Applied Sciences

23 pages, 1664 KiB

Open AccessArticle

Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning

by Gabriela Laura Sălăgean, Monica Leba and Andreea Cristina Ionica

Appl. Sci. 2025, 15(12), 6417; https://doi.org/10.3390/app15126417 - 6 Jun 2025

This paper presents a real-time system for the detection and classification of facial micro-expressions, evaluated on the CASME II dataset. Micro-expressions are brief and subtle indicators of genuine emotions, posing significant challenges for automatic recognition due to their low intensity, short duration, and [...] Read more.

This paper presents a real-time system for the detection and classification of facial micro-expressions, evaluated on the CASME II dataset. Micro-expressions are brief and subtle indicators of genuine emotions, posing significant challenges for automatic recognition due to their low intensity, short duration, and inter-subject variability. To address these challenges, the proposed system integrates advanced computer vision techniques, rule-based classification grounded in the Facial Action Coding System, and artificial intelligence components. The architecture employs MediaPipe for facial landmark tracking and action unit extraction, expert rules to resolve common emotional confusions, and deep learning modules for optimized classification. Experimental validation demonstrated a classification accuracy of 93.30% on CASME II, highlighting the effectiveness of the hybrid design. The system also incorporates mechanisms for amplifying weak signals and adapting to new subjects through continuous knowledge updates. These results confirm the advantages of combining domain expertise with AI-driven reasoning to improve micro-expression recognition. The proposed methodology has practical implications for various fields, including clinical psychology, security, marketing, and human-computer interaction, where the accurate interpretation of emotional micro-signals is essential. Full article

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31 pages, 2119 KiB

Open AccessArticle

Optimizing Vehicle Placement in the Residual Spaces of Unmarked Parking Areas: A Comparative Study of Heuristic Methods

by Mustafa Hüsrevoğlu, Artur Janowski and Ahmet Emin Karkınlı

Appl. Sci. 2025, 15(12), 6416; https://doi.org/10.3390/app15126416 - 6 Jun 2025

Abstract

Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle [...] Read more.

Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle Swarm Optimization, Artificial Bee Colony, Gray Wolf Optimizer, Harris Hawks Optimizer, Phasor Particle Swarm Optimization, Multi-Population Based Differential Evolution, and the Colony-Based Search Algorithm. The experiments were conducted in two different parking areas, one designed for parallel parking and the other for perpendicular parking, under three scenarios allowing different levels of cars’ rotational flexibility. The results indicate that MDE consistently outperforms other methods in both speed and robustness, achieving the highest vehicle capacity. These findings provide a foundation for smart parking systems, enabling real-time optimization, reduced congestion, and improved urban mobility. Full article

(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)

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13 pages, 7588 KiB

Open AccessArticle

Investigating the Physical Mechanisms of Quicksand Using a Custom-Designed Experimental Apparatus

by Jianhui Long, Rui Dong, Kaixin Zhang, Hangyu Weng and Zhiqiang Yi

Appl. Sci. 2025, 15(12), 6415; https://doi.org/10.3390/app15126415 - 6 Jun 2025

Abstract

Quicksand initiation in saturated sandy soils represents a critical geohazard with significant implications for geotechnical infrastructure stability. Despite its importance, the granular-scale mechanisms driving the physical state transitions during quicksand remain insufficiently understood. This study employs a custom-designed hydrodynamic seepage testing system to [...] Read more.

Quicksand initiation in saturated sandy soils represents a critical geohazard with significant implications for geotechnical infrastructure stability. Despite its importance, the granular-scale mechanisms driving the physical state transitions during quicksand remain insufficiently understood. This study employs a custom-designed hydrodynamic seepage testing system to investigate these mechanisms, enabling precise regulation of hydrodynamic velocity and real-time monitoring of pressure variations. Through experiments on quartz sand specimens with varying particle gradations, we demonstrate that particle gradation primarily governs quicksand susceptibility, while hydrodynamic velocity controls its initiation timing and exhibits a linear correlation with seepage discharge. The quicksand process evolves through three distinct stages: self-consolidation, reorganization, and quicksand initiation, with the reorganization stage identified as the pivotal phase where particle rearrangement dictates system stability. These findings elucidate the intrinsic physical mechanisms of quicksand as a hydraulic failure phenomenon, offering valuable insights for predictive modeling and geohazard mitigation in granular media. Full article

(This article belongs to the Section Civil Engineering)

22 pages, 2764 KiB

Open AccessArticle

Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions

by Rebeka Anna Jáger and Géza Szabó

Appl. Sci. 2025, 15(12), 6414; https://doi.org/10.3390/app15126414 - 6 Jun 2025

Abstract

As air traffic control (ATC) automation advances, simulation environments become essential for testing and validating novel solutions before deployment. This study presents a modular framework that integrates real air traffic data to simulate controlled and uncontrolled airspace environments for automation assessment. The framework [...] Read more.

As air traffic control (ATC) automation advances, simulation environments become essential for testing and validating novel solutions before deployment. This study presents a modular framework that integrates real air traffic data to simulate controlled and uncontrolled airspace environments for automation assessment. The framework consists of a two-layer structure: a traffic simulation layer for generating and updating aircraft positions, and an upper layer for managing control agents and traffic commands. It uses ADS-B data to simulate realistic conditions, incorporates randomized traffic generation, and enables pilot–controller interactions. The system supports various operational modes, from simple data recording to fully interactive control scenarios. Interfaces allow external algorithm integration for traffic prediction, conflict resolution, and controller workload evaluation. A case study demonstrates the framework’s ability to assess a basic control algorithm’s performance under increasing traffic density. This open-source, MATLAB-based simulation environment supports robust, repeatable ATC automation testing using real-time or recorded traffic data. Its flexible architecture and clearly defined interfaces enable customization for diverse research applications, including sectorization studies, flow management, and workload estimation. Full article

(This article belongs to the Special Issue Innovative Research on Transportation Means)

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14 pages, 1734 KiB

Open AccessArticle

Differential Effects of Low-Frequency TMS of the Motor Cortex on Voluntary and Non-Voluntary Rhythmic Arm Movements

by Irina A. Solopova, Victor A. Selionov, Irina Y. Dolinskaya, Germana Cappellini and Yury Ivanenko

Appl. Sci. 2025, 15(12), 6413; https://doi.org/10.3390/app15126413 - 6 Jun 2025

Abstract

Given the cervical spinal cord’s role in locomotor and rhythmic upper limb tasks, its neuromodulation has emerged as an important area of study for understanding human spinal rhythmogenesis. We previously demonstrated that, under unloading conditions, arm muscle vibrostimulation can elicit non-voluntary upper limb [...] Read more.

