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The low stability and bioaccessibility of polyphenols limit their application in functional foods. To address this, chlorogenic acid (CGA) and curcumin (CU) were selected as model compounds and co-encapsulated in spray-dried linseed oil (LO) multiple emulsions (MEs), using octenyl succinic anhydride-modified waxy maize starch as encapsulating agent. Water-in-oil-in-water MEs were prepared by two-step high-pressure homogenization and spray-dried under optimized conditions determined by response surface methodology to minimize surface oil. The resulting microparticles were characterized for encapsulation efficiency (EE), morphology, oxidative stability, and performance under simulated gastrointestinal digestion (INFOGEST protocol). Both CGA and CU exhibited high EE in microparticles (~88–90%), with spray drying significantly improving CGA retention compared to liquid emulsions. Microparticles also showed improved oxidative stability due to the presence of antioxidants. During digestion, CU bioaccessibility decreased (62.7%) relative to liquid MEs (83.6%), consistent with reduced lipid digestion. Conversely, CGA bioaccessibility was higher in microparticles (47.6%) than in MEs (29.2%), indicating a protective effect of the encapsulating agent under intestinal conditions. Overall, spray drying stabilized linseed oil-based MEs and enabled effective co-encapsulation of hydrophilic and lipophilic compounds, supporting their potential as multifunctional delivery systems for functional foods. Full article

(This article belongs to the Special Issue Innovations in Drug Delivery Systems for Antioxidant-Based Therapeutic Agents)

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43 pages, 2436 KB

Open AccessReview

Fabricating Three-Dimensional Metamaterials Using Additive Manufacturing: An Overview

by Balakrishnan Subeshan, Abdulhammed K. Hamzat and Eylem Asmatulu

J. Manuf. Mater. Process. 2025, 9(10), 343; https://doi.org/10.3390/jmmp9100343 (registering DOI) - 19 Oct 2025

Abstract

Metamaterials are artificial materials composed of special microstructures that have properties with unusual and useful features and can be applied to many fields. With their unique properties and sensitivity to external stimuli, metamaterials offer design flexibility to users. Traditional manufacturing is often not up to the task of creating metamaterials, which are now more accurately and more effectively analyzed than they were in the past. Recent advances in additive manufacturing (AM) have achieved remarkable success, with ensemble machine learning models demonstrating R² values exceeding 0.97 and accuracy improvements of 9.6% over individual approaches. State-of-the-art multiphoton polymerization (MPP) techniques now reach submicron resolution (<1 μm), while selective laser melting (SLM) processes provide 20–100 μm precision for metallic metamaterials. This work offers a comprehensive review of additively manufactured 3D metamaterials, focusing on three categories of their fabrication: electromagnetic (achieving bandgaps up to 470 GHz), acoustic (providing 90% sound suppression at targeted frequencies), and mechanical (demonstrating Poisson’s ratios from −0.8 to +0.8). The relationship between different types of AM processes used in creating 3D objects and the properties of the resulting materials has been systematically reviewed. This research aims to address gaps and develop new applications to meet the modern demand for the broader use of metamaterials in advanced devices and systems that require high efficiency for sophisticated, high-performance applications. Full article

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24 pages, 3779 KB

Open AccessArticle

Ecosystem Service Value Dynamics in the Yellow River Delta National Nature Reserve, China: Conservation Implications from Two Decades of Change

by Shuxin Shi, Shengyuan Xu and Ziqi Meng

Sustainability 2025, 17(20), 9291; https://doi.org/10.3390/su17209291 (registering DOI) - 19 Oct 2025

Abstract

Yellow River Delta National Nature Reserve plays a critical role in ecological conservation, and assessing its ecosystem service value (ESV) is essential for guiding sustainable management strategies that harmonize development and preservation. This study was motivated by the need to generate actionable insights for adaptive conservation planning in this vulnerable coastal region. We evaluated the spatiotemporal dynamics of ESV from 2000 to 2020 using a combination of remote sensing, geographic information system analyses, and statistical modeling. Primary drivers influencing the spatial heterogeneity of ecosystem service value were identified through geographical detector analysis, and future trends were projected based on historical patterns. The results revealed that (1) ESV showed a clear spatial gradient, with higher values in coastal zones, moderate values along river channels, and lower values inland, and exhibited an overall significant increase over the two decades, primarily driven by improvements in regulating services; (2) wetland area and precipitation were the most influential factors, though socio-economic elements and environmental conditions also contributed to ESV distribution; and (3) future ESV is expected to follow current trends, reinforcing the importance of current management practices. Given that the continuous increase in ESV from 2000 to 2020 was predominantly attributed to water body expansion, future conservation strategies should prioritize the protection and restoration of these water resources. Full article

