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Recent Articles  

33 pages, 1420 KB  
Review
Nutritional Supplements for Muscle Hypertrophy: Mechanisms and Morphology—Focused Evidence
by Andreea Maria Mănescu, Simona Ștefania Hangu and Dan Cristian Mănescu
Nutrients 2025, 17(22), 3603; https://doi.org/10.3390/nu17223603 (registering DOI) - 18 Nov 2025
Abstract
Nutritional supplementation is widely used in resistance training, yet assessment of “hypertrophy” is often confounded by body-composition surrogates. This narrative review, anchored in mechanistic plausibility, integrates trials reporting morphology-direct outcomes (ultrasound/MRI). Across 46 eligible trials, protein/essential amino acids (EAA) showed consistent benefits when [...] Read more.
Nutritional supplementation is widely used in resistance training, yet assessment of “hypertrophy” is often confounded by body-composition surrogates. This narrative review, anchored in mechanistic plausibility, integrates trials reporting morphology-direct outcomes (ultrasound/MRI). Across 46 eligible trials, protein/essential amino acids (EAA) showed consistent benefits when daily intake was <1.6 g·kg−1·day−1 or when per-meal leucine provision was <2–3 g; effects plateaued once intakes exceeded ~2.0 g·kg−1·day−1. Creatine monohydrate (3–5 g·day−1, with or without loading) produced measurable increases in muscle thickness or cross-sectional area in interventions lasting ≥8–12 weeks, mediated by enhanced training volume and quality. β-hydroxy-β-methylbutyrate (HMB, 3 g·day−1) demonstrated conditional utility during high training stress or caloric deficit, but was largely neutral in well-fed, resistance-trained cohorts. Adjuncts such as omega-3 fatty acids (1–2 g·day−1), citrulline (6–8 g pre-exercise), and collagen (10–15 g·day−1 plus vitamin C) primarily facilitated training tolerance, recovery, or connective-tissue adaptation, rather than driving hypertrophy directly. A tiered model is proposed: protein/EAA as the foundation, creatine as amplifier, HMB as conditional agent, and adjuncts as facilitators. Methodological heterogeneity, short intervention length, and inconsistent imaging protocols remain limiting factors, underscoring the need for standardized ultrasound/MRI and adequately powered, preregistered trials. Full article
(This article belongs to the Special Issue Effects of Physical Activity and Nutritional Supplementation on Muscle Health)
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33 pages, 27676 KB  
Article
A Generative AI Framework for Adaptive Residential Layout Design Responding to Family Lifecycle Changes
by Yinlin Zhou and Yonggang Pan
Buildings 2025, 15(22), 4155; https://doi.org/10.3390/buildings15224155 (registering DOI) - 18 Nov 2025
Abstract
Rapidly evolving family structures have intensified the demand for residential layouts that can flexibly adapt to diverse spatial and functional needs. Conventional design approaches, whether manual or computer-aided, often fail to maintain user-centered adaptability across the household lifecycle. Meanwhile, advances in generative artificial [...] Read more.
Rapidly evolving family structures have intensified the demand for residential layouts that can flexibly adapt to diverse spatial and functional needs. Conventional design approaches, whether manual or computer-aided, often fail to maintain user-centered adaptability across the household lifecycle. Meanwhile, advances in generative artificial intelligence have introduced new opportunities for intelligent design generation; however, existing models tend to prioritize visual aesthetics over behavioral and functional coherence. This study proposes an integrated text-to-design workflow that transforms user requirements, extracted from different family lifecycle stages, into structured prompts for AI-driven spatial generation. A dedicated interior dataset is constructed to incorporate lifecycle responsiveness, user preferences, and spatial functionality, while a composite loss function is introduced to enhance diffusion model precision and contextual fidelity. Comparative experiments against mainstream models such as Stable Diffusion and MidJourney reveal superior adaptability, spatial rationality, and user alignment. Overall, the findings demonstrate the potential of generative AI to bridge user behavior analysis with architectural logic, promoting data-driven, adaptive, and human-oriented residential design practices. Full article
(This article belongs to the Special Issue Sustainable Buildings and Cities: Integrated Research on Smart Strategies for Renewal and Resilience)
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43 pages, 28745 KB  
Review
A Review on the Chassis Configurations and Key Technologies of Agricultural Robots
by Renkai Ding, Xiangyuan Qi, Xiangpeng Meng, Xuwen Chen, Le Zhang, Yixin Mei, Anze Li and Qing Ye
Agriculture 2025, 15(22), 2379; https://doi.org/10.3390/agriculture15222379 (registering DOI) - 18 Nov 2025
Abstract
The chassis configuration serves as the mobility foundation of agricultural robots, directly determining their trafficability, stability, and intelligent operation in complex fields. Existing research lacks a systematic analysis of the evolution and adaptation principles of mainstream chassis technologies. This review addresses this gap [...] Read more.
The chassis configuration serves as the mobility foundation of agricultural robots, directly determining their trafficability, stability, and intelligent operation in complex fields. Existing research lacks a systematic analysis of the evolution and adaptation principles of mainstream chassis technologies. This review addresses this gap by proposing a dual-dimensional framework—“structural design principles and dynamic adaptive control”—to evaluate wheeled, tracked, and wheel-legged hybrid chassis. Our analysis reveals that (1) wheeled chassis achieve refinement through efficiency-driven operation in structured environments but are limited by rigid wheel–ground contact; (2) tracked chassis enhance performance on soft or sloped terrain via technologies like contour-adaptive tracks, albeit with increased energy consumption; and (3) wheel-legged hybrid chassis represent a shift towards active terrain overcoming, offering superior adaptability at the cost of high control complexity. Finally, we synthesize persistent challenges and identify future breakthroughs in terrain–vehicle coupled modeling and multi-modal control, which will drive the evolution towards intelligent, mechatronic–hydraulic integrated platforms. Full article
(This article belongs to the Section Agricultural Technology)
17 pages, 3703 KB  
Article
A Non-Intrusive DSMC-FEM Coupling Method for Two-Dimensional Conjugate Heat Transfer in Rarefied Hypersonic Conditions
by Ziqu Cao and Chengyu Ma
Aerospace 2025, 12(11), 1021; https://doi.org/10.3390/aerospace12111021 (registering DOI) - 18 Nov 2025
Abstract
Accurate conjugate heat transfer (CHT) analysis is critical to the thermal management of hypersonic vehicles operating in rarefied environments, where non-equilibrium gas dynamics dominate. While numerous sophisticated CHT solvers exist for continuum flows, they are physically invalidated by rarefaction effects. This paper presents [...] Read more.
