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Background: Preeclampsia (PE) is a pregnancy-specific disease and hypertensive disorder with a multifactorial pathogenesis involving complex molecular regulatory networks. Recent studies highlight the critical role of non-coding RNAs, particularly miRNAs and lncRNAs, in PE development. This study investigates the molecular interaction between miR-7151-5p and the lncRNA KCNQ1OT1 and their functional contributions to PE pathogenesis. Methods: An integrative approach combining RNAhybrid-based bioinformatics, dual-luciferase reporter assays, qRT-PCR, Transwell migration and invasion assays, and RNA sequencing was employed to characterize the binding between miR-7151-5p and KCNQ1OT1 and assess their influence on trophoblast cell function and gene expression. Results: A bioinformatic analysis predicted a stable binding site between miR-7151-5p and KCNQ1OT1 (minimum free energy: –37.3 kcal/mol). The dual-luciferase reporter assay demonstrated that miR-7151-5p directly targets KCNQ1OT1, leading to suppressed transcriptional activity. In HTR8/SVneo cells, miR-7151-5p overexpression significantly downregulated both KCNQ1OT1 and Notch1 mRNA, whereas its inhibition showed no significant changes, suggesting additional regulatory mechanisms of Notch1 expression. Transwell assays indicated that miR-7151-5p overexpression suppressed trophoblast cell migration and invasion, whereas its inhibition enhanced these cellular behaviors. RNA-seq analysis further revealed that miR-7151-5p overexpression altered key signaling pathways, notably the TGF-β pathway, and significantly modulates PE-associated genes, including PLAC1, ANGPTL6, HIRA, GLA, HSF1, and BAG6. Conclusions: The regulatory effect of miR-7151-5p on KCNQ1OT1, along with its influence on trophoblast cell dynamics via Notch1 and TGF-β signaling pathways, highlights its role in PE pathogenesis and supports its potential as a biomarker in early PE screening. Full article

(This article belongs to the Section Molecular Genetics and Genetic Diseases)

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17 pages, 4338 KiB

Open AccessArticle

Lightweight Attention-Based CNN Architecture for CSI Feedback of RIS-Assisted MISO Systems

by Anming Dong, Yupeng Xue, Sufang Li, Wendong Xu and Jiguo Yu

Mathematics 2025, 13(15), 2371; https://doi.org/10.3390/math13152371 (registering DOI) - 24 Jul 2025

Abstract

Reconfigurable Intelligent Surface (RIS) has emerged as a promising enabling technology for wireless communications, which significantly enhances system performance through real-time manipulation of electromagnetic wave reflection characteristics. In RIS-assisted communication systems, existing deep learning-based channel state information (CSI) feedback methods often suffer from excessive parameter requirements and high computational complexity. To address this challenge, this paper proposes LwCSI-Net, a lightweight autoencoder network specifically designed for RIS-assisted multiple-input single-output (MISO) systems, aiming to achieve efficient and low-complexity CSI feedback. The core contribution of this work lies in an innovative lightweight feedback architecture that deeply integrates multi-layer convolutional neural networks (CNNs) with attention mechanisms. Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. This approach not only improves network representational efficiency but also reduces redundant computations, leading to optimized computational complexity. Additionally, the proposed cross-channel residual block (CRBlock) establishes inter-channel information-exchange paths, strengthening feature fusion and ensuring outstanding stability and robustness under high compression ratio (CR) conditions. Our experimental results show that for CRs of 16, 32, and 64, LwCSI-Net significantly improves CSI reconstruction performance while maintaining fewer parameters and lower computational complexity, achieving an average complexity reduction of 35.63% compared to state-of-the-art (SOTA) CSI feedback autoencoder architectures. Full article

(This article belongs to the Special Issue Data-Driven Decentralized Learning for Future Communication Networks)

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16 pages, 5555 KiB

Open AccessArticle

Optimization of a Navigation System for Autonomous Charging of Intelligent Vehicles Based on the Bidirectional A* Algorithm and YOLOv11n Model

by Shengkun Liao, Lei Zhang, Yunli He, Junhui Zhang and Jinxu Sun

Sensors 2025, 25(15), 4577; https://doi.org/10.3390/s25154577 (registering DOI) - 24 Jul 2025

