An LLMS Remotely Controlled Experiment for Smart Hydro Energy Storage and Irrigation System Powered by Photo-Voltaic Array and IoT

Many students all over the world have faced some educational issues due to the Covid-19 epidemic. As a consequence, many educational institutes focused on shifting to an E-learning system. This paper introduces a design and implementation steps of a remotely controlled experiment representing a smart hydro energy storage and irrigation system with monitoring capability using photovoltaic power and the Internet of Things (IoT). The experiment is running within the newly proposed Laboratory Learning Management System (LLMS). The remotely controlled experiment is a smart hydro energy storage and irrigation system, where the stored water during the daytime is used at night for smart irrigation of three different types of plants based on the moisture and temperature, in addition to the amount of water that the user sets for every area. In this experiment, during the daytime, the utilities are feeding from the solar panel and battery, but at night, the utilities are feeding from the battery or the hydro turbine that converts the water potential energy to electric energy. The overall Experiment is controlled using IoT sensors and relays which are connected and driven by the parameters that the user sets and can be communicated with the system using the Internet which allows the system to be proactive and take the needed decision in the right time. The main contribution of this system's experiment is the pumping of underground water in irrigation using a renewable and clean energy source, in addition to controlling the systems using IoT through the proposed LLMS. © 2022 IEEE.

Generic Laboratory Authoring Tool for Virtual and Remote Controlled Laboratories

Due to the impact of Covid-19, many students all over the world have faced some educational issues. Therefore, many educational institutes focused on shifting their learning process to E-learning system. To provide a complete E-learning system, the performing of virtual and remote Laboratory experiments is needed. In this paper, a generic and flexible online authoring tool for the Laboratory Learning System (LLS) is presented. The LLS system is a platform that provides teachers and students with a flexible environment for virtual and remote controlled labs using the proposed authoring tool. The heart of the LLS system is the authoring tool which facilities the ease and flexibility of designing various laboratory experiments which includes a number of pages, and each page has a number of steps with many draggable components. Furthermore, the proposed authoring tool is the first authoring tool that provides general and reusable virtual laboratory resource (VLR) for automatically managing laboratory software and hardware resources. To support the new VRL feature of the authoring tool, the LLS supports the ability to remotely control the laboratory equipment while performing laboratory experiments and also has the capability to run any type of simulation tool for virtually simulated labs. The proposed authoring tool is designed considering all the needed components with well-defined interfaces to achieve an effective and flexible Laboratory learning system. © 2022 IEEE.

Determining Critical Success Factors for an Online Laboratory Learning System Using Delphi Method

To prevent the spread of the Covid-19 pandemic, governments have been forced to stop offering educational services directly on campus. Thus, education has moved towards a new path;homes have been transformed into online educational classes through Learning Management Systems (LMS). Despite the many advantages of LMS such as availability, accessibility, and usability, which helps to monitor student learning and manage synchronous and asynchronous learning tools, there are many challenges facing students of applied disciplines such as sciences, engineering, and technology. Among these challenges are the following: how can laboratory experiments be conducted from a distance? How can students' achievement be measured while conducting their scientific experiment tasks? The current study aimed to reach design criteria for a new system for managing a virtual learning laboratory system (LLS). The Delphi method was used to obtain the opinions of experts and those interested in the field of e-learning design. The responses of (31) experts were analyzed using NVivo software, then the results were analyzed using statistical methods to rank them according to importance through three rounds. The results revealed that the criteria for applying artificial intelligence mechanisms, content management systems through virtual machine, assessment, and accessibility through cloud computing are among the key criteria for designing LLS for science, engineering, and technology disciplines. © 2022 IEEE.

