A drug recommender system Based on Collaborative Filtering for Covid-19 patients

The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field. © 2023 IEEE.

Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting

Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets. © 2023 IEEE.

Survival Analysis and Its Application of Discharge Time Likelihood Prediction Using Clinical Data on COVID-19 Patients-Machine-Learning Approaches

COVID-19 has significant fatality rate since its appearance in December 2019 as a respiratory ailment that is extremely contagious. As the number of cases in reduction zones rises, highly health officials are control that authorized treatment centers may become overrun with corona virus patients. Artificial neural networks (ANNs) are machine coding that can be used to find complicate relationships between datasets. They enable the detection of category in complicated biological datasets that would be impossible to identify with traditional linear statistical analysis. To study the survival characteristics of patients, several computational techniques are used. Men and older age groups had greater mortality rates than women, according to this study. COVID-19 patients discharge times were predicted;also, utilizing various machine learning and statistical tools applied technically. In medical research, survival analysis is a regularly used technique for identifying relevant predictors of adverse outcomes and developing therapy guidelines for patients. Historically, demographic statistics have been used to predict outcomes in such patients. These projections, on the other hand, have little meaning for the individual patient. We present the training of neural networks to predict outcomes for individual patients at one institution, as well as their predictive performance using data from another institution in a different region. The research output show that the Gradient boosting longevity model beats the all other different models, also in this research study for predicting patient longevity. This study aims to assist health officials in making more informed decisions during the outbreak. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Recent Advances of Blockchain and Its Applications

Blockchain is an emerging decentralized data collection, sharing, and storage technology, which have provided abundant transparent, secure, tamper-proof, secure, and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-enabled applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus and storage mechanisms and communication schema in general blockchain systems. Then extensive literature review is conducted on blockchain-enabled Internet of Things (IoT), edge computing, federated learning, and several emerging applications including healthcare, COVID-19 pandemic, online social network, and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges, and opportunities in both academia and industry. © 2020 Tsinghua University Press.

Nonparametric Homogeneity Test for Small Samples and Its Applications to COVID-19 Epidemic Curves Forecasting

The COVID-19 pandemic is accompanied by intensive attempts to build mathematical models to predict it. For this, various models are used, both traditional differential equations and machine learning models. Classical epidemiological compartment models contain parameters that are difficult to measure. Their results are used to model various scenarios, but it is difficult to obtain a reliable forecast with their help. Machine learning models, on the other hand, do not use prior assumptions, and their inferences are based only on training samples. This usually results in more reliable forecasts. In both the first and second cases, it is necessary not only to estimate the forecast error, but to compare the prediction accuracy of different models by checking the error homogeneity also. An additional factor complicating the problem is the small size of available samples in some cases. This forces one to resort to resampling methods. The article describes the Klyushin-Petunin test for testing the homogeneity of samples with ties in a multi-sample design and compares it with the traditional Anderson-Darling, Kruskal-Wallis and Friedman tests using the example of three methods for predicting the COVID-19 epidemic in the basis of epidemic data in Germany, Japan, South Korea and Ukraine. © 2022 IEEE.

Adoption of Artificial Intelligence Technologies by Often Marginalized Populations

Artificial Intelligence (AI) has found its application in many aspects of our lives. The COVID-19 pandemic has further allowed AI to play an increasingly important and beneficial role in our society, but it has also exposed the limitation of AI, particularly related to marginalized populations. This chapter first provides an overview of AI and equity pre-COVID, and then discusses what we know about AI during COVID-19. At the end, we conduct a systematic literature review to examine marginalized populations and their use of AI technologies during COVID-19. The populations examined in this review are children, older adults, people with disabilities, racial and ethnic minorities (in a country or region), low-income, gender, or general marginalized populations. The results indicate a huge gap for research on the use, adoption, and perception of AI technologies by communities that have previously experienced inequities in AI and COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Floating Structure Integrity Management Program (FSIMP) Towards Digitalization

