Search Results (253)

Wireless power transfer (WPT) is increasingly used in smart manufacturing, unmanned platforms, and contactless power-supply applications. However, weak coupling, load-dependent impedance drift, and spatial misalignment can shift the resonant condition, leading to unstable output voltage and reduced transfer efficiency. This paper proposes a constant-voltage WPT method that combines a non-uniform winding coupler, parasitic coils, and dynamic capacitor compensation. A composite magnetic coupler with dense outer windings, loose inner windings, and parasitic coils is first developed, and a region-based electromagnetic model is established to characterise self-inductance, mutual inductance, and coupling coefficients. An improved LCC-S compensation network with a dynamic capacitor compensation matrix is then derived to keep the system close to resonant operation at the nominal 85 kHz operating point under load variation and coil-displacement-induced coupling changes. A zero-voltage-switching-angle tracking method with mutual-inductance correction is further introduced to compensate for phase deviation and maintain soft-switching operation through limited switching-frequency adjustment. Experimental validation demonstrates that the system maintains a stable constant-voltage output across a load range of 20–50 Ω and under 5 cm lateral and longitudinal offsets. The measured efficiency remains above 89% and reaches 93.7% under the optimal coupling and load-matching condition. Full article

Soil erosion (SE) is a constant, complex land degradation process, a common natural disaster that occurs all over the world and severely impacts soil fertility, food security, and environmental balance. Soil erosion depends on many factors, including soil properties, slope, vegetation, rainfall amount and intensity, and anthropogenic activities. There are two main natural erosive forces by which soil is eroded and transported—water and wind. Water erosion refers to the detachment, transportation, and deposition of soil particles (solid runoff) into river networks. These particles, varying in size and composition, are the main products of soil erosion and most strongly affect river ecosystems. Solid runoff, or sediment-laden runoff, affects water quality, destroying habitats, carrying pollutants, reducing reservoir storage, and causing flooding. Erosion control activities also influence river ecosystems in different ways. Hydrotechnical facilities, a major erosion control practice, can alter the composition of aquatic biota by disrupting longitudinal connectivity and isolating populations. Reforestation and afforestation are other erosion control practices that have a strong impact on ecosystems. Stormwater retention systems in urban and forest areas are also important measures addressed in this review. This review examines complex environmental interactions and the roles of erosion and erosion control activities in river ecosystems. During the research, several key points were established: erosion and erosion control activities significantly affect river ecosystems. There is a lack of quantitative analysis of erosion intensity and its influence on ecosystems. This is probably due to the exceptional complexity and diversity of river ecosystems, but such a study would provide important information about complex relationships in nature. Full article

Energy-intensive industries deploying hybrid renewable energy systems require performance monitoring frameworks that evolve with phased system implementation. This paper introduces the performance and sustainability framework, a simulation-grounded evolution of the sustainability balanced scorecard for longitudinal assessment of renewable energy infrastructure. The framework requires that key performance indicators derive from validated techno-economic simulations, that assessment is repeated at temporal checkpoints corresponding to physical system changes, and that each balanced scorecard perspective includes at least one environmental or circular-economy indicator. The framework is demonstrated in a cheese manufacturing facility in Crete, Greece, where a 38 kW cheese whey biomass generator, 72.2 kW photovoltaic system, and 10 kW wind turbine are deployed over five years. Annual HOMER Pro re-simulations are combined with weighted SWOT scoring to track technical, economic, environmental, and organisational performance. By Year 5, the system achieves an 88.7% electrical renewable fraction, 60.0% gross-operational CO₂-eq reduction, 0.1148 EUR/kWh levelised cost of energy, and 22.3% internal rate of return. The longitudinal trajectory also reveals declining delivered thermal renewable contribution and cheese whey utilisation, exposing operational trade-offs that single-point scorecard assessments would miss. Applicability of the PSF to community-scale governance under ISO 37101:2016 and to renewable energy communities under Directive (EU) 2018/2001 is examined exclusively as a conceptual scaling framework for future research. The present empirical demonstration is restricted to a single-facility case study, and no community-level stakeholder data are collected or analysed. Full article

