Search Results (651)

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

In-wheel motor (IWM)-driven electric vehicles (EVs) are susceptible to road excitation, which can induce eccentricity between the stator and rotor of the IWM. This eccentricity leads to unbalanced electromagnetic forces (UEFs) and electromechanical coupling (EMC) effects, severely degrading vehicle dynamic performance. To address this issue, this study first established an EMC system model encompassing UEF, IWM drive, and vehicle dynamics. Based on this model, four typical operating conditions—constant speed, acceleration, deceleration, and steering—were designed to thoroughly analyze the influence of EMC effects on vehicle dynamic response characteristics. The analysis results were validated through real-vehicle experiments. The results indicate that the EMC effects caused by motor eccentricity primarily affect the vehicle’s vertical dynamics performance (especially during acceleration and deceleration), leading to increased vertical body acceleration and reduced ride comfort, while having a relatively minor impact on longitudinal and lateral dynamics performance. Additionally, these effects significantly increase the relative eccentricity of the motor under various operating conditions, further degrading motor performance. To mitigate these negative effects, this paper designs an active suspension controller based on distributed model predictive control (DMPC). Simulation and experimental validation demonstrate that the proposed controller effectively improves ride comfort and body posture stability while significantly suppressing the growth of the motor’s relative eccentricity, thereby enhancing motor operational performance. Full article

This study examines the moderating effect of bank capitalization on the relationship between economic freedom and banking performance, offering comparative evidence from both advanced and emerging economies. Using an unbalanced panel of 213 countries from 1993 to 2018, this study applies a two-step System Generalized Method of Moments approach to address dynamic effects, endogeneity, and unobserved heterogeneity. The results show that economic freedom exerts a negative and significant impact on bank profitability (ROA and ROE), particularly in emerging markets with weaker institutional safeguards. Strong internal capital buffers, on the other hand, mitigate these adverse effects and enhance resilience, supporting stable profitability under liberalized conditions. Regulatory capital shows a less consistent and sometimes restrictive role. Disaggregated results indicate that equity buffers most effectively cushion the risks of financial and investment freedom, whereas trade freedom is less sensitive to capital levels. The findings emphasize that successful liberalization depends on institutional capacity and capitalization strength, highlighting the importance of tailored prudential frameworks. The study contributes to debates on financial liberalization, Basel III, macroprudential regulation, and bank risk management, underscoring that a “one-size-fits-all” liberalization strategy may undermine stability and efficiency unless supported by robust capital buffers. Full article

Multiphase electric drives (EDs) offer important advantages for high-demand applications. However, they require appropriate high-performance control strategies. In this context, finite-control-set model predictive control (FCS-MPC) emerges as a promising strategy, offering a notable flexibility to implement multiobjective regulation schemes. When applied to multiphase EDs, standard FCS-MPC exhibits degraded current quality at low and medium control frequencies. Multivector solutions address this issue by properly combining multiple voltage vectors within a single control period to create the so-called virtual voltage vectors (VVVs). In this way, this approach achieves flux and torque regulation while minimizing current injection into the secondary subspace. For this purpose, the VVV synthesis typically prioritizes active vectors with low contribution in secondary subspaces, avoiding the average deception phenomenon. VVV solutions commonly enable an open-loop regulation of secondary currents. Nevertheless, the absence of closed-loop control in the secondary subspace hinders the compensation of nonlinearities, machine asymmetries, and unbalanced conditions in the ED. Considering this scenario, this work implements a multivector FCS-MPC recovering closed-loop control for the secondary subspace. The capability of the proposal to mitigate secondary current injection and compensate for possible dissymmetries is experimentally evaluated in a six-phase ED. Its performance is compared against a benchmark technique in which secondary current regulation is handled in open-loop mode. The proposed control solution significantly improves in current quality, achieving a reduction in harmonic distortion of 54% at medium speed. Full article

