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Search Results (2,409)

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29 pages, 2806 KiB  
Review
Bridging Design and Climate Realities: A Meta-Synthesis of Coastal Landscape Interventions and Climate Integration
by Bo Pang and Brian Deal
Land 2025, 14(9), 1709; https://doi.org/10.3390/land14091709 - 23 Aug 2025
Abstract
This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they [...] Read more.
This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they focused on climate adaptation, flood protection, or other goals) and how they tracked performance. We are hoping to bring some clarity to a very scattered field, helping us to see patterns in what is actually being carried out in terms of landscape interventions and increasing sea levels. We are hoping to provide a practical reference for making better, more climate-responsive design decisions. Coastal cities face escalating climate-driven threats from increasing sea levels and storm surges to urban heat islands. These threats are driving increased interest in nature-based solutions (NbSs) as green adaptive alternatives to traditional gray infrastructure. Despite an abundance of individual case studies, there have been few systematic syntheses aimed at landscape designers and managers linking design typologies, project framing, and performance outcomes. This study addresses this gap through a meta-synthesis of 123 implemented coastal landscape interventions aimed directly at landscape-oriented research and professions. Flood risk reduction was the dominant framing strategy (30.9%), followed by climate resilience (24.4%). Critical evidence gaps emerged—only 1.6% employed integrated monitoring approaches, 30.1% provided ambiguous performance documentation, and mean monitoring quality scored 0.89 out of 5.0. While 95.9% of the projects acknowledged SLR as a driver, only 4.1% explicitly integrated climate projections into design parameters. Community monitoring approaches demonstrated significantly higher ecosystem service integration, particularly cultural services (36.4% vs. 6.9%, p<0.001), and enhanced monitoring quality (mean score 1.64 vs. 0.76, p<0.001). Implementation barriers spanned technical constraints, institutional fragmentation, and data limitations, each affecting 20.3% of projects. Geographic analysis revealed evidence generation inequities, with systematic underrepresentation of high-risk regions (Africa: 4.1%; Latin America: 2.4%) versus concentration in well-resourced areas (North America: 27.6%; Europe: 17.1%). Full article
38 pages, 4394 KiB  
Article
Adaptive Spectrum Management in Optical WSNs for Real-Time Data Transmission and Fault Tolerance
by Mohammed Alwakeel
Mathematics 2025, 13(17), 2715; https://doi.org/10.3390/math13172715 - 23 Aug 2025
Abstract
Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This [...] Read more.
Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article
(This article belongs to the Special Issue Advances in Mobile Network and Intelligent Communication)
26 pages, 1541 KiB  
Article
Assessing the Socioeconomic and Environmental Impact of Hybrid Renewable Energy Systems for Sustainable Power in Remote Cuba
by Israel Herrera Orozco, Santacruz Banacloche, Yolanda Lechón and Javier Dominguez
Sustainability 2025, 17(17), 7592; https://doi.org/10.3390/su17177592 - 22 Aug 2025
Abstract
This study evaluates the viability of a specific hybrid renewable energy system (HRES) installation designed for a remote community as a case study in Cuba. The system integrates solar, wind, and biomass resources to address localised challenges of energy insecurity and environmental degradation. [...] Read more.
This study evaluates the viability of a specific hybrid renewable energy system (HRES) installation designed for a remote community as a case study in Cuba. The system integrates solar, wind, and biomass resources to address localised challenges of energy insecurity and environmental degradation. Rather than offering a generalised evaluation of HRES technologies, this work focuses on the performance, impacts, and viability of this particular configuration within its unique geographical, social, and technical context. Using life cycle assessment (LCA) and input–output modelling, the research assesses environmental and socioeconomic impacts. The proposed HRES reduces greenhouse gas emissions by 60% (from 1.14 to 0.47 kg CO2eq/kWh) and fossil energy consumption by 50% compared to diesel-based systems. Socioeconomic analysis reveals that the system generates 40.3 full-time equivalent (FTE) jobs, with significant employment opportunities in operation and maintenance. However, initial investments primarily benefit foreign suppliers due to Cuba’s reliance on imported components. The study highlights the potential for local economic gains through workforce training and domestic manufacturing of renewable energy technologies. These findings underscore the importance of integrating multiple renewable sources to enhance energy resilience and sustainability in Cuba. Policymakers should prioritise strategies to incentivise local production and capacity building to maximise long-term benefits. Future research should explore scalability across diverse regions and investigate policy frameworks to support widespread adoption of HRES. This study provides valuable insights for advancing sustainable energy solutions in Cuba and similar contexts globally. Full article
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42 pages, 591 KiB  
Article
Leveraging Network Analysis and NLP for Intelligent Data Mining of Taxonomies and Folksonomies of PornHub
by Jan Sawicki, Loizos Bitsikokos, Yulia Belinskaya, Maria Ganzha and Marcin Paprzycki
Appl. Sci. 2025, 15(17), 9250; https://doi.org/10.3390/app15179250 - 22 Aug 2025
Abstract
This study explores graph-based methods to model and analyze the semantic interplay between editorial taxonomies and user-generated folksonomies on the PornHub platform, using a dataset of over 97,000 videos (2015–2024). We construct and examine a graph of user-assigned tags and platform-defined categories, applying [...] Read more.