This study investigates the effects of filament winding parameters (tension and mosaic pattern) on the mechanical performance of thin-walled fiber-reinforced polymer composite tubes under internal pressure. The pressure was generated through axial compression of an elastomeric insert, providing a controlled alternative to conventional hydrostatic burst testing. Tubes were manufactured with different combinations of winding tension (10–50 N) in the ±55° and hoop layers. Within the ±55° layer, several mosaic pattern configurations were tested. Structural responses were evaluated using pressure testing, Digital Image Correlation (DIC), and Scanning Electron Microscopy (SEM). 20 N was identified as the most efficient tension level, improving interlaminar integrity and increasing hoop tensile strength by approximately 8–13%. Specimens with a hoop layer failed abruptly by hoop-dominated brittle fracture, characterized by longitudinal splitting and fiber rupture in the circumferential direction. Among the investigated mosaic configurations, the 3/3 pattern demonstrated the most efficient structural response—the mean hoop tensile strength (1088 ± 43 MPa) was approximately 31–40% higher than that of the remaining configurations (722–798 MPa). Overall, the results indicate that both winding tension and mosaic pattern influence the failure pressure, with optimized configurations contributing to improved pressure resistance and structural consistency. Full article

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R² of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy. Full article

The hot-spot temperature in oil-immersed 3D wound core transformer has a significant impact on its performance. The complexity of the winding structure and the characteristics of oil flow increase the difficulty of temperature field analysis. To address this challenge, this study aims to propose a comprehensive thermal network model for oil-immersed 3D wound core transformers to accurately calculate the winding average temperature rise and local hot-spot temperature rise with high efficiency. First, based on the principle of constant thermal resistance, a detailed model of high- and low-voltage winding is calculated using 2D finite element simulation technology. An equivalent model is established to obtain the equivalent thermal conductivity. This model considers various variables, including wire diameter, external insulation dimensions, and the vertical and longitudinal spacing of the windings. Next, multiple types of thermal resistance are defined using the thermoelectric analogy method, and a global thermal network model of the oil-immersed 3D wound core transformer is constructed. Using the Gauss–Seidel method and relevant heat transfer theory, factors such as the flow of transformer cooling oil are taken into account, which allows for the calculation of the average temperature rise and local hot-spot temperature rise in the windings. This approach effectively reduces calculation time while ensuring accuracy. Finally, a 50 kVA oil-immersed amorphous alloy 3D wound core transformer is used as a case study, and temperature field experimental tests are conducted to verify the accuracy of the proposed analytical model. Full article

This study investigates subauroral phenomena during the main phase of the 10 May 2024 geomagnetic storm using a combination of ground-based observations from the WD IZMIRAN observatory (magnetometer, ionosonde, and all-sky imager) and Global Self-consistent Model of the Thermosphere, Ionosphere, Protonosphere (GSM TIP) simulations. During 18:00–20:00 UT, we identified the simultaneous occurrence of ionospheric signatures of Polarization Jets (PJ)/Sub-Auroral Ion Drifts (SAID) and Strong Thermal Emission Velocity Enhancement (STEVE) over Kaliningrad, consistent with previously reported PJ/SAID identification from DMSP drift velocity measurements. This identification is supported by: (1) characteristic purple emissions (clearly visible in all three channels) moving rapidly westward; (2) U-shaped structures in ionogram sequences; (3) the reproduction of supersonic westward plasma drifts within a narrow latitudinal band by the first-principles model; and (4) observed and simulated significant Ne depletion. The estimated ion drift velocity from all-sky imaging (assuming an emission altitude of 200 km) is consistent with GSM TIP simulations, which predicted PJ/SAID velocities of ~750 m/s driven by a latitudinally narrow (~3°) but longitudinally extended (>50°) poleward electric field (40 mV/m). Simulations reveal that this PJ/SAID phenomenon causes a reversal of the zonal thermospheric wind at 250 km and induces Ne disturbances across the 200–700 km altitude range. The electron temperature enhancement (up to 1500 K) exhibits a “falling drop” shape, peaking at 350 km, while ion heating exceeds 150 K. The neutral temperature shows a dual response: frictional heating at 120–160 km and localized cooling at 175–250 km due to drop in electron density. Additionally, an increase in atomic oxygen concentration was predicted within the 90–200 km range across the PJ/SAID longitudinal sector. Full article

