Search Results (13,155)

In order to improve the localization accuracy and model interpretability of single-phase ground fault sections in distribution networks, a knowledge-integrated and data-driven fault localization model is proposed. The model transforms the transient zero-sequence currents into Markov Transition Field (MTF) images based on transfer entropy, and improves the two-channel feature expression with both causal and temporal structures. On this basis, a knowledge guidance mechanism based on a physical mechanism is introduced to focus on the waveform backpropagation characteristics of upstream and downstream nodes of the fault through the feature attention module, and a similarity weighting strategy is constructed by integrating the Hausdorff distance in the all-connectivity layer in order to enhance the model’s capability of discriminating between the key segments. The dataset is constructed in an improved IEEE 14-node simulation system, and the effectiveness of the proposed method is verified by t-SNE feature visualization, comparison experiments with different parameters, misclassification correction analysis, and anti-noise performance evaluation. For misclassified sample datasets, this method achieves an accuracy rate of 99.53%, indicating that it outperforms traditional convolutional neural network models in terms of fault section localization accuracy, generalization capability, and noise robustness. Research shows that the deep integration of knowledge and data can significantly enhance the model’s discriminative ability and engineering practicality, providing new insights for the construction of intelligent power systems with explainability. Full article

Background: Transcription factors (TFs) critically regulate gene expression, orchestrating plant growth, development, and stress responses. The conserved IDD (INDETERMINATE DOMAIN) TF family modulates key developmental processes, including root, stem, and seed morphogenesis. Dendrocalamus latiflorus Munro, an economically vital sympodial bamboo in southern China, suffers significant yield losses due to prevalent bamboo shoot abortion, impacting both edible shoot production and timber output. Despite the documented roles of IDD TFs in shoot apical meristem expression and lateral organ regulation, their genome-wide characterization in D. latiflorus remains unstudied. Methods: Using IDD members from Arabidopsis thaliana, Oryza sativa, and Phyllostachys edulis as references, we identified 45 DlIDD genes in D. latiflorus. Comprehensive bioinformatics analyses included gene characterization, protein physicochemical assessment, phylogenetic reconstruction, and examination of gene structures/conserved domains. Differential expression of DlIDD genes was profiled between dormant and sprouting bamboo shoots to infer putative functions. Results: The 45 DlIDD genes were phylogenetically classified into three subfamilies and unevenly distributed across 34 chromosomes. Whole-genome duplication (WGD) events drove the expansion of this gene family. Promoter analyses revealed enriched cis-regulatory elements associated with hormone response and developmental regulation. Functional analyses suggested potential roles for DlIDD genes in bamboo shoot development. Conclusions: This study provides a foundation for future research to elucidate the functions of IDD TFs and their regulatory mechanisms in bamboo shoot morphogenesis and lateral bud development within woody monocots. Full article

Monkeypox virus (MPXV) experienced an unprecedented global outbreak in 2022, characterized by a significant departure from historical patterns: a rapid spread of the epidemic to more than 110 non-traditional endemic countries, with more than 90,000 confirmed cases; a fundamental shift in the mode of transmission, with human-to-human transmission (especially among men who have sex with men (MSM)) becoming the dominant route (95.2%); and genetic sequencing revealing a key adaptive mutation in a novel evolutionary branch (Clade IIb) that triggered the outbreak. These features highlight the significant evolution of MPXV in terms of host adaptation, transmission efficiency, and immune escape ability. The aim of this paper is to provide insights into the viral adaptive evolutionary mechanisms driving this global outbreak, with a particular focus on the role of immune escape (e.g., novel mechanisms of M2 proteins targeting the T cell co-stimulatory pathway) in enhancing viral transmission and pathogenicity. At the same time, we systematically evaluate the cross-protective efficacy and limitations of existing vaccines (ACAM2000, JYNNEOS, and LC16), as well as recent advances in novel vaccine platforms, especially mRNA vaccines, in inducing superior immune responses. The study further reveals the constraints to outbreak control posed by grossly unequal global vaccine distribution (e.g., less than 10% coverage in high-burden regions such as Africa) and explores the urgency of optimizing stratified vaccination strategies and facilitating technology transfer to promote equitable access. The core of this paper is to elucidate the dynamic game between viral evolution and prevention and control strategies (especially vaccines). The key to addressing the long-term epidemiological challenges of MPXV in the future lies in continuously strengthening global surveillance of viral evolution (early warning of highly transmissible/pathogenic variants), accelerating the development of next-generation vaccines based on new mechanisms and platforms (e.g., multivalent mRNAs), and resolving the vaccine accessibility gap through global collaboration to build an integrated defense system of “Surveillance, Research and Development, and Equitable Vaccination,” through global collaboration to address the vaccine accessibility gap. Full article