Accurate conjugate heat transfer (CHT) analysis is critical to the thermal management of hypersonic vehicles operating in rarefied environments, where non-equilibrium gas dynamics dominate. While numerous sophisticated CHT solvers exist for continuum flows, they are physically invalidated by rarefaction effects. This paper presents a novel partitioned coupling framework that bridges this methodological gap by utilizing the preCICE library to non-intrusively integrate the Direct Simulation Monte Carlo (DSMC) method, implemented in SPARTA, with the finite element method (FEM) via FEniCS for high-fidelity simulations of rarefied hypersonic CHT. The robustness and accuracy of this approach are validated through three test cases: a quasi-1D flat plate benchmark confirms the fundamental coupling mechanism against a reference finite difference solution; a 2D flat-nosed cylinder demonstrates the capability of the framework to handle highly non-uniform heat flux distributions and resolve the ensuing transient thermal response within the solid; finally, a standard cylinder case confirms the compatibility with curved geometries and its stability and accuracy in long-duration simulations. This work establishes a validated and accessible pathway for high-fidelity aerothermal analysis in rarefied gas dynamics, effectively decoupling the complexities of multi-physics implementation from the focus on fundamental physics. Full article
(This article belongs to the Section Aeronautics)
26 pages, 3749 KB  
Article
Promoter Motif Profiling and Binding Site Distribution Analysis of Transcription Factors Predict Auto- and Cross-Regulatory Mechanisms in Arabidopsis Flowering Genes
by Eszter Virág, Beáta B. Tóth, Barbara Kutasy, Ágnes Nagy, Klaudia Pákozdi, József Péter Pallos, Gábor Kardos and Géza Hegedűs
Int. J. Mol. Sci. 2025, 26(22), 11152; https://doi.org/10.3390/ijms262211152 (registering DOI) - 18 Nov 2025
Abstract
The transition to flowering in Arabidopsis thaliana is governed by complex transcriptional regulatory networks, in which promoter-associated cis-regulatory elements integrate both developmental and environmental cues. To investigate these regulatory interactions, we analyzed promoter motifs of 18 flowering-related genes using curated motif resources, [...] Read more.
The transition to flowering in Arabidopsis thaliana is governed by complex transcriptional regulatory networks, in which promoter-associated cis-regulatory elements integrate both developmental and environmental cues. To investigate these regulatory interactions, we analyzed promoter motifs of 18 flowering-related genes using curated motif resources, including the Eukaryotic Promoter Database (EPD) and JASPAR, applying stringent statistical thresholds. Transcription factors (TFs), which were predicted to bind across all examined promoters, were designated as putative master regulators, resulting in the identification of 36 candidates, predominantly belonging to the MADS-box, DOF, and IDD families. Positional analyses revealed both proximal and distal binding sites, including a notable motif at −1024 in PISTILLATA and at +466 in SEPALLATA3, potentially indicative of autoregulatory control. Comparative analysis further identified 96 gene-specific associations, reflecting a balance between shared and specialized regulatory mechanisms. Treatment with β-aminobutyric acid (BABA), which has a flowering delaying effect, repressed SQUAMOSA and increased DOF-type TFs, indicating a chromatin-associated reprogramming process, which may coordinate the transcriptional suppression of flowering activators. These findings refine current models of floral regulatory networks and provide testable hypotheses regarding autoregulatory and cross-regulatory circuits in the control of flower development. Full article
(This article belongs to the Special Issue Transcription Factors in Plant Gene Expression Regulation, 2nd Edition)
26 pages, 496 KB  
Article
Simultaneous State and Parameter Estimation Methods Based on Kalman Filters and Luenberger Observers: A Tutorial & Review
by Amal Chebbi, Matthew A. Franchek and Karolos Grigoriadis
Sensors 2025, 25(22), 7043; https://doi.org/10.3390/s25227043 (registering DOI) - 18 Nov 2025
Abstract
Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are [...] Read more.
Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are two classes of state and parameter estimation methods: Kalman Filters and Luenberger Observers. The Kalman Filter framework, including its major variants such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Ensemble Kalman Filter (EnKF), has been widely applied for joint and dual estimation in linear and nonlinear systems under uncertainty. In parallel, Luenberger observers, typically used in deterministic settings, offer alternative approaches through high-gain, sliding mode, and adaptive observer structures. This review focuses on the theoretical foundations, algorithmic developments, and application domains of these methods and provides a comparative analysis of their advantages, limitations, and practical relevance across diverse engineering scenarios. Full article
(This article belongs to the Section Physical Sensors)
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33 pages, 4091 KB  
Article
Seismic Risk Assessment and Sustainable Geotechnical Solutions for Building Heritage: A Case Study in Southeastern Sicily
by Angela Fiamingo, Ettore Mangione, Glenda Abate and Maria Rossella Massimino
Heritage 2025, 8(11), 485; https://doi.org/10.3390/heritage8110485 (registering DOI) - 18 Nov 2025
Abstract
The protection of historic buildings in seismic-prone regions is a critical challenge requiring strategies that balance structural safety with cultural preservation. This study proposes an integrated methodological framework for assessing seismic risk in heritage contexts by combining Geographic Information System (GIS)-based large-scale analyses [...] Read more.
