Applied Sciences

17 pages, 1517 KB

Open AccessArticle

Photocatalytic Degradation of Methyl Orange, Eriochrome Black T, and Methylene Blue by Silica–Titania Fibers

by Omar Arturo Aldama-Huerta, Nahum A. Medellín-Castillo, Francisco Carrasco Marín and Simón Yobanny Reyes-López

Appl. Sci. 2025, 15(22), 12084; https://doi.org/10.3390/app152212084 (registering DOI) - 13 Nov 2025

Abstract

The photocatalytic activity of silica–titania (S-T) fibers synthesized via sol–gel and electrospinning was evaluated using methyl orange (MO), eriochrome black T (EB), and methylene blue (MB) as model dyes. Characterization by X-ray diffraction confirmed the presence of anatase and rutile TiO₂ phases, [...] Read more.

The photocatalytic activity of silica–titania (S-T) fibers synthesized via sol–gel and electrospinning was evaluated using methyl orange (MO), eriochrome black T (EB), and methylene blue (MB) as model dyes. Characterization by X-ray diffraction confirmed the presence of anatase and rutile TiO₂ phases, while UV-Vis spectroscopy determined a bandgap energy of 3.2 eV. Scanning electron microscopy revealed fibers with an average diameter of 214 nm. Under UV irradiation, nearly complete dye removal (initial concentration: 30 mg/L; catalyst dosage: 0.1 g/L) was achieved within 8 h. The reaction kinetics followed the Langmuir–Hinshelwood model, with significant differences in apparent reaction rates (k_a) among the dyes, attributable to their distinct structural and functional properties. This study establishes silica–titania fibers as a high-performance, highly versatile composite photocatalyst. Achieving 98% degradation efficiency, their key innovation is their fibrous morphology, which solves the critical problem of powder catalyst recovery. This enables a paradigm shift from simple lab efficiency to practical, sustainable application. Full article

(This article belongs to the Special Issue Applications of Nanoparticles in the Environmental Sciences)

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18 pages, 3079 KB

Open AccessArticle

Prefabricated Reinforced Guide Walls for Mountainous River Locks: Numerical Analysis and Performance Evaluation

by Liguo Wu, Yonglong Li, Xiang Lu and Zhenyu Wu

Appl. Sci. 2025, 15(22), 12083; https://doi.org/10.3390/app152212083 (registering DOI) - 13 Nov 2025

Abstract

In the field of shipping engineering, guide walls serve as core flow-guiding structures for river regulation and waterway maintenance. Their structural stability, construction efficiency, and maintainability directly determine shipping safety and construction costs. Currently, guide walls in mountainous rivers predominantly utilize cast-in-place monolithic [...] Read more.

In the field of shipping engineering, guide walls serve as core flow-guiding structures for river regulation and waterway maintenance. Their structural stability, construction efficiency, and maintainability directly determine shipping safety and construction costs. Currently, guide walls in mountainous rivers predominantly utilize cast-in-place monolithic structures, which suffer from issues such as complicated construction, high cement consumption, and poor adaptability. This study proposes a novel prefabricated reinforced guide wall, consisting of a base plate, prefabricated concrete units, intra-layer bolts, and inter-layer reinforcement bars, and develops a nonlinear numerical framework, integrating contact mechanics, metal plasticity, and finite element analysis to investigate the mechanical behavior of the proposed wall structure under hydraulic loads. The results show that the prefabricated reinforced guide wall exhibits stable stress and deformation responses and maintains reliable inter-layer stability. Benefiting from its hollow prefabricated configuration, which replaces part of the concrete with rockfill, the proposed system substantially reduces cement demand and supports low-carbon and sustainable construction. This study provides both theoretical insights and engineering evidence for the safe, efficient, and sustainable application of prefabricated reinforced guide walls in mountainous river locks. Full article

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19 pages, 780 KB

Open AccessArticle

Speaking Through an Avatar: Emotional Expressiveness, Individual Differences, User Experience and Performance

by David Ponce, Sara Garces-Arilla, Marta Mendez, Magdalena Mendez-Lopez and M.-Carmen Juan

Appl. Sci. 2025, 15(22), 12082; https://doi.org/10.3390/app152212082 (registering DOI) - 13 Nov 2025

Abstract

Emotionally expressive avatars are often used to increase engagement in virtual environments, but their effects on users’ emotional outcomes and experience during evaluative tasks are not well established. This study examined whether differences in avatar emotional expressiveness are associated with affective responses and [...] Read more.