(This article belongs to the Special Issue Remote Sensing and Spatial Analysis for Monitoring and Assessing Landscape and Ecosystem Sustainability)

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31 pages, 3840 KB

Open AccessReview

Efficient and Secure GANs: A Survey on Privacy-Preserving and Resource-Aware Models

by Niovi Efthymia Apostolou, Elpida Vasiliki Balourdou, Maria Mouratidou, Eleni Tsalera, Ioannis Voyiatzis, Andreas Papadakis and Maria Samarakou

Appl. Sci. 2025, 15(20), 11207; https://doi.org/10.3390/app152011207 (registering DOI) - 19 Oct 2025

Abstract

Generative Adversarial Networks (GANs) generate synthetic content to support applications such as data augmentation, image-to-image translation, and training models where data availability is limited. Nevertheless, their broader deployment is constrained by limitations in data availability, high computational and energy demands, as well as privacy and security concerns. These factors restrict their scalability and integration in real-world applications. This survey provides a systematic review of research aimed at addressing these challenges. Techniques such as few-shot learning, consistency regularization, and advanced data augmentation are examined to address data scarcity. Approaches designed to reduce computational and energy costs, including hardware-based acceleration and model optimization, are also considered. In addition, strategies to improve privacy and security, such as privacy-preserving GAN architectures and defense mechanisms against adversarial attacks, are analyzed. By organizing the literature into these thematic categories, the review highlights available solutions, their trade-offs, and remaining open issues. Our findings underline the growing role of GANs in artificial intelligence, while also emphasizing the importance of efficient, sustainable, and secure designs. This work not only concentrates the current knowledge but also sets the basis for future research. Full article

(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)

14 pages, 2063 KB

Open AccessArticle

Impact of AI Assistance in Pneumothorax Detection on Chest Radiographs Among Readers of Varying Experience

by Chen-Wei Ho, Yu-Lun Wu, Yi-Chun Chen, Yu-Jeng Ju and Ming-Ting Wu

Diagnostics 2025, 15(20), 2639; https://doi.org/10.3390/diagnostics15202639 (registering DOI) - 19 Oct 2025

Abstract

Objectives: We aimed to investigate whether AI assistance could improve the performance of pneumothorax detection on chest radiographs (CXR) by readers with varying experience from radiologists to the frontline healthcare providers, and whether AI assistance could diminish the potential confounders for readers’ detecting pneumothorax. Methods: In this retrospective, single-center, blinded, multi-reader diagnostic accuracy study, 125 CXRs were prepared from radiological information system (March 2024 to August 2024) for test. The 18 readers were composed of six groups, each had 3 persons: board-certified radiologists (Group-1), senior radiology residents (Group-2), junior radiology residents (Group-3), postgraduate year residents (Group-4), senior radiographers (Group-5), and junior radiographers (Group-6). They read the CXR independently twice, without and with AI assistance, at an interval of one month. We used receiver operating characteristic curve for performance analysis and generalized estimating equation (GEE) model for confounding factor analysis. Results: AI software alone achieved a high area under curve of 0.965 (95% CI: 0.926, 0.995). With AI assistance, the performance in all groups significantly improved (p < 0.01) especially the junior readers (the frontline healthcare providers, Group-3, 4, 6) and diminished the difference among all groups except some related to Group-1. GEE model showed that AI assistance, reader’s experience, and projection type interfere with the readers’ performance (all p < 0.05). Conclusions: AI assistance could improve the performance of pneumothorax detection by varying experience of readers, especially the frontline healthcare providers. The influence of confounders, such as reader’s experience, also be diminished by AI assistance. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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24 pages, 4033 KB

Open AccessArticle

Integrating PC Splitting Design and Construction Organization Through Multi-Agent Simulation for Prefabricated Buildings

by Yi Shen, Jing Wang and Guan-Hang Jin

Buildings 2025, 15(20), 3773; https://doi.org/10.3390/buildings15203773 (registering DOI) - 19 Oct 2025

Abstract

Prefabricated building projects represent industrialized and intelligent construction through factory production, standardized design, and mechanized assembly. This study presents a multi-agent simulation approach to model the prefabricated construction process, allowing for the concurrent optimization of the prefabricated component (PC) splitting design and the construction organization plan through iterative simulation. (1) Employing a questionnaire survey, it identifies critical factors affecting schedule and cost from a design–construction coordination perspective. (2) Based on these findings, an agent-based model was developed incorporating PC installation, crane operations, and storage yard spatial constraints, along with interaction rules governing these agents. (3) Data interoperability was achieved among Revit, NetLogo3D and Navisworks. This integrated environment offers project managers digital management of design and construction plans, simulation support, and visualization tools. Simulation results confirm that a hybrid resource allocation strategy utilizing both tower cranes and mobile cranes enhances resource leveling, accelerates schedule performance, and improves cost efficiency. Full article

