A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Global Systemic Risk Dynamic Network Connectedness during the Covid-19: Evidence from Nonlinear Granger Causality

This study examined the global systemic risk network connectedness during the COVID-19 pandemic by focusing on the stock, bond, and foreign exchange markets of 14 countries (2000–2021). We found that the commonality among multiple markets was high, while the systemic risk of COVID-19 was smaller than that of the 2007–2008 financial crisis. Additionally, the exposure of bond markets to systemic risk was larger than the exchange rate and stock markets. Although the stock and bond markets were the main sources of risk during the pandemic, the foreign exchange market had the strongest connection with the global financial network.

Characterization and Source Apportionment of PM in Handan—A Case Study during the COVID-19

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10;the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. © 2023 by the authors.

Time evolution of cytokine profiles associated with mortality in COVID-19 hospitalized patients.

Background: High cytokine levels have been associated with severe COVID-19 disease. Although many cytokine studies have been performed, not many of them include combinatorial analysis of cytokine profiles through time. In this study we investigate the association of certain cytokine profiles and its evolution, and mortality in SARS-CoV2 infection in hospitalized patients. Methods: Serum concentration of 45 cytokines was determined in 28 controls at day of admission and in 108 patients with COVID-19 disease at first, third and sixth day of admission. A principal component analysis (PCA) was performed to characterize cytokine profiles through time associated with mortality and survival in hospitalized patients. Results: At day of admission non-survivors present significantly higher levels of IL-1α and VEGFA (PC3) but not through follow up. However, the combination of HGF, MCP-1, IL-18, eotaxine, and SCF (PC2) are significantly higher in non-survivors at all three time-points presenting an increased trend in this group through time. On the other hand, BDNF, IL-12 and IL-15 (PC1) are significantly reduced in non-survivors at all time points with a decreasing trend through time, though a protective factor. The combined mortality prediction accuracy of PC3 at day 1 and PC1 and PC2 at day 6 is 89.00% (p<0.001). Conclusions: Hypercytokinemia is a hallmark of COVID-19 but relevant differences between survivors and non-survivors can be early observed. Combinatorial analysis of serum cytokines and chemokines can contribute to mortality risk assessment and optimize therapeutic strategies. Three clusters of cytokines have been identified as independent markers or risk factors of COVID mortality.

Convolutional Neural Network Model based Students' Engagement Detection in Imbalanced DAiSEE Dataset

The COVID-19 pandemic has significantly changed learning processes. Learning, which had generally been carried out face-to-face, has now turned online. This learning strategy has both advantages and challenges. On the bright side, online learning is unbound by space and time, allowing it to take place anywhere and anytime. On the other side, it faces a common challenge in the lack of direct interaction between educators and students, making it difficult to assess students' engagement during an online learning process. Therefore, it is necessary to conduct research with the aim of automatically detecting students' engagement during online learning. The data used in this research were derived from the DAiSEE dataset (Dataset for Affective States in E-Environments), which comprises ten-second video recordings of students. This dataset classifies engagement levels into four categories: low, very low, high, and very high. However, the issue of imbalanced data found in the DAiSEE dataset has yet to be addressed in previous research. This data imbalance can cause errors in the classification model, resulting in overfitting and underfitting of the model. In this study, Convolutional Neural Network, a deep learning model, was utilized for feature extraction on the DAiSEE dataset. The OpenFace library was used to perform facial landmark detection, head pose estimation, facial expression unit recognition, and eye gaze estimation. The pre-processing stages included data selection, dimensional reduction, and normalization. The PCA and SVD techniques were used for dimensional reduction. The data were later oversampled using the SMOTE algorithm. The training and testing data were distributed at an 80:20 ratio. The results obtained from this experiment exceeded the benchmark evaluation values on the DAiSEE dataset, achieving the best accuracy of 77.97% using the SVD dimensional reduction technique. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.

Alpha-factor integrated risk parity portfolio strategy in global equity fund of funds

This study proposes a principal alpha-style factor integrated risk parity strategy that can diversify style risk factors and the stock selection risk of external managers in Fