Search Results (1,065)

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

Accurate traffic flow forecasting is a fundamental component of Intelligent Transportation Systems and proactive urban mobility management. However, the inherent complexity of urban traffic flow, characterized by non-stationary dynamics and multi-scale temporal dependencies, poses significant modeling challenges. Existing spatio-temporal models often struggle to reconcile the discrepancy between static physical road constraints and highly dynamic, state-dependent spatial correlations, while their reliance on fixed temporal receptive fields limits the capacity to disentangle overlapping periodicities and stochastic fluctuations. To bridge these gaps, this study proposes a novel Multi-scale Graph-Decoupling Spatial–temporal Network (MS-GSTN). MS-GSTN leverages a Hierarchical Moving Average decomposition module to recursively partition raw traffic flow signals into constituent patterns across diverse temporal resolutions, ranging from systemic daily trends to high-frequency transients. Subsequently, a Tri-graph Spatio-temporal Fusion module synergistically models scale-specific dependencies by integrating an adaptive temporal graph, a static spatial graph, and a data-driven dynamic spatial graph within a unified architecture. Extensive experiments on four large-scale real-world benchmark datasets demonstrate that MS-GSTN consistently achieves superior forecasting accuracy compared to representative state-of-the-art models. Quantitatively, the proposed framework yields an overall reduction in Mean Absolute Error of up to 6.2% and maintains enhanced stability across multiple forecasting horizons. Visualization analysis further confirms that MS-GSTN effectively identifies scale-dependent spatial couplings, revealing that long-term traffic flow trends propagate through global network connectivity while short-term variations are governed by localized interactions. Full article

The black soil region of Northeast China (NEC) is China’s most important food production base. Long-term inefficient land use has made its ecosystem vulnerable to widespread degradation, prompting the implementation of ecological restoration projects (ERPs) to enhance ecosystem service (ES) resilience. Yet, the complex interactions among key ESs, including grain production (GP), water yield (WY), soil conservation (SC), and carbon storage (CS), as well as the spatial non-stationarity of their driving factors post-ERPs, have caused spatially heterogeneous, scale-dependent ES relationships. To address these gaps, this study aims to analyze temporal changes in ESs across multiple scales in NEC from 2000 to 2020. By mapping the interactions and quantifying their intensities, we revealed spatial variations in driving factors under different ERPs. The results show that the Natural Wetland Conservation Project (NWCP) and Three-North Shelterbelt Program (TNSP) have led to overall improvements in all ESs. In contrast, the Grain for Green Program (GFGP), the Land Salinity/Sodicity Amelioration Project (LASP), and the Natural Forests Conservation Program (NFCP) are associated with trade-offs between ESs. Interactions between ESs exhibited clear spatial scale dependence, and the dominant drivers varied across scales and restoration contexts. These findings highlight the importance of considering spatial scale and non-stationarity when evaluating ecological restoration outcomes. This study provides a scientific basis for the development and management of ecological restoration programs in intensively managed agricultural regions worldwide, particularly those undergoing multiple, overlapping restoration interventions, from a multi-scale spatial perspective. Full article

Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric fluctuations and seasonal variability, makes short-term GI prediction a challenging task. To overcome these limitations, this work introduces a new hybrid forecasting architecture referred to as WTX–CBO, which integrates a Wavelet Transform (WT)-based decomposition module, an encoder–decoder Transformer model, and an XGBoost regressor, optimized using the Chaotic Billiards Optimizer (CBO) combined with the Adam optimization algorithm. In the proposed architecture, WT decomposes solar irradiance data into multi-scale components, capturing both high-frequency transients and long-term seasonal patterns. The Transformer module effectively models complex temporal and spatio-temporal dependencies, while XGBoost enhances nonlinear learning capability and mitigates overfitting. The CBO ensures efficient hyperparameter tuning and accelerated convergence, outperforming traditional meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Comprehensive experiments conducted on real-world GI datasets from diverse climatic conditions demonstrate the outperformance of the proposed model. The WTX–CBO ensemble consistently outperformed benchmark models, including LSTM, SVR, standalone Transformer, and XGBoost, achieving improved accuracy, stability, and generalization capability. The proposed WTX–CBO framework is designed as a high-accuracy decision-support forecasting tool that provides short-term global irradiance predictions to enable intelligent energy management, predictive charging, and adaptive control strategies in solar-powered applications, including solar electric vehicles (SEVs), rather than performing end-to-end vehicle or photovoltaic power simulations. Overall, the proposed hybrid framework provides a robust and scalable solution for short-term global irradiance forecasting, supporting reliable PV integration, smart charging control, and sustainable energy management in next-generation solar systems. Full article