Given the cervical spinal cord’s role in locomotor and rhythmic upper limb tasks, its neuromodulation has emerged as an important area of study for understanding human spinal rhythmogenesis. We previously demonstrated that, under unloading conditions, arm muscle vibrostimulation can elicit non-voluntary upper limb oscillations. In this study, we investigated the effects of transcranial magnetic stimulation (TMS) of the motor cortex during both voluntary and non-voluntary (vibration-induced) rhythmic arm movements. We analyzed motor-evoked potentials, mean arm muscle activity, and kinematic parameters of arm movements, including cycle duration and shoulder and elbow joint angular oscillations. Motor-evoked potentials in proximal arm muscles were significantly modulated during both movement types. Notably, low-frequency TMS markedly enhanced non-voluntary arm oscillations, whereas its effect on voluntary movements was statistically non-significant. This differential response is likely due to the absence of characteristic supraspinal influences in sensory-induced spinal activation during non-voluntary movements. These findings align with previous evidence showing that supraspinal pathways facilitate rhythmogenesis in the lower limbs, and they now extend this concept to the upper limbs. Overall, our results suggest that therapies aimed at modulating cervical central pattern generators may benefit from the active engagement of supraspinal motor circuits. Full article

(This article belongs to the Special Issue Neuromechanics: The Relationship between Neural Control and Musculoskeletal Biomechanics)

11 pages, 1698 KiB

Open AccessArticle

Quantifying Fermentable Sugars in Beer: Development and Validation of a Reliable HPLC-ELSD Method

by Pedro F. Lopes, Fábio B. Oliveira and Luis F. Guido

Appl. Sci. 2025, 15(12), 6412; https://doi.org/10.3390/app15126412 - 6 Jun 2025

Abstract

A high-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) method was developed and validated for analyzing fermentable and reducing sugars in brewing matrices. The method exhibited detection limits of 2.5–12.5 mg/L and quantification limits of 12.0–30.0 mg/L. Linearity was achieved for all [...] Read more.

A high-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) method was developed and validated for analyzing fermentable and reducing sugars in brewing matrices. The method exhibited detection limits of 2.5–12.5 mg/L and quantification limits of 12.0–30.0 mg/L. Linearity was achieved for all sugars, fitted with a quadratic calibration model (R² = 0.9998). Precision metrics revealed relative standard deviations (RSDs) below 2% for repeatability and below 6% for intermediate precision. Recovery rates between 86 and 119% confirmed robustness and minimal matrix interference. Application to brewing samples highlighted variability in sugar profiles, with sucrose concentrations in wort ranging from 3.5 to 22.0 g/L and maltose and maltotriose in finished beers between 0.80 and 1.50 g/L and 1.10–2.50 g/L, respectively. Batch variability analysis showed that brewing conditions had a greater impact on sugar concentrations than malt batch origin, with maltose variation reaching 34.6%. This HPLC-ELSD method provides a robust and reliable tool for sugar analysis in brewing, offering valuable insights into fermentation dynamics and batch consistency. Its application to industrial contexts underscores its potential for improving quality control and optimizing brewing processes. Full article

(This article belongs to the Special Issue Emerging Analytical Techniques in Food Industry and Agricultural Products)

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20 pages, 2538 KiB

Open AccessArticle

A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision

by Ming Tao, Ziheng Xiao, Yulong Liu, Lei Huang, Gongliang Xiang and Yuanquan Xu

Appl. Sci. 2025, 15(12), 6411; https://doi.org/10.3390/app15126411 - 6 Jun 2025

Abstract

As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. This study presents [...] Read more.

As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. This study presents a YOLOv8-based binocular vision model for real-time recognition of blasting fragmentation. The model is trained on a dataset comprising 1536 samples, which were annotated using an automatic labeling algorithm and expanded to 7680 samples through data augmentation techniques. The YOLOv8 instance segmentation model is employed to detect and classify rock fragments. By integrating binocular vision-based automatic image capture with Welzl’s algorithm, the actual particle size of each rock fragment is calculated. Furthermore, region of interest (ROI) extraction and shadow-based data enhancement techniques are incorporated to focus the model on the blasting fragmentation area and reduce environmental interference. Finally, software and a system were independently developed based on this integrated model and successfully deployed at engineering sites. The dynamic recognition Mean Average Precision of this integrated model is 0.84, providing a valuable reference for evaluating blasting effects and improving work efficiency. Full article

17 pages, 4706 KiB

Open AccessArticle

Evaluation of the Inclusion of the Seaweed Ulva lactuca Produced in an Integrated System with Biofloc in the Diet of Juvenile Tilapia Oreochromis niloticus

by Andrezza Carvalho, Larissa Müller, Victor Rosas, Marcelo Borges Tesser, Juliane Ventura-Lima, Gamze Turan, Marcelo Pias and Luís H. Poersch

Appl. Sci. 2025, 15(12), 6410; https://doi.org/10.3390/app15126410 - 6 Jun 2025

Abstract

Macroalgae biomass produced in integrated biofloc systems can become a high-quality nutritional product to replace ingredients in the diet of tilapia. This study aimed to evaluate the effects of different concentrations of Ulva lactuca on the performance and antioxidant capacity of Nile tilapia. [...] Read more.

Macroalgae biomass produced in integrated biofloc systems can become a high-quality nutritional product to replace ingredients in the diet of tilapia. This study aimed to evaluate the effects of different concentrations of Ulva lactuca on the performance and antioxidant capacity of Nile tilapia. There were four isoprotein and isolipid diets with 5%, 10%, and 15% macroalgae meal, and a control treatment without macroalgae inclusion. The experiment lasted for 42 days in a recirculating system, with animal performance, blood sampling, and proximal composition being carried out. To assess the potential benefits of including algal biomass, a salinity stress test was carried out on the fish, and samples were collected for biochemical analysis. There were no significant differences in carcass performance and composition between the treatments. The results showed that the inclusion of 10% macroalgae resulted in a higher granulocyte count, while the antioxidant capacity obtained better results in the 5 and 10% macroalgae inclusions, followed by the modulation of the antioxidant system, as evidenced by an increase in glutathione-S-transferase activity and reduced glutathione levels. However, protein and lipid oxidation did not occur only in the 5% macroalgae inclusion compared with the treatments with higher algae inclusion. Therefore, the inclusion of 5% macroalgae in the tilapia diet is indicated to improve antioxidant capacity in the face of stress. Full article

(This article belongs to the Special Issue Advances in Aquatic Animal Nutrition and Aquaculture)

13 pages, 611 KiB

Open AccessArticle

Color Variation in 3D-Printed Orthodontic Aligners as a Compliance Indicator: A Prospective Pilot Study

by Francesca Cremonini, Giuseppe Chiusolo, Filippo Pepe and Luca Lombardo

Appl. Sci. 2025, 15(12), 6409; https://doi.org/10.3390/app15126409 - 6 Jun 2025

Abstract

Patient compliance remains a significant challenge in orthodontic treatment with clear aligners, as adherence to prescribed wear time is often suboptimal. This study investigated the potential of colorimetric analysis as a method to assess compliance with NOXI 3D-printed night-time aligners. Specifically, it evaluated [...] Read more.

