Applying blockchain technology to ensure compliance with sustainability standards in the PPE multi-tier supply chain

Because of the Covid-19 pandemic, urgent surging demand for healthcare products such as personal protective equipment (PPE) has caused significant challenges for multi-tier supply chain management. Although a given firm may predominantly focus on an arms-length solution by targeting the first-tier supplier, the firm can still struggle with extended multi-tier suppliers it cannot choose which use unsustainable practices. One key goal is compliance across various dimensions with production, environmental and labour standards across the multi-tier supply chain, a goal that blockchain technology can be applied to manage. Based on a comprehensive literature review, this research develops a system architecture of blockchain-based multi-tier sustainable supply chain management in the PPE industry designed to identify and coordinate standards in production and social and environmental sustainability in multi-tier PPE supply chains. The architecture was validated by theoretical basis, expert opinions and technical solutions. We conclude with managerial implications for implementing blockchain technology to advance sustainable multi-tier supply chain practices.

Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

A Shuffling Building Block and Augmentation Parameter Tuning Techniques to Handle Small Medical Dataset

The recent COVID-19 outbreak showed us the importance of faster disease diagnosis using medical image processing as it is considered the most reliable and accurate diagnostic tool. In a CNN architecture, performance improves with the increasing number of trainable parameters at the cost of processing time. We have proposed an innovative approach of combining efficient novel architectures like Inception, ResNet, and ResNet-Xt and created a new CNN architecture that benefits Extreme Cardinal dimensions. We have also created four variations of the same base architecture by varying the position of each building block and used X-Ray, Microscopic, MRI, and pathMNIST datasets to train our architecture. For learning curve optimization, we have applied learning rate changing techniques, tuned image augmentation parameters, and chose the best random states value. For a specific dataset, we reduced the validation loss from 0.22 to 0.18 by interchanging the architecture's building block position. Our results indicate that image augmentation parameters can help to decrease the validation loss. We have also shown rearrangement of the building blocks reduces the number of parameters, in our case, from 5,689,008 to 3,876,528. © 2023 IEEE.

Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.

Sistema de recomendación integrado para el autocontrol emocional en adultos mayores con cuadro postcovid-19 en Panamá

: The effects of the pandemic can translate into a variety of physical and emotional reactions that are affecting the population, particularly the elderly Panamanian population, who have not been able to overcome the mainly emerging challenges of an infectious disease with health implications. physical and has also profoundly affected their well-being and mental health. To allow the Panamanian elderly population to improve emotional self-control and mental relaxation, we propose a software architecture for the development of a recommendation system integrating: artificial intelligence (AI), internet of things (IoT) and mobile applications. Keywords: Covid-19, AI, IoT, Mobile apps, Machine learning. 1.Introducción La Covid-19 es, sin lugar a duda, la mayor catástrofe del siglo XXI, probablemente la crisis global más significativa después de la segunda guerra mundial. En este artículo, proponemos el diseño de una arquitectura altamente integral y flexible basada en diferentes elementos de TIC que permitirá extraer datos de un sensor, analizarlos y realizar recomendaciones a pacientes panameños adultos mayores con afecciones psicológicas o reacciones emocionales posteriores al contagio de la Covid-19 (post-covid-19), basado en la utilización de componentes como IA, IoT y aplicaciones móviles para lograr el autocontrol emocional y relajación mental.

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation: IMS

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel;human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products' waste avoidance.

PCTran-Cloud: an Accident Simulator for Nuclear Power Plants Based on Cloud Technology

In the current environment where COVID-19 is serious, the space, place and resources required for teaching nuclear power plants are restricted to a great extent. To solve such problems and improve the utilization of education resources, this study improved an accident simulator for nuclear power plants based on the concept of cloud technology. We build the Browser / Server architecture so that the platform has successfully implemented multiterminal, multiplatform and multiuser simultaneous applications. Through the simulation results of the Small Break Loss of Coolant Accident (SBLOCA) and the test results of platform performance by PCTran-Cloud, the correctness of PCTran-Cloud in the accident simulation function and results were verified. In general, PCTran-Cloud has the characteristics of high scalability, high concurrency and high security. The platform can provide an environment for the training and education of nuclear power professionals. © 2022 IEEE.

Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities

Healthcare systems are under increasing strain due to a myriad of factors, from a steadily ageing global population to the current COVID-19 pandemic. In a world where we have needed to be connected but apart, the need for enhanced remote and at-home healthcare has become clear. The Internet of Things (IoT) offers a promising solution. The IoT has created a highly connected world, with billions of devices collecting and communicating data from a range of applications, including healthcare. Due to these high volumes of data, a natural synergy with Artificial Intelligence (AI) has become apparent –big data both enables and requires AI to interpret, understand, and make decisions that provide optimal outcomes. In this extensive survey, we thoroughly explore this synergy through an examination of the field of the Artificial Intelligence of Things (AIoT) for healthcare. This work begins by briefly establishing a unified architecture of AIoT in a healthcare context, including sensors and devices, novel communication technologies, and cross-layer AI. We then examine recent research pertaining to each component of the AIoT architecture from several key perspectives, identifying promising technologies, challenges, and opportunities that are unique to healthcare. Several examples of real-world AIoT healthcare use cases are then presented to illustrate the potential of these technologies. Lastly, this work outlines promising directions for future research in AIoT for healthcare. IEEE

Collaborative E-Learning Process Discovery in Multi-tenant Cloud

With the appearance of the COVID-19 pandemic, the practice of e-learning in the cloud makes it possible to: avoid the problem of overloading the institutions infrastructure resources, manage a large number of learners and improve collaboration and synchronous learning. In this paper, we propose a new e-leaning process management approach in cloud named CLP-in-Cloud (for Collaborative Learning Process in Cloud). CLP-in-Cloud is composed of two steps: i) design general, configurable and multi-tenant e-Learning Process as a Service (LPaaS) that meets different needs of institutions. ii) to fulfill the user needs, developpe a functional and non-functional awareness LPaaS discovery module. For functional needs, we adopt the algorithm A* and for non-functional needs we adopt a linear programming algorithm. Our developed system allows learners to discover and search their preferred configurable learning process in a multi-tenancy Cloud architecture. In order to help to discover interesting process, we come up with a recommendation module. Experimentations proved that our system is effective in reducing the execution time and in finding appropriate results for the user request.

A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day.

Cloud-Based Architecture for Early Detection and Postoperative Monitoring of Hypospadias Patients in Indonesia

Covid-19 Epidemic has significantly changed how hypospadias patients are delivered to healthcare services, particularly after hypospadias repairs (postoperative care). Some studies reported that using telemedicine schemes by sending digital documentation such as images and videos through cell phones can facilitate an assessment of postoperative monitoring of hypospadias patients. However, this approach raises various concerns, such as managing digital documentation of hypospadias patients, analyzing the data, and the security of individuals' health information. This study proposes a design of cloud-based architecture for early detection and postoperative monitoring of hypospadias patients to address the concerns above. The user acceptance test shows that most users agree that this application may be used for early detection, monitoring hypospadias patients, and helping capture videos and provide labeling to patients' data. © 2022 IEEE.

Reconfigurable and In-sensor Computing Pb-free Perovskite Array towards Intelligent X-ray Imaging

