The development of biomimetic scaffolds requires balancing structural integrity with biological signaling. This study evaluates kefiran, a microbial exopolysaccharide, as a bioactive component in establishing printing feasibility of 3D composite constructs. Kefiran from Romanian artisanal cultures was characterized via ¹H-NMR, HPLC, and SEM/TEM, confirming a high-quality hexasaccharide repeating unit. Three composite inks (K100, K70, and K50) were developed by integrating kefiran, chondroitin sulfate, and Si-substituted hydroxyapatite into an alginate matrix and processed using a Bio X 3D-printer. Results showed that higher kefiran concentrations improved printing feasibility, providing enhanced structural fidelity and stability during the layer-by-layer deposition process. All bioprinted scaffolds demonstrated high cytocompatibility with L929 fibroblasts, maintaining viability above 70%. Notably, kefiran exhibited dual-functional therapeutic potential: concentrations above 500 mg/L showed a concentration-dependent antiproliferative effect against HT-29 cells at 72 h while remaining safe for normal cells. These findings establish kefiran-based biomaterial inks as robust, bioactive platforms for regenerative medicine. By enhancing both the mechanical printability of alginate composites and the biological response of cultured cells, kefiran proves to be a versatile component for advanced tissue engineering and potential biological activity applications. Full article

(This article belongs to the Special Issue Hydrogels for Tissue Repair: Innovations and Applications)

27 pages, 1763 KB

Open AccessArticle

Optimizing Session Frequency in EEG Biofeedback: A Comparative Study of Protocol Dynamics and Neuromuscular Adaptation in Elite Judo Athletes

by Alicja Markiel, Dariusz Skalski, Kinga Łosińska, Marcin Żak and Adam Maszczyk

Sensors 2026, 26(7), 2077; https://doi.org/10.3390/s26072077 (registering DOI) - 26 Mar 2026

Abstract

Background: The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. Methods: This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neurophysiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Results: Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre–post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue resistance (MF_drop = −2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG–load dynamics. A robust dose–response relationship was evident. Conclusions: Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing. Full article

(This article belongs to the Special Issue Machine Learning in Biomedical Signal Processing)

24 pages, 1224 KB

Open AccessReview

AI-Enabled Sensor Technologies for Remote Arrhythmic Monitoring in High-Risk Cardiomyopathy Genotypes

by Nardi Tetaj, Andrea Segreti, Francesco Piccirillo, Aurora Ferro, Virginia Ligorio, Alberto Spagnolo, Michele Pelullo, Simone Pasquale Crispino and Francesco Grigioni

Sensors 2026, 26(7), 2078; https://doi.org/10.3390/s26072078 (registering DOI) - 26 Mar 2026

Abstract

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensors in Cardiovascular Disease Monitoring)

25 pages, 22071 KB

Open AccessArticle

The Impact of Meteorological Parameters and Air Pollution on the Spatiotemporal Distribution of Nighttime Light in China

by Dan Wang, Wei Shan, Song Hong, Qian Wu, Shuai Shi and Bin Chen

Sustainability 2026, 18(7), 3256; https://doi.org/10.3390/su18073256 (registering DOI) - 26 Mar 2026

Abstract

Nighttime light (NTL), a crucial indicator of human activity intensity, has not been systematically analyzed for its interactive mechanisms with air pollution and climate change. This study first investigates the spatiotemporal evolution of China’s total nighttime light (TNTL) and average nighttime light (ANTL), alongside key indicators of meteorological parameters and air pollution, at the grid scale from 2000 to 2023. We then employ prefecture-level city data and a geographically and temporally weighted regression (GTWR) model to quantify the spatiotemporally heterogeneous associations of temperature (TMP), precipitation (PRE), fine particulate matter (PM_2.5), ozone (O₃), land use (LUL), topography, and socioeconomic factors with NTL. The results indicate that (1) China’s NTL exhibits a significant overall upward trend, with areas of increase or significant increase comprising 92.04% of the total study area. TNTL growth demonstrates regional heterogeneity, expanding by a factor of 4.91 in East China and 2.65 in Northeast China; (2) meteorological and air pollution indicators display spatiotemporal non-stationarity, with the synergistic effect between O₃ and PRE being the strongest; (3) among NTL drivers, LUL contributes most significantly (0.44), followed by TMP (0.14) > PM_2.5 (−0.33 × 10⁻¹) > O₃ (0.17 × 10⁻¹) > PRE (−0.33 × 10⁻⁶); (4) TMP and PRE may primarily influence NTL by altering ecological conditions and nighttime activity patterns. TMP shows a strong positive correlation with NTL in the junction zone of South, East, and Central China, whereas PRE predominantly exerts a negative influence; (5) air pollution exhibits distinct spatiotemporal effects: high PM_2.5 and O₃ generally correspond to lower NTL, though positive correlations persist in some areas due to industrial structures, highlighting the need for integrated policies that balance air quality management with sustainable urban planning; (6) the 2013 “Air Pollution Prevention and Control Action Plan” significantly strengthened the negative correlation between PM_2.5 and NTL in North China. However, O₃ concentrations increased by 28.9% after 2017, underscoring the challenge of coordinating VOC and NOx controls for long-term atmospheric sustainability. Full article

