Search Results (3,201)

This study aims to develop an event-triggered control strategy of grid-connected inverters, based on the linear parameter-varying (LPV) modeling approach. Regarding the changes in grid voltage, filter capacitance and inductance, and random electromagnetic interference, a stochastic LPV model for three-phase two-level inverters is established. To reduce computation burden, an event trigger with a continuous-time form is adopted to derive the state feedback controller for the LPV plant. Unlike the existing common approach to dealing with event-triggered mechanisms, a predesignated event-triggering threshold is used to determine the triggering instant of the event condition. Using parameter-dependent Lyapunov functions, sufficient conditions reliant on parameters are introduced. Based on the derived conditions, the corresponding event-triggered controllers are engineered to ensure uniform ultimate bounded stability for the resulting event-triggered LPV inverter system subject to exogenous disturbance. The simulation results are presented to confirm the efficacy of the proposed methods. Full article

Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R² value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R² = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control. Full article

Batteries are important components of electrochemical energy storage systems used in mobile devices, electric vehicles, and power generation systems. This paper proposes a compact battery model dedicated to SPICE. This model takes into account properties of a real battery, such as limited electrical capacity, limited charge and discharge current, limited voltage change at its terminals, the self-discharge effect, the dependence of the battery’s internal resistance on its state of charge, and an influence of temperature on its characteristics. The developed model is presented, along with equations describing the parameters of its components. The results of experimental verification of the correctness of the developed model for different types of batteries are presented and discussed. Good agreement was achieved between the calculation and measurement results for AGM, LiPo, LiFePO₄, and Na-ion batteries. High accuracy of the proposed model was demonstrated for all tested batteries. Full article

Modeling ion transport through membrane channels is crucial for understanding cellular processes, and Poisson–Nernst–Planck (PNP) equations provide a fundamental continuum framework for such ionic fluxes. We investigate a quasi-one-dimensional steady-state PNP system for two oppositely charged ion species, focusing on how large permanent charges within the channel and realistic boundary conditions impact ion transport. In contrast to classical models that impose ideal electroneutrality at the channel ends (a simplification that eliminates boundary layers near the membrane interfaces), we adopt relaxed neutral boundary conditions that allow small charge imbalances at the boundaries. Using asymptotic analysis treating the large permanent charge as a singular perturbation, we derive explicit first-order expansions for each ionic flux, incorporating boundary layer parameters $(\sigma ,\rho )$ to quantify slight deviations from electroneutrality. This analysis enables a qualitative characterization of individual cation and anion flux behaviors. Notably, we identify two critical transmembrane potentials, $V_{1c}$ and $V_{2c}$, at which the cation and anion fluxes, respectively, vanish, signifying flux-reversal thresholds that delineate distinct monotonic regimes in the flux-voltage response; these critical values depend on the permanent charge magnitude and the boundary layer parameters. We further show that both ionic fluxes exhibit saturation: as the applied voltage becomes extreme, each flux approaches a finite limiting value, with the saturation level modulated by the degree of boundary charge imbalance. Moreover, allowing even small boundary charge deviations reveals non-intuitive discrepancies in flux behavior relative to the ideal electroneutral case. For example, in certain parameter regimes, a large permanent charge that enhances an ionic current under strict electroneutral conditions will instead suppress that current under relaxed-neutral conditions (and vice versa). This new analytical framework exposes subtle yet essential nonlinear dynamics that classical electroneutral assumptions would otherwise obscure. It provides deeper insight into the interplay between large fixed charges and boundary-layer effects, emphasizing the importance of incorporating such realistic boundary conditions to ensure accurate modeling of ion transport through membrane channels. Numerical simulations are performed to provide more intuitive illustrations of our analytical results. Full article

Electronic transport through T-shaped double quantum dots (TDQDs) connected to normal metallic leads is studied theoretically by using a nonequilibrium Green’s function method. It is assumed that the Coulomb interaction exists only in the central QD (QD-1) sandwiched between the leads, and it is absent in the other reference QD (QD-2) side-coupled to QD-1. We also consider the impacts of Majorana bound states (MBSs), which are prepared at the opposite ends of a topological superconductor nanowire (hereafter called a Majorana nanowire) connected to QD-2, on the electrical current and differential conductance. Our results show that by the combined effects of the Coulomb interaction in QD-1 and the MBSs, a negative differential conductance (NDC) effect emerges near the zero-bias point, where MBSs play significant roles. Now, the electrical current decreases despite the increasing bias voltage. The NDC is prone to occur under conditions of low temperature, and both of the two QDs’ energy levels are resonant to the leads’ zero Fermi energy. Its magnitude, which is characterized by a peak-to-valley ratio, can be enhanced up to 3 by increasing the interdot coupling strength, and it depends on the dot-MBS hybridization strength nonlinearly. This prominent NDC combined with the previously found zero-bias anomaly (ZBA) of the differential conductance is useful in designing novel quantum electric devices, and it may also serve as an effective detection means for the existence of MBSs, which is still a challenge in solid-state physics. Full article