The protection of historic buildings in seismic-prone regions is a critical challenge requiring strategies that balance structural safety with cultural preservation. This study proposes an integrated methodological framework for assessing seismic risk in heritage contexts by combining Geographic Information System (GIS)-based large-scale analyses with detailed Finite Element Method (FEM) simulations. At the urban scale, the framework is applied to more than 70 buildings in the historic center of Bronte (Eastern Sicily, Italy) to evaluate Soil–Structure Interaction (SSI) effects and identify priority areas for mitigation. At a detailed scale, the approach is validated through an in-depth investigation of the San Giovanni Evangelista bell-tower, a representative historic structure within the study area. For this case, sustainable Geotechnical Seismic Isolation (GSI) systems using well-graded Gravel–Rubber Mixtures (wgGRMs) are numerically tested as a low-impact retrofitting strategy. The results demonstrate that combining large-scale mapping with detailed structural modeling provides both broad urban insight and accurate site-specific evaluations, offering a replicable decision-support tool for seismic risk reduction in heritage environments. Additionally, wgGRMs-based GSI system significantly reduces seismic accelerations and drifts, offering a low-impact, sustainable retrofitting solution that reuses waste materials and fully preserves architectural integrity. Full article
(This article belongs to the Special Issue History, Conservation and Restoration of Cultural Heritage)
31 pages, 1779 KB  
Review
Synergistic Computing for Sustainable Energy Systems: A Review of Genetic Algorithm-Enhanced Approaches in Hydrogen, Wind, Solar, and Bioenergy Applications
by Jacek Lukasz Wilk-Jakubowski, Łukasz Pawlik, Leszek Ciopiński and Grzegorz Wilk-Jakubowski
Energies 2025, 18(22), 6027; https://doi.org/10.3390/en18226027 (registering DOI) - 18 Nov 2025
Abstract
The imperative for sustainable energy solutions has spurred extensive research into renewable resources such as hydrogen, wind, solar, and bioenergy. This paper presents a comprehensive review of recent advancements (2015–2024) in the application of Genetic Algorithms and associated computational technologies for the optimisation [...] Read more.
The imperative for sustainable energy solutions has spurred extensive research into renewable resources such as hydrogen, wind, solar, and bioenergy. This paper presents a comprehensive review of recent advancements (2015–2024) in the application of Genetic Algorithms and associated computational technologies for the optimisation and forecasting of these energy systems. This study synthesizes findings across diverse areas including hydrogen storage design, wind farm layout optimization, solar irradiance prediction, and bioenergy production and utilization. The review categorizes the literature based on renewable energy sources and their specific areas of application, such as system optimization, energy management, and forecasting. Furthermore, it examines the role of sensitivity analysis and decision-making frameworks enhanced by Genetic Algorithm-based approaches across these domains. By highlighting the synergistic potential of computational intelligence in addressing the complexities of renewable energy deployment, this review provides valuable insights for researchers and practitioners seeking to accelerate the transition towards a more sustainable energy future. Full article
(This article belongs to the Special Issue New Challenges and Prospects of Smart Energy Systems for Sustainable Communities)
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30 pages, 2202 KB  
Review
Integrating IoT and AI for Sustainable Energy-Efficient Smart Building: Potential, Barriers and Strategic Pathways
by Dillip Kumar Das
Sustainability 2025, 17(22), 10313; https://doi.org/10.3390/su172210313 (registering DOI) - 18 Nov 2025
Abstract
The global drive toward sustainability and energy efficiency has accelerated the development of smart buildings integrating the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies optimise energy use, enhance occupant comfort, and advance building management systems. This study examines the integration [...] Read more.
The global drive toward sustainability and energy efficiency has accelerated the development of smart buildings integrating the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies optimise energy use, enhance occupant comfort, and advance building management systems. This study examines the integration of IoT and AI in energy-efficient smart buildings, emphasising applications and challenges. A qualitative methodology, combining systematic literature review, case study analysis, and systems analysis, underpins the research. Findings indicate that IoT enables smart metering, real-time energy monitoring, automated lighting and HVAC, occupancy-based energy optimisation, and renewable energy integration. AI complements these functions through predictive maintenance, energy forecasting, demand-side management, intelligent climate control, indoor air quality automation, and behaviour-driven analytics. Together, they reduce carbon emissions, lower operational costs, and improve occupant well-being. However, challenges remain, including data security and privacy risks, interoperability gaps, scalability and cost constraints, and retrofitting difficulties. To address these, the paper proposes a systems thinking-enabled conceptual framework structured around three pillars: adopting IoT and AI as enabling technologies, overcoming integration barriers, and identifying application areas that advance sustainability in smart buildings. This framework supports strategic decision-making toward net-zero and resilient building design. Full article
(This article belongs to the Special Issue Sustainability of Smart Building: The Intelligence Application, Industry 4.0, BIM, Environmental Comfort, and Safety Application)
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21 pages, 801 KB  
Article
Boundary Control for Consensus in Fractional-Order Multi-Agent Systems Under DoS Attacks and Actuator Failures
by Qiang Qi, Xiao Chen, Dejian Wang, Jiashu Dai, Yuqian Yang and Chengdong Yang
Fractal Fract. 2025, 9(11), 745; https://doi.org/10.3390/fractalfract9110745 (registering DOI) - 18 Nov 2025
Abstract
This paper investigates the consensus problem in fractional-order multi-agent systems (FOMASs) under Denial of Service (DoS) attacks and actuator faults. A boundary control strategy is proposed, which reduces dependence on internal sensors and actuators by utilizing only the state information at the system [...] Read more.
This paper investigates the consensus problem in fractional-order multi-agent systems (FOMASs) under Denial of Service (DoS) attacks and actuator faults. A boundary control strategy is proposed, which reduces dependence on internal sensors and actuators by utilizing only the state information at the system boundaries, significantly lowering control costs. To address DoS attacks, a buffer mechanism is designed to store valid control signals during communication interruptions and apply them once communication is restored, thereby enhancing the system’s robustness and stability. Additionally, this study considers the impact of actuator performance fluctuations on control effectiveness and proposes corresponding adjustment strategies to ensure that the system maintains consensus and stability even in the presence of actuator failures or performance variations. Finally, the effectiveness of the proposed method is validated through numerical experiments. The results show that, even under DoS attacks and actuator faults, the system can still successfully achieve consensus and maintain good stability, demonstrating the feasibility and effectiveness of this control approach in complex environments. Full article
(This article belongs to the Special Issue Fractional Dynamics and Control in Multi-Agent Systems and Networks)
13 pages, 1374 KB  
Article
β-Antithrombin Levels in Patients with Venous Thromboembolism
by Edith Alexandra Uj, Éva Molnár, Tünde Miklós, Réka Gindele, Amir Houshang Shemirani, Zsuzsanna Bereczky and Éva Katona
Int. J. Mol. Sci. 2025, 26(22), 11151; https://doi.org/10.3390/ijms262211151 (registering DOI) - 18 Nov 2025
Abstract
Beta-antithrombin (β-AT), the isoform of antithrombin (AT) with a higher affinity for heparin, constitutes 5–10% of total AT in plasma. There are limited data regarding β-AT activity levels in thrombotic disorders. In our study, we analyzed samples from 200 non-AT-deficient patients who had [...] Read more.
