A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

Web 3.0 Resources in the Development of Autonomous Work of Higher Education Students in Times of Covid-19 Pandemic

This research on web 3.0 tools and autonomous work analyzes new and interactive possibilities for the generation of educational content in web environments. The research aims to determine the use of web 3.0 tools and the autonomous work of higher education students in times of pandemic. The research methodology was of an experimental type through a quantitative approach, with a documentary bibliographic modality for the understanding of the variables and field where direct contact was maintained with the study population. For the collection of information, the survey technique was used based on a questionnaire on a Likert scale. The study population was 68 students of the Tourism major, a population to which the experimentation was applied based on the ADDIE methodology for the development of digital tools and the application of the TAM model survey. The statistic used to test the hypothesis is Kolmogorov-Smirnov with a value less than 0.05. The results of this research were that the students favorably accepted the technology, that is, the web 3.0 tools in autonomous work since they contribute to generating self-learning skills, motivation, and commitment to the construction of knowledge in a playful way. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Detecting -Vertex Cuts in Sparse Networks via a Fast Local Search Approach

The k-vertex cut (k-VC) problem belongs to the family of the critical node detection problems, which aims to find a minimum subset of vertices whose removal decomposes a graph into at least k connected components. It is an important NP-hard problem with various real-world applications, e.g., vulnerability assessment, carbon emissions tracking, epidemic control, drug design, emergency response, network security, and social network analysis. In this article, we propose a fast local search (FLS) approach to solve it. It integrates a two-stage vertex exchange strategy based on neighborhood decomposition and cut vertex, and iteratively executes operations of addition and removal during the search. Extensive experiments on both intersection graphs of linear systems and coloring/DIMACS graphs are conducted to evaluate its performance. Empirical results show that it significantly outperforms the state-of-the-art (SOTA) algorithms in terms of both solution quality and computation time in most of the instances. To evaluate its generalization ability, we simply extend it to solve the weighted version of the k-VC problem. FLS also demonstrates its excellent performance. IEEE

Path Planning Algorithms for Mobile Robots in Hospital Environment during Covid-19

Since 2019, the COVID-19 virus has spread worldwide, posing a significant health and safety concern. The application of mobile robots in the medical field has gradually demonstrated their unique advantages. Therefore, we focus on the application of mobile robots inwards. By collating and summarizing some of the most popular existing path planning algorithms, this paper illustrates that different algorithms can produce varying outcomes depending on different environments and hardware used. MATLAB is used in this study to simulate four algorithms: To determine the most efficient path, A∗, RRT, RRT∗, and PRM in a specific hospital map are compared, as well as parameters including path length, average execution time, and resource consumption. Modelling a single-layer hospital map makes it possible for mobile robots in the medical field to execute tasks more efficiently between entry and ward in the COVID-19 hospital environment. Based on a comparison and comprehensive consideration of the data derived from the simulations, it is found that the A∗algorithm is superior in terms of optimality, completeness, time complexity, and spatial complexity. Therefore, the A∗algorithm is more valuable in finding the best path for a mobile robot in a hospital environment. © 2022 ACM.

Comparative Analysis of the Characteristics of the YoY Inflation Rate Cities in Indonesia before and during the Covid-19 Pandemic

In this study, the identification of the characteristics of the Year of Year (YoY) inflation rates in cities throughout Indonesia is presented before the pandemic and during the Covid-19 pandemic. The data used are the YoY inflation rates from January 2018 to April 2022 using non-parametric statistical methods. At the time before the pandemic, the YoY inflation rate of cities in Indonesia tended to be unrelated to each other, but during the pandemic, data on the YoY inflation rate of cities in Indonesia tended to be related to one another. The results also obtained are that there are significant differences in the distribution of the YoY inflation rate before the pandemic and during the Covid-19 pandemic for cities in Indonesia. © 2022 IEEE.