Abstract

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

(This article belongs to the Special Issue Vision-Guided System in Intelligent Autonomous Robots)

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25 pages, 3790 KiB

Open AccessArticle

Studying Inverse Problem of Microscale Droplets Squeeze Flow Using Convolutional Neural Network

by Aryan Mehboudi, Shrawan Singhal and S.V. Sreenivasan

Fluids 2025, 10(8), 190; https://doi.org/10.3390/fluids10080190 (registering DOI) - 24 Jul 2025

Abstract

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption. Full article

(This article belongs to the Special Issue Flow Control Across Varying Length Scales: Nanofluidics, Microfluidics and Millifluidics)

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16 pages, 2230 KiB

Open AccessArticle

Three-Dimensional-Printed Biomimetic Scaffolds for Investigating Osteoblast-Like Cell Interactions in Simulated Microgravity: An In Vitro Platform for Bone Tissue Engineering Research

by Eleonora Zenobi, Giulia Gramigna, Elisa Scatena, Luca Panizza, Carlotta Achille, Raffaella Pecci, Annalisa Convertino, Costantino Del Gaudio, Antonella Lisi and Mario Ledda

J. Funct. Biomater. 2025, 16(8), 271; https://doi.org/10.3390/jfb16080271 (registering DOI) - 24 Jul 2025

Abstract

Three-dimensional cell culture systems are relevant in vitro models for studying cellular behavior. In this regard, this present study investigates the interaction between human osteoblast-like cells and 3D-printed scaffolds mimicking physiological and osteoporotic bone structures under simulated microgravity conditions. The objective is to assess the effects of scaffold architecture and dynamic culture conditions on cell adhesion, proliferation, and metabolic activity, with implications for osteoporosis research. Polylactic acid scaffolds with physiological (P) and osteoporotic-like (O) trabecular architectures were 3D-printed by means of fused deposition modeling technology. Morphometric characterization was performed using micro-computed tomography. Human osteoblast-like SAOS-2 and U2OS cells were cultured on the scaffolds under static and dynamic simulated microgravity conditions using a rotary cell culture system (RCCS). Scaffold biocompatibility, cell viability, adhesion, and metabolic activity were evaluated through Bromodeoxyuridine incorporation assays, a water-soluble tetrazolium salt assay, and an enzyme-linked immunosorbent assay of tumor necrosis factor-α secretion. Both scaffold models supported osteoblast-like cell adhesion and growth, with an approximately threefold increase in colonization observed on the high-porosity O scaffolds under dynamic conditions. The dynamic environment facilitated increased surface interaction, amplifying the effects of scaffold architecture on cell behavior. Overall, sustained cell growth and metabolic activity, together with the absence of detectable inflammatory responses, confirmed the biocompatibility of the system. Scaffold microstructure and dynamic culture conditions significantly influence osteoblast-like cell behavior. The combination of 3D-printed scaffolds and a RCCS bioreactor provides a promising platform for studying bone remodeling in osteoporosis and microgravity-induced bone loss. These findings may contribute to the development of advanced in vitro models for biomedical research and potential countermeasures for bone degeneration. Full article

(This article belongs to the Special Issue Functional Biomaterial for Bone Regeneration)

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Graphical abstract

20 pages, 4630 KiB

Open AccessArticle

A Novel Flow Characteristic Regulation Method for Two-Stage Proportional Valves Based on Variable-Gain Feedback Grooves

by Xingyu Zhao, Huaide Geng, Long Quan, Chengdu Xu, Bo Wang and Lei Ge

Machines 2025, 13(8), 648; https://doi.org/10.3390/machines13080648 (registering DOI) - 24 Jul 2025

