Search Results (752)

Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies. Full article

Sanicro 25 (X7NiCrWCuCoNb25-23-3-3-2) steel is specifically designed for use in superheater components within the latest generation of conventional power plants. These power plants operate under conditions often referred to as super-ultra-supercritical, with steam parameters that can reach up to 30 MPa and temperatures of 653 °C for fresh steam and 672 °C for reheated steam. While last-generation supercritical power plants still rely on fossil fuels, they represent a significant step forward in more sustainable energy production. The most sophisticated facilities of this kind can achieve thermodynamic efficiencies exceeding 47%. This study aimed to conduct a detailed analysis of the initial precipitation processes occurring in Sanicro 25 steel within the temperature range of 700–900 °C. The temperature of 700 °C corresponds to the operational conditions of this material, particularly in secondary steam superheaters in thermal power plants that operate under ultra-supercritical parameters. Understanding precipitation processes is crucial for optimizing mechanical performance, particularly in terms of long-term strength and creep resistance. To accurately assess the microstructural changes that occur during the early stages of service, a digital twin approach was employed, which included CALPHAD simulations and experimental heat treatments. Experimental annealing tests were conducted in air within the temperature range of 700–900 °C. Precipitation behavior was simulated using the Thermo-Calc 2025a with Dictra software package. The results from Prisma simulations correlated well with the experimental data related to the kinetics of phase transformations; however, it was noted that the predicted sizes of the precipitates were generally smaller than those observed in experiments. Additionally, computational limitations were encountered during some simulations due to the complexity arising from the numerous alloying elements present in Sanicro 25 steel. The microstructural evolution was investigated using various methods, including light microscopy (LM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Full article

High-voltage diodes, as key devices in power electronic systems, have important significance for system reliability and preventive maintenance in terms of storage life prediction. In this paper, we propose a hybrid modeling framework that integrates the Long Short-Term Memory Network (LSTM) and Transformer structure, and is hyper-parameter optimized by the Improved Artificial Bee Colony Algorithm (IABC), aiming to realize the high-precision modeling and prediction of high-voltage diode storage life. The framework combines the advantages of LSTM in time-dependent modeling with the global feature extraction capability of Transformer’s self-attention mechanism, and improves the feature learning effect under small-sample conditions through a deep fusion strategy. Meanwhile, the parameter type-aware IABC search mechanism is introduced to efficiently optimize the model hyperparameters. The experimental results show that, compared with the unoptimized model, the average mean square error (MSE) of the proposed model is reduced by 33.7% (from 0.00574 to 0.00402) and the coefficient of determination (R²) is improved by 3.6% (from 0.892 to 0.924) in 10-fold cross-validation. The average predicted lifetime of the sample was 39,403.3 h, and the mean relative uncertainty of prediction was 12.57%. This study provides an efficient tool for power electronics reliability engineering and has important applications for smart grid and new energy system health management. Full article

Fibrate pharmaceuticals (fibrates), as a widespread class of emerging contaminants, pose potential risks to both ecological systems and human health. The photocatalytic system based on nitrogen (N) and fluorine (F) co-doped TiO₂ nanotube arrays (NF-TNAs) provides a renewable solution for fibrate pharmaceutical removal from water, powered by inexhaustible sunlight. In this study, the degradation of two typical fibrates, i.e., bezafibrate (BZF) and ciprofibrate (CPF), under simulated sunlight irradiation through NF-TNAs were investigated. The photocatalytic degradation of BZF/CPF was achieved through combined radical and non-radical oxidation processes, while the generation and reaction mechanisms of associated reactive oxygen species (ROS) were examined. Electron paramagnetic resonance detection and quenching tests confirmed the existence of h⁺, •OH, O₂^•−, and ¹O₂, with O₂^•− playing the predominant role. The transformation products (TPs) of BZF/CPF were identified through high-resolution mass spectrometry analysis combined with quantum chemical calculations to elucidate the degradation pathways. The influence of co-existing ions and typical natural organic matters (NOM) on BZF/CPF degradation were also tested. Eventually, the ecological risk of BZF/CPF transformation products was assessed through quantitative structure–activity relationship (QSAR) modeling, and the results showed that the proposed photocatalytic system can largely alleviate fibrate toxicity. Full article