(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)

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35 pages, 3526 KB

Open AccessArticle

Multi-Objective Optimization of Mobile Battery Energy Storage and Dynamic Feeder Reconfiguration for Enhanced Voltage Profiles in Active Distribution Systems

by Phuwanat Marksan, Krittidet Buayai, Ritthichai Ratchapan, Wutthichai Sa-nga-ngam, Krischonme Bhumkittipich, Kaan Kerdchuen, Ingo Stadler, Supapradit Marsong and Yuttana Kongjeen

Energies 2025, 18(20), 5515; https://doi.org/10.3390/en18205515 (registering DOI) - 19 Oct 2025

Abstract

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)

18 pages, 3617 KB

Open AccessArticle

Sliding Mode Observer-Based Sensorless Control Strategy for PMSM Drives in Air Compressor Applications

by Rana Md Sohel, Wenhao Wu, Renzi Ji, Zihao Fang and Kai Liu

Appl. Sci. 2025, 15(20), 11206; https://doi.org/10.3390/app152011206 (registering DOI) - 19 Oct 2025

Abstract

This paper presents a sensorless control strategy for permanent magnet synchronous motor (PMSM) drives in industrial and automotive air compressor applications. The strategy utilizes an adaptive-gain sliding mode observer integrated with a refined back-EMF model to suppress chattering and improve convergence. The proposed approach achieves precise rotor position and speed estimation across a wide operational range without mechanical sensors. It directly addresses the critical needs of reliability, compactness, and resilience in automotive environments. Unlike conventional observers, its originality lies in the enhanced gain structure, enabling accurate and robust sensorless control validated through both simulation and hardware tests. Comprehensive simulation results demonstrate effective performance from 2000 to 8500 rpm, with steady-state speed tracking errors maintained below 0.4% at 2000 rpm and 0.035% at 8500 rpm under rated load. The control methodology exhibits excellent disturbance rejection capabilities, maintaining speed regulation within ±5 rpm under an 80% load disturbance at 8500 rpm while limiting q-axis current ripple to 2.5% of rated values. Experimental validation on a 2.2 kW PMSM-driven compressor test platform confirms stable operation at 4000 rpm with speed fluctuations constrained to 20 rpm (0.5% error) and precise current regulation, maintaining the d-axis current within ±0.07 A. The system demonstrates rapid dynamic response, achieving acceleration from 1320 rpm to 2365 rpm within one second during testing. The results confirm the method’s practical viability for enhancing reliability and reducing maintenance in industrial and automotive compressors systems. Full article

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33 pages, 4863 KB

Open AccessArticle

Optimal Control of MSWI Processes Using an RBF-IPOA Strategy for Enhanced Combustion Efficiency and NO_X Reduction

by Jinxiang Pian, Peng Deng and Jian Tang

Processes 2025, 13(10), 3350; https://doi.org/10.3390/pr13103350 (registering DOI) - 19 Oct 2025

Abstract

As urbanization accelerates, solid waste volume increases, making municipal solid waste incineration (MSWI) a primary disposal method. However, low combustion efficiency and harmful gas emissions, such as nitrogen oxides (NO_X), contribute to significant environmental pollution. Improving combustion efficiency and reducing pollutants are critical challenges in waste incineration. Due to the process’s complexity and operational fluctuations, traditional PID and model-based methods often fail to deliver optimal results, making this a key research focus. To address this, this paper proposes an optimal control method for the solid waste incineration process, aimed at improving combustion efficiency and reducing emissions. By establishing Radial Basis Function (RBF) neural network prediction models for CO, CO₂, and NO_X, and integrating an improved Pelican Optimization Algorithm (IPOA), an optimized control strategy for air volume and pressure settings is developed. Experimental results demonstrate that the proposed method significantly enhances combustion efficiency while effectively reducing NO_X emissions. Furthermore, under varying operational conditions, the method can adaptively adjust the air volume and pressure settings, ensuring system adaptability to new conditions and maintaining both combustion efficiency and NO_X emission concentrations within target ranges. Full article

(This article belongs to the Special Issue Advances in Detection, Control and Optimization of Low-Carbon Energy Systems)

