A Comparative Study of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Covid Variants

The pandemic situation created by COVID'19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation. The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result © 2023, Neutrosophic Sets and Systems.All Rights Reserved.

Right Under Our Noses

Air quality and environmental justice To introduce how socioeconomic status affects the physical aspects of exposure to differing air-quality levels, students used an anthropological technique of comparison to "make the strange familiar and the familiar strange." Students analyzed a New York Times story revealing the air-quality inequities of two teens residing in India: "Who Gets to Breathe Clean Air in New Delhi?" For 25 minutes, students interact with the website and reflect on paper: * One new and interesting fact that they encountered in the article about air quality, * How they think the information might relate to air quality in the United States, and * What, if anything, they think we could do to help increase awareness about these types of environmental disparities. For the next 35 minutes, students search online for articles about air quality and environmental justice in the area near our school's location. The data from real-time air quality index reports are available on every cell phone, and students decided to record it on a calendar to chart in Excel.

Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

PurposeThe COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively.Design/methodology/approachThe importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis.FindingsThe findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic.Originality/valueWhile the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.

Robotic Assistance in Radiology: A Covid-19 Scenario

During the COVID-19 Pandemic, the need for rapid and reliable alternative COVID-19 screening methods have motivated the development of learning networks to screen COVID-19 patients based on chest radiography obtained from Chest X-ray (CXR) and Computed Tomography (CT) imaging. Although the effectiveness of developed models have been documented, their adoption in assisting radiologists suffers mainly due to the failure to implement or present any applicable framework. Therefore in this paper, a robotic framework is proposed to aid radiologists in COVID-19 patient screening. Specifically, Transfer learning is employed to first develop two well-known learning networks (GoogleNet and SqueezeNet) to classify positive and negative COVID-19 patients based on chest radiography obtained from Chest X-Ray (CXR) and CT imaging collected from three publicly available repositories. A test accuracy of 90.90%, sensitivity and specificity of 94.70% and 87.20% were obtained respectively for SqueezeNet and a test accuracy of 96.40%, sensitivity and specificity of 95.50% and 97.40% were obtained respectively for GoogleNet. Consequently, to demonstrate the clinical usability of the model, it is deployed on the Softbank NAO-V6 humanoid robot which is a social robot to serve as an assistive platform for radiologists. The strategy is an end-to-end explainable sorting of X-ray images, particularly for COVID-19 patients. Laboratory-based implementation of the overall framework demonstrates the effectiveness of the proposed platform in aiding radiologists in COVID-19 screening. Author

Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images.

COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.

A QGIS plugin for BONDS project: integrating field data with geographical, remote sensing and health information

Open source Geographic Information System (GIS) have been fostering spatial data research such as Earth observation and environmental monitoring for more than 30 years. More recently, globally available geospatial information combined with web technologies are providing new environments and tools for data handling. Thus, binding the mapping and processing capabilities of traditional GIS to the accessibility and reliability of web-based data providers can bring new opportunities for research. In this paper, we built a QGIS plugin to explore the integration of different public data providers in Brazil along with field data produced by the BONDS project. The biOdiversity conservatioN with Development in Amazon wetlandS project (BONDS) proposes to develop biodiversity scenarios for the Amazonian floodplains aiming to support solutions to preserve biodiversity and ecosystem services. The use of web services enabled dynamic and fast access to several products ranging from remote sensing images, land use and land cover, territorial cartography, water quality, to COVID-19 health data, and more. © 2022 National Institute for Space Research, INPE. All rights reserved.

One Year of COVID-19: Mapping the Spread of a Global Pandemic

The significance of the COVID-19 pandemic has resulted in the availability of an unprecedented amount of data having become available unlike in any comparable health emergency before. Global situation updates were made available on a daily basis. This provides the unique opportunity to gain a better understanding of the underlying spatial patterns that developed during the spread of the virus. This contribution makes use of these data and provides a geographical overview of the spread of the COVID-19 pandemic in 2020, the first year of the (known) spread of the virus. A main emphasis is put on the utilisation of innovative data visualisation approaches by deploying cartogram techniques as a method to emphasise the underlying quantities of global cases and deaths. The cartographic analysis is accompanied by a critical reflection on the sometimes problematic nature of the data and the patterns that have emerged from it. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The Impact of the Covid-19 Pandemic on the Digital Transformation in the Banking Sector in Portugal