(This article belongs to the Special Issue Ecology, Environment, and Watershed Management)

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26 pages, 2036 KB

Open AccessArticle

Multi-Objective Optimization of a Modular Unequal Tooth-Shoe PMLSM via an ARD-Kriging Surrogate-Assisted Framework

by Cheng Fang, Liang Guo, Jiawei Jiang, Bochen Wang and Wenqi Lu

Appl. Sci. 2026, 16(7), 3218; https://doi.org/10.3390/app16073218 (registering DOI) - 26 Mar 2026

Abstract

This paper presents a novel dual-module Permanent Magnet Linear Synchronous Motor (PMLSM) featuring an unequal tooth-shoe topology, alongside a highly efficient surrogate-assisted framework to maximize average thrust and minimize thrust ripple. To overcome the computational bottleneck of expensive Finite Element Analysis (FEA), we propose a Constraint-Preserving Maximin Latin Hypercube Design (CP-MmLHD) coupled with an ARD-Kriging model and the Expected Hypervolume Improvement (EHVI) criterion. This closed-loop framework expertly handles strict geometric constraints and anisotropic parameter sensitivities. Within a strict budget of only 150 FEA evaluations, the framework successfully identifies a high-quality Pareto front. Notably, a representative optimal design reduces thrust ripple by over 80% without compromising average thrust. Furthermore, comparative experiments demonstrate superior computational efficiency over conventional algorithms, while multi-run statistical benchmarking and stochastic Monte Carlo analysis rigorously confirm the framework’s algorithmic robustness and manufacturing reliability. Full article

42 pages, 2250 KB

Open AccessArticle

Data-Driven Yield Estimation and Maximization Using Bayesian Optimization Under Uncertainty

by Kei Sano, Daiki Kawahito, Yukiya Saito, Hironori Moki and Dragan Djurdjanovic

Appl. Sci. 2026, 16(7), 3213; https://doi.org/10.3390/app16073213 - 26 Mar 2026

Abstract

In this paper, we propose a novel method which utilizes samples of measured product quality characteristics to efficiently estimate the probabilities of those quality characteristics being within the desired specifications and, consequently, the process yield. Specifically, when dealing with 1D Gaussian distributions, we formally prove that the proposed yield estimator asymptotically gives a lower Mean Squared Error compared to the best unbiased estimator. In order to enable maximization of yield, this novel estimator is incorporated into the framework of Bayesian Optimization which iteratively seeks controllable tool parameters under which the outgoing product yield is maximized. The newly proposed yield maximization method is demonstrated in an application involving high-fidelity simulations of a reactive ion etch chamber, a tool component commonly used in semiconductor manufacturing. The aim of these simulations was to rapidly and reliably determine tool parameters that maximize the probability of delivering desired plasma density characteristics under stochastic variations in chamber conditions. The novel yield estimation and optimization methods show superiority when the number of experimental observations is limited and the distributions of outgoing product characteristics can be approximated well by a Gaussian distribution. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

