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The timely detection of falls among the elderly remains challenging. Single modality sensing approaches using inertial measurement units (IMUs) or vision-based monitoring systems frequently exhibit high false positives and compromised accuracy under suboptimal operating conditions. We propose a novel bimodal deep learning-based bimodal sensing framework to address the problem, by leveraging a memory-based autoencoder neural network for inertial abnormality detection and an attention-based neural network for visual pose assessment, with late fusion at the decision level. Our experimental evaluation with a custom dataset of simulated falls and routine activities, captured with waist-mounted IMUs and RGB cameras under dim lighting, shows significant performance improvement by the described bimodal late-fusion system, with an F1-score of 97.3% and, most notably, a false-positive rate of 3.6% significantly lower than the 11.3% and 8.9% with IMU-only and vision-only baselines, respectively. These results confirm the robustness of the described fall detection approach and validate its applicability to real-time fall detection under different light settings, including nighttime conditions. Full article

(This article belongs to the Special Issue Sensor-Based Human Activity Recognition)

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15 pages, 1081 KB

Open AccessArticle

Digital Tools for Decision Support in Social Rehabilitation

by Valeriya Gribova and Elena Shalfeeva

J. Pers. Med. 2025, 15(10), 468; https://doi.org/10.3390/jpm15100468 (registering DOI) - 1 Oct 2025

Abstract

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning. Full article

(This article belongs to the Section Personalized Medical Care)

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24 pages, 22010 KB

Open AccessArticle

Improving the Temporal Resolution of Land Surface Temperature Using Machine and Deep Learning Models

by Mohsen Niroomand, Parham Pahlavani, Behnaz Bigdeli and Omid Ghorbanzadeh

Geomatics 2025, 5(4), 50; https://doi.org/10.3390/geomatics5040050 (registering DOI) - 1 Oct 2025

Abstract

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

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20 pages, 284 KB

Open AccessArticle

A Comparison of Chinese and Korean Older Adult Immigrants’ Transnational Healthcare Practices in Toronto, Canada: A Mixed-Methods Study

by Leah Czukar, Lu Wang, Sepali Guruge, Janet Lum and Meira Greenbaum

Healthcare 2025, 13(19), 2493; https://doi.org/10.3390/healthcare13192493 (registering DOI) - 1 Oct 2025

Abstract

Background/Objectives: While immigrants represent 21% of Canada’s total population, they represent 30% of the country’s older population. Sociocultural and economic barriers to the Canadian healthcare system have been frequently reported among older adult immigrants. These barriers are intricately linked to a vastly understudied phenomenon-transnational health practices (THP), which may involve travelling to home countries for healthcare, accessing medicine and health-related information and resources linked to home countries. This study aimed to explore the relationships among local healthcare experiences in Canada, individual characteristics and use of THP among older adult immigrants. Methods: A mixed-methods approach was used combining statistical, spatial and qualitative methods to analyze group patterns of THP and its influencing factors. Primary data was collected through surveys and focus groups of older Mainland Chinese and older South Korean immigrants residing in Toronto. They are the two largest East Asian groups in Canada, with documented transnational ties with their home country. Results: The study found that THP were sought by both groups but were more prevalent among older Chinese immigrants. By integrating quantitative and qualitative analyses, the study revealed complex relationships between THP and barriers in local healthcare access relating to wait times, cost, language, availability, spatial accessibility and quality of care, for different types of care including primary, specialist, eye and dental care. Conclusions: The study generates new knowledge on THP in Canada and adds to the growing body of literature on transnational healthcare practices and behaviours among migrants across different countries and regions. It provides implications to inform health policy and deliver care for older adult immigrants as their populations continue to increase. Full article

(This article belongs to the Special Issue Healthcare for Migrants and Minorities)

41 pages, 724 KB

Open AccessArticle

The Impact of Integrity-Related Factors on Consumer Shopping Intention. An Interactive Marketing Approach Based on Digital Integrity Model

by Nicoleta-Valentina Florea, Gabriel Croitoru and Aurelia-Aurora Diaconeasa

J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 262; https://doi.org/10.3390/jtaer20040262 (registering DOI) - 1 Oct 2025

