Search Results (13,757)

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

Conventional space vector pulse width modulation (CSVPWM) with the fixed switching frequency generates significant sideband harmonics in the three-phase voltage. Discrete random switching frequency SVPWM (DRSF-SVPWM) methods have been widely applied in motor control systems for the suppression of tone harmonic energy. To further reduce the amplitude of the high-frequency harmonic with a limited switching frequency variation range, this paper proposes a time-division subbands beta distribution random SVPWM (TSBDR-SVPWM) method. The overall frequency band of the switching frequency is equally divided into N subbands, and each fundamental cycle of the line voltage is segmented into 2*(N-1) equal time intervals. Additionally, within each time segment, the switching frequency is randomly selected from the corresponding subband and follows the optimal discrete beta distribution. The switching frequency harmonic energy in the line voltage spectrum spreads across multiple frequency subbands and discrete frequency components, thereby forming a more uniform power spectrum of the line voltage. Both simulation and experimental results validate that, compared with CSVPWM, the sideband harmonic amplitude is reduced by more than 8.5 dB across the entire range of speed and torque conditions in the TSBDR-SVPWM. Furthermore, with the same variation range of the switching frequency, the proposed method achieves the lowest switching frequency harmonic amplitude and flattest line voltage spectrum compared with several state-of-the-art random modulation methods. Full article

This study proposes an iterative method based on thermal equilibrium equations to calculate the radial temperature distribution of long-span overhead transmission lines under forced convection. This paper takes the ACSR 500/280 conductor as the research object, establishes the three-dimensional finite element model considering the helix angle of the conductor, and carries out the experimental validation for the LGJ 300/40 conductor under the same conditions. The model captures internal temperature distribution through contour analysis and examines the effects of current, wind speed, and ambient temperature. Unlike traditional models assuming uniform conductor temperature, this method reveals internal thermal gradients and introduces a novel three-stage radial attenuation characterization. The iterative method converges and accurately reflects temperature variations. The results show a non-uniform radial distribution, with a maximum temperature difference of 8 °C and steeper gradients in aluminum than in steel. Increasing current raises temperature nonlinearly, enlarging the radial difference. Higher wind speeds reduce both temperature and radial difference, while rising ambient temperatures increase conductor temperature with a stable radial profile. This work provides valuable insights for the safe operation and optimal design of long-span transmission lines and supports future research on dynamic and environmental coupling effects. Full article

A standard approach to design begins with scaling up state-of-the-art machines to new target dimensions, moving towards larger rotors with lower specific energy to maximize revenue and enable power production in lower wind speed areas. This trend is particularly crucial in floating offshore wind in the Mediterranean Sea, where the high levelized cost of energy poses significant risks to the sustainability of investments in new projects. In this context, the conventional approach of scaling up machines designed for fixed foundations and strong offshore winds may not be optimal. Additionally, modern large-scale wind turbines for offshore applications face challenges in achieving high aerodynamic performance in thick root regions. This study proposes a holistic optimization framework that combines multi-fidelity analyses and tools to address the new challenges in wind turbine rotor design, accounting for the novel demands of this application. The method is based on a modular optimization framework for the aerodynamic design of a new wind turbine rotor, where the cost function block is defined with the aid of a model reduction strategy. The link between the full-order model required to evaluate the target rotor’s performance, the physical aspects of blade aerodynamics, and the optimization algorithm that needs several evaluations of the cost function is provided by the definition of a surrogate model (SM). An intelligent SM definition strategy is adopted to minimize the computational effort required to build a reliable model of the cost function. The strategy is based on the construction of a self-adaptive, automatic refinement of the training space, while the particular SM is defined by the use of stochastic radial basis functions. The goal of this paper is to describe the new aerodynamic design strategy, its performance, and results, presenting a case study of a 15 MW wind turbine blades optimized for specific deepwater sites in the Mediterranean Sea. Full article