This paper focuses on the dynamics modeling and control methods for an underactuated Dual-Unmanned Aerial Helicopter Slung Load System (DUH-SLS), which consists of two Unmanned Aerial Helicopters (UAHs) connected to the suspended load via two sling cables. The DUH-SLS is a multi-body coupled system with internal ideal constraint forces and has seven motion degrees of freedom (DOFs) in the longitudinal plane. In this paper, a set of independent and complete generalized coordinates is selected to describe the system’s motion. The dynamics model of DUH-SLS is established using Lagrange analytical mechanics. This approach, which avoids system internal forces, greatly improves modeling efficiency. Finally, the correctness of this dynamics model is validated using a virtual prototype of the DUH-SLS developed in the multi-body dynamics simulation software ADAMS. The DUH-SLS is a complex nonlinear controlled object, and the iterative Linear Quadratic Regulator (iLQR) method is introduced to design an integrated optimal controller to achieve trajectory tracking and swing suppression for the DUH-SLS. This method transforms the quadratic optimal control problem of nonlinear systems into a series of linear quadratic optimal control (LQR) problems through iterative optimization in function space, thus obtaining an optimal solution. The iLQR optimal controller requires offline iterative computation, but the optimal control obtained has a state feedback closed-loop form, which ensures robustness during online control. Numerical simulation results demonstrate that the proposed iLQR optimal controller exhibits excellent control performance in complex multi-task scenarios. Particularly in trajectory tracking tasks, the maximum average position tracking error of the iLQR controller is only 0.14 m, compared to 3.57 m and 3.11 m for the LQR and LMC (Lyapunov Method Controller) controllers, respectively. Furthermore, the controller demonstrates strong robustness against internal parameter perturbations and external complex wind disturbances, fully validating the effectiveness and superiority of the proposed approach. Full article

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. Full article

The parafoil is highly susceptible to deviations from its reference trajectory under wind disturbances. Given its constrained longitudinal control authority, it has limited capability to correct these deviations and regain the intended glide path. To overcome this limitation, we propose a dynamic planning framework based on a layered homing strategy. The airdrop mission trajectory is initially designed as a traditional multi-segment path. To approximate non-uniform glide characteristics under wind disturbances, this planning problem incorporates a predicted wind model as an external input. Node parameters of the segmented trajectory are then solved using an improved grey wolf optimizer (IGWO). By tracking this reference trajectory, the parafoil is guided into the proximity of the target. To ensure landing precision, the terminal phase is formulated and discretized using an adaptive pseudo-spectral method (APSM). The online planner computes a real-time trajectory to account for actual motion characteristics. This dynamic replanning (DRP) compensates for deviations caused by model mismatches and external disturbances. The proposed homing method is statistically verified via extensive Monte Carlo simulations under different wind conditions. Finally, the airdrop experiment is conducted to validate the DRP method. Full article