This study explores graph-based methods to model and analyze the semantic interplay between editorial taxonomies and user-generated folksonomies on the PornHub platform, using a dataset of over 97,000 videos (2015–2024). We construct and examine a graph of user-assigned tags and platform-defined categories, applying the Leiden community detection algorithm to uncover latent semantic groupings. To enrich the graph structure, we embed textual metadata using state-of-the-art language models (Qwen3-Embedding-4B and all-MiniLM-L6-v2), enabling the integration of natural language processing within graph-based learning. Our analysis reveals that folksonomies partially align with taxonomies through synonymous structures but also diverge by capturing nuanced attributes such as body features and aesthetic styles. These asymmetries highlight how folksonomies introduce higher-resolution semantic layers absent from fixed-category systems. By fusing graph mining, NLP-driven embeddings, and network-based clustering, this work contributes a hybrid methodology for semantic knowledge extraction in large-scale, user-generated content. It offers implications for graph-based recommendation, content moderation, and metadata enrichment—demonstrating the utility of graph-centric AI techniques in real-world multimedia data settings. Full article
30 pages, 3417 KiB  
Article
A Lightweight Deep Learning Model for Automatic Modulation Classification Using Dual-Path Deep Residual Shrinkage Network
by Prakash Suman and Yanzhen Qu
AI 2025, 6(8), 195; https://doi.org/10.3390/ai6080195 - 21 Aug 2025
Viewed by 545
Abstract
Efficient spectrum utilization is critical for meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying modulation schemes in received signals—an essential capability for dynamic spectrum allocation and [...] Read more.
Efficient spectrum utilization is critical for meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying modulation schemes in received signals—an essential capability for dynamic spectrum allocation and interference mitigation, particularly in cognitive radio (CR) systems. With the increasing deployment of smart edge devices, such as IoT nodes with limited computational and memory resources, there is a pressing need for lightweight AMC models that balance low complexity with high classification accuracy. In this study, we propose a low-complexity, lightweight deep learning (DL) AMC model optimized for resource-constrained edge devices. We introduce a dual-path deep residual shrinkage network (DP-DRSN) with garrote thresholding for effective signal denoising, and we designed a compact hybrid CNN-LSTM architecture comprising only 27,072 training parameters. The proposed model achieved average classification accuracies of 61.20%, 63.78%, and 62.13% on the RML2016.10a, RML2016.10b, and RML2018.01a datasets, respectively, demonstrating a strong balance between model efficiency and classification performance. These results highlight the model’s potential for enabling accurate and efficient AMC on edge devices with limited resources, despite not surpassing state-of-the-art accuracy owing to its deliberate emphasis on computational efficiency. Full article
(This article belongs to the Section AI Systems: Theory and Applications)
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29 pages, 1124 KiB  
Review
From Mathematical Modeling and Simulation to Digital Twins: Bridging Theory and Digital Realities in Industry and Emerging Technologies
by Antreas Kantaros, Theodore Ganetsos, Evangelos Pallis and Michail Papoutsidakis
Appl. Sci. 2025, 15(16), 9213; https://doi.org/10.3390/app15169213 - 21 Aug 2025
Viewed by 237
Abstract
Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within [...] Read more.
Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article
(This article belongs to the Special Issue Feature Review Papers in Section Applied Industrial Technologies)
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27 pages, 1970 KiB  
Review
Artificial Intelligence in Alzheimer’s Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval
by Rafail C. Christodoulou, Amanda Woodward, Rafael Pitsillos, Reina Ibrahim and Michalis F. Georgiou
J. Clin. Med. 2025, 14(16), 5913; https://doi.org/10.3390/jcm14165913 - 21 Aug 2025
Viewed by 138
Abstract
Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to [...] Read more.
Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. Methods: This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. Results: A total of 111 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. Conclusions: AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice. Full article
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35 pages, 3129 KiB  
Article
Spatiotemporal Meta-Reinforcement Learning for Multi-USV Adversarial Games Using a Hybrid GAT-Transformer
by Yang Xiong, Shangwen Wang, Hongjun Tian, Guijie Liu, Zihao Shan, Yijie Yin, Jun Tao, Haonan Ye and Ying Tang
J. Mar. Sci. Eng. 2025, 13(8), 1593; https://doi.org/10.3390/jmse13081593 - 20 Aug 2025
Viewed by 121
Abstract
Coordinating Multi-Unmanned Surface Vehicle (USV) swarms in complex, adversarial maritime environments is a significant challenge, as existing multi-agent reinforcement learning (MARL) methods often fail to capture intricate spatiotemporal dependencies, leading to suboptimal policies. To address this, we propose Adv-TransAC, a novel Spatio-Temporal Meta-Reinforcement [...] Read more.
Coordinating Multi-Unmanned Surface Vehicle (USV) swarms in complex, adversarial maritime environments is a significant challenge, as existing multi-agent reinforcement learning (MARL) methods often fail to capture intricate spatiotemporal dependencies, leading to suboptimal policies. To address this, we propose Adv-TransAC, a novel Spatio-Temporal Meta-Reinforcement Learning framework. Its core innovation is a hybrid GAT-transformer architecture that decouples spatial and temporal reasoning: a Graph Attention Network (GAT) models instantaneous tactical formations, while a transformer analyzes their temporal evolution to infer intent. This is combined with an adversarial meta-learning mechanism to enable rapid adaptation to opponent tactics. In high-fidelity escort and defense simulations, Adv-TransAC significantly outperforms state-of-the-art MARL baselines in task success rate and policy robustness. The learned policies demonstrate the emergence of complex cooperative behaviors, such as intelligent risk-aware coordination and proactive interception maneuvers. The framework’s practicality is further validated by a communication-efficient federated optimization architecture. By effectively modeling spatiotemporal dynamics and enabling rapid adaptation, Adv-TransAC provides a powerful solution that moves beyond reactive decision-making, establishing a strong foundation for next-generation, intelligent maritime platforms. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
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40 pages, 725 KiB  
Article
Upper and Lower Bounds of Performance Metrics in Hybrid Systems with Setup Time
by Ken’ichi Kawanishi and Yuki Ino
Mathematics 2025, 13(16), 2685; https://doi.org/10.3390/math13162685 - 20 Aug 2025
Viewed by 121
Abstract
To address the increasing demand for computational and communication resources, modern networked systems often rely on heterogeneous servers, including those requiring setup times, such as virtual machines or servers, and others that are always active. In this paper, we model and analyze the [...] Read more.
To address the increasing demand for computational and communication resources, modern networked systems often rely on heterogeneous servers, including those requiring setup times, such as virtual machines or servers, and others that are always active. In this paper, we model and analyze the performance of such hybrid systems using a level-dependent quasi-birth-and-death (LDQBD) process. Building upon an existing queueing model, we extend the analysis by considering scalable approximation methods. Since matrix analytic methods become computationally expensive in large-scale settings, we propose a stochastic bounding approach that derives upper and lower bounds for the stationary distribution, thereby significantly reducing computational cost. This approach further provides bounds on the performance metrics of the hybrid system. Full article
(This article belongs to the Special Issue Recent Research in Queuing Theory and Stochastic Models, 2nd Edition)
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21 pages, 8034 KiB  
Article
Decoding Forage-Driven Microbial–Metabolite Patterns: A Multi-Omics Comparison of Eight Tropical Silage Crops
by Xianjun Lai, Siqi Liu, Yandan Zhang, Haiyan Wang and Lang Yan
Fermentation 2025, 11(8), 480; https://doi.org/10.3390/fermentation11080480 - 20 Aug 2025
Viewed by 208
Abstract
Tropical forage crops vary widely in biochemical composition, resulting in inconsistent silage quality. Understanding how plant traits shape microbial and metabolic networks during ensiling is crucial for optimizing fermentation outcomes. Eight tropical forages—Sorghum bicolor (sweet sorghum), Sorghum × drummondii (sorghum–Sudangrass hybrid), Sorghum [...] Read more.
