Search Results (323)

To address the coordinated operation of renewable generation, load, and storage in zero-carbon parks, this paper proposes a source–load–storage (SLS) coordinated multi-resource optimization method. First, a parallel forecasting model combining partial least squares regression (PLSR) and ModernTCN, denoted PLSR-Modern TCN, is developed. PLSR extracts an eight-dimensional latent representation from each lagged-load window, while ModernTCN independently captures nonlinear temporal dependencies from the original one-channel sequence. The two representations are aligned by sample index, concatenated, and mapped to the one-day-ahead forecasting horizon. The model achieves an R² of 0.987, outperforming traditional TCN, convolutional neural network (CNN), random forest, and linear regression models. Based on the forecasts, a multi-objective SLS optimization model is established by considering time-of-use electricity prices, supply–demand balance, renewable curtailment, operation cost, carbon emissions, energy storage operation, PCC voltage, and equivalent harmonic power. The entropy weight method determines the objective weights, and an improved genetic algorithm (IGA) solves the optimization model. Simulation results show that IGA achieves the lowest mean comprehensive fitness and the smallest repeated-run variation among the compared algorithms. In the illustrative scheduling result, IGA provides a modest energy-related operating-cost reduction of approximately 0.11–0.13% and a carbon-emission reduction of approximately 2.2–4.5%. Full article

Traditional sequential recommender systems have primarily relied on implicit pattern recognition in user interaction sequences, achieving strong performance but functioning as “black boxes” that lack transparent reasoning. This paper introduces LLM-RS, a novel framework that leverages Large Language Models to enable explicit reasoning chains in sequential recommendation. Our approach transforms the recommendation task from mere pattern matching to interpretable reasoning by developing a multi-stage architecture that: (1) extracts structured preference profiles from user interaction sequences, (2) generates explicit reasoning chains analyzing candidate items against inferred preferences, and (3) produces persuasive explanations alongside recommendations. We propose three model variants—fine-tuned reasoning, retrieval-augmented generation, and hybrid ensemble—that integrate LLM-based reasoning with traditional collaborative filtering. The framework addresses key challenges in modern recommender systems by providing transparent, persuasive rationales while maintaining competitive performance, marking a significant step toward more interpretable and trustworthy recommendation systems. Comprehensive evaluations across the Amazon Reviews, MovieLens, MIND, and KuaiSAR datasets demonstrate that LLM-RS not only matches state-of-the-art methods in accuracy but also significantly enhances explanation quality, user trust, and recommendation diversity. Our findings reveal that reasoning-enabled recommendations increase user adherence in online experiments and improve long-term engagement metrics. Full article

Driven by the low-carbon transformation in the construction industry, Moso bamboo has emerged as a promising green material. However, a critical gap exists in current structural design theories: they predominantly rely on homogeneous assumptions, failing to capture the inherent spatial variability and coupled strength–stiffness degradation of bamboo. Experimental results reveal a distinct longitudinal gradient, where the top section (5–8 m) exhibits approximately 15% higher average tensile strength and 12% higher average elastic modulus compared to the bottom section (1–3 m). This oversight severely compromises the accuracy of structural reliability evaluations. To address this, this study pioneers a high-precision digital representation method by developing a novel three-dimensional (3D) anisotropic bivariate coupled random field model. Experimental and stochastic finite element simulations demonstrate that the model accurately replicates spatial variations, maintaining relative errors for primary statistical indicators strictly below 1% while robustly capturing the bivariate coupling characteristics. Crucially, by integrating non-parametric probability box (P-box) theory, the macroscopic tensile resistance of full-scale bamboo members is rigorously bounded within a definitive statistical interval. Furthermore, the extracted Interval Skewness Ratio generally remains greater than 1.0 (averaging 1.38), providing robust quantitative proof of an asymmetric structural degradation governed by the brittle weakest-link failure mechanism. By effectively eliminating non-physical sample generation associated with traditional univariate models, this research makes a significant contribution to the field, providing a rigorous digital twin framework and theoretical foundation for the advanced non-probabilistic safety design of modern bamboo structures. Full article