Patient compliance remains a significant challenge in orthodontic treatment with clear aligners, as adherence to prescribed wear time is often suboptimal. This study investigated the potential of colorimetric analysis as a method to assess compliance with NOXI 3D-printed night-time aligners. Specifically, it evaluated color variations in polyamide aligners due to thermo-oxidation, using the RGB (Red, Green, Blue) color model as a non-invasive indicator. In total, 10 patients participated in this prospective study, wearing aligners for either 7 or 12 h daily over a 14-day period. Colorimetric measurements were collected via a smartphone-based application, and statistical analyses examined correlations between wear duration and color changes. The results revealed a significant association between a longer wear time and increased discoloration (p < 0.001), supporting the feasibility of RGB-based monitoring as a reliable compliance tool. However, individual variability in saliva composition, diet, and oral hygiene may have influenced the results, highlighting the need for further research into potential confounding variables. These findings underscore the promise of integrating digital monitoring technologies to improve adherence tracking and patient management in orthodontics. Future studies should refine the methodology and validate its efficacy in larger, more diverse populations. Full article

(This article belongs to the Special Issue Orthodontics: Advanced Techniques, Methods and Materials)

22 pages, 583 KiB

Open AccessArticle

Novel Bacterial Strains for Nonylphenol Removal in Water and Sewage Sludge: Insights from Gene Expression and Toxicity

by Alba Lara-Moreno, Inés Aguilar-Romero, Fernando Madrid, Jaime Villaverde, Jorge D. Carlier, Juan Luís Santos, Esteban Alonso and Esmeralda Morillo

Appl. Sci. 2025, 15(12), 6408; https://doi.org/10.3390/app15126408 - 6 Jun 2025

Abstract

4-Nonylphenols (4-NPs) are persistent endocrine disruptors frequently found in wastewater treatment plant (WWTP) effluents and sewage sludge. This study evaluated the ability of eight bacterial strains that were isolated from sewage sludge to degrade 4-n-NP in an aqueous solution. Bacillus safensis CN12, Shewanella [...] Read more.

4-Nonylphenols (4-NPs) are persistent endocrine disruptors frequently found in wastewater treatment plant (WWTP) effluents and sewage sludge. This study evaluated the ability of eight bacterial strains that were isolated from sewage sludge to degrade 4-n-NP in an aqueous solution. Bacillus safensis CN12, Shewanella putrefaciens CN17, and Alcaligenes faecalis CN8 showed the highest degradation rates, removing 100%, 75%, and 74% of 4-n-NP (10 mg L⁻¹), with DT₅₀ values of 0.90, 8.9, and 10.4 days, respectively. Despite the reduction in 4-n-NP concentrations, ecotoxicity assays revealed that the resulting transformation products (TPs) were more toxic than the parent compound. To investigate the potential degradation mechanisms, in silico and gene expression analyses were conducted on B. safensis CN12, revealing a significant upregulation of the multicopper oxidase gene, cotA (7.25-fold), and the ring-cleaving dioxygenase gene, mhqO (13.9-fold). Although the CN12 strain showed potential for mineralization based on gene expression studies, this was not observed in the aqueous solution. However, when 4-n-NP was adsorbed on sludge and treated with CN12 in the presence of hydroxypropyl-β-cyclodextrin (HPBCD) as a bioavailability enhancer, mineralization reached up to 33%, indicating a synergistic effect with the native sludge microbiota. Full article

(This article belongs to the Section Applied Microbiology)

22 pages, 2323 KiB

Open AccessArticle

Finite Mixture Model-Based Analysis of Yarn Quality Parameters

by Esra Karakaş, Melik Koyuncu and Mülayim Öngün Ükelge

Appl. Sci. 2025, 15(12), 6407; https://doi.org/10.3390/app15126407 - 6 Jun 2025

Abstract

This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture [...] Read more.

This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry. Full article

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27 pages, 1304 KiB

Open AccessReview

Review on Soft Mobility Infrastructure Design Codes

by Chang Chen, Zoi Christoforou and Nadir Farhi

Appl. Sci. 2025, 15(12), 6406; https://doi.org/10.3390/app15126406 - 6 Jun 2025

Abstract

Soft mobility is gaining popularity in urban spaces due to its various benefits in terms of carbon footprint, air quality, congestion mitigation, and public health. Soft mobility infrastructure mainly includes urban road adjustments to accommodate pedestrian and bicycle flows. Relevant design codes are [...] Read more.

Soft mobility is gaining popularity in urban spaces due to its various benefits in terms of carbon footprint, air quality, congestion mitigation, and public health. Soft mobility infrastructure mainly includes urban road adjustments to accommodate pedestrian and bicycle flows. Relevant design codes are being developed worldwide, and important investments are being made in soft mobility. This paper provides a review and comparative analysis of 17 design codes and regulations from different countries and regions across the world. Furthermore, the German road design code for motorized traffic is used as a reference to assess the level of detail and eventual gaps in the soft mobility infrastructure design codes. Results indicate that, in contrast to road codes, soft mobility infrastructure codes vary significantly from country to country. Most importantly, the limit and recommended values of geometric parameters are fewer in number and less documented compared to road design parameters. Evidence-based recommendations are needed to enhance the design, construction, operation, maintenance, and safe management of soft mobility infrastructure. Full article

(This article belongs to the Special Issue Infrastructure Resilience Analysis)

20 pages, 1465 KiB

Open AccessArticle

Lightweight Periodic Scheduler in Wearable Devices for Real-Time Biofeedback Systems in Sports and Physical Rehabilitation

by Anton Kos, Árpád Bűrmen, Matevž Hribernik, Sašo Tomažič, Anton Umek, Iztok Fajfar and Janez Puhan

Appl. Sci. 2025, 15(12), 6405; https://doi.org/10.3390/app15126405 - 6 Jun 2025

Abstract

This study explores the application of wireless wearable devices within the emerging domain of biomechanical feedback systems for sports and rehabilitation. A critical aspect of these systems is the need for real-time operation, where wearable devices must execute multiple processes concurrently while ensuring [...] Read more.