The objective of the paper is to demonstrate digitalization of Floating Structures Integrity Management Program (FSIMP) and its application for the structural integrity of floating structure assets. The framework of FSIMP is being developed by adopting Risk Based Inspection (RBI) methodology and complemented with technical know-how and industry best-practices. Implementing the methodology provides strategic planning for maintenance by reducing the anticipated risk. Hence, ensuring uninterrupted service of the floating structure assets throughout the service life. This paper presents a systematic approach for digitalization of the integrity management program for a nominated floating structure asset. The methodology offers a procedure to acquire necessary data management gathering, risk assessment, and RBI survey plan to maintain the structural integrity in the centralized web-based platform of FSIMP. RBI process is adopted into the FSIMP to investigate all deterioration and failure mechanisms. These structures will be identified by qualitative and quantitative risk assessment methods. The implementation of FSIMP offers a wide range of capabilities in structural integrity management such as integrating all floating structure fleet assets in a single dashboard of web-based platform, clear line of sight for reliable structural integrity, and an holistic overview across all levels of management. FSIMP with RBI methodology evaluates all data gathering to optimize inspection resources based on the risk assessment through an optimum combination of inspection methods and frequencies. The whole process is aligned to the requirements from Classification to ensure reliability for continuous operations. It also observes the essential need of digitalization for FSIMP during the time of post-COVID19 pandemic and the ever-expanding offshore oil, gas and energy frontiers that demand the adoption of new and advanced technologies, especially in the field of digitalization. It is shown that FSIMP has great potential as a digitalization tool and system to integrate with the RBI risk assessment that aligns to the requirements from Classification. It is strategically to maximize the effectiveness and improved efficiency for inspection and monitoring plan. The paper provides information on the solution of digitalization to the Floating Structures Integrity Management Program (FSIMP) in ensuring that the integrity of floating structure asset during the service life is intact for continuous operation and a holistic overview for all the assigned fleet assets in a centralized dashboard web-based platform. In addition to that, RBI is as added benefit to the FSIMP with its structure methodology of data evaluation and risk assessment in order to objectively optimizing inspection and maintenance resources. Copyright © 2023, International Petroleum Technology Conference.

Generation of microbial protein feed (MPF) from waste and its application in aquaculture in China

Aquaculture is regarded as one of the fastest methods for preparing food and may be relied upon more and more in the future. Production can be seeded from fish caught in the wild and can be maintained with imported fish food however, aquaculture output and quality is limited by cost and resources, and there is an incentive to make it more environmentally sustainable. If these goals can be achieved, we will produce better quality fish and in higher volumes. Microbial protein feed (MPF) offers a sustainable feedstuff solution for the aquaculture industry in China, with the net benefits of taking less time to prepare, using less water and land, being recyclable and also reducing carbon emissions. MPF provides stable and high quality proteins and is produced through the fermentation of microorganisms by utilizing agricultural and industrial waste as substrates and been extensively used in fish and shrimp production in China. This review describes the microorganisms, raw materials, fermentation processes and nutritional components used in MPF production in aquaculture. We shall discuss also MPF large-scale production processes in detail and then finally, what opportunities and challenges are faced by MPF in Chinese aquaculture in the context of "double carbon"targets and Covid-19. High-efficiency biosynthesis technology using mono-carbon gases to produce protein will become an important field in the future, as it shall facilitate sustainable and healthy feedstocks for the aquaculture industry, and allow China to achieve the goal of lower carbon emissions. © 2023 Elsevier Ltd.

Visualization of Covid-19 Pandemic Data: An Analysis

This paper focuses on data visualization techniques and its efficiency. Data visualization refers to pictorial representation of data. Though it is easy to view and understand, the time is yet to come when this technique would see its full utilization. It has a lot of applications in data presentation as well as data exploration, pattern mining and predictive analysis. Its applications would be studied in relation to Covid-19 pandemic. The world is suffering from novel corona virus since December 2019. It has been more than two years and there seems no end to this pandemic. Data visualization techniques will be applied using R programming language, it has been widely adopted by statisticians to study and analyse data, so it will be used here as a tool to study data generated by corona virus outbreak in the last two years. However, the scope of the study is limited to data generated in India for number of confirmed cases, deaths and recovered cases. © 2022 IEEE.