This study addresses the issues of unbalanced capacity allocation and rigid train formations in urban metro systems under tidal passenger flow conditions. By integrating temporal–spatial passenger demand with real-time dynamic train formation, we propose a virtual formation optimization method driven by carriage load factors. This method enhances the flexibility of train formation strategies by coordinating virtual coupling and decoupling operations between trains traveling in opposite directions. A mixed-integer linear programming (MILP) model is developed, with train unit allocation and turnover scheduling as the main decision variables. The model aims to minimize total passenger waiting time and system operating costs, while incorporating constraints related to unit allocation, turnover, and passenger assignment. The model can be efficiently solved using commercial solvers such as CPLEX. To evaluate the proposed method, a case study is conducted on a metro line in a major city. Numerical experiments demonstrate that, compared with a fixed 6-car formation scheme, the proposed method reduces total passenger waiting time by approximately 4.2% and operating costs by 11.6%. When compared to a fixed 8-car formation scheme, it achieves a 48.8% reduction in operating costs with only a 4.3% increase in passenger waiting time. These results highlight the potential of the proposed virtual formation strategy to enhance operational efficiency and resource utilization in urban metro systems, offering both practical value and implementation feasibility. Full article

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article

This paper presents a heuristic methodology for the optimal expansion of unbalanced three-phase distribution systems in rural areas, simultaneously addressing feeder routing and conductor sizing to minimize the total annualized cost—defined as the sum of investments in conductors and operational energy losses. The planning strategy explores two radial topological models: the Minimum Spanning Tree (MST) and the Steiner Tree (ST). The latter incorporates auxiliary nodes to reduce the total line length. For each topology, an initial conductor sizing is performed based on three-phase power flow calculations using Broyden’s method, capturing the unbalanced nature of the rural networks. These initial solutions are refined via four metaheuristic algorithms—the Chu–Beasley Genetic Algorithm (CBGA), Particle Swarm Optimization (PSO), the Sine–Cosine Algorithm (SCA), and the Grey Wolf Optimizer (GWO)—under a master–slave optimization framework. Numerical experiments on 15-, 25- and 50-node rural test systems show that the ST combined with GWO consistently achieves the lowest total costs—reducing expenditures by up to 70.63% compared to MST configurations—and exhibits superior robustness across all performance metrics, including best-, average-, and worst-case solutions, as well as standard deviation. Beyond its technical contributions, the proposed methodology supports the United Nations Sustainable Development Goals by promoting universal energy access (SDG 7), fostering cost-effective rural infrastructure (SDG 9), and contributing to reductions in urban–rural inequalities in electricity access (SDG 10). All simulations were implemented in MATLAB 2024a, demonstrating the practical viability and scalability of the method for planning rural distribution networks under unbalanced load conditions. Full article

In low-voltage (LV) distribution networks, system operating conditions are always unbalanced due to the unpredictability of the load demand in each phase, coupled with a potentially asymmetrical network structure due to different phase conductors’ sizes and lengths. The widespread diffusion of distributed generators (DGs) among network users has significantly contributed to reducing the overall load of the electrical system, but at the cost of making voltages slightly more unbalanced. In this article, an LV distribution test network equipped with several single-phase DGs has been considered, and all During-Fault Voltages (DFVs) have been studied, according to each possible type of short circuit. To provide a measure of the asymmetry of unsymmetrical voltage dips, three different indices based on the symmetrical components of the voltages have been considered; moreover, the Monte Carlo simulation (MCS) method has allowed for studying faults and asymmetries in a probabilistic manner. Through the probability density functions (pdfs) of the DFVs, it has been possible to assess the impact of single-phase DGs on the asymmetry of bus voltages due to short-circuits. Full article

Background: Probabilistic genotyping software has become an essential tool in forensic genetics, particularly for interpreting complex DNA mixtures. Previous studies measured the impact of considering widely divergent statistical approaches in quantifying evidence, both inter- and intra-software. At a much smaller scale, this data-driven study shows how different models implemented on distinct versions of the same tool may affect the results. Among the available tools, EuroForMix stands out as a quantitative, open-source software that models various aspects of the DNA profile, including artefacts like stutter peaks. Its freeware nature allowed the use of both versions 1.9.3. and 3.4.0, between which several updates were made, including the possibility to model both back and forward stutter, compared to only modeling back stutters inputted by the expert in the earlier version. Methods: A total of 156 real casework sample pairs (comprising mixtures with two or three estimated contributors and associated reference) from the Portuguese Scientific Police Laboratory were analyzed using both software versions. The same input data, containing alleles and artefactual peaks, were used to reflect operational conditions. Statistical measurements were compared and further investigated. Results: Most Likelihood Ratio values differed in less than one order of magnitude across versions. However, exceptions were found in more complex samples, such as those with more contributors, unbalanced contributions, or greater degradation. Conclusions: This work emphasizes the relevance of model selection in forensic evidence quantification, even when considering different versions of the same tool. The impact of different models in statistical evaluation depends on several factors, such as sample technical conditions, genotypic profiles, and population distribution. Full article