Abstract

The purpose of this study is to examine the impact of integrity-related considerations, such as ethics, privacy, protection, security, and trust, on online consumer shopping intention within the interactive marketing environment. To achieve this, the research uses partial least squares structural equation modelling (PLS-SEM), analysing data from a sample of 260 respondents collected through an online survey. The findings reveal that protection is the most influential factor driving consumer buying intentions, followed by trust, ethics and security. Privacy, while significant, has a more moderate influence on consumer behaviour compared to other factors. The study makes a key theoretical contribution by advancing the understanding of how these constructs interact to shape consumer behaviour in the digital marketplace, particularly highlighting the importance of data protection and ethical practices. Practically, the research offers actionable recommendations for e-commerce businesses, based on building a digital integrity model, suggesting the focus on enhancing data security and ethical transparency to build consumer trust. Furthermore, the findings highlight the need for policymakers to strengthen data privacy regulations and harmonise international security standards in e-commerce. Future research should consider longitudinal studies and explore these dynamics in different regulatory environments. Full article

(This article belongs to the Topic Innovations in New Media: Shaping the Future of Interactive Marketing)

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37 pages, 5285 KB

Open AccessArticle

Assessing Student Engagement: A Machine Learning Approach to Qualitative Analysis of Institutional Effectiveness

by Abbirah Ahmed, Martin J. Hayes and Arash Joorabchi

Future Internet 2025, 17(10), 453; https://doi.org/10.3390/fi17100453 (registering DOI) - 1 Oct 2025

Abstract

In higher education, institutional quality is traditionally assessed through metrics such as academic programs, research output, educational resources, and community services. However, it is important that their activities align with student expectations, particularly in relation to interactive learning environments, learning management system interaction, curricular and co-curricular activities, accessibility, support services and other learning resources that ensure academic success and, jointly, career readiness. The growing popularity of student engagement metrics as one of the key measures to evaluate institutional efficacy is now a feature across higher education. By monitoring student engagement, institutions assess the impact of existing resources and make necessary improvements or interventions to ensure student success. This study presents a comprehensive analysis of student feedback from the StudentSurvey.ie dataset (2016–2022), which consists of approximately 275,000 student responses, focusing on student self-perception of engagement in the learning process. By using classical topic modelling techniques such as Latent Dirichlet Allocation (LDA) and Bi-term Topic Modelling (BTM), along with the advanced transformer-based BERTopic model, we identify key themes in student responses that can impact institutional strength performance metrics. BTM proved more effective than LDA for short text analysis, whereas BERTopic offered greater semantic coherence and uncovered hidden themes using deep learning embeddings. Moreover, a custom Named Entity Recognition (NER) model successfully extracted entities such as university personnel, digital tools, and educational resources, with improved performance as the training data size increased. To enable students to offer actionable feedback, suggesting areas of improvement, an n-gram and bigram network analysis was used to focus on common modifiers such as “more” and “better” and trends across student groups. This study introduces a fully automated, scalable pipeline that integrates topic modelling, NER, and n-gram analysis to interpret student feedback, offering reportable insights and supporting structured enhancements to the student learning experience. Full article

(This article belongs to the Special Issue Machine Learning and Natural Language Processing)

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29 pages, 2052 KB

Open AccessArticle

Comparison of Alternative Port-Hamiltonian Dynamics Extensions to the Thermodynamic Domain Toward IDA-PBC-Like Control: Application to a Heat Transfer Model

by Oleksiy Kuznyetsov

Dynamics 2025, 5(4), 42; https://doi.org/10.3390/dynamics5040042 (registering DOI) - 1 Oct 2025

Abstract

The dynamics of port-Hamiltonian systems is based on energy balance principles (the first law of thermodynamics) embedded in the structure of the model. However, when dealing with thermodynamic subsystems, the second law (entropy production) should also be explicitly taken into account. Several frameworks were developed as extensions to the thermodynamic domain of port-Hamiltonian systems. In our work, we study three of them, namely irreversible port-Hamiltonian systems, entropy-based generalized Hamiltonian systems, and entropy-production-metric-based port-Hamiltonian systems, which represent alternative approaches of selecting the state variables, the storage function, simplicity of physical interpretation, etc. On the example of a simplified lumped-parameter model of a heat exchanger, we study the frameworks in terms of their implementability for an IDA-PBC-like control and the simplicity of using these frameworks for practitioners already familiar with the port-Hamiltonian systems. The comparative study demonstrated the possibility of using each of these approaches to derive IDA-PBC-like thermodynamically consistent control and provided insight into the applicability of each framework for the modeling and control of multiphysics systems with thermodynamic subsystems. Full article