Tropical forage crops vary widely in biochemical composition, resulting in inconsistent silage quality. Understanding how plant traits shape microbial and metabolic networks during ensiling is crucial for optimizing fermentation outcomes. Eight tropical forages—Sorghum bicolor (sweet sorghum), Sorghum × drummondii (sorghum–Sudangrass hybrid), Sorghum sudanense (Sudangrass), Pennisetum giganteum (giant Napier grass), Pennisetum purpureum cv. Purple (purple elephant grass), Pennisetum sinese (king grass), Leymus chinensis (sheep grass), and Zea mexicana (Mexican teosinte)—were ensiled under uniform conditions. Fermentation quality, bacterial and fungal communities (16S rRNA and ITS sequencing), and metabolite profiles (untargeted liquid chromatography–mass spectrometry, LC-MS) were analyzed after 60 days. Sweet sorghum and giant Napier grass showed optimal fermentation, with high lactic acid levels (111.2 g/kg and 99.4 g/kg, respectively), low NH4+-N (2.4 g/kg and 3.1 g/kg), and dominant Lactiplantibacillus plantarum. In contrast, sheep grass and Mexican teosinte exhibited poor fermentation, with high NH4+-N (6.7 and 6.1 g/kg) and Clostridium dominance. Fungal communities were dominated by Kazachstania humilis (>95%), while spoilage-associated genera such as Cladosporium, Fusarium, and Termitomyces proliferated in poorly fermented silages. Metabolomic analysis identified 15,827 features, with >3000 significantly differential metabolites between silages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealed divergence in flavonoid biosynthesis, lipid metabolism, and amino acid pathways. In the sweet sorghum vs. sheep grass comparison, oxidative stress markers ((±) 9-HODE, Agrimonolide) were elevated in sheep grass, while sweet sorghum accumulated antioxidants like Vitamin D3. Giant Napier grass exhibited higher levels of antimicrobial flavonoids (e.g., Apigenin) than king grass, despite both being dominated by lactic acid bacteria. Sorghum–Sudangrass hybrid silage showed enrichment of lignan and flavonoid derivatives, while Mexican teosinte accumulated hormone-like compounds (Gibberellin A53, Pterostilbene), suggesting microbial dysbiosis. These findings indicate that silage fermentation outcomes are primarily driven by forage-intrinsic traits. A “forage–microbiota–metabolite” framework was proposed to explain how plant-specific properties regulate microbial assembly and metabolic output. These insights can guide forage selection and development of precision inoculant for high-quality tropical silage. Full article
(This article belongs to the Section Industrial Fermentation)
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20 pages, 3116 KiB  
Article
A Residential Droop-Controlled AC Nanogrid with Power Quality Enhancement
by Ayesha Wajiha Aslam, Víctor Minambres-Marcos and Carlos Roncero-Clemente
Electronics 2025, 14(16), 3306; https://doi.org/10.3390/electronics14163306 - 20 Aug 2025
Viewed by 221
Abstract
Harmonic distortion from non-linear loads poses a significant challenge to power quality in residential nanogrids, often requiring complex control strategies and communication between distributed resources. This paper presents a parallel hybrid inverter system for an AC nanogrid that enhances power quality using only [...] Read more.
Harmonic distortion from non-linear loads poses a significant challenge to power quality in residential nanogrids, often requiring complex control strategies and communication between distributed resources. This paper presents a parallel hybrid inverter system for an AC nanogrid that enhances power quality using only decentralized droop-based primary control, without the need for secondary control or communication links. The system features two inverters with strategic placement: one maintains voltage stability at the point of common coupling, while the other directly supplies the harmonic and reactive current demanded by non-linear loads. A compensation mechanism allows the second inverter to dynamically switch from supplying sinusoidal current to injecting targeted harmonic components, effectively isolating distortion from the main grid. Simulation results confirm that this approach significantly reduces voltage distortion at the PCC and ensures balanced power sharing, all while simplifying the control architecture. The proposed method offers a scalable, cost-effective solution for residential nanogrids seeking to integrate diverse loads and distributed energy resources while maintaining high power quality. Full article
(This article belongs to the Special Issue Recent Advances in Control and Optimization in Microgrids)
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17 pages, 2784 KiB  
Article
Enhanced Distributed Coordinated Control Strategy for DC Microgrid Hybrid Energy Storage Systems Using Adaptive Event Triggering
by Fawad Nawaz, Ehsan Pashajavid, Yuanyuan Fan and Munira Batool
Electronics 2025, 14(16), 3303; https://doi.org/10.3390/electronics14163303 - 20 Aug 2025
Viewed by 351
Abstract
Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded [...] Read more.
Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded DC microgrids (MGs). We propose a hierarchical distributed control framework integrating ANN-based controllers and adaptive event-triggered mechanisms to dynamically regulate power flow and minimise communication. This system utilises a hierarchical coordinated control method (HCCM) with primary virtual resistance droop control integrated with state-of-charge (SoC) management and secondary control for voltage regulation and proportional current distribution through optimised communication networks. The integration of artificial neural network (ANN)-based controllers alongside traditional PI control leads to an improvement in system responsiveness. The control approach dynamically adjusts the trigger parameters to minimise communication overhead with tight voltage regulation. An extensive simulation using MATLAB/Simulink shows how the system can effectively manage variability in renewable energy sources and maintain stable voltage profiles with precise power distribution and minimal bus voltage fluctuations. Simulations confirm enhanced voltage regulation (±0.5% deviation), proportional current sharing (98% accuracy), and 60% communication reduction under load transients (outcomes). Full article
(This article belongs to the Section Industrial Electronics)
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26 pages, 5952 KiB  
Article
A Hybrid Short-Term Prediction Model for BDS-3 Satellite Clock Bias Supporting Real-Time Applications in Data-Denied Environments
by Ye Yu, Chaopan Yang, Yao Ding, Yuanliang Xue and Yulong Ge
Remote Sens. 2025, 17(16), 2888; https://doi.org/10.3390/rs17162888 - 19 Aug 2025
Viewed by 215
Abstract
High-precision satellite clock bias (SCB) prediction is essential for GNSS applications, including real-time precise point positioning (RT-PPP), Earth observation, planetary exploration, and spaceborne geodetic missions. However, during communication outages or when real-time SCB products are unavailable, RT-PPP may fail due to missing clock [...] Read more.
High-precision satellite clock bias (SCB) prediction is essential for GNSS applications, including real-time precise point positioning (RT-PPP), Earth observation, planetary exploration, and spaceborne geodetic missions. However, during communication outages or when real-time SCB products are unavailable, RT-PPP may fail due to missing clock corrections. This underscores the necessity of reliable short-term SCB prediction in data-denied environments. To address this challenge, a hybrid model that integrates wavelet transform, a particle swarm optimization-enhanced gray model, and a first-order weighted local method is proposed for short-term SCB prediction. First, the novel model employs the db1 wavelet to perform three-level multi-resolution decomposition and single-branch reconstruction on preprocessed SCB, yielding one trend term and three detailed terms. Second, the particle swarm optimization algorithm is adopted to globally optimize the parameters of the traditional gray model to avoid falling into local optima, and the optimization-enhanced gray model is applied to predict the trend term. For the three detailed terms, the embedding dimension and time delay are calculated, and they are constructed in phase space to establish a first-order weighted local model for prediction. Third, the final SCB prediction is obtained by summing the predicted results of the trend term and the three detailed terms correspondingly. The BDS-3 SCB products from the GNSS Analysis Center of Wuhan University (WHU) are selected for experiments. Results indicate that the proposed model surpasses conventional linear polynomial (LP), quadratic polynomial (QP), gray model (GM), and Legendre (Leg.) polynomial models. The average precision and stability improvements reach (80.00, 79.16, 82.14, and 72.22) % and (36.36, 41.67, 41.67, and 61.11) % for 30 min prediction, (79.31, 78.57, 80.65, and 76.92) % and (44.44, 44.44, 47.37, and 74.36) % for 60 min prediction, and the average precision of the predicted SCB products is better than 0.20 ns and 0.21 ns for 30 min and 60 min, respectively. Furthermore, the proposed model exhibits strong robustness and is less affected by changes in clock types and the amount of modeling data. Therefore, in practical applications, the short-term SCB products predicted by the novel model are fully capable of satisfying the requirements of centimeter-level RT-PPP for clock bias precision. Full article
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15 pages, 2026 KiB  
Article
Planting Diversification Enhances Phosphorus Availability and Reshapes Fungal Community Structure in the Maize Rhizosphere
by Yannan Li, Yuming Zhang, Xiaoxin Li, Hongjun Li, Wenxu Dong, Shuping Qin, Xiuping Liu, Lijuan Zhang, Chunsheng Hu, Hongbo He, Pushan Zheng and Jingyun Zhao
Agronomy 2025, 15(8), 1993; https://doi.org/10.3390/agronomy15081993 - 19 Aug 2025
Viewed by 196
Abstract
Intercropping with green manures is an effective practice for increasing agricultural production and reducing environmental issues. However, the effects of green manure type and intercropping patten on soil nutrient availability and microbial communities remains underexplored. In the present study, the impacts of three [...] Read more.