To address the issue of surface glazing that occurs during prolonged polishing with gel tools, this study employs a triethanolamine (TEA)-based polishing fluid system to enhance the self-sharpening capability of the gel polishing disc. The inhibitory mechanism of TEA concentration on disc glazing is systematically analyzed, along with its impact on the gel disc’s frictional wear behaviour. Furthermore, the synergistic effects of process parameters on both surface quality and material removal rate (MRR) of SiC are examined. The results demonstrate that TEA concentration is a critical factor in regulating polishing performance. At an optimal concentration of 4 wt%, an ideal balance between chemical chelation and mechanical wear is achieved, effectively preventing glazing while avoiding excessive tool wear, thereby ensuring sustained self-sharpening capability and process stability. Through orthogonal experiment optimization, the best parameter combination for SiC polishing is determined: 4 wt% TEA concentration, 98 N polishing pressure, and 90 rpm rotational speed. This configuration delivers both superior surface quality and desirable MRR. Experimental data confirm that TEA significantly enhances the self-sharpening performance of gel discs through its unique complex reaction. During the rough polishing stage, the MRR increases by 34.9% to 0.85 μm/h, while the surface roughness S_a is reduced by 51.3% to 6.29 nm. After subsequent CMP fine polishing, an ultra-smooth surface with a final roughness of 2.33 nm is achieved. Full article

Conventional methods for isolating extracellular vesicles (EVs) are often limited by long processing times, a low purity, and a reliance on specialized equipment. To overcome these challenges, we developed the DeSEI (amine-functionalized Diatomaceous earth-based Syringe platform for EV Isolation), a novel platform employing low-cost, amine-functionalized diatomaceous earth (ADe) within a simple syringe–filter system. The capture mechanism leverages the electrostatic interaction between the positively charged ADe and the negatively charged EV surface, enabling a rapid and efficient isolation. The optimized 30 min protocol yields intact EVs with morphology, size, and protein markers comparable to those from ultracentrifugation, ensuring minimal cellular contamination. Notably, DeSEI exhibited a nearly 60-fold higher recovery efficiency of EV-derived miRNA compared to ultracentrifugation. The platform further proved its versatility with a rapid one-step miRNA extraction protocol and a user-friendly cartridge format. The direct miRNA extraction capability is particularly advantageous for a streamlined biomarker analysis, while the cartridge design illustrates a clear pathway toward developing point-of-care diagnostic tools. The DeSEI offers a promising alternative to existing methods for EV-based research by providing a combination of speed, simplicity, and procedural flexibility that does not require specialized equipment. Full article

This study, in response to the optimization needs of fall risks for the elderly in the context of cultural and tourism integration in Hebei Province, China, established a quantitative correlation system between ten gait parameters and ten types of spatial risk factors. By collecting gait data (Qualisys infrared motion capture system, sampling rate 200 Hz) and spatial parameters from 30 elderly subjects (with mild, moderate, and severe functional impairments), a multi-level regression model was established. This study revealed that step frequency, step width, and step length were nonlinearly associated with corridor length, door opening width, and step depth (R² = 0.53–0.68). Step speed, ankle dorsiflexion, and foot pressure were key predictive factors (OR = 0.04–8.58, p < 0.001), driving the optimization of core spatial factors such as threshold height, handrail density, and friction coefficient. Step length, cycle, knee angle, and lumbar moment, respectively, affected bed height (45–60 cm), switch height (1.2–1.4 m), stair riser height (≤35 mm), and sink height adjustment range (0.7–0.9 m). The prediction accuracy of the ten optimized values reached 86.7% (95% CI: 82.1–90.3%), with Hosmer–Lemeshow goodness-of-fit x² = 7.32 (p = 0.412) and ROC curve AUC = 0.912. Empirical evidence shows that the graded optimization scheme reduced the fall risk by 42–85%, and the estimated fall incidence rate decreased by 67% after the renovation. The study of the “abnormal gait—spatial threshold—graded optimization” quantitative residential layout optimization provides a systematic solution for the data-quantified model of elderly-friendly residential renovations. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article