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16 pages, 6360 KB

Open AccessArticle

Landscape Afterlives: A Geospatial Approach to the History of African Burial Grounds in New York City and the Hudson Valley

by Sebastian Wang Gaouette

Humans 2025, 5(4), 25; https://doi.org/10.3390/humans5040025 (registering DOI) - 1 Oct 2025

Abstract

Throughout the eighteenth and early nineteenth centuries, slavery was a central element of life in colonial and early national New York. The places where the enslaved buried their dead, referred to today as African Burial Grounds, remain important sites of reflection and remembrance for many New Yorkers. However, little literature exists discussing New York’s African Burial Ground sites from a broad, comparative perspective. This study examines seven African Burial Grounds in New York City and the Hudson Valley, two historically significant regions of New York State. GIS data from all seven sites, considered alongside GIS data from nearby coeval white Christian cemeteries, reveal that while the individuals interred in New York’s African Burial Grounds represent a variety of lived experiences, certain unifying patterns nonetheless emerge in the spatial dialectics of their final resting places. The findings have implications for the preservation of Black cultural heritage throughout southeastern New York State. Full article

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33 pages, 4190 KB

Open AccessArticle

Preserving Songket Heritage Through Intelligent Image Retrieval: A PCA and QGD-Rotational-Based Model

by Nadiah Yusof, Nazatul Aini Abd. Majid, Amirah Ismail and Nor Hidayah Hussain

Computers 2025, 14(10), 416; https://doi.org/10.3390/computers14100416 (registering DOI) - 1 Oct 2025

Abstract

Malay songket motifs are a vital component of Malaysia’s intangible cultural heritage, characterized by intricate visual designs and deep cultural symbolism. However, the practical digital preservation and retrieval of these motifs present challenges, particularly due to the rotational variations typical in textile imagery. This study introduces a novel Content-Based Image Retrieval (CBIR) model that integrates Principal Component Analysis (PCA) for feature extraction and Quadratic Geometric Distance (QGD) for measuring similarity. To evaluate the model’s performance, a curated dataset comprising 413 original images and 4956 synthetically rotated songket motif images was utilized. The retrieval system featured metadata-driven preprocessing, dimensionality reduction, and multi-angle similarity assessment to address the issue of rotational invariance comprehensively. Quantitative evaluations using precision, recall, and F-measure metrics demonstrated that the proposed PCAQGD + Rotation technique achieved a mean F-measure of 59.72%, surpassing four benchmark retrieval methods. These findings confirm the model’s capability to accurately retrieve relevant motifs across varying orientations, thus supporting cultural heritage preservation efforts. The integration of PCA and QGD techniques effectively narrows the semantic gap between machine perception and human interpretation of motif designs. Future research should focus on expanding motif datasets and incorporating deep learning approaches to enhance retrieval precision, scalability, and applicability within larger national heritage repositories. Full article

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25 pages, 7560 KB

Open AccessArticle

Research on Green Distribution Problems of Mixed Fleets Considering Multiple Charging Methods

by Lvjiang Yin, Ruixue Zhu and Dandan Jian

Energies 2025, 18(19), 5220; https://doi.org/10.3390/en18195220 (registering DOI) - 1 Oct 2025

Abstract

Against the backdrop of global emissions reduction and transportation electrification, electric vehicles are gradually replacing traditional fuel vehicles for delivery. However, issues such as limited range and charging times often conflict with time window service requirements. To balance economic and environmental performance, mixed fleets and multi-method charging strategies have emerged as viable approaches. This study addresses the problem by developing a mixed-integer programming model that incorporates multiple charging methods and carbon emission accounting. An Improved Adaptive Large Neighborhood Search (IALNS) algorithm is proposed, featuring multiple Removal and Insertion operators tailored for customers and charging stations, along with two local optimization operators. The algorithm’s superiority and applicability are validated through simulation and comparative analysis on benchmark instances and real-world data from an urban courier network. Sensitivity analysis further demonstrates that the proposed algorithm effectively coordinates vehicle type and charging mode selection, reducing total costs and carbon emissions while ensuring service quality. This approach provides practical reference value for operational decision-making in mixed fleet delivery. Full article

(This article belongs to the Special Issue Advanced Low-Carbon Energy Technologies)

