Search Results (6,281)

With the development of distributed energy sources such as photovoltaic and wind power, power grids have imposed increasingly higher requirements on power quality. As common nonlinear loads in power grids, multi-pulse rectifiers (MPRs) inject significant harmonics into the grid side. To reduce harmonic pollution at the source, this paper proposes a novel triple-passive harmonic suppression method to reduce the input current harmonics of MPRs. The proposed 48-pulse rectifier comprises a main circuit based on delta-connected auto-transformer (DCT) and a triple-passive harmonic suppression circuit (TPHSC). The TPHSC consists of two interphase reactors (IPRs) and eight diodes. Based on Kirchhoff’s Current Law (KCL), the output currents of the main circuit are calculated, and the operating modes of the TPHSC are analyzed. From the main circuit’s output currents and the DCT topology, the rectifier’s input currents are derived, and the optimal turns ratio of the IPRs for minimizing the input current total harmonic distortion (THD) is determined. The total capacity of the IPRs accounts for only 2.3% of the output load power. Experimental results show that the measured input current THD is close to the theoretical value of 3.8%. Overall, the proposed rectifier offers a cost-effective solution with stronger harmonic suppression capability, making it suitable for applications requiring low grid harmonic pollution. Full article

The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions. Full article

Background: Obesity is a risk factor for breast cancer mortality in postmenopausal women. However, it remains unclear which specific components of adipose tissue and skeletal muscle are associated with risk. This study assessed the associations between MRI-assessed adiposity, skeletal mass, and breast cancer risk in a population-based cohort. Methods: We analyzed data from 15,669 postmenopausal women in the UK Biobank who underwent MRI for body composition assessment. Age- and multivariable-adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using Cox proportional-hazards regression to evaluate the associations between body composition and breast cancer risk, adjusting for relevant confounders. Sensitivity analyses were conducted by excluding breast cancer cases diagnosed within 2 years of the MRI scan. To explore nonlinear relationships, we applied restricted cubic splines with three knots to model associations between visceral adipose tissue (VAT), muscle-fat infiltration (MFI), and breast cancer risk. Results: The mean age of participants was 58.6 years (SD = 5.2; range = 40–69). Higher VAT was significantly associated with increased breast cancer risk (3rd vs. 1st tertile aHR = 1.24, 95% CI: 1.10–1.45). Elevated MFI was also linked with greater risk (3rd vs. 1st tertile aHR = 1.53, 95% CI: 1.25–1.87). These associations persisted after excluding early cancer cases. We observed a J-shaped relationship between VAT, MFI, and breast cancer risk. Conclusions: Higher levels of VAT and MFI are associated with elevated breast cancer risk in postmenopausal women, suggesting that imaging-derived body composition measures may enhance risk prediction and inform prevention strategies. Full article

Drilling tool attitude parameters are crucial for achieving precise directional drilling and trajectory control. Navigation systems based on redundant micro-electro-mechanical systems inertial measurement units (MEMS-IMU) significantly improve the reliability and accuracy of drilling tool attitude measurements. To achieve redundant arrangement of MEMS-IMUs, this paper proposes uniformly arranging MEMS-IMUs on a hollow hexagonal prism carrier, taking into account the actual structure of the drilling tool. However, under dynamic conditions, when updating drilling tool attitude using the strapdown inertial navigation system (SINS), the nonlinear errors of the MEMS-IMU accumulate over time, leading to distortion in the attitude calculation results. Therefore, this paper proposes a composite inertial measurement unit (CIMU) attitude measurement method. A virtual inertial measurement unit (VIMU) is generated through multi-IMU data fusion. Furthermore, the geometric constraints between each IMU and the VIMU, combined with Kalman filtering, are used to achieve real-time suppression of attitude errors, thereby improving the accuracy of the drilling tool attitude calculation results. Experimental results show that, compared with conventional data fusion methods, the CIMU algorithm reduces the overall drilling tool attitude error level by 40–70%. Full article

HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the problems of insufficient modeling ability of nonlinear features and insufficient portrayal of long time-series dependencies in prediction methods, this paper proposes an HVAC energy consumption prediction model that combines time-sequence convolutional network (TCN), bi-directional gated recurrent unit (BiGRU), and Attention mechanism. The model takes advantage of TCN’s parallel computing and multi-scale feature extraction, BiGRU’s bidirectional temporal dependency modeling, and Attention’s weight assignment of key features to effectively improve the prediction accuracy. In this work, the HVAC load is represented by the building-level electricity meter readings of office buildings equipped with centralized, electrically driven heating, ventilation, and air-conditioning systems. Therefore, the proposed method is mainly applicable to building-level HVAC energy consumption prediction scenarios where aggregated hourly electricity or cooling energy measurements are available, rather than to the control of individual terminal units. The experimental results show that the model in this paper achieves better performance compared to the method on ASHRAE dataset, the proposed model outperforms the baseline by 2.3%, 22.2%, and 34.7% in terms of MAE, RMSE, and MAPE, respectively, on the one-year time-by-time data of the office building, and meanwhile it is significant 54.1% on the MSE metrics. Full article

The last kilometer connection problem of metro transit stations is the core factor to measure the connection efficiency and service quality. Establishing the spatiotemporal distribution pattern of the connection distance is conducive to clarifying the interaction mechanism between bike-sharing connections and urban space. This study focuses on the travel behavior of shared bicycle users accessing metro stations, aiming to reveal the access distance decay patterns and their relationship with influence factors. Finally, the random forest algorithm was used to explore the nonlinear relationship between the influencing factors and the connection decay distance, and to clarify the importance of the factors. Multiple linear regression was applied to examine the linear correlation between the distance decay coefficient and the factors influence. The geographically weighted regression was further employed to explore spatial variations in their effects. Finally, the random forest algorithm was used to rank the importance of the impact factors. The results indicate that proximity distance to metro stations, proximity distance to bus stops, and the number of bus routes serving the station area have significant negative correlations with the distance decay coefficient. Significant spatial heterogeneity was observed in the influence of each factor on the distance decay coefficient, based on the geographically weighted regression analysis. With a high goodness-of-fit (R² = 0.8032), the Random Forest regression model furthermore quantified the relative importance of each factor influencing the distance decay coefficient. The findings can be directly applied to optimize the layout of shared bicycle parking, metro access facilities planning, and multi-modal transportation system design. Full article

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions. Full article

Accurate modeling of proton exchange membrane fuel cells (PEMFCs) is essential for predicting system performance under diverse operating conditions. This study introduces a refined semi-empirical modeling that combines experimental validation with an enhanced parameter estimation method based on the Cloud Drift Optimization (CDO) algorithm. The approach focuses on identifying seven key parameters of the nonlinear PEMFC model by minimizing the difference between experimentally measured and simulated cell voltages. To assess its effectiveness, the proposed CDO-based estimator was compared with several established metaheuristic algorithms, including the particle swarm optimizer and the tetragonula carbonaria optimization algorithm. The evaluation was performed using three commercial PEMFC stacks rated at 250 W, 500 W, and the NedStack PS6, as well as experimental data obtained from the Renewable Energy Laboratory at A’Sharqiyah University. Results demonstrate that the CDO algorithm consistently produced the lowest sum of squared errors (SSE) of 1.0337 and exhibited stable convergence across multiple independent runs with a standard deviation of 1.2114 × 10⁻⁷. Its reliable performance under both normal and degraded conditions confirms the algorithm’s robustness and adaptability, establishing CDO as an efficient and dependable technique for PEMFC modeling and parameter identification. Full article

During train operation, the adhesion characteristics between the wheels and rails, which are influenced by driving environments and operating conditions, result in a traction force lower than the motor’s nominal output. Traditional control strategies often overlook the nonlinear relationship between wheel–rail adhesion limits and traction motor output, which can lead to wheel slippage, accelerated wear, and excessive energy consumption. This paper establishes an energy-efficient train control model considering wheel–rail adhesion characteristics. Based on convex optimization methods, the model jointly optimizes the train’s speed trajectory and motor control strategy. Before optimization, nonlinear constraints are simplified through function approximation and tightened McCormick envelope relaxation, significantly reducing the computational complexity of the model. Numerical experiments demonstrate that the proposed driving strategy can adjust the train’s speed in response to poor rail conditions, ensuring adherence to adhesion safety limits. Simulations based on real-world high-speed rail line data in China show that, compared to the traditional EETC model with anti-skid control measures, the proposed model achieves a safer driving strategy. Additionally, in the context of speed trajectory tracking control, it reduces energy consumption by 19.49% compared to the traditional EETC model with anti-skid control measures. Furthermore, the model demonstrates high computational efficiency, indicating its potential for integration into a real-time driving strategy optimization framework. Full article

