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Search Results (166)

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21 pages, 4796 KB  
Article
Early Oral Cancer Detection with AI: Design and Implementation of a Deep Learning Image-Based Chatbot
by Pablo Ormeño-Arriagada, Gastón Márquez, Carla Taramasco, Gustavo Gatica, Juan Pablo Vasconez and Eduardo Navarro
Appl. Sci. 2025, 15(19), 10792; https://doi.org/10.3390/app151910792 - 7 Oct 2025
Viewed by 614
Abstract
Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents [...] Read more.
Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents a novel system that combines a patient-centred chatbot with a deep learning framework to support early diagnosis, symptom triage, and health education. The system integrates convolutional neural networks, class activation mapping, and natural language processing within a conversational interface. Five deep learning models were evaluated (CNN, DenseNet121, DenseNet169, DenseNet201, and InceptionV3) using two balanced public datasets. Model performance was assessed using accuracy, sensitivity, specificity, diagnostic odds ratio (DOR), and Cohen’s Kappa. InceptionV3 consistently outperformed the other models across these metrics, achieving the highest diagnostic accuracy (77.6%) and DOR (20.67), and was selected as the core engine of the chatbot’s diagnostic module. The deployed chatbot provides real-time image assessments and personalised conversational support via multilingual web and mobile platforms. By combining automated image interpretation with interactive guidance, the system promotes timely consultation and informed decision-making. It offers a prototype for a chatbot, which is scalable and serves as a low-cost solution for underserved populations and demonstrates strong potential for integration into digital health pathways. Importantly, the system is not intended to function as a formal screening tool or replace clinical diagnosis; rather, it provides preliminary guidance to encourage early medical consultation and informed health decisions. Full article
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12 pages, 736 KB  
Review
Decentralized Clinical Trials: Governance, Ethics and Medico-Legal Issues for the New Paradigm of Research with a Focus on Cardiovascular Field
by Elena Tenti, Giuseppe Basile, Claudia Giorgetti, Diego Sangiorgi, Elisa Mikus, Gaia Sebastiani, Vittorio Bolcato, Livio Pietro Tronconi and Elena Tremoli
Med. Sci. 2025, 13(4), 222; https://doi.org/10.3390/medsci13040222 - 7 Oct 2025
Viewed by 338
Abstract
The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can [...] Read more.
The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can be conducted remotely, increasing trial accessibility and reducing population selection biases, particularly for cardiovascular patients. However, this also presents complex regulatory and ethical challenges. The article explores how digital platforms and emerging technologies like block chain, AI, and advanced cryptography can promote traceability, security, and transparency throughout the trial process, ensuring participant identification and documentation of each procedural step. Clear, legally compliant informed consent, often managed through electronic systems, both for research participation and data management in line with GDPR, is essential. Ethical considerations include ensuring participants understand trial information, with adaptations such as simplified language, visual aids, and multilingual support. The transnational nature of decentralized trials highlights the need for coordinated regulatory standards to overcome jurisdictional barriers and reinforce accountability. This framework promotes trust, shared responsibility, and the protection of participants rights while upholding high ethical standards in scientific research. Full article
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10 pages, 294 KB  
Article
Performance Differences Between Spanish AzBio and Latin American HINT: Implications for Test Selection
by Chrisanda Marie Sanchez, Jennifer Coto, Sandra Velandia, Ivette Cejas and Meredith A. Holcomb
Audiol. Res. 2025, 15(5), 129; https://doi.org/10.3390/audiolres15050129 - 2 Oct 2025
Viewed by 197
Abstract
Background/Objectives: Spanish-speaking patients face persistent barriers in accessing equitable audiological care, particularly when standardized language-appropriate tools are lacking. Two Spanish-language sentence recognition tests, the Spanish AzBio Sentence (SAzB) and the Latin American Hearing in Noise Test (LAH), are commonly used to evaluate speech [...] Read more.
