Search Results (7,924)

The degradation of municipal solid waste (MSW) in landfills involves complex physical, chemical, and biological interactions that span multiple spatial and temporal scales. To better understand these dynamics, this study develops a comprehensive model that couples microbial, chemical, thermal, and hydraulic fields. The model captures bidirectional feedback mechanisms, such as heat and acid production from microbial metabolism, which in turn influence microbial activity and reaction pathways. A simplified one-dimensional formulation was solved using the finite difference method and validated against historical temperature data from real landfills. Simulation results indicate that temperature peaks at approximately 45 °C around the fifth year, followed by a gradual decline. pH and substrate concentration decrease over time but exhibit minimal variation with depth. The degradation rate reaches its maximum within two years and subsequently declines. These trends highlight the critical roles of temperature in initiating rapid degradation and substrate concentration in determining the endpoint of the reaction. This model provides a theoretical foundation for interpreting energy and mass transformation processes in landfills and offers practical insights for optimizing landfill management, reducing pollution, facilitating resource recovery and providing a theoretical model and prediction tool for sustainable waste management. Full article

Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to reconstruct information under cloud occlusions. While Transformer-based models excel at handling such spatial modeling, their quadratic computational complexity limits practical application. The recently proposed Mamba, a state space model, offers a computationally efficient alternative for long-range modeling, but its inherent 1D sequential processing is ill-suited to capturing complex 2D spatial contexts in images. To bridge this gap, we propose the multi-pattern scanning Mamba (MPSM) block. Our MPSM block adapts the Mamba architecture for vision tasks by introducing a set of diverse scanning patterns that traverse features along horizontal, vertical, and diagonal paths. This multi-directional approach ensures that each feature aggregates comprehensive contextual information from the entire spatial domain. Furthermore, we introduce a dynamic path-aware (DPA) mechanism to adaptively recalibrate feature contributions from different scanning paths, enhancing the model’s focus on position-sensitive information. To effectively capture both global structures and local details, our MPSM blocks are embedded within a U-Net architecture enhanced with multi-scale supervision. Extensive experiments on the RICE1, RICE2, and T-CLOUD datasets demonstrate that our method achieves state-of-the-art performance while maintaining favorable computational efficiency. Full article

The spatiotemporal dynamics of soil nutrients in the crop row zone are critical determinants of crop yield, necessitating precision fertilization for optimal plant growth. However, previous studies have predominantly focused on plant-available nutrient status at the scale of entire cropping systems, yet a granular understanding of their distribution patterns across precise temporal and spatial dimensions remains limited. Therefore, this study investigated maize–legume intercropping systems to quantify the dynamics of soil alkaline-hydrolyzable nitrogen (AN), available phosphorus (AP), and available potassium (AK) across distinct growth stages, soil depths, and row positions. The experiment comprised five treatments: maize–soybean intercropping, maize–peanut intercropping, and monocultures of maize, soybean, and peanut. Throughout the two-year study, maize–soybean intercropping significantly enhanced the plant height of both maize and soybean relative to their respective monocultures (p < 0.05). In contrast, within the maize–peanut system, intercropping significantly promoted peanut plant height but suppressed stem diameter in both species (p < 0.05); these effects were consistent across both study years. Both systems exhibited a “benefit-sacrifice” pattern, where dry matter was preferentially allocated to maize, thereby increasing total system productivity despite suppressing legume growth. Furthermore, during the mid-to-late growth stages, intercropped maize showed an enhanced capacity for nitrogen uptake from deeper soil layers. In contrast, the alkaline-hydrolyzable nitrogen content in intercropped soybean and peanut remained lower than in their respective monocultures throughout the growth period, with reductions ranging from 8.49% to 34.79%. Intercropping significantly increased the soil available phosphorus content in the root zones of maize, soybean, and peanut compared to their respective monocultures. The available phosphorus content in the 0–20 cm soil layer was consistently higher than in monoculture systems, with a maximum increase of 41.70%. Moreover, intercropping effectively mitigated soil potassium depletion, resulting in a smaller decline in available potassium. This effect was most pronounced in the maize–peanut intercropping pattern within the 20–40 cm soil layer. The distribution of soil available nutrients (N, P, K) was also influenced by drip tape placement. The levels of these nutrients for soybean and peanut were higher at 50 cm from the drip tape than at 30 cm, while for maize, levels were higher at 80 cm than at 40 cm. Intercropping increased the thousand-kernel weight of maize and soybean but decreased that of peanut. Overall, the strategic row configuration optimized the yield performance of both intercropping systems, resulting in land equivalent ratios greater than 1, which indicates distinct yield advantages for both intercropping patterns. Full article