To address the challenges of unbalanced demand and high operational costs in highway port logistics, this study investigates the scheduling of tractors and semitrailers under time window constraints in a networked environment, where geographically distributed ports are interconnected by fixed routes, and tractors dynamically transport semitrailers between ports to balance asymmetric demands. A mathematical optimization model is developed, incorporating multiple car yards, diverse transport demands, and temporal constraints. To solve the model efficiently, an Adaptive Large Neighborhood Search (ALNS) algorithm is proposed and benchmarked against an improved Ant Colony System (IACS). Simulation results show that, compared to traditional scheduling methods, the proposed approach reduces the number of required tractors by up to 61% and operational costs by up to 21%, depending on tractor working hours. The tractor-to-semitrailer ratio improves from 1.00:1.10 to 1.00:2.59, demonstrating the enhanced resource utilization enabled by the ALNS algorithm. These findings offer practical guidance for optimizing tractor and semitrailer configurations in highway port operations under varying conditions. Full article

This paper proposes a nonlinear control strategy for a microgrid, comprising a PV generator, wind turbine, battery, solid oxide fuel cell (SOFC), electrolyzer, and a three-phase four-leg voltage source inverter (VSI) with an LC filter. The microgrid is designed to supply unbalanced AC loads while maintaining high power quality. To address chattering and enhance control precision, a super-twisting algorithm (STA) is integrated, outperforming traditional PI, IP, and classical SMC methods. The four-leg VSI enables independent control of each phase using a dual-loop strategy (inner voltage, outer current loop). Stability is ensured through Lyapunov-based analysis. Scalar PWM is used for inverter switching. The battery, SOFC, and electrolyzer are controlled using integral backstepping, while the SOFC and electrolyzer also use Lyapunov-based voltage control. A hybrid integral backstepping–STA strategy enhances PV performance; the wind turbine is managed via integral backstepping for power tracking. The system achieves voltage and current THD below 0.40%. An energy management algorithm maintains power balance under variable generation and load conditions. Simulation results confirm the control scheme’s robustness, stability, and dynamic performance. Full article

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

This study explores how headquarters (HQ) digital transformation affects foreign subsidiaries’ performance in emerging market multinational enterprises (EMNEs). Based on the Technology–Organization–Environment (TOE) framework, parenting advantage theory, and loose coupling theory, we propose a multi-level contingency model. Using unbalanced panel data from 5543 foreign subsidiaries of Chinese A-share listed firms (2011–2021), we find that HQ digital transformation significantly improves subsidiary performance. However, this effect is shaped by key organizational and environmental factors. At the organizational level, excessive HQ control weakens the positive impact, while business group affiliation strengthens it. At the environmental level, strong intellectual property rights (IPR) protection enhances the benefits of digital transformation, whereas advanced host-country digital infrastructure substitutes internal support, reducing the effect. Robustness checks with alternative measures and instrumental variable estimation confirm our results. Theoretically, this study opens the “black box” of intra-MNE digital value transmission and identifies boundary conditions under which digital parenting is effective. Practically, it offers insights for EMNEs on optimizing digital strategies amid governance complexity and institutional diversity. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

Osteoporosis is a skeletal disorder characterized by decreased bone strength, which leads to an increased risk of developing fractures. Interestingly, this metabolic disorder is often related to sarcopenia, defined as decreased muscle mass, strength, and function. These two conditions appear to be closely connected, leading to a clinical condition named osteosarcopenia (OS). Aging may explain the link between muscle and bone loss through genetic, mechanical, endocrine, and nutritional factors. Further, aging increases the amount of adipose tissue, often due to sedentary behavior and unbalanced nutritional pattern, leading to a clinical condition defined as osteosarcopenic obesity, characterized by concurrent obesity, sarcopenia, and osteoporosis, where each condition exacerbates the others. Moreover, sarcopenia leads to decreased physical (PA) activity, worsening skeletal homeostasis, and creating a vicious cycle, which increases falls, fracture risk, and disability. This review underscores the importance of a systemic approach, focusing on nutritional therapy integrated with PA and, eventually, pharmacological interventions to efficiently manage (OS). Full article