The increasing demand for high-speed, energy-efficient computing has propelled the development of integrated photonic logic gates, which utilize the speed of light to surpass the limitations of traditional electronic circuits. These gates enable ultrafast, parallel data processing with minimal power consumption, making them ideal for next-generation computing, telecommunications, and quantum applications. Recent advancements in nanofabrication, nonlinear optics, and phase-change materials have facilitated the seamless integration of all-optical logic gates onto compact photonic chips, significantly enhancing performance and scalability. This paper explores the latest breakthroughs in photonic logic gate design, key material innovations, and their transformative applications. While challenges such as fabrication precision and electronic–photonic integration remain, integrated photonic logic gates hold immense promise for revolutionizing optical computing, artificial intelligence, and secure communication. Full article

This study explores using polyvinylidene fluoride (PVDF) membranes modified with multi-walled carbon nanotubes (MWCNTs) to treat simulated and industrial brine from coal power stations. The MWCNTs were acid-treated and characterized using Fourier Transform Infrared Spectroscopy (FTIR), Raman, and nitrogen sorption at 77 K, Thermogravimetric analysis (TGA), and Transmission electron microscopy (TEM). The desired membranes were obtained by casting from a solution of N-Methyl-2-pyrrolidone, PVDF, various weight percentages of MWCNTs, and a small amount of polyvinylpyrrolidone. The acid treatment of the MWCNTs introduced oxygen moieties on the surface, and increased pore volume and surface area while maintaining crystallinity and structural integrity remain preserved. The maximum rejection rate achieved was 41.82% with 1 wt.% of acid-treated MWCNTs in the PVDF membrane. Acid-treated MWCNTs loaded membranes had an improved rejection rate, which was 5× higher than membranes without MWCNTs. Full article

S355NL structural steel is extensively employed in bridges, ships, and power station equipment owing to its excellent tensile strength, weldability, and low-temperature toughness. However, pronounced fluctuations in its Charpy impact energy at low temperatures significantly compromise the reliability and service life of critical components. In this study, vacuum-induction-melted ingots of S355NL steel containing 0–0.086 wt.% rare earth cerium were prepared. The effects of Ce on microstructures, inclusions, and impact toughness were systematically investigated using optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and Charpy V-notch testing. The results indicate that appropriate Ce additions (0.0011–0.0049 wt.%) refine the average grain size from 5.27 μm to 4.88 μm, reduce the pearlite interlamellar spacing from 204 nm to 169 nm, and promote the transformation of large-size Al₂O₃-MnS composite inclusions into fine, spherical, Ce-rich oxysulfides. Charpy V-notch tests at –50 °C reveal that 0.0011 wt.% Ce enhances both longitudinal (269.7 J) and transverse (257.4 J) absorbed energies while minimizing anisotropy (E_t/E_l  =  1.01). Conversely, excessive Ce addition (0.086 wt.%) leads to coarse inclusions and deteriorates impact performance. These findings establish an optimal Ce window (0.0011–0.0049 wt.%) for microstructural and inclusion engineering to enhance the low-temperature impact toughness of S355NL steel. Full article

This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt active filter (SAF) mechanism within the GFM control structure to achieve a real-time suppression of harmonic distortions from the inverter and grid currents. In parallel, a virtual impedance-based dynamic current limiting strategy is incorporated to constrain fault current magnitudes, ensuring the protection of power electronic components and maintaining system stability. The SAF operates in a current-injection mode aligned with harmonic components, derived via instantaneous reference frame transformations and selective harmonic extraction. The virtual impedance control (VIC) dynamically modulates the inverter’s output impedance profile based on grid conditions, enabling adaptive response during fault transients to limit overcurrent stress. A detailed analysis is performed for the coordinated control of the grid-forming inverter. Supported by simulations and analytical methods, the approach ensures system stability while addressing overcurrent limitations and active harmonic filtering under nonlinear load conditions. This establishes a viable solution for the next-generation inverter-dominated power systems where reliability, power quality, and fault resilience are paramount. Full article

