Search Results (11,743)

Amine-functionalized solid adsorbents are widely recognized as promising candidates for direct air capture of CO₂; however, their practical deployment remains constrained by humidity-dependent adsorption behavior and poor packed-bed operability arising from irregular particle morphology and fines generation. Rather than focusing solely on maximizing intrinsic adsorption capacity, this study addresses these process-level limitations through an integrated design strategy combining particle morphology control with surface chemistry optimization. Uniform spherical magnesium silicate particles with a mean diameter of approximately 15 μm were synthesized via a water-in-oil emulsion route to suppress fines formation and reduce hydrodynamic resistance. Controlled acid pretreatment was subsequently applied to adjust surface hydroxyl accessibility and enable efficient amine grafting without altering bulk composition. The optimized spherical magnesium silicate amine adsorbents exhibited pronounced humidity-enhanced carbon dioxide capture, achieving capacities of 1.7 to 1.8 millimoles/g at 50% relative humidity, representing an approximately fourfold increase compared with dry conditions. This enhancement is attributed to a humidity-induced mechanistic transition from carbamate formation under dry conditions to water-assisted bicarbonate formation under humid conditions. Complete regeneration was achieved at 100 °C, with stable adsorption desorption behavior maintained over ten consecutive cycles, demonstrating short-term reversibility. These findings highlight morphology controlled scalability. Future work should prioritize durability beyond 100 cycles, mechanical robustness, and techno-economic viability at scale. Full article

Accurate identification of road roughness is pivotal for optimizing vehicle suspension control and enhancing passenger comfort. However, existing data-driven methods often struggle to balance classification accuracy with the strict computational constraints of real-time onboard monitoring. To address this challenge, this paper proposes a lightweight and robust road roughness classification framework utilizing a single sprung mass accelerometer. First, to overcome the scarcity of labeled real-world data and the limitations of linear models, a high-fidelity co-simulation platform combining CarSim and Simulink is established. This platform generates physically consistent vibration datasets covering ISO A–F roughness levels, effectively capturing nonlinear suspension dynamics. Second, we introduce DB-MLP, a novel Dual-Branch Multi-Layer Perceptron architecture. In contrast to computationally intensive Transformer or RNN-based models, DB-MLP employs a dual-branch strategy with multi-resolution temporal projection to efficiently capture multi-scale dependencies, and integrates dual-domain (time and position-wise) feature transformation blocks for robust feature extraction. Experimental results demonstrate that DB-MLP achieves a superior accuracy of 98.5% with only 0.58 million parameters. Compared to leading baselines such as TimeMixer and InceptionTime, our model reduces inference latency by approximately 20 times (0.007 ms/sample) while maintaining competitive performance on the specific road classification task. This study provides a cost-effective, high-precision solution suitable for real-time deployment on embedded vehicle systems. Full article

Time-dependent structural systems (TDSSs) in engineering involve high dimensionality, nonlinearity, and complex uncertainties, complicating the reliability analysis compared to time-independent assessments. To address these challenges, this paper proposes an extremum-based back propagation neural network (BPNN) method for TDSS reliability analysis. The method adopts a double-loop structure. Specifically, the inner loop finds the minimum of the time-dependent performance function for a given realization of the random variables. This transformation converts the time-dependent problem into an equivalent time-invariant one. Then, the outer loop constructs a BPNN surrogate model to map the relationship between the random variables and the performance function minima. To improve computational efficiency, an adaptive sample selection strategy is integrated into the training process. This technique selects samples near the failure boundary to iteratively update the BPNN, ensuring high accuracy with a small training set. Once the stopping criterion is satisfied, the failure probability is estimated using Monte Carlo simulation (MCS). The trained BPNN model is used to rapidly predict the extremum for the large-scale sample pool. The proposed method is verified through three practical engineering cases: a four-bar mechanism, an aero-engine turbine disc, and a cantilever tube. Results show that the method remains accurate and efficient. The successful applications confirm the rationality and engineering applicability of the proposed model. Full article

The agri-food sector is a major contributor to global greenhouse gas emissions while facing increasing demand for food production driven by population growth. Transitioning towards sustainable and low-carbon agricultural systems is therefore critical. Green hydrogen, produced from renewable energy sources, holds significant promise as a clean energy carrier and chemical feedstock to decarbonize multiple stages of the agri-food supply chain. This systematic review is based on a structured analysis of peer-reviewed literature retrieved from Web of Science, Scopus, and Google Scholar, covering over 120 academic publications published between 2010 and 2025. This review provides a comprehensive overview of hydrogen’s current and prospective applications across agriculture and the food industry, highlighting opportunities to reduce fossil fuel dependence and greenhouse gas emissions. In agriculture, hydrogen-powered machinery, hydrogen-rich water treatments for crop enhancement, and the use of green hydrogen for sustainable fertilizer production are explored. Innovative waste-to-hydrogen strategies contribute to circular resource utilization within farming systems. In the food industry, hydrogen supports fat hydrogenation and modified atmosphere packaging to extend product shelf life and serves as a sustainable energy source for processing operations. The analysis indicates that near-term opportunities for green hydrogen deployment are concentrated in fertilizer production, food processing, and controlled-environment agriculture, while broader adoption in agricultural machinery remains constrained by cost, storage, and infrastructure limitations. Challenges such as scalability, economic viability, and infrastructure development are also discussed. Future research should prioritize field-scale demonstrations, technology-specific life-cycle and techno-economic assessments, and policy frameworks adapted to decentralized and rural agri-food contexts. The integration of hydrogen technologies offers a promising pathway to achieve carbon-neutral, resilient, and efficient agri-food systems that align with global sustainability goals and climate commitments. Full article

Fine-grained food image segmentation is essential for accurate dietary assessment and nutritional analysis, yet remains highly challenging due to ambiguous boundaries, inter-class similarity, and dense layouts of meals containing many different ingredients in real-world settings. Existing methods based solely on CNNs, Transformers, or Mamba architectures often fail to simultaneously preserve fine-grained local details and capture contextual dependencies over long distances. To address these limitations, we propose HDF (Hybrid Decoder for Food Image Segmentation), a novel decoding framework built upon the MambaVision backbone. Our approach first employs a convolution-based feature pyramid network (FPN) to extract multi-stage features from the encoder. These features are then thoroughly fused across scales using a Cross-Layer Mamba module that models inter-level dependencies with linear complexity. Subsequently, an Attention Refinement module integrates global semantic context through spatial–channel reweighting. Finally, a Food Co-occurrence Module explicitly enhances food-specific semantics by learning dynamic co-occurrence patterns among categories, improving segmentation of visually similar or frequently co-occurring ingredients. Evaluated on two widely used, high-quality benchmarks, FoodSeg103 and UEC-FoodPIX Complete, which are standard datasets for fine-grained food segmentation, HDF achieves a 52.25% mean Intersection-over-Union (mIoU) on FoodSeg103 and a 76.16% mIoU on UEC-FoodPIX Complete, outperforming current state-of-the-art methods by a clear margin. These results demonstrate that HDF’s hybrid design and explicit co-occurrence awareness effectively address key challenges in food image segmentation, providing a robust foundation for practical applications in dietary logging, nutritional estimation, and food safety inspection. Full article

Forest cover condition is a key determinant of ecosystem functioning and ecological resilience, yet its spatial variability across large and environmentally heterogeneous regions remains insufficiently understood. Leaf area index (LAI) provides a continuous and physically meaningful indicator of forest canopy condition, reflecting variations in canopy density associated with climate and landscape structure. Here, we develop a spatially explicit and interpretable analytical framework to diagnose the dominant climatic and landscape controls on forest cover condition across mainland China during 2000–2020. By integrating machine-learning modelling with SHapley Additive exPlanations, GeoDetector interaction analysis, and nonlinear dependence diagnostics, we quantify the relative contributions and interactions of precipitation, temperature, topography, and forest landscape structure to spatial patterns in forest LAI. The results reveal pronounced spatial heterogeneity in forest cover control regimes. Precipitation dominates forest cover condition in humid regions but exhibits nonlinear saturation, whereas forest fragmentation strongly constrains canopy development and moderates climate-LAI relationships in arid and semi-arid forested landscapes. In high-elevation regions, topographic and thermal factors exert primary control. Overall, the findings demonstrate that forest cover condition reflects climate-conditioned and landscape-dependent control regimes, providing a transparent basis for large-scale forest cover assessment and ecological monitoring. Full article

