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The rise in online learning, accelerated by the COVID-19 pandemic, has led to greater use of synchronous hybrid learning (SHL) in higher education. SHL allows simultaneous teaching of in-person and online learners through videoconferencing tools. Previous studies have identified various benefits (e.g., flexibility) and challenges (e.g., student engagement) to SHL. Whilst systematic reviews have emerged on this topic, few studies have considered the experiences of staff. The aim of this review was threefold: (i) to better understand how staff experiences and perceptions are assessed, (ii) to understand staff experiences in terms of the benefits and challenges of SHL and (iii) to identify recommendations for effective teaching and learning using SHL. In line with the PRISMA guidance, we conducted a systematic review across four databases, identifying 14 studies for inclusion. Studies were conducted in nine different countries and covered a range of academic disciplines. Most studies adopted qualitative methods, with small sample sizes. Measures used were typically novel and unvalidated. Four themes were identified relating to (i) technology, (ii) redesigning teaching and learning, (iii) student engagement and (iv) staff workload. In terms of recommendations, ensuring adequate staff training and ongoing classroom support were considered essential. Additionally, active and collaborative learning were considered important to address issues with interactivity. Whilst these findings largely aligned with previous work, this review also identified limited reporting in research in this area, and future studies are needed to address this. Full article

(This article belongs to the Section Higher Education)

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24 pages, 1376 KiB

Open AccessArticle

Smart Agriculture in Ecuador: Adoption of IoT Technologies by Farmers in Guayas to Improve Agricultural Yields

by Ruth Rubí Peña-Holguín, Carlos Andrés Vaca-Coronel, Ruth María Farías-Lema, Sonnia Valeria Zapatier-Castro and Juan Diego Valenzuela-Cobos

Agriculture 2025, 15(15), 1679; https://doi.org/10.3390/agriculture15151679 (registering DOI) - 2 Aug 2025

Abstract

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

(This article belongs to the Special Issue Internet of Things and Wireless Sensor Networks for Smart Vegetable Growing)

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18 pages, 5178 KiB

Open AccessArticle

Quantification of Suspended Sediment Concentration Using Laboratory Experimental Data and Machine Learning Model

by Sathvik Reddy Nookala, Jennifer G. Duan, Kun Qi, Jason Pacheco and Sen He

Water 2025, 17(15), 2301; https://doi.org/10.3390/w17152301 (registering DOI) - 2 Aug 2025

Abstract

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)

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15 pages, 1361 KiB

Open AccessArticle

Radiomics with Clinical Data and [¹⁸F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study

by Gijs D. van Praagh, Francine Vos, Stijn Legtenberg, Marjan Wouthuyzen-Bakker, Ilse J. E. Kouijzer, Erik H. J. G. Aarntzen, Jean-Paul P. M. de Vries, Riemer H. J. A. Slart, Lejla Alic, Bhanu Sinha and Ben R. Saleem

Diagnostics 2025, 15(15), 1944; https://doi.org/10.3390/diagnostics15151944 (registering DOI) - 2 Aug 2025

Abstract

Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [¹⁸F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on pre-treatment criteria to diagnose a vascular graft infection (“MAGIC-light features”), another using radiomics features from diagnostic [¹⁸F]FDG-PET scans, and a third combining both datasets. The training set included 92 patients (72 iVGEI-positive, 20 iVGEI-negative), and the external test set included 20 iVGEI-positive and 12 iVGEI-negative patients. The abdominal aorta and iliac arteries in the PET/CT scans were automatically segmented using SEQUOIA and TotalSegmentator and manually adjusted, extracting 96 radiomics features. The best-performing models for the MAGIC-light features and PET-radiomics features were selected from 343 unique models. Most relevant features were combined to test three final models using ROC analysis, accuracy, sensitivity, and specificity. Results: The combined model achieved the highest AUC in the test set (mean ± SD: 0.91 ± 0.02) compared with the MAGIC-light-only model (0.85 ± 0.06) and the PET-radiomics model (0.73 ± 0.03). The combined model also achieved a higher accuracy (0.91 vs. 0.82) than the diagnosis based on all the MAGIC criteria and a comparable sensitivity and specificity (0.70 and 1.00 vs. 0.76 and 0.92, respectively) while providing diagnostic information at the initial presentation. The AUC for the combined model was significantly higher than the PET-radiomics model (p = 0.02 in the bootstrap test), while other comparisons were not statistically significant. Conclusions: This study demonstrated the potential of ML models in supporting diagnostic decision making for iVGEI. A combined model using pre-treatment clinical features and PET-radiomics features showed high diagnostic performance and specificity, potentially reducing overtreatment and enhancing patient outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Radiomics in Medical Diagnosis)