With the development of the socio-economic landscape and the increasing demand for urban power supply, user expectations for power supply reliability have risen significantly. To address this challenge, dual-petal distribution networks, characterized by multiple tie-line structures and inter-regional load transfer paths, have significantly enhanced fault recovery capability and are gradually replacing traditional radial configurations as a key form of modern distribution systems. However, their multi-regional coupling characteristics introduce complex issues such as dynamic changes in load transfer paths and islanded operation, resulting in significant limitations in the accuracy and adaptability of existing reliability assessment methods. To this end, this paper proposes an analytical method for calculating reliability indices of dual-petal distribution networks, considering the characteristics of load transfer zones. First, typical operation modes of dual-petal distribution networks are extracted, and a time-sequential component reliability analysis model is established. Second, a load transfer zone matrix is constructed based on the impact of distribution network faults on load nodes across different regions. Third, based on the fault ride-through capability of distributed generation (DG), a load restoration strategy considering load transfer zone characteristics is formulated, and the DG Island Recovery Matrix (DGIRM) is derived. Finally, by performing algebraic operations among various matrices and reliability parameter vectors, an explicit analytical calculation of reliability indices for dual-petal distribution networks with different DG configurations is achieved. The effectiveness of the proposed method is validated using a typical dual-petal network. The results demonstrate that the proposed method offers high computational efficiency and accuracy, effectively quantifying the impact of DG on the power supply reliability of dual-petal distribution networks, and providing theoretical and methodological support for the reliability assessment and planning of complex distribution systems. Full article

The terrain of tea gardens in Yunnan is complex, and the branches and leaves of tea trees are densely interlaced. Traditional manual picking methods are labor-intensive and inefficient, and can no longer meet the needs of modern tea industry development. To achieve automatic recognition of tea buds and leaves and accurate two-dimensional localization of picking points in complex natural environments, this study constructs a lightweight and high-precision tea bud and leaf detection and keypoint localization model, YOLOv11-pose-BEH, based on the YOLOv11-pose model. Based on the original network, this model introduces the BiFPN feature fusion module to achieve efficient bidirectional transmission of multi-scale information. The EMA (Efficient Multi-scale Attention) mechanism is integrated to form the C2PSA_EMA module, improving the effect of multi-scale feature extraction. HetConv is introduced to form the C3K2_HetConv module, enhancing the extraction of local texture and edge features. Compared with the baseline network, the improved YOLOv11-pose-BEH achieved significant improvements in both tea bud object detection and picking point localization. For the tea bud object detection task, the precision, recall, mAP0.5, and F1-score increased by 7.41, 6.39, 5.28, and 6.88 percentage points, respectively. For the picking point localization task, the precision, recall, mAP0.5, and F1-score increased by 5.86, 5.83, 4.83, and 5.85 percentage points, respectively. These results indicate that the proposed model can achieve more accurate and stable tea bud and leaf detection and two-dimensional picking point localization under complex backgrounds, providing efficient and reliable technical support for the visual perception of intelligent tea-picking robots in tea gardens. Full article

The genus Cynara is native to the Mediterranean region and is widely used in food and traditional medicine worldwide. Cynara is characterized by its diverse phytochemical composition, with sesquiterpene lactones, a subclass of terpenoids, being particularly distinctive. These compounds are naturally synthesized as defense mechanisms against herbivores and pathogens while acting as allelochemicals. The sesquiterpene lactones found in Cynara exhibit potential anticancer, anti-inflammatory, and antimicrobial activities. They also possess significant phytotoxic activity, making them promising natural bioherbicides for agricultural applications. The effective exploitation of these compounds requires the use of appropriate extraction solvents and techniques. Compared with conventional solvents and extraction methods, green solvents, including ionic liquids and deep eutectic solvents, together with modern extraction techniques, particularly ultrasound-assisted extraction, supercritical fluid extraction, and Naviglio extraction, have proven highly effective for their recovery. In addition, the application of elicitation strategies, such as salt stress, shading, hormones, and microbial biostimulants, has emerged as a promising approach for enhancing the production of these compounds during cultivation. Therefore, this review highlights Cynara as a valuable source of sesquiterpene lactones with broad applications in medicine and agriculture and provides guidance on technical approaches relevant to their extraction and the enhancement of their production. Full article