In order to solve the problems of slow convergence speed, insufficient accuracy, and easily falling into the local optimum of the traditional whale optimization algorithm (WOA) in mobile robot path planning, an improved whale optimization algorithm (IWOA) combined with the bird navigation mechanism was proposed. Specific improvement measures include using logical chaos mapping to initialize the population to enhance the randomness and diversity of the initial solution, designing a nonlinear convergence factor to prevent the algorithm from prematurely entering the shrinking surround phase and extending the global search time, introducing an adaptive spiral shape constant to dynamically adjust the search range to balance exploration and development capabilities, optimizing the individual update strategy in combination with the bird navigation mechanism, and optimizing the algorithm through companion position information, thereby improving the stability and convergence speed of the algorithm. Path planning simulations were performed on 30 × 30 and 50 × 50 grid maps. The results show that compared with WOA, MSWOA, and GA, in the 30 × 30 map, the path length of IWOA is shortened by 3.23%, 7.16%, and 6.49%, respectively; in the 50 × 50 map, the path length is shortened by 4.88%, 4.53%, and 28.37%, respectively. This study shows that IWOA has significant advantages in the accuracy and efficiency of path planning, which verifies its feasibility and superiority. Full article

In this article, the authors present an in-depth analysis of a PTAT sensor and its role as one of the analogue blocks in a test ASIC. The authors propose some modifications to the PTAT sensor to reduce output signal non-linearities observed following measurements that are more accurate than those in their previous article on a PTAT sensor. The obtained PTAT sensor linearity ranges from R² = 0.9990 to R² = 0.9999 in a temperature range from −40 °C to 150 °C for the entire set of measured specimens, and the details of these test sessions are discussed in this manuscript. Moreover, it is demonstrated that at least some of the implemented circuits may have a discernible impact on the operation of the others. This is particularly evident regarding the bandgap reference, whose operation is also presented and analysed. The integrated circuit specimens containing all analysed circuits were manufactured using custom 3 µm CMOS technology on an n-type wafer. Measurements showed that some circuits containing p-diff resistors behave differently compared to those consisting solely of MOS transistors in symmetrical and matched configurations. The spread of resistor values is approximately 20%, thus requiring their skilful operation in this technology. The likely cause of the bandgap reference’s operation modification has been identified, and promising results have been obtained by recreating its malfunction via simulation. The authors found that in this technology, analogue circuits should be designed with a large margin for component dimensions, especially those implanted in p-wells. Full article

The steering linkage represents a key subsystem of any automobile, playing a direct role in vehicle handling, driving safety, and overall comfort. Within this mechanism, the tie rod and tie rod end are crucial for transmitting steering forces from the gear to the wheel hub. A typical issue that gradually develops in these components is the clearance appearing in the spherical joint, caused by wear, corrosion, and repeated operational stresses. Even small clearances can noticeably reduce stiffness and natural frequencies, making the system more sensitive to vibration and premature failure. In this work, the effect of spherical joint clearance on the dynamic behavior of the tie rod-tie rod end assembly was analyzed through numerical simulation combined with experimental observation. Three-dimensional CAD models were meshed with tetrahedral elements and subjected to modal analysis under several clearance conditions, while boundary constraints were set to replicate real operating conditions. Experimental measurements on a dedicated test rig were used to assess joint clearance and wear in service parts. The results indicate a strong nonlinear relationship between clearance magnitude and modal response, with PTFE bushing degradation identified as the main source of clearance. These findings link the evolution of clearance to the change in vibration characteristics, providing useful insight for diagnostic approaches and predictive maintenance aimed at improving steering reliability and vehicle safety. Full article

