Exploring the Learning Gains of Implementing Teacher Humanoid Robots in STEM Education: A Systematic Review

Εducational (or pedagogical) robotics has received increased attention over the last few years based on its effectiveness on the learning process. Humanoid robots have been recently introduced in school settings to mainly support teaching of curriculum-based subjects. However, humanoid robots’ benefits on STEM education in typical classroom settings are less examined by the research literature. Instead, most research studies take place in non-typical school or classroom settings (such as laboratories). The main goal of the current review is to sum up results of relevant research studies about the positive impact of teacher humanoid robots on STEM education. The learning benefits in STEM subjects, programming and reasoning skills are examined too. Sample subject of this review are mainstream students aged 4 to 18 years old and research studies are grouped based on their commonalities such as common learning areas and results

Web-Based Virtual Laboratory Design in Class XI Chemistry Subject

Practicum activities are crucial for supporting students’ success in the teaching and learning process. To conduct the practicum, several factors are required, which vary depending on the field of practice. Adequate facilities and equipment are also necessary. The purpose of this research is to develop a web-based interactive virtual learning application for chemistry lab experiments. The stages involved in creating an application using the multimedia development life cycle (MDLC) method include planning, designing, collecting material, manufacturing, testing, and distribution. Creating applications using Unity software and designing assets with Adobe Illustrator. Virtual labs can help overcome the lack of costs associated with procuring laboratory equipment and materials, reduce the risk of work accidents, and can be accessed from anywhere

IoT-Based Automatic Hydro-Organic Smart Farming System in Greenhouse with Solar Panels for Khok Nong Na

In this research, we propose IoT-Based Automatic Hydro-Organic Smart Farming System (AHOSFS) in Greenhouse with Solar Panels for Khok Nong Na, design and development with Internet of Things and solar energy. Data is collected from sensors for the following factors that affect plant growth: humidity, temperature, light, pH, EC, air quality, water temperature, and water level. The Firebase cloud-based application was used to collect all the data, and it enables farmers to monitor and control the system using their smartphones. The results showed AHOSFS can mix water and nutrient solution, which measure pH of 6.61 and EC of 1.23, control water level and mix nutrient solution in appropriate quantities. Green oak can thrive at pH levels between 6.0 and 7.0, EC levels between 1.1 and 1.7, and humidity level between 75 and 85. The sensor measured temperature value between 18 and 25 °C, which was adequate for the growth of green oak. Sensor also measured water temperature between 25 and 28 °C. The comparative value of cultivation in a hydro-organic system deployed between AB solution, organic fertilizer Formula 1 and Formula 2 found that organic fertilizer formula 1 has an effective and approximate AB solution at 81.64%. The fertilizer formula 2 has AB solution at 89.72%, which was more secure at a comparable level

An Analytical Study on the Implementation of a Healthcare App to Assist People with Disabilities Using Cloud Computing and IoT

This study targets a group of people who require care, that is, people with special needs. The significance of this study lies in addressing the main problem that this group suffers from, which is the lack of awareness and information that leads to the acceptance of that group in society. This work aims to create a mobile application that contributes to spreading knowledge among people with special needs and enhancing their skills to help them become accepted by community members. This application supports people with special needs with training resources, education, suitable jobs, and other services helping them in developing their experiences and knowledge to be active in society. In addition, an evaluation questionnaire has been developed to collect data from both the private and public sectors to classify the building blocks necessary for KSA to incorporate the Internet of Things (IoT) and cloud computing into the healthcare sector. As a result, most respondents acknowledge the importance of a streamlined data-gathering process, the IoT, and cloud-based computing to meet their healthcare needs. Lastly, six main blocks for checking suppliers and the public to accept IoT and cloud healthcare applications are then acknowledged in this paper

Exploring Campus through Web-Based Immersive Adventures Using Virtual Reality Photography: A Low-Cost Virtual Tour Experience

This study aims to assess the incorporation of virtual reality (VR) photography into the web-based immersive application “virtual interactive campus tour (VICT).” This application offers users an immersive experience, allowing them to virtually explore university campuses and access information about the facilities and services available. The VICT application offers a cost-effective, attractive, and sustainable alternative for universities to display their resources and interact with potential students. Through black box testing, we conducted user acceptance testing (UAT) and functionality testing, confirming the application’s readiness for deployment and its capability to meet institutional and end-user requirements. This study also examined the potential for universities to use VR to meet the expectations of prospective students. The application is compatible with both desktop and mobile devices. The results indicated that the overall average validity score was 0.88, suggesting that the measure is valid. The validation results were thoroughly tested and reliable. This study emphasizes the potential of immersive web-based tours in higher education and aims to bridge the divide between virtual exploration and physical visits. By offering an immersive virtual campus experience, this innovative tool has the potential to revolutionize university marketing strategies, increase student engagement, and transform campus visit approaches