This study explores the application of wireless wearable devices within the emerging domain of biomechanical feedback systems for sports and rehabilitation. A critical aspect of these systems is the need for real-time operation, where wearable devices must execute multiple processes concurrently while ensuring specific tasks are performed within precise time constraints. To address this challenge, we developed a specialized, lightweight periodic scheduler for microcontrollers. Extensive testing under various conditions demonstrated that sensor sampling frequencies of 650 Hz and 1750 Hz are achievable when utilizing one and 26 sensor samples per packet, respectively. Receiver delays were observed to be a few milliseconds or more, depending on the application scenario. These findings offer practical guidelines for developers and practitioners working with real-time biomechanical feedback systems. By optimizing sensor sampling frequencies and packet configurations, our approach enables more responsive and accurate feedback for athletes and patients, improving the reliability of motion analysis, rehabilitation monitoring, and training assessments. Additionally, we outline the limitations of such systems in terms of transmission delays and jitter, providing insights into their feasibility for different real-world applications. Full article

(This article belongs to the Special Issue Research on Biomechanics, Equipment Development, Motor Control and Learning of Human Movements)

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31 pages, 10476 KiB

Open AccessArticle

An Intelligent Framework for Multiscale Detection of Power System Events Using Hilbert–Huang Decomposition and Neural Classifiers

by Juan Vasquez, Manuel Jaramillo and Diego Carrión

Appl. Sci. 2025, 15(12), 6404; https://doi.org/10.3390/app15126404 - 6 Jun 2025

Abstract

This article proposes a multiscale classification framework for detecting voltage disturbances in electrical distribution systems using artificial neural networks (ANNs) combined with the Hilbert–Huang transform (HHT). The framework targets four core power quality (PQ) events defined in the IEEE 1159-2019 standard: normal operation [...] Read more.

This article proposes a multiscale classification framework for detecting voltage disturbances in electrical distribution systems using artificial neural networks (ANNs) combined with the Hilbert–Huang transform (HHT). The framework targets four core power quality (PQ) events defined in the IEEE 1159-2019 standard: normal operation and voltage sag, swell, and interruption. Unlike traditional methods that operate on a fixed disturbance duration, our approach incorporates multiple time scales (0.2 s, 0.4 s, and 0.8 s) to improve detection robustness across varied event lengths, a critical factor in real-world scenarios where disturbance durations are unpredictable. Features are extracted using empirical mode decomposition (EMD) and Hilbert spectral analysis, enabling accurate representation of the signals’ non-stationary and nonlinear characteristics. The ANN is trained using statistical descriptors derived from the first two intrinsic mode functions (IMFs), capturing both amplitude and frequency content. The method was validated in MATLAB on the IEEE 33-bus radial distribution test system using simulated disturbances. The proposed model achieved a classification accuracy of 94.09% and demonstrated consistent performance across all time windows, supporting its suitability for real-time monitoring in smart distribution networks. This study contributes a scalable and adaptable solution for automated PQ event classification under variable conditions. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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36 pages, 14071 KiB

Open AccessArticle

Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam

by Xudong Li, Lin Sun, Xiaopei Liu and Yili Duo

Appl. Sci. 2025, 15(12), 6403; https://doi.org/10.3390/app15126403 - 6 Jun 2025

Abstract

The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever [...] Read more.

The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever structure is subjected to an evenly distributed load, and a series of chaotic vibrations are observed corresponding to different amplitudes and angular velocities of the load. Then, the RNN data-driven model is applied to predict chaotic vibrations, and the chaotic vibration prediction of RNN is evaluated. The prediction results are primarily evaluated using two metrics: mean absolute error (MAE) and root mean square error (RMSE). The analysis results show that the maximum MAE is 0.041 and the maximum RMSE is 0.067. Even under perturbed initial conditions, the RNN model maintained high prediction accuracy, with a maximum MAE of and RMSE of , highlighting its robustness and reliability in predicting chaotic vibrations. The error analysis indicates that the RNN accurately predicts chaotic vibrations with a high degree of precision. Full article

54 pages, 2029 KiB

Open AccessReview

Electromagnetic Techniques Applied to Cultural Heritage Diagnosis: State of the Art and Future Prospective: A Comprehensive Review

by Patrizia Piersigilli, Rocco Citroni, Fabio Mangini and Fabrizio Frezza

Appl. Sci. 2025, 15(12), 6402; https://doi.org/10.3390/app15126402 - 6 Jun 2025

Abstract

When discussing Cultural Heritage (CH), the risk of causing damage is inherently linked to the artifact itself due to several factors: age, perishable materials, manufacturing techniques, and, at times, inadequate preservation conditions or previous interventions. Thorough study and diagnostics are essential before any [...] Read more.

When discussing Cultural Heritage (CH), the risk of causing damage is inherently linked to the artifact itself due to several factors: age, perishable materials, manufacturing techniques, and, at times, inadequate preservation conditions or previous interventions. Thorough study and diagnostics are essential before any intervention, whether for preventive, routine maintenance or major restoration. Given the symbolic, socio-cultural, and economic value of CH artifacts, non-invasive (NI), non-destructive (ND), or As Low As Reasonably Achievable (ALARA) approaches—capable of delivering efficient and long-lasting results—are preferred whenever possible. Electromagnetic (EM) techniques are unrivaled in this context. Over the past 20 years, radiography, tomography, fluorescence, spectroscopy, and ionizing radiation have seen increasing and successful applications in CH monitoring and preservation. This has led to the frequent customization of standard instruments to meet specific diagnostic needs. Simultaneously, the integration of terahertz (THz) technology has emerged as a promising advancement, enhancing capabilities in artifact analysis. Furthermore, Artificial Intelligence (AI), particularly its subsets—Machine Learning (ML) and Deep Learning (DL)—is playing an increasingly vital role in data interpretation and in optimizing conservation strategies. This paper provides a comprehensive and practical review of the key achievements in the application of EM techniques to CH over the past two decades. It focuses on identifying established best practices, outlining emerging needs, and highlighting unresolved challenges, offering a forward-looking perspective for the future development and application of these technologies in preserving tangible cultural heritage for generations to come. Full article

(This article belongs to the Section Energy Science and Technology)

27 pages, 6492 KiB

Open AccessArticle

Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network

by Lingfeng Qi, Jiafang Pan, Tianping Huang, Zhenfeng Zhou and Faguo Huang

Appl. Sci. 2025, 15(12), 6401; https://doi.org/10.3390/app15126401 - 6 Jun 2025

Abstract

Remaining useful life (RUL) prediction of rolling bearings is of significance for improving the reliability and durability of rotating machinery. Aiming at the problem of suboptimal RUL prediction precision under cross-working conditions due to distribution discrepancies between training and testing data, enhanced cross-working [...] Read more.