Background/Objectives: Spanish-speaking patients face persistent barriers in accessing equitable audiological care, particularly when standardized language-appropriate tools are lacking. Two Spanish-language sentence recognition tests, the Spanish AzBio Sentence (SAzB) and the Latin American Hearing in Noise Test (LAH), are commonly used to evaluate speech perception in adults with hearing loss. However, performance differences between these measures may influence referral decisions for hearing intervention, such as cochlear implantation. This study compared test performance under varying noise and spatial conditions to guide appropriate test selection and reduce the risk of misclassification that may contribute to healthcare disparities. Methods: Twenty-one bilingual Spanish/English speaking adults with normal bilateral hearing completed speech perception testing using both the SAzB and LAH. Testing was conducted under two spatial configurations: (1) speech and noise presented from the front (0° azimuth) and (2) speech to the simulated poorer ear and noise to the better ear (90°/270° azimuth). Conditions included quiet and three signal-to-noise ratios (+10, +5, and 0 dB). Analyses included paired t-tests and one-way ANOVAs. Results: Participants scored significantly higher on the LAH than on the SAzB across all SNR conditions and configurations, with ceiling effects observed for the LAH. SAzB scores varied by language dominance, while LAH scores did not. No other differences were observed based on any further demographic information. Conclusions: The SAzB provides a more challenging and informative assessment of speech perception in noise. Relying on easier tests like the LAH may obscure real-world difficulties and delay appropriate referrals for hearing loss intervention, including cochlear implant evaluation. Selecting the most appropriate test is critical to avoiding under-referral and ensuring Spanish-speaking patients receive equitable and accurate care. Full article
(This article belongs to the Section Speech and Language)
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29 pages, 3929 KB  
Article
Large Language Model-Based Autonomous Agent for Prognostics and Health Management
by Minhyeok Cha, Sang-il Yoon, Seongrae Kim, Daeyoung Kang, Keonwoo Nam, Teakyong Lee and Joon-Young Kim
Machines 2025, 13(9), 831; https://doi.org/10.3390/machines13090831 - 9 Sep 2025
Viewed by 926
Abstract
Prognostics and Health Management (PHM), including fault diagnosis and Remaining Useful Life (RUL) prediction, is critical for ensuring the reliability and efficiency of industrial equipment. However, traditional AI-based methods require extensive expert intervention in data preprocessing, model selection, and hyperparameter tuning, making them [...] Read more.
Prognostics and Health Management (PHM), including fault diagnosis and Remaining Useful Life (RUL) prediction, is critical for ensuring the reliability and efficiency of industrial equipment. However, traditional AI-based methods require extensive expert intervention in data preprocessing, model selection, and hyperparameter tuning, making them less scalable and accessible in real-world applications. To address these limitations, this study proposes an autonomous agent powered by Large Language Models (LLMs) to automate predictive modeling for fault diagnosis and RUL prediction. The proposed agent processes natural language queries, extracts key parameters, and autonomously configures AI models while integrating an iterative optimization mechanism for dynamic hyperparameter tuning. Under identical settings, we compared GPT-3.5 Turbo, GPT-4, GPT-4o, GPT-4o-mini, Gemini-2.0-Flash, and LLaMA-3.2 on accuracy, latency, and cost, using GPT-4 as the baseline. The most accurate model is GPT-4o with an accuracy of 0.96, a gain of six percentage points over GPT-4. It also reduces end-to-end time to 1.900 s and cost to $0.00455 per 1 k tokens, which correspond to reductions of 32% and 59%. For speed and cost efficiency, Gemini-2.0-Flash reaches 0.964 s and $0.00021 per 1 k tokens with accuracy 0.94, an improvement of four percentage points over GPT-4. The agent operates through interconnected modules, seamlessly transitioning from query analysis to AI model deployment while optimizing model selection and performance. Experimental results confirmed that the developed agent achieved stable performance under ideal configurations, attaining accuracy 0.97 on FordA for binary fault classification, accuracy 0.95 on CWRU for multi-fault classification, and an asymmetric score of 380.74 on C-MAPSS FD001 for RUL prediction, while significantly reducing manual intervention. By bridging the gap between domain expertise and AI-driven predictive maintenance, this study advances industrial automation, improving efficiency, scalability, and accessibility. The proposed approach paves the way for the broader adoption of autonomous AI systems in industrial maintenance. Full article
(This article belongs to the Section Automation and Control Systems)
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59 pages, 3596 KB  
Review
Beginner-Friendly Review of Research on R-Based Energy Forecasting: Insights from Text Mining
by Minjoong Kim, Hyeonwoo Kim and Jihoon Moon
Electronics 2025, 14(17), 3513; https://doi.org/10.3390/electronics14173513 - 2 Sep 2025
Viewed by 865
Abstract
Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise [...] Read more.
Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article
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17 pages, 335 KB  
Article
Intelligent Virtual Assistant for Mobile Workers: Towards Hybrid, Frugal and Contextualized Solutions
by Karl Alwyn Sop Djonkam, Gaëtan Rey and Jean-Yves Tigli
Appl. Sci. 2025, 15(17), 9638; https://doi.org/10.3390/app15179638 - 2 Sep 2025
Viewed by 715
Abstract
Field workers require expeditious and pertinent access to information to execute their duties, frequently in arduous environments. Conventional document search interfaces are ill-suited to these contexts, while fully automated approaches often lack the capacity to adapt to the variability of situations. This article [...] Read more.
Field workers require expeditious and pertinent access to information to execute their duties, frequently in arduous environments. Conventional document search interfaces are ill-suited to these contexts, while fully automated approaches often lack the capacity to adapt to the variability of situations. This article explores a hybrid approach based on the use of specialized small language models (SLMs), combining natural language interaction, context awareness (static and dynamic), and structured command generation. The objective of this study is to demonstrate the feasibility of providing contextualized assistance for mobile agents using an intelligent conversational agent, while ensuring that reasonable resource consumption is maintained. The present case study pertains to the supervision of illumination systems on a university campus by technical agents. The static and the dynamic contexts are integrated into the user command to generate a prompt that queries a previously fine-tuned SLM. The methodology employed, the construction of five datasets for the purposes of evaluation, and the refinement of selected SLMs are presented herein. The findings indicate that models of smaller scale demonstrate the capacity to comprehend natural language queries and generate responses that can be effectively utilized by a tangible system. This work opens prospects for intelligent, resource-efficient, and contextualized assistance in industrial or constrained environments. Full article
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22 pages, 1021 KB  
Systematic Review
Scientific Evidence in Public Health Decision-Making: A Systematic Literature Review of the Past 50 Years
by Emmanuel Kabengele Mpinga, Sara Chebbaa, Anne-Laure Pittet and Gabin Kayumbi
Int. J. Environ. Res. Public Health 2025, 22(9), 1343; https://doi.org/10.3390/ijerph22091343 - 28 Aug 2025
Viewed by 1718
Abstract
Background: Scientific evidence plays a critical role in informing public health decision-making processes. However, the extent, nature, and effectiveness of its use remain uneven across contexts. Despite the increasing volume of literature on the subject, previous syntheses have often suffered from narrow thematic, [...] Read more.
Background: Scientific evidence plays a critical role in informing public health decision-making processes. However, the extent, nature, and effectiveness of its use remain uneven across contexts. Despite the increasing volume of literature on the subject, previous syntheses have often suffered from narrow thematic, temporal, or geographic scopes. Objectives: This study undertook a comprehensive systematic literature review spanning 50 years to (i) synthesise current knowledge on the use of scientific evidence in public health decisions, (ii) identify key determinants, barriers, and enablers, (iii) evaluate implementation patterns, and (iv) propose future directions for research and practice. Methods: We adopted the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Moreover, we researched three large databases (Web of Science, Embase, and PubMed), and this study focused on articles published in the English and French languages between January 1974 and December 2024. Studies were analysed thematically and descriptively to identify trends, patterns, and knowledge gaps. Results: This review reveals a growing corpus of scholarship with a predominance of qualitative studies mainly published in public health journals. Evidence use is most frequently analysed at the national policy level. Analyses of the evolution of scientific production over time revealed significant shifts beginning as early as 2005. Critical impediments included limited access to reliable and timely data, a lack of institutional capacity, and insufficient training among policy-makers. In contrast, enablers encompass cross-sector collaboration, data transparency, and alignment between researchers and decision-makers. Conclusions: Addressing persistent gaps necessitates a more nuanced appreciation of interdisciplinary and contextual factors. Our findings call for proactive policies aimed at promoting the use of scientific evidence by improving the accessibility of health data (addressing the absence or lack of data, as well as its reliability, timeliness, and accessibility), and by training decision-makers in the use of scientific evidence for decision making. Furthermore, our findings advocate for better alignment between the agendas of healthcare professionals (e.g., data collection), researchers (e.g., the selection of research topics), and decision-makers (e.g., expectations and needs) in order to develop and implement public health policies that are grounded in and informed by scientific evidence. Full article
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13 pages, 234 KB  
Review
Liver Transplantation for Unresectable Colorectal Liver Metastases: A Scoping Review on Redefining Boundaries in Transplant Oncology
by Berkay Demirors, Vrishketan Sethi, Abiha Abdullah, Charbel Elias, Francis Spitz, Jason Mial-Anthony, Godwin Packiaraj, Sabin Subedi, Shwe Han, Timothy Fokken and Michele Molinari
Curr. Oncol. 2025, 32(9), 481; https://doi.org/10.3390/curroncol32090481 - 28 Aug 2025
Viewed by 1187
Abstract
Historically, colorectal liver metastases (CRLMs) have been considered a contraindication for liver transplantation (LT), primarily due to limited organ availability and concerns about oncologic efficacy. However, emerging evidence indicates that highly selected patients with unresectable CRLM can achieve long-term survival following LT—often with [...] Read more.