A New Intelligent System for Evaluating and Assisting Students in Laboratory Learning Management System

Due to the Covid-19 epidemic the need for digital E-learning systems become mandatory. Also, most sectors that faced a shortage in E-learning systems are performing laboratory experiments remotely. For this reason, this research paper focuses on providing a complete Laboratory Learning Management System (LLMS) with generic and intelligent performance evaluation for experiments. The new LLMS offers many services from intelligently and automatically doing performance assessments and assistance for the students while performing the experiments online. The new performance assessment module provides regular assessment for experimental steps added to it the intelligent automatic assessment that detects if the students performed the experiments correctly from their mouse dynamics using an AI algorithm. Moreover, the new LLMS uses an analytic module to provide the teachers with analyzed results and charts to describe the behavior of students in various performed experiments. Regarding, the new performance assistant module provides students with complete assistance by pressing the help button to trigger the virtual tutor to explain any experimental steps. Furthermore, it intelligently to collects the mouse dynamics of the student performing the experiments and uses AI algorithms to detect if students face difficulties and provide them with suitable help automatically. Moreover, it can open a chat session with a real teaching assistant or a classmate to help the students. Furthermore, the new performance assessment and assistant services are considered generic because they used the mouse dynamic behavior of students which is suitable for any type of software used in the laboratory, without the need for a special device or extra cost. © 2022 IEEE.

AI Virtual Assistant for Online Laboratory Experiments Based on Multi-threshold Technique and Genetic Algorithm for Analyzing the Student's Mouse Interaction Activities

There is a great interest in online learning systems, especially due to COVID-19 pandemic. However, there are a lot of limitations and challenges of online laboratory learning systems. This paper presents an efficient technique that provides an intelligent virtual tutor for online laboratory environment, as in engineering and science sectors. Based on the analysis of the student's mouse activities, the AI virtual Assistant or virtual tutor will automatically estimate the difficulties that the student stuck during conducting the steps of lab's experiment. Hence, the virtual tutor can assist the student, accordingly. The technique is based on multi-threshold that are used to discriminate different levels of difficulties. The values of these thresholds are estimated and optimized via the genetic algorithm. The experimental results show that discrimination between different student behaviors can be achieved accurately and efficiently. © 2022 IEEE.

A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories

Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, the adoption of e-learning in sectors such as engineering, science, and technology faces a particular challenge as it needs a special Laboratory Learning Management System (LLMS) capable of supporting online lab activities through virtual and controlled remote labs. One of the most challenging tasks in designing such LLMS is how to assess a student's performance while an experiment is being conducted and how stuttering students can be automatically detected while experimenting and providing the appropriate assistance. For this, a generic technique based on Artificial Intelligence (AI) is proposed in this paper for assessing student performance while conducting online labs and implemented as a performance evaluation module in the LLMS. The performance evaluation module is designed to automatically detect the student performance during the experiment run time and triggers the LLMS virtual assistant service to provide struggling students with the appropriate help when they need it. Also, the proposed performance assessment technique is used during the lab exam sessions to support the automatic grading process conducted by the LLMS Auto-Grading Module. The proposed performance evaluation technique has been developed based on analyzing the student's mouse dynamics to work generally with any type of simulation or control software used by virtual or remote controlled laboratories;without the need for special interfacing. The study has been applied to a novel dataset built by the course instructors and students simulating a circuit on TinkerCad. Using mouse dynamics fetching, the system extracts features and evaluates them to determine if the student has built the experiment steps in the right way or not. A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.

Geofencing-based Congestion Control in Workplaces Environment using Sequential Pattern Mining