This study investigates the wind-resistant configuration of a seven-row single-span double-layer cable-supported photovoltaic (PV) array through conducting systematic analysis of the interference effect. Wind tunnel pressure measurement tests were conducted on a rigid model to obtain the wind force coefficients and torque coefficients under different wind directions. The time histories of wind pressure obtained from the tests were imported into a finite element (FE) model to calculate the vertical displacement and torsional angle responses. The wind-induced responses of different configurations with varying quantities and arrangements of longitudinal connections and wind-resistant cables were analyzed. The results indicate that in the case of head-on wind, wind force is the most unfavorable, and the correlation between wind force and torque is relatively low. In the case of oblique incoming flow, torque is the most adverse, and the correlation between wind force and torque increases. Directions of vertical displacement are opposite in windward and leeward wind scenarios, but directions of torsion angle remain consistent. Overall, the wind-induced responses at mid-span are greater than those at the edge, and the first-row response is more significant than that of the subsequent rows. The wind-induced vibration under windward flow conditions is more adverse when compared to that under leeward flow conditions. However, the downstream adverse interference effect caused by leeward incoming flow is more prominent. Based on the comprehensive analysis of wind loads and wind-induced responses, the whole structure is divided into three zones, namely, wind-induced response control zone, local wind pressure control zone, and wind effect transition zone. A wind-resistant configuration with longitudinal connection arrangements considering both safety and economic benefits is proposed, which provides a reference for the wind-resistant design of similar structures. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by persistent synovial inflammation and vascular abnormalities. Emerging evidence suggests that dysbiosis of the microbiome contributes to the pathogenesis of this disease, while seasonal and meteorological variations represent significant factors influencing microbial community dynamics. However, the specific pathological mechanisms mediated by microbial populations within knee joint effusions of RA patients remain poorly elucidated. The present study employs 16S rRNA high-throughput sequencing technology to characterize seasonal variation patterns affecting microbial communities in knee joint effusions of RA patients and to investigate the relationship between microbial community structures and climatic lag effects. Methods: Microbial communities in knee joint effusion samples obtained from RA patients were analyzed using 16S rRNA high-throughput sequencing methodologies. A Distributed Lag Non-linear Model (DLNM) was applied to quantify the delayed effects of climatic variables on microbial community composition. The correlation patterns between meteorological parameters and community structure were elucidated through the integration of ridge regression and redundancy analysis (RDA). Preliminary identification of potential biomarkers was conducted using random forest algorithms. Results: According to research findings, the microbial composition of knee joint effusions in RA patients shows seasonal fluctuation patterns that are compatible with those seen in RA patients, even though there is no discernible seasonal change in β-diversity. Compared with samples obtained during other seasons, spring specimens exhibited significantly elevated relative abundances of both beneficial microorganisms and opportunistic pathogenic taxa. Random forest modeling identified Escherichia-Shigella and Curtobacterium as preliminary candidate biomarkers; however, external validation is required to establish their specificity as disease indicators. Further analysis revealed that although short-term meteorological fluctuations exert minimal influence on overall microbial diversity, specific alterations in mean wind speed (MWS) and relative humidity (RH) drive compositional changes in the microbial community, manifested as rapid responses from dominant bacterial taxa and compensatory buffering effects from rare taxa. Conclusions: This study suggests that the synovial cavity microbiota in RA patients may exhibit seasonal variation patterns that are statistically associated with environmental parameters, particularly humidity and temperature. Due to the inherent limitations of the cross-sectional study design, the preliminary candidate biomarkers identified herein require validation through external cohorts. Additional investigations incorporating healthy controls and osteoarthritis (OA) cohorts are necessary to confirm specificity and to elucidate the therapeutic potential of these microbial targets for RA microbiome interventions. Currently, insufficient evidence exists to establish causal relationships among microbial populations, joint pathology, and climatic factors. Longitudinal cohort studies are imperative to validate the temporal dynamics and clinical significance of these associations. Full article