Remaining useful life (RUL) prediction of rolling bearings is of significance for improving the reliability and durability of rotating machinery. Aiming at the problem of suboptimal RUL prediction precision under cross-working conditions due to distribution discrepancies between training and testing data, enhanced cross-working condition RUL prediction for rolling bearings via an initial degradation detection-enabled joint transfer metric network is proposed. Specifically, the health indicator, called reconstruction along projection pathway (RAPP), is calculated for initial degradation detection (IDD), in which RAPP is obtained from a novel deep adversarial convolution autoencoder network (DACAEN) and compares discrepancies between the input and the reconstruction by DACAEN, not only in the input space, but also in the hidden spaces, and then RUL prediction is triggered after IDD via RAPP. After that, a joint transfer metric network is proposed for cross-working condition RUL prediction. Joint domain adaptation loss, which combines representation subspace distance and variance discrepancy representation, is designed to act on the final layer of the mapping regression network to decrease data distribution discrepancies and ultimately obtain cross-domain invariant features. The experimental results from the PHM2012 dataset show that the proposed method has higher prediction accuracy and better generalization ability than typical and advanced transfer RUL prediction methods under cross-working conditions, with improvements of 0.047, 0.053, and 0.058 in the MSE, RMSE, and Score. Full article

(This article belongs to the Special Issue Advanced Technologies for Industry 4.0 and Industry 5.0)

24 pages, 1510 KiB

Open AccessArticle

Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications

by Patrycja Rowińska, Marcin Sypka, Aneta M. Białkowska, Maria Stryjek, Adriana Nowak, Regina Janas, Beata Gutarowska and Justyna Szulc

Appl. Sci. 2025, 15(12), 6400; https://doi.org/10.3390/app15126400 - 6 Jun 2025

Abstract

This study evaluates various strains of soil bacterial for use in the development of new biopreparations. Mesophilic spore-forming bacteria were isolated from cultivated soil and analysed for their enzymatic activity, ability to decompose crop residues, and antagonistic properties towards selected phytopathogens. Notably, this [...] Read more.

This study evaluates various strains of soil bacterial for use in the development of new biopreparations. Mesophilic spore-forming bacteria were isolated from cultivated soil and analysed for their enzymatic activity, ability to decompose crop residues, and antagonistic properties towards selected phytopathogens. Notably, this is the first cytotoxicity assessment of soil bacterial metabolites on Spodoptera frugiperda Sf-9 (fall armyworm). Bacillus subtilis, Bacillus licheniformis, Bacillus velezensis, Paenibacillus amylolyticus, and Prestia megaterium demonstrated the highest hydrolytic potential for the degradation of post-harvest residues from maize, winter barley, and triticale. They exhibited antimicrobial activity against at least three of the tested phytopathogens and demonstrated the ability to solubilize phosphorus. Metabolites of B. licheniformis (IC50 = 8.3 mg/mL) and B. subtilis (IC50 = 144.9 mg/mL) were the most cytotoxic against Sf-9. We recommend the use of the tested strains in industrial practice as biocontrol agents, plant growth biostimulants, crop residue decomposition stimulants, and bioinsecticides. Future studies should focus on assessing the efficacy of using these strains under conditions simulating the target use, such as plant microcosms and greenhouses and the impact of these strains on the abundance and biodiversity of native soil microbiota. This research can serve as a model procedure for screening other strains of bacteria for agricultural purposes. Full article

30 pages, 12166 KiB

Open AccessArticle

An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model

by Lilan Huang, Hongze Leng, Junqiang Song, Dongzi Wang, Wuxin Wang, Ruisheng Hu and Hang Cao

Appl. Sci. 2025, 15(12), 6399; https://doi.org/10.3390/app15126399 - 6 Jun 2025

Abstract

Accurate initial conditions are crucial for improving numerical weather prediction (NWP). Variational data assimilation relies on a static background error covariance matrix (B), yet its variance estimation is often inaccurate, affecting assimilation and forecast performance. This study introduces DRL-AST, a deep [...] Read more.

Accurate initial conditions are crucial for improving numerical weather prediction (NWP). Variational data assimilation relies on a static background error covariance matrix (B), yet its variance estimation is often inaccurate, affecting assimilation and forecast performance. This study introduces DRL-AST, a deep reinforcement learning-based adaptive variance rescaling strategy that dynamically adjusts the variances of B to optimize forecast skill through improved assimilation performance. By formulating variance rescaling as a Markov Decision Process and employing an actor–critic framework with Proximal Policy Optimization, DRL-AST autonomously selects spatio-temporal rescaling factors, enhancing assimilation and forecast accuracy without additional computational cost. As a new paradigm for adaptive variance tuning, DRL-AST demonstrates competitive improvements in forecast skill in experiments with the Lorenz-96 model by generating initial states that better conform to model dynamical consistency. Given its adaptability and efficiency, DRL-AST holds great potential for application in high-dimensional NWP models, where deep learning-based dimensionality reduction and reinforcement learning techniques could further enhance its feasibility and effectiveness in complex assimilation frameworks. Full article

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26 pages, 6302 KiB

Open AccessArticle

Predicting Urban Rail Transit Network Origin–Destination Matrix Under Operational Incidents with Deep Counterfactual Inference

by Qianqi Fan, Chengcheng Yu and Jianyong Zuo

Appl. Sci. 2025, 15(12), 6398; https://doi.org/10.3390/app15126398 - 6 Jun 2025

Abstract

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and [...] Read more.

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

(This article belongs to the Special Issue Applications of Big Data in Public Transportation Systems)

37 pages, 959 KiB

Open AccessArticle

Renewable Energy and Price Stability: An Analysis of Volatility and Market Shifts in the European Electricity Sector (2015–2025)

by Marek Pavlík, František Kurimský and Kamil Ševc

Appl. Sci. 2025, 15(12), 6397; https://doi.org/10.3390/app15126397 - 6 Jun 2025

Abstract

This research paper analyses the evolution of electricity price volatility in six European countries between 2015 and 2025, focusing on the relationship between the increasing penetration of renewable energy sources (RES) and short-term price fluctuations. Based on high-frequency data (at 15 min to [...] Read more.