Metal oxide nanoparticles serve as crucial drivers in modern biomedical, catalytic, environmental, and energy technologies due to their high surface-to-volume ratios and quantum confinement properties. Traditional chemical and physical synthesis methods remain limited by significant energy footprints, high costs, and the use of hazardous reagents. To address these challenges, bioinspired (“green”) synthesis has emerged as a sustainable paradigm that employs biological systems as nature nanofactories. This critical review provides a provides a comprehensive and systematic analysis of the green synthesis of major metal oxide systems (ZnO, TiO₂, Fe₃O₄/Fe₂O₃, CuO, Co₃O₄, CeO_2, and MnO₂) using diverse biological templates, including plant extracts, bacteria, fungi, algae, and biopolymers. Moving beyond simple descriptive summaries, we critically evaluate the foundational electron-transfer and nucleation mechanism, systematically correlate processing parameters with physical outcomes, and offer a rigorous comparative analysis across different biological kingdoms. Finally, we directly address the underlying challenges facing the field: reproducibility bottlenecks, scalability limits, environmental safety variations, and regulatory hurdles necessary for industrial translation. Full article

The growing complexity of modern systems has pushed safety science beyond tradition-al analysis methods. In a world where the unknown matters as much as the known, knowledge graphs emerge as a powerful means for representing, connecting, and extending knowledge. However, the intersection between safety science and knowledge graphs remains largely unexplored. Which communities of researchers are leveraging knowledge graphs for safety? Is there any common pattern in how they are being used? This paper addresses these questions by presenting a systematic review of the literature on the use of knowledge graphs in the context of safety. Based on 173 eligible documents, we propose a classification framework structured around three dimensions: the originality of knowledge characterization, the originality of knowledge extraction, and the maturity of safety analysis. The framework identifies three archetypes of knowledge graph users: Assemblers, who rely on existing models and tools; Alchemists, who adapt available knowledge structures or extraction procedures; and Shapers, who develop novel ontologies, extraction methods, or both. The obtained results show how the latter represents the largest group among the reviewed studies, suggesting a tension between analytical maturity and the need for customized solutions. More broadly, the classification framework presented in this review may support researchers from both the safety and the artificial intelligence communities in fostering a shared path for the scientific development of these disciplines. Full article

Pesticide application efficiency is fundamentally governed by the spatial distribution of droplet deposition. However, characterizing this dynamic process is challenging due to complex environmental and canopy variables. Consequently, conventional offline sampling methods lack the temporal and spatial resolution required for modern intelligent spraying systems. This review systematically examines recent progress in droplet deposition detection. We first revisit traditional methods like water-sensitive paper, addressing high-coverage quantification biases and fluorescence-based techniques. Next, we analyze real-time sensing technologies, including capacitive and optical sensors, highlighting their responsiveness and inherent physical constraints. Furthermore, deep learning approaches for droplet detection, overlap segmentation, and geometric-to-physical regression are discussed. While these methods substantially enhance feature extraction, they often struggle with cross-scenario generalization. Ultimately, current techniques face inherent trade-offs among real-time capability, quantification accuracy, and environmental adaptability, remaining insufficient for complex field conditions. To enable reliable closed-loop control in precision plant protection, future research must prioritize multi-modal sensor fusion, the integration of data-driven and physics-based models, and real-time deployment via edge computing. Full article

Semantic segmentation is a fundamental task in computer vision that aims to assign a categorical label to each pixel in an image, facilitating dense and detailed scene understanding. This pixel-level classification is especially crucial in autonomous driving, where accurate environmental perception is vital for dependable object detection and safe decision-making. In this study, we propose MultiDecNet, a novel multi-decoder semantic segmentation framework designed to capture both macroscopic scene layouts and fine-grained spatial boundaries in complex urban environments. Drawing inspiration from classical networks, MultiDecNet incorporates a parallel dual-branch decoding strategy that simultaneously leverages the multi-scale context modeling of the Pyramid Pooling Module (PPM) and the structural refinement capabilities of Atrous Spatial Pyramid Pooling (ASPP). To explore the impact of modern backbone representations, we structurally modernize the feature extraction pipeline by introducing the contemporary ConvNeXt convolutional architecture as an alternative to traditional ResNet101 backbones. We extensively evaluate and compare the baseline configurations alongside our proposed MultiDecNet using both ResNet101 and ConvNeXt-Large backbones on the benchmark Cityscapes dataset. The quantitative assessments demonstrate that the MultiDecNet architecture consistently provides highly competitive performance within the scope of this comparative study, with the MultiDecNet-ConvNeXt variant achieving favorable overall scores among the evaluated methods. Furthermore, a granular, class-wise IoU and training dynamics analysis reveals that while traditional networks retain competitive boundaries for localized minority targets, the modern ConvNeXt backbone ensures faster convergence stability and balanced contextual mastery over large-scale driving layouts. Ultimately, these findings offer critical insights into architectural synergy and backbone selection, presenting a robust, scalable, and well-balanced solution for advanced autonomous navigation systems. Full article