In recent years, Transformer-based methods have demonstrated proficiency in capturing complex patterns for time series forecasting. However, their quadratic complexity relative to input sequence length poses a significant bottleneck for scalability and real-world deployment. Recently, the Mamba architecture has emerged as a compelling alternative by mitigating the prohibitive computational overhead and latency inherent in Transformers. Nevertheless, a vanilla Mamba backbone often struggles to adequately characterize intricate temporal dynamics, particularly long-term trend shifts and non-stationary behaviors. To bridge the gap between Mamba’s global scanning and local dependency modeling, we propose C-T-Mamba, a hybrid framework that synergistically integrates a Mamba block, channel attention, and a temporal convolution block. Specifically, the Mamba block is leveraged to capture long-range temporal dependencies with linear scaling, the channel attention mechanism filters redundant information, and the temporal convolution block extracts multi-scale local and global features. Extensive experiments on five public benchmarks demonstrate that C-T-Mamba consistently outperforms state-of-the-art (SOTA) baselines (e.g., PatchTST and iTransformer), achieving average reductions of 4.3–18.5% in MSE and 3.9–16.2% in MAE compared to representative Transformer-based and CNN-based models. Inference scaling analysis reveals that C-T-Mamba effectively breaks the computational bottleneck; at a horizon of 1536, it achieves an $8.8\times$ reduction in GPU memory and over 10× speedup compared to standard Transformers. At 2048 steps, its latency remains as low as 8.9 ms, demonstrating superior linear scaling. These results underscore that C-T-Mamba achieves SOTA accuracy while maintaining a minimal computational footprint, making it highly effective for long-term multivariate time series forecasting. Full article

Stimuli-responsive hydrogels are an emerging class of smart materials with immense potential across biomedical engineering, soft robotics, environmental systems, and advanced manufacturing. In this review, we present an in-depth exploration of their material design, classification, fabrication strategies, and real-world applications. We examine how a wide range of external stimuli—such as temperature, pH, moisture, ions, electricity, magnetism, redox conditions, and light—interact with polymer composition and crosslinking chemistry to shape the responsive behavior of hydrogels. Special attention is given to the growing field of 4D printing, where time-dependent shape and property changes enable dynamic, programmable systems. Unlike existing reviews that often treat materials, stimuli, or applications in isolation, this work introduces a multidimensional comparative framework that connects stimulus-response behavior with fabrication techniques and end-use domains. We also highlight key challenges that limit practical deployment—including mechanical fragility, slow actuation, and scale-up difficulties—and outline engineering solutions such as hybrid material design, anisotropic structuring, and multi-stimuli integration. Our aim is to offer a forward-looking perspective that bridges material innovation with functional design, serving as a resource for researchers and engineers working to develop next-generation adaptive systems. Full article

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems. Full article

Background: Adiponectin is an adipokine with insulin-sensitizing, anti-inflammatory, and cytoprotective properties that also plays a key role in metabolic adaptation to exercise. Although its regulation after resistance exercise has been extensively documented, less is known about its short-term modulation and its correlation with muscle damage markers following resistance training. Methods: Nine resistance-trained young men completed two sessions of total-body resistance exercise: (1) high time under tension (TUT) (5-1-2-1 cadence, to failure; ETS1) and (2) moderate TUT (2-1-2-1 cadence, two repetitions in reserve; ETS2). Plasma and saliva samples were collected before exercise and at 15 min, 24 h, and 48 h after exercise to assess total adiponectin by ELISA. Plasma creatine kinase (CK) and a Visual Analog Scale (VAS) were also measured for muscle soreness. Results: Plasma adiponectin significantly decreased from baseline to 48 h post-exercise in both sessions (p < 0.001), with no differences between the TUT conditions. Salivary adiponectin remained unchanged. Although a significant increase in CK and a decrease in adiponectin were observed at the group level, correlation analysis revealed no significant linear relationship between the magnitude of CK elevation and adiponectin reduction. Conclusions: Overall, these findings support the role of adiponectin as a marker of acute metabolic adaptation to resistance exercise. Acute resistance exercise elicited a time-dependent decrease in circulating adiponectin, irrespective of TUT. The temporal pattern of adiponectin decrease coincided with the rise in muscle damage markers, yet the lack of direct correlation suggests distinct regulatory mechanisms, while the lack of salivary changes underscores the complexity of adipokine regulation in vivo and suggests that saliva is not a reliable indicator of changes in circulating adiponectin. Full article