For power electronic interfaces, Direct Power Control (DPC) has emerged as a leading control technique, especially in applications such as synchronous motors, induction motors, and other electric drives; renewable energy sources (such as photovoltaic inverters and wind turbines); and converters that are grid-connected, such as Virtual Synchronous Generator (VSG) and Static Compensator (STATCOM) configurations. DPC accomplishes several significant goals by avoiding the inner current control loops and doing away with coordinating transformations. The application of STATCOM based on three- and five-level diode-clamped inverters is covered in this work. The study checks the abilities of DPC during power control adjustments during diverse grid operation scenarios while detailing how multilevel inverters affect system stability and power reliability. Proportional Integral (PI) controllers are used to control active and reactive power levels as part of the control approach. This study shows that combining DPC with Sinusoidal Pulse Width Modulation (SPWM) increases the system’s overall electromagnetic performance and control accuracy. The performance of STATCOM systems in power distribution and transient response under realistic operating conditions is assessed using simulation tools applied to three-level and five-level inverter topologies. In addition to providing improved voltage quality and accurate reactive power control, the five-level inverter structure surpasses other topologies by maintaining a total harmonic distortion (THD) below 5%, according to the main findings. The three-level inverter operates efficiently under typical grid conditions because of its straightforward design, which uses less processing power and computational complexity. Full article

The Polish Free-Electron Laser (PolFEL), which is currently under construction in the National Centre for Nuclear Research in Świerk near Warsaw, will comprise an electron gun and from four to six cryomodules, each accommodating two nine-cell TESLA RF superconducting resonant cavities. To cool the superconducting resonant cavities, the cryomodules will be supplied with superfluid helium at a temperature of 2 K. Other requirements regarding the cooling power of PolFEL result from the need to cool the power couplers for the accelerating cryomodules (5 K) and thermal shields, which limit the heat inleaks due to radiation (40–80 K). The machine will utilize several thermodynamic states of helium, including two-phase superfluid helium, supercritical helium, and low-pressure helium vapours. Supercritical helium will be supplied from a cryoplant by a cryogenic distribution system (CDS)—transfer line and valve boxes—where it will be thermodynamically transformed into a superfluid state. This article presents the architecture of the CDS, discusses several design solutions that could have been decided on with the use of second law analysis, and presents the design methodology of the chosen CDS elements. Full article

This study investigates pre-seismic ionospheric anomalies preceding the 2023 Jishishan Ms6.2 earthquake using total electron content (TEC) data derived from BDS geostationary orbit (GEO) satellites. Multi-scale analysis integrating Butterworth filtering and wavelet transforms resolved TEC disturbances into three distinct frequency regimes: (1) high-frequency perturbations (0.56–3.33 mHz) showed localized disturbances (amplitude ≤ 4 TECU, range < 300 km), potentially associated with near-field acoustic waves from crustal stress adjustments; (2) mid-frequency signals (0.28–0.56 mHz) exhibited anisotropic propagation (>1200 km) with azimuth-dependent N-shaped waveforms, consistent with the characteristics of acoustic–gravity waves (AGWs); and (3) low-frequency components (0.18–0.28 mHz) demonstrated phase reversal and power-law amplitude attenuation, suggesting possible lithosphere–atmosphere–ionosphere (LAI) coupling oscillations. The stark contrast between near-field residuals and far-field weak fluctuations highlighted the dominance of large-scale atmospheric gravity waves over localized acoustic disturbances. Geometry-based velocity inversion revealed incoherent high-frequency dynamics (5–30 min) versus anisotropic mid/low-frequency traveling ionospheric disturbance (TID) propagation (30–90 min) at 175–270 m/s, aligning with theoretical AGW behavior. During concurrent G1-class geomagnetic storm activity, spatial attenuation gradients and velocity anisotropy appear primarily consistent with seismogenic sources, providing insights for precursor discrimination and contributing to understanding multi-scale coupling in seismo-ionospheric systems. Full article

