Search Results (45,359)

As complex agricultural machinery, traditional combine harvesters face numerous challenges during operation due to their reliance on manual observation. To meet the demands of modern agriculture, intelligent combine harvesters have emerged. Intelligent sensing uses multi-sensor fusion and deep learning to monitor crop lodging, feed rate, loss rate, and impurity content. Under suboptimal conditions, multi-source fusion strategies improve perception reliability. Information processing and decision-making enable dynamic optimization of operational parameters and reduce harvest losses. Multi-machine coordination transforms single-machine operations into fleet control, while remote monitoring leverages a cloud edge collaboration architecture to enable status visualization, remote control, and predictive maintenance for faults. Unmanned operations utilize high-precision positioning and intelligent path planning to improve fleet efficiency and field coverage. However, the field still faces common challenges, including insufficient real-time processing capabilities for multi-source heterogeneous data, poor adaptability to complex agronomic scenarios, and limited economic feasibility. In this review, we examine the complete technology chain, which includes intelligent perception, intelligent decision-making and coordination, remote monitoring, and unmanned operations. We conduct a comparative analysis of the current state of these systems and the challenges they face, providing a systematic reference for future research and industrial applications. Full article

Photovoltaic (PV) power stations are pivotal for the renewable energy transition, yet their operational efficiency is often compromised by defects such as surface dust accumulation and cracks. Traditional manual inspections are labor-intensive and subjective, while conventional monitoring methods struggle with environmental interference. This study proposes YOLO-MSG, a lightweight framework specifically designed for the automated detection of PV module defects during system operation, including normal panels as well as defective conditions such as dusty and cracked panels. The methodology integrates a Multi-Scale Grouped Convolution (MSGC) module for enhanced feature extraction and a Group-Stem Decoupled Head (GSD-Head) to reduce parameter redundancy. Furthermore, a joint optimization strategy involving LAMP and logits-based knowledge distillation is employed to facilitate edge deployment. Experimental results on a specialized PV defect dataset demonstrate that YOLO-MSG achieves a superior balance between detection accuracy and computational cost. Compared to state-of-the-art models like YOLO11 and YOLOv12, YOLO-MSG significantly reduces GFLOPs and parameter count while maintaining highly competitive mean Average Precision (mAP), with improvements of 1.35% in mAP and 2.37% in mAP50-95 over the baseline models. Specifically, the model achieves an average inference speed of 90.30 FPS on the NVIDIA Jetson AGX platform. These findings confirm the algorithm’s industrial viability, providing a robust and efficient solution for the real-time automated maintenance of photovoltaic infrastructures. Full article

Understanding how urban morphology regulates Land Surface Temperature (LST) is important in the context of rapid urbanization and increasingly frequent extreme climate events. Although both two-dimensional (2D) and three-dimensional (3D) morphological factors are known to affect urban thermal environments, their relative explanatory roles, factor-specific optimal scales, and nonlinear responses are still insufficiently quantified within a unified multi-scale framework. This study focuses on the area within Beijing’s Fifth Ring Road and applies an interpretable LightGBM-SHAP framework to examine the multi-scale relationships between integrated 2D/3D urban morphology and LST using a Landsat 8 image acquired during a typical summer daytime heatwave event. Five analytical scales (150, 300, 600, 900, and 1200 m) are evaluated to compare factor importance, identify optimal explanatory scales, and characterize threshold-like response patterns. The LightGBM models maintained relatively strong predictive performance across all scales under spatial cross-validation, with the highest mean R² observed at 600 m, followed closely by 300 m. The results indicate a clear scale-dependent contrast in explanatory dominance: 2D factors show stronger associations with LST at fine-to-medium scales, whereas 3D factors become more influential at coarser scales. From a process perspective, this contrast is consistent with differences in surface-cover-related and vertical-structure-related thermal regulation, although the underlying physical mechanisms are not directly tested in this study. SHAP analysis further identifies factor-specific nonlinear response intervals for several key indicators under the selected extreme-heat condition. For example, a cooling tendency is observed when Mean Building Height (MBH) exceeds 15 m at the 150 m scale. These findings provide scale-explicit and context-specific evidence for interpreting urban morphology–LST relationships and support heat-mitigation strategies that combine micro-scale surface-cover optimization with larger-scale regulation of building height variation and urban roughness. The identified response intervals should be interpreted as empirical references under a typical daytime heatwave condition rather than as universally transferable climatological thresholds. Full article