Beta-antithrombin (β-AT), the isoform of antithrombin (AT) with a higher affinity for heparin, constitutes 5–10% of total AT in plasma. There are limited data regarding β-AT activity levels in thrombotic disorders. In our study, we analyzed samples from 200 non-AT-deficient patients who had experienced venous thromboembolism (VTE) compared to 200 healthy controls. Total AT activity was measured using a chromogenic anti-factor Xa assay. To measure β-AT, we used elevated NaCl (1.1 M) in the reagent to inhibit the heparin binding of α-AT. There were no significant differences in total AT activity (median (IQR)) levels between the control and VTE groups (100 (93–109)% and 99 (94–109)%, respectively; p = 0.955). However, the β-AT activity levels (median (IQR)) and the ratio of β-AT to total AT (mean ± SD) were significantly higher in the VTE group compared to the control group (93.3 (90.3–97.3)% vs. 89.3 (84.0–95.0)% and 9.34 ± 0.68% vs. 8.86 ± 0.88%; p < 0.001). β-AT activity levels and the ratios in the upper third were strongly associated with a higher risk of VTE (OR (95% CI): 5.78 (3.08–10.87) and 6.15 (3.36–11.24), respectively). Our study demonstrated an elevation of plasma levels of β-AT in patients with VTE. Further research is necessary to clarify the pathophysiological significance of this finding. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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32 pages, 21764 KB  
Article
Robust Sparse Non-Negative Matrix Factorization for Identifying Signals of Interest in Bearing Fault Detection
by Hamid Shiri and Anna Michalak
Sensors 2025, 25(22), 7041; https://doi.org/10.3390/s25227041 (registering DOI) - 18 Nov 2025
Abstract
Bearings are among the most failure-prone components in rotating systems, making early fault detection crucial in industrial applications. While recent publications have focused on this issue, challenges remain, particularly in dealing with heavy-tailed or non-cyclic impulsive noise in recorded signals. Such noise poses [...] Read more.
Bearings are among the most failure-prone components in rotating systems, making early fault detection crucial in industrial applications. While recent publications have focused on this issue, challenges remain, particularly in dealing with heavy-tailed or non-cyclic impulsive noise in recorded signals. Such noise poses significant challenges for classical fault selectors like kurtosis-based methods. Moreover, many deep-learning approaches struggle in these environments, as they often assume Gaussian or stationary noise and rely on large labeled datasets that are rarely available in practice. To address this, we propose a robust sparse non-negative matrix factorization (NMF) method based on the maximum-correntropy criterion, which is known for its robustness in the presence of heavy-tailed noise. This methodology is applied to identify fault frequency bands in the spectrogram of the signal. The effectiveness of the approach is validated using simulated fault signals under both Gaussian and heavy-tailed noise conditions through Monte Carlo simulations. A statistical efficiency analysis confirms robustness to random perturbations. Additionally, three real datasets are used to evaluate the performance of the proposed method. Results from both simulations and real-world data demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
30 pages, 3280 KB  
Article
Flash Drought Assessment: Insights from a Selection of Mediterranean Islands, Greece
by Chrysoula Katsora, Evangelos Leivadiotis, Nektaria Papadopoulou, Isavela Monioudi, Efthymia Kostopoulou, Petros Gaganis, Aris Psilovikos and Ourania Tzoraki
Hydrology 2025, 12(11), 308; https://doi.org/10.3390/hydrology12110308 (registering DOI) - 18 Nov 2025
Abstract
Flash droughts are a significant natural hazard, characterized by rapid onset and potential to cause substantial economic and environmental impacts. This study utilizes ERA5 soil moisture data to identify and define historical flash drought (FD) events in the Northeastern Aegean islands (specifically Chios, [...] Read more.
Flash droughts are a significant natural hazard, characterized by rapid onset and potential to cause substantial economic and environmental impacts. This study utilizes ERA5 soil moisture data to identify and define historical flash drought (FD) events in the Northeastern Aegean islands (specifically Chios, Lemnos, Lesvos and Samos). Hourly soil moisture data, spanning from 1990 to the present, covering three soil layers (0–7 cm, 7–28 cm and 28–100 cm), were analyzed and mapped onto a 0.1° × 0.1° grid with a native resolution of approximately 9 km. Additionally, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied to the island of Lesvos, using precipitation and average temperature data from the local meteorological stations. The number and characteristics of these events—including frequency, duration, decline rate, magnitude, intensity, recovery rate and recovery duration—were produced to construct a regional overview of FD risk across the Northeastern Aegean Islands. These results reveal a considerable variability in the spatial, seasonal and temporal distribution of past FD events. Furthermore, this study highlights the value of using satellite-derived soil moisture data for identifying FD events and demonstrates that analyzing this data with field temperature and precipitation measurements enables a more localized and accurate interpretation of past events. This approach facilitates the definition of FD “hotspot” areas, which, when combined with further investigation, can lead to the development of a predictive FD model. Full article
(This article belongs to the Section Hydrology–Climate Interactions)
21 pages, 5309 KB  
Article
Implementation Efficiency of Falcon Digital Signature Scheme on Arty-7 XC7A35T Board
by Tat-Thang Nguyen, Duc-Duy Nguyen, Toan-Thanh Dao and Nhu-Quynh Luc
Electronics 2025, 14(22), 4504; https://doi.org/10.3390/electronics14224504 (registering DOI) - 18 Nov 2025
Abstract
In this study, the authors propose a solution for implementing a digital signature system using the post-quantum digital signature scheme Falcon on the FPGA hardware platform XC7A35T-CPG236. The system is combined with a software component developed in C# using the NET Framework 4.7, [...] Read more.