A Probabilistic Approach to Evaluate the Likelihood of Artificial Genetic Modification and Its Application to SARS-CoV-2 Omicron Variant

A method to find a probability that a given bias of mutations occur naturally is proposed to test whether a newly detected virus is a product of natural evolution or a product of non-natural process such as genetic manipulation. The probability is calculated based on the neutral theory of molecular evolution and binominal distribution of non-synonymous (N) and synonymous (S) mutations. Though most of the conventional analyses, including dN/dS analysis, assume that any kinds of point mutations from a nucleotide to another nucleotide occurs with the same probability, the proposed model takes into account the bias in mutations, where the equilibrium of mutations is considered to estimate the probability of each mutation. The proposed method is applied to evaluate whether the Omicron variant strain of SARS-CoV-2, whose spike protein includes 29 N mutations and only one S mutation, can emerge through natural evolution. The result of binomial test based on the proposed model shows that the bias of N/S mutations in the Omicron spike can occur with a probability of 2.0 × 10−3 or less. Even with the conventional model where the probabilities of any kinds of mutations are all equal, the strong N/S mutation bias in the Omicron spike can occur with a probability of 3.7 × 10−3, which means that the Omicron variant is highly likely a product of non-natural process including artifact. © 2022 Information Processing Society of Japan.

Hayyakum (حيَّاكم) - COVID-19 Vaccine Digital Certificate: A Blockchain Approach

As a consequence of the global pandemic, many restrictions and rules were enforced. One predicament was the travel restrictions and requirements put into place with regard to vaccinations. Countries worldwide now require people to be vaccinated upon entry. The process of validating vaccine doses requires lots of paperwork and is inefficient. Blockchain is an uprising technology that is secure and fast at carrying out transactions. We propose implementing vaccine dose verifications between countries through vaccine certificates using Blockchain as an effective solution. The need for a common shared database, avoiding a trusted third party to administrate the network, having several countries involved, ensuring privacy and security, and accountability logs make Blockchain needed in this scenario. Digital vaccine certificates are very sensitive information that must be kept private and secure but accessible to several entities. Blockchain ensures the aforementioned requirements are met while preserving the integrity of the VDCs. This paper describes blockchain technology and its application in digital vaccine certificates. © 2022 IEEE.

Hydrogen Related Technologies and Application a Major Pathway for the Energy Transition

The capabilities of hydrogen as a key role in the upcoming transition to a more sustainable green energy future have increased rapidly in recent years and gained interest globally. COVID-19 Outbreak drew attention to how important it is for us as societies to have Clean Air, Water, Food And re-established consumers behavior regarding the consumption of energy which pointed the attention at hydrogen Starting from the first meeting to fight climate change until today, the biggest steps and strategies taken against global warming focusing on hydrogen cost-reduction technologies and Carbon-based industries where hydrogen is a promising solution to transform them into Emission-free industries. This chapter reviews the most recent publications and papers on green hydrogen, its applications, and the challenges that faces us as societies to empower green hydrogen utilization in a transition to a carbon-free future, and how it can play a vital role in the energy transition with Europe latest hydrogen-based strategies to become a climate-neutral continent. And how hydrogen and its application will lead the energy transition to renewables in Turkey. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Systematic Literature Review of Knowledge Management Models for E-learning

E-learning refers to a technology-based learning system that allows it to be accessed anytime and anywhere. The application of e-learning in the world of education today is very much needed. The Covid-19 pandemic has encouraged to be used as a backup and solution in the learning system. Currently, knowledge management has been widely applied to digital-based learning. In its application, it is necessary to know what methods and models are applied to support the successful implementation of e-learning. It is hoped that this research will find out the knowledge management model for e-learning suitable for educational institutions. This study aims to find out the models and impact that arise in applying knowledge management in e-learning. This research was conducted using a systematic literature review method that took references from ACM, IEEE, ProQuest, ScienceDirect, and Scopus from 2017-2021 with the final paper extracted as many as 16 articles. In using KM to e-learning, using quantitative methods is more widely used. The SECI model can be a solution as a material for analyzing existing KM processes. Several other models can be applied according to the needs and objectives of KM implementation on e-learning. The application results show positive impacts, including improving better perception, user satisfaction, and improving existing knowledge processing. The implementation of KM in e-learning is also one of the success factors needed. © 2022 IEEE.

