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A New Ontology-Based Recommender System for Academic Guidance in Paramedical Studies
Get PDFThis paper presents NurSHT-RS (Nursing Sciences and Health Techniques-Recommender System). The purpose is to help students choose study options within the paramedical education sector in Morocco. Recognizing the challenges students face due to insufficient knowledge about study specialties and the reliance on subjective advice, NurSHT-RS aims to address the risks of uninformed career decisions. The system leverages machine learning (ML) and ontology techniques to personalize recommendations based on students’ academic profiles, demographic data, and physical conditions, including disabilities. The system also incorporates NSHTOri-Onto (Nursing Sciences and Health Techniques Orientation-Ontology), a comprehensive ontology that semantically represents student profiles, paramedical specialties, and training institutions. This ontology not only supports accurate recommendations but also provides reusable and extensible knowledge frameworks for similar applications. NurSHT-RS stands out by offering students detailed and up-to-date information, including study modules, admission criteria, and career opportunities, to ensure informed decision-making. In addition, it could be used by educational advisors, Moroccan students, or foreign students wishing to continue their studies in Morocco
Design and Development of a 3D-Printed Tissue Equivalent Phantom for Cobalt-60 HDR Brachytherapy
Get PDFThe use of 3D phantoms represents patient anatomy, enabling more effective treatment planning optimization. This study aims to develop a tissue-equivalent 3D-printed phantom as a dosimetry application for the HDR-BT Cobalt-60 source. Polylactic Acid (PLA) with a single infill density of 85% was used to achieve tissue-equivalent characteristics. The phantom features a 200 mm diameter spherical structure composed of three distinct components, including an applicator channel and cavities in each layer to accommodate the HDR-BT source dose measurement detector. The characteristics of the 3D-PLA phantom were analyzed and compared with soft tissue, muscle, and water based on ICRU-44 reports, as well as with the RW3 phantom (PTW, Germany) as a standard phantom. The resulting 3D-PLA phantom density of 1.05 g/cm3 was in good agreement with muscle, RW3 phantom, soft tissue, and water, respectively. The linear attenuation coefficient of the 3D-PLA phantom at energies >0.1 MeV and its effective atomic number (Zeff) ≈ 8.26 closely resemble soft tissue properties. The HU value of the 3D-PLA phantom is −116.6 ± 7.5 HU, also within the soft tissue HU values range. These findings confirm that the developed 3D-PLA phantom exhibits the necessary characteristics for dosimetry applications
Vulnerabilities and Impacts: Climate Change and Public Health in Bangladesh
Get PDFThe escalating impacts of climate change are increasingly affecting human health, particularly in vulnerable groups. Rising temperatures have intensified heat stress, resulting in more cases of heat-related illnesses, especially among the elderly and individuals with chronic conditions. Simultaneously, climate change is extending the geographic spread of vector-borne diseases (VBDs) such as malaria and dengue while increasing the prevalence of waterborne illnesses due to contaminated supplies-placing additional pressure on healthcare infrastructure. Mental health issues are also growing due to climate-related anxiety, displacement, and trauma from extreme weather events. Additionally, worsening air quality, driven by industrial emissions and wildfires, contributes to respiratory and cardiovascular problems, disproportionately impacting urban populations. Forced migration due to climate-induced disasters further deepens health vulnerabilities. Together, these interconnected effects are amplifying the strain on public health systems, underscoring the urgent need for integrated adaptation strategies to address the health risks posed by a changing climate. Addressing these challenges requires coordinated policy action, cross-sector collaboration, and increased investment in climate-resilient healthcare systems
Market Risk in Sustainable and Traditional Exchange-Traded Funds during Global Uncertainty
Get PDFAmid global market turbulence triggered by the COVID-19 pandemic and subsequent geopolitical disruptions, investor interest in environmental, social, and governance (ESG) criteria has grown significantly. This study investigates the comparative market risk and performance of traditional and ESG-focused exchange-traded funds (ETFs) in U.S. and European markets from January 2020 to April 2025. The aim was to identify which ETF type delivers superior performance in terms of returns and risk mitigation during periods of heightened volatility. Using daily return data from eight ETFs (four U.S., four European; both ESG and traditional), we applied Value at Risk (VaR), Conditional Value at Risk (CVaR), and Sharpe ratio analysis. The results revealed notable regional differences: traditional U.S. ETFs achieved the highest returns and risk-adjusted performance, while European ESG ETFs demonstrated the lowest downside risk. ESG ETFs in the U.S. performed comparably to traditional funds, suggesting that ESG integration does not necessarily compromise financial outcomes. These findings highlight that ESG-related risk and return profiles vary by region. For investors and portfolio managers, this suggests that ESG allocation decisions should consider regional market dynamics to strike a balance between sustainability goals and financial performance