Background: The genus Satureja L. (savory) includes approximately 200 aromatic herb and shrub species distributed worldwide. These plants are widely used in traditional and modern medicine, culinary practices, and agriculture. This review summarises knowledge on the traditional uses, phytochemistry, and pharmacological activities of Satureja species published between March 2014 and 2025. Methods: Peer-reviewed literature was searched on Web of Knowledge, PubMed, Scopus, and SciFinder using the keywords “Satureja” and “savory.” A total of 171 relevant articles were analyzed, focusing on ethnomedicinal use, chemical constituents, and pharmacological effects. Results: Recent ethnobotanical studies documented the use of local medicinal plants, including Satureja, in several European regions. Phytochemical research identified major groups of compounds such as essential oils, flavonoids, phenolic acids, jasmonates, di- and triterpenes, and steroids. Essential oils are the most studied and show high variability among species due to environmental and genetic factors. Pharmacological research largely highlights antimicrobial, antioxidant, and antitumor activities, as well as protective effects against chemotherapy-induced side effects. Additional studies report neurological benefits, including prevention of opioid analgesic tolerance, antiepileptic activity, and memory-enhancing effects. Satureja species have been the subject of various innovative developments aimed at preserving food quality, improving coating materials in the food industry, and developing new environmentally friendly biopesticides. Conclusions: Future research should prioritize the study of individual bioactive compounds, their mechanisms of action, and structure–activity relationships. Advances in nanoformulations and modern extraction technologies offer promising directions to support the medicinal and food-industry applications of Satureja-derived products. Full article

Accurate fault diagnosis is critical for the reliable and secure operation of modern industrial equipment; however, traditional unimodal data representations often fail to fully capture the complex, multi-dimensional characteristics of mechanical faults. To address this limitation, this paper proposes CFNET, a novel multimodal cross-fusion recognition framework designed for the robust fault diagnosis of rotating machinery, including vane pumps and bearings. The proposed framework employs an innovative three-modal input strategy that systematically extracts deep spatial and temporal features using parallel ResNet50 and stacked GRU networks. To break through the limitations of conventional decision-level fusion, CFNET introduces a dynamic mid-layer cross-fusion mechanism driven by adaptive attention weights, effectively enhancing feature interaction depth while preventing redundancy during back-propagation. Furthermore, handcrafted prior features are integrated via skip connections to supplement the deep learning representations. The aggregated multi-dimensional features are ultimately processed by a Transformer architecture for feature sparsification and high-precision classification. Extensive experiments rigorously validate the superiority of the proposed method. On a custom-built industrial vane pump test rig, CFNET achieved a maximum accuracy improvement of 25% over traditional unimodal methods. Furthermore, it demonstrated state-of-the-art performance on two authoritative benchmark datasets, achieving impressive recognition accuracies of 98.12% and 99.92% on the Paderborn University and Case Western Reserve University bearing datasets, respectively. These results underscore the exceptional robustness, efficiency, and industrial applicability of the CFNET framework. Full article