Emotionally expressive avatars are often used to increase engagement in virtual environments, but their effects on users’ emotional outcomes and experience during evaluative tasks are not well established. This study examined whether differences in avatar emotional expressiveness are associated with affective responses and user experience during a socially evaluative speech task in virtual reality (VR), and how individual characteristics and emotional variables relate to performance and user experience. Sixty-three university students were randomly assigned to deliver a five-minute self-presentation, simulating a job interview, in front of a virtual mirror while embodied in either a high-expressive or low-expressive avatar. In the present study, the manipulation of avatar expressiveness was implemented using Meta Quest 2 and Meta Quest Pro headsets, differing mainly in facial-tracking capability. Participants completed a structured three-phase protocol: pre-avatar embodiment (baseline questionnaires), avatar embodiment (speech task), and post-avatar embodiment (post-task measures). Emotional state and trait variables, speech fluency and engagement during the task, and user experience variables were assessed. No significant effects of avatar expressiveness were found on emotional or experiential variables. Correlation analyses revealed a positive association between extraversion and avatar embodiment. These findings contribute to our understanding of the factors that are associated with user experience and behaviour in avatar-based VR environments and suggest that individual traits, such as extraversion, should be considered when designing VR applications for training, education, and therapeutic purposes. Full article

(This article belongs to the Special Issue Advanced Virtual, Augmented, and Mixed Reality: Immersive Applications and Innovative Techniques: 2nd Edition)

29 pages, 3420 KB

Open AccessArticle

Characterization of Upper Extremity Joint Angle Error for Virtual Reality Motion Capture Compared to Infrared Motion Capture

by Skyler A. Barclay, Trent Brown, Tessa M. Hill, Ann Smith, Timothy Reissman, Allison L. Kinney and Megan E. Reissman

Appl. Sci. 2025, 15(22), 12081; https://doi.org/10.3390/app152212081 (registering DOI) - 13 Nov 2025

Abstract

Virtual reality (VR) offers built-in wearable sensor-based tracking capabilities. Current research focusses on position and orientation error, with limited results on more clinically relevant metrics, such as joint angles. This leads us to our first objective, to characterize the accuracy of upper extremity [...] Read more.

Virtual reality (VR) offers built-in wearable sensor-based tracking capabilities. Current research focusses on position and orientation error, with limited results on more clinically relevant metrics, such as joint angles. This leads us to our first objective, to characterize the accuracy of upper extremity VR motion capture. Since the intent is for clinical translation, our second objective is to compare the errors across people identified as healthy controls and people who had experienced a spinal cord injury (SCI). Spatially and temporally synced VR and infrared motion capture data were collected during a variety of custom VR Beat Saber levels. Error values were found with infrared motion capture as the ground truth. The median RMSE was found to be below 7° for shoulder horizontal adduction and elbow flexion and 5° for shoulder elevation and wrist joint metrics. The percentage median error for the range of motion was found to be below 30%, 15%, and 5% for the frontal wrist, sagittal wrist, and all other joints, respectively. Larger standard deviations suggest that repetitions are needed to obtain reliable measurements. No statistical difference in any error metric was found between the control cohort and SCI cohort, providing evidence for clinical translation for post-SCI treatment. Full article

(This article belongs to the Special Issue Virtual Reality in Physical Therapy)

23 pages, 4818 KB

Open AccessArticle

Multispectral-NeRF: A Multispectral Modeling Approach Based on Neural Radiance Fields

by Hong Zhang, Fei Guo, Zihan Xie and Dizhao Yao

Appl. Sci. 2025, 15(22), 12080; https://doi.org/10.3390/app152212080 (registering DOI) - 13 Nov 2025

Abstract

3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems. Traditional 3D reconstruction techniques based on 2D images typically rely on RGB spectral [...] Read more.