Abstract

The two-stage proportional valve is a key control component in heavy-duty equipment, where its signal-flow characteristics critically influence operational performance. This study proposes an innovative flow characteristic regulation method using variable-gain feedback grooves. Unlike conventional throttling notch optimization, the core mechanism actively adjusts pilot–main valve mapping through feedback groove shape and area gain adjustments to achieve the desired flow curves. This approach avoids complex throttling notch issues while retaining the valve’s high dynamics and flow capacity. Mathematical modeling elucidated the underlying mechanism. Subsequently, trapezoidal and composite feedback grooves are designed and investigated via simulation. Finally, composite feedback groove spools tailored to construction machinery operating conditions are developed. Comparative experiments demonstrate the following: (1) Pilot–main mapping inversely correlates with area gain; increasing gain enhances micro-motion control, while decreasing gain boosts flow gain for rapid actuation. (2) This method does not significantly increase pressure loss or energy consumption (measured loss: 0.88 MPa). (3) The composite groove provides segmented characteristics; its micro-motion flow gain (2.04 L/min/0.1 V) is 61.9% lower than conventional valves, significantly improving fine control. (4) Adjusting groove area gain and transition point flexibly modifies flow gain and micro-motion zone length. This method offers a new approach for high-performance valve flow regulation. Full article

(This article belongs to the Section Machine Design and Theory)

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23 pages, 13580 KiB

Open AccessArticle

Enabling Smart Grid Resilience with Deep Learning-Based Battery Health Prediction in EV Fleets

by Muhammed Cavus and Margaret Bell

Batteries 2025, 11(8), 283; https://doi.org/10.3390/batteries11080283 (registering DOI) - 24 Jul 2025

Abstract

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012,

R^{2}

: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

(This article belongs to the Special Issue Recent Progress of Battery Design, Modeling and Testing in Electric Vehicles)

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24 pages, 319 KiB

Open AccessArticle

Indigenous Contestations of Carbon Markets, Carbon Colonialism, and Power Dynamics in International Climate Negotiations

by Zeynep Durmaz and Heike Schroeder

Climate 2025, 13(8), 158; https://doi.org/10.3390/cli13080158 (registering DOI) - 24 Jul 2025

Abstract

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the height of their negotiation during COP25 and COP26 by drawing attention to their role in perpetuating “carbon colonialism,” thereby revealing deeper power dynamics in global climate governance. Utilising a political ecology framework, this study explores these power dynamics at play during the climate negotiations, focusing on the instrumental, structural, and discursive forms of power that enable or limit Indigenous participation. Through a qualitative case study approach, the research reveals that while Indigenous Peoples have successfully used discursive strategies to challenge market-based solutions, their influence remains limited due to entrenched structural and instrumental power imbalances within the UNFCCC process. This study highlights the need for equitable policies that integrate human rights safeguards and prioritise Indigenous-led, non-market-based approaches to ecological restoration. Full article

20 pages, 557 KiB

Open AccessArticle

Systems Thinking and Entrepreneurial Persistence Among Technology Entrepreneurs in China

by Zhuo Tao and Jianmin Sun

Systems 2025, 13(8), 626; https://doi.org/10.3390/systems13080626 (registering DOI) - 24 Jul 2025

Abstract

Based on the theoretical framework of systems thinking, this study investigates the mechanism of systems thinking in promoting entrepreneurial persistence among technology entrepreneurs in China’s digital economy development. From a dynamic complex systems perspective, 409 technology entrepreneurs from China, were measured using the systems thinking scale, the psychological ownership scale, the resource bricolage scale, and the entrepreneurial persistence scale. Systems thinking among technology entrepreneurs has been found to enhance entrepreneurial persistence significantly. Psychological ownership of technology entrepreneurs partially mediates the process of systems thinking influencing entrepreneurial persistence. Resource bricolage positively moderates the systems thinking process, influencing entrepreneurial persistence among technology entrepreneurs. This study innovatively introduces systems thinking into the field of technology entrepreneurship, reveals the relationship between systems thinking and entrepreneurial persistence of technology entrepreneurs, and provides theoretical guidance for Chinese technology entrepreneurs to enhance entrepreneurial persistence through systems thinking. Full article

(This article belongs to the Special Issue Harnessing Systems Thinking in Entrepreneurship: Interdisciplinary Perspectives and Emerging Trends)

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13 pages, 573 KiB

Open AccessReview

Developmental Programming and Postnatal Modulations of Muscle Development in Ruminants

by Kiersten Gundersen and Muhammad Anas

Biology 2025, 14(8), 929; https://doi.org/10.3390/biology14080929 (registering DOI) - 24 Jul 2025

Abstract

Prenatal and postnatal skeletal muscle development in ruminants is coordinated by interactions between genetic, nutritional, epigenetic, and endocrine factors. This review focuses on the influence of maternal nutrition during gestation on fetal myogenesis, satellite cell dynamics, and myogenic regulatory factors expression, including MYF5, MYOD1, and MYOG. Studies in sheep and cattle indicate that nutrient restriction or overnutrition alters muscle fiber number, the cross-sectional area, and the transcriptional regulation of myogenic genes in offspring. Postnatally, muscle hypertrophy is primarily mediated by satellite cells, which are activated via PAX7, MYOD, and MYF5, and regulated through mechanisms such as CARM1-induced chromatin remodeling and miR-31-mediated mRNA expression. Hormonal signaling via the GH–IGF1 axis and thyroid hormones further modulate satellite cell proliferation and protein accretion. Genetic variants, such as myostatin mutations in Texel sheep and Belgian Blue cattle, enhance muscle mass but may compromise reproductive efficiency. Nutritional interventions, including the plane of nutrition, supplementation strategies, and environmental stressors such as heat and stocking density, significantly influence muscle fiber composition and carcass traits. This review provides a comprehensive overview of skeletal muscle programming in ruminants, tracing the developmental trajectory from progenitor cell differentiation to postnatal growth and maturation. These insights underscore the need for integrated approaches combining maternal diet optimization, molecular breeding, and precision livestock management to enhance muscle growth, meat quality, and production sustainability in ruminant systems. Full article

(This article belongs to the Special Issue New Insights into Skeletal Muscle Metabolism in Pathological and Physiological Conditions)

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8 pages, 2843 KiB

Open AccessProceeding Paper

Coastal Erosion in Tsunami and Storm Surges-Exposed Areas in Licantén, Maule, Chile: Case Study Using Remote Sensing and In-Situ Data

by Joaquín Valenzuela-Jara, Idania Briceño de Urbaneja, Waldo Pérez-Martínez and Isidora Díaz-Quijada

Eng. Proc. 2025, 94(1), 10; https://doi.org/10.3390/engproc2025094010 - 24 Jul 2025

Abstract

This study examines urban expansion, coastal erosion, and extreme wave events in Licantén, Maule Region, following the 2010 earthquake and tsunami. Using multi-source data—Landsat and Sentinel-2 imagery, ERA5 reanalysis, high-resolution Maxar images, UAV surveys, and the CoastSat algorithm—we detected significant urban growth in tsunami-prone areas: Iloca (36.88%), La Pesca (33.34%), and Pichibudi (20.78%). A 39-year shoreline reconstruction (1985–2024) revealed notable changes in erosion rates and shoreline dynamics using DSAS v6.0, influenced by tides, storm surges, and wave action modeled in R to quantify storm surge events over time. Results underscore the lack of urban planning in hazard-exposed areas and the urgent need for resilient coastal management under climate change. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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17 pages, 6623 KiB

Open AccessArticle

Numerical Study on Flow Field Optimization and Wear Mitigation Strategies for 600 MW Pulverized Coal Boilers

by Lijun Sun, Miao Wang, Peian Chong, Yunhao Shao and Lei Deng

Energies 2025, 18(15), 3947; https://doi.org/10.3390/en18153947 (registering DOI) - 24 Jul 2025

Abstract

To compensate for the instability of renewable energy sources during China’s energy transition, large thermal power plants must provide critical operational flexibility, primarily through deep peaking. To investigate the combustion performance and wear and tear of a 600 MW pulverized coal boiler under deep peaking, the gas–solid flow characteristics and distributions of flue gas temperature, wall heat flux, and wall wear rate in a 600 MW tangentially fired pulverized coal boiler under variable loads (353 MW, 431 MW, 519 MW, and 600 MW) are investigated in this study employing computational fluid dynamics numerical simulation method. Results demonstrate that increasing the boiler load significantly amplifies gas velocity, wall heat flux, and wall wear rate. The maximum gas velocity in the furnace rises from 20.9 m·s⁻¹ (353 MW) to 37.6 m·s⁻¹ (600 MW), with tangential airflow forming a low-velocity central zone and high-velocity peripheral regions. Meanwhile, the tangential circle diameter expands by ~15% as the load increases. The flue gas temperature distribution exhibits a “low-high-low” profile along the furnace height. As the load increases from 353 MW to 600 MW, the primary combustion zone’s peak temperature rises from 1750 K to 1980 K, accompanied by a ~30% expansion in the coverage area of the high-temperature zone. Wall heat flux correlates strongly with temperature distribution, peaking at 2.29 × 10⁵ W·m⁻² (353 MW) and 2.75 × 10⁵ W·m⁻² (600 MW) in the primary combustion zone. Wear analysis highlights severe erosion in the economizer due to elevated flue gas velocities, with wall wear rates escalating from 3.29 × 10⁻⁷ kg·m⁻²·s⁻¹ (353 MW) to 1.23 × 10⁻⁵ kg·m⁻²·s⁻¹ (600 MW), representing a 40-fold increase under full-load conditions. Mitigation strategies, including ash removal optimization, anti-wear covers, and thermal spray coatings, are proposed to enhance operational safety. This work provides critical insights into flow field optimization and wear management for large-scale coal-fired boilers under flexible load operation. Full article

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28 pages, 2724 KiB

Open AccessArticle

Data-Driven Dynamic Optimization for Hosting Capacity Forecasting in Low-Voltage Grids

by Md Tariqul Islam, M. J. Hossain and Md Ahasan Habib

Energies 2025, 18(15), 3955; https://doi.org/10.3390/en18153955 (registering DOI) - 24 Jul 2025

Abstract

The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an

R^{2}

score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions. Full article

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22 pages, 4611 KiB

Open AccessArticle

MMC-YOLO: A Lightweight Model for Real-Time Detection of Geometric Symmetry-Breaking Defects in Wind Turbine Blades

by Caiye Liu, Chao Zhang, Xinyu Ge, Xunmeng An and Nan Xue

Symmetry 2025, 17(8), 1183; https://doi.org/10.3390/sym17081183 (registering DOI) - 24 Jul 2025

Abstract

Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background interference. To address this, based on the high-speed detection model YOLOv10-N, this paper proposes a novel detection model named MMC-YOLO. First, the Multi-Scale Perception Gated Convolution (MSGConv) Module was designed, which constructs a full-scale receptive field through multi-branch fusion and channel rearrangement to enhance the extraction of geometric asymmetry features. Second, the Multi-Scale Enhanced Feature Pyramid Network (MSEFPN) was developed, integrating dynamic path aggregation and an SENetv2 attention mechanism to suppress background interference and amplify damage response. Finally, the Channel-Compensated Filtering (CCF) module was constructed to preserve critical channel information using a dynamic buffering mechanism. Evaluated on a dataset of 4818 wind turbine blade damage images, MMC-YOLO achieves an 82.4% mAP [0.5:0.95], representing a 4.4% improvement over the baseline YOLOv10-N model, and a 91.1% recall rate, an 8.7% increase, while maintaining a lightweight parameter count of 4.2 million. This framework significantly enhances geometric asymmetry defect detection accuracy while ensuring real-time performance, meeting engineering requirements for high efficiency and precision. Full article

(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)

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21 pages, 2210 KiB

Open AccessArticle

Iterative Learning Control for Virtual Inertia: Improving Frequency Stability in Renewable Energy Microgrids

by Van Tan Nguyen, Thi Bich Thanh Truong, Quang Vu Truong, Hong Viet Phuong Nguyen and Minh Quan Duong

Sustainability 2025, 17(15), 6727; https://doi.org/10.3390/su17156727 (registering DOI) - 24 Jul 2025

Abstract

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article

(This article belongs to the Special Issue Synergistic Approaches to Energy Transition, Circular Economy and the Sustainable Development Goals (SDGs))

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