Search Results (11,788)

Blade impact faults degrade power generation quality, if not detected in time, may lead to turbine malfunction or even complete failure. Moreover, the accuracy of blade impact fault detection in tidal current turbine (TCT) is significantly affected by variations in flow velocity and tidal flow period. To solve this problem, a self-adaptive detection method based on stator current signals and k-nearest neighbor-multiplicative score (KNN-MS) is proposed. The method first employs the KNN algorithm to characterize local feature distributions. Then, robustness under unstable flow conditions is improved through variance-based weighting. Finally, a cumulative multiplicative scoring mechanism is proposed to amplify and quantify fault-related anomaly indicators. The experimental results show that the proposed method achieves high diagnostic accuracy and stability across steady, periodic, and variable-period flow scenarios. Full article

X-ray free-electron lasers (XFELs) have revolutionized structural biology by enabling “diffraction-before-destruction” and capturing the ultrafast dynamics of life. However, the intrinsic sparsity and noise of XFEL diffraction snapshots, often complicated by multi-lattice overlaps, create a formidable computational bottleneck that limits data utilization and structural fidelity. Here, we present MCDPS-SFX, a robust indexing framework based on a reference-based, whole-pattern matching principle integrated with parallelized iterative refinement. By exhaustively sampling orientation space and progressively rejecting outliers, MCDPS-SFX significantly outperforms legacy algorithms—more than doubling crystal yields in heterogeneous datasets (e.g., 21,807 vs. 8792 for MOSFLM)—and achieves highly competitive yields comparable to state-of-the-art indexers, such as extracting over 90,000 lattices in the lysozyme benchmark. We demonstrate its efficacy on standard benchmarks and technically demanding G-protein-coupled receptor (GPCR) systems, including the rhodopsin–arrestin complex and the glucagon receptor. MCDPS-SFX consistently produces high-quality data statistics, enabling the high-resolution visualization of functionally critical, flexible regions such as phosphorylated receptor tails. Our results provide a powerful tool for enhancing the scientific output of XFEL experiments, offering a robust alternative for maximizing information recovery from weakly diffracting or overlapping crystalline samples. Full article

Social Virtual Reality (VR) is emerging as a powerful medium for remote social interaction and collaboration, enabling multiple users to share experiences together while apart. Likewise, recent advances in multimedia technologies have proposed strategically combining diverse content formats and introducing interaction techniques for recreating virtual environments and engaging with them, respectively. This study pioneers the joint exploration of Social VR enhanced with holographic communication, multimodal content integration, and advanced interaction methods to deliver realistic and interactive group visits to reconstructed cultural heritage sites, specifically an existing restaurant–museum. The reconstructed space is further augmented with Points of Interest (PoIs), which can be freely visited and dynamically activated to provide rich contextual and historical information about the venue. The proposed technology and scenario have been evaluated objectively and subjectively. Results from objective tests offer relevant insights into the technical requirements, performance metrics (including bandwidth usage and latency), and overall system stability. Results from subjective tests with 22 participant pairs reveal high levels of user satisfaction, particularly in terms of immersion, presence, togetherness, and interaction quality regardless of whether participants acted as Guides (interacting with the VR environment) or Followers (observing and following the Guide’s actions). Beyond demonstrating feasibility, the findings from this study prove, for the first time, how strategically combining multi-user holoportation with multimodal content and role-based interactions can enable guided, collaborative cultural or touristic visits that preserve social presence while supporting rich exploration and contextual learning. Full article

Using a coordinated multi-year monitoring dataset collected during the 2020–2024 partial-opening management period, we examined benthic diatom assemblages across the Sejong, Gongju, and Baekje weirs in the Geum River, Republic of Korea. Seasonal surveys at eight stations were used to evaluate spatiotemporal variation in water quality and benthic diatom community structure under this hydrological management regime. Annual basin-wide averages showed gradual interannual changes in water quality, including declines in total phosphorus, total nitrogen, chlorophyll-a, turbidity, and biochemical oxygen demand after 2021, accompanied by increased dissolved oxygen. Diatom community indices based on relative-abundance data showed corresponding temporal variation, with decreased dominance and increased Shannon diversity, evenness, and taxon richness. Ordination analyses indicated gradual differentiation between the earlier (2020–2021) and later (2022–2024) monitoring groups within the study period, whereas random forest models showed limited explanatory power and were treated as exploratory. Overall, the results support benthic diatoms as sensitive descriptors of ecological change in flow-regulated monsoonal rivers while underscoring the value of long-term monitoring where true pre-intervention biological baselines are unavailable. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