To control congestion in the workplace environment especially in crises like the COVID-19 pandemic, this requires careful control of highly crowded workplace locations. Therefore, innovative technologies, such as geofencing and sequential pattern mining can be used to estimate people movement pattern and combat the spread of COVID-19. In this paper, the workplace area is divided into a set of geofences by using geofencing technology. Then, the movement profiles of each user are estimated to control the possible congestion in the workplace's enviroment. To accomplish this, the user's historical geofence transitions are used to anticipate the next time the user will leave the current geofence. The Sequential Pattern Discovery using Equivalence classes (CM-SPADE), Succinct BWT-based Sequence prediction model (SuBSeq) and Compact Prediction Tree + (CPT+) algorithms are adopted to predict the user's next geofence. In the CM-SPADE algorithm, a vertical database is obtained from the available database and the frequent sequence is found based on relative support, confidence, and lift measures. Meanwhile, in the training phase of the SuBSeq algorithm, Ferragina and Manzini (FM)-index and Burrows-Wheeler Transform string are generated. Then, in the ready-to-predict phase, the next geofence is anticipated. The CPT+ algorithm is based on generating Prediction Tree (PT), Lookup Table (LT), and Inverted Index (IIdx) for the training data. Then, Frequent Subsequence Compression (FSC) and Simple Branches Compression (SBC) are used to reduce the size of the PT. In addition, the Prediction with improved Noise Reduction (PNR) method is utilized to reduce the execution time. The results show remarkable superiority for SuBSeq algorithm over CM-SPADE and CPT+ with the accuracy greater than 90% withh an average of 8 input geofences to predict the next geofence. © 2022 IEEE.

Efficient Collaborative and Cooperative Laboratory Experimentation System for Online Engineering, Science and Technology Education

The presence of the COVID-19 pandemic forced the educational systems all over the world to shift their activities to be hold remotely using online learning systems. Creating an efficient remote learning system that facilitate the transition to e-learning and distance education has become a must, especially in practical sectors such as Engineering, Science and Technology that require laboratory-demanded courses. Focusing only on the individual-based experiment where a single user can access and conduct the experiment, dismissing the structure of group-based laboratory experiment, can’t reflect comprehension construction as in the real on-site laboratories. In this paper, a group-based online learning system is proposed to provide a collaborative and cooperative virtual learning environment for laboratory experimentation taking into consideration different aspects that may impact the interactions between students. We divided the whole group-based laboratory experimentation platform process into four main parts: experiment creation using integrated authoring tool, experiment configuration and scheduling, monitored run-time process, and pre/post session configuration. We also proposed a runtime experiment student’s web-based graphical user interface that represents developed features that successfully achieve flexible, scalable and reusable system with the aim of maintaining satisfactory and effective user experience. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Symbiotic Relationship Between Machine Learning and Industry 4.0: A Review

Industry 4.0 though launched less than a decade ago, has revolutionized the way technologies are being used. It has found its application in almost every field of manufacturing, cybersecurity, health, banking, and other services. Industry 4.0 is heavily dependent on interconnectivity and data. Machine learning (ML) acts as a foundation for building industry 4.0 applications. In this paper, we have provided a broad view of how ML is necessary to accomplish the benefits of industry 4.0. The paper includes ML usage in companies and the limitations of ML, which need to be mitigated. There are also some instances of the failure of ML algorithms and their repercussions. Though industry 4.0 requires a lot more inputs and capital than normal processes, the long-run benefits outweigh the initial costs. ML is gaining popularity, and extensive research is happening to exploit its potential and develop full smart applications.

IoT Enabled Geofencing-Based System for Monitoring and Enforcing COVID-19 Control Measures in Workplaces, Service Areas and Distributed Home Quarantine

Due to the outbreak of COVID-19, the whole world is thinking of new mechanisms, preventive measures to protect human life from the widespread of the pandemic. Many countries imposed COVID-19 control measures to limit further infection spread. However, controlling the spread of COVID-19 without taking severe measures (e.g. lockdown and full quarantine) that have undesirable economic effects becomes a major challenge. By exploiting the modern and available information and communication technologies, innovative solutions may emerge to face and deal with this crisis. In this paper, an innovative and flexible solution based on the exploitation and integration of geofencing, and Internet of Things (IoT) technologies is proposed to enhance the crisis management framework in response to the COVID-19 pandemic. The proposed solution is designed to monitor and impose COVID-19 control measures in different environments such as distributed home quarantine, workplaces, service areas, and COVID-19 infected zones. © 2022 IEEE.