Optimal Vaccination Strategies for Multiple Dose Vaccinations

Due to the COVID-19 pandemic and the shortage of vaccinations during its roll-out, the question regarding the best strategy to achieve immunity in the population by adjusting the time between the two necessary vaccination doses was intensively discussed. This strategy has already been studied from various angles by various researches. However, the combinatorial optimization problem and its complexity has not been the focus of attention. In this paper, we study the complexity of different versions of this problem by first proposing a simple approach using a matching algorithm. Then, we extend the approach by adding constraints and multiple manufacturers. Finally, we discuss a variation of the problem where three vaccinations are necessary, including the so-called "booster". This problem turns out to be NP-hard.

Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.

Genome sequences indicate a wide variety of characteristics, which include species and sub-species type, genotype, diseases, growth indicators, yield quality, etc. To analyze and study the characteristics of the genome sequences across different species, various deep learning models have been proposed by researchers, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Multilayer Perceptrons (MLPs), etc., which vary in terms of evaluation performance, area of application and species that are processed. Due to a wide differentiation between the algorithmic implementations, it becomes difficult for research programmers to select the best possible genome processing model for their application. In order to facilitate this selection, the paper reviews a wide variety of such models and compares their performance in terms of accuracy, area of application, computational complexity, processing delay, precision and recall. Thus, in the present review, various deep learning and machine learning models have been presented that possess different accuracies for different applications. For multiple genomic data, Repeated Incremental Pruning to Produce Error Reduction with Support Vector Machine (Ripper SVM) outputs 99.7% of accuracy, and for cancer genomic data, it exhibits 99.27% of accuracy using the CNN Bayesian method. Whereas for Covid genome analysis, Bidirectional Long Short-Term Memory with CNN (BiLSTM CNN) exhibits the highest accuracy of 99.95%. A similar analysis of precision and recall of different models has been reviewed. Finally, this paper concludes with some interesting observations related to the genomic processing models and recommends applications for their efficient use.

LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

Kernel density visualization (KDV) has been widely used in many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. Although KDV can be supported by many scientific, geographical, and visualization software tools, none of these tools can support high-resolution KDV with large-scale datasets. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. Given the high efficiency of these algorithms, LIBKDV not only accelerates the KDV computation but also enriches KDV-based geospatial analytics, including bandwidth-tuning analysis and spatiotemporal analysis, which cannot be natively and feasibly supported by existing software tools. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets. © 2022, VLDB Endowment. All rights reserved.

VVC intra prediction decoder: Feature improvement and performance analysis

Nowadays, streaming applications have been in great demand, especially due to covid-19 (teleworking, online teaching, virtual reality, etc.). In addition, artificial intelligence has become widely used especially in video processing domains, so a video with high quality improves the accuracy rate of this application. To meet these needs, the Versatile Video Coding standard (VVC) has appeared to give a high compression efficiency compared to high-efficiency video coding. This norm consists of a high complexity algorithm that offers an improvement in processing time and decreases the bit rate by 50 % thanks to several new compression techniques. In this context, we propose the implementation of an intra prediction decoding chain of this standard on a system on chip. In this work, we highlight the VVC feature enhancements, we present the suitable method for VVC intra-prediction decoder implementation on the PYNQ-Z2, and we provide profiling in terms of decoding time and power consumption. As a future work, this study is helpful to distinguish the block that will be a candidate for a Hardware acceleration. © 2022 IEEE.

An Improved Particle Swarm Optimization Algorithm for Irregular Flight Recovery Problem

As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper. © 2022, Springer Nature Switzerland AG.