DISINFECTION WITH VAPORIZED HYDROGEN PEROXIDE FLUID IN DIFFERENT ENVIRONMENTS AND ITS APPLICATIONS AND ADVANTAGES IN CRISIS MANAGEMENT

During a global pandemic, mitigating the impact of the disease and coordinating efforts to manage not only the medical but also the logistical and administrative aspects of such an all-encompassing phenomenon are of paramount importance. An extremely important but less publicised issue in this context is laboratory management and safety in analytical laboratories. In times of high capacity utilisation, as is the case during a pandemic or endemic outbreak of disease, other routine processes have to be abbreviated or are cancelled altogether due to lack of planning owing to the rapid emergence of the outbreak. In order to achieve high level of cleanliness in laboratories of all shapes and sizes and with different requirements, a universal solution seems unimaginable. Our experiments show a promising, automated approach of disinfection of various spaces. Within a short timeframe of 1 h – 3 h it is possible to disinfect any desired room to achieve a laboratory grade hygiene status. This was proven by employing biological indicators validated for this procedure. The tested technology reduced the indicator germs by a concentration of the mathematical log 6 reduction. Achieving this high level of cleanliness is possible by assigning a single person to the task for the set-up at the scene. Steering and monitoring of the process can be done remotely. While the machine used in our experiments is not a completely new concept, our experiments in a real-life setting such as laboratories and clinics alike, show that the applied hydrogen peroxide vapour distributed by a specialized fogger, disinfects even hard to reach spots within closed-off spaces. This program can be performed while automated (PCR) machines are running and highly trained personnel can apply their expertise elsewhere. Moreover, while the program is running real-time data is available and the process can be remotely monitored and steered digitally. It is of major concern to ensure maintainability of infrastructure e.g. COVID labs, ambulances, laboratories or veterinary practitioners to ensure treatment of directly and indirectly related health issues within a crisis. We concentrated on evaluating the usability of the disinfection technology presented in real-life settings. © 2022 IDIMT. All rights reserved.

Group Testing on General Set-Systems

Group testing is one of the fundamental problems in coding theory and combinatorics in which one is to identify a subset of contaminated items from a given ground set. There has been renewed interest in group testing recently due to its applications in diagnostic virology, including pool testing for the novel coronavirus. The majority of existing works on group testing focus on the uniform setting in which any subset of size d from a ground set V of size n is potentially contaminated.In this work, we consider a generalized version of group testing with an arbitrary set-system of potentially contaminated sets. The generalized problem is characterized by a hypergraph H = (V, E), where V represents the ground set and edges e ∈ E represent potentially contaminated sets. The problem of generalized group testing is motivated by practical settings in which not all subsets of a given size d may be potentially contaminated, rather, due to social dynamics, geographical limitations, or other considerations, there exist subsets that can be readily ruled out. For example, in the context of pool testing, the edge set E may consist of families, work teams, or students in a classroom, i.e., subsets likely to be mutually contaminated. The goal in studying the generalized setting is to leverage the additional knowledge characterized by H = (V, E) to reduce the number of tests.The paper considers both adaptive and non-adaptive group testing and makes the following contributions. First, for the non-adaptive setting, we show that finding an optimal solution for the generalized version of group testing is NP-hard. For this setting, we present a solution that requires O(d log ;E;) tests, where d is the maximum size of a set e ∈ E. Our solutions generalize those given for the traditional setting and are shown to be of order-optimal size O(log ;E;) for hypergraphs with edges that have 'large' symmetric differences. For the adaptive setting, when edges in E are of size exactly d, we present a solution of size O(log ;E;+ d log2 d) that comes close to the lower bound of Ω (log ;E;+ d) © 2022 IEEE.

Patients rehabilitation with musculoskeletal sequel from covid-19 using vision artificial in the city of Huancayo

Technology and its applications are here to improve our lives, it is used ever more these days with the pandemic Covid-19. This article is aimed to reduce the attendance to Hospitals and clinics where you would be treated with musculoskeletal muscular treatments in the city of Huancayo. With the help of modern technology it is offered an alternative software with artificial vision in order to monitor most patients in real time. The development of this investigation is set in 5 stages, the first stage talks about a posture recognition with artificial vision with framework mediapipe. The second stage explains the design interface and the mathematics formula which controls a patient development, the third stage describes the integration from the first and the second stage with a treat method. The fourth stage describes de development of a webpage using services to develop and monitor in real time. The last stage describes the process of the software validation having the last usuary with a chart of questions. Finally, the results of validations show the patient acceptation, as so 63.6% of patients who had no difficulties doing the software exercises. As Such a monitoring from the initial stage from the patien is hey factor before starting the therapy. © 2022 IEEE.