In the parallel operation of islanded microgrids, line impedance mismatches and random load fluctuations, along with the dynamic response lag and difficulty in multidimensional parameter tuning of traditional control strategies, lead to power sharing imbalances and instability in frequency and voltage. To address these issues, this paper proposes a hierarchical cooperative control strategy based on consistency that integrates load-observation-based dynamic reference feedforward (LODRF) and adaptive particle swarm optimization (APSO). First, an improved adaptive virtual impedance (IAVI) strategy based on consistency is introduced into the virtual synchronous generator control framework. Second, an LODRF mechanism is applied at the secondary control layer to actively reconstruct the power baseline by observing the load status at the point of common coupling (PCC) in real time. Furthermore, an APSO algorithm utilizing the integral of time-weighted absolute error (ITAE) as a global performance index is constructed to optimize key proportional–integral controller parameters cooperatively. Simulation results from a four-unit heterogeneous parallel system in MATLAB/Simulink demonstrate that the IAVI strategy enables stable convergence of frequency and voltage and proportional power sharing. Compared with the system without LODRF, the proposed strategy reduces maximum frequency and voltage dynamic deviations under load disturbances by 78.5% and 53.3%, respectively, and shortens effective recovery times by 0.01 s and 0.09 s, respectively. Moreover, compared with the standard PSO algorithm, the APSO-optimized system reduces maximum frequency and voltage deviations by 3.1% and 36.4%, respectively. Additionally, average active and reactive power sharing errors in the steady state are kept below 0.9%, verifying the significant advantages of the strategy in improving dynamic disturbance rejection and steady-state precision. Full article

Although radiotherapy is a cornerstone in the management of several pediatric malignancies, its administration in children poses unique anesthetic challenges. Unlike adults, pediatric patients, particularly younger children, often require repeated sedation or general anesthesia to ensure immobility and reduce psychological distress during daily treatment sessions that may extend over several weeks. This narrative review summarizes current evidence on anesthetic strategies for children undergoing radiotherapy, focusing on clinical indications, pharmacological approaches, safety considerations, and organizational aspects. We discuss the main sedation and anesthesia techniques used in non-operating room anesthesia (NORA) settings, including deep sedation with midazolam, propofol, ketamine, and dexmedetomidine, as well as general anesthesia with laryngeal mask airway management. Particular attention is given to the cumulative effects of repeated anesthetic exposure, airway management challenges in remote radiation environments, and the risk of respiratory and hemodynamic complications. The review also highlights the importance of individualized, protocol-driven management, rapid recovery strategies, and continuous remote monitoring systems. Non-pharmacological interventions and audiovisual-assisted techniques are also discussed as potential strategies to reduce anesthesia requirements in selected patients. A multidisciplinary approach involving anesthesiologists, radiation oncologists, nurses, psychologists, and technical staff is essential to optimize safety, treatment adherence, and overall quality of care. Tailored anesthetic management, supported by standardized protocols and specialized pediatric expertise, remains crucial to balancing procedural efficacy with short- and long-term safety in this vulnerable population. Full article

Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) represent fundamental cellular adaptive mechanisms that maintain protein homeostasis and metabolic balance. Many RNA viruses, particularly flaviviruses such as dengue virus (DENV), Zika virus (ZIKV), West Nile virus (WNV), yellow fever virus (YFV), and Japanese encephalitis virus (JEV), extensively remodel the ER to establish replication compartments and assemble progeny virions. This massive reorganization disrupts ER homeostasis, leading to UPR activation. Emerging evidence reveals that flaviviruses not only trigger but also manipulate the three UPR branches—PERK, IRE1, and ATF6—to optimize viral translation, replication, and egress. In parallel, flavivirus infection profoundly alters host lipid metabolism and promotes dynamic changes in lipid droplets (LDs), key organelles that mediate lipid storage and serve as scaffolds for viral replication and assembly. The UPR intimately connects to LD biogenesis through transcriptional and translational programs mediated by XBP1, ATF4, and ATF6, thereby coupling ER stress responses to lipid remodeling and energy homeostasis. This intricate crosstalk between UPR and LDs creates a metabolic and structural niche favorable for viral replication but detrimental to host cell integrity. This review provides a comprehensive analysis of the molecular mechanisms by which flaviviruses exploit ER stress and the UPR to reprogram lipid metabolism and LD dynamics. We highlight the dual role of UPR signaling in promoting adaptive lipid synthesis and initiating cell death under prolonged stress, discuss recent insights into ER–LD interactions during flavivirus infection, and explore therapeutic opportunities targeting UPR–lipid metabolic pathways as broad-spectrum antiviral strategies. Understanding this interconnected network will advance our knowledge of viral pathogenesis and identify new avenues for host-directed antiviral intervention. Full article

Acute ischemic stroke (AIS) is characterized by complex interactions among vascular occlusion, endothelial injury, and inflammatory activation, which collectively influence clinical outcomes. Increasing attention has focused on the endothelial glycocalyx, a critical regulator of vascular permeability, mechanotransduction, and inflammatory signaling. Disruption of the endothelial glycocalyx during ischemia and subsequent reperfusion contributes to blood–brain barrier (BBB) dysfunction and secondary brain injury. Mechanical thrombectomy has emerged as the reference standard treatment for large vessel occlusion in AIS. This review synthesizes current evidence on endothelial glycocalyx degradation and associated inflammatory cascades in cute ischemic stroke, with particular emphasis on patients undergoing mechanical thrombectomy. We examine the mechanisms underlying endothelial and BBB injury, ischemia–reperfusion-mediated vascular dysfunction, and systemic inflammatory responses (SIRS). In addition, the potential clinical relevance of circulating biomarkers indicative of endothelial glycocalyx shedding and endothelial damage is discussed. By integrating molecular pathophysiology with contemporary reperfusion strategies, this review highlights the importance of endothelial protection as a potential adjunct to mechanical thrombectomy. While mechanical thrombectomy remains the gold standard therapy for AIS due to large vessel occlusion, targeting endothelial glycocalyx integrity and post-reperfusion inflammation may represent a promising approach to optimizing neurological outcomes and reducing complications. Further research is required to elucidate specific pathophysiological mechanisms and to develop targeted therapeutic strategies aimed at reducing stroke-related morbidity and mortality. Full article

Fruit anthocyanins are primary determinants of color, sensory quality, and nutritional value in grapes; however, their endogenous biosynthesis is governed by complex interactions among genetic, environmental, agronomic, and postharvest factors. This review elaborates recent advances in physiology and molecular biology to clarify the biosynthetic mechanisms in grapes, including the coordinated action of structural enzymes, MYB–bHLH–WD40 regulatory complexes, hormone-mediated signaling pathways, and vacuolar transport processes. Key environmental factors, such as temperature fluctuations, light exposure, water availability, and soil properties, regulate these networks, contributing to significant variation in pigmentation profiles across cultivars and growing regions. Strategic agronomic practices, including canopy management, regulated deficit irrigation, balanced nutrient management, and temperature-mitigation techniques, further influence pigmentation by modifying the microclimate of the fruit zone during development. Based on these mechanistic insights, this review evaluates targeted strategies for enhancing anthocyanin accumulation, highlighting recent progress in genetic improvement through CRISPR/Cas genome editing, transgenic approaches, and marker-assisted selection (MAS), which enable precise modulation of biosynthetic and regulatory genes. Complementary postharvest interventions, such as optimized cold storage, modified-atmosphere packaging, hormonal elicitors, and controlled oxidative technologies, provide additional opportunities to maintain or enhance pigment stability after harvest. Collectively, these advances establish a comprehensive framework linking molecular regulation with practical vineyard, breeding, and postharvest strategies, offering an integrated pathway to improve anthocyanin consistency, berry quality, and the phenolic characteristics of grape-derived products. Full article