3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems. Traditional 3D reconstruction techniques based on 2D images typically rely on RGB spectral information. With advances in sensor technology, additional spectral bands beyond RGB have been increasingly incorporated into 3D reconstruction workflows. Existing methods that integrate these expanded spectral data often suffer from expensive scheme prices, low accuracy, and poor geometric features. Three-dimensional reconstruction based on NeRF can effectively address the various issues in current multispectral 3D reconstruction methods, producing high-precision and high-quality reconstruction results. However, currently, NeRF and some improved models such as NeRFacto are trained on three-band data and cannot take into account the multi-band information. To address this problem, we propose Multispectral-NeRF—an enhanced neural architecture derived from NeRF that can effectively integrate multispectral information. Our technical contributions comprise threefold modifications: Expanding hidden layer dimensionality to accommodate 6-band spectral inputs; redesigning residual functions to optimize spectral discrepancy calculations between reconstructed and reference images; and adapting data compression modules to address the increased bit-depth requirements of multispectral imagery. Experimental results confirm that Multispectral-NeRF successfully processes multi-band spectral features while accurately preserving the original scenes’ spectral characteristics. Full article

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24 pages, 3892 KB

Open AccessArticle

Corrosion and Fracture Localization in Grounding Grids and State Evaluation Based on Analysis of the Evolution of Magnetic Field Distributions

by Jiao Xue, Fei Gao, Zhen Li, Xiaoming Li, Yufeng Yin and Fuqiang Tian

Appl. Sci. 2025, 15(22), 12079; https://doi.org/10.3390/app152212079 (registering DOI) - 13 Nov 2025

Abstract

The grounding grid of a substation is a crucial component for ensuring normal operation. However, since it is buried underground for long periods, it is highly susceptible to electrochemical corrosion. This corrosion leads to a reduction in its grounding performance, and severe corrosion [...] Read more.

The grounding grid of a substation is a crucial component for ensuring normal operation. However, since it is buried underground for long periods, it is highly susceptible to electrochemical corrosion. This corrosion leads to a reduction in its grounding performance, and severe corrosion may endanger the reliable operation of high-voltage equipment and secondary relay-protection equipment, as well as the safety of personnel. In this paper, the electromagnetic field analysis method is used to conduct simulation modeling of the grounding grid. A different-frequency current is injected into the grounding grid to study the variation law of the surface magnetic field distribution when corrosion occurs to different degrees at different positions in the grounding grid. Through the analysis of the evolutionary characteristics of the magnetic field distribution, the corrosion-induced breakages in the grounding grid are located and a comprehensive state evaluation is carried out. The results show that when a fault occurs in a conductor at the same position, the variation amplitude of the surface magnetic field gradually increases with increased corrosion. Based on this finding, an online monitoring algorithm for the location of corrosion-induced breakages and state evaluation of the grounding grid is proposed. A comprehensive evaluation model is constructed by combining the grounding resistance value and corrosion characteristic value to accurately locate the fault. Full article

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

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18 pages, 1267 KB

Open AccessArticle

Estimation of the Soil–Water Retention Curve from the Grain Size Distribution and Relative Density of Coarse-Grained Soils

by Xin Liu, Ruixuan Li, Xi Sun, Jie Li and Xiaonan Wang

Appl. Sci. 2025, 15(22), 12078; https://doi.org/10.3390/app152212078 (registering DOI) - 13 Nov 2025

Abstract

The soil–water retention curve (SWRC) is a fundamental property that governs the hydraulic and mechanical behavior of unsaturated soils. Laboratory SWRC determination remains time-consuming and costly, promoting indirect estimation methods. However, existing methods often oversimplify the pore structure and particle arrangement of soils [...] Read more.

The soil–water retention curve (SWRC) is a fundamental property that governs the hydraulic and mechanical behavior of unsaturated soils. Laboratory SWRC determination remains time-consuming and costly, promoting indirect estimation methods. However, existing methods often oversimplify the pore structure and particle arrangement of soils and neglect the effect of capillary menisci, resulting in discrepancies from natural soil behavior. This study proposes a novel method to estimate the SWRC of coarse-grained soils based on grain size distribution (GSD) and relative density. In the proposed method, soil particles are idealized as spheres in a two-dimensional (2D) plane, and the packing structure is modeled using representative quadrilaterals composed of four poly-disperse particles. The GSD is employed to calculate the probability of different particle sizes occupying the corners of the quadrilateral elements, while the relative density defines their geometric configuration. The water retention behavior is then evaluated using the geometric relationships between the air–water interface and particle radii. The predicted SWRCs are in good agreement with experimental data, indicating that the method can effectively capture the water retention characteristics of coarse-grained soils governed by capillary effects. The method’s applicability is limited to coarse-grained soils and excludes clayey soils where adsorbed water dominates retention mechanisms. Full article

(This article belongs to the Section Civil Engineering)

17 pages, 2930 KB

Open AccessArticle

Dexterous Ungrasping in Three-Dimensional Space: Stability and Planning ^†

by Jungwon Seo

Appl. Sci. 2025, 15(22), 12077; https://doi.org/10.3390/app152212077 (registering DOI) - 13 Nov 2025

Abstract

This work examines the robotic technique of ungrasping, in which an object held by a gripper is intentionally released into the environment. The proposed method achieves controlled object release through non-static contact interactions that permit rolling and sliding. This form of dexterous manipulation [...] Read more.