Historically, colorectal liver metastases (CRLMs) have been considered a contraindication for liver transplantation (LT), primarily due to limited organ availability and concerns about oncologic efficacy. However, emerging evidence indicates that highly selected patients with unresectable CRLM can achieve long-term survival following LT—often with outcomes superior to those obtained through conventional systemic therapies. To evaluate the evolving role of LT in this setting, we conducted a scoping review of the literature. A comprehensive search was performed across PubMed, Embase, Web of Science, Scopus, and ClinicalTrials.gov, as well as ProQuest Dissertations & Theses and Google Scholar to capture gray literature. The search included English-language articles published between January 2015 and April 2025. Eligible studies included those reporting on the application of LT for patients with unresectable CRLM. This scoping review synthesizes current evidence on patient selection criteria, overall and disease-free survival, recurrence patterns, and emerging biomarkers that may guide transplant eligibility. In addition, we explore innovations in organ utilization—including living donor LT and machine perfusion technologies—that aim to expand access while addressing ethical concerns related to organ allocation. As LT for CRLM transitions from investigational use to clinical implementation, this review outlines the key challenges and future opportunities that will shape its role in the landscape of transplant oncology. Full article
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26 pages, 389 KB  
Article
Integrating AI with Meta-Language: An Interdisciplinary Framework for Classifying Concepts in Mathematics and Computer Science
by Elena Kramer, Dan Lamberg, Mircea Georgescu and Miri Weiss Cohen
Information 2025, 16(9), 735; https://doi.org/10.3390/info16090735 - 26 Aug 2025
Viewed by 463
Abstract
Providing students with effective learning resources is essential for improving educational outcomes—especially in complex and conceptually diverse fields such as Mathematics and Computer Science. To better understand how these subjects are communicated, this study investigates the linguistic structures embedded in academic texts from [...] Read more.
Providing students with effective learning resources is essential for improving educational outcomes—especially in complex and conceptually diverse fields such as Mathematics and Computer Science. To better understand how these subjects are communicated, this study investigates the linguistic structures embedded in academic texts from selected subfields within both disciplines. In particular, we focus on meta-languages—the linguistic tools used to express definitions, axioms, intuitions, and heuristics within a discipline. The primary objective of this research is to identify which subfields of Mathematics and Computer Science share similar meta-languages. Identifying such correspondences may enable the rephrasing of content from less familiar subfields using styles that students already recognize from more familiar areas, thereby enhancing accessibility and comprehension. To pursue this aim, we compiled text corpora from multiple subfields across both disciplines. We compared their meta-languages using a combination of supervised (Neural Network) and unsupervised (clustering) learning methods. Specifically, we applied several clustering algorithms—K-means, Partitioning around Medoids (PAM), Density-Based Clustering, and Gaussian Mixture Models—to analyze inter-discipline similarities. To validate the resulting classifications, we used XLNet, a deep learning model known for its sensitivity to linguistic patterns. The model achieved an accuracy of 78% and an F1-score of 0.944. Our findings show that subfields can be meaningfully grouped based on meta-language similarity, offering valuable insights for tailoring educational content more effectively. To further verify these groupings and explore their pedagogical relevance, we conducted both quantitative and qualitative research involving student participation. This paper presents findings from the qualitative component—namely, a content analysis of semi-structured interviews with software engineering students and lecturers. Full article
(This article belongs to the Special Issue Advancing Educational Innovation with Artificial Intelligence)
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26 pages, 630 KB  
Article
Multivariate Probit Model Analysis of the Factors Influencing Smallholder Farmers’ Choice of ICT Tools: A Case Study of Mpumalanga, South Africa
by Melga Meta Ntsoane, Jorine Tafadzwa Ndoro and Ntombovuyo Wayi-Mgwebi
Agriculture 2025, 15(17), 1817; https://doi.org/10.3390/agriculture15171817 - 26 Aug 2025
Viewed by 745
Abstract
This study examined factors influencing smallholder farmers’ decisions to use Information and Communication Technologies (ICTs) for agricultural information in Mbombela Local Municipality, Mpumalanga, South Africa. Data were collected from 308 respondents through a quantitative cross-sectional survey using a structured questionnaire, with systematic sampling [...] Read more.