Intercropping with green manures is an effective practice for increasing agricultural production and reducing environmental issues. However, the effects of green manure type and intercropping patten on soil nutrient availability and microbial communities remains underexplored. In the present study, the impacts of three green manure–maize intercropping patterns on maize yield, rhizosphere nutrient availability, and soil fungal community were evaluated. Four treatments (three replicate plots for each) were involved, including a monoculture treatment (MC) as a control and three intercropping patterns as follows: maize–ryegrass (Lolium perenne L.) (IntL), maize–forage soybean (Fen Dou mulv 2, a hybrid soybean cultivar) (IntF), and maize–ryegrass–forage soybean (IntLF) intercropping. The results showed that all three intercropping patterns significantly increased maize yield and rhizosphere available phosphorus (AP) compared with MC. Intercropping shifted the dominant assembly process of the maize rhizosphere fungal community from stochastic to deterministic processes, shaping a community rich in arbuscular mycorrhizal fungi (AMF) and limited in plant pathogens, primarily Exserohilum turcicum. AP showed significant correlations with fungal community and AMF, while maize yield was negatively correlated with plant pathogens. In addition, the dual-species green manure intercropping pattern (IntLF) had the strongest positive effects on maize yield, AP content, and fungal community compared with single-species patterns (IntL and IntF). These results illustrate the advantages of planting diversification in boosting crop production by improving nutrient availability and soil health in the rhizosphere and suggest that the maize–ryegrass–forage soybean intercropping system is a potential strategy for improving soil fertility and health. Full article
(This article belongs to the Special Issue Plant Nutrition Eco-Physiology and Nutrient Management)
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21 pages, 563 KiB  
Review
Addressing Patient–Provider Communication Gaps in Vanishing Twin Syndrome: Implications for Patient Care and Clinical Guidelines
by Nichole M. Cubbage, Samantha L. P. Schilit, Allison Groff, Stephanie Ernst and Marc A. Nascarella
Healthcare 2025, 13(16), 2048; https://doi.org/10.3390/healthcare13162048 - 19 Aug 2025
Viewed by 525
Abstract
Background: Vanishing twin syndrome (VTS) represents a complex and under-recognized phenomenon in multifetal pregnancies, associated with both clinical uncertainty and significant psychosocial impact. Despite its frequency, gaps remain in diagnostic clarity, international guidelines, and communication strategies with patients and families. Materials and [...] Read more.
Background: Vanishing twin syndrome (VTS) represents a complex and under-recognized phenomenon in multifetal pregnancies, associated with both clinical uncertainty and significant psychosocial impact. Despite its frequency, gaps remain in diagnostic clarity, international guidelines, and communication strategies with patients and families. Materials and Methods: This hybrid review integrates narrative and systematic elements to assess the diagnostic, clinical, and psychosocial gaps in VTS. A systematic literature search was conducted across Medline/PubMed, CINAHL, PsycINFO, EBM Reviews, and Scopus using terms such as “vanishing twin syndrome,” “patient-provider communicat*,” and “bereave* care.” Sources included systematic reviews, randomized controlled trials, cohort studies, and qualitative studies. Exclusion criteria were outdated publications (>10 years old). Results: Evidence revealed multiple domains of concern. Clinical risks and diagnostics remain poorly defined, with inconsistent recognition of maternal and neonatal complications. Psychosocial impacts were prominent, encompassing grief, identity disruption, and unmet support needs. Patient–provider communication was frequently inadequate, with insufficient training and lack of standardized language. International guidelines varied widely in scope, with only a few of them providing clear recommendations for bereavement care in multifetal loss contexts. Discussion: Emerging discourse highlights the limitations of the traditional fission model and alternative conceptual frameworks, such as Herranz’s model, for understanding VTS. These theoretical differences underscore the need for precise terminology and consistent diagnostic practices. Clinical implications extend to prenatal screening, obstetric management, and the integration of psychosocial support. Patient-centered communication and structured support initiatives (e.g., the Butterfly Project) demonstrate the potential to bridge communication gaps and improve care experiences. Conclusions: VTS requires recognition as both a medical and psychosocial condition. Improved clinical definitions, harmonized international guidelines, and emphasis on empathetic communication are essential to address the current gaps. Integrating these elements into practice may enhance patient outcomes and provide families with validation and support following multifetal loss. Full article
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