Computer Vision-Based Approach for Automated Monitoring and Assessment of Gait Rehabilitation at Home

This study presents a markerless video-based human gait analysis system for automatic assessment of at-home rehabilitation. A marker-based MoCap system (Vicon) is used to evaluate the accuracy of the proposed approach. Additionally, a novel gait rehabilitation score based on the Dynamic Time Warping (DTW) algorithm is introduced, enabling quantification of rehabilitation progress. The accuracy of the proposed approach is assessed by comparing it to a marker-based MoCap system (Vicon), which is used to evaluate the proposed approach. This evaluation results in mean absolute errors (MAE) of 4.8° and 5.2° for the left knee, and 5.9° and 5.7° for the right knee, demonstrating an acceptable accuracy in knee angle measurements. The obtained scores effectively distinguish between normal and abnormal gait patterns. Subjects with normal gait exhibit scores around 97.5%, 98.8%, while those with abnormal gait display scores around 30%, 29%, respectively. Furthermore, a subject at an advanced stage of rehabilitation achieved a score of 65%. These scores provide valuable insights for patients, allowing them to assess their rehabilitation progress and distinguish between different levels of gait recovery. The proposed markerless approach demonstrates acceptable accuracy in measuring knee joint angles during a sagittal walk and provides a reliable rehabilitation score, making it a convenient and cost-effective alternative for automatic at-home rehabilitation monitoring

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

Electronic Prototype of Autonomous Learning for the Crossing of Pedestrians with Visual Disabilities in Lima

Difficulties related to vehicular chaos and obstacles in public spaces hinder the orientation of visually impaired individuals, limiting their autonomy and exposing them to potential accidents. Considering these factors, the objective was to develop a prototype that facilitates autonomous learning by utilizing different electronic components. The aim is to ensure the safe movement of blind pedestrians, promote self-reliance, and minimize the risk of accidents. The proposed prototype is based on the concept of implementing intelligent traffic lights that detect the presence of pedestrians, allowing for safe crossing for both pedestrians and vehicles. The proposed circuit utilizes two ESP32 modules. One module is placed in the traffic light and configured as a Bluetooth master to transmit signals. It is also equipped with an ultrasonic sensor. The other module is located in the user’s wristband and configured as a Bluetooth slave to receive signals. It is also equipped with a horn. The communication between the modules has been developed using the C programming language for microcontrollers in the Arduino IDE development environment. A master-slave communication system was implemented, resulting in the constant reporting of the distance between the pedestrian and the sidewalk within the pedestrian crossing by the ultrasonic sensor. This system controls the safe crossing by regulating the traffic lights. The HC-SR04 ultrasonic sensor can detect distances ranging from 2 cm to 450 cm. Therefore, the prototype can be used as a foundation for future advancements in various cities and contexts, ultimately benefiting blind pedestrians by improving their mobility

Stakeholders of Cardiovascular Innovation Ecosystems in Germany: A First Level Analysis and an Example

This paper aims to provide a first attempt towards analysis innovation ecosystems for cardiovascular pathologies in Germany through the use of a stakeholder model. We present essential stakeholders for the development and deployment of innovations in the field of cardiovascular research and medicine, and the primary functions they fulfill in the context of these innovation ecosystems. The adopted approach consists of the implementation of a multilevel system model for analyzing stakeholders in this particular field. Data acquisition transpired through systematic literature review of multiple articles and studies. Data analysis phases were executed until reaching a point at which the considerable amount of data was discovered, ensuring consistency across various sources. We demonstrate that innovation ecosystems in cardiovascular medicine involve interconnected networks of stakeholders across different fields. Moreover, through an investigation of innovation ecosystems of cardiovascular pathologies particularly in Germany, we present the functions undertaken by each stakeholder, which are essential for the participation in the innovation ecosystems. The findings presented in this paper hold the potential to bring better understanding of cardiovascular pathology innovation ecosystems in Germany. This assertion is substantiated through a comprehensive examination of relevant scientific literature