This paper investigates the dependencies between the design parameters of the armature (stator) winding of a high-speed PMSM machine and the electrical losses in its windings resulting from eddy currents. In addition, the factors accounting for the occurrence of parasitic circulating currents, whose presence in the phase windings is associated with the design specificity, are analyzed. Quantitative analysis is carried out by the application of a newly developed mathematical model for the calculation of fundamental and additional losses in a multi-turn coil enclosed in the slots of a ferromagnetic core. The analysis takes into account the actual design of the slot and the conductor, the variable arrangement of individual conductors in the slot, the core saturation and the presence of the excitation field—to represent the main factors that affect the process of additional losses in the slot of the electric machine. The verification of the mathematical model developed in this study was carried out by comparing the distribution of power losses in the slot section of the coil, consisting of several elementary conductors connected in parallel and located in a rectangular open slot, with an identical distribution derived on the basis of an analytical method from the classical circuit theory. For the purpose of confirming the results and conclusions derived from simulation studies, a number of physical experiments were carried out, consisting in determining the power losses in multi-turn coils of different designs. Recommendations have been developed to minimize additional losses by optimizing the arrangement of conductors within the slot, selecting the appropriate cross-sectional size of a single conductor and the saturation level of the tooth zone. Full article

With the continuous expansion and increasing operational complexity of high-speed railway networks, there is a growing need for more flexible and efficient EMU (Electric Multiple Unit) routing strategies. To address these challenges, in this paper, we propose a multi-day flexible circulation model that minimizes total connection time and deadheading mileage. A multi-commodity network flow model is formulated, incorporating constraints such as first-level maintenance intervals, storage capacity, train coupling/decoupling operations, and train types, with across-day consistency. To solve this complex model efficiently, a heuristic decomposition algorithm is designed to separate the problem into daily service chain generation and EMU assignment. A real-world case study in the Beijing–Baotou high-speed corridor demonstrates the effectiveness of the proposed approach. Compared to a fixed strategy, the flexible strategy reduces EMU usage by one unit, lowers deadheading mileage by up to 16.4%, and improves maintenance workload balance. These results highlight the practical value of flexible EMU deployment for large-scale, multi-day railway operations. Full article

This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in blade number, expanded area ratio (EAR), pitch-to-diameter ratio (P/D), FCR, and cupping percentage. A multi-layer artificial neural network (ANN) is trained to predict thrust, torque, and open-water efficiency (η_o) with a high coefficient of determination (R²), greater than 0.9999. The ANN is integrated into an optimization algorithm to identify optimal propeller designs for the KRISO Container Ship (KCS) using empirical constraints for cavitation and tip speed. Unlike prior studies that rely on boundary element method (BEM)-ML hybrids or multi-fidelity simulations, this study introduces a geometry-coupled analysis of FCR and cupping—parameters often treated independently—and applies empirical cavitation and acoustic (tip speed) limits to guide the design process. The results indicate that incorporating 1.0–1.5% cupping leads to a significant improvement in efficiency, up to 9.3% above the reference propeller, while maintaining cavitation safety margins and acoustic limits. Conversely, designs with non-zero FCR values (0.5–1.5%) show a modest efficiency penalty (up to 4.3%), although some configurations remain competitive when compensated by higher EAR, P/D, or blade count. The study confirms that the combination of cupping with optimized geometric parameters yields high-efficiency, cavitation-safe propellers. Furthermore, the ML-based framework demonstrates excellent potential for rapid, accurate, and scalable propeller design optimization that meets both performance and regulatory constraints. Full article

Kirigami-inspired geometries offer a lightweight, bioinspired strategy for friction enhancement and anchoring in soft robotics. This study presents a bioinspired kirigami structure designed to enhance the anchoring performance of soft robotic systems through systematic geometric and actuation parameter optimization. Drawing inspiration from the anisotropic friction mechanisms observed in reptilian scales, we integrated linear, triangular, trapezoidal, and hybrid kirigami cuts onto flexible plastic sheets. A compact 12 V linear actuator enabled cyclic actuation via a custom firmware loop, generating controlled buckling and directional friction for effective locomotion. Through experimental trials, we quantified anchoring efficiency using crawling distance and stride metrics across multiple cut densities and actuation conditions. Among the tested configurations, the triangular kirigami with a 4 × 20 unit density on 100 µm PET exhibited the most effective performance, achieving a stride efficiency of approximately 63% and an average crawling speed of ~47 cm/min under optimized autonomous operation. A theoretical framework combining buckling mechanics and directional friction validated the observed trends. This study establishes a compact, tunable anchoring mechanism for soft robotics, offering strong potential for autonomous exploration in constrained environments. Full article

