Search Results (2,692)

With the increasing size of datasets in data mining applications, feature selection has become critical for enhancing classification accuracy and reducing computational complexity. In this study, a novel binary feature selection algorithm, called bWSO-log, is proposed based on the White Shark Optimizer (WSO). Unlike the commonly used S-shaped and V-shaped transfer functions in the literature, the WSO algorithm is converted into a binary form for the first time using a logarithmic transfer function. The performance of the proposed method was tested on nineteen benchmark datasets and compared with eight widely used metaheuristic algorithms. The results show that the bWSO-log algorithm demonstrates superior or competitive performance in terms of classification accuracy and the number of selected features. These findings reveal the effectiveness of the proposed logarithmic function and highlight the potential of WSO-based binary optimization in feature selection problems. Full article

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the etiologic agent that causes the coronavirus disease (COVID-19) identified in Wuhan, in 2019. Men are more prone to developing severe manifestations than women, suggesting a possible crucial role of sex hormones. 17,β-Estradiol (E2) and 1,25 α dihydroxyvitamin D₃ (calcitriol) act upon gene pathways as immunomodulators in several infectious respiratory diseases. In this study, we aimed to evaluate the influence of E2 and calcitriol on the VSV-based pseudovirus SARS-CoV-2 and SARS-CoV-2 infection in vitro. We infected Vero E6 cells with the recombinant VSV-based pseudovirus SARS-CoV-2 and the SARS-CoV-2 viruses according to the pre-treatment and pre–post-treatment models. The Angiotensin-Converting Enzyme 2 (ACE2) and Vitamin D Receptor (VDR) gene expression did not change under different treatments. The VSV-based pseudovirus SARS-CoV-2 infection showed a significant decrease in the focus-forming unit count in the presence of E2 and calcitriol (either alone or in combination) in the pre-treatment model, while in the pre–post-treatment model, the infection was inhibited only in the presence of E2. Th SARS-CoV-2 infection highlighted a decrease in viral titres in the presence of E2 and calcitriol only in the pre–post-treatment model. 17,β-Estradiol and calcitriol can exert an inhibitory effect on SARS-CoV-2 infections, demonstrating their protective role against viral infections. Full article

Efficient and accurate panel assignment is critical in expert and peer review processes. Traditional methods—based on manual preferences or Heuristic rules—often introduce bias, inconsistency, and scalability challenges. We present an automated framework that combines transformer-based document similarity modeling with optimization-based reviewer assignment. Using the all-mpnet-base-v2 from model (version 3.4.1), our system computes semantic similarity between proposal texts and reviewer documents, including CVs and Google Scholar profiles, without requiring manual input from reviewers. These similarity scores are then converted into rankings and integrated into an Integer Linear Programming (ILP) formulation that accounts for workload balance, conflicts of interest, and role-specific reviewer assignments (lead, scribe, reviewer). The method was tested across 40 researchers in two distinct disciplines (Chemical Engineering and Philosophy), each with 10 proposal documents. Results showed high self-similarity scores (0.65–0.89), strong differentiation between unrelated fields (−0.21 to 0.08), and comparable performance between reviewer document types. The optimization consistently prioritized top matches while maintaining feasibility under assignment constraints. By eliminating the need for subjective preferences and leveraging deep semantic analysis, our framework offers a scalable, fair, and efficient alternative to manual or Heuristic assignment processes. This approach can support large-scale review workflows while enhancing transparency and alignment with reviewer expertise. Full article

An Analog-to-Digital Converter (ADC) is an indispensable part of image sensor systems. This paper presents a silicon-based 13-bit 100 kS/s two-step single-slope analog-to-digital converter (TS-SS ADC) for infrared image sensors with a frame rate of 100 Hz. For the charge leakage and offset voltage issues inherent in conventional TS-SS ADC, a four-terminal comparator was employed to resolve the fine ramp voltage offset caused by charge redistribution in storage and parasitic capacitors. In addition, a current-steering digital-to-analog converter (DAC) was adopted to calibrate the voltage reference of the dynamic comparator and mitigate differential nonlinearity (DNL)/integral nonlinearity (INL). To eliminate quantization dead zones, a 1-bit redundancy was incorporated into the fine quantization circuit. Finally, the quantization scheme consisted of 7-bit coarse quantization followed by 7-bit fine quantization. The ADC was implemented using an SMIC 55 nm processSemiconductor Manufacturing International Corporation, Shanghai, China. The post-simulation results show that when the power supply is 3.3 V, the ADC achieves a quantization range of 1.3 V–3 V. Operating at a 100 kS/s sampling rate, the proposed ADC exhibits an effective number of bits (ENOBs) of 11.86, a spurious-free dynamic range (SFDR) of 97.45 dB, and a signal-to-noise-and-distortion ratio (SNDR) of 73.13 dB. The power consumption of the ADC was 22.18 mW. Full article