In this study, the authors propose a solution for implementing a digital signature system using the post-quantum digital signature scheme Falcon on the FPGA hardware platform XC7A35T-CPG236. The system is combined with a software component developed in C# using the NET Framework 4.7, capable of running on the Windows operating system. Falcon is selected for its compact signature size and high computational performance, optimizing the signing and verification processes on hardware. The system is designed to leverage the hardware acceleration capabilities of an FPGA to enhance performance compared to software-only signature methods. The integration of the Falcon scheme into both hardware (FPGA) and software components ensures high flexibility for real-world deployment. The obtained results show that key generation execution time is approximately 1516.2 ms, while the signing and verification times are around 2–5 ms. Hardware resource usage has significantly improved, and the power consumption is 0.097 W (57%). However, the software component has not yet undergone thorough security testing. Given the achieved results in terms of execution time and energy consumption, the proposed device and software may be suitable for certain applications that do not require stringent performance and energy constraints. Furthermore, this research demonstrates the feasibility of deploying post-quantum cryptographic solutions on embedded hardware platforms, contributing to the development of secure communication systems in the future. Full article
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23 pages, 1916 KB  
Review
The Atmospheric Gray-Zone (a.k.a. Terra Incognita) Problem: A Strategy Analysis from an Engineering Viewpoint
by Stefan Heinz
Fluids 2025, 10(11), 301; https://doi.org/10.3390/fluids10110301 (registering DOI) - 18 Nov 2025
Abstract
The Terra Incognita (or gray-zone) problem seen in atmospheric flow simulations causes serious consequences: it implies, e.g., significantly incorrect flow predictions and results that often simply depend on flow simulation settings as the computational grid applied. There is definitely the need for a [...] Read more.
The Terra Incognita (or gray-zone) problem seen in atmospheric flow simulations causes serious consequences: it implies, e.g., significantly incorrect flow predictions and results that often simply depend on flow simulation settings as the computational grid applied. There is definitely the need for a robust gray-zone modeling to ensure that research and technology decisions are based on reliable results. As a matter of fact, solution approaches to deal with this problem in atmospheric and engineering type simulations reveal remarkable differences. In contrast to atmospheric flow simulations, there exists a broad spectrum of solution concepts for engineering applications. Driven by these conceptual differences, the paper presents an analysis of the Terra Incognita problem and corresponding solution concepts. Specifically, the paper presents a modeling approach that overcomes the core problem of currently applied methods. A new method of providing a resolution-aware turbulence length scale (one of the major problems in atmospheric flow simulations) is presented. This approach is capable of seamlessly covering the full range of microscale to mesoscale simulations, and to appropriately deal with mesoscale to microscale couplings. Full article
(This article belongs to the Special Issue Feature Reviews for Fluids 2025–2026)
16 pages, 1840 KB  
Article
FloCyT: A Flow-Aware Centroid Tracker for Cell Analysis in High-Speed Capillary-Driven Microfluidic Flow
by Suraj K. Maurya, Matt Stark and Cédric Bessire
Sensors 2025, 25(22), 7040; https://doi.org/10.3390/s25227040 (registering DOI) - 18 Nov 2025
Abstract
Capillary-driven microfluidic chips have emerged as promising platforms for point-of-care diagnostics, offering portable, inexpensive, and pump-free operation. Accurate tracking of cell flow in these systems is vital for quantitative applications such as on-chip cytometry, cell counting, and biomechanical analysis. However, tracking in capillary-driven [...] Read more.
Capillary-driven microfluidic chips have emerged as promising platforms for point-of-care diagnostics, offering portable, inexpensive, and pump-free operation. Accurate tracking of cell flow in these systems is vital for quantitative applications such as on-chip cytometry, cell counting, and biomechanical analysis. However, tracking in capillary-driven devices is challenging due to rapid cell displacements, flow instabilities, and visually similar cells. Under these conditions, conventional tracking algorithms such as TrackPy, TrackMate, SORT, and DeepSORT exhibit frequent identity switches and trajectory fragmentation. Here, we introduce FloCyT, a robust, high-speed centroid tracking tool specifically designed for capillary-driven and microfluidic flow. FloCyT leverages microchannel geometry for tracking and uses anisotropic gating for association, global flow-aware track initialisation, and channel-specific association. This enables precise tracking even under challenging conditions of capillary-driven flow. FloCyT was evaluated on 12 simulated and 4 real patient datasets using standard multi-object tracking metrics, including IDF1 and MOTA, ID switches, and the percentage of mostly tracked objects. The results demonstrate that FloCyT outperforms both standard and flow-aware-modified versions of TrackPy and SORT, achieving higher accuracy, more complete trajectories, and fewer identity switches. By enabling accurate and automated cell tracking in capillary-driven microfluidic devices, FloCyT enhances the quantitative sensing capability of image-based microfluidic diagnostics, supporting novel, low-cost, and portable cytometry applications. Full article
(This article belongs to the Section Intelligent Sensors)
26 pages, 8122 KB  
Article
Mapping Relationship Between Field and Laboratory Direct Shear Strength Indicators of Soil and Rock Layers at Shallow Depths in Arid–Hot Valley Regions
by Qinghe Zeng, Zhibin Li, Jin Liao, Hong Ke, Xionghui Huang, Xiangqing Li, Shoukui Wang, Zhen Liu and Cuiying Zhou
Appl. Sci. 2025, 15(22), 12241; https://doi.org/10.3390/app152212241 (registering DOI) - 18 Nov 2025
Abstract
The arid–hot valley regions in southwestern China are characterized by developed geological structures and frequent local heavy rainfalls, which often trigger flash floods. The mechanical properties of soil and rock masses in these regions are critical for the construction of regional projects. Field [...] Read more.
The arid–hot valley regions in southwestern China are characterized by developed geological structures and frequent local heavy rainfalls, which often trigger flash floods. The mechanical properties of soil and rock masses in these regions are critical for the construction of regional projects. Field direct shear tests can accurately reflect the mechanical properties of the soil and rock masses in their natural state, but they are costly and cause significant disturbance to the surrounding environment. In contrast, laboratory direct shear tests are more straightforward and cost-effective but cannot fully replicate the complex stress conditions and structural characteristics of in situ soil and rock masses. The lack of correlation between field and laboratory direct shear strength indicators significantly hinders the accurate assessment of geotechnical properties, thereby affecting the precision of engineering applications. To this end, this paper focuses on the soil and rock layers in the arid–hot valley regions in southwestern China. This research took into account the effects of soil depth and moisture content, proposing a solution that fully correlates field and laboratory direct shear strength test indicators. Field and laboratory direct shear tests were conducted at shallow depths to investigate the relationship between the shear strength indicators of various geological formations. The results show that laboratory remolded sample tests generally yield lower shear strength values compared to field direct shear tests. The laboratory shear strength and internal friction angle of each rock and soil layer show a linear increase with depth. A mathematical relationship between soil layer depth, laboratory shear strength indicators, and field shear strength indicators can be established using a quadratic polynomial function. This resolved the “disconnect” between field and laboratory test results, significantly reducing engineering survey costs and providing important theoretical basis and reference for engineering construction in arid and hot river valley regions. Full article
(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources, 2nd Edition)
26 pages, 1375 KB  
Article
Lightweight Multi-Class Autoencoder Model for Malicious Traffic Detection in Private 5G Networks
by Jinha Kim, Seungjoon Na and Hwankuk Kim
Appl. Sci. 2025, 15(22), 12242; https://doi.org/10.3390/app152212242 (registering DOI) - 18 Nov 2025
Abstract
This study proposes a lightweight autoencoder-based detection framework for the efficient detection of multi-class malicious traffic within a private 5G network slicing environment. Conventional deep learning-based detection approaches encounter difficulties in real-time processing and edge environment applications because of their significant computational complexity [...] Read more.