This research paper analyses the evolution of electricity price volatility in six European countries between 2015 and 2025, focusing on the relationship between the increasing penetration of renewable energy sources (RES) and short-term price fluctuations. Based on high-frequency data (at 15 min to hourly resolution) on electricity prices, solar and wind generation, and residual load, both year-on-year and structural changes in volatility are quantified. The results show a significant increase in volatility after 2021, with outliers appearing particularly during the 2022 energy crisis, most notably in countries with a high share of RES and limited system flexibility. The analysis identifies non-linear relationships between RES generation and the occurrence of negative prices, with country-specific threshold levels. Annual regression models show that the predictive power of these relationships is time-varying and influenced by externalities. The correlation matrices confirm regional differences in the impact of RES on price dynamics. The results support the design of rules for forecasting risk periods and point to the need for market mechanisms increasing flexibility, including accumulation, demand management, and cross-border integration. Full article

(This article belongs to the Section Energy Science and Technology)

14 pages, 5556 KiB

Open AccessCommunication

Biofabricating Three-Dimensional Bacterial Cellulose Composites Using Waste-Derived Scaffolds

by Jula Kniep, Manu Thundathil, Kurosch Rezwan and Ali Reza Nazmi

Appl. Sci. 2025, 15(12), 6396; https://doi.org/10.3390/app15126396 - 6 Jun 2025

Abstract

Microorganisms metabolising low-value carbon sources can produce a diverse range of bio-based and biodegradable materials compatible with circular economy principles. One such material is bacterial cellulose (BC), which can be obtained in high purity through the fermentation of sweetened tea by a Symbiotic [...] Read more.

Microorganisms metabolising low-value carbon sources can produce a diverse range of bio-based and biodegradable materials compatible with circular economy principles. One such material is bacterial cellulose (BC), which can be obtained in high purity through the fermentation of sweetened tea by a Symbiotic Culture of Bacteria and Yeast (SCOBY). In recent years, there has been a growing research interest in SCOBYs as a promising solution for sustainable material design. In this work, we have explored a novel method to grow SCOBYs vertically using a waste-based scaffold system. Waste sheep wool and cotton fabric were soaked in a SCOBY infusion to serve as scaffolds, carrying the infusion and facilitating vertical growth through capillary forces. Remarkably, vertical membrane growth up to 5 cm above the liquid–air interface (LAI) was observed after just one week. Membranes with different microstructures were found in sheep wool and cotton, randomly oriented between the scaffold fibre, resulting in a high surface area. This study demonstrated that vertical growth in scaffolds is possible, proving the concept of a new method of growing composite materials with potential high-value applications in biomedicine, energy storage, or filtration. Full article

(This article belongs to the Special Issue Emerging Biomaterials and Bio-Composites Across Disciplines: Design, Characterization and Applications)

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26 pages, 1844 KiB

Open AccessArticle

Using Graph-Based Maximum Independent Sets with Large Language Models for Extractive Text Summarization

by Cengiz Hark

Appl. Sci. 2025, 15(12), 6395; https://doi.org/10.3390/app15126395 - 6 Jun 2025

Abstract

Large Language Models (LLMs) have shown a strong performance across various tasks but still face challenges in automatic text summarization. While they are effective in capturing semantic patterns from large corpora, they typically lack mechanisms for encoding structural relationships between sentences or paragraphs. [...] Read more.

Large Language Models (LLMs) have shown a strong performance across various tasks but still face challenges in automatic text summarization. While they are effective in capturing semantic patterns from large corpora, they typically lack mechanisms for encoding structural relationships between sentences or paragraphs. Their high hardware requirements and limited analysis as to processing efficiency further constrain their applicability. This paper proposes a framework employing the Graph Independent Set approach to extract the essence of textual graphs and address the limitations of LLMs. The framework encapsulates nodes and relations into structural graphs generated through Natural Language Processing (NLP) techniques based on the Maximum Independent Set (MIS) theory. The incorporation of graph-derived structural features enables more semantically cohesive and accurate summarization outcomes. Experiments on the Document Understanding Conference (DUC) and Cable News Network (CNN)/DailyMail datasets are conducted with different summary lengths to evaluate the performance of the framework. The proposed method provides up to a 41.05% (Recall-Oriented Understudy for Gisting Evaluation, ROUGE-2 F1) increase in summary quality and a 60.71% improvement in response times on models such as XLNet, Pegasus, and DistilBERT. The proposed framework enables more informative and concise summaries by embedding structural relationships into LLM-driven semantic representations, while reducing computational costs. In this study, we explore whether integrating MIS-based graph filtering with LLMs significantly enhances both the accuracy and efficiency of extractive text summarization. Full article

28 pages, 2054 KiB

Open AccessArticle

The Influence of the Spillover Punishment Mechanism Under P-MA Theory on the Balance of Perceived Value in the Intelligent Construction of Coal Mines

by Yanyu Guo, Jizu Li and David Cliff

Appl. Sci. 2025, 15(12), 6394; https://doi.org/10.3390/app15126394 - 6 Jun 2025

Abstract

The objective of this paper is to examine the game-theoretic relationship between local governments and coal mining enterprises with regard to the issue of coal mine intelligent construction. Firstly, this paper employs prospect theory to construct the value perception function and the decision [...] Read more.

The objective of this paper is to examine the game-theoretic relationship between local governments and coal mining enterprises with regard to the issue of coal mine intelligent construction. Firstly, this paper employs prospect theory to construct the value perception function and the decision weight function, which are then used to optimize the parameters of the traditional income matrix. The equilibrium point is then analyzed for stability under different conditions. Subsequently, Vensim PLE and MATLAB simulation software are employed to substantiate the impact of spillover penalties and associated parameters on the value perception equilibrium of the two parties. The results of the simulation demonstrate that, in addition to the initial strategy selected, the spillover penalty exerts a considerable inhibitory effect on the process of enterprise intelligence construction. Secondly, from the perspective of value perception, the lower the costs to enterprises of carrying out intelligent construction in terms of labor and mental effort, the more enterprises are inclined to engage in this construction. The higher the costs to enterprises of complying with strict government regulation, and the lower the costs to enterprises of deregulation, the more the government can govern by non-interference. Finally, the behavioral trends of local government departments are also correlated with additional revenue they receive from firms and the factor of fines linked to government performance. Full article

13 pages, 2332 KiB

Open AccessArticle

Apoptotic Potential of Polyphenol Extract of Mexican Oregano Lippia graveolens Kunth on Breast Cancer Cells MDA-MB-231

by Marilyn S. Criollo-Mendoza, José Basilio Heredia, Laura A. Contreras-Angulo, Israel García-Aguiar and Erick Paul Gutiérrez-Grijalva

Appl. Sci. 2025, 15(12), 6393; https://doi.org/10.3390/app15126393 - 6 Jun 2025

Abstract

Some oregano species have been related to antiproliferative activity against various types of cancer cells, such as colon, liver, and breast; this has been mainly associated with their rich content of flavonoid-type compounds due to their ability to induce the activation of intracellular [...] Read more.