Influence of Health Interests and Technological Trends on the Acceptance of Wearable Technologies and Their Applications in Saudi Arabia
Get PDFWearable technologies have developed relatively quickly in the modern years. This development has impacted the management of personal health and wellness to a considerable extent. The purpose of this study is to understand to what extent the interest of individuals in health and fitness, coupled with their general orientation towards technology, affects the acceptance of wearable technologies and related applications in the Saudi Arabian environment. Utilizing the UTAUT model, the research examines six hypotheses, revealing that health information- seeking behavior (β = 0.316, p < 0.001) and personal innovativeness in IT (β = 0.273, p < 0.001) are strong predictors of behavioral intention (BI) to adopt wearables. Performance expectancy (β = 0.186, p = 0.030) also significantly influences BI, while facilitating conditions (β = 0.267, p < 0.001) and BI (β = 0.528, p < 0.001) are crucial for actual use behavior (UB). These findings provide valuable insights for healthcare practitioners promoting wearable fitness devices, as they underscore the importance of enhancing public understanding of exercise benefits and active living. These would have significant implications for healthcare practitioners when promoting wearable fitness gadgets to the general population. In that respect, healthcare professionals could play significant roles in enhancing the general population’s understanding of physical exercise benefits and an active life by disseminating information and knowledge gained from these equipment and gadgets. Wearable technology companies can subsequently learn from and integrate such valuable inputs. At the same time, designers and marketing strategists design plans to satisfy the unique needs and tastes of the Saudi market. The results may further explain how personal health concerns and technical trends interact in embracing new, creative, health-focused solutions
The Impact of Mobile Applications on Personalized Learning Paths in Dance Education
Get PDFWith the rapid development of smart mobile applications, mobile devices have become an essential learning tool in dance education. Traditional dance teaching methods often fail to effectively meet the individualized learning needs of students, especially in the dynamic learning and feedback of skills. As a result, the design and implementation of personalized learning paths have become a key issue in current dance education. Although existing research has preliminarily explored dance teaching on mobile platforms, problems still exist, such as inaccurate student dance posture assessments and incomplete personalized learning path recommendation mechanisms. Therefore, utilizing mobile application technologies to achieve precise dance posture recognition and evaluation, while providing personalized learning paths for each student, is a critical issue that needs to be addressed. This paper aims to explore the role of mobile applications in optimizing personalized learning paths in dance education. The study consists of two main parts: first, a dance posture evaluation method based on intelligent posture recognition technology is proposed to accurately match personalized learning content based on students’ progress and skill levels. Second, a personalized learning path recommendation system based on collaborative filtering algorithms is designed to help students receive tailored content during their dance learning process. The study demonstrates that combining posture evaluation with content recommendation can significantly improve the personalization and learning outcomes of dance education, providing new technological support and practical applications for intelligent dance teaching
Patent Landscapes in Digital Health: Insights from Smartphone Technologies