25 pages, 4108 KB

Open AccessArticle

Comprehensive Explorations and Preliminary Experimental Verification of RNA Modification-Related Diagnostic Markers in the Subtype Classification of Peripheral Blood-Derived Mononuclear Cells Derived from Post-Traumatic Stress Disorder Patients

by Lesheng Wang, Gaomeng Luo, Sha Liu, Zhipeng Xu, Wei Wei and Xiang Li

Diseases 2025, 13(10), 323; https://doi.org/10.3390/diseases13100323 (registering DOI) - 1 Oct 2025

Abstract

Background: The precise role of RNA modification in post-traumatic stress disorder (PTSD) remains incompletely understood. This study aims to elucidate the effects of five common RNA modifications in PTSD, specifically m⁶A, m⁵C, m¹A, m⁷G, and ψ. Methods: We extracted data from the GEO repository to conduct a series of bioinformatics analyses. These included differential analysis to identify key regulators of five common RNA modifications, model construction using random forest (RF), least absolute shrinkage and selection operator (LASSO), and nomogram techniques, as well as consensus clustering of RNA modification subtypes. Furthermore, GO enrichment analysis was performed on DEGs associated with various RNA modification patterns. Immune cell infiltration was assessed using PCA and ssGSEA. RT-qPCR was performed to validate RNA modification-related genes (RMGs). Results: Twenty-one differentially expressed RMGs were identified. LASSO and RF intersection yielded eight signature genes (YTHDC1, IGFBP1, IGF2BP1, ALKBH5, NSUN4, TET2, TET3, WDR4) that robustly diagnosed PTSD (AUC = 0.804). Furthermore, these feature genes were validated using RT-qPCR, which was basically consistent with the results of bioinformatics analysis. Consensus clustering analysis may reveal two distinguishable subtypes: clusterA marked by high immunoinflammation, and clusterB characterized by high-neuroendocrine dysregulation. Conclusions: RMGs may play a crucial role in the pathogenesis of PTSD. Analyzing RNA modification patterns could offer potential diagnostic markers and help to guide immunotherapeutic approaches or neurotransmitter system interventions for PTSD in the future. Full article

(This article belongs to the Section Neuro-psychiatric Disorders)

24 pages, 4032 KB

Open AccessArticle

Enhancing Automated Breast Cancer Detection: A CNN-Driven Method for Multi-Modal Imaging Techniques

by Khadija Aguerchi, Younes Jabrane, Maryam Habba, Mustapha Ameur and Amir Hajjam El Hassani

J. Pers. Med. 2025, 15(10), 467; https://doi.org/10.3390/jpm15100467 (registering DOI) - 1 Oct 2025

Abstract

Background/Objectives: Breast cancer continues to be one of the primary causes of death among women worldwide, emphasizing the necessity for accurate and efficient diagnostic approaches. This work focuses on developing an automated diagnostic framework based on convolutional neural networks (CNNs) capable of handling multiple imaging modalities. Methods: The proposed CNN model are trained and evaluated on several benchmark datasets, including mammography (DDSM, MIAS, INbreast), ultrasound, magnetic resonance imaging (MRI), and histopathology (BreaKHis). Standardized preprocessing procedures were applied across all datasets, and the outcomes were compared with leading state-of-the-art techniques. Results: The model attained strong classification performance with accuracy scores of 99.2% (DDSM), 98.97% (MIAS), 99.43% (INbreast), 98.00% (Ultrasound), 98.43% (MRI), and 86.42% (BreaKHis). These findings indicate superior results compared to many existing approaches, confirming the robustness of the method. Conclusions: This study introduces a reliable and scalable diagnostic system that can support radiologists in early breast cancer detection. Its high accuracy, efficiency, and adaptability across different imaging modalities make it a promising tool for integration into clinical practice. Full article

(This article belongs to the Special Issue Advanced Imaging and Artificial Intelligence in Personalized Medicine: Innovations and Future Directions)

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17 pages, 4348 KB

Open AccessArticle

[1,2,5]Oxadiazolo[3,4-b]dithieno[2,3-f:2′,3′-h]quinoxaline as a Versatile Scaffold for the Construction of Various Polycyclic Systems as Potential Organic Semiconductors

by Elizaveta M. Krynina, Yuriy A. Kvashnin, Ekaterina F. Zhilina, Denis A. Gazizov, Pavel A. Slepukhin, Gennady L. Rusinov, Egor V. Verbitskiy and Valery N. Charushin

Chemistry 2025, 7(5), 158; https://doi.org/10.3390/chemistry7050158 (registering DOI) - 1 Oct 2025