This work examines the robotic technique of ungrasping, in which an object held by a gripper is intentionally released into the environment. The proposed method achieves controlled object release through non-static contact interactions that permit rolling and sliding. This form of dexterous manipulation is especially important for thin or slender objects, as demonstrated through physical experiments. This study first addresses how three-dimensional stability can be established using a minimal number of contacts. It then introduces a planning framework for three-dimensional ungrasping that extends our prior planar formulation. Experimental results obtained with a two-fingered gripper—the most common gripper type—validate the feasibility, effectiveness, and practicality of the presented approach. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications, 2nd Edition)

27 pages, 7827 KB

Open AccessArticle

Mechanical and Durability Performance of Sustainable Concrete Incorporating Stone Dust as Sand Substitute and Sugarcane Bagasse Ash as Cement Replacement

by Prachoom Khamput, Mahamasuhaimi Masae, Kiatsuda Somna and Tawich Klathae

Appl. Sci. 2025, 15(22), 12076; https://doi.org/10.3390/app152212076 (registering DOI) - 13 Nov 2025

Abstract

This study investigates the mechanical and durability performance of sustainable concrete using stone dust (SD) and ground sugarcane bagasse ash (GSCBA) to partially replace natural sand and cement, respectively. The experimental program was conducted with concrete containing 0–40 wt% GSCBA and 100% SD [...] Read more.

This study investigates the mechanical and durability performance of sustainable concrete using stone dust (SD) and ground sugarcane bagasse ash (GSCBA) to partially replace natural sand and cement, respectively. The experimental program was conducted with concrete containing 0–40 wt% GSCBA and 100% SD were prepared and tested. The results showed that full replacement of natural sand with SD did not significantly affect compressive strength. Concrete containing 10% GSCBA and 100% SD (10GSCBA) exhibited comparable compressive strength to the control concrete (CON) up to 90 days. However, the modulus of elasticity and modulus of rupture decreased slightly with increasing GSCBA content, indicating a close correlation with compressive strength. The mix containing 40% GSCBA and 100% SD (40GSCBA) achieved a compressive strength of 42.6 MPa at 90 days, representing 91% of the CON, with acceptable durability performance. These findings demonstrate that the combined utilization of SD and GSCBA offers an innovative and eco-efficient solution for concrete production, contributing to reduced cement consumption, lower production costs, and minimized carbon emissions without necessarily affecting mechanical strength or the long-term viability of the system. Full article

(This article belongs to the Special Issue Advances in Sustainable and Green Building Materials)

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38 pages, 2097 KB

Open AccessReview

Signal Preprocessing, Decomposition and Feature Extraction Methods in EEG-Based BCIs

by Bandile Mdluli, Philani Khumalo and Rito Clifford Maswanganyi

Appl. Sci. 2025, 15(22), 12075; https://doi.org/10.3390/app152212075 (registering DOI) - 13 Nov 2025

Abstract

Brain–Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices by interpreting brain wave patterns associated with specific motor imagery tasks, which are derived from EEG signals. Although BCIs allow applications such as robotic arm control and smart assistive [...] Read more.

Brain–Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices by interpreting brain wave patterns associated with specific motor imagery tasks, which are derived from EEG signals. Although BCIs allow applications such as robotic arm control and smart assistive environments, they face major challenges, mainly due to the large variation in EEG characteristics between and within individuals. This variability is caused by low signal-to-noise ratio (SNR) due to both physiological and non-physiological artifacts, which severely affect the detection rate (IDR) in BCIs. Advanced multi-stage signal processing pipelines, including efficient filtering and decomposition techniques, have been developed to address these problems. Additionally, numerous feature engineering techniques have been developed to identify highly discriminative features, mainly to enhance IDRs in BCIs. In this review, several pre-processing techniques, including feature extraction algorithms, are critically evaluated using deep learning techniques. The review comparatively discusses methods such as wavelet-based thresholding and independent component analysis (ICA), including empirical mode decomposition (EMD) and its more sophisticated variants, such as Self-Adaptive Multivariate EMD (SA-MEMD) and Ensemble EMD (EEMD). These methods are examined based on machine learning models using SVM, LDA, and deep learning techniques such as CNNs and PCNNs, highlighting key limitations and findings, including different performance metrics. The paper concludes by outlining future directions. Full article