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

Over efficient photocatalysts, CO₂ photoreduction typically converts CO₂ into low-carbon chemicals, which serve as raw materials for downstream synthesis processes. Here, an efficient composite photocatalyst heterojunction (Cu₂O-u/g-C₃N₄) has been fabricated to reduce CO₂. Graphitic carbon nitride (g-C₃N₄) was synthesized via thermal polymerization of urea at 550 °C, while pre-dispersed Cu₂O derived from urea pyrolysis (Cu₂O-u) was prepared by thermal reduction of urea and CuCl₂·2H₂O at 180 °C. The heterojunction Cu₂O-u/g-C₃N₄ was subsequently constructed through hydrothermal treatment at 180 °C. This heterojunction exhibited a bandgap of 2.10 eV, with dual optical absorption edges at 485 nm and above 800 nm, enabling efficient harvesting of solar light. Under 175 W mercury lamp irradiation, the heterojunction catalyzed liquid-phase CO₂ photoreduction to formic acid, acetic acid, and methanol. Its formic acid production activity surpassed that of pristine g-C₃N₄ by 3.14-fold and TiO₂ by 8.72-fold. Reaction media, hole scavengers, and reaction duration modulated product selectivity. In acetonitrile/isopropanol systems, formic acid and acetic acid production reached 579.4 and 582.8 μmol·h⁻¹·g_cat⁻¹. Conversely, in water/triethanolamine systems, methanol production reached 3061.6 μmol·h⁻¹·g_cat⁻¹, with 94.79% of the initial conversion retained after three cycles. Finally, this work ends with the conclusions of the CO₂ photocatalytic reduction to formic acid, acetic acid, and methanol, and recommends prospects for future research. Full article

The stable operation of micromachine systems relies on reliable power management, where DC-DC converters provide energy with high efficiency to extend operational endurance. However, these converters also constitute significant electromagnetic interference (EMI) sources that may interfere with the normal functioning of micro-electromechanical systems. This paper proposes a boost converter utilizing Pulse Width Modulation (PWM) with peak current mode control to address the EMI issues inherent in the switching operation of DC-DC converters. The converter incorporates a Hybrid Spread Spectrum (HSS) technique to effectively mitigate EMI noise. The HSS combines a 1.2 MHz pseudo-random spread spectrum with a 9.4 kHz triangular periodic spread spectrum. At a standard switching frequency of 2 MHz, the spread spectrum range is set to ±7.8%. Simulations conducted using a 0.5 μm Bipolar Complementary Metal-Oxide-Semiconductor Double-diffused Metal-Oxide-Semiconductor (BCD) process demonstrate that the HSS technique reduces EMI around the switching frequency by 12.29 dBμV, while the converter’s efficiency decreases by less than 1%. Full article

With the advancement of computer vision and image processing technologies, scene recognition has gradually become a research hotspot. However, in practical applications, it is necessary to detect the categories and locations of objects in images while recognizing scenes. To address these issues, this paper proposes an indoor object detection and scene recognition algorithm based on the Apriori algorithm and the Mobile-EFSSD model, which can simultaneously obtain object category and location information while recognizing scenes. The specific research contents are as follows: (1) To address complex indoor scenes and occlusion, this paper proposes an improved Mobile-EFSSD object detection algorithm. An optimized MobileNetV3 with ECA attention is used as the backbone. Multi-scale feature maps are fused via FPN. The localization loss includes a hyperparameter, and focal loss replaces confidence loss. Experiments show that the method achieves stable performance, effectively detects occluded objects, and accurately extracts category and location information. (2) To improve classification stability in indoor scene recognition, this paper proposes a naive Bayes-based method. Object detection results are converted into text features, and the Apriori algorithm extracts object associations. Prior probabilities are calculated and fed into a naive Bayes classifier for scene recognition. Evaluated using the ADE20K dataset, the method outperforms existing approaches by achieving a better accuracy–speed trade-off and enhanced classification stability. The proposed algorithm is applied to indoor scene images, enabling the simultaneous acquisition of object categories and location information while recognizing scenes. Moreover, the algorithm has a simple structure, with an object detection average precision of 82.7% and a scene recognition average accuracy of 95.23%, making it suitable for practical detection requirements. Full article