In the mining of deep mineral resources and tunnel engineering, the degradation of mechanical properties and the evolution of energy of through-double-joint sandy slate under triaxial loading and unloading conditions are key scientific issues affecting the stability design of the project. The existing research has insufficiently explored the joint inclination angle effect, damage evolution mechanism, and energy distribution characteristics of this type of rock mass under the path of increasing axial pressure and removing confining pressure. Based on this, in this study, uniaxial compression, conventional triaxial compression and increasing axial pressure, and removing confining pressure tests were conducted on four types of rock-like materials with prefabricated 0°, 30°, 60°, and 90° through-double-joint inclinations under different confining pressures. The axial stress/strain curve, failure characteristics, and energy evolution law were comprehensively analyzed, and damage variables based on dissipated energy were proposed. The test results show that the joint inclination angle significantly affects the bearing capacity of the specimen, and the peak strength shows a trend of first increasing and then decreasing with the increase in the inclination angle. In terms of failure modes, the specimens under conventional triaxial compression exhibit progressive compression/shear failure (accompanied by rock bridge fracture zones), while under increased axial compression and relief of confining pressure, a combined tensioning and shear failure is induced. Moreover, brittleness is more pronounced under high confining pressure, and the joint inclination angle also has a significant control effect on the failure path. In terms of energy, under the same confining pressure, as the joint inclination angle increases, the dissipated energy and total energy of the cemented filling body at the end of triaxial compression first decrease and then increase. The triaxial compression damage constitutive model of jointed rock mass established based on dissipated energy can divide the damage evolution into three stages: initial damage, damage development, and accelerated damage growth. Verified by experimental data, this model can well describe the damage evolution characteristics of rock masses with different joint inclination angles. Moreover, an increase in the joint inclination angle will lead to varying degrees of damage during the loading process of the rock mass. The research results can provide key theoretical support and design basis for the stability assessment of surrounding rock in deep and high-stress plateau tunnels, the optimization of support parameters for jointed rock masses, and early warning of rockburst disasters. Full article

Under the context of global climate change, the frequent occurrence of extreme low-temperature events poses a severe challenge to plant distribution and ecosystem stability. The arid and semi-arid regions of Northwestern China, as a sensitive response area to global change, have proven to possess significant development potential with their unique desert vegetation systems. This study focuses on the ecological adaptability mechanisms of Nitraria tangutorum Bobr., a key species of the desert ecosystem in Northwestern China, and systematically analyzes the evolution patterns of its geographical distribution under the coupled effects of climate change and human activities through a MaxEnt model. The research conclusions are as follows: (i) This study constructs a Human Footprint-MaxEnt (HF-MaxEnt) coupling model. After incorporating human footprint variables, the AUC value of the model increases to 0.914 (from 0.888), demonstrating higher accuracy and reliability. (ii) After incorporating human footprint variables, the predicted area of the model decreases from 2,248,000 ${km}^2$ to 1,976,000 ${km}^2$, with the High Suitability experiencing a particularly sharp decline of up to 79.4%, highlighting the significant negative impact of human disturbance on Nitraria tangutorum. (iii) Under the current climate baseline period, solar radiation, precipitation during the wettest season, and mean temperature of the coldest month are the core driving factors for suitable areas of Nitraria tangutorum. (iv) Under future climate scenarios, the potential distribution area of Nitraria tangutorum is significantly positively correlated with carbon emission levels. Under the SSP370 and SSP585 emission pathways, the area of potential distribution reaches 172.24% and 161.3% of that in the current climate baseline period. (v) Under future climate scenarios, the distribution center of potential suitable areas for Nitraria tangutorum shows a dual migration characteristic of “west–south” and “high altitude”, and the mean temperature of the hottest month will become the core constraint factor in the future. This study provides theoretical support and data backing for the delineation of habitat protection areas, population restoration, resource management, and future development prospects for Nitraria tangutorum. Full article