Currently, digital transformation is a process that cuts across different sectors of activity, including banks. One of the examples is the availability of products and services on digital channels. This study aims to identify the changes implemented in the digital channels of banks in Portugal that were driven by the covid-19 pandemic, contributing to the acceleration of digital transformation. 30 Reports and Accounts from 10 banks were selected from 2019 to 2021 and analyzed using the Leximancer software to identify the main themes and concepts. The results allowed the identification of 4 themes and 40 most relevant concepts. It is concluded that banks have reinforced digital channels in terms of products and services with evident concerns associated with security. This study, at an academic level, aims to contribute with a conceptual map of the measures adopted. At the business level, it aims to enable managers to define other initiatives and enhance those already implemented. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

IncreasedUse of Maps During theCOVID-19 Pandemic: An Example from Morocco

This chapter discusses the use and interpretation of graphs and maps concerning COVID-19 by ordinary people. Epidemiological data have experienced unprecedented communication in the official media as well as social networks. Media are using a vocabulary that includes such words and terms as "curve, " "flattening” and "inflection point” to describe the evolution of the pandemic. It can be assumed that there is an appropriation of this language about the impact that COVID-19 has had on people's daily lives. This impact concerns both the fear of infection and the expectation of the end of containment imposed by a majority of countries in the world. The maps presenting the epidemic on a global scale were used by people as a grid for reading, but above all for the extrapolation to the country of origin. Contrary to the wide availability of COVID-19 international maps, national and local maps in some countries such as Morocco have not had the same degree of usage. The use of non-graphical information at the local level has helped to balance this scale of knowledge about the spread and evolution of COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation.

BACKGROUND: A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. METHODS: We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. RESULTS: The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints. CONCLUSIONS: Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

Analyzing and visualizing global research trends on COVID-19 linked to sustainable development goals.

This study is intended to afford a comprehensive overview of the implications of COVID-19 on progress toward achieving the sustainable development goals (SDGs) set out in the United Nations (UN) 2030 Agenda and the state of related research activities on COVID-19 linked to the SDGs. Bibliometric techniques and visual mapping are proposed as methodological tools to better approach the objectives of the present work. This includes: retrieving related publications from Scopus database, investigating the trends and growth trajectories of research works, and analyzing the scenarios post-COVID-19 either optimistic or pessimistic outlooks. The national and international contributions and collaboration toward this theme of research are further analyzed at countries, institutions, and sources levels. This analysis indicates that research works conducted on the impacts of COVID-19 on the achievement of the SDGs are still in the immaturity level. The global research productivity on this topic was just 160 documents (0.19% of total global research productivity in all fields of science with relevance to COVID-19). The implications of COVID-19 on good health and well-being, SDG-3, have attracted considerable attention. It is followed by SDG-13 that concerned with climate changes. The post-COVID-19 scenarios showed deep and justified worries in relation to achieving the SDGs by 2030. This study figures the major issues debated in the literature with respect to COVID-19 and its implications on the SDGs. The study, furthermore, attempts to assess the required actions to advance the SDGs post-COVID-19.

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.

Using the DTFM Method to Analyse the Degradation Process of Bilateral Trade Relations between China and Australia

Quantitative assessment and visual analysis of the multidimensional features of international bilateral product trade are crucial for global trade research. However, current methods face poor salience and expression issues when analysing the characteristics of China—Australia bilateral trade from 1998 to 2019. To address this, we propose a new perspective that involves period division, feature extraction, construction of product space, and spatiotemporal analysis by selecting the display competitive advantage index using the digital trade feature map (DTFM) method. Our results reveal that the distribution of product importance in China—Australia bilateral trade is heavy-tailed, and that the number of essential products has decreased by 68% over time. The proportion of products in which China dominates increased from 71% to 77%. Furthermore, Australia consistently maintains dominance in the most crucial development in trade, and the supremacy of the head product is becoming stronger. Based on these findings, the stability of bilateral trade between Australia and China is declining, and the pattern of polarisation in the importance of traded products is worsening. This paper proposes a novel method for studying Sino—Australian trade support. The analytical approach presented can be extended to analyse the features of bilateral trade between other countries.