Socio-ecological systems (SESs) exhibit nonlinear feedback across environmental, social, and economic processes, requiring integrative analytical tools capable of representing such coupled dynamics. This study presents a quantitative framework that integrates a compartmental model of a global human–ecosystem with two complementary optimization approaches (Fisher Information (FI) and Multi-Objective Optimization (MOO)) to evaluate policy strategies for sustainability. The model represents biophysical and socio-economic interactions across 15 compartments, incorporating feedback loops between greenhouse gas (GHG) accumulation, temperature anomalies, and trophic–economic dynamics. Six policy-relevant decision variables were selected (wild plant mortality, sectoral prices (agriculture, livestock, and industry), base wages, and resource productivity) and optimized under temporal (25-year) and magnitude (±10%) constraints to ensure policy realism. FI-based optimization enhances system stability, whereas the MOO framework balances environmental, social, and economic objectives using the Ideal Point Method. Both approaches prevent the systemic collapse observed in the baseline scenario. The FI and MOO strategies reduce terminal global temperature by 11.4% and 15.0%, respectively, relative to the baseline (35 °C → 31.0 °C under FI; 35 °C → 29.7 °C under MOO). Resource-use efficiency, measured through the resource requirement coefficient (λ), improves by 8–10% under MOO (0.6767 → 0.6090) and by 6–7% under FI (0.6668 → 0.6262). These outcomes offer actionable guidance for long-term climate policy at national and international scales. The MOO framework provided the most balanced outcomes, enhancing environmental and social performance while maintaining economic viability. Overall, the integration of optimization and information-theoretic approaches within SES models can support evidence-based public policy design, offering actionable pathways toward resilient, efficient, and equitable sustainability transitions. Full article

Glyphosate is the most widely used herbicide in the world for weed control; however, due to lixiviation, wind and runoff effects, an important fraction can reach the soil, aquifers and surface waters, affecting environmental and human health. The behavior of glyphosate in two agricultural soils (C1: silty clay texture, and C2: silty loam texture) was analyzed in this study using a laboratory-scale model. Water transfer was modeled with the Richards equation, while glyphosate transport was modeled using the advection–dispersion equation, with both solved using finite difference methods. The glyphosate dispersion coefficient was obtained from laboratory concentration data derived from the soil profile via inverse modeling using a non-linear optimization algorithm. The goals of this study were to (i) quantify glyphosate retention in soils with different physical and chemical properties, (ii) calibrate a numerical model for the estimation of dispersivity and simulation of short- and long-term scenarios, and (iii) assess vulnerability to groundwater contamination. The results showed that C1 retained a greater amount of glyphosate in the soil profile, while C2 was considered more vulnerable as it liberated the contaminant more easily. The model accurately reproduced the measured concentrations, as evidenced by the RMSE and R² statistics, thus supporting further scenario simulations allowing for prediction of the fate of the herbicide in soils. The approach utilized in this study may be useful as a tool for authorities in environmental fields, enabling better control and monitoring of soil contamination. These findings highlight potential risks of contamination and reinforce the importance of agricultural management strategies. Full article

This paper presents an analytically derived optimal control strategy for wound-rotor synchronous machines (WRSM) based on minimising the Joule losses in both the stator and rotor windings. The presented control strategy is analysed in terms of analytical derivation, machine current ratios, working regions and constraints. It is experimentally verified on a salient-pole wound-rotor synchronous machine with strongly non-linear equivalent circuit parameters. The verification was performed in two stages: first, considering constant equivalent circuit parameters while assessing strong non-linear behaviour of the machine leading to significant discrepancies in the resulting machine torque. In the second stage, after determination of non-linear machine parameters using measured flux maps, identical control methodology is analysed in terms of variations in ratios between machine currents. Using pre-calculated current ratios the same control methodology is extended for machines with strongly non-linear equivalent circuit parameters and verified in a real environment. The measurement confirms expected machine behaviour in all available control regions achievable by used synchronous motor as well as limits between these control regions. The results of the verification in a real environment show a discrepancy of approximately 5% in measured machine torque in comparison to expected value confirming the validity of the analytically derived approach and introduced modifications for machines with non-linear equivalent circuit parameters. Full article