The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance with healthcare regulations like HIPAA and GDPR. This study presents C3-VULMAP, a novel and large-scale dataset explicitly designed for privacy-aware vulnerability detection in healthcare software. The dataset comprises over 30,000 vulnerable and 7.8 million non-vulnerable C/C++ functions, annotated with CWE categories and systematically mapped to LINDDUN privacy threat types. The objective is to support the development of automated, privacy-focused detection systems that can identify fine-grained software vulnerabilities in healthcare environments. To achieve this, we developed a hybrid construction methodology combining manual threat modeling, LLM-assisted synthetic generation, and multi-source aggregation. We then conducted comprehensive evaluations using traditional machine learning algorithms (Support Vector Machines, XGBoost), graph neural networks (Devign, Reveal), and transformer-based models (CodeBERT, RoBERTa, CodeT5). The results demonstrate that transformer models, such as RoBERTa, achieve high detection performance (F1 = 0.987), while Reveal leads GNN-based methods (F1 = 0.993), with different models excelling across specific privacy threat categories. These findings validate C3-VULMAP as a powerful benchmarking resource and show its potential to guide the development of privacy-preserving, secure-by-design software in embedded and electronic healthcare systems. The dataset fills a critical gap in privacy threat modeling and vulnerability detection and is positioned to support future research in cybersecurity and intelligent electronic systems for healthcare. Full article

Microbial Fuel Cell (MFC) is a novel bioelectrochemical system that catalyzes the oxidation of chemical energy in organic waste and converts it directly into electrical energy through the attachment and growth of electroactive microorganisms on the electrode surface. This technology realizes the synergistic effect of waste treatment and renewable energy production. A CF-NiO-PANI capacitor composite anode was prepared by loading polyaniline on a CF-NiO electrode to improve the capacitance of a CF electrode. The electrochemical characteristics of the composite anode were evaluated using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), and the electrode materials were analyzed comprehensively by scanning electron microscopy (SEM), energy diffusion spectrometer (EDS), and Fourier transform infrared spectroscopy (FTIR). MFC system based on CF-NiO-PANI composite anode showed excellent energy conversion efficiency in potato starch wastewater treatment, and its maximum power density increased to 0.4 W/m³, which was 300% higher than that of the traditional CF anode. In the standard charge–discharge test (C1000/D1000), the charge storage capacity of the composite anode reached 2607.06 C/m², which was higher than that of the CF anode (348.77 C/m²). Microbial community analysis revealed that the CF-NiO-PANI anode surface formed a highly efficient electroactive biofilm dominated by electrogenic bacteria (accounting for 47.01%), confirming its excellent electron transfer ability. The development of this innovative capacitance-catalytic dual-function anode material provides a new technical path for the synergistic optimization of wastewater treatment and energy recovery in MFC systems. Full article

Stretchable display technology has rapidly evolved, enabling a new generation of flexible electronics with applications ranging from wearable healthcare and smart textiles to implantable biomedical devices and soft robotics. This review systematically presents recent advances in stretchable displays, focusing on intrinsic stretchable materials, wavy surface engineering, and hybrid integration strategies. The paper highlights critical breakthroughs in device architectures, energy-autonomous systems, durable encapsulation techniques, and the integration of artificial intelligence, which collectively address challenges in mechanical reliability, optical performance, and operational sustainability. Particular emphasis is placed on the development of high-resolution displays that maintain brightness and color fidelity under mechanical strain, and energy harvesting systems that facilitate self-powered operation. Durable encapsulation methods ensuring long-term stability against environmental factors such as moisture and oxygen are also examined. The fusion of stretchable electronics with AI offers transformative opportunities for intelligent sensing and adaptive human–machine interfaces. Despite significant progress, issues related to large-scale manufacturing, device miniaturization, and the trade-offs between stretchability and device performance remain. This review concludes by discussing future research directions aimed at overcoming these challenges and advancing multifunctional, robust, and scalable stretchable display systems poised to revolutionize flexible electronics applications. Full article

This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics. Full article