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12 pages, 2836 KB

Open AccessArticle

A Study on Improving Separation Efficiency Through Weir Curvature Optimization in an FWKO with a Dish-Head Inlet

by Hyun-Su Jeong and Youn-Jea Kim

Separations 2025, 12(10), 287; https://doi.org/10.3390/separations12100287 (registering DOI) - 19 Oct 2025

Abstract

The Free Water Knock Out (FWKO) vessel is a critical device in the oil sands treatment process, responsible for separating water, oil, and gas. This study investigates the gas–oil interface within the FWKO and analyzes the flow characteristics of the unresolved mixture near the interface. To enhance the separation efficiency by increasing the residence time of the mixture, a concave-shaped weir was introduced. Numerical simulations were conducted using ANSYS Fluent 2023 R1, applying the Volume of Fluid (VOF) model to capture the multiphase flow behavior. Optimization was performed using a genetic algorithm, and the optimal weir curvature with a minor radius of 0.017333 m and a major radius of 0.19032 m yielded the highest separation efficiency. The model incorporating the optimized weir demonstrated a 1.26% improvement in separation efficiency compared to the reference model, and a 2.13% improvement over the baseline model without curvature. These findings confirm that applying curvature to the traditionally flat weir can achieve higher separation efficiency. Moreover, improving separation efficiency through such a simple geometric modification demonstrates significant economic effectiveness. Full article

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24 pages, 1326 KB

Open AccessArticle

Multi-Attribute Decision-Making Model for Security Perception in Smart Apartments from a User Experience Perspective

by Jingbo Zhang and Shuxuan Meng

Urban Sci. 2025, 9(10), 430; https://doi.org/10.3390/urbansci9100430 (registering DOI) - 19 Oct 2025

Abstract

With an aging population and the widespread adoption of smart technologies, elderly residents’ perceived safety in smart apartments has become a critical determinant of their quality of life and their acceptance of technology. However, much of the current research remains confined to either technical or psychological dimensions, with insufficient attention to the systematic interactions among multiple factors as experienced by elderly populations. This study aims to systematically evaluate and optimize the living environments of older adults, with the goal of enhancing their overall quality of life and subjective well-being. This study employs the DANP–mV model to empirically analyze the safety perception of older adults in smart apartments, integrating case-based investigation and evaluation to propose targeted optimization strategies and improvement pathways. Unlike traditional approaches that treat criteria as independent, this hybrid model reveals the interdependencies among factors and establishes a more realistic prioritization of improvement actions. The study found that, compared with merely reinforcing physical security measures, factors such as enhanced remote security support, a stronger sense of control and coping confidence, and higher satisfaction with the protective system exert a more fundamental influence on the overall safety perception. These results demonstrate that adopting a systems-thinking approach shifts the focus of decision-making from superficial safety risks to underlying causal drivers, thereby mitigating resource allocation imbalances and enhancing the effectiveness and sustainability of safety improvement measures. Full article

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25 pages, 2105 KB

Open AccessArticle

Research on Lightweight Design Performance of Offshore Structures Based on 3D Printing Technology

by Haoyu Jiang, Yifan Xie, Shengqing Zeng, Sixing Guo, Zehan Chen, Zhenjie Liang and Dapeng Zhang

J. Mar. Sci. Eng. 2025, 13(10), 2007; https://doi.org/10.3390/jmse13102007 (registering DOI) - 19 Oct 2025

Abstract

Traditional manufacturing methods struggle to incorporate complex internal configurations within structures, thus restricting the potential for enhancing the strength of offshore structures through internal design. However, the advent of 3D printing technology presents innovative solutions to this challenge. Previous research has investigated the use of 3D printing to integrate lattice-like structures within conventional frameworks to achieve lightweight designs. Building upon this foundation, this paper models an embedded structure and other marine structures subjected to similar loads using simplified models and conducts a thorough investigation into their mechanical properties. Specifically, it examines the effects of the 3D-printed infill structure, infill rate, and tilt angle of printed specimens on the mechanical properties of 3D-printed components. The goal is to identify the optimal parameter combinations that ensure structural strength while also achieving a lightweight design and a secondary lightweight design for the embedded structure. This paper concludes, from tensile, torsional, and compressive experiments, that honeycomb infill structures, with specimens printed at an inclination angle of 0°, exhibit superior performance across all properties. Additionally, the bonding between the layers of the printed parts is identified as a key factor influencing the tensile and torsional properties. While increasing the infill rate can significantly improve the overall mechanical properties of specimens, it also results in a corresponding reduction in the lightweighting index. Full article

(This article belongs to the Special Issue Structural Modelling, Safety Assessment, and Advanced Material Application of Marine Structures)