With the progressive depletion of conventional oil and gas resources and the increasing demand for alternative energy, organic-rich sedimentary rock—oil shale—has attracted widespread attention as a key unconventional hydrocarbon resource. Pyrolysis is the essential process for converting the organic matter in oil shale into recoverable hydrocarbons, and a detailed understanding of its behavior is crucial for improving development efficiency. This review systematically summarizes the research progress on the pyrolysis characteristics of oil shale under laboratory conditions. It focuses on the applications of thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) in identifying pyrolysis stages, extracting kinetic parameters, and analyzing thermal effects; the role of coupled spectroscopic techniques (e.g., TG-FTIR, TG-MS) in elucidating the evolution of gaseous products; and the effects of key parameters such as pyrolysis temperature, heating rate, particle size, and reaction atmosphere on product distribution and yield. Furthermore, the mechanisms and effects of three distinct heating strategies—conventional heating, microwave heating, and autothermic pyrolysis—are compared, and the influence of inherent minerals and external catalysts on reaction pathways is discussed. Despite significant advances, challenges remain in quantitatively describing reaction mechanisms, accurately predicting product yields, and generalizing kinetic models. Future research should integrate multiscale experiments, in situ characterization, and molecular simulations to construct pyrolysis mechanism models tailored to various oil shale types, thereby providing theoretical support for the development of efficient and environmentally friendly oil shale conversion technologies. Full article

The historic landscape and the value of the ancient city in the stock era present a diversified and mixed problem; as such, this study explores a quantifiable spatial correlation method for landscape layering characteristics and value space, in order to provide support for the urban renewal paths that integrate historical and contemporary needs. Taking as an example Chaozhou Ancient City, a renowned historical and cultural city in China, this study draws on the theory of historical urban landscape layering and comprehensively uses historical graphic interpretation, GIS spatial quantitative analysis, the single-land-use dynamic degree model, the Analytic Network Process, and the Delphi method to quantitatively analyze and evaluate the landscape layering characteristics and value space of the ancient city. Meanwhile, it explores the relationship between the historical landscape layering characteristics and value space of ancient cities using the spatial autocorrelation model and the coupling coordination modulus model. The key findings are as follows: (1) The high-layer space (66.1%) and high-value space (31.1%) of the historic landscape of Chaozhou Ancient City show significant mismatch and imbalance. Spatially, layer spaces increase from the city center toward the periphery, whereas value spaces decrease from the center outward, demonstrating marked spatial heterogeneity. (2) The layer–value space shows a spatial distribution of agglomeration, with Moran’s I index values of 0.2712 and 0.6437, respectively. The agglomeration degree of the value space is much higher than that of the layer space, and both show significant non-equilibrium and associative coupling. (3) Coupling coordination: basically balanced (D = 0.56) indicates a transition toward a more integrated state, although 48% of the region remains in a state of severe dysfunction, mainly consisting of two types of spaces: “high-layer–high-value” and “low-layer–low-value.” These two dysfunctional types should be prioritized in future conservation and renewal strategies. This study provides a more comprehensive quantitative analysis path for identifying and evaluating the landscape layer–value space of the ancient city, providing visualization tools and decision-making support for the future protection and renewal of Chaozhou Ancient City and the declaration of the World Heritage. Full article