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30 pages, 3080 KiB

Open AccessArticle

Unsupervised Multimodal Community Detection Algorithm in Complex Network Based on Fractal Iteration

by Hui Deng, Yanchao Huang, Jian Wang, Yanmei Hu and Biao Cai

Fractal Fract. 2025, 9(8), 507; https://doi.org/10.3390/fractalfract9080507 (registering DOI) - 2 Aug 2025

Abstract

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

14 pages, 392 KiB

Open AccessArticle

Development of Asymmetrical, Symmetrical Tonic Neck Reflex Test and Tonic Labyrinth Reflex Test (TASHUN) for the Assessment of Neurotypical Children: Validity and Reliability

by Ágnes Virág Nagy, Ferenc Rárosi, Mihály Domokos and Márta Wilhelm

Appl. Sci. 2025, 15(15), 8601; https://doi.org/10.3390/app15158601 (registering DOI) - 2 Aug 2025

Abstract

The ongoing secular changes in human movement development means that an assessment of primitive reflexes is now required not only in disabled but also in neurotypical children. This study had three aims: (1) presenting the TASHUN test battery as suitable for the assessment of primitive reflex activity in normal children and child athletes; (2) analyzing reflex characteristics of neurotypical children; (3) verifying validity and reliability of tests. Spearman’s rank correlation and ROC analysis were used for validation. Intraclass Correlation Coefficient and RM ANOVA analyzed reliability. The test on 242 schoolgirls has demonstrated that retained primitive reflexes are present in almost every individual (84.7–95.7%). Correlations showed strong positive association, with all values exceeding 0.8, and ROC analysis demonstrated excellent predictive strength (AUC values over 0.9). Interobserver reliability showed excellent agreement (ICC values above 0.9). No significant offset was present among the scoring by evaluators. Therefore, testing for primitive reflexes is necessary in neurotypical children in order to obtain a realistic image about the physiology of reflexes and their role in motor development. Our screening could be useful for practicing sport professionals, researchers and academics, to identify deficiencies, to further explore reflexes and to train future PE teachers and trainers. Full article

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25 pages, 28131 KiB

Open AccessArticle

Landslide Susceptibility Assessment in Ya’an Based on Coupling of GWR and TabNet

by Jiatian Li, Ruirui Wang, Wei Shi, Le Yang, Jiahao Wei, Fei Liu and Kaiwei Xiong

Remote Sens. 2025, 17(15), 2678; https://doi.org/10.3390/rs17152678 (registering DOI) - 2 Aug 2025

Abstract

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article

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31 pages, 3464 KiB

Open AccessArticle

An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent

by Serhii Semenov, Oleksii Kolomiitsev, Mykhailo Hulevych, Patryk Mazurek and Olena Chernyk

Appl. Sci. 2025, 15(15), 8596; https://doi.org/10.3390/app15158596 (registering DOI) - 2 Aug 2025