20 pages, 81766 KB

Open AccessArticle

Experimental Biomechanical Analysis of the Bone-to-Implant Connection in Single-Piece Implants

by Karina Krawiec, Adam Kurzawa, Jakub J. Słowiński, Calin Romulus Fodor and Łukasz Pałka

J. Funct. Biomater. 2025, 16(10), 393; https://doi.org/10.3390/jfb16100393 (registering DOI) - 19 Oct 2025

Abstract

The mechanical properties of dental implants are critical for their durability. The purpose of this study was to determine the maximum force required to induce full pull-out of a titanium implant from the bone and to characterize the mechanical behavior during this process. First, pull-out tests were performed on monolithic implants embedded in bovine ribs and foam blocks that mimic the mechanical parameters of human bone, allowing a quantitative evaluation of implant–bone interface strength and a comparison of geometric variants. Second, the extraction process was recreated in a three dimensional finite element model incorporating nonlinear interface contact and parameterization, enabling the reproduction of load–displacement curves; the results obtained showed good agreement with the experiment. Third, the fracture surfaces were observed macroscopically and by scanning electron microscopy/energy dispersive spectroscopy. The results demonstrated significant distinctions in the forces required to extract implants with varying thread geometries, clearly indicating the impact of implant design on their mechanical stability. The presented FEM-based methodology provides a reliable tool to study mechanical interactions at the implant–bone interface. The findings obtained can improve our understanding of implant behavior in biological systems and provide a basis for further optimization of their design. Full article

(This article belongs to the Special Issue Biomechanical Studies and Biomaterials in Dentistry (2nd Edition))

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35 pages, 546 KB

Open AccessArticle

Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder

by Georgios Kostopoulos, Nikos Fazakis, Sotiris Kotsiantis and Yiannis Dimakopoulos

Algorithms 2025, 18(10), 663; https://doi.org/10.3390/a18100663 (registering DOI) - 19 Oct 2025

Abstract

This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE)-sampled synthetic data injection into the training process to enhance model performance under low-label data conditions in Educational Data Mining tasks. The algorithm begins with training a TVAE on the given labeled data to generate imitative synthetic samples of the underlying data distribution. These synthesized samples are treated as additional unlabeled data and combined with the original unlabeled ones in order to form an augmented training pool. A standard SSL algorithm (e.g., Self-Training) is trained using a base classifier (e.g., Random Forest) on the combined dataset. By expanding the pool of unlabeled samples with realistic synthetic data, TVAE-SSL improves training sample quantity and diversity without introducing label noise. Large-scale experiments on a variety of datasets demonstrate that TVAE-SSL can outperform baseline supervised models in the full labeled dataset in terms of accuracy, F1-score and fairness metrics. Our results demonstrate the capacity of generative augmentation to enhance the effectiveness of semi-supervised learning for tabular data. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms and Generative AI in Education (2nd Edition))

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11 pages, 1589 KB

Open AccessArticle

Two-Step Statistical and Physical–Mechanical Optimization of Electric Arc Spraying Parameters for Enhanced Coating Adhesion

by Nurtoleu Magazov, Bauyrzhan Rakhadilov and Moldir Bayandinova

Processes 2025, 13(10), 3349; https://doi.org/10.3390/pr13103349 (registering DOI) - 19 Oct 2025

Abstract

This paper presents the development and experimental verification of a second-order polynomial regression model for predicting the adhesion strength of coatings produced by electric arc metallization (EAM). The aim of the study is to optimize three key process parameters: current strength (I), carrier gas pressure (P) and nozzle-to-substrate distance (L) in order to maximize the adhesion strength of the coating to the substrate. Experimental data were obtained from the central composite plan within the response surface method (RSM) and processed using analysis of variance (ANOVA). A pronounced synergistic interaction between pressure and distance was found (P × L), whereas current strength had no statistically significant effect in the range investigated. Optimal parameters (I = 200 A, P = 6.5 bar, L = 190 mm) provided an adhesion strength of ~15.4 kN, which was within 8.5% of the model’s prediction, confirming its accuracy. The proposed two-stage approach—combining statistical modeling with experimental fine-tuning in the global extremum zone—made it possible to improve the accuracy of the forecast and link statistical dependencies with the physical and mechanical mechanisms of adhesion formation (kinetic energy of particles, residual thermoelastic stresses). This method provides engineering-based recommendations for industrial application of EAM, reduces the cost of parameter selection, and improves the reproducibility of coating properties. Full article

(This article belongs to the Section Manufacturing Processes and Systems)

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