Accurate sales prediction is crucial for inventory and marketing in e-commerce. Cross-border sales involve complex patterns that traditional models cannot capture. To address this, we propose an improved Bidirectional Long Short-Term Memory (BiLSTM) model, enhanced with an attention mechanism and Bayesian hyperparameter optimization. The attention mechanism focuses on key temporal features, improving trend identification. The BiLSTM captures both forward and backward dependencies, offering deeper insights into sales patterns. Bayesian optimization fine-tunes hyperparameters such as learning rate, hidden-layer size, and dropout rate to achieve optimal performance. These innovations together improve forecasting accuracy, making the model more adaptable and efficient for cross-border e-commerce sales. Experimental results show that the model achieves an Root Mean Square Error (RMSE) of 13.2, Mean Absolute Error (MAE) of 10.2, Mean Absolute Percentage Error (MAPE) of 8.7 percent, and a Coefficient of Determination (R²) of 0.92. It outperforms baseline models, including BiLSTM (RMSE 16.5, MAPE 10.9 percent), BiLSTM with Attention (RMSE 15.2, MAPE 10.1 percent), Temporal Convolutional Network (RMSE 15.0, MAPE 9.8 percent), and Transformer for Time Series (RMSE 14.8, MAPE 9.5 percent). These results highlight the model’s superior performance in forecasting cross-border e-commerce sales, making it a valuable tool for inventory management and demand planning. Full article

This paper presents an analytical model for the hereditary vibrations of a coupled circular plate system interconnected by viscoelastic creep layers. The system is represented as a discrete-continuous chain of thin, isotropic plates with time-dependent material properties. Based on the theory of hereditary viscoelasticity and D’Alembert’s principle, a system of partial integro-differential equations is derived and reduced to ordinary integro-differential equations using Bernoulli’s method and Laplace transforms. Analytical expressions for natural frequencies, mode shapes, and time-dependent response functions are obtained. The results reveal the emergence of multi-frequency vibration regimes, with modal families remaining temporally uncoupled. This enables the identification of resonance conditions and dynamic absorption phenomena. The fractional parameter serves as a tunable damping factor: lower values result in prolonged oscillations, while higher values cause rapid decay. Increasing the kinetic stiffness of the coupling layers raises vibration frequencies and enhances sensitivity to hereditary effects. This interplay provides deeper insight into dynamic behavior control. The model is applicable to multilayered structures in aerospace, civil engineering, and microsystems, where long-term loading and time-dependent material behavior are critical. The proposed framework offers a powerful tool for designing systems with tailored dynamic responses and improved stability. Full article

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation. Full article

In GPS-denied environments, the spatio-temporal coupling of errors caused by dynamic network topologies poses a fundamental challenge to cooperative localization, presenting existing methods with a dilemma: approaches pursuing global optimization lack dynamic adaptability, while those focusing on local adaptation struggle to guarantee global convergence. To address this challenge, this paper proposes ST-DCL, a cooperative localization framework based on a novel principle of closed-loop spatio-temporal decoupling. The core of ST-DCL comprises two modules: a Dynamic Weighted Multidimensional Scaling (DW-MDS) optimizer, responsible for providing a globally consistent coarse estimate with provable convergence, and a specially designed Spatio-Temporal Graph Neural Network (ST-GNN) corrector, tasked with compensating for local nonlinear errors. The DW-MDS effectively suppresses interference from historical errors via an adaptive sliding window and confidence weights derived from our error propagation model. The key innovation of the ST-GNN lies in its two newly designed components: a Dynamic Topological Attention Module for actively modulating neighbor aggregation to inhibit spatial error diffusion, and a Dilated Causal Convolution Module for modeling long-term temporal dependencies to curb error accumulation. These two modules form a closed loop via a confidence feedback mechanism, working in synergy to achieve continuous error suppression. Theoretical analysis indicates that the framework exhibits bounded-error convergence under dynamic topologies. In simulations involving 200 nodes, velocities up to 50 m/s, and 15% NLOS links, the ST-DCL achieves a normalized root mean square error (NRMSE) of 0.0068, representing a 21% performance improvement over state-of-the-art methods. The practical efficacy and real-time capability are further validated through real-world flight experiments with a 10-UAV swarm in complex, GPS-denied scenarios. Full article