25 pages, 6567 KB

Open AccessArticle

Manufacturing-Induced Defect Taxonomy and Visual Detection in UD Tapes with Carbon and Glass Fiber Reinforcements

by Gönenç Duran

Polymers 2026, 18(7), 807; https://doi.org/10.3390/polym18070807 - 26 Mar 2026

Abstract

Continuous unidirectional (UD) thermoplastic composite tapes are increasingly used in aerospace, automotive, and energy applications because of their high specific strength, low weight, recyclability, and compatibility with automated manufacturing. Since final component performance strongly depends on tape quality, reliable defect characterization and detection are essential. In this study, manufacturing-induced defects in polypropylene-based UD tapes reinforced with carbon and glass fibers were investigated using real images acquired directly from laboratory-scale production without synthetic data. Defects related to interfacial integrity, matrix distribution, fiber architecture, and surface irregularities were systematically analyzed, and a practical four-class defect taxonomy was established. To enable automated inspection under limited-data conditions, lightweight YOLOv8, YOLOv11, and the new YOLO26 models were comparatively evaluated using a UD tape-specific augmentation strategy combining physically constrained Albumentations and on-the-fly augmentation. Among the tested models, YOLO26-s achieved the best overall performance, reaching a mean mAP@0.5 of 0.87 ± 0.03, outperforming YOLOv11 (0.83) and YOLOv8 (0.78), with 0.90 precision and 0.85 recall. Interfacial (0.92 mAP) and matrix-related (0.90 mAP) defects were detected most reliably, whereas fiber-related (0.89 mAP) and surface defects (0.79 mAP) remained more challenging, particularly in glass-fiber-reinforced tapes due to transparency-masking effects. The results demonstrate the potential of compact deep learning models for computationally efficient and manufacturing-relevant in-line quality monitoring of UD tape production. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

15 pages, 1110 KB

Open AccessArticle

A Multi-Stakeholder Perspective on Integrating Genomic Sequencing into Newborn Screening: An Interview Study

by Saskia G. Smits, Suzanne M. Onstwedder, Tessel Rigter, Wendy Rodenburg and Lidewij Henneman

Int. J. Neonatal Screen. 2026, 12(2), 19; https://doi.org/10.3390/ijns12020019 - 26 Mar 2026

Abstract

Interest in the genomic sequencing of healthy newborns has raised a discussion on whether this technology should be introduced into existing newborn screening (NBS) programs. This qualitative study explores a multi-stakeholder perspective on the future of genomic sequencing in NBS. Semi-structured interviews were conducted with 26 professionals involved in NBS or in clinical genome sequencing in the Netherlands. Participants highlighted opportunities such as the possibility to use one test for a wide range of genetic conditions, reducing diagnostic odyssey, expanding the scope of NBS, and increasing program efficiency. Challenges were raised regarding genetic variant interpretation, expected increased parental anxiety, data privacy issues, difficulties with information provision, and high costs. Three areas of tension between participants’ perspectives were identified: screening strategy, screening performance, and roles and responsibilities. It was emphasized that implementing genomic sequencing should not risk reducing the current high NBS participation, and that enhancing knowledge, communication, and collaboration between all stakeholders is needed. Although most participants did not believe genomic sequencing as a first-tier test is currently desirable and feasible, they acknowledged it has a role to play in the future of NBS. Future decision-making should consider the potential impact on the participation rate, program quality, and balancing benefits and harms. Full article

29 pages, 1955 KB

Open AccessSystematic Review

Effects of Rhythmic Auditory Stimulation Using Sensory Feedback-Based Wearable Devices on the Gait and Balance in Patients with Parkinson’s Disease: A Systematic Review and Meta-Analysis

by Ju-Hak Kim, Myoung-Ho Lee and Myoung-Kwon Kim

Brain Sci. 2026, 16(4), 359; https://doi.org/10.3390/brainsci16040359 - 26 Mar 2026

Abstract

Background: This paper presents a systematic review and meta-analysis to identify the effects of Rhythmic Auditory Stimulation (RAS) delivered via wearable devices on the gait and balance in patients with Parkinson’s disease. Method: The PICO criteria were established according to the PRISMA 2020 guidelines, and literature searches were performed across five databases covering studies published between 2015 and 2025: PubMed, Embase, Cochrane, Scopus, and Web of Science. After applying the inclusion criteria, eleven randomized controlled trials (RCTs) were selected. The quality of the studies was evaluated using the PEDro Scale and ROB-2. Statistical analyses were performed using Review Manager 5.4 based on the number of samples, means, and standard deviations to calculate the effect sizes. Result: The analysis results showed that wearable RAS significantly improved the gait speed (SMD = 0.49, p < 0.05) and balance ability (SMD = 0.40, p < 0.05), while no significant differences in the gait pattern, FOG-Q, or UPDRS-III were observed. The heterogeneity among studies was low, and the funnel plots were distributed symmetrically, indicating minimal publication bias. The average PEDro score was 7.33, suggesting moderate-to-high methodological quality. Conclusion: wearable RAS was identified as an evidence-based intervention effective in improving the gait speed and balance in patients with Parkinson’s disease. Full article