Identifying Success Criteria for Sustainable AI-based Online Laboratory Courseware System

The great transformation of e-learning during the Covid-19 pandemic has led to the emergence of new learning tools and virtual learning environments through Internet. Despite many advantages of e-Learning courseware systems such as availability, flexibility and accessibility, students of some sectors such as engineering, science and technology are facing several limitations in conducting their practical works remotely through online platform for laboratory experiments. The specific objective of this study was to come up with an updated success criteria and list of requirements that should be considered for developing a sustainable artificial intelligence-based online laboratory courseware system. Data for this study were collected using online questionnaire distributed to a group of e-Learning experts. NVivo software was used to analyze experts' comments on how to construct the online laboratory courseware systems. This research revealed 16 basic design and development criteria for the online laboratory courseware system, which are distributed into eight sub-branches and organized into four primary aspects. These findings suggest that in general 30 accurate indicators for the design of an effective laboratory learning system (LLS) for engineering, science and technology sectors dealing with content management, assessment, accessibility and usability as well as the adoption of artificial intelligence techniques. © 2022 IEEE.

Violation Detection Technique for COVID-19 Self-Isolation and Control Measures using Wireless and Geofencing Technologies

The spread of COVID-19 pandemic forced the governments to apply severe control measures such as lockdowns and quarantines. This lead to the need for innovative digital health technological solutions to increase the effectiveness of the necessary precautionary. In this regard, a smart geofencing solution is proposed in this paper to effectively support the implementation of COVID-19 self-isolation and control measures in different areas where each intended area is defined as a geofence with specific control actions. The proposed solution is based on a mobile client application that exploits specific wireless metrics for Wi-Fi and cellular networks to generate a digital signature for the defined geofence and detects any violation of its virtual borders. A detailed description along with a mathematical model of how the geofence digital signature can be created is presented. Then, a geofence matching criterion based on a weighted score is proposed to detect the geofence violation by comparing the periodically measured wireless metrics with the created geofence digital signature. The proposed solution can be used for detecting violation in different cases such as distributed home quarantine and controlled areas. The performance of the proposed solution is studied on different scenarios, and the results show the significance of the proposed solution compared to others. Moreover, the effect of the decision time window parameter on the performance of the proposed geofence matching criterion is illustrated. © 2022 IEEE.

Automatic Identification of Student's Cognitive Style from Online Laboratory Experimentation using Machine Learning Techniques

Online learning has emerged as powerful learning methods for the transformation from traditional education to open learning through smart learning platforms due to Covid-19 pandemic. Despite its effectiveness, many studies have indicated the necessity of linking online learning methods with the cognitive learning styles of students. The level of students always improves if the teaching methods and educational interventions are appropriate to the cognitive style of each student individually. Currently, psychological measures are used to assess students' cognitive styles, but about the application in virtual environment, the matter becomes complicated. The main goal of this study is to provide an efficient solution based on machine learning techniques to automatically identify the students' cognitive styles by analyzing their mouse interaction behaviors while carrying out online laboratory experiments. This will help in the design of an effective online laboratory experimentation system that is able to individualize the experiment instructions and feedback according to the identified cognitive style of each student. The results reveal that the KNN and SVM classifiers have a good accuracy in predicting most cognitive learning styles. In comparison to KNN, the enlarged studies ensemble the KNN, linear regression, neural network, and SVM reveal a 13% increase in overall total RMS error. We believe that this finding will enable educators and policy makers to predict distinct cognitive types in the assessment of students when they interact with online experiments. We believe that integrating deep learning algorithms with a greater emphasis on mouse location traces will improve the accuracy of our classifiers' predictions.