Understanding the interactions among ecosystem services (ESs) and their spatiotemporal dynamics is pivotal for sustainable ecosystem management, particularly in arid regions where water scarcity imposes significant constraints. This study focuses on the Ili River Valley, a representative arid region, to investigate the evolution of ESs, their trade-offs and synergies, and the underlying driving mechanisms from a water-resource-constrained perspective. We assessed five key ESs—soil retention (SR), habitat quality (HQ), water purification (WP), carbon sequestration (CS), and water yield (WY)—utilizing multi-source remote sensing and statistical data spanning 2000 to 2020. Employing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, Spearman correlation analysis, Geographically Weighted Regression (GWR), and the Geodetector method, we conducted a comprehensive analysis at both sub-watershed and 500 m grid scales. Our findings reveal that, except for SR and WP, the remaining three ESs exhibited an overall increasing trend over the two-decade period. Trade-off relationships predominantly characterize the ESs in the Ili River Valley; however, these interactions vary temporally and across spatial scales. Natural factors, including precipitation, temperature, and soil moisture, primarily drive WY, CS, and SR, whereas anthropogenic factors significantly influence HQ and WP. Moreover, the impact of these driving factors exhibits notable differences across spatial scales. The study underscores the necessity for ES management strategies tailored to specific regional characteristics, accounting for scale-dependent variations and the dual influences of natural and human factors. Such strategies are essential for formulating region-specific conservation and restoration policies, providing a scientific foundation for sustainable development in ecologically vulnerable arid regions. Full article

Batch normalization (BN) has become a foundational component in modern deep neural networks. However, one of its disadvantages is its reliance on batch statistics that may be unreliable or unavailable during inference, particularly under test-time domain shifts. While batch-statistics-free affine transformation methods alleviate this by learning per-sample scale and shift parameters, most treat samples independently, overlooking temporal or sequential correlations in streaming or episodic test-time settings. We propose LSTM-Affine, a memory-based normalization module that replaces BN with a recurrent parameter generator. By leveraging an LSTM, the module produces channel-wise affine parameters conditioned on both the current input and its historical context, enabling gradual adaptation to evolving feature distributions. Unlike conventional batch-statistics-free designs, LSTM-Affine captures dependencies across consecutive samples, improving stability and convergence in scenarios with gradual distribution shifts. Extensive experiments on few-shot learning and source-free domain adaptation benchmarks demonstrate that LSTM-Affine consistently outperforms BN and prior batch-statistics-free baselines, particularly when adaptation data are scarce or non-stationary. Full article

State-of-the-art historical global burned area (BA) products largely rely on MODIS data, offering long temporal coverage but limited spatial resolution. As a result, small fires and complex landscapes remain underrepresented in global fire history reconstructions. By contrast, Landsat provides the only continuous satellite record extending back to the 1980s, with substantially finer resolution. However, its use at a global scale has long been hindered by infrequent revisit times, cloud contamination, massive data volumes, and processing demands. We compared MODIS FireCCI51 with the only existing Landsat-based global product, GABAM, in a mountainous region characterized by frequent, small-scale fires. GABAM detected a higher number of burn scars, including small events, with higher Producer’s Accuracy (0.68 vs. 0.08) and similar User’s Accuracy (0.85 vs. 0.83). These results emphasize the value of Landsat for reconstructing past fire regimes in complex landscapes. Crucially, recent advances in cloud computing, data cubes, and processing pipelines now remove many of the former barriers to exploiting the Landsat archive globally. A more systematic integration of Landsat data into MODIS-based routines may help produce more complete and accurate databases of historical fire activity, ultimately enabling improved understanding of long-term global fire dynamics. Full article