This paper presents the design and implementation of a high-efficiency, full silicon carbide (SiC)-based center-tapped phase-shifted full-bridge (PSFB) converter for NiCd battery charging applications in railway systems. The converter utilizes SiC MOSFET modules on the primary side and SiC diodes on the secondary side, resulting in significant efficiency improvements due to the superior switching characteristics and high-temperature tolerance inherent in SiC devices. A nanocrystalline-cored center-tapped transformer is optimized to minimize voltage stress on the rectifier diodes. Additionally, the use of a nanocrystalline core provides high saturation flux density, low core loss, and excellent permeability, particularly at high frequencies, which significantly enhances system efficiency. The converter also compensates for temperature fluctuations during operation, enabling a wide and adjustable output voltage range according to the temperature differences. A prototype of the 10-kW, 50-kHz PSFB converter, operating with an input voltage range of 700–750 V and output voltage of 77–138 V, was developed and tested both through simulations and experimentally. The converter achieved a maximum efficiency of 97% and demonstrated a high power density of 2.23 kW/L, thereby validating the effectiveness of the proposed design for railway battery charging applications. Full article

The synthesis and structural characterization of three new triple-stranded helical complexes ([Dy₂(L²)₃]2Cl∙15H₂O (C1), [Y₂(L²)₃]3(NO₃)Cl∙14H₂O∙DMSO (C2), and [Eu₂(L⁴)₃]∙12H₂O (C3), where L² and L⁴ are ligands derived from pyridoxal hydrochloride and succinic or adipic acid dihydrazides, respectively, were described. The X-ray data, combined with spectroscopic measurements, indicated that L² and L⁴ act as bis-tridentate ligands, presenting two tridentate chelating cavities O,N,O to obtain the dinuclear complexes C1–C3. Their antiviral profile was predicted via in silico calculations in terms of interaction with the structural severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein in the down- and up-states and complexed with the cellular receptor angiotensin-converting enzyme 2 (ACE2). The best affinity energy values (−9.506, −9.348, and −9.170 kJ/mol for C1, C2, and C3, respectively) were obtained for the inorganic complexes docked in the model spike-ACE2, with C1 being suggested as the most promising candidate for a future in vitro validation. The obtained in silico antiviral trend was supported by the prediction of the electronic and physical–chemical properties of the inorganic complexes via the density functional theory (DFT) approach, representing an original and relevant contribution to the bioinorganic and medicinal chemistry fields. Full article

Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

The renin–angiotensin system (RAS) plays a central role in cardiovascular regulation and has gained prominence in the pathogenesis of Coronavirus Disease 2019 (COVID-19) due to the critical function of angiotensin-converting enzyme 2 (ACE2) as the entry receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Angiotensin IV, but not angiotensin II, has recently been reported to enhance the binding between the viral spike protein and ACE2. To investigate the virological significance of this effect, we developed a single-round infection assay using SARS-CoV-2 viral-like particles expressing the spike protein. Our results demonstrate that while angiotensin II does not affect viral infectivity across concentrations ranging from 40 nM to 400 nM, angiotensin IV enhances viral entry at a low concentration but exhibits dose-dependent inhibition at higher concentrations. These findings highlight the unique dual role of angiotensin IV in modulating SARS-CoV-2 entry. In silico molecular docking simulations indicate that angiotensin IV was predicted to associate with the S1 domain near the receptor-binding domain in the open spike conformation. Given that reported plasma concentrations of angiotensin IV range widely from 17 pM to 81 nM, these levels may be sufficient to promote, rather than inhibit, SARS-CoV-2 infection. This study identifies a novel link between RAS-derived peptides and SARS-CoV-2 infectivity, offering new insights into COVID-19 pathophysiology and informing potential therapeutic strategies. Full article

Current–voltage (I–V) characteristics are an important measure of photovoltaic (PV) generators, corresponding to environmental conditions regarding solar irradiance and temperature. The I–V curve tracer is a widely used instrument in power engineering to evaluate system performance and detect fault conditions in PV power systems. Several technologies have been applied to develop the device and trace I–V characteristics, improving accuracy, speed, and portability. Focusing on the outdoor environment, this paper presents an in-depth analysis and comparison of the system design and dynamics to identify the I–V tracing performance based on different power conversion topologies and data acquisition methods. This is a valuable reference for industry and academia to further the technology and promote sustainable power generation. Full article

This paper introduces an enhanced bi-directional full-bridge resonant converter designed for Vehicle-to-Grid (V2G) systems. A key innovation lies in the incorporation of an auxiliary LC resonant circuit connected via a tertiary transformer winding. This circuit dynamically modifies the magnetizing inductance based on operating frequency, enabling soft-switching across all primary switches, specifically, Zero-Voltage Switching (ZVS) at turn-on and near Zero-Current Switching (ZCS) at turn-off across the entire load spectrum. Additionally, the converter supports both Constant Current (CC) and Constant Voltage (CV) charging modes at distinct, fixed operating frequencies, thus avoiding wide frequency variations. A 3.3 kW prototype developed for onboard electric vehicle charging applications demonstrates the effectiveness of the proposed topology. Experimental results confirm high efficiency in both charging and discharging operations, achieving up to 98.13% efficiency in charge mode and 98% in discharge mode. Full article