Get PDFDigital health, such as mobile health apps, has garnered significant attention, leading to an increase in patent applications. This study investigates patent applications related to digital health using smartphones to elucidate the overall state of patenting in this domain and the characteristics of applicants based on nationality and industry type. We analyzed 11,139 patent families filed between 2015 and 2019, featuring the international patent classification (IPC) code G16H (healthcare informatics) and keywords related to smartphones. Our findings indicate that the United States (US) is the most significant market and leader in digital health technology. Correspondence analysis revealed that applications from the US and Europe share similar content, while those from Japan and China exhibit distinct characteristics. Additionally, industry-based correspondence analysis showed that patents from the machinery industry are often related to imaging technology, whereas those from the information technology (IT) sector are primarily associated with medical information-based applications
Application of Convolutional Neural Networks in Skin Disease Prediction: Accuracy and Efficiency in Dermatological Image Analysis
Get PDFThe use of dermatological images and convolutional neural networks (CNNs) to predict skin diseases is one of the most promising applications of data science to improve the diagnosis and treatment of skin diseases. The aim of this work was to achieve maximum accuracy and efficiency in skin disease prediction using dermatological images and CNN models. Based on dermatological images, the ability of five CNN models to predict skin diseases was evaluated. The ResNet50, Inception V3, VGG-19, DenseNet201, and EfficientNet models were evaluated using the Kaggle HAM10000 (human against machine with 10000 training images) dataset. The metrics used were accuracy, recall, and F1 score. As a result, the study found that skin disease classification has variable performance. VGG-19 and DenseNet201 showed high values for accuracy, recall, and F1 score, with accuracy close to 98%. These models demonstrated an effective ability to identify and classify different types of skin diseases. In contrast, ResNet50 and Inception V3 obtained mixed results, while EfficientNet showed variable results in predicting skin diseases from dermatological images. Finally, the importance of choosing the right CNN model to predict skin diseases from dermatological images can be highlighted. VGG-19 and DenseNet201 performed well in classifying various skin diseases, which could be useful for developing dermatological diagnostic support systems
Innovative Applications and Future Development of Intelligent Diagnostic Devices in Traditional Chinese Medicine: An AI Review
Get PDFThis paper reviews the current applications, challenges, and future development trends of intelligent diagnostic devices in Traditional Chinese Medicine (TCM). With continuous advancements in artificial intelligence (AI), machine learning (ML), and data analytics, intelligent diagnostic devices are gradually being integrated into TCM diagnostics, covering various aspects such as pulse diagnosis, tongue diagnosis, and facial diagnosis. These advanced technologies not only enhance the objectivity and accuracy of TCM diagnosis but also suggest promising trends for personalized treatment development. This paper examines recent progress in intelligent devices for tongue image analysis, pulse diagnosis, and facial image analysis, highlighting existing challenges related to data quality, interdisciplinary collaboration, and ethical issues. Furthermore, the paper envisions future trends in intelligent diagnostic devices, emphasizing the importance of miniaturization, portability, telemedicine, and standardization. We expect intelligent technology to accelerate the modernization and globalization of TCM. Finally, the paper calls for future research to focus on the deep integration of TCM and modern technology, promoting the standardization of intelligent diagnostic devices and improving diagnostic reliability to foster innovative developments in TCM diagnostics
Fostering Empathy and Social-Emotional Learning in Students with Learning Disabilities through HCI and Augmented Reality
Get PDFThis systematic review synthesizes the suggested approach to designing human-computer interaction (HCI) in conjunction with augmented reality (AR) applications to enhance learner empathy with learning disabilities (LDs). However, as the popularity of both AR and HCI increases within the context of education, there is a dearth of literature on integrating the two in supporting social and emotional learning (SEL) in students with LDs. SEL refers to the learning process by which a person gains academic and social competencies and can also apply, cipher, and internalize the competencies related to emotional, social, and academic aspects of human life. This review summarizes the studies published between 2015 and 2024. It discusses how the AR-based immersive and interactive learning contexts can improve these students’ social and emotional learning and reduce their technophobia. It is evident from the highlighted gaps that comprehensive features, user-centered design (UCD) principles, and gradual implementation of technology are possible solutions to enhance the learning and emotional development of students with LDs. This paper highlights the existing gaps in literature and provides recommendations for future and practical research