Background/Objectives: The Naxi people in Northwest Yunnan of China have used alcohol-soaked Pleione Pseudobulbus, which is the Pseudobulbus of Pleione bulbocodioides Rolfe (PBR), for lung diseases and tumors for a long period of time. This study aims to explore underlying mechanisms of bioactive ingredients in PBR, as well as to underscore the synergy between traditional medicine and modern pharmacological research. Methods: We verified the anti-tumor effects of the PBR extract through in vitro cell experiments, and explored its underlying mechanisms by combining untargeted metabolomics with network pharmacology to predict the related targets. Results: The anti-tumor potential of PBR extracts was systematically evaluated by integrating chemical profiling with in vitro cell-based assays. Untargeted metabolomics tentatively annotated metabolites spanning 12 major chemical classes, several of which have been previously reported to possess anti-tumor activity. To validate these annotations, prioritized candidates were further examined by LC-MS/MS against authentic reference standards at the nanogram scale, which confirmed the presence of sclareol—a naturally occurring diterpene with documented anti-tumor properties—as a constituent of PBR. Consistent with this chemical evidence, the PBR extract exerted multi-faceted anti-tumor effects in A549 lung cancer cells: it significantly suppressed proliferation, migration, and invasion; induced G0/G1-phase cell-cycle arrest; disrupted mitochondrial membrane potential; and modulated the expression of apoptosis-related proteins. Conclusions: By highlighting the pharmacological properties of cultivated PBR, we identified 118 overlapping targets between PBR compounds and lung disease-related targets, and we further selected 25 core lung cancer targets with high topological importance. This study suggests that the primary active compounds of Pleione bulbocodioides (Franch.) Rolfe may exert anti-lung cancer activity, potentially through targeting the EGFR tyrosine kinase inhibitor resistance pathway and the PI3K-Akt signaling pathway. Furthermore, in silico molecular docking suggested that the two major active compounds exhibited favorable predicted binding affinities with four core targets, particularly EGFR and AKT1, providing a basis for further experimental validation. These results support the potential value of Naxi traditional medicine and the need to further research onthese medicinal plants, thereby promoting Chinese herb medicine conservation efforts in the Naxi region. Full article

Medicinal mushrooms have become an important component of modern dietary supplementation and functional nutrition due to their diverse biological activities and long-standing use in traditional medicine. Among the most widely studied and utilized species are Ganoderma lucidum, Lentinula edodes, Grifola frondosa, Cordyceps militaris, Cordyceps sinensis, Trametes versicolor, and Inonotus obliquus. Their therapeutic potential is associated with a wide range of biologically active constituents, including polysaccharides, triterpenoids, phenolic compounds, and other secondary metabolites. Experimental and clinical studies indicate that extracts derived from these species may support immune function, modulate inflammatory responses, and exhibit antioxidant, antimicrobial, and anticancer properties. In addition to extensive in vitro and in vivo investigations, a growing number of clinical studies have evaluated the safety and potential therapeutic benefits of medicinal mushroom preparations in humans. In recent years, increasing attention has been directed toward their incorporation into nutraceutical formulations and functional foods aimed at supporting health and preventing chronic diseases. Advances in cultivation technologies and extraction methods have also contributed to improved availability and standardization of mushroom-derived products. This review provides a comprehensive overview of selected medicinal mushroom species commonly used in dietary supplements, focusing on their bioactive constituents, reported biological activities, and potential applications in contemporary medicine. Full article

Moldy pear core constitutes a severe internal defect that compromises fruit quality. This study proposes a nondestructive detection method for Korla pear moldy core using Vis/NIR spectral signals, aimed at supporting post-harvest quality control and automated industrial sorting. We collected spectral signals from pears and quantified the moldy pear core area to classify samples into healthy (S = 0%), slightly moldy (0 < S ≤ 10%), and severely moldy (S > 10%) categories. We constructed a three-tier comparative framework to evaluate the progression from conventional machine learning to advanced deep learning: traditional methods using univariate selection (US) and random forest (RF) for feature extraction followed by support vector machine (SVM) classification; 1D-ResNet for direct processing of spectral signals; and two-dimensional approaches transforming signals into improved gramian angular field (IGAF) or Laplacian pyramid Markov transition field (LPMTF) images processed through deep belief network (DBN), MobileNetv3, and Vision Transformer (ViT). The LPMTF-ViT combination delivered the best performance with 98.98% test accuracy and 94.44% external validation accuracy, significantly exceeding traditional approaches and 1D-ResNet. This innovative approach delivers effective technical support for early-stage, nondestructive detection of internal fruit defects. It also establishes a scalable foundation for automated industrial inspection systems, potentially reducing post-harvest losses while ensuring premium quality control in modern fruit supply chains. Full article