The Gaia System is a tabletop projection mapping system for museum exhibitions, now in its third iteration and installed at the Sortland Museum (Norway). It presents socio-economic, environmental, and historical topics through an interactive spatial display. The system supports both multi-user interaction—allowing many visitors to engage simultaneously—and a tour guide mode for staff-led presentations. It combines scientific, data-driven visualizations with popular-science, story-driven content and integrates both real-time and locally stored data streams. Its design and development processes are thoroughly described. A field study with 32 participants yielded a System Usability Scale (SUS) score of 84.14 and a mean User Experience Questionnaire (UEQ-S) overall score of 1.93, indicating high usability and a positive user experience. The participants found the projection technology impressive and the content informative while noting challenges such as information overload, unclear temporal structuring of the content, and minor technical issues. Planned developments focus on restructuring the content for shorter sessions, implementing a new content management system, and refining the technical stability. Finally, this work reframes projection mapping as operational infrastructure rather than a fixed display, offering practical guidance for researchers advancing PM methodologies and museum practitioners deploying innovative, technology-driven exhibitions. Full article

Industrial control systems (ICS), as critical infrastructure supporting national operations, are increasingly threatened by sophisticated stealthy network attacks. These attacks often break malicious behaviors into multiple highly camouflaged packets, which are embedded into large-scale background traffic with low frequency, making them semantically and temporally indistinguishable from normal traffic and thus evading traditional detection. Existing methods largely rely on flow-level statistics or long-sequence modeling, resulting in coarse detection granularity, high latency, and poor byte-level interpretability, falling short of industrial demands for real-time and actionable detection. To address these challenges, we propose Avocado, a fine-grained, multi-level intrusion detection model. Avocado’s core innovation lies in contextual flow-feature fusion: it models each packet jointly with its surrounding packet sequence, enabling independent abnormality detection and precise localization. Moreover, a shared-query multi-head self-attention mechanism is designed to quantify byte-level importance within packets. Experimental results show that Avocado significantly outperforms state-of-the-art flow-level methods on NGAS and CLIA-M221 datasets, improving packet-level detection ACC by 1.55% on average, and reducing FPR and FNR to 3.2%, 3.6% (NGAS), and 3.7%, 4.3% (CLIA-M221), respectively, demonstrating its superior performance in both detection and interpretability. Full article

Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load forecasting. The model integrates three core components synergistically: Graph Attention Network (GAT) dynamically captures spatial correlations via adaptive node weighting, resolving static topology constraints; a CNN-LSTM module extracts multi-scale temporal features—convolutional kernels decompose load fluctuations, while bidirectional LSTM layers model long-term trends; and a gated fusion mechanism adaptively weights and fuses spatio-temporal features, suppressing noise and enhancing sensitivity to critical load periods. Experimental validations on multi-city datasets show significant improvements: the model outperforms baseline models by a notable margin in error reduction, exhibits stronger robustness under extreme weather, and maintains superior stability in multi-step forecasting. This study concludes that the hybrid model balances spatial topological analysis and temporal trend modeling, providing higher accuracy and adaptability for STLF in complex power grid environments. Full article