Intelligent Virtual Tutor for Online Laboratory Experiments Based on Modelling the Student's Mouse Interaction Behavior

COVID-19 pandemic has led to a great interest in online learning systems. However, the lack of suitable online laboratory learning systems has posed a particular challenge for sectors that need laboratory experimentation activities as in engineering and science domains. This paper presents a simple but efficient technique for providing intelligent virtual tutor that can assist students in online laboratory experimentation environment. The proposed technique is based on analyzing and modelling the student's mouse interaction behavior for identifying the difficulties that the student faced during conducting the lab's experiment, and hence providing the suitable assistance. The different levels of difficulties will be detected using the trajectory of mouse movement activities. The obtained results verify accurate and very fast operation for identifying the student's difficulties.

Understanding the role and capabilities of Internet of Things-enabled Additive Manufacturing through its application areas

The purpose of this paper is to highlight the importance of the effective utilisation of internet-based technologies in Additive Manufacturing (AM) through its practical implications in various areas. The key technologies that comprise the Industry 4.0 paradigm and have been previously implemented into the AM process possess immense potential to bring about miraculous manufacturing changes. The ongoing pandemic situation, too, has pushed the industries into the deployment of advanced technologies to deal effectively with the major challenges that followed COVID-19 and to keep up with customers' expectations. Therefore, manufacturing industries need to adopt the latest internet-based technologies into AM, especially for lean and optimised manufacturing and perceived proficiency of advanced printing technologies. This paper identifies goals of sustainable manufacturing that can be accomplished as a result of implementation of Internet of Things (IoT) in 3D printing technology. Also, since the Internet of Things-enabled AM process is an entirely new concept, limited sources are available in this field. Pubmed, Scopus, and Google Scholar databases are used to conduct literature reviews. The study conducted is the most up-to-date information on Internet of Things-enabled AM and is rigorously analyzed to identify prospective application areas. Thus, IoT-enabled AM helps in producing sustainable solutions for humanity and in meeting customers' demands in the stipulated period, thereby contributing to mass personalisation, which is the ultimate goal of Industry 4.0. © 2021 Kingfa Scientific and Technological Co. Ltd.

Quality 4.0 technologies to enhance traditional Chinese medicine for overcoming healthcare challenges during COVID-19

The Quality 4.0 concept is derived from the industrial fourth revolution, i.e., Industry 4.0. Quality 4.0 is the future of quality, where new digital and disruptive technologies are used to maintain quality in organizations. It is also suitable for traditional Chinese medicine (TCM) to maintain quality. This quality revolution aims to improve industrial and service sectors’ quality by incorporating emerging technologies to connect physical systems with the natural world. The proposed digital philosophy can update and enhance the entire TCM treatment methodology to become more effective and attractive in the current competitive structure of the pharmaceutical and clinical industries. Thus, in healthcare, this revolution empowers quality treatment during the COVID-19 pandemic. There is a major requirement in healthcare to maintain the quality of medical tools, equipment, and treatment processes during a pandemic. Digital technologies can widely be used to provide innovative products and services with excellent quality for TCM. In this paper, we discuss the significant role of Quality 4.0 and how it can be used to maintain healthcare quality and fulfill challenges during the pandemic. Additionally, we discuss 10 significant applications of Quality 4.0 in healthcare during the COVID-19 pandemic. These technologies will provide unique benefits to maintain the quality of TCM throughout the treatment process. With Quality 4.0, quality can be maintained using innovative and advanced digital technologies. © 2021 Digital Chinese Medicine