This study proposes a lightweight autoencoder-based detection framework for the efficient detection of multi-class malicious traffic within a private 5G network slicing environment. Conventional deep learning-based detection approaches encounter difficulties in real-time processing and edge environment applications because of their significant computational complexity and resource demands. To address this issue, this study balances traffic data using slice-label-based hierarchical sampling and performs domain-specific feature grouping to reflect semantic similarity. Independent autoencoders are trained for each group, and the latent vectors from the encoder outputs are combined to be used as input for an SVM-based multi-class classifier. This structure reflects traffic differences between slices while also improving computational efficiency. Four sets of experiments were constructed to verify the model’s performance and evaluate its structural performance, resource usage efficiency, classifier generalization performance, and whether it met SLA constraints from various perspectives. As a result, the proposed Multi-AE model achieved an accuracy of 0.93, a balanced accuracy of 0.93, and an ECE of 0.03, demonstrating high stability and detection reliability. Regarding resource utilization efficiency, GPU utilization was under 7%, and the average memory usage was approximately 5.7 GB, demonstrating resource efficiency. In SLA verification, inference latency below 10 ms and a throughput of 564 samples/s were achieved based on URLLC. This study is significant in that it experimentally demonstrated a detection structure that achieves a balance of accuracy, lightweight design, and real-time performance in a 5G slicing environment. Full article
(This article belongs to the Special Issue AI-Enabled Next-Generation Computing and Its Applications)
20 pages, 693 KB  
Article
Social–Emotional Competence Growth Profiles in Upper Elementary School Years and Pathways to Mental Health Outcomes in Middle School
by Juyeon Lee and Chenxiao Wang
Int. J. Environ. Res. Public Health 2025, 22(11), 1744; https://doi.org/10.3390/ijerph22111744 (registering DOI) - 18 Nov 2025
Abstract
Social–emotional competence (SEC) is an essential factor for healthy youth development. However, few studies have examined patterns of SEC growth trajectories among non-Western youth, and whether and how their SEC growth patterns during elementary school years predict later mental health. Using five-year panel [...] Read more.
Social–emotional competence (SEC) is an essential factor for healthy youth development. However, few studies have examined patterns of SEC growth trajectories among non-Western youth, and whether and how their SEC growth patterns during elementary school years predict later mental health. Using five-year panel data on a nationally representative sample of South Korean youth (N = 2607; 49.6% girl, Mage = 10, SDage = 0.1 at baseline), we first identified three latent profiles of SEC growth trajectories throughout upper elementary years (Grades 4 to 6), distinguished by initial and continued mean-level differences in both self-management and group collaboration. Informed by self-determination theory, we found that these SEC growth profiles significantly predicted depression and life satisfaction in middle school (Grade 8), mediated by peer relatedness and academic competence during the middle school transition (Grade 7). This study discusses implications for future research and practice to promote young adolescents’ social–emotional development and mental health. Full article
(This article belongs to the Special Issue Mental Health and Health Promotion in Young People)
23 pages, 5739 KB  
Article
DCD-Net: Decoupling-Centric Decomposition Network for Low-Light Image Enhancement
by Wei Wang, Yi Zhu, Mingming Zhang and Chao Xie
Sensors 2025, 25(22), 7038; https://doi.org/10.3390/s25227038 (registering DOI) - 18 Nov 2025
Abstract
This paper presents a Decoupling-Centric Decomposition network for Low-Light Image Enhancement (DCD-Net). The DCDNet addresses two key challenges: (1) existing methods center on how to design the enhancement network and ignore the decomposition network’s critical role to decouple reflectance and illuminations as the [...] Read more.
This paper presents a Decoupling-Centric Decomposition network for Low-Light Image Enhancement (DCD-Net). The DCDNet addresses two key challenges: (1) existing methods center on how to design the enhancement network and ignore the decomposition network’s critical role to decouple reflectance and illuminations as the first step and (2) existing decomposition networks process images directly (or with pre-denoising), ignore their compliance with the Retinex theory. Specifically, by centering on illumination–reflectance decoupling refinement, DCDNet operates without reliance on supplementary enhancement networks. It consists of a preprocessing network and a decomposition network. The preprocessing network adopts a self-supervised learning mechanism to suppress Retinex-incompatible features in the input image, thereby improving the quality of Retinex decomposition. Within the decomposition network, the reflectance net is designed to suppress the contamination of illumination on reflectance restoration by the Dual-Gated Directional Reflectance Module (DGD-RM) and Reflectance-Guided Multi-head Self-Attention (RG-MSA), while the illumination net utilizes DCT to achieve local–global illumination estimates. Comprehensive experiments were conducted on two benchmark datasets (LOL and MIT) and five unpaired datasets. The quantitative results on different datasets are as follows (measured by PSNR and SSIM): LOL v1 (20.87, 0.770) and MIT (21.66, 0.864). The average NIQE across five unpaired datasets is 3.420. Both qualitative and quantitative analyses demonstrated the superiority of DCDNet over state-of-the-art methods. Moreover, ablation studies demonstrated the effectiveness of each module in DCDNet. Full article
(This article belongs to the Section Sensing and Imaging)
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37 pages, 9182 KB  
Article
Stratification-Induced Porosity Variations in Granular Packings–Part I: From Laboratory Measurement to Numerical Modelling
by Wenjia Xu and Catrina Brüll
Geotechnics 2025, 5(4), 77; https://doi.org/10.3390/geotechnics5040077 (registering DOI) - 18 Nov 2025
Abstract
This study investigates how stratification—layering of particles of different sizes—affects porosity in granular sediment packings. While most existing porosity models are developed for well-mixed, homogeneous grain structures, natural riverbed sediments can be stratified, which may lead to significant deviations in porosity. To address [...] Read more.