Background: Olive leaves are a rich source of bioactive phenolic compounds, but extraction yields vary depending on methodological choices. The aim was to identify optimal parameters for maximizing recovery and preserving antioxidant activity. Methods: Fourteen studies (149 samples) were included, following predefined eligibility criteria and PRISMA guidelines for systematic review. Data on TPC, TFC, and antioxidant assays (DPPH, FRAP, ABTS) were extracted and analyzed according to extraction method, solvent type, and processing conditions. Results: Soxhlet extraction and shaking achieved the highest TPC and antioxidant capacity, whereas ultrasound-assisted and high-voltage electrical discharge extractions showed lower averages unless intensity or duration was increased. Solvent polarity was critical: ≥75% aqueous methanol provided the highest TPC and FRAP, while ≥75% ethanol yielded the greatest TFC and ABTS activity. Pure water consistently gave the lowest yields. Extractions at >50 °C increased TPC up to fivefold compared to room temperature but did not proportionally improve radical-scavenging capacity. Most phenolic compounds were recovered within ≤1 h under optimized, heated, or assisted conditions, with longer times offering no significant advantage. Conclusions: Optimizing solvent composition, temperature, and extraction time is essential for maximizing yield and maintaining antioxidant quality in olive leaf extracts, and standardized protocols are needed to enable direct comparisons across studies. Full article

Intravascular aspiration thrombectomy catheters are widely used to treat stroke, pulmonary embolism, and deep venous thrombosis. However, their performance is frequently compromised by clot material becoming lodged within the catheter tip. To address this, we develop a novel ultrasound-enhanced aspiration catheter approach that generates cavitation within the tip to mechanically degrade clots, with a view to facilitate extraction. The design employs hollow cylindrical transducers that produce inwardly propagating cylindrical waves to generate sufficiently high pressures to perform histotripsy. This study investigates the feasibility of self-sensing cavitation detection by analyzing voltage signals across the transducer during treatment. Experiments were conducted for two transmit pulse lengths at varying driving voltages with water or clot in the lumen. Cavitation clouds within the lumen were assessed using 40 MHz ultrasound imaging. Changes in the signal envelope during the pulse body and ringdown phases occurred above the cavitation threshold, the latter being associated with more rapid wave damping in the presence of bubble clouds within the lumen. In the frequency domain, voltage-dependent cavitation signals—subharmonics, ultra-harmonics, and broadband—emerged alongside transmit pulses. This work demonstrates a highly sensitive, sensor-free method for detecting cavitation within the lumen, enabling feedback control to further improve histotripsy-assisted aspiration. Full article

Mitochondria-associated membranes (MAMs) are essential for cellular homeostasis. MAMs are specialized contact sites located between the endoplasmic reticulum (ER) and mitochondria and control apoptotic pathways, lipid metabolism, autophagy initiation, and calcium signaling, processes critical to the survival and function of neurons. Although this area of membrane biology remains understudied, increasing evidence links MAM dysfunction to the etiology of major neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). MAMs consist of a network of protein complexes that mediate molecular exchange and ER–mitochondria tethering. MAMs regulate lipid flow in the brain, including phosphatidylserine and cholesterol; disruption of this process causes membrane instability and impaired synaptic function. Inositol 1,4,5-trisphosphate receptor—voltage-dependent anion channel 1 (IP3R-VDAC1) interactions at MAMs maintain calcium homeostasis, which is required for mitochondria to produce ATP; dysregulation promotes oxidative stress and neuronal death. An effective therapeutic approach for altering neurodegenerative processes is to restore the functional integrity of MAMs. Improving cell-to-cell interactions and modulating MAM-associated proteins may contribute to the restoration of calcium homeostasis and lipid metabolism, both of which are key for neuronal protection. MAMs significantly contribute to the progression of neurodegenerative diseases, making them promising targets for future therapeutic research. This review emphasizes the increasing importance of MAMs in the study of neurodegeneration and their potential as novel targets for membrane-based therapeutic interventions. Full article

