribution of plant roots, soil water content and stable isotopes within the root zone in the subsidence and non-subsidence plots located in mining areas with sand-capped loess and sandy soil. Our results show that coal mining subsidence induces cracks and fissures in both sand-capped loess and sandy soil, enhancing soil infiltration and increasing deep soil water (>1 m). The increase in deep soil water was more pronounced in sand-capped loess, where subsidence exhibited near-precipitation lc-excess values (−5.9‰ to −0.2‰) and also shifted the soil water infiltration mechanism from piston flow to preferential flow. Moreover, land subsidence provides a more suitable soil physical environment that supports the growth of deeper and more extensive plant roots. The coupling degree (D) between the soil water system and root system was significantly higher in subsidence areas (D > 0.4), indicating enhanced root water absorption. These changes benefit plant physiological activities and stress response, providing an adaptive mechanism for plants in subsidence regions. This study provides new insights into the effects of coal mining subsidence on the root-soil interface in Earth’s Critical Zones and serves as a foundation for ecological restoration and management in subsidence-impacted areas. Full article

This study systematically investigated the influence of approach kinematics on the subsequent kinetics and power production strategies during the approach to running jumps with a single leg (ARJSL). Twenty-five physically active male university students performed ARJSL trials under two prescribed approach speeds (fast and slow) and three approach distances (3, 6, and 9 m) in a 2 × 3 within-subjects design. Three-dimensional motion capture synchronized with force platform data was used to quantify jump height (JH), vertical touchdown velocity (TDv), reactive strength index (RSI), peak joint power (hip, knee, and ankle), and joint stiffness. Significant approach speed × distance interactions were observed for JH (p = 0.006), TDv (p < 0.001), RSI (p = 0.014), ankle stiffness (p = 0.006), and peak power generation at all lower-limb joints (all p < 0.034). The results demonstrate that changes in approach strategy systematically alter the distribution of mechanical power among the hip, knee, and ankle joints, thereby influencing the effectiveness of horizontal-to-vertical momentum conversion during take-off. Notably, RSI and ankle stiffness were particularly sensitive to combined manipulations of speed and distance, highlighting their value as neuromechanical indicators of stretch–shortening cycle intensity and joint loading demands. In conclusion, ARJSL performance depends on finely tuned, speed- and distance-specific biomechanical adaptations within the lower extremity. These findings provide a constrained, joint-level mechanical characterization of how approach speed and distance interact to influence power redistribution and stiffness behavior during ARJSL, without implying optimal or performance-maximizing strategies. Full article

We present a new method for mapping global gas flaring using a multiyear spatio-temporal database of VIIRS Nightfire (VNF) nighttime infrared detections from the Suomi NPP, NOAA-20, and NOAA-21 satellites. The method is designed to resolve closely spaced industrial combustion sources and to produce a stable, physically meaningful flare catalog suitable for long-term monitoring and emissions analysis. The method combines adaptive spatial aggregation of high-temperature detections with a hierarchical clustering that super-resolves individual flare stacks within oil and gas fields. Post-processing yields physically consistent flare footprints and attraction regions, allowing separation of closely spaced sources. Flare clusters are assigned to operational categories (e.g., upstream, midstream, LNG) using prior catalogs combined with AI-assisted expert interpretation. In this step, a multimodal large language model (LLM) provides contextual classification suggestions based on geospatial information, high-resolution daytime imagery, and detection time-series summaries, while final attribution is performed and validated by domain experts. Compared with annual flare catalogs commonly used for national flaring estimates, the new catalog demonstrates substantially improved performance. It is more selective in the presence of intense atmospheric glow from large flares, identifies approximately twice as many active flares, and localizes individual stacks with ~50 m precision, resolving emitters separated by ~400–700 m. For the well-defined class of downstream flares at LNG export facilities, the catalog achieves complete detectability. These improvements support more accurate flare inventories, facility-level attribution, and policy-relevant assessments of gas flaring activity. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

Daily nighttime light (NTL) observations made by remote sensing satellites can monitor human activity at high temporal resolution, but are often constrained by residual physical disturbances. Even in standard products, such as NASA’s Black Marble VNP46A2, factors related to sensor viewing geometry, lunar illumination, atmospheric conditions, and seasonality can introduce noise into daily radiance retrievals. This study develops a locally adaptive framework to diagnose and correct residual disturbances in daily NTL data. By estimating location-specific regression models, we quantify the residual sensitivity of VNP46A2 radiance to multiple disturbance factors and selectively remove statistically significant components. The results show that the proposed approach effectively removes statistically significant residual disturbances from daily NTL data in the VNP46A2 product. An application for COVID-19 containment periods in China demonstrates the effectiveness of the proposed approach, where corrected daily NTL data exhibit enhanced temporal stability and improved interpretability. Further analysis based on event study approaches demonstrates that corrected daily NTL data enable the identification of short-run policy effects that are difficult to detect with lower-frequency indicators. Overall, this study enhances the suitability of daily NTL data for high-frequency socioeconomic applications and extends existing preprocessing approaches for daily NTL observations. Full article