Accurate, real-time, and cost-effective detection and counting of pre-weaning piglets are critical for improving piglet survival rates. However, achieving this remains technically challenging due to high computational demands, frequent occlusion, social behaviors, and cluttered backgrounds in commercial farming environments. To address these challenges, this study proposes a lightweight and attention-enhanced piglet detection and counting network based on an improved YOLOv8n architecture. The design includes three key innovations: (i) the standard C2f modules in the backbone were replaced with an efficient novel Multi-Scale Spatial Pyramid Attention (MSPA) module to enhance the multi-scale feature representation while a maintaining low computational cost; (ii) an improved Gather-and-Distribute (GD) mechanism was incorporated into the neck to facilitate feature fusion and accelerate inference; and (iii) the detection head and the sample assignment strategy were optimized to align the classification and localization tasks better, thereby improving the overall performance. Experiments on the custom dataset demonstrated the model’s superiority over state-of-the-art counterparts, achieving 88.5% precision and a 93.8% $mA{P}_{0.5}$. Furthermore, ablation studies showed that the model reduced the parameters, floating point operations (FLOPs), and model size by 58.45%, 46.91% and 56.45% compared to those of the baseline YOLOv8n, respectively, while achieving a 2.6% improvement in the detection precision and a 4.41% reduction in the counting MAE. The trained model was deployed on a Raspberry Pi 4B with $ncnn$ to verify the effectiveness of the lightweight design, reaching an average inference speed of <87 ms per image. These findings confirm that the proposed method offers a practical, scalable solution for intelligent pig farming, combining a high accuracy, efficiency, and real-time performance in resource-limited environments. Full article

Due to the widespread distribution of F. oxysporum, the search for mechanisms of tolerance to this disease in Solanum lycopersicum L. is an ongoing endeavor. This research aimed to identify F. oxysporum-tolerant genotypes at the germination and seedling stages in order to use them as sources of resistance. Ninety-six tomato lines were inoculated with the F. oxysporum strain with NCBI accession key PQ187438. The germination test was carried out in a germination chamber at a constant temperature of 28 ± 2 °C with 70 ± 5% relative humidity in darkness for the first 3 days and then 7 days with light. Clustering and discriminant analysis identified 14 genotypes with tolerance, showing great seed vigor and lower disease severity. Seedling evaluation was conducted in a floating raft system for 10 days after inoculation. Nine genotypes showed greater tolerance to the pathogen by developing a larger leaf area and accumulating more dry matter (p ≤ 0.05). No genotypes with tolerance were identified at both phenological stages (germination and seedling), indicating that tolerance mechanisms are independent at both phenological stages, so genotype selection should be carried out independently. Full article

With the recent proliferation of the Internet and the ever-evolving threat landscape, developing a reliable and effective intrusion detection system (IDS) has become an urgent need. However, one of the key challenges hindering the success of IDS development is class imbalance, which often leads to biased models and poor detection rates. To address this challenge, this paper proposes a GAN-AHR algorithm which adaptively balances the dataset by augmenting minority classes using CGAN or BSMOTE, based on class-specific characteristics such as compactness and density. By leveraging BSMOTE to oversample classes with high compactness and high density, we can exploit its simplicity and effectiveness. However, the quality of BSMOTE-generated data is significantly lower when the classes are sparse and lacking clear boundaries. In such cases, CGAN is better suited in this scenario given its ability to capture complex data distributions. We present empirical results on the NF-UNSW-NB15 dataset using a Random Forest (RF) classifier, reporting a significant improvement in the precision, recall, and F1-score of several minority classes. Specifically, a remarkable increase in the F1-score for the Shellcode and DoS classes was reported, reaching 0.90 and 0.51, respectively. Full article