The weight of battery modules keeps hindering them from being commercially attractive as the sole power supply for short-range electric passenger flights. Furthermore, the challenging requirements for aerospace applications limit the range of options for module elements and complicate the implementation of lightweight solutions. Hence, the objective of this study is to elaborate and evaluate concepts for battery modules to identify promising solutions for electrified aircraft propulsion systems. For that purpose, a house of quality is compiled to assess the relations between options for module elements and module requirements, as well as correlations between options. Potential concepts are elaborated by combining suitable elements. Finally, the concepts are evaluated to highlight the most preferable and compatible ones for aircraft battery modules. Full article

This study systematically evaluated the potential of five native starches, including corn (CS), potato (PS), tapioca (TS), rice (RS), and sweet potato (SPS), as fat replacers in low-fat pork sausages. The obtained results showed that amylose content varied significantly, with PS and SPS having the highest levels (30.06% and 28.60%, respectively), which were beneficial for forming starch gels. Correspondingly, PS and SPS demonstrated the highest solubility and swelling power. In sausage applications, PS and SPS exhibited superior water-retention capacities, with drying losses of 6.75% and 7.03%, and cooking losses of 2.23% and 2.52%, which were lower than those of the normal control (NC) and low-fat control (LFC) groups. Moreover, the results of texture profile analysis revealed that PS and SPS enabled the sausages to achieve the highest levels of hardness and springiness, contributing to maintaining the moisture retention and toughness of the sausages. Electronic tongue and nose analyses indicated that incorporating these starches did not adversely affect the taste and odor profiles of the sausages, except for RS, which showed distinct flavor encapsulation properties. Overall, PS and SPS served as excellent fat replacers in the meat industry, offering healthier alternatives without compromising product quality. Full article

The collaboration of ground vehicles (GVs) and drones offers a powerful approach for enhancing drone capabilities. Current research focuses on drone-only or single-type target reconnaissance, failing to adequately account for practical scenarios. This paper introduces a GV–drone collaboration routing problem with multi-type target reconnaissance (GVD-MTR), which explicitly integrates GV–drone collaboration with simultaneous reconnoitering of point, line, and area targets. To address this problem, we propose a knowledge-driven two-stage hybrid algorithm (KDHA). In the first stage, K-means clustering combined with heuristic operators is applied to generate and refine routes for the GV. In the second stage, an improved Adaptive Large Neighborhood Search (IALNS) method is implemented to produce optimized drone routes. KDHA leverages hybrid search strategies, such as a population-based initialization strategy and a multi-level acceptance strategy, to efficiently generate high-quality solutions. Regarding recognizing the impacts of different target types on the total travel distance, we incorporate the related domain knowledge to design problem-specific search operators. Extensive simulation experiments demonstrate that KDHA consistently outperforms several state-of-the-art heuristics in terms of solution quality and runtime. Sensitivity analyses further confirm the robustness of the proposed approach across a range of parameter settings and problem instances. Full article

Objective: Seaweed and microalgae provide antioxidants, polyunsaturated fatty acids, and bioactive compounds that may enhance exercise performance and accelerate recovery. However, evidence remains inconsistent. This systematic review and meta-analysis aimed to evaluate the effects of algae-derived supplementation on exercise performance and physiological recovery outcomes in healthy and athletic adults. Methods: This review was registered in PROSPERO (CRD420251166723) and conducted in accordance with PRISMA 2020 guidelines. PubMed, Web of Science, Embase, Cochrane Library, EBSCO, and CNKI were systematically searched for randomized controlled trials (RCTs) evaluating algae supplementation in exercise contexts. Inclusion and exclusion criteria were defined based on the PICOS framework. Primary outcomes included VO₂max, Time to exhaustion (TTE), maximal power output (WRmax), Time-Trial (TT) performance, and creatine kinase (CK). Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup, sensitivity, and publication bias analyses were performed. Results: Twenty-two RCTs (n = 822) investigating Spirulina, Chlorella, brown-algal polysaccharides, or astaxanthin met inclusion criteria. Algae supplementation showed a suggestive improvement in VO₂max (SMD = 0.88, 95%CI: 0.00–1.75) and significantly improved in TTE (SMD = 1.06, 95%CI: 0.16–1.96), with smaller effects on WRmax (SMD = 0.29, 95%CI: 0.03–0.55), and no significant benefit for TT performance (SMD = −0.27, 95%CI: −0.74 to 0.21). Regarding recovery, CK concentrations were significantly reduced (SMD = −0.78, 95%CI: −1.28 to −0.28). Subgroup analysis suggested greater effects for Chlorella supplementation, higher dosages, and aerobic training contexts; reductions in muscle-damage markers were more evident following resistance exercise. Sensitivity analyses supported the robustness of the main findings with minimal evidence of publication bias. Conclusions: Algae-derived supplements—particularly Spirulina and Chlorella—may modestly enhance aerobic exercise performance and attenuate exercise-induced muscle damage under certain conditions. Effects appear to depend on algae species, dosing strategies, intervention duration, and training modality. High-quality, multi-center RCTs incorporating mechanistic endpoints are needed to clarify optimal application and to develop athlete-specific recommendations. Full article