On Fractional Differential Inclusion for an Epidemic Model via L-Fuzzy Fixed Point Results

The real world is filled with uncertainty, vagueness, and imprecision. The concepts we meet in everyday life are vague rather than precise. In real-world situations, if a model requires that conclusions drawn from it have some bearings on reality, then two major problems immediately arise, viz. real situations are not usually crisp and deterministic;complete descriptions of real systems often require more comprehensive data than human beings could recognize simultaneously, process and understand. Conventional mathematical tools which require all inferences to be exact, are not always efficient to handle imprecisions in a wide variety of practical situations. Following the latter development, a lot of attention has been paid to examining novel L-fuzzy analogues of conventional functional equations and their various applications. In this paper, new coincidence point results for single-valued mappings and an L-fuzzy set-valued map in metric spaces are proposed. Regarding novelty and generality, the obtained invariant point notions are compared with some well-known related concepts via non-trivial examples. It is observed that our principal results subsume and refine some important ones in the corresponding domains. As an application, one of our results is utilized to discuss more general existence conditions for realizing the solutions of a non-integer order inclusion model for COVID-19.

Groundwater Contamination Monitoring for Pollution Measurement and Transmission Applying WQI Approaches from a Region of the Erode District, Tamilnadu, India

The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.

Multi-National Topics Maps for Parliamentary Debate Analysis

In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact. © 2022 IEEE Computer Society. All rights reserved.

Recent Urban Issues Related to Particulate Matter in Ploiesti City, Romania

The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.

Touring in the time of COVID-19: digital maps and the virtual teaching of Palestinian travel writing

The employment of digital media and e-materials in the classroom in the time of Covid-19 in Palestine has generated much attention among scholars, researchers and teachers. One of these electronic resources is digital maps which have recently become enriching and transformative ways of learning in different educational and pedagogical settings in Palestinian academic institutions. The lack of physical mobility due to continued governmental enforcements of lockdown laws in the Palestinian Occupied Territories hindered many teachers, students and researchers in the field of national cartography and human geography, many of whom were faced with dire challenges in exploring local landscapes outlined in Palestinian travel writing. This article examines the vital role of using digital maps in teaching Palestinian cartographic fiction. While it notes the value of students' geographic practices in the field, it explores the benefits of using digital maps in the higher teaching of Palestinian literary cartography. The article, in particular, reflects on the development and re-construction of the meanings of students' subjectivity and nationalism in the light of their virtual performance, responses and imaginary relationship to "place” during the outbreak of Covid-19. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Teaching Geology in Higher Education Institutions under COVID-19 Conditions

Teaching geology under COVID-19 pandemic conditions led to teaching limitations for educators and learning difficulties for students. The lockdown obstructed face-to-face teaching, laboratory work, and fieldtrips. To minimize the impact of this situation, new distance learning teaching methods and tools were developed. The current study presents the results of an empirical study, where distance learning teaching tools were constructed and used to teach geology to university students. A mineralogical mobile phone application was used to replace laboratory mineral identification and a flow chart to replace laboratory rock identification. Additionally, exercises on faults and maps were developed to fill the gap that was created as field work was impossible. A university course on geology was designed on the basis of the constructed distance learning teaching tools, and more than 100 students from the Department of Civil Engineering attended the course. The results show that the proposed tools helped the students to considerably understand scientific information on geology and supported the learning outcomes. Thus, it is suggested that the teaching tools, constructed for the purposes of the study, could be used in conditions when distance learning is required, or even under typical learning conditions after laboratories, as well as before or after fieldtrips, for better learning outcomes.

A Chaotic-Based Interactive Autodidactic School Algorithm for Data Clustering Problems and Its Application on COVID-19 Disease Detection

In many disciplines, including pattern recognition, data mining, machine learning, image analysis, and bioinformatics, data clustering is a common analytical tool for data statistics. The majority of conventional clustering techniques are slow to converge and frequently get stuck in local optima. In this regard, population-based meta-heuristic algorithms are used to overcome the problem of getting trapped in local optima and increase the convergence speed. An asymmetric approach to clustering the asymmetric self-organizing map is proposed in this paper. The Interactive Autodidactic School (IAS) is one of these population-based metaheuristic and asymmetry algorithms used to solve the clustering problem. The chaotic IAS algorithm also increases exploitation and generates a better population. In the proposed model, ten different chaotic maps and the intra-cluster summation fitness function have been used to improve the results of the IAS. According to the simulation findings, the IAS based on the Chebyshev chaotic function outperformed other chaotic IAS iterations and other metaheuristic algorithms. The efficacy of the proposed model is finally highlighted by comparing its performance with optimization algorithms in terms of fitness function and convergence rate. This algorithm can be used in different engineering problems as well. Moreover, the Binary IAS (BIAS) detects coronavirus disease 2019 (COVID-19). The results demonstrate that the accuracy of BIAS for the COVID-19 dataset is 96.25%.