Background/Objectives: The triglyceride-to-High-density lipoprotein cholesterol (TG/HDL) ratio is an established marker of atherogenic dyslipidaemia and insulin resistance. Although its association with subclinical atherosclerosis has been reported, the relative contributions of long-term TG/HDL burden and visit-to-visit variability to carotid intima media thickness (CIMT) remain unclear. This study aimed to evaluate the differential associations of the longitudinal mean and temporal variability of the TG/HDL ratio with CIMT. Methods: This retrospective single-center observational cohort study included 260 adult patients with at least three years of longitudinal lipid measurements and a standardized carotid ultrasonography assessment. The longitudinal mean TG/HDL ratio and variability indices, including standard deviation, coefficient of variation, average real variability and variability independent of the mean, were calculated. CIMT was measured using B-mode ultrasonography. Associations were assessed using correlation analyses, multivariable linear regression, joint category analyses and stratified analyses according to statin therapy. Results: The longitudinal mean TG/HDL ratio was independently associated with increased CIMT after adjustment for traditional cardiovascular risk factors. In contrast, TG/HDL variability indices showed no independent association with CIMT and did not improve model performance beyond the mean TG/HDL ratio. Restricted cubic spline analysis demonstrated a significant non-linear association between TG/HDL mean and CIMT, suggesting a threshold-dependent relationship. Joint category analyses demonstrated higher CIMT values in groups with elevated TG/HDL mean regardless of variability status. A significant interaction was observed between TG/HDL variability and statin therapy (p for interaction = 0.011). Conclusions: These findings indicate that cumulative exposure to atherogenic dyslipidaemia, reflected by the long-term mean TG/HDL ratio, is more strongly associated with subclinical carotid atherosclerosis than short-term lipid fluctuations. Full article

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R²) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article

The introduction of Super-High-Density (SHD) olive orchards represents a crucial innovation in modern olive growing, enhancing sustainability. However, the long-term success of these planting systems depends strongly on cultivar selection, combining suitable vegetative and reproductive traits. This three-year field study investigated key floral biology parameters—flowering phenograms, gynosterility, and self-compatibility—of ten olive cultivars grown under irrigated conditions in southern Italy: ‘Arbequina’, ‘Arbosana’, ‘Cima di Bitonto’, ‘Coratina’, ‘Don Carlo’, ‘Frantoio’, ‘Favolosa’ (=‘Fs-17’), ‘I-77’, ‘Koroneiki’, and ‘Urano’ (=‘Tosca’). Flowering phenograms varied significantly across years and cultivars, showing temporal shifts related to chilling accumulation and yield of the previous year. Early blooming cultivars (‘Arbequina’, ‘Arbosana’, and ‘Coratina’) exhibited partial flowering overlap with mid-season ones, enhancing cross-pollination opportunities. Quantitative analysis of flowering overlap revealed that most cultivar combinations exceeded the 70% threshold required for effective pollination, although specific genotypes (‘Coratina’, ‘Fs-17’, and especially ‘I-77’) showed critical mismatches, while ‘Frantoio’ and ‘Arbequina’ emerged as the most reliable pollinizers. Gynosterility exhibited statistical differences among cultivars and canopy positions: ‘I-77’ showed the highest values (71.4%), while ‘Coratina’ and ‘Cima di Bitonto’ showed the lowest ones (7.3 and 8.4%, respectively). The median portions of the canopies generally displayed a greater number of sterile flowers (29.4%), reflecting the combined effect of genetic and environmental factors such as light exposure. In the inflorescence, the majority of gynosterile flowers were concentrated in the lower part, for all canopy portions (modal value). Self-compatibility tests were performed considering a fruit set of 1% as a threshold to discriminate. For open pollination, the fruit set was highly variable among cultivars, ranging from 0.5% in ‘I-77’ to 4.7% in ‘Arbosana’. Apart from ‘I77’, all varieties achieved a fruit set greater than 1%. Instead, for the self-pollination, only ‘Arbequina’, ‘Koroneiki’, ‘Frantoio’, and ‘Cima di Bitonto’ could be identified as pseudo-self-compatible, whereas ‘Coratina’, ‘Fs-17’, and the others were clearly self-incompatible and therefore unsuitable for monovarietal orchards in areas with limited availability of pollen. By integrating self-compatibility and gynosterility data, the cultivars were ranked according to reproductive aptitude, identifying ‘Cima di Bitonto’ and ‘Frantoio’ as the most fertile genotypes, whereas ‘Don Carlo’ and particularly ‘I-77’ showed severe genetic sterility constraints. The findings underline the critical role of floral biology in defining reproductive efficiency and varietal adaptability in SHD systems. This research provides valuable insights for optimizing cultivar selection, orchard design, and management practices, contributing to the development of sustainable, climate-resilient olive production models for Mediterranean environments. Full article