Global urban development has fully entered the stage of stock renewal, and the synergy between public health and historic heritage conservation has become a core issue of urban sustainable development in the post-pandemic era. As special spatial units carrying urban cultural memories, historic districts generally face problems such as chaotic traffic functions, a lack of slow traffic spaces, and insufficient public health support. Existing studies lack a public health-oriented special evaluation system and a sustainable renewal path adapted to their characteristics. This paper systematically sorts out eight core impact paths of street built environment elements on public health and constructs a healthy street evaluation system for historic districts, including six dimensions (transportation facilities, green squares, ancillary facilities, street-front commerce, urban furniture, and street network) and 30 core elements combined with the spatial and cultural characteristics of historic districts. Taking five typical streets in the Liuhe Area of Hankou Historic District as an empirical case, a comprehensive evaluation is carried out using a combination of quantitative surveys, questionnaire surveys, and spatial analyses. The results show that the overall health performance of street space in the study area is low, with extremely unbalanced development across dimensions. The core shortcomings are concentrated in incomplete slow traffic systems, lack of public spaces, prominent parking chaos, and fragmented historic styles, and the health problems of streets with different functional types show significant typological differentiation characteristics. Based on this, this paper proposes five systematic renewal strategies, transportation system optimization, public space improvement, landscape system perfection, historic style activation, and long-term mechanism construction, for achieving the synergistic goals of historic culture conservation, public health promotion, and urban sustainable development. This study not only enriches the theoretical system of research on healthy spaces in historic districts but also provides a referable evaluation framework and practical approach for modern historic districts in China and other similar historic districts with comparable spatial textures and functional characteristics. Full article

Proppant flowback during the flowback phase after hydraulic fracturing in coal reservoirs critically impacts fracture conductivity and wellbore integrity. However, experimental studies on its critical conditions and controlling mechanisms within coal’s complex fracture networks are scarce compared to sandstone or shale. This study conducted physical simulation experiments using outcrop coal samples from the XD block in China and a modified fracture conductivity system. By establishing a determination method for the critical backflow rate (Q_c), the dynamic evolution process of proppant backflow—characterized by the stages of initial stability, critical instability, severe backflow, and re-equilibration—was revealed. The influences of proppant size, flowback fluid viscosity, proppant concentration, and effective stress on Q_c were systematically analyzed, and the relative weight of each influencing factor was quantified through orthogonal experimental design. Results show that proppant backflow initiates and concentrates preferentially at the fracture outlet region, implying a higher risk of proppant failure in the near-wellbore fracture section. The Q_c decreases with reducing proppant size, increasing flowback fluid viscosity, increasing proppant concentration, and decreasing effective stress, among which effective stress is identified as the dominant controlling factor. Furthermore, no necessary correlation is observed between Q_c and the critical backflow ratio, suggesting that the initiation threshold and post-instability flowback intensity are governed by different mechanisms. This work provides experimental data and a quantitative basis for optimizing flowback strategies in coal reservoir fracturing operations. Full article

Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management. Full article

This study investigates the influence of agronomic management on the accumulation of bioactive compounds and the antioxidant capacity of Achillea millefolium L., a medicinal species of increasing relevance for pharmaceutical and nutraceutical applications. Different cultivation strategies were applied, including controlled drought stress, foliar fertilization, and inoculation with plant growth–promoting rhizobacteria (PGPR), in order to evaluate their impact on tissue-specific metabolic responses. The total antioxidant capacity (TAC) of flowers and roots was determined using FRAP, DPPH, and ABTS spectrophotometric assays, while metabolite profiling was performed by UHPLC–MS/MS analysis. Clear differences in antioxidant activity were observed among plant organs and cultivation treatments. Flower extracts showed intermediate antioxidant capacity, with FRAP values ranging from 55.86 to 66.55 mg TE g⁻¹ extract and the highest activity consistently recorded for treatment F_010 (addition of K, P fertilizers under water stress conditions and PGPR absence) across all assays. Root extracts exhibited substantially lower antioxidant values (FRAP 19.40–33.69 mg TE g⁻¹), although samples R_000 (no foliar fertilization, under water stress conditions and PGPR absence) and R_100 (no foliar fertilization, under water stress conditions and presence of PGPR) displayed comparatively higher radical scavenging activity. Metabolic profiling revealed a shared presence of caffeic acid derivatives and flavonoids, including mono- and di-caffeoylquinic acids and apigenin-related compounds, with marked quantitative differences among tissues. Overall, the results demonstrate that agronomic practices significantly influence the accumulation and distribution of antioxidant metabolites in A. millefolium L., highlighting the importance of cultivation strategies for optimizing the production of bioactive phytochemicals. Full article

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes K-out-of-N sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. Full article

Visually rich document understanding (VrDU) models rely on tightly coupled textual, layout, and visual representations. In real-world deployments, these models must continuously adapt to new document domains over time. However, naïve sequential fine-tuning leads to severe catastrophic forgetting due to shared parameters and strong cross-task interference. Existing continual learning approaches either constrain parameter updates, preserve output distributions, or uniformly suppress gradient directions associated with previous tasks. While effective in limited settings, these strategies fail to balance stability and plasticity in large multimodal transformers. We propose AMD-Proj, an adaptive memory-driven selective gradient projection framework for continual learning in document understanding. It models task knowledge using specific gradient subspaces and adaptively modulates incoming gradients based on their alignment with this memory, selectively blocking interfering directions while reinforcing reusable ones. An efficient truncated SVD mechanism with online subspace merging ensures bounded memory usage and scalability to large transformer-based architectures. We evaluate AMD-Proj on four VrDU benchmarks (FUNSD, SROIE, CORD, and BuDDIE) under a task-incremental learning setting using LayoutLMv2 and LayoutLMv3 backbones. Results show that AMD-Proj reduces catastrophic forgetting and improves F1-based stability over EWC, GPM, LwF, OWM, CUBER, TRGP and parameter-efficient fine-tuning methods. Extensive mechanistic analyses, including gradient spectrum decomposition and layer-wise reuse versus block dynamics, provide insight into how selective gradient projection controls optimization geometry during continual adaptation. These findings establish selective gradient projection as a principled and interpretable approach for continual learning in visually rich document understanding. Full article

Microbial lipids have emerged as a promising sustainable alternative to plant- and petroleum-derived oils, with applications spanning biofuels, oleochemicals, nutraceuticals, and specialty materials. Significant advances in metabolic engineering and strain development have increased lipid production capacity across diverse microorganisms. Numerous reviews have summarized the biological and metabolic advances in this field, highlighting significant progress in metabolic engineering and strain development that has increased lipid production capacity across diverse microorganisms. However, translating these gains into economically viable industrial processes remains a major challenge. This review examines process engineering strategies for microbial lipid production across the full bioprocessing pipeline, from laboratory-scale strain evolution to industrial-scale operation. We discuss recent developments in adaptive laboratory evolution, systems-guided strain optimization, and robustness engineering, emphasizing their implications for process performance. Key bioprocess parameters—including substrate selection, nutrient limitation strategies, reactor design, oxygen transfer, and process control—are critically evaluated for their impact on lipid yield, productivity, and scalability. Furthermore, downstream processing considerations and techno-economic constraints are analyzed in the context of large-scale implementation. By integrating strain-level innovations with process engineering principles, this review highlights current bottlenecks, emerging solutions, and future directions for achieving efficient and scalable microbial lipid biomanufacturing. Full article