Abstract

A straightforward synthetic method is advanced to produce hard-to-reach polycyclic compounds belonging to the [1,2,5]oxadiazolo[3,4-b]quinoxaline ring system. This approach draws on a combination of the nucleophilic aromatic substitution of hydrogen (S_N^H) and Scholl cross-coupling reactions, followed by reduction of the 1,2,5-oxadiazole fragment under mild reaction conditions. All compounds were obtained for the first time with moderate to excellent yields. Electrochemical and photophysical measurements show that the synthesized compounds may serve as narrow-band n-type organic semiconductors, with energy levels ranging from 2.00 to 2.28 eV, comparable to those of the best commercially available electronic semiconductors. Full article

(This article belongs to the Special Issue Oxygen-Containing Heterocyclic Compounds: Recent Advances in Chemistry)

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17 pages, 5960 KB

Open AccessArticle

Impacts of Humic Acid and Potassium Fulvate on Cadmium and Lead Accumulation and Translocation in Maize (Zea mays L.) Grown in Co-Contaminated Soil

by Qi Liu, Xuchao Sun, Sheng Wang, Rongteng Zhao, Lanfeng Li, Jijiang Zhou, Li Bao, Wenbing Zhou and Naiming Zhang

Agriculture 2025, 15(19), 2064; https://doi.org/10.3390/agriculture15192064 (registering DOI) - 1 Oct 2025

Abstract

To explore strategies for the safe utilization of farmland co-contaminated with cadmium (Cd) and lead (Pb), this field study systematically evaluated the impacts of humic acid (HA) and potassium fulvate (PF) at different application rates (0, 1500, 3000, and 4500 kg·ha⁻¹) on the growth, yield, and translocation of Cd and Pb within the soil–plant system of maize (Zea mays L.). The results showed that while HA and PF did not significantly alter total soil Cd and Pb concentrations, they markedly reduced their bioavailable fractions. This mitigation of heavy metal phytotoxicity significantly promoted maize growth and yield, with the high-dose HA treatment increasing yield by a maximum of 32.9%. Both amendments dose-dependently decreased Cd and Pb concentrations, bioconcentration factors (BCF), and translocation factors (TF) in all maize tissues, particularly in the grains. At equivalent application rates, PF was slightly more effective than HA in reducing heavy metal concentrations in the grains. Notably, a significant positive correlation was observed between Cd and Pb concentrations across all plant parts, confirming a synergistic accumulation and translocation mechanism. This synergy provides a physiological explanation for the broad-spectrum immobilization efficacy of these humic substances. In conclusion, applying HA and PF presents a dual-benefit strategy for increasing yield and reducing risks in Cd- and Pb-contaminated farmlands. This study proposes a differentiated application approach: PF is the preferred option when ensuring food-grade safety is the primary goal, whereas high-dose HA is more advantageous for maximizing yield in soils with low-to-moderate contamination risk. Full article

(This article belongs to the Section Agricultural Soils)

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23 pages, 7422 KB

Open AccessArticle

Adaptive–Predictive Lateral Web Movement Control Algorithm for Flexible Material Winding Systems

by Piotr Urbanek, Andrzej Fraczyk and Jacek Kucharski

Appl. Sci. 2025, 15(19), 10638; https://doi.org/10.3390/app151910638 (registering DOI) - 1 Oct 2025

Abstract

Various industrial technologies require flexible material webs to undergo processes such as thermal treatment (e.g., drying), printing, or laminating. Such processes are usually performed within winding systems, where the web goes through a set of rolls, and the precision of the web movement determines the quality of the final product. Therefore, high accuracy in the control of both the longitudinal and lateral movement of the web is of paramount importance. Designing the proper control system requires insightful analysis of the technological setup and precise modeling of its dynamic properties. In this paper, the transfer function model of the roll-to-roll system with closed-loop web circulation has been developed based on the mathematical description of the open-loop system. It has been proven that the analyzed system can be efficiently represented by an integral block with negligible inertia. Having established this, several control algorithms have been analyzed, and, as a result, the dedicated adaptive–predictive control algorithm has been proposed. The developed solutions have been verified both by simulations and real experiments performed using the semi-industrial laboratory setup. The high control quality of the proposed algorithm (e.g., considerable reductions in overshoot and settling time compared to PI control), outperforming classical approaches, has been confirmed under various disturbances. Full article

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