Covid-19 Disease Detection with Improved Deep Learning Algorithms on X-Ray Data

The COVID-19 pandemic has brought human life to a startling halt around the world from the moment it emerged and took thousands of lives. The health system has come to the point of collapse, many people in the world have died from being infected, and many people who have survived the disease have had permanent lung damage with the spread of COVID-19 in 212 countries and regions. In this study, an answer is sought to diagnose the disease-causing virus through Artificial Intelligence Algorithms. The aim of the study is to accelerate the diagnosis and treatment process of COVID-19 disease. Enhancements were made using Deep Learning methods, including CNN, VGG16, DenseNet121, and ResNet50. For this study, the disease was detected by using X-Ray images of patients with and without COVID-19 disease, and then it was evaluated how to increase the accuracy rate with the limited available data. To increase the accuracy rate, the results of data augmentation on the image data were examined and the time complexity of the algorithms with different layers was evaluated. As a result of the study, it was seen that data augmentation increased the performance rate in all algorithms and the ResNet50 algorithm was more successful than other algorithms. © 2022 IEEE.

Collaborative tools web 3.0 in the teaching of mathematics in times of Covid 19 pandemic

The limited use of collaborative tools in the teaching of Mathematics with students of Middle Basic General Education has allowed us to think about the three essential axes that this work entails: collaborative tools, Didactics of Mathematics, and learning and knowledge technologies (LKT). This research aimed to analyze the use of web 3.0 collaborative tools in the teaching of Mathematics. The methodology applied with a mixed experimental-exploratory approach, through a structured questionnaire of 24 questions on a Likert scale which was validated by Cronbach's alpha statistic with a result of 0.846, the TAM model was applied to measure the acceptance of digital resources developed through collaborative web3.0 tools in virtual learning during the global pandemic covid-19, the ADDIE methodology was applied in the execution and application of collaborative tools for students in the eighth year of General Basic education. The contrast of the hypothesis was carried out using the Kolmogorov-Smirnov statistical test for a sample where a value less than 0.000 was obtained, stating that the development of web 3.0 resources by the teacher improves collaborative work focused on learning mathematics and, in this way, a paradigm shift is manifested in its conceptualization and modifications in its didactic development. The results show that the role of technology and the Internet in the learning of mathematics can generate some motivation, they represent for students and teachers a significant factor of high impact in the learning of mathematics in the long term, not because of their use or access to them, but for the competence to apply them in their learning. © 2022 IEEE.

The use of web 3.0 tools for reading skill development: case blogs in virtual education

The COVID-19 pandemic has brought drastic changes in the teaching and learning of EFL, not least through the increased use of ICT and web 3.0 technologies. As part of this phenomenon, blogs have become a widely used technique for assisting foreign language acquisition. The aim of this study was to test the hypothesis that the use of blogs has a significant impact on the development of reading skills in English learners. The study was based on 106 participants (57 male and 49 female) from a private secondary school in the central highland region of Ecuador, with an age-range from 12 to 19 years. A nonparametric-experimental design was applied to the whole sample, who were pre-tested, given reading development instruction, and then post-tested. The pre-test and post-test were adapted from the Cambridge Preliminary English Test (PET) and consisted of 5 multiple choice comprehension questions, 5 scanning questions and 6 fill in the gap questions. In the instructional phase, the participants engaged in 6 sessions of asynchronous and 6 sessions of synchronous study, in which a variety of digital resources such as Kahoot, Nearpod, Educaplay, Liveworksheets, Padlet, Quizzis, British Council, Quizzlet and others, were employed to contribute to students' communicative competence. Data tendencies and normality were measured through the Kolmogorov-Smirnov test, which was 0.000;while Wilcoxon was used to corroborate the hypothesis. The results showed a significant improvement of 66.9% in the students' reading skills through the use of web 3.0 blogs. Findings of the research show that the integration of web 3.0 technologies, specifically blogs foster learners' oral and written interaction, being reading the keystone of the whole process. © 2022 IEEE.