Some oregano species have been related to antiproliferative activity against various types of cancer cells, such as colon, liver, and breast; this has been mainly associated with their rich content of flavonoid-type compounds due to their ability to induce the activation of intracellular signaling pathways, such as apoptosis induction. This study aimed to determine the antiproliferative activity mechanism of the polyphenol extract of Mexican oregano (Lippia graveolens Kunth) on MDA-MB-231 breast cancer cells. The flavonoid content with the antiproliferative potential was quantified by ESI-QTOF-MS/MS chromatography, finding naringenin in a higher concentration (7758.71 µg/g extract) compared to the other identified compounds (quercetin, luteolin, and apigenin). Subsequently, the cytotoxicity of the extract was evaluated in the normal human fibroblasts CCD-18Co cell line, where the extract did not present cytotoxic activity at the evaluated concentration (150 µg/mL). In MDA-MB-231 cells treated with the same extract concentration, the activation of proteins associated with apoptosis was observed by western blot. Therefore, the previous antiproliferative activity shown by this extract on breast cancer cells may be due to the activation of this cell death pathway. Thus, the polyphenol extract of Mexican oregano L. graveolens has the potential for future research as an adjuvant in treating breast cancer. Full article

(This article belongs to the Special Issue Advanced Phytochemistry and Its Applications)

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24 pages, 20634 KiB

Open AccessArticle

WarehouseGame Training: A Gamified Logistics Training Platform Integrating ChatGPT, DeepSeek, and Grok for Adaptive Learning

by Juan José Romero Marras, Luis De la Torre and Dictino Chaos García

Appl. Sci. 2025, 15(12), 6392; https://doi.org/10.3390/app15126392 - 6 Jun 2025

Abstract

Modern warehouses play a fundamental role in today’s logistics, serving as strategic hubs for the reception, storage, and distribution of goods. However, training warehouse operators presents a significant challenge due to the complexity of logistics processes and the need for efficient and engaging [...] Read more.

Modern warehouses play a fundamental role in today’s logistics, serving as strategic hubs for the reception, storage, and distribution of goods. However, training warehouse operators presents a significant challenge due to the complexity of logistics processes and the need for efficient and engaging learning methods. Training in logistics operations requires practical experience and the ability to adapt to real-world scenarios, which can result in high training costs. In this context, gamification and artificial intelligence emerge as innovative solutions to enhance training by increasing operator motivation, reducing learning time, and optimizing costs through personalized approaches. But is it possible to effectively apply these techniques to logistics training? This study introduces WarehouseGame Training, a gamified training tool developed in collaboration with Mecalux Software Solutions and implemented in Unity 3D. The solution integrates large language models (LLMs) such as ChatGPT, DeepSeek, and Grok to enhance adaptive learning. These models dynamically adjust challenge difficulty, provide contextual assistance, and evaluate user performance in logistics training scenarios. Through this gamified training tool, the performance of these AI models is analyzed and compared, assessing their ability to improve the learning experience and determine which one best adapts to this type of training. Full article

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18 pages, 3910 KiB

Open AccessArticle

Simulation-Based Assessment of Urban Pollution in Almaty: Influence of Meteorological and Environmental Parameters

by Lyazat Naizabayeva, Kateryna Kolesnikova and Victoriia Khrutba

Appl. Sci. 2025, 15(12), 6391; https://doi.org/10.3390/app15126391 - 6 Jun 2025

Abstract

Background: Air pollution is a persistent and critical challenge for Almaty, Kazakhstan’s largest city. The city’s unique topographical and meteorological conditions—being located in a mountain basin with dense urban development—restrict natural ventilation and contribute to frequent exceedances of air quality standards. These factors [...] Read more.

Background: Air pollution is a persistent and critical challenge for Almaty, Kazakhstan’s largest city. The city’s unique topographical and meteorological conditions—being located in a mountain basin with dense urban development—restrict natural ventilation and contribute to frequent exceedances of air quality standards. These factors make accurate assessment and management of atmospheric pollution particularly urgent for the region. Aim: This study aims to develop and apply a novel, high-resolution three-dimensional numerical model to analyze the spatial distribution of key atmospheric indicators—air velocity, temperature, and pollutant concentrations in Almaty. The goal is to provide a comprehensive understanding of how meteorological and urban factors influence air quality, with a focus on both horizontal and vertical stratification. Methods: A three-dimensional computational model was constructed, integrating real meteorological data and detailed urban topography. The model solves the compressible Navier–Stokes, energy, and pollutant transport equations using the finite volume method over a 1000 × 1000 × 500 m domain. Meteorological fields are synthesized along all spatial axes to account for vortex structures, urban heat islands, and stratification effects. This approach enables the simulation of atmospheric parameters with unprecedented spatial resolution for Almaty. Results: The simulation reveals significant spatial heterogeneity in atmospheric parameters. Wind velocity ranges from 0.31 to 5.76 m/s (mean: 2.14 m/s), temperature varies between 12.03 °C and 19.47 °C (mean: 16.12 °C), and pollutant concentrations fluctuate from 5.02 to 102.35 μg/m³ (mean: 44.87 μg/m³). Notably, pollutant levels in the city center exceed those at the periphery by more than two-fold (68.23 μg/m³, 29.14 μg/m³), and vertical stratification leads to a marked decrease in concentrations with altitude. These findings provide, for the first time, a comprehensive and quantitative picture of air quality dynamics in Almaty. Conclusion: The developed model advances the scientific understanding of urban air pollution in complex terrains and offers practical tools for city planners and policymakers. By identifying pollution hotspots and elucidating the influence of meteorological factors, the model supports the optimization of urban infrastructure, zoning, and environmental monitoring systems. This research lays the groundwork for evidence-based strategies to mitigate air pollution and improve public health in Almaty and similar urban environments. Full article

(This article belongs to the Section Ecology Science and Engineering)

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13 pages, 1275 KiB

Open AccessArticle

Evaluation of the Antimicrobial Capacity of a White Grape Marc Extract Through Gastrointestinal Digestion

by Lorena G. Calvo, María Celeiro, Rosa-Antía Villarino, Ana G. Abril, Sandra Sánchez, José Luis R. Rama and Trinidad de Miguel

Appl. Sci. 2025, 15(12), 6390; https://doi.org/10.3390/app15126390 - 6 Jun 2025

Abstract

Polyphenols are extensively studied for their antimicrobial and prebiotic properties, but concerns about their stability persist. In order to elucidate the antimicrobial stability of such molecules in the gastrointestinal environment and their potential effect as antimicrobials and microbiota modulators, a white grape marc [...] Read more.