Conventional X-ray imaging architectures feature data redundancy and hardware consumption due to the separated sensory terminal and computing units. In-sensor computing architectures is promising to overcome such drawbacks. However, its realization in X-ray range remains elusive. We propose ion distribution induced reconfigurable mechanism, and demonstrate the first X-ray band in-sensor computing array based on Pb-free perovskite. Redistribution of Br- ion in perovskite induces the switching of PN and NP modes under electrical pooling. X-ray detection sensitivity can be switched between two stable self-power sensing modes with 4373±298 and -7804±429 mu mathrm{CGy}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{cm}{-2} respectively, which are superior than that of commercial a-Se detectors (20 mu mathrm{C} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{c} mathrm{m}{-2}). Both modes exhibit low detection limit of 48.4 mathrm{n} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}, which is two orders lower than typical medical dose rate of 5.5 mu mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}. The perovskite array sensors can integrate with thin film transistors (TFTs) with low-temperature (80oC) process with good uniformity. An in-sensor computing algorithm of attention mechanism is performed on array sensors for chest X-ray images COVID-19 recognition, which enables an accuracy improvement up to 98.2%. Our results can pave the way for future intelligent X-ray imaging. © 2022 IEEE.

A Supervised Learning-Based Framework for Predicting COVID-19 in Patients

The integration of ML and loT can provide insightful details for critical decision making, automated responses, etc. Predicting future trends and detecting anomalies are some of the areas where loT and ML are being used at a rapid rate. Machine learning can help decode the hidden patterns in IoT data. It may complement or replace manual processes in critical areas with automated systems that use statistically derived behavior. In healthcare, wearable sensors used for tracking patient activity have been continuously producing a staggering amount of data. This paper proposes an IoT-based scalable architecture for detecting COVID-19-positive patients and storing and processing such massive amount of data on the cloud. The proposed architecture also employs machine learning algorithms for correct classification of patients. The proposed architecture employs gradient boosting classifier method for early detection of COVID-19 in the patient's body. In order to make the architecture scalable and faster in terms of computational power, the architecture employs cloud computing for data storage. © 2023 IGI Global. All rights reserved.

Design and implementation of cloud platform for nuclear accident simulation

Introduction: To meet the multi-user, cross-time-and-space, cross-platform online demand of work, and professional training teaching in nuclear reactor safety analysis under the normalization of Coronavirus Disease 2019. Method: Taking the nuclear accident simulation software PCTRAN as an example, this study adopts cloud computing technology to build the NasCloud, a nuclear accident simulation cloud platform based on Browser/Server architecture, and successfully realizes multi-user, cross-time-and-space, cross-platform applications. Targeting the AP1000, a pressurized water reactor nuclear power plant, the simulation of cold-leg Small Break Loss of Coolant Accident and cold-leg Large Break Loss of Coolant Accident were carried out to verify the correctness of the NasCloud's accident simulation function. Results: The result shows that the simulation functions and results of the NasCloud in multi-terminal are consistent with the single version of PCTRAN. At the same time, the platform has high scalability, concurrency and security characteristics. Discussion: Therefore, the nuclear accident simulation cloud platform built in this study can provide solutions for the work and training of nuclear reactor safety analysis, and provide reference for other engineering design and simulation software cloud to computing transformation. Copyright © 2023 Chen, Chen, Xie, Xiong and Yu.

Maintainability of Service-Oriented Architecture using Hybrid K-means Clustering Approach

The process of making changes to software after it has been delivered to the client is known as maintainability. Maintainability deals with new or changed client requirements. Service-oriented architecture (SOA) is a method for developing applications that helps services work on different environments. SOA works on patterns of distributed systems that help different applications communicate with each other using different protocols. To assess the maintainability of service-oriented architecture, different factors are required. Some of these factors are analyzability, changeability, stability, and testability. Modification is the process of upgrading the software functionality. After modification of service-oriented architecture, the module will go to the testing phase. The evaluation and verification of whether a software product or application performs as intended is known as testing. The testing phase is a combination of various stages, such as individual module testing and testing after collaborations between them. This testing stage is time-consuming in the maintenance process. The term "outlier" refers to a module in software systems that deviates significantly from the rest of the module. It represents the collection of data, variables, and methods. For instance, the program might have been coded mistakenly or an investigation might not have been run accurately. To detect the outlier module, test cases are needed. A methodology is proposed to reduce the predefined test cases. K-means clustering is the best approach to calculate the number of test cases, but the outlier is not automatically determined. In this paper, a hybrid clustering approach is applied to detect the outlier. This clustering method is used in software testing to count the number of comments in various software and in medical science to diagnose the disease of Covid patients. The experimental outcomes show that our strategy achieves better results.