Mitochondria, as the metabolic hubs of cells, play a pivotal role in maintaining cardiovascular homeostasis through dynamic regulation of energy metabolism, redox balance, and calcium signaling. Cardiovascular diseases (CVDs), including heart failure, ischemic heart disease, cardiomyopathies, and myocardial infarction, remain the leading cause of global mortality, with mitochondrial dysfunction emerging as a unifying pathological mechanism across these conditions. Emerging evidence suggests that impaired mitochondrial transport systems—critical gatekeepers of metabolite flux, ion exchange, and organelle communication—drive disease progression by disrupting bioenergetic efficiency and exacerbating oxidative stress. This review synthesizes current knowledge on mitochondrial transport proteins, such as the voltage-dependent anion channels, transient receptor potential channels, mitochondrial calcium uniporter, and adenine nucleotide translocator, focusing on their structural–functional relationships and dysregulation in CVD pathogenesis. We highlight how aberrant activity of these transporters contributes to hallmark features of cardiac pathology, including metabolic inflexibility, mitochondrial permeability transition pore destabilization, and programmed cell death. Furthermore, we critically evaluate preclinical advances in targeting mitochondrial transport systems through pharmacological modulation, gene editing, and nanoparticle-based delivery strategies. By elucidating the mechanistic interplay between transport protein dysfunction and cardiac metabolic reprogramming, we address a critical knowledge gap in cardiovascular biology and provide a roadmap for developing precision therapies. Our insights underscore the translational potential of mitochondrial transport machinery as both diagnostic biomarkers and therapeutic targets, offering new avenues to combat the growing burden of CVDs in aging populations. Full article

QX-314 is a positively charged lidocaine derivative with the membrane-impermeant property. This compound applied at the intracellular side has been shown to suppress the voltage-gated Na⁺ current (I_Na), while lidocaine itself acts to suppress the hyperpolarization-activated cation current (I_h). To what extent this drug may exert any effects on various plasmalemmal ionic currents still remains largely unknown. This investigation focused on the impact of QX-314 on ionic currents in GH₃ cells derived from pituitary tumors. This compound applied extracellularly was noted to differentially suppress the amplitude of transient and late I_Na with an IC₅₀ value of 93 and 42 μM, respectively. In GH₃ cells dialyzed with QX-314 (10 μM), the I_Na(T) amplitude evoked by a brief depolarizing step was decreased, and its inactivation was increased. Moreover, QX-314, when applied extracellularly at 100 μM, diminished the amplitude of the I_h current with an IC₅₀ of 68 μM. Intracellular dialysis with QX-314 also suppressed I_h amplitude; moreover, the later application of oxaliplatin reversed this suppression. As cells were extracellularly and continually exposed to QX-314, the magnitude of the erg-mediated K⁺ current (I_K(erg)) was also effectively suppressed with an IC₅₀ value of 73 μM. Furthermore, upon intracellular dialysis with QX-314 (10 μM), the degree of the voltage-dependent hysteresis (Hys_(V)) of I_K(erg) during the long-lasting isosceles-triangular ramp voltage was decreased; during continued exposure to QX-314, further extracellular bath additions of PD118057 (10 μM) counteracted QX-314-induced suppression. However, the extracellular addition of QX-314 (100 μM) mildly suppressed the outward delayed rectifier K⁺ current in GH₃ cells. Collectively, QX-314 effectively suppressed I_Na, I_h, and I_K(erg) in GH₃ cells, a model of endocrine function, and these actions may contribute to their physiological functions, if similar effects are observed in vivo. Full article

The optimal allocation of Wireless Smart Concentrators (WSCs) in low-voltage (LV) distribution networks poses significant challenges due to signal attenuation caused by varying building densities and vegetation. This paper proposes a Variable Neighborhood Search (VNS) algorithm to optimize the placement of WSCs in LV distribution networks. To comprehensively assess the proposed approach, both linear and nonlinear mathematical formulations are considered, depending on whether the distance between meters and concentrators is treated as a fixed parameter or as a decision variable. The performance of the proposed VNS algorithm is benchmarked against both exact solvers and metaheuristics such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Tabu Search (TS). In the linear formulation, VNS achieved the exact optimal solution with execution times up to 75% faster than competing methods. For the more complex nonlinear model, VNS consistently identified superior solutions while requiring less computational effort. These results underscore the algorithm’s ability to balance solution quality and efficiency, making it particularly well-suited for large-scale, resource-constrained utility planning. Full article