This study examined factors influencing smallholder farmers’ decisions to use Information and Communication Technologies (ICTs) for agricultural information in Mbombela Local Municipality, Mpumalanga, South Africa. Data were collected from 308 respondents through a quantitative cross-sectional survey using a structured questionnaire, with systematic sampling to select participants. Multivariate probit regression identified factors affecting ICT tool choices. Analysis revealed a significant positive relationship between gender, age and language use with smallholder farmers’ preference for using radio. Factors like farm size, off-farm income, and language positively influence the choice of basic cell phones. In contrast, educational level, marital status, and electricity supply negatively influence the choice to use radio and basic cell phones. Network connectivity and ICT awareness positively influence TV use, while household size and ICT costs have a negative effect. Educational level and ICT awareness positively influenced the use of computers and smartphones, whereas age, gender, off-farm income, electricity supply, farm size, household size and network connectivity had a negative influence. When smallholder farmers have access to multiple ICT tools, they can select the most beneficial combination for improving crop productivity. To maximise ICTs’ potential, policymakers should promote inclusive ICT access, awareness and training tailored to farmers’ needs, focusing on affordability, connectivity and literacy to support agricultural information dissemination. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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28 pages, 2252 KB  
Review
Technical Review: Architecting an AI-Driven Decision Support System for Enhanced Online Learning and Assessment
by Saipunidzam Mahamad, Yi Han Chin, Nur Izzah Nasuha Zulmuksah, Md Mominul Haque, Muhammad Shaheen and Kanwal Nisar
Future Internet 2025, 17(9), 383; https://doi.org/10.3390/fi17090383 - 26 Aug 2025
Viewed by 1356
Abstract
The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to [...] Read more.
The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to 2025. By integrating machine learning, natural language processing, knowledge-based systems, and deep learning, AI-DSSs enhance educational outcomes through predictive analytics, automated grading, and personalized learning paths. This study examines system architecture, data requirements, model selection, and user-centric design, emphasizing their roles in achieving scalability and inclusivity. Through case studies of a MOOC platform using NLP and an adaptive learning system employing reinforcement learning, this paper highlights significant improvements in grading efficiency (up to 70%) and student performance (12–20% grade increases). Performance metrics, including accuracy, response time, and user satisfaction, are analyzed alongside evaluation frameworks combining quantitative and qualitative approaches. Technical challenges, such as model interpretability and bias, ethical concerns like data privacy, and implementation barriers, including cost and adoption resistance, are critically assessed, with proposed mitigation strategies. Future directions explore generative AI, multimodal integration, and cross-cultural studies to enhance global accessibility. This review offers a robust framework for researchers and practitioners, providing actionable insights for designing equitable, efficient, and scalable AI-DSSs to transform online education. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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20 pages, 3244 KB  
Article
SOUTY: A Voice Identity-Preserving Mobile Application for Arabic-Speaking Amyotrophic Lateral Sclerosis Patients Using Eye-Tracking and Speech Synthesis
by Hessah A. Alsalamah, Leena Alhabrdi, May Alsebayel, Aljawhara Almisned, Deema Alhadlaq, Loody S. Albadrani, Seetah M. Alsalamah and Shada AlSalamah
Electronics 2025, 14(16), 3235; https://doi.org/10.3390/electronics14163235 - 14 Aug 2025
Viewed by 649
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven [...] Read more.
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven by demographic shifts in aging populations, and the lifetime risk of developing ALS is 1 in 350–420. Despite international advancements in assistive technologies, a recent national survey in Saudi Arabia revealed that 100% of ALS care providers lack access to eye-tracking communication tools, and 92% reported communication aids as inconsistently available. While assistive technologies such as speech-generating devices and gaze-based control systems have made strides in recent decades, they primarily support English speakers, leaving Arabic-speaking ALS patients underserved. This paper presents SOUTY, a cost-effective, mobile-based application that empowers ALS patients to communicate using gaze-controlled interfaces combined with a text-to-speech (TTS) feature in Arabic language, which is one of the five most widely spoken languages in the world. SOUTY (i.e., “my voice”) utilizes a personalized, pre-recorded voice bank of the ALS patient and integrated eye-tracking technology to support the formation and vocalization of custom phrases in Arabic. This study describes the full development life cycle of SOUTY from conceptualization and requirements gathering to system architecture, implementation, evaluation, and refinement. Validation included expert interviews with Human–Computer Interaction (HCI) expertise and speech pathology specialty, as well as a public survey assessing awareness and technological readiness. The results support SOUTY as a culturally and linguistically relevant innovation that enhances autonomy and quality of life for Arabic-speaking ALS patients. This approach may serve as a replicable model for developing inclusive Augmentative and Alternative Communication (AAC) tools in other underrepresented languages. The system achieved 100% task completion during internal walkthroughs, with mean phrase selection times under 5 s and audio playback latency below 0.3 s. Full article
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15 pages, 553 KB  
Systematic 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 - 2 Aug 2025
Viewed by 715
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 [...] Read more.