14 pages, 8714 KB

Open AccessArticle

LuCa: A Novel Method for Lung Cancer Delineation

by Mattia Carletti, Giulia Bruschi, MHD Jafar Mortada, Laura Burattini and Agnese Sbrollini

Appl. Sci. 2025, 15(22), 12074; https://doi.org/10.3390/app152212074 (registering DOI) - 13 Nov 2025

Abstract

Lung cancer remains the leading cause of cancer-related deaths worldwide, with over 2.4 million new diagnoses in 2022. Early diagnosis remains challenging due to the non-specificity of symptoms, often resulting in late-stage detection. Although 2-D and 3-D medical imaging, particularly computed tomography (CT), [...] Read more.

Lung cancer remains the leading cause of cancer-related deaths worldwide, with over 2.4 million new diagnoses in 2022. Early diagnosis remains challenging due to the non-specificity of symptoms, often resulting in late-stage detection. Although 2-D and 3-D medical imaging, particularly computed tomography (CT), is widely used for detecting lung cancer, it is associated with manual segmentation, which remains time-consuming and user-dependent. This study proposes LuCa as an innovative 2.5-D deep learning model for lung cancer delineation, which combines the benefits of 2-D segmentation with 3-D volume delineation. The main novelty of LuCa is focused on its pipeline, specifically designed to be of clinical use, in order to guarantee the usability of the method. LuCa employs a U-Net architecture for segmentation, followed by a post-image-processing step for 3-D tumor volume delineation and false-positive correction. The method was trained and evaluated using the “NSCLC-Radiomics” database, comprising CT images of 422 non-small cell lung cancer patients, with clinical manual tumor annotations as ground truth. The model achieved strong performance, with high dice coefficients (87 ± 12%), intersection over union (81 ± 17%), sensitivity (84 ± 16%), and positive predictive value (94 ± 10%) on the test set. Performance was particularly high for larger tumors, reflecting the ability of the model to delineate more visible lesions accurately. Statistical analysis confirmed the high correlation and minimal error between predicted and ground truth tumor volumes. The results highlight the potential of the 2.5-D approach to improve clinical efficiency by enabling accurate tumor segmentation with reduced computational cost, compared to traditional 3-D methods. Future research will focus on assessing the use of LuCa as real-time clinical decision support, particularly for assessing tumors during treatment. Full article

(This article belongs to the Special Issue Deep Learning and Data Mining: Latest Advances and Applications)

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31 pages, 787 KB

Open AccessArticle

Interpretable Ensemble Learning Models for Credit Card Fraud Detection

by Saria Iqbal, Khalid Mahmood Awan, Shahid Kamal and Zahoor Ur Rehman

Appl. Sci. 2025, 15(22), 12073; https://doi.org/10.3390/app152212073 (registering DOI) - 13 Nov 2025

Abstract

With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting [...] Read more.

With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting fraudulent transactions in financial sectors, such as banking, is a major issue because existing fraud detection methods are rule-based and unable to detect unknown patterns. The tactics and techniques used by fraudsters are far more advanced than they are, making machine learning (ML) a valuable approach to improve detection efficiency. While numerous studies have explored machine learning models for credit card fraud detection, most have prioritized accuracy metrics alone, offering little attention to how or why models make decisions. This lack of interpretability creates barriers for financial institutions, where regulatory compliance and user trust are critical. In particular, the systematic application of explainable AI (XAI) techniques such as SHAP and LIME to fraud detection remains scarce. This study addresses this gap by combining high-performing ensemble models (Random Forest and XGBoost) with advanced interpretability methods (SHAP and LIME), providing both strong predictive performance and transparent feature-level explanations. Such integration not only improves fraud detection but also strengthens the trustworthiness and deployability of AI systems in real-world financial contexts. A real-world credit card dataset is used to evaluate both models, and experimental results show that Random Forest achieved higher precision (89.09%) and F1 score (0.9159), while XGBoost yielded better recall (95.56%) and ROC AUC (0.9997). To address the crucial need for interpretability, SHAP and LIME analyses were applied, revealing the most influential features behind model predictions and enhancing transparency in decision-making. Overall, this study demonstrates the potential of integrating explainable artificial intelligence (XAI) into fraud detection systems, thereby enhancing trust and reliability in financial institutions. Full article

38 pages, 1249 KB

Open AccessArticle

A Composite Index to Identify Appropriate Locations for Rural Community Renewable Energy Projects

by Noelia Romero-Castro, Vanessa Miramontes-Viña, M. Ángeles López-Cabarcos and Helena Santos-Rodrigues

Appl. Sci. 2025, 15(22), 12072; https://doi.org/10.3390/app152212072 - 13 Nov 2025

Abstract

This study develops a composite index to identify and prioritize the most suitable geographical locations for rural Community Renewable Energy (CRE) projects. CRE is central to the sustainable energy transition, but its strategic deployment in rural areas is challenging due to uneven development, [...] Read more.