With the growing emphasis on healthy eating and nutrition management in modern society, food image recognition has become increasingly important. However, it faces challenges such as large intra-class differences and high inter-class similarities. To tackle these issues, we present a Food Feature Fusion Network (FFFNet), which leverages a multi-head cross-attention mechanism to integrate the local detail-capturing capability of Convolutional Neural Networks with the global modeling capacity of Vision Transformers. This enables the model to capture key discriminative features when addressing such challenging food recognition tasks. FFFNet also introduces self-supervised clustering, generating pseudo-labels from the feature space distribution and employing a clustering objective derived from Kullback–Leibler divergence to optimize the feature space distribution. By maximizing similarity between features and their corresponding cluster centers, and minimizing similarity with non-corresponding centers, it promotes intra-class compactness and inter-class separability, thereby addressing the core challenges. We evaluated FFFNet across the ISIA Food-500, ETHZ Food-101, and UEC Food256 datasets, attaining Top-1/Top-5 accuracies of 65.31%/88.94%, 89.98%/98.37%, and 80.91%/94.92%, respectively, outperforming existing approaches. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

The increasing demand for renewable energy has intensified research on lignocellulosic biomass pyrolysis as a versatile route for sustainable energy and resource recovery. This study provides a comparative overview of main pyrolysis regimes (slow, intermediate, fast, and flash), emphasizing operational parameters, typical product yields, and technological readiness levels (TRLs). Reactor configurations, including fixed-bed, fluidized-bed, rotary kiln, auger, and microwave-assisted systems, are analyzed in terms of design, advantages, limitations, and TRL status. Key process parameters, such as temperature, heating rate, vapor residence time, reaction atmosphere, and catalyst type, critically influence the yields and properties of biochar, bio-oil, and syngas. Increased temperatures and fast heating rates favor liquid and gas production, whereas lower temperatures and longer residence times enhance biochar yield and carbon content. CO₂ and H₂O atmospheres modify product distribution, with CO₂ increasing gas formation and biochar surface area and steam enhancing bio-oil yield at the expense of solid carbon. Catalytic pyrolysis improves selectivity toward target products, though trade-offs exist between char and oil yields depending on feedstock and catalyst choice. These insights underscore the interdependent effects of process parameters and reactor design, highlighting opportunities for optimizing pyrolysis pathways for energy recovery, material valorization, and sustainable bioeconomy applications. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article

Historically, traditional transportation planning has promoted public policies focused on building and maintaining infrastructure for private cars to improve travel efficiency. This approach presents a significant challenge for cities in the Global South due to their unique socioeconomic conditions and urban development patterns. Dedicated public transport infrastructure can make better use of the road network by moving more people and reducing congestion. Beyond its environmental benefits, it also provides the population with greater accessibility, creating new development opportunities. This study uses Mexicali, Mexico, a medium-sized city with dispersed urban growth and a high dependence on cars, as a case study. The goal is to identify the relationship between the supply of public bus routes and actual work-related commuting patterns. The methodology considers that, given the scarcity of economic resources and prior studies in the Global South, using Geographic Information Systems (GIS) for the spatial analysis of travel is a key tool for redesigning more inclusive and sustainable public transport systems. Specifically, this study utilized origin–destination survey data from 14 urban areas to assess modal coverage, work-related commuting patterns, and the spatial distribution of employment centres. The findings reveal a marked misalignment between the existing public transport network and the population’s travel needs, particularly in marginalized areas. Users face long travel times, multiple transfers, low service frequency, and limited connectivity to key employment areas. This configuration reinforces an exclusionary urban structure, with negative impacts on equity, modal efficiency, and sustainability. The study concludes that GIS-based spatial analysis generates sufficient evidence to redesign the public transport system and reorient urban mobility policy toward sustainability and social inclusion. Full article