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 and Children’s Health)
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24 pages, 3328 KB  
Review
Ergonomic and Psychosocial Risk Factors and Their Relationship with Productivity: A Bibliometric Analysis
by Gretchen Michelle Vuelvas-Robles, Julio César Cano-Gutiérrez, Jesús Everardo Olguín-Tiznado, Claudia Camargo-Wilson, Juan Andrés López-Barreras and Melissa Airem Cázares-Manríquez
Safety 2025, 11(3), 74; https://doi.org/10.3390/safety11030074 - 1 Aug 2025
Viewed by 1709
Abstract
This study analyzes the relationship between ergonomic and psychosocial risk factors and labor productivity using a bibliometric approach through a general analysis and one that includes inclusion criteria such as English language, open access, and primary research publications to identify only those articles [...] Read more.
This study analyzes the relationship between ergonomic and psychosocial risk factors and labor productivity using a bibliometric approach through a general analysis and one that includes inclusion criteria such as English language, open access, and primary research publications to identify only those articles that explicitly address the relationship between ergonomic and psychosocial risk factors and labor productivity. It is recognized that both physical and psychosocial conditions of the work environment directly influence workers’ health and organizational performance. For this purpose, a bibliometric review was conducted in academic databases, including Scopus, Web of Science, ScienceDirect, and Taylor & Francis, resulting in the selection of 4794 relevant articles for general analysis. Additionally, 116 relevant articles were selected based on the inclusion criteria. Tools and methodologies, such as Rayyan, Excel, VOSviewer 1.6.20, and PRISMA, were used to classify the studies and identify trends, collaboration networks, and geographical distribution. The results reveal a sustained growth in scientific production, with clusters on occupational safety and health, work environment factors, and the characteristics of the population, approach, and methodologies used in the studies. Likewise, Procedia Manufacturing, International Journal of Occupational Safety and Ergonomics, and Ergonomics stand out as the main sources of publication, while countries such as Sweden, Poland, and the United States lead the scientific production in this field. In addition, the network of co-occurrence of keywords evidences a comprehensive approach that articulates physical or ergonomic and psychosocial risk factors with organizational performance, while the network of authors shows consolidated collaborations and studies focused on analyzing the relationship between physical demands and musculoskeletal disorders from advanced ergonomic approaches. Full article
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15 pages, 1515 KB  
Article
Ontology-Based Data Pipeline for Semantic Reaction Classification and Research Data Management
by Hendrik Borgelt, Frederick Gabriel Kitel and Norbert Kockmann
Computers 2025, 14(8), 311; https://doi.org/10.3390/computers14080311 - 1 Aug 2025
Viewed by 580
Abstract
Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. [...] Read more.
Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. To improve this, semantic structuring through ontologies is essential. This work extends the established ontologies by refining logical relations and integrating semantic tools such as the Web Ontology Language or the Shape Constraint Language. It incorporates application programming interfaces from chemical databases, such as the Kyoto Encyclopedia of Genes and Genomes and the National Institute of Health’s PubChem database, and builds upon established ontologies. A key innovation lies in automatically decomposing chemical substances through database entries and chemical identifier representations to identify functional groups, enabling more generalized reaction classification. Using new semantic functionality, functional groups are flexibly addressed, improving the classification of reactions such as saponification and ester cleavage with simultaneous oxidation. A graphical interface (GUI) supports user interaction with the knowledge graph, enabling ontological reasoning and querying. This approach demonstrates improved specificity of the newly established ontology over its predecessors and offers a more user-friendly interface for engaging with structured chemical knowledge. Future work will focus on expanding ontology coverage to support a wider range of reactions in catalysis research. Full article
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