Polyphenols are extensively studied for their antimicrobial and prebiotic properties, but concerns about their stability persist. In order to elucidate the antimicrobial stability of such molecules in the gastrointestinal environment and their potential effect as antimicrobials and microbiota modulators, a white grape marc extract from the variety Albariño has been exposed to simulated digestions. In vitro digestions were performed following the INFOGEST protocol and samples were taken after each digestive phase and submitted to bacterial resazurin viability assays. The results reveal that the extract presents a potential antimicrobial effect against foodborne pathogens, such as Staphylococcus aureus, Listeria monocytogenes, Escherichia coli, and Salmonella enterica, which is enhanced during the intestinal phase. Modulation of the bacterial growth at concentrations below 2% (v/v) of the extract against pathogenic bacteria was observed. Although gastrointestinal digestion reduces the extract’s polyphenolic content, with procyanidin and quercetin-3-glucoside identified as the most unstable compounds, cell viability assays confirmed that its antimicrobial efficacy is maintained. In conclusion, the Albariño marc extract demonstrates a promising microbial modulation capacity, which persists during the digestive process despite variations in the polyphenolic composition. Full article

(This article belongs to the Special Issue Advances in Food Safety and Microbial Control)

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12 pages, 2922 KiB

Open AccessArticle

Comparative Experimental Study on the Dynamic and Static Stiffness of Sandy Soils Utilizing Alpan’s Empirical Approach

by Guldem Korkmaz, Sinan Sargin and Sadik Oztoprak

Appl. Sci. 2025, 15(12), 6389; https://doi.org/10.3390/app15126389 - 6 Jun 2025

Abstract

Stiffness parameters are very important and effective in the constitutive models used in finite element analysis. It is not easy or common to obtain these parameters in the laboratory. However, even if the modulus is determined in the small and medium deformation range, [...] Read more.

Stiffness parameters are very important and effective in the constitutive models used in finite element analysis. It is not easy or common to obtain these parameters in the laboratory. However, even if the modulus is determined in the small and medium deformation range, there is a need to make transitions in both static and dynamic parameters. In almost all studies, the Alpan approach is used for the relationship between static and dynamic moduli of elasticity. Therefore, a better understanding of this approach is required. In this study, the relationship between static and dynamic stiffness was determined by monotonic triaxial and resonant column tests on five different sand samples with different relative stiffness and grain distributions, and the results were compared with Alpan’s approach. It is not clear which of the initial or maximum modulus of elasticity (E₀), unloading-reloading modulus (E_ur) or secant modulus of elasticity (E₅₀) are used by Alpan for static modulus of elasticity (E_stat). Therefore, the coefficient R_sec = E_stat/E₅₀ was introduced and queried to indicate which E_stat is a multiple of E₅₀. In connection with this, the dynamic modulus of elasticity (E_dyn) was calculated using the small deformation shear modulus (G₀) obtained from resonant column experiments and assuming Poisson’s ratios (ν = 0.2, 0.3, 0.4). It was found that Alpan’s empirical approach achieved a significant degree of agreement for the sands in this study and the studies of other researchers. It was observed that the best agreement between dynamic and static stiffness ratio (E_dyn/E_stat) and static modulus of elasticity (E_stat) for sand specimens in this study was obtained with υ = 0.2 and R_sec = 2. According to the experimental results, it is safe to say that Alpan’s empirical approach is still valid when the values of Poisson’s ratio and E_stat in the very small deformation region are used. Since there are limited studies on E_dyn/E_stat ratio in the literature, it is thought that the findings in this paper will assist engineers and researchers. However, this work would also assist engineers in selecting appropriate stiffness parameters for calibrating constitutive models. Full article

(This article belongs to the Section Civil Engineering)

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18 pages, 2913 KiB

Open AccessArticle

Occupant Kinematic and Injury Responses in Zero-Gravity Seat Under Low-, Medium-, and High-Speed Rear Impacts with Different Seat Belt Systems

by Wenqiong Tu, Peiwen Zhang, Jing Zhang, Yang Liu, Xin Ye and Xuerong Zhang

Appl. Sci. 2025, 15(12), 6388; https://doi.org/10.3390/app15126388 - 6 Jun 2025

Abstract

This study investigates occupant kinematic and injury responses in zero-gravity seats under rear impacts at 16 km/h, 40 km/h, and 56 km/h and evaluates the protective performance of a conventional three-point seat belt system and a four-point seat belt system. First, a THUMS [...] Read more.

This study investigates occupant kinematic and injury responses in zero-gravity seats under rear impacts at 16 km/h, 40 km/h, and 56 km/h and evaluates the protective performance of a conventional three-point seat belt system and a four-point seat belt system. First, a THUMS (Total Human Model for Safety)-based finite element assembly consisting of a regular seat model and a conventional three-point seat belt system was verified by comparing the kinematic responses and time-history curves of head acceleration, head rotation, and the T1 acceleration of PMHS (Postmortem Human Subject) tests. Then, a THUMS-based finite element assembly in a zero-gravity seat with a three-point seat belt system was created, and computational biomechanical analyses revealed that at low-to-medium impact speeds (16 and 40 km/h), the occupant exhibited backward sliding in the zero-gravity seat along the seatback with lower limb rotation and did not experience head and neck injury. However, a 56 km/h impact induced an excessive seatback rotation and caused the head to become out of position. The neck collided with the upper part of the headrest and caused a surge in the contact force between the neck and the headrest. The head injury and neck injury were comprehensively analyzed via the head injury metrics and neck injury metrics, including cervical spine injury metrics and cervical ligament injury metrics. Further, a four-point seat belt system was adopted and demonstrated better and more balanced restraining effects by reducing the relative displacement between the occupant’s head and chest in the x- and y-directions by 26% and 84%, respectively. Therefore, the occupant’s head remains in position and the collision between the neck and the headrest can be avoided. Maximum reductions in the head and neck injury metrics reached 70% and 57%, respectively. The current study illustrates the disadvantages of the traditional three-point seat belt system in restraining the occupant in a zero-gravity seat under rear impact and shows the four-point seat belt to be a better alternative. This study sheds light on seat belt system design and optimization towards future zero-gravity seats under rear impact. Full article

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Appl. Sci., Volume 15, Issue 12 (June-2 2025) – 40 articles

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