Traditional villages, as carriers of agricultural civilization and ecological wisdom, represent important sites for fostering new-quality productive forces. In the context of rapid urbanization, they function as key spaces for rural development while also confronting vulnerabilities such as spatial functional imbalance and ecological degradation. Within the production–living–ecology (PLE) spaces, dependence on labor-intensive and capital-intensive agricultural models often results in resource misallocation and systemic dysfunction. New-quality productive forces, driven by innovation and green transition, provide a fresh perspective for sustainable rural spatial restructuring. However, their micro-scale mechanisms within traditional villages remain underexplored. This study focuses on 22 nationally recognized traditional villages in Foshan, China. Based on land-use and socioeconomic data from 1993, 2003, 2013, and 2023, we applied land-use transition matrices, a coupling coordination degree model, and geographical detector analysis to examine the evolution of PLE spatial patterns and their driving mechanisms. The findings show that (1) spatially, the share of living space increased significantly, while ecological and agricultural production spaces continued to shrink, reflecting heightened competition among the three; (2) the overall coupling coordination degree exhibited a declining trend, indicating weakened synergy among PLE functions; (3) key drivers of system coordination include per capita disposable income of rural residents, agricultural labor productivity, regional technological innovation capacity, and forest coverage, underscoring the synergistic role of socioeconomic and ecological factors in new countryside development. This study elucidates the micro-spatial pathways through which new rural construction and conservation mechanisms operate, providing a reference for context-sensitive conservation and high-quality development of traditional villages in rapidly industrializing regions. The analytical framework can also be extended to other rural areas undergoing transition. Full article

This article proposes a strategic framework for Kyiv’s post-conflict sustainability and resilience under brittle, anxious, non-linear, and incomprehensible (BANI) conditions. We integrate adaptive governance, circular-economy reconstruction, and city-scale digital capabilities, including AI-enabled analytics, IoT sensing, and urban digital twins. Building on recent assessments of Ukraine’s reconstruction needs, we outline a socio-technical model that links sustainability and resilience objectives under shock risk and budget constraints and show how an illustrative five-year optimisation can rebalance investments toward distributed renewables and early-warning infrastructure. The example portfolio achieves an end-horizon composite performance of F_optimized(5) = 0.65 (on a 0–1 normalised index where 1 indicates achieving the policy-defined targets; 0.65 indicates ~65% progress toward those targets at year 5, improving on the baseline allocation under shocks), indicating improved robustness relative to a baseline allocation. We emphasise that effective implementation depends on secure-by-design digital architecture, participatory prioritisation of indicators and weights, and iterative monitoring that supports rapid adaptation as conditions evolve. The framework provides a pragmatic roadmap for Kyiv and similarly vulnerable cities seeking a low-carbon recovery while reducing systemic brittleness and mitigating anxiety-driven decision-making delays. Full article

The sustainable management of water resources and the development of climate-resilient infrastructure depend on the precise identification of water bodies in satellite imagery. This paper presents a novel deep learning architecture that integrates a convolutional block attention module (CBAM) into a modified EfficientNet–UNet backbone. This integration allows the model to prioritize informative features and spatial areas. The model robustness is ensured through a rigorous training regimen featuring five-fold cross-validation, dynamic test-time augmentation, and optimization with the Lovász loss function. The final model achieved the following values on the independent test set: precision = 90.67%, sensitivity = 86.96%, specificity = 96.18%, accuracy = 93.42%, Dice score = 88.78%, and IoU = 79.82%. These results demonstrate improvement over conventional segmentation pipelines, highlighting the effectiveness of attention mechanisms in extracting complex water-body patterns and boundaries. The key contributions of this paper include the following: (i) adaptation of CBAM within a UNet-style architecture tailored for remote sensing water-body extraction; (ii) a rigorous ablation study detailing the incremental impact of decoder complexity, attention integration, and loss function choice; and (iii) validation of a high-fidelity, computationally efficient model ready for deployment in large-scale water-resource and ecosystem-monitoring systems. Our findings show that attention-guided segmentation networks provide a robust pathway toward high-fidelity and sustainable water-body mapping. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article

Central nervous system (CNS) disorders such as depression severely impair human health. Targeted inhibition of the GluN1-GluN2A receptor is a promising therapeutic strategy, but current drugs often have adverse effects. To develop novel candidate drugs, this study utilized the (S)-ketamine and GluN1-GluN2A receptor complex as a structural template and conducted de novo drug design with the DrugFlow platform. An integrated strategy of molecular docking-based virtual screening combined with high-throughput binding free energy (∆G_binding) calculations from large-scale molecular dynamics (MD) simulations identified three promising antagonists. The ∆G_binding values of these compounds are all below −18.98 kcal/mol, indicating stronger binding affinity than (S)-ketamine, and they demonstrate promising drug-like properties and development potential. 200-ns MD simulations confirmed their stable receptor binding and mechanism consistent with (S)-ketamine. Electrophysiological recordings revealed that, at a concentration of 10 μM, Compounds A1, A2, and A3 produced concentration-dependent inhibition of GluN1-GluN2A receptor-mediated currents, with fractional inhibition values of 24.26%, 35.36%, and 41.76%, respectively. These findings demonstrate the compounds’ potential as CNS disorder therapeutics, requiring further experiments to validate efficacy and advance development for conditions like depression. Full article