Aiming to address the safety and stability issues caused by typical faults of Unmanned Aerial Vehicle (UAV) navigation sensors, a novel fault-tolerant method is proposed, which can capture the temporal dependencies of fault feature evolution, and complete the classification, prediction, and data reconstruction of fault data. In this fault-tolerant method, the feature extraction module adopts the FNKPCA method—integrating the Frobenius Norm (F-norm) with Kernel Principal Component Analysis (KPCA)—to optimize the kernel function’s ability to capture signal features, and enhance the system reliability. By combining FNKPCA with Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM), an active fault-tolerant processing method, namely FNKPCA–SVM–BiLSTM, is obtained. This study conducts comparative experiments on public datasets, and verifies the effectiveness of the proposed method under different fault states. The proposed approach has the following advantages: (1) It achieves a detection accuracy of 98.64% for sensor faults, with an average false alarm rate of only 0.15% and an average missed detection rate of 1.16%, demonstrating excellent detection performance. (2) Compared with the Long Short-Term Memory (LSTM)-based method, the proposed fault-tolerant method can reduce the RMSE metrics of Global Positioning System (GPS), Inertial Measurement Unit (IMU), and Ultra-Wide-Band (UWB) sensors by 77.80%, 14.30%, and 75.00%, respectively, exhibiting a significant fault-tolerant effect. Full article

Returning biogas slurry to fields is an important approach for the resource utilization of organic waste. It enhances soil fertility and crop growth, but its long-term effects on soil ecosystems need evaluation. A meta-analysis approach was employed to comprehensively assess the influence of biogas slurry application from multiple perspectives (soil enzyme activities, heavy-metal accumulation, microbial communities, and fundamental nutrients) across different time scales: short-term (<1 year), medium-term (1–3 years), and long-term (>3 years). The results demonstrate that the ecological effects of biogas slurry application exhibited a pronounced time-dependency. Specifically, short-term application (less than 1 year) significantly increased soil urease (23.0%) and sucrase activity (22.1%), along with organic matter and available nutrients. However, it also resulted in the rapid accumulation of heavy metals, including Hg and As. Under medium-term application, sucrase activity further surged (108.1%). Meanwhile, phosphatase activity and total potassium content decreased, and heavy-metal accumulation showed declining trends. Following long-term application, microbial richness substantially improved, but certain heavy metals (Cu, Zn and Cd) continued to accumulate. In summary, this study reveals distinct temporal-scale mechanisms by which field biogas slurry application regulates soil functions. Full article

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as battery degradation, inverter anomalies, and motor failures. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal dependency handling of Long Short-Term Memory (LSTM) networks. Through data preprocessing, model training, and validation, the approach achieves high-precision fault identification. Experimental results demonstrate an accuracy rate exceeding 95% on simulated datasets, outperforming traditional machine learning methods. This research provides a practical framework for HEV fault diagnosis and explores its potential in real-world applications. Full article