Geo-Spatial Context Provision for Digital Twin Generation

Light detection and ranging technology allows for the creation of detailed 3D point clouds of physical objects and environments. Therefore, it has the potential to provide valuable information for the operation of various kinds of cyber-physical systems that need to be aware of, and interact with, their surroundings, such as autonomous vehicles and robots. Point clouds can also become the basis for the creation of digital representations of different assets and a system's operational environment. This article outlines a system architecture that integrates the geo-spatial context information provided by LiDAR scans with behavioral models of the components of a cyber-physical system to create a digital twin. The clear distinction between behavior and data sets the proposed digital twin architecture apart from existing approaches (that primarily focus on the data aspect), and promotes contextual digital twin generation through executable process models. A vaccine logistics automation use case is detailed to illustrate how information regarding the environment can be used for the operation of an autonomous robot carrying out transport preparation tasks. Besides supporting operation, we propose to combine context data retrieved from the system at different points in the logistics process with information regarding instances of executable behavior models as part of the digital twin architecture. The twin can subsequently be used to facilitate system and process monitoring through relevant stakeholders and structure context data in a user-centric fashion.

Face Mask Detection Using Image Processing and Convolutional Neural Networks

Face masks have become a crucial part of our everyday lives as the number of COVID cases around the world has increased, and new varieties appear every few months. We must wear a mask every time we walk outside, so the installation of face mask detectors in public places has become quite significant. In this study, we use image processing to create a face mask detection system on cascading multiple architectures of convolutional neural networks (CNN) and deep neural networks (DNN), with investigate the findings using MobileNetV2, Xception and ResNet152V2 models. The proposed technique was able to obtain excellent accuracy by training the system with CNN and DNN architectures. © 2022 IEEE.

Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico

The emergence of the COVID-19 pandemic has led to an unprecedented change in the lifestyle routines of millions of people. Beyond the multiple repercussions of the pandemic, we are also facing significant challenges in the population's mental health and health programs. Typical techniques to measure the population's mental health are semiautomatic. Social media allow us to know habits and daily life, making this data a rich silo for understanding emotional and mental well-being. This study aims to build a resilient and flexible system that allows us to track and measure the sentiment changes of a given population, in our case, the Mexican people, in response to the COVID-19 pandemic. We built an extensive data system utilizing modern cloud-based serverless architectures to analyze 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. We provide metrics, advantages, and challenges of developing serverless cloud-based architectures for a natural language processing project of a large magnitude. © 2022, The Author(s).

DevOps and IaC to Automate the Delivery of Hands-On Software Lab Exams

During the recent COVID-19 outbreak, many educational institutions had to operate fully remotely and conduct examinations online. Conducting hands-on software lab exams online raises serious issues and concerns such as: 1) the heterogeneity of examinees' personal computers, 2) the computers may not be powerful enough to run the required software for the hands-on exam, especially hardware intensive programs, 3) cheating and plagiarism are hardly controllable since examinees are using their personal computers and they can look up whatever information they need. The paper proposes a highly available and scalable software cloud architecture that utilizes modern cloud technologies, DevOps principles, and infrastructure as code tools of various categories to facilitate the construction of a highly available and scalable architectural solution that automates the delivery of software lab exams. Evaluation and results of the proposed architecture illustrate that a cloud instance that is preconfigured with all the required exam material can be instantiated and completely ready to use in an average of 149 seconds. Moreover, deploying the backend server on a Kubernetes Cluster allowed the system to automatically scale and handle sudden loads due to Kubernetes' auto-scaling and self-healing features. © 2022 IEEE.