This study develops a composite index to identify and prioritize the most suitable geographical locations for rural Community Renewable Energy (CRE) projects. CRE is central to the sustainable energy transition, but its strategic deployment in rural areas is challenging due to uneven development, the necessity of coordinating diverse resources, and the need for governments to guarantee the prudent use of scarce funds. Framed under the Resource Mobilization Theory, the proposed index helps mitigate these uncertainties by providing a structure for site evaluation. Although the empirical application is performed in a specific Portuguese region, the methodological approach is explicitly designed to be transferable to other national and regional contexts. The index provides significant practical implications for CRE promoters, investors, and spatial planners, offering a transparent, clear-cut tool to define targets and optimize resource allocation. Furthermore, this study contributes to rural development literature by merging entrepreneurship and renewable energy fields, demonstrating how local CRE can effectively leverage available resources to deliver both private and community benefits. Full article

(This article belongs to the Special Issue Energy Transition in Sustainable Buildings)

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15 pages, 1153 KB

Open AccessArticle

Investigation of Antioxidant Capacity, Chemical Composition, and Sensory Characteristics Using Camu-Camu Powder in the Production of Fresh Cow’s Cheese

by Mihaela Adriana Tița, Maria Adelina Constantinescu, Cecilia Georgescu, Adriana Maria Canciu, Maria Lidia Iancu and Ovidiu Tița

Appl. Sci. 2025, 15(22), 12071; https://doi.org/10.3390/app152212071 - 13 Nov 2025

Abstract

(1) Background: Research into incorporating plant powders into dairy products is growing because they significantly increase the nutritional value of the finished products, making them a more attractive option for consumers seeking healthier alternatives. The objective of this study is to develop a [...] Read more.

(1) Background: Research into incorporating plant powders into dairy products is growing because they significantly increase the nutritional value of the finished products, making them a more attractive option for consumers seeking healthier alternatives. The objective of this study is to develop a novel dairy product by incorporating camu-camu powder into fresh cow’s cheese. The material has been identified as a promising candidate due to its multiple health benefits and high antioxidant content, particularly vitamin C; (2) Methods: The stability of the product during storage was therefore evaluated by analysing its acidity, pH, dry matter content, water activity, syneresis and water holding capacity. The impact of camu-camu powder on the antioxidant activity of the cheese samples was determined using the DPPH method. A sensory evaluation was conducted to ascertain the potential functional properties and consumer acceptability of the subject; (3) Results: The bioactive compounds present in the powder have been shown to enhance the antioxidant capacity of fresh cheese, with the 2% sample demonstrating the most effective antioxidant performance. From a sensory perspective, the 1.5% sample received the highest ratings from tasters. The 1% sample is distinguished by its notable colour stability during storage. Physicochemical analysis shows that camu-camu powder is a sustainable ingredient that improves the quality and extends the shelf life of the finished product; (4) Conclusions: This information is indispensable for the evaluation of the safety and efficacy of camu-camu powder in dairy products. Moreover, it may serve as a point of departure for future studies involving the development of other food products with bioactive compounds using unconventional raw materials. Full article

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25 pages, 5567 KB

Open AccessArticle

Monitoring and Prediction of Deformation and Failure of Roadway Surrounding Rock Based on Binocular Vision and Random Forest

by Pengfei Shan, Long Zhang, Chengwei Yan, Huicong Xu, Zheng Meng, Bojia Xi and Gang Xu

Appl. Sci. 2025, 15(22), 12070; https://doi.org/10.3390/app152212070 - 13 Nov 2025

Abstract

The deformation and failure of surrounding rock in underground roadways are governed by complex mechanical interactions and environmental factors, yet the fundamental scientific patterns behind these processes remain unclear. This lack of real-time, data-driven understanding limits the development of intelligent monitoring and prediction [...] Read more.