The flipped classroom in meaningful mathematics learning: case study eighth year of basic general education

Quality academic training at the different educational levels enhances students' meaningful learning. Due to new requirements of the Covid 19 pandemic, education started to rely more on the use of information and communication technologies. This research article aims to describe and analyze the contribution of the 'flipped classroom' model in the meaningful learning of mathematics through the use and design of web 3.0 digital tools. The methodology implemented was experimental - exploratory research, through a structured questionnaire of 17 questions on the Likert scale, which was validated using Cronbach's alpha statistic with a result of 0.846. The TAM model was applied to measure the acceptance of digital resources in virtual learning and the ADDIE methodology was used in the design and application of the 3.0 technological tools for eighth-grade students of basic general education. The contrast of the hypothesis was made by the statistical test of KolmogorovSmirnov for a sample, where a value less than 0.05 was obtained. Therefore, the results proved that the flipped classroom model contributes to the meaningful learning of mathematics, allowing greater development of 30 students in their daily educational activities. In addition, there was an increase in the frequency of the use of digital documents, multimedia resources, and especially web 3.0 resources developed by the teacher to improve virtual teaching, which enhanced optimal and flexible learning within a social setting. © 2022 IEEE.

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards. © 2022 Tech Science Press. All rights reserved.

Identification of Face Mask Detection Using Convolutional Neural Networks

This paper delineates the algorithms to decipher the persons who are wearing mask. Sensing faces with sealings is a thought-provoking task due to the nonappearance of huge datasets of masked faces, thereby we have gathered a colossal of datasets from various resources and arranged them in a structured form. Preprocessing methods were applied on the dataset for reducing time and space complexity of an algorithm. The minimized dataset is used for training, utilizing this dataset we have developed different models using Convolution Neural Networks (CNN). The results have been comprehended from this model and an impeccable result of 91% accuracy is obtained using convolution neural networks. The construction of Covid-19 face mask detector here today can possibly be used to help ensure your safety and the security of others. This detection system can also be widely used in travel scenes, with mobile phone and surveillance images. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A STUDY OF COMPUTATIONAL AND CONCEPTUAL COMPLEXITIES OF COMPARTMENT AND AGENT BASED MODELS

The ongoing COVID-19 pandemic highlights the essential role of mathematical models in understanding the spread of the virus along with a quantifiable and science-based prediction of the impact of various mitigation measures. Numerous types of models have been employed with various levels of success. This leads to the question of what kind of a mathematical model is most appropriate for a given situation. We consider two widely used types of models: equation-based models (such as standard compartmental epidemiological models) and agent-based models. We assess their performance by modeling the spread of COVID-19 on the Hawaiian island of Oahu under different scenarios. We show that when it comes to information crucial to decision making, both models produce very similar results. At the same time, the two types of models exhibit very different characteristics when considering their computational and conceptual complexity. Consequently, we conclude that choosing the model should be mostly guided by available computational and human resources.

Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network.

Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.

Fundamental limitations on efficiently forecasting certain epidemic measures in network models.

The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.

Digital literacy and cognitive abilities of preservice physics teacher in physics for school course using LMS3: How are the both correlated?

The Covid 19 pandemic that hit Indonesia demands the physics for school course to be conducted online using a Learning Management System Supported Smartphone (LMS3). By using this application, prospective physics teachers can practice their digital literacy and strengthen cognitive abilities. This study aims to determine the correlation between digital literacy and cognitive abilities, in general and by gender, which are trained through physics for school course using LMS3. The descriptive research was conducted with survey method and involving 20 students at a university in Tasikmalaya. They are five males and fifteen females spread 18-20 years old. The instruments used in this research were digital literacy test and cognitive ability test. Both have been validated by 5 experts and have high reliability. The collected data were analyzed statistically using the Kolmogorov Smirnov test and the Pearson correlation test. The results of the normality test obtained α = 0.636 which indicates that the data is normally distributed. The correlation coefficient r = 0.626 which indicates that digital literacy and cognitive ability are strong correlated. Based on the results the digital literacy of prospective physics teachers must be adequate to support learning success, one of which is shown by cognitive abilities. © 2021 Institute of Physics Publishing. All rights reserved.