(This article belongs to the Special Issue Clinical Research on Neurological Rehabilitation After Stroke)

41 pages, 1354 KB

Open AccessReview

From Biomass to Functional Biochar: Modification Approaches to Targeted Environmental Pollution Remediation Applications

by Ana Rita Alves, Antón Puga, João Vilaça, Sónia A. Figueiredo and Cristina Delerue-Matos

Agriculture 2026, 16(7), 734; https://doi.org/10.3390/agriculture16070734 - 26 Mar 2026

Abstract

Soil health is a major environmental concern. Biochars are a promising solution to address both soil contamination and amendment. They represent a sustainable valorisation alternative for solid wastes produced in huge amounts, namely agroforestry residues and sludge from wastewater treatment plants. Biochar’s superior properties, enhanced pore structure and high specific surface area can contribute to restoring soil quality, by adsorbing several pollutants (e.g., pharmaceutical compounds, pesticides, and metals) from water and soil, enhancing water retention capacity, improving soil aggregation, regulating pH, and reducing the need for synthetic fertilisers. Multiple studies have reported removal efficiencies exceeding 70% for metals and 60% for organic compounds in soils, as well as over 40% for both organic compounds and metals in waters. These efficiencies depend on factors such as feedstock, pyrolysis conditions, modification strategies, and target contaminants. Recent advancements in the field have introduced both chemical and physical modifications that can enhance adsorption selectivity. This review provides a comprehensive analysis of the fundamentals of biochar production, modification strategies, and their environmental applications in soil remediation and water treatment. By comparing unmodified and modified biochars, this review highlights the crucial factors that influence the performance of this highly versatile and cost-effective solution. Full article

(This article belongs to the Special Issue Impacts of Emerging Agricultural Pollutants on Environmental Health)

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31 pages, 5672 KB

Open AccessArticle

D-SOMA: A Dynamic Self-Organizing Map-Assisted Multi-Objective Evolutionary Algorithm with Adaptive Subregion Characterization

by Xinru Zhang and Tianyu Liu

Computers 2026, 15(4), 207; https://doi.org/10.3390/computers15040207 - 26 Mar 2026

Abstract

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

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26 pages, 1455 KB

Open AccessArticle

Energy-Aware Time-Dependent Routing of Electric Vehicles for Multi-Depot Pickup and Delivery with Time Windows

by Ying Wang, Qiang Li, Jicong Duan, Qin Zhang and Yu Ding

Sustainability 2026, 18(7), 3255; https://doi.org/10.3390/su18073255 - 26 Mar 2026

Abstract

The rapid expansion of e-commerce and on-demand logistics has intensified the need for cost-effective and reliable urban distribution systems. This paper investigates an energy-aware routing problem for electric vehicle fleets operating from multiple depots under time-varying traffic conditions. We propose a novel multi-depot vehicle routing model that jointly incorporates time-dependent travel speeds, simultaneous pickup and delivery operations, and time window constraints. The model explicitly captures key operational realities, including battery capacity limitations, load- and speed-dependent energy consumption, synchronized pickup-delivery requirements, and soft time windows. The objective is to minimize total operational cost by simultaneously optimizing depot assignments, vehicle routes, and service schedules. Given the NP-hard nature of the problem, we develop a two-stage heuristic solution framework. In the first stage, a spatio-temporal clustering strategy is employed to assign customers to depots efficiently. In the second stage, route construction and improvement are performed using an enhanced Adaptive Large Neighborhood Search (ALNS) algorithm equipped with problem-specific destroy and repair operators. Computational experiments on adapted benchmark instances demonstrate that the proposed approach consistently produces high-quality solutions and exhibits robust convergence behavior. In addition, sensitivity analyses provide managerial insights, revealing an optimal range of vehicle energy capacity and an economically efficient speed band that balances travel time and energy consumption. Full article