The demand for reliable, compact, and highly dependable energy conversion systems has grown significantly due to their application in renewable energy systems and electric vehicles for transportation. One of the main converters used in this type of conversion system is the DC–AC converter, known as an inverter. The common study of inverter behavior has focused on addressing, in isolation, the topologies and modulation strategies that activate/deactivate the converter switches, whose main objectives are to improve power quality, increase power density under different operating conditions, and reduce losses. Some of the above objectives were addressed by oversized passive filters, which resulted in increased system volume, high cost, and reduced adaptability. This systematic review analyzes and organizes the state of the art regarding the relationship between the selection of inverter topology, modulation strategy (ranging from conventional modulation approaches to more advanced adaptive strategies), and optimization in conjunction with passive components to observe DC bus voltage management. The review was conducted following the PRISMA 2020 guidelines. A structured search was performed in IEEE Xplore, ScienceDirect, MDPI, and Scielo databases up to 2025, retrieving 9547 records. After duplicate removal and multi-stage screening of titles, abstracts, and full-text, 104 studies met the predefined technical inclusion criteria. Eligible studies were required to report quantitative performance metrics, validated modulation techniques, and explicit focus on inverter architectures or DC bus optimization. The selected studies were examined through comparative technical analysis of topology–modulation interaction, harmonic distortion performance, efficiency, and system-level integration. The study highlights the importance of taking a comprehensive approach at the complete system level by designing the elements addressed together, rather than being optimized in isolation for renewable energy and electric vehicle applications. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

Cadmium (Cd) contamination is widely recognized as a major risk factor affecting the security and quality of crop production. Watermelon (Citrullus lanatus) is a globally cultivated fruit that is susceptible to Cd stress. 24-Epibrassinolide (EBR), an active brassinosteroid, is essential for plant growth and abiotic stress responses. However, its protective role in watermelon under Cd stress remains unclear. This study elucidates the physiological and molecular processes underlying EBR-mediated alleviation of Cd toxicity in watermelon seedlings. The results showed that exogenous EBR application effectively mitigated Cd-induced growth inhibition through decreased Cd deposition, reduced the accumulation of reactive oxygen species (ROS), lowered membrane lipid peroxidation, and increased antioxidant capacity in watermelon leaves under Cd treatment. Transcriptome (RNA-Seq) analysis revealed that EBR triggered substantial reprogramming of gene expression patterns, identifying 530 differentially expressed genes (DEGs) in Cd + EBR co-treatment compared with Cd treatment alone, including 204 down-regulated genes and 326 up-regulated genes. These DEGs are vital for controlling several physiological processes, including phenylpropane metabolism, phenylpropanoid biosynthesis, endoplasmic reticulum’s protein production, cell wall organization, and others. Further physiological assays confirmed that EBR increased the activities of PAL and 4CL, the core enzymes driving phenylpropanoid biosynthesis, leading to a significant accumulation of total phenols and flavonoids. Together, the above results give concrete proof of the powerful functions of 24-EBR, acting as an enhancer of plant performance under Cd stress by enhancing the antioxidant system and by activating the phenylpropanoid pathway and its derived metabolic networks. Full article

Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article

To address the demand for wide-swath, high-resolution short-wave infrared (SWIR) imaging on resource-constrained spaceborne platforms, this study presents the design and on-orbit validation of a compact dual-channel push-broom (line-scanning) imaging system. The system adopts a transmissive optical architecture and a centralized, compact electronic control unit (ECU) configuration. By interleaving and mosaicking sixteen InGaAs linear array detectors, the system achieves an imaging swath of approximately 187 km and a nominal ground sampling distance of about 24 m, while maintaining a total instrument mass of 10.62 kg and a power consumption of approximately 12 W, thereby demonstrating a high level of integration and efficient resource utilization. To address focal plane consistency issues arising from multi-detector mosaicking, a closed-loop leveling method was developed using the modulation transfer function (MTF) as the primary performance metric. Through defocus estimation and quantitative correction of protrusions on a SiC substrate, convergence toward a unified confocal focal plane among multiple detectors was achieved. On-orbit image quality assessment indicates that the full width at half maximum (FWHM) of the line spread function (LSF) for both channels is approximately 1.38 pixels, with favorable signal-to-noise ratio (SNR) performance. These results validate the effectiveness of the proposed focal plane leveling strategy as well as the opto-mechanical-thermal design of the system. The proposed approach provides a practical pathway for the engineering implementation and consistency control of multi-detector mosaicked SWIR payloads under stringent resource constraints. Full article