This study investigates how stratification—layering of particles of different sizes—affects porosity in granular sediment packings. While most existing porosity models are developed for well-mixed, homogeneous grain structures, natural riverbed sediments can be stratified, which may lead to significant deviations in porosity. To address this, a novel, cost-effective, and non-destructive laboratory method was developed to measure the vertical porosity distribution in stratified samples using glass beads. Results confirmed the presence of transition layers at the interface between coarse and fine sediments, where porosity follows a distinct trend of decrease and recovery. A Discrete Element Method (DEM)–based simulation model (Particula 1.3) was calibrated and validated against laboratory results, enabling broader parameter studies beyond the physical experiments. An improved algorithm based on a density threshold was also introduced to efficiently and objectively determine the transition layer extent in simulations. Empirical formulas linking transition layer thickness and porosity metrics to the grain-size ratio were derived, enabling the calculation of the average porosity of a stratified sample. Part I focuses on the experimental setup, model validation, and foundational insights into transition zone formation. A companion paper (Part II) will build on these results to develop predictive models for porosity in stratified sediment. Full article
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26 pages, 7950 KB  
Article
Impact of Grouping Behavior Among School-Aged Children on Evacuation Efficiency Under Ordered and Disordered Evacuation Forms
by Yufeng Zhou, Changzheng Xuan, Xiaochen Zhao and Xiaohu Jia
Buildings 2025, 15(22), 4154; https://doi.org/10.3390/buildings15224154 (registering DOI) - 18 Nov 2025
Abstract
School-aged children are a vulnerable group in emergencies, showing distinct grouping behaviors under ordered and disordered evacuations. This study investigated how these behaviors affect evacuation efficiency and how spatial conditions shape outcomes. Two drills were conducted on 216 children aged 6–12. Indicators including [...] Read more.
School-aged children are a vulnerable group in emergencies, showing distinct grouping behaviors under ordered and disordered evacuations. This study investigated how these behaviors affect evacuation efficiency and how spatial conditions shape outcomes. Two drills were conducted on 216 children aged 6–12. Indicators including movement speed, crowd density, and grouping type were analyzed from video data. Disordered evacuation featured unstable group structures, variable speeds, and faster but less consistent movement. In contrast, ordered evacuation improved group stability and coordination, with only slight speed reductions (16% in corridors and 12% in stairways). Spatial conditions also affected grouping behavior. Wider corridors encouraged lateral dispersion and required stronger guidance, whereas stairways benefited from reduced control to alleviate congestion. These findings highlight how grouping behavior affects evacuation efficiency across evacuation forms and spatial settings, and underscore the importance of coordinating evacuation management strategies with building circulation design. The results provide empirical evidence for enhancing the safety of school-aged children during evacuations and offer practical guidance for optimizing school evacuation strategies and educational building design. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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17 pages, 2561 KB  
Article
LDLK-U-Mamba: An Efficient and Highly Accurate Method for 3D Rock Pore Segmentation
by Guojun Chen, Huihui Li, Chang Liu, Pengxia Li and Yunyi Kong
Sensors 2025, 25(22), 7039; https://doi.org/10.3390/s25227039 (registering DOI) - 18 Nov 2025
Abstract
Three-dimensional rock pore segmentation is crucial in fields such as geology and petroleum exploration, holding significant importance for oil and gas resource exploration and development. However, existing segmentation methods still present two main limitations: (1) they fail to capture the spatial relationships of [...] Read more.
Three-dimensional rock pore segmentation is crucial in fields such as geology and petroleum exploration, holding significant importance for oil and gas resource exploration and development. However, existing segmentation methods still present two main limitations: (1) they fail to capture the spatial relationships of pores in 3D when directly applied to 3D rock pore segmentation, inevitably leading to inaccurate segmentation results; (2) they struggle to apply efficiently in resource-constrained scenarios due to the high computational complexity and costly computational demands. To solve the above issues, we propose a novel and lightweight method based on the Mamba architecture, termed LDLK-U-Mamba, for precise and efficient 3D rock pore segmentation. Specifically, we design a Lightweight Dynamic Large Kernel (LDLK) module to capture global contextual information and develop an InceptionDSConv3d module for multi-scale feature fusion and refinement, further yielding more accurate segmentation results. In addition, the Basic Residual Depthwise Separable Block (BasicResDWSBlock) module is proposed to utilize depthwise separable convolutions and the Squeeze-and-Excitation (SE) module to reduce model parameters and computational complexity. Extensive qualitative and quantitative experiments demonstrate that our LDLK-U-Mamba outperforms current mainstream segmentation approaches, validating its effectiveness for rock pore segmentation—particularly in capturing the 3D spatial relationships of pores. Full article
(This article belongs to the Section Intelligent Sensors)
14 pages, 557 KB  
Article
Evaluating the Role of Social Media in Veterinary Anatomy and Clinical Education: A Student-Based Study
by Ebru Eravci Yalin, Simge Özüner, Zeynep Nilüfer Akçasız, Sevim Güllü and Ozan Gündemir
Vet. Sci. 2025, 12(11), 1098; https://doi.org/10.3390/vetsci12111098 (registering DOI) - 18 Nov 2025
Abstract
Social media is increasingly present in higher education, particularly in fields that rely on visual learning such as veterinary anatomy and clinical sciences. This study investigated how veterinary students perceived the use of social media for educational and professional purposes, with specific attention [...] Read more.