The escalating heat flux densities in high-performance electronics necessitate superior thermal management. This study enhanced pool-boiling heat transfer, a method offering high heat removal capacity, by leveraging Binder Jetting 3D Printing (BJ3DP) to create complex porous copper structures without the need for chemical treatments. This approach enables a reliable utilization of phenomena like capillarity for improved performance. Three types of porous copper structures, namely Large Lattice, Small Lattice, and Staggered, were fabricated on pure copper substrates and tested via pool boiling of de-ionized and de-gassed water at atmospheric pressure. Compared to a plain polished copper surface, which exhibited a critical heat flux (CHF) of 782 kW/m² at a wall superheat of 18 K, the 3D-printed porous copper surfaces showed significantly improved heat transfer performance. The Staggered surface achieved a conventional CHF of 2342.4 kW/m² (a 199.7% enhancement) at a wall superheat of 24.6 K. Notably, the Large Lattice and Small Lattice structures demonstrated exceptionally stable boiling without reaching the typical catastrophic CHF within the experimental parameters. These geometries continued to increase in heat flux, reaching maximums of 2397.7 kW/m² (206.8% higher at a wall superheat of 55.6 K) and 2577.2 kW/m² (229.7% higher at a wall superheat of 39.5 K), respectively. Subsequently, a gradual decline in heat flux was observed with an increasing wall superheat, demonstrating an outstanding resistance to the boiling crisis. These improvements are attributed to the formation of distinct vapor–liquid pathways within the porous structures, which promotes the efficient rewetting of the heated surface through capillary action. This mechanism supports a highly efficient, self-sustaining boiling configuration, emphasizing the superior rewetting and vapor management capabilities of these 3D-printed porous structures, which extend the boundaries of sustained high heat flux performance. The porous surfaces also demonstrated a higher heat transfer coefficient (HTC), particularly at lower heat fluxes (≤750 kW/m²). High-speed digital camera visualization provided further insight into the boiling phenomenon. Overall, the findings demonstrate that these BJ3DP structured surfaces produce optimized vapor–liquid pathways and capillary-enhanced rewetting, offering significantly superior heat transfer performance compared to smooth surfaces and highlighting their potential for advanced thermal management. Full article

Wheat disease detection is a crucial component of intelligent agricultural systems in modern agriculture. However, at present, its detection accuracy still has certain limitations. The existing models hardly capture the irregular and fine-grained texture features of the lesions, and the results of spatial information reconstruction caused by standard upsampling operations are inaccuracy. In this work, the GDFC-YOLO method is proposed to address these limitations and enhance the accuracy of detection. This method is based on YOLOv11 and encompasses three key aspects of improvement: (1) a newly designed Ghost Dynamic Feature Core (GDFC) in the backbone, which improves the efficiency of disease feature extraction and enhances the model’s ability to capture informative representations; (2) a redesigned neck structure, Disease-Focused Neck (DF-Neck), which further strengthens feature expressiveness, to improve multi-scale fusion and refine feature processing pipelines; and (3) the integration of the Powerful Intersection over Union v2 (PIoUv2) loss function to optimize the regression accuracy and convergence speed. The results showed that GDFC-YOLO improved the average accuracy from 0.86 to 0.90 when the cross-overmerge threshold was 0.5 (mAP@0.5), its accuracy reached 0.899, its recall rate reached 0.821, and it still maintained a structure with only 9.27 M parameters. From these results, it can be known that GDFC-YOLO has a good detection performance and stronger practicability relatively. It is a solution that can accurately and efficiently detect crop diseases in real agricultural scenarios. Full article