In response to the key issues of complex internal structure, significant attenuation of partial discharge (PD) ultrasound signal propagation, and low sensor sensitivity in large oil–immersed power transformers, this paper analyzes the multi–order resonant mode vibration characteristics of the shaft–type fiber optic ultrasound sensor core structure. The displacement distribution patterns of the core structure in both transverse and longitudinal resonant modes are clarified. A strategy using oblique fiber winding rings is proposed to eliminate the problems of strain cancellation and non–accumulation of displacement in transverse and longitudinal resonant modes, which are common in traditional fiber optic ultrasound sensors with parallel fiber windings. Furthermore, design principles are provided to enhance the coverage of the free end and the high–strain regions with semi–high symmetry, as well as the vector–integrated response suitable for multi–order modes. Experimental results show that, in typical PD model detection, the oblique winding sensor exhibits a more prominent response near the high–order resonances of the core, with a detection sensitivity approximately 2.5 times higher than that of the parallel winding structure, and an overall sensitivity at least 7.4 times greater than that of traditional Piezoelectric (PZT) sensors. This demonstrates that the fiber winding method is a key design parameter determining the acoustic–solid coupling efficiency and high sensitivity performance of shaft–type fiber optic interferometric PD sensors, providing a feasible path for high–reliability fiber optic sensing solutions for online monitoring of transformer partial discharges. Full article

With the rapid development of mega-cities, clarifying the wind field characteristics of high-density urban areas is crucial for the accurate assessment of wind loads on newly built or temporary structures. Taking the high-density urban area of Shanghai as a case study, this research utilizes long-term wind field monitoring data obtained from a super high-rise building under construction. Statistical methods are employed to analyze the mean wind and fluctuating wind characteristics of such sites. The results indicate the following: the mean wind direction distribution is generally consistent with code statistics, with dominant wind directions varying significantly by season; the mean wind profile exponent at the site is 0.39, which is slightly higher than the reference value for Terrain Category D specified in codes; turbulence intensity tends to stabilize as wind speed increases, and the ratio of along-wind to cross-wind turbulence intensity is 1:0.59, which is slightly lower than the code-suggested value and shows a significant positive correlation with the gust factor. The mean peak factor is 2.52, while the mean longitudinal and lateral turbulence integral length scales are 118 m and 45 m, respectively. For strong wind samples, the longitudinal wind spectrum agrees well with the Davenport spectrum, whereas the lateral power spectrum correlates well with the Von Karman spectrum. This study provides a scientific basis and data support for wind load calculation and structural safety assessment in Shanghai and other high-density cities. Full article

In semi-arid areas, water loss from small agricultural water storage facilities is significant, owing to evaporation. A longitudinal study was conducted between 2019 and 2022 at the Agricultural Research Station, Ananthapuramu, located in the semi-arid climate of Peninsular India, which compared 12 distinct treatments designed to reduce evaporation. These treatments included bamboo sheets, agricultural residues, Azolla (Azolla pinnata), monomolecular alcohol films, and oil-based films, along with an untreated control. Evaporation rates and meteorological data were measured using the depth loss method and automatic weather station. Results indicated substantial treatment effects, such as bamboo sheets decreasing evaporation by 88%, reducing daily loss from 5.2 mm to 0.8 mm, while Azolla achieved a 62% reduction (2.8 mm). Organic residues decreased evaporation by 37–47%, and chemical monolayers and oils by 21–42%. Ridge regression models demonstrated strong performance (R² = 0.789–0.808), with bamboo sheets exhibiting the lowest Root Mean Square Error (0.127 mm day⁻¹). Economic analysis revealed annual water savings of 4700–4800 m³ ha⁻¹ for bamboo sheets and 2300–2500 m³ ha⁻¹ for less effective covers. Assuming a baseline water value of 0.20 US$ m⁻³, annual net benefits ranged from 250 to 900 US$ ha⁻¹, with Net Present Values spanning from 7000 to 160,000 US$ ha⁻¹ across various scenarios. Overall, bamboo sheets and Azolla were identified as the most effective and economically viable options for mitigating evaporation in semi-arid smallholder water systems. Maximum air temperature (T_max) was a key meteorological variable used to model daily evaporation, together with wind speed, followed by relative humidity and sunshine duration. Full article