Significant Applications of Big Data in Industry 4.0

Industry 4.0 is being implemented with the help of advanced technologies. Big data, Artificial Intelligence (AI), Robotics, Internet of Things (IoT), Cloud computing, and 3D printing are the major technologies used to adopt Industry 4.0 successfully. Here, the study's need is to discuss the major potential of big data for Industry 4.0. These technologies' primary purpose is to collect the right data to solve the relevant issue during manufacturing and other required services. This technology plays a significant role in creating advancements in this fourth industrial revolution. Conclusively, big data applications are useful for in-process management and productivity improvement in the automation sector. Complex systems of drivers and intelligent sensors can be easily optimized based on information collected using this technology. Big data is the key to gain a competitive leap by reconnoitring the fundamental issues like deviations during the process, quality discriminations, and energy efficiency squander in a manufacturing process. The study discusses the significant applications of big data in Industry 4.0. For a proper surveillance system, industries need to have an immensely technical or personalized way, making big data a valuable source for predicting analysis and operation management based on market insight statistics or information. In upcoming days, big data will provide further advancement in Industry 4.0 and is supposed to play an efficient role in its successful adoption.

Factors Affecting Telehealth Availability Among Breast Centers During the Pandemic

Introduction: To provide breast cancer care during the COVID-19 pandemic, many centers shifted toward offering telehealth visits. We sought to determine the availability of telehealth services at Commission on Cancer (CoC)-accredited centers in the United States and factors associated with this virtual accessibility. Methods: Using a secret shopper model from June-September 2020, we contacted 371 CoC-accredited centers providing breast cancer care to determine whether they offered telehealth appointments. We analyzed factors associated with telehealth availability using bivariate and multivariate logistic regression analyses. Results: There were 316 of 371 (85.2%) hospitals that reported having telehealth capacity for breast cancer patients. Facility type (p=0.027), teaching hospital status (p=0.0001), geographic location (p=0.014), and hospital size (based on bed number, p=0.036) were all associated with telemedicine use on bivariate analysis (see table). For-profit vs not-for-profit status and the population base in which a center was located did not affect telehealth availability. On multivariate analysis, controlling for facility type, teaching hospital status and hospital size, only geographic location (p=0.004) was found to be an independent predictor of telehealth access, with centers located in the West being more than 6 times more likely to offer this provision than other regions, including the Northeast (OR:6.38;95% CI:1.27-32.00, p=0.024). Conclusion: While several hospital characteristics, including CoC designation, size, and teaching hospital status affected availability of telehealth visits, significant geographic disparities remained in telehealth provision independent of these factors. As COVID-19 forces medicine to increase its telehealth focus, particular attention should be paid to the geographic variation that may exacerbate access disparities. [Formula presented]

AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design

The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.

Accessibility of Telehealth services for cancer care at cancer hospital in the United States

Background: The COVID-19 pandemic has dramatically accelerated the availability of telehealth services for patients with cancer. However, little national cross-sectional data is available to inform potential gaps in access. We aimed to characterize overall access to and trends in telehealth availability for new cancer care patients at hospitals across the United States. Methods: We performed a cross sectional secret-shopper study to evaluate the availability of telehealth services for new patients for three major cancer types- colorectal, breast, and skin cancer-at Commission on Cancer accredited hospitals during the period of April to November 2020. American Hospital Association and Center for Medicare and Medicaid Service databases were queried to determine hospital characteristics. We described hospital variation in access to telehealth services using descriptive statistics. Univariable and multivariable logistic regression were used to identify factors associated with telehealth availability. Results: Of 334 successfully contacted facilities, 248 (74%) offered new patient telehealth services for at least one cancer type. However, access differed by cancer site: telehealth availability for new patients with skin, colorectal, and breast cancer was 47%, 42%, and 38%, respectively. Of the facilities sampled, 47% offered telehealth for one cancer type, 40% for two cancer types, and 14% for all three cancer types. Rates of any telehealth access among the cancer types ranged from 61% at Community Cancer Programs to 100% at NCI Designated Programs. In multivariable logistic regression, facility type was significantly associated with telehealth access while factors such as bed size, ownership, and volume were not significantly associated. Conclusions: Although access to telehealth services for patients with cancer has increased, overall gaps in access remain. Within facility differences in telehealth access imply opportunities to better align services within institutions, though further investigation is warranted as these offerings mature. (Table Presented).