Social media is increasingly present in higher education, particularly in fields that rely on visual learning such as veterinary anatomy and clinical sciences. This study investigated how veterinary students perceived the use of social media for educational and professional purposes, with specific attention to its role in learning anatomy and observing clinical procedures. An online cross-sectional survey was administered to students across different academic levels at a veterinary faculty. The questionnaire included demographic questions and eight Likert-scale items addressing the educational value of social media, its role in accessing learning materials, engagement with professional visuals, and ethical concerns regarding the sharing of clinical or surgical content. Data were analyzed using one-way ANOVA and post hoc Tukey tests to assess differences by academic year, age group, and daily social media usage. Analysis showed that students in earlier academic years generally held more favorable views on the use of social media for accessing learning materials and understanding complex subjects. Students in advanced years expressed greater ethical concern, particularly about sharing surgical or clinical videos online. Daily social media usage showed limited influence on general perceptions, though some variation appeared in topic-specific responses. The results suggest that veterinary programs may benefit from structured educational components on digital professionalism and responsible media use to better align student engagement with ethical standards and educational objectives. Full article
(This article belongs to the Special Issue Advances in Veterinary Anatomy: Connecting Basic Research with Clinical, Biotechnological, and Educational Innovations)
20 pages, 361 KB  
Article
Novel Error Bounds of Milne Formula Type Inequalities via Quantum Calculus with Computational Analysis and Applications
by Amjad E. Hazma, Abdul Mateen, Talha Anwar and Ghada AlNemer
Mathematics 2025, 13(22), 3698; https://doi.org/10.3390/math13223698 (registering DOI) - 18 Nov 2025
Abstract
Quantum calculus is a powerful extension of classical calculus, providing novel tools for deriving sharper and more efficient analytical results without relying on limits. This study investigates error estimations for Milne formula-type inequalities within the framework of quantum calculus, offering a fresh perspective [...] Read more.
Quantum calculus is a powerful extension of classical calculus, providing novel tools for deriving sharper and more efficient analytical results without relying on limits. This study investigates error estimations for Milne formula-type inequalities within the framework of quantum calculus, offering a fresh perspective on numerical integration theory. New variants of Milne’s formula-type inequalities are established for q-differentiable convex functions by first deriving a key quantum integral identity. The primary aim of this work is to obtain sharper and more accurate bounds for Milne’s formula compared to existing results in the literature. The validity of the proposed results is demonstrated through illustrative examples and graphical analysis. Furthermore, applications to special means of real numbers, the Mittag–Leffler function, and numerical integration formulas are presented to emphasize the practical significance of the findings. This study contributes to advancing the theoretical foundations of both classical and quantum calculus and enhances the understanding of integral inequality theory. Full article
(This article belongs to the Section C: Mathematical Analysis)
35 pages, 5308 KB  
Article
AI-Based Time-Series Ensemble Approach Coupled with a Hydrological Model for Reservoir Storage Prediction in Korea
by Jaeseong Park, Jason Sung-uk Joh, Minha Choi, Taejung Kim, Jaeil Cho and Yangwon Lee
Water 2025, 17(22), 3296; https://doi.org/10.3390/w17223296 (registering DOI) - 18 Nov 2025
Abstract
In regions like South Korea, erratic seasonal rainfall creates a dual vulnerability for agricultural reservoirs: rapid storage increases during the rainy season risk flooding and structural damage, while insufficient storage during dry periods leads to inadequate irrigation. Accurate reservoir storage prediction is therefore [...] Read more.
In regions like South Korea, erratic seasonal rainfall creates a dual vulnerability for agricultural reservoirs: rapid storage increases during the rainy season risk flooding and structural damage, while insufficient storage during dry periods leads to inadequate irrigation. Accurate reservoir storage prediction is therefore crucial. It enables pre-emptive storage and release planning, ensuring stable reservoir management and efficient water utilization despite unpredictable weather conditions. AI-based prediction offers a solution to the aforementioned challenges. However, previous studies had two key limitations: (1) they could not account for inflow and outflow variables in reservoirs that do not provide these data, and (2) they relied on Recurrent Neural Network (RNN) models with a recursive prediction mechanism, leading to decreased accuracy as the lead time increased. To overcome this, we propose a framework that simulates reservoir inflow and outflow using a rainfall–runoff hydrological model and utilizes these variables as inputs for time-series AI models. We then predict the storage rate using a Bayesian Model Averaging (BMA) ensemble of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Fusion Transformer (TFT) models, which resulted in a substantial accuracy improvement. The Mean Absolute Error (MAEs) for 1-day, 2-day, and 3-day ahead predictions were 0.820%p, 1.339%p, and 1.766%p, respectively, with corresponding correlation coefficients of 0.994, 0.987, and 0.980. This framework maintains high accuracy even as the lead time increases. The proposed framework can predict reservoir storage rates with high accuracy, even for reservoirs characterized by irregular seasonal rainfall patterns and a lack of explicit inflow/outflow data, thus contributing to more effective reservoir operation. Full article
(This article belongs to the Special Issue Big Data and Machine Learning for Hydrology Research: Methods, Applications and Future Directions)
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23 pages, 4409 KB  
Article
Effect of Corn Starch as Stabilizer Particle in Combination with Egg White Proteins in Natural Rubber Latex Biofoams Produced by Microwave Foaming
by Clara Amezúa-Arranz, Leandra Oliveira Salmazo, Alberto López-Gil and Miguel-Ángel Rodríguez-Pérez
Polymers 2025, 17(22), 3057; https://doi.org/10.3390/polym17223057 (registering DOI) - 18 Nov 2025
Abstract
Current ecological and environmental concerns have led to a rapid increase in social interest in research and innovation in the field of sustainable plastics, which directly affects foamed plastic products. In this study, we present our contribution by investigating the effects of egg [...] Read more.
Current ecological and environmental concerns have led to a rapid increase in social interest in research and innovation in the field of sustainable plastics, which directly affects foamed plastic products. In this study, we present our contribution by investigating the effects of egg white protein and corn starch particles on open-cell biofoams produced from natural rubber latex in a two-step process based on an initial aeration that leads to a liquid foam precursor and its dehydration by microwave radiation. By incorporating corn starch and either replacing or maintaining the levels of egg white protein, two independent series of foams were examined. We observed how the reduction in egg white led to bigger and heterogeneous cells, although the density values were practically maintained around 100 kg/m3. In contrast, the formulations with corn starch at a fixed level of egg white protein created foams with homogeneous structures and smaller cells (≤120 µm). In addition, in terms of density, both series present values around 100 kg/m3 for the final solid foams, indicating that the addition of starch does not involve density increments. On the contrary, densities are still low, and the cellular structure homogeneity improves, confirming that starch is a very promising stabilizer bio-particle in the development of biofoams from liquids. Full article
(This article belongs to the Section Biobased and Biodegradable Polymers)
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