The deformation and failure of surrounding rock in underground roadways are governed by complex mechanical interactions and environmental factors, yet the fundamental scientific patterns behind these processes remain unclear. This lack of real-time, data-driven understanding limits the development of intelligent monitoring and prediction systems in mining engineering. To address this challenge, this study aims to establish an intelligent system for the dynamic monitoring and prediction of roadway surrounding rock deformation based on binocular vision and machine learning. An improved Semi-Global Block Matching (SGBM) algorithm is developed for real-time 3D deformation measurement, while a physical similarity model is constructed to visualize the deformation–failure evolution. The Random Forest (RF) algorithm is employed for deep deformation prediction, and its optimal parameters are determined by minimizing the mean square error. Experimental results show that the average measurement errors of the binocular vision method are 1.22 mm and 0.92 mm, outperforming total station monitoring. The gradient-enhanced Random Forest (GERF) model achieves RMSE values of 0.0164 and 0.0113, with R² values of 0.8856 and 0.8356, respectively. Compared with AdaBoost, XGBoost, and Vision Transformer models, GERF improves predictive accuracy by 7.82%, 8.68%, and 3.87%, respectively. These findings demonstrate the scientific feasibility and technical advantage of the proposed intelligent system, offering a new approach to understanding and predicting roadway deformation and failure in intelligent mining. Full article

(This article belongs to the Special Issue Advances and Techniques in Rock Fracture Mechanics)

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18 pages, 3296 KB

Open AccessArticle

Automatic Baseline Correction of 1D Signals Using a Parameter-Free Deep Convolutional Autoencoder Algorithm

by Łukasz Górski and Małgorzata Jakubowska

Appl. Sci. 2025, 15(22), 12069; https://doi.org/10.3390/app152212069 - 13 Nov 2025

Abstract

Baseline correction techniques are highly applicable in analytical chemistry. Consequently, there is a constant demand for universal and automated baseline correction methods. Our new procedure, based on the Convolutional Autoencoder (ConvAuto) model and combined with an automated implementation algorithm (ApplyModel [...] Read more.

Baseline correction techniques are highly applicable in analytical chemistry. Consequently, there is a constant demand for universal and automated baseline correction methods. Our new procedure, based on the Convolutional Autoencoder (ConvAuto) model and combined with an automated implementation algorithm (ApplyModel procedure), meets these expectations. The key advantage of this approach is its ability to handle 1D signals of various lengths and resolutions, which is a common limitation encountered in deep neural network models. The proposed procedure is fully automatic and does not require any parameter optimization. As our experiments show, the ApplyModel procedure can also be easily combined with other baseline correction methods that utilize deep neural networks, such as the ResUNet model, which also extends its practical applicability. The usability of our new approach was tested by implementing it for both simulated and experimental signals, ranging from 200 to 4000 points in length. For complex signals characterized by multiple peaks and a nonlinear background, the ConvAuto model achieved an RMSE of 0.0263, compared to 1.7957 for the ResUNet model. In the determination of Pb(II) in a certified reference material, a recovery of 89.6% was obtained, which was 1% higher than that achieved with the ResUNet model. Full article

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16 pages, 9022 KB

Open AccessArticle

Influence of Ground Conditions on Vibration Propagation and Response Under Accidental Impact Loads

by Jae-Kwang Ahn, Yong-Gook Lee, Sang-Rae Lee, Mintaek Yoo, Cheolwoo Park and Jae Sang Moon

Appl. Sci. 2025, 15(22), 12068; https://doi.org/10.3390/app152212068 - 13 Nov 2025

Abstract

Vibrations of unknown origin can cause fear and confusion when their sources are unrecognized. In modern construction environments, such vibrations may result not only from earthquakes but also from accidental impacts during industrial operations. However, due to the absence of established safety standards, [...] Read more.

Vibrations of unknown origin can cause fear and confusion when their sources are unrecognized. In modern construction environments, such vibrations may result not only from earthquakes but also from accidental impacts during industrial operations. However, due to the absence of established safety standards, evaluating and compensating for the effects of short-duration, high-intensity vibrations has remained difficult. This study investigates the characteristics of ground motions induced by accidental impact loads through finite element-based numerical simulations. The analyses identify key factors that control vibration propagation under various subsurface conditions. The results show that an impact load produces a single impulsive motion dominated by a vertical component, which decays exponentially with time. The amplitude of vibration increases with drop height and girder mass, confirming the relationship between potential energy and vibration intensity. The attenuation of peak particle velocity (PPV) follows a logarithmic pattern with distance, and the variation in attenuation depends on soil thickness and the presence of a weathered-rock layer. These results demonstrate that both the magnitude of impact and the ground composition control the amplitude, frequency content, and duration of impact-induced vibrations, providing a basis for assessing unmonitored accidental events. Full article