Batteries are ubiquitous, with their presence ranging from electric vehicles to portable electronics. Research focused on increasing average voltage, improving stability, and extending cycle longevity of batteries is pivotal for the advancement of battery technology. These advancements can be accelerated through research into battery chemistries. The traditional approach, which examines each material combination individually, poses significant challenges in terms of resources and financial investment. Physics-based simulations, while detailed, are both time-consuming and resource-intensive. Researchers aim to mitigate these concerns by employing Machine Learning (ML) techniques. In this study, we propose a Transformer-based deep learning model for predicting the average voltage of battery electrodes. Transformers, known for their ability to capture complex dependencies and relationships, are adapted here for tabular data and regression tasks. The model was trained on data from the Materials Project database. The results demonstrated strong predictive performance, with lower mean absolute error (MAE) and mean squared error (MSE), and higher R² values, indicating high accuracy in voltage prediction. Additionally, we conducted detailed per-ion performance analysis across ten working ions and apply sample-wise loss weighting to address data imbalance, significantly improving accuracy on rare-ion systems (e.g., Rb and Y) while preserving overall performance. Furthermore, we performed SHAP-based feature attribution to interpret model predictions, revealing that gravimetric energy and capacity dominate prediction influence, with architecture-specific differences in learned feature importance. This work highlights the potential of Transformer architectures in accelerating the discovery of advanced materials for sustainable energy storage. Full article

A physics-informed neural network (PINN) framework is developed to model the large deformation and coupled electromechanical response of dielectric elastomer tubes for energy harvesting. The system integrates incompressible neo-Hookean elasticity with radial electric loading and compressible gas inflation, leading to nonlinear equilibrium equations with deformation-dependent boundary conditions. By embedding the governing equations and boundary conditions directly into its loss function, the PINN enables accurate, mesh-free solutions without requiring labeled data. It captures realistic pressure–volume interactions that are difficult to address analytically or through conventional numerical methods. The results show that internal volume increases by over 290% during inflation at higher reference pressures, with residual stretch after deflation reaching 9.6 times the undeformed volume. The axial force, initially tensile, becomes compressive at high voltages and pressures due to electromechanical loading and geometric constraints. Harvested energy increases strongly with pressure, while voltage contributes meaningfully only beyond a critical threshold. To ensure stable training across coupled stages, the network is optimized using the Optuna algorithm. Overall, the proposed framework offers a robust and flexible tool for predictive modeling and design of soft energy harvesters. Full article

In this work, we investigate the electrical properties of a metal–insulator–semiconductor (MIS) heterojunction based on porous silicon dioxide (Ag/pSiO₂/Si). The porous silicon (PS) films were elaborated by electrochemical anodization under specific experimental conditions to obtain a porosity of about 55%. Porous silicon (PS) was oxidized by IR-RTP at different oxidation temperatures (Tox) ranging from 200 to 950 °C under an oxygen atmosphere. The morphology of the samples was analyzed using a scanning electron microscope (SEM). Ag/Al and Ag contacts were screen printed on the back and front sides of the heterojunction, respectively. Both the series and shunt resistances were derived from dark current–voltage (I–V) characteristics related to the various Ag/pSiO₂/Si heterojunctions. In this context, the reflectance was also measured at different oxidation temperatures to investigate its correlation with the series resistance (Rs) and shunt resistance (Rsh). The optimum electrical performance was obtained for an oxidation temperature close to 400 °C. Depending on the pSiO₂ thickness, two conduction mechanisms were highlighted within the devices. For a Tox below 200 °C, as well as for the non-oxidized devices, the conduction mechanism is governed by the tunneling current through the pSiO₂ film. However, when the Tox increases and exceeds 200 °C, the pSiO₂ thickness increases, leading to the switching of the conduction mechanism to a thermionic instead of a tunneling effect mechanism. Full article

It is well known that for low-intensity incident light within a certain frequency range, the stopping voltage of the photoelectric effect is independent of the intensity but dependent on the frequency of the light, which is described by the equation $V=h\nu /e-W_0/e$, where V is the stopping voltage, h is the Planck constant, $\nu$ is the frequency of incident light, e is the basic charge, and $W_0$ is the work function. This implies that the stopping voltage increases with the frequency of the incident light. However, our experiments reveal that for non-monochromatic incident light, the stopping voltage is not determined by the maximum frequency component of the incident light, but by the maximum center frequency among all wave train components (with different center frequencies) involved in the incident light; that is to say, in the photon energy expression $h\nu$, the physical quantity $\nu$ does not refer to the frequency of monochromatic light, but represents the center frequency of a wave train spectrum. The spectral bandwidth of a wave train component can be as large as 122 nm in the visible and near-infrared regions. These findings highlight the need for greater attention to such effects in photoelectric detection and the study of energy exchange between light and matter. Full article