Blockchain technology with its application in medical and healthcare systems: A survey

With the advent of the 21st century, healthcare systems all around the world are facing challenges at an unprecedented scale. The recent covid-19 outbreak is a glaring example of such challenges. After facing such situations anyone can conclude that there is a need to change the way our current health system works and the recent blockchain technology emerged as a way to bring out this change. From universal health blueprint to medical supply chain management and connecting vetted suppliers, blockchain is making an impact. This paper introduces the concept of blockchain along with its applications with the main focus on healthcare and medical systems. © 2022 Wiley Periodicals LLC.

Analyzing Resilience of Healthcare Systems with COVID-19 Case Study

The recent COVID-19 has posed unprecedented challenges in many aspects of societal welfare, especially in healthcare systems. While resilience is a core concept in disaster risk reduction in other engineering and systems disciplines, its application to healthcare systems is relatively recent. In healthcare systems, resilience has been sparsely explored without widely adopted metrics in place. Although there are some suggested frameworks to analyze resilience, the methods of calculating resilience value are still obscure. This research will review and explore the resilience framework and metrics from other fields and assess their fitness for use in healthcare systems. The resilience property of healthcare systems will be analyzed for the State of North Dakota in the United States based on the COVID-19 data from the Centers for Disease Control and Prevention (CDC). The proposed resilience value is quantified from different factors such as the number of positive cases, deaths, ICU admittance, and hospitalizations in use. A case study incorporating this information to obtain a resilience value will be presented. Analyzing healthcare system resilience can aid in helping decision-makers to plan for better mitigation strategies geared toward current and future pandemic catastrophes. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Revision of the 5G Concept Rollout and Its Application in Smart Cities: A Study Case in South America

This work aims to address and emphasize the importance of the fifth-generation mobile networks (5G) rollout in the smart cities, focusing on the South American countries and bringing a theoretical analysis about the implementation in each country from the perspective of the national regulatory agencies, as well as the significance of this system for the future of the countries with the benefits expected for the 5G networks and their application in the smart cities. It was made a bibliographic review of the involved technical concepts and a collection of information from the regulatory agencies and telecommunication companies of all the countries and one French territory of South America, aiming to identify in which stage of roadmap implementation each country is, considering that many countries in other continents (e.g., South Korea and the USA) already have an implemented and functional 5G architecture. The whole region suffered a delay due to COVID-19, but all the studied countries already have or are creating the required standards for an implementation that follows the international guidelines, which shows that, though they are not as evolved as other countries, their rulers and administrators are concerned about this global trend. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Multilayered Triboelectric Energy Harvester as a Smart Floor Mat

This paper develops a multilayered triboelectric energy harvester and demonstrates its application as a smart floor mat. Triboelectrification is the process in which contact and separation between two triboelectric electrode surfaces result in the generation of opposite charges on them. Due to the tendency of conductive materials to attain charge equilibrium, the electrons flow from the ground to the conductor or vice versa to make it neutral. As a result, an alternating current (AC) flows in the external circuit as the materials contact and separate. In this work, we fabricated an array of triboelectric nanogenerators (TENG) by connecting eight zigzag-shaped multilayered TENGs (each containing three units) in series to realize a smart floor mat. The TENG array was sandwiched between two wooden slabs and was placed in front of the library entrance to control the occupancy by tracking the number of people entering/leaving. This smart floor mat generated a maximum output power of 119.7 μW, which lit up to 40 light-emitting diodes (2mA current with 10μF capacitance) when the mat was compressed and released periodically. The device will have potential applications in tracking the number of people entering/leaving a facility. In this Covid-19 era, the control of occupancy rate becomes more crucial in an indoor setting such as in libraries, shopping malls, etc. This study provides a simple, straightforward, and low-cost solution to achieving the control measure. In addition, the traditional occupancy tracking systems based on cameras, processors, and sensors are expensive compared to our low-cost and energy-efficient smart floor mat. Hence, our design has the potential to provide a promising alternative to the existing solutions. © 2022 IEEE.