Abstract

Ensuring software quality during development requires effective regression testing. However, test suites in open-source libraries often grow large, redundant, and difficult to maintain. Most traditional test suite optimization methods treat test cases as atomic units, without analyzing the utility of individual instructions. This paper presents an intelligent method for test case synthesis using a Q-learning agent. The agent learns to construct compact test cases by interacting with an execution environment and receives rewards based on branch coverage improvements and simultaneous reductions in test case length. The training process includes a pretraining phase that transfers knowledge from the original test suite, followed by adaptive learning episodes on individual test cases. As a result, the method requires no formal documentation or API specifications and uses only execution traces of the original test cases. An explicit synthesis algorithm constructs new test cases by selecting API calls from a learned policy encoded in a Q-table. Experiments were conducted on two open-source C++ libraries of differing API complexity and original test suite size. The results show that the proposed method can reach up to 67% test suite reduction while preserving branch coverage, confirming its effectiveness for regression test suite minimization in resource-constrained or specification-limited environments. Full article

(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)

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14 pages, 841 KiB

Open AccessArticle

Enhanced Deep Learning for Robust Stress Classification in Sows from Facial Images

by Syed U. Yunas, Ajmal Shahbaz, Emma M. Baxter, Mark F. Hansen, Melvyn L. Smith and Lyndon N. Smith

Agriculture 2025, 15(15), 1675; https://doi.org/10.3390/agriculture15151675 (registering DOI) - 2 Aug 2025

Abstract

Stress in pigs poses significant challenges to animal welfare and productivity in modern pig farming, contributing to increased antimicrobial use and the rise of antimicrobial resistance (AMR). This study involves stress classification in pregnant sows by exploring five deep learning models: ConvNeXt, EfficientNet_V2, MobileNet_V3, RegNet, and Vision Transformer (ViT). These models are used for stress detection from facial images, leveraging an expanded dataset. A facial image dataset of sows was collected at Scotland’s Rural College (SRUC) and the images were categorized into primiparous Low-Stressed (LS) and High-Stress (HS) groups based on expert behavioural assessments and cortisol level analysis. The selected deep learning models were then trained on this enriched dataset and their performance was evaluated using cross-validation on unseen data. The Vision Transformer (ViT) model outperformed the others across the dataset of annotated facial images, achieving an average accuracy of 0.75, an F1 score of 0.78 for high-stress detection, and consistent batch-level performance (up to 0.88 F1 score). These findings highlight the efficacy of transformer-based models for automated stress detection in sows, supporting early intervention strategies to enhance welfare, optimize productivity, and mitigate AMR risks in livestock production. Full article

(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)

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22 pages, 2498 KiB

Open AccessArticle

SceEmoNet: A Sentiment Analysis Model with Scene Construction Capability

by Yi Liang, Dongfang Han, Zhenzhen He, Bo Kong and Shuanglin Wen

Appl. Sci. 2025, 15(15), 8588; https://doi.org/10.3390/app15158588 (registering DOI) - 2 Aug 2025

Abstract

How do humans analyze the sentiments embedded in text? When attempting to analyze a text, humans construct a “scene” in their minds through imagination based on the text, generating a vague image. They then synthesize the text and the mental image to derive the final analysis result. However, current sentiment analysis models lack such imagination; they can only analyze based on existing information in the text, which limits their classification accuracy. To address this issue, we propose the SceEmoNet model. This model endows text classification models with imagination through Stable diffusion, enabling the model to generate corresponding visual scenes from input text, thus introducing a new modality of visual information. We then use the Contrastive Language-Image Pre-training (CLIP) model, a multimodal feature extraction model, to extract aligned features from different modalities, preventing significant feature differences caused by data heterogeneity. Finally, we fuse information from different modalities using late fusion to obtain the final classification result. Experiments on six datasets with different classification tasks show improvements of 9.57%, 3.87%, 3.63%, 3.14%, 0.77%, and 0.28%, respectively. Additionally, we set up experiments to deeply analyze the model’s advantages and limitations, providing a new technical path for follow-up research. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications of Emotion Recognition)

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14 pages, 588 KiB

Open AccessSystematic Review

Muslim Women Inmates and Religious Practices: What Are Possible Solutions?

by Maria Garro

Healthcare 2025, 13(15), 1890; https://doi.org/10.3390/healthcare13151890 (registering DOI) - 2 Aug 2025