Accurate prediction of the degradation trajectory and estimation of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the reliability and safety of modern energy storage systems. However, many existing approaches rely on deep or highly complex models to achieve high accuracy, often at the cost of computational efficiency and practical applicability. To tackle this challenge, we propose a novel hybrid deep-learning framework, IVCLNet, which predicts the battery’s state-of-health (SOH) evolution and estimates RUL by identifying the end-of-life threshold (SOH = 80%). The framework integrates Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Variational Mode Decomposition (VMD), and an attention-enhanced Long Short-Term Memory (LSTM) network. IVCLNet leverages a cascade decomposition strategy to capture multi-scale degradation patterns and employs multiple indirect health indicators (HIs) to enrich feature representation. A lightweight Convolutional Block Attention Module (CBAM) is embedded to strengthen the model’s perception of critical features, guiding the one-dimensional convolutional layers to focus on informative components. Combined with LSTM-based temporal modeling, the framework ensures both accuracy and interpretability. Extensive experiments conducted on two publicly available lithium-ion battery datasets demonstrated that IVCLNet significantly outperforms existing methods in terms of prediction accuracy, robustness, and computational efficiency. The findings indicate that the proposed framework is promising for practical applications in battery health management systems. Full article

Integrated energy load forecasting plays a crucial role in optimizing the operation and economic dispatch of integrated energy systems. Its forecasting accuracy is not only time-dependent but also influenced by the coupling characteristics among energy sources. Solely relying on time-scale training methods cannot adequately capture the strong correlations among multiple energy sources. To address challenges in extracting coupled load forecasting features, obtaining periodic characteristics, and setting model network structures, this paper proposes an Integrated Energy Short-Term Adaptive Load Forecasting Method Based on Coupled Feature Extraction (AP-CFE). This approach integrates high-dimensional coupling features and periodic temporal features effectively using ensemble algorithms. To prevent overfitting or underfitting issues, an Adaptive learning algorithm (AP) is introduced. The load demonstrates highly stochastic behavior in response to external factors, resulting in rapid, volatile fluctuations in grid demand. The strategy of employing sparse self-attention to approximate the residual terms effectively mitigates this issue. Simulation results using comprehensive energy load data from Australia demonstrate that the proposed model outperforms existing models, achieving better capture of energy coupling characteristics with average absolute percentage errors reduced by 20.75%, 28.48%, and 21.64% for electricity, heat, and gas loads, respectively. Full article

Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity. Full article

Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features. Full article

With the deepening of power market reform and the large-scale integration of bidirectional systems such as energy storage and electric vehicles, achieving sustainable carbon management has become increasingly urgent. Traditional carbon emission accounting methods face challenges, including insufficient dynamics and unclear responsibility boundaries. To address these issues, this paper proposes a sustainability-oriented accounting method for indirect carbon emissions from electricity in the context of bidirectional system integration in the power market environment. First, the dynamic carbon emission characteristics of bidirectional systems such as energy storage and vehicle-to-grid (V2G) systems are analyzed, and a carbon emission accounting model is constructed to address the fairness issue of emission responsibility allocation during charging and discharging. Second, on the basis of the theory of carbon emission flows and incorporating electricity trading contract data, an accounting method for indirect carbon emissions from electricity in green electricity trading, coal-fired electricity trading, and hybrid scenarios under bidirectional system integration is developed. Finally, the case study demonstrates that the proposed method accurately captures the temporal variation of carbon emission factors, ensures conservation of total emissions, and fairly redistributes carbon responsibility among users under different market scenarios, while revealing how bidirectional systems and green electricity trading reshape nodal carbon intensities and spatial emission distributions without causing double counting. Full article

A collaborative learning approach between citizen scientists, experts and managers transformed metrics of coral reef fish biodiversity into indicators for use in regional waterway health report cards. We tested a citizen science tool, iNaturalist, to identify species and monitor annual changes in fish biodiversity at a regional scale in the Great Barrier Reef, Australia. The participation of almost 1000 citizen scientists between 2013 and 2025 resulted in 13,131 research grade observations of 684 species of fish. Annual biodiversity data from three years (2023–2025) was compared to 10 years of baseline data (2013–2022) and calibrated for effort. Report cards scores for fish ecological health were generally ‘very good’ to ‘good’ and we conclude that a citizen science methodology is potentially suitable for fish ecological health at multiple spatial and temporal scales. Full article