(This article belongs to the Special Issue Soil Dynamics and Earthquake Engineering)

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18 pages, 247 KB

Open AccessArticle

Artificial Intelligence and Formative and Shared Assessment in Teacher Education

by T. Fuentes-Nieto, J. L. Aparicio-Herguedas and V. M. López-Pastor

Appl. Sci. 2025, 15(22), 12067; https://doi.org/10.3390/app152212067 - 13 Nov 2025

Abstract

(1) AI is increasingly being used in teacher education (TE), but there seem to be gaps and deficits regarding how to make good use of AI to enhance learning processes. One possibility comes from the integration of AI in formative and shared assessment [...] Read more.

(1) AI is increasingly being used in teacher education (TE), but there seem to be gaps and deficits regarding how to make good use of AI to enhance learning processes. One possibility comes from the integration of AI in formative and shared assessment (F&SA) processes. Therefore, the purpose of this study was to analyse the results found after the implementation of learning activities that combined the use of AI and F&SA. (2) Methods: A double case study was carried out on two learning tasks applied in TE. The data collection techniques were as follows: activity reports (students), teacher observations, dialogical interviews, focus groups and students’ reflective video diaries. Quantitative and qualitative data were combined. (3) Results: In relation to Case 1, the results show that there was a considerable variety of students’ competences in using AI and that the students managed to improve some aspects of their theoretical frameworks with AI (writing, links between paragraphs and generating new relationships between the topics covered). In addition, the main learnings pointed out by the students were as follows: to be aware of the need to review what AI offers slowly, as it may contain important mistakes, and that using it well can help them to improve their work. In relation to Case 2, the integration of an AI simulator in an F&SA protocol improved the students’ communication skills and the formative power of the assessment protocol created was proven; the students found the integrated AI simulator, the complementary feedback from the teacher and the self-assessment carried out through reflective video diaries on the experience useful. (4) Conclusions: The proper integration of AI with F&SA processes seems to favour the learning and competences of TE students, but teachers are shown to be a key part in making them literate in the appropriate use of AI. More research seems to be needed on this topic. Full article

(This article belongs to the Special Issue Recent Developments in E-learning: Learning and Teaching AI, Learning and Teaching with AI)

15 pages, 1410 KB

Open AccessArticle

Railway Transition Curves Curvature—Should It Be Smooth in the Extreme Points or Not, or Something Else?

by Krzysztof Zboinski and Piotr Woznica

Appl. Sci. 2025, 15(22), 12066; https://doi.org/10.3390/app152212066 - 13 Nov 2025

Abstract

This work addresses the features of railway transition curves’ curvature, especially at extreme points. In particular, should it be smooth at the extreme points or not, or something else? Such a question is not accidental. This is based on the main results that [...] Read more.

This work addresses the features of railway transition curves’ curvature, especially at extreme points. In particular, should it be smooth at the extreme points or not, or something else? Such a question is not accidental. This is based on the main results that the present authors obtained while optimizing the shape of polynomial railway transition curves. It appeared for the same quality function that, depending on the transition curve degree, the optimum shapes represented curvatures either possessing bends or close to smooth at the extreme points. Such a discrepancy raises the question of its reason, i.e., the factor influencing the appearance of bends at the extreme points of the curvatures the most. The continuity of G₀ and G₁ at such points was considered. At the same time, the hypothesis was formulated that a long time taken while negotiating the curve is the most influential factor of the existence of the mentioned bends in the extremities of curvature. Two cases were considered to ensure the long time of vehicle passage through the curve, with a great curve length and a small vehicle velocity, respectively. To verify the hypothesis, the optimizations of the shape of the transition curves of 5th, 9th, and 11th degrees and the simulations of the railway vehicle behaviour were performed. The hypothesis turned out to be true, although easier in application to long transition curves. It was shown that the transition curves’ shapes obtained in assumed circumstances did not have bends at the extreme points. The tendency to smoothen the curvature can be univocally noticed. It resulted in calmer vehicle movement, expressed by vehicle body lateral dynamical characteristics. Corresponding results of the simulations and transition curve optimizations were presented and compared. Full article

(This article belongs to the Special Issue Research Advances in Rail Transport Infrastructure)

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