Abstract

Background/Objectives: Despite legal frameworks acknowledging the need to protect the rights of female prisoners, penitentiary systems often neglect gender-specific needs, particularly for foreign women. Among them, Muslim women face distinct challenges linked to cultural and religious practices, which are frequently unmet in prison contexts. This review aims to explore the academic literature on the experiences of Muslim women in detention. Methods: A systematic review was conducted using three major bibliographic databases—Scopus, PubMed, and Web of Science—covering the period from 2010 to 2024. Inclusion criteria focused on peer-reviewed studies examining the condition of Muslim women in prison. Of the initial pool, only four articles met the criteria and were included in the final analysis. Results: The review reveals a marked scarcity of research on Muslim women in prison at both national and international levels. This gap may be due to their limited representation or cultural factors that hinder open discourse. The selected studies highlight key issues, including restricted access to services, limited ability to practice religion, and language and cultural barriers. These challenges contribute to increased psychological vulnerability, which is often underestimated in prison settings. Conclusions: There is an urgent need for targeted research and culturally competent training for prison staff to adequately support Muslim women in detention. Greater academic and institutional attention is essential to develop inclusive policies that consider the intersection of gender, religion, and migration, particularly in the post-release reintegration process. Full article

(This article belongs to the Section Women's Health Care)

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18 pages, 6891 KiB

Open AccessArticle

Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry

by Siqi Liu, Ruina Li, Jiayi Zhou, Chaoyuan Dai, Jingui Yu and Qiaoxin Zhang

Appl. Sci. 2025, 15(15), 8587; https://doi.org/10.3390/app15158587 (registering DOI) - 2 Aug 2025

Abstract

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

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27 pages, 2496 KiB

Open AccessArticle

A Context-Aware Tourism Recommender System Using a Hybrid Method Combining Deep Learning and Ontology-Based Knowledge

by Marco Flórez, Eduardo Carrillo, Francisco Mendes and José Carreño

J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 194; https://doi.org/10.3390/jtaer20030194 (registering DOI) - 2 Aug 2025

Abstract

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

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17 pages, 3061 KiB

Open AccessArticle

Model-Agnostic Meta-Learning in Predicting Tunneling-Induced Surface Ground Deformation

by Wei He, Guan-Bin Chen, Wenlian Qian, Wen-Li Chen, Liang Tang and Xiangxun Kong

Symmetry 2025, 17(8), 1220; https://doi.org/10.3390/sym17081220 (registering DOI) - 2 Aug 2025

Abstract

The present investigation presents the field measurement and prediction of tunneling-induced surface ground settlement in Tianjin Metro Line 7, China. The cross-section of a metro tunnel exhibits circular symmetry, thereby making it suitable for tunneling with a circular shield machine. The ground surface may deform during the tunneling stage. In the early stage of tunneling, few measurement data can be collected. To obtain a better usable prediction model, two kinds of neural networks according to the model-agnostic meta-learning (MAML) scheme are presented. One kind of deep learning strategy is a combination of the Back-Propagation Neural Network (BPNN) and the MAML model, named MAML-BPNN. The other prediction model is a mixture of the MAML model and the Long Short-Term Memory (LSTM) model, named MAML-LSTM. Founded on several measurement datasets, the prediction models of the MAML-BPNN and MAML-LSTM are successfully trained. The results show the present models possess good prediction ability for tunneling-induced surface ground settlement. Based on the coefficient of determination, the prediction result using MAML-LSTM is superior to that of MAML-BPNN by 0.1. Full article

(This article belongs to the Special Issue Symmetry, Asymmetry and Other Patterns in Structural Engineering with Its Application)

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18 pages, 7965 KiB

Open AccessArticle

Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram

by Qianlong Ding, Shuangquan Chen, Jinsong Shen and Borui Wang

Appl. Sci. 2025, 15(15), 8586; https://doi.org/10.3390/app15158586 (registering DOI) - 1 Aug 2025

Abstract

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows. Full article

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