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29 pages, 16603 KB

Open AccessArticle

Hierarchical Neural-Guided Navigation with Vortex Artificial Potential Field for Robust Path Planning in Complex Environments

by Boyi Xiao, Lujun Wan, Jiwei Tian, Yuqin Zhou, Sibo Hou and Haowen Zhang

Drones 2026, 10(4), 240; https://doi.org/10.3390/drones10040240 - 26 Mar 2026

Abstract

Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architecture integrating deep representation learning with an improved Vortex Artificial Potential Field (APF). At the decision layer, a Convolutional Neural Network (CNN) encodes the environment as a fixed-dimensional tensor and generates global waypoints with constant-time inference, independent of obstacle count. At the control layer, a Vortex APF resolves the Goal Non-Reachable with Obstacles Nearby (GNRON) problem and limit-cycle oscillations through tangential rotational potentials, achieving significant improvement in trajectory smoothness compared to traditional APF methods. A closed-loop replanning mechanism further ensures robust performance under execution drift. Experiments across varying obstacle densities demonstrate that the combined system achieves high navigation success rates in dense environments with substantially reduced computation time compared to sampling-based planners such as Rapidly exploring Random Tree star (RRT*), while maintaining superior trajectory quality. This architecture provides a computationally efficient solution for resource-constrained UAV platforms operating in GPS-denied or obstacle-rich environments such as warehouses, forests, and disaster sites. Full article

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35 pages, 14791 KB

Open AccessArticle

Optimal Voltage Control for Remote Marine Loads via Subsea Cables: A Solution Circle-Based Comparative Efficiency Analysis of UPFC, SSSC, and TCSC

by Izabel Nikolaeva, Nikolay Nikolaev, Ara Panosyan and Jens Denecke

Energies 2026, 19(7), 1638; https://doi.org/10.3390/en19071638 - 26 Mar 2026

Abstract

Maintaining voltage stability and minimizing power losses for remote marine loads powered by long submarine cables is the challenging context of this paper. Flexible Alternating Current Transmission Systems (FACTS) are well-studied for terrestrial grids. However, their comparative performance and efficiency in the context of high-capacity submarine links remain a gap in the literature. This paper presents a rigorous analysis of the performance of a Unified Power Flow Controller (UPFC), Static Synchronous Series Compensator (SSSC), and Thyristor Controlled Series Capacitor (TCSC). A mathematical framework is developed to introduce the “solution circle” concept, which demonstrates that the series impedance values required to maintain a specific load voltage define a circle in the complex plane. A theoretical analysis is performed, revealing that the UPFC, with its two degrees of freedom, is significantly more efficient because it can select the minimum impedance magnitude on this circle. In contrast, SSSC and TCSC are limited to the reactive axis, which, under certain operating conditions, may not cross the solution circle; therefore, they may not meet the power quality objective. The results of a practical case study show that UPFC requires approximately half the rated power (22.4 MVA) compared to its counterparts (39.4 MVA) to achieve the same control objectives. Full article

(This article belongs to the Section F1: Electrical Power System)

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37 pages, 3141 KB

Open AccessArticle

Multi-Stage Power Conversion and Coordinated Voltage Control for Battery-Based Power Barges Supplying LV and HV AC Loads

by Allahyar Akhbari, Kasper Jessen and Amin Hajizadeh

Electronics 2026, 15(7), 1386; https://doi.org/10.3390/electronics15071386 - 26 Mar 2026

Abstract

The growing electrification of ports and maritime transport requires flexible power systems capable of supplying multiple voltage levels with high efficiency and power quality. Battery-based power barges offer a promising solution, but their power conversion systems must handle wide voltage and power ranges while remaining stable under dynamic operating conditions. This paper presents a scalable multi-stage power conversion architecture for battery-based power barges that can supply both low-voltage and high-voltage AC loads from a common DC source. The system combines isolated Dual Active Bridge (DAB) DC–DC converters with a three-level Neutral-Point-Clamped (NPC) inverter. An input-parallel output-series DAB configuration is used for high-voltage operation, enabling modularity and scalability within semiconductor limits. A coordinated control strategy ensures stable DC-link regulation, balanced module operation, and high-quality AC voltage generation. Simulation results confirm stable operation, fast dynamic response, a voltage THD below 4%, and overall efficiency above 95%, demonstrating the suitability of the proposed architecture for future power barge and port electrification applications. Full article

(This article belongs to the Section Industrial Electronics)

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