Lessons taught and learned from the operation of the solar energy e-learning laboratory

The solar energy e learning laboratory (solar e-lab) in Cyprus is a good example of a web-based, remote engineering laboratory. It comprises a pilot solar energy conversion plant which is equipped with all necessary instrumentation, data acquisition, and communication devices needed for remote access, control, data collection and processing. The impact that the solar e-lab had during its nearly 5 years of operation is indeed high. Throughout this period, the solar e-lab has been accessed by users from over 500 locations from 79 countries spread all over the world. In the period of November 2004 to October 2008, more than a million visits were recorded, out of which 25000 have registered on the site and surfed through studying the supplied material. Around 1000 hits concerned registered users that passed the pre-lab test and performed the experimentation part. The four years of operation of the solar e-lab demonstrated how the Internet can be used as a tool to make the laboratory facilities accessible to engineering students and technicians located outside the laboratory, including overseas. In this way, the solar energy e-learning lab, its equipment and experimental facilities were made available and shared by a number of interested people, thus widening educational experiences. Judging from the online evaluation reports that were received from the solar e-lab users during the last 2 years of operation, it can be concluded that there is nearly excellent satisfaction by the users

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

A State Table SPHIT Approach for Modified Curvelet-based Medical Image Compression

Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds

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

Guarding the Cloud: An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms

Cloud computing has gained significant popularity due to its reliability and scalability, making it a compelling area of research. However, this technology is not without its challenges, including network connectivity dependencies, downtime, vendor lock-in, limited control, and most importantly, its vulnerability to attacks. Therefore, guarding the cloud is the objective of this paper, which focuses, in a novel approach, on two prevalent cloud attacks: Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks. To tackle the detection of these malicious activities, machine learning algorithms, namely Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN), are utilized. Experimental simulations of DDoS and MitC attacks are conducted within a cloud environment, and the resultant data is compiled into a dataset for training and evaluating the machine learning algorithms. The study reveals the effectiveness of these algorithms in accurately identifying and classifying malicious activities, effectively distinguishing them from legitimate network traffic. The finding highlights Decision Trees algorithm with most promising potential of guarding the cloud and mitigating the impact of various cyber threats

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

Providing equivalent learning activities with software-based remote access laboratories

Laboratory-based learning activities are important components of engineering and surveying education and it is difficult to offering practical activities to distance education students. Remote Access Laboratory (RAL) systems are widely discussed as learning tools to offer students remote access to rigs or hardware. In some disciplines laboratory activities are purely software based and RAL systems can be used to provide access to software. As part of a larger study into the transferability of the remote laboratory concept to non-engineering disciplines this project evaluates the effectiveness of RAL based software activities in supporting student learning is investigated. In the discipline of Surveying and Spatial Science, RAL technology is used to provide Geographic Information System software access to distance students. The key research question discussed in this paper is whether RALbased software activities can address the same learning outcomes as face-to-face practical classes for software activities. Data was collected from students' discussion forums, teaching staff diaries and teaching staff interviews. The project demonstrates that students undertaking learning activities remotely achieve similar learning outcomes than student in practice classes using the same software. Ease of system access and usability are critical and the learning activity needs to be supported by comprehensive learning materials. This research provides a clear case in which the use of RAL technology has provided inclusive educational opportunities more efficiently and these general results are also applicable to experiments that involve physical hardware

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

A Learning Health-Care System for Improving Renal Health Services in Peru Using Data Analytics

The health sector around the world faces the continuous challenge of improving the services provided to patients. Therefore, digital transformation in health services plays a key role in integrating new technologies such as artificial intelligence. However, the health system in Peru has not yet taken the big step towards digitising its services, currently ranking 71st according to the World Health Organisation (WHO). This article proposes a learning health system for the management and monitoring of private health services in Peru based on the three key components of intelligent health care: (1) a health data platform (HDP); (2) intelligent technologies (IT); and (3) an intelligent health care suite (HIS). The solution consists of four layers: (1) data source, (2) data warehousing, (3) data analytics, and (4) visualization. In layer 1, all data sources are selected to create a database. The proposed learning health system is built, and the data storage is executed through the extract, transform and load (ETL) process in layer 2. In layer 3, the Kaggle dataset and the decision tree (DT) and random forest (RF) algorithms are used to predict the diagnosis of disease, resulting in the RF algorithm having the best performance. Finally, in layer 4, the intelligent health-care suite dashboards and interfaces are designed. The proposed system was applied in a clinic focused on preventing chronic kidney disease. A total of 100 patients and six kidney health experts participated. The results proved that the diagnosis of chronic kidney disease by the learning health system had a low error rate in positive diagnoses (err = 1.12%). Additionally, it was demonstrated that experts were “satisfied” with the dashboards and interfaces of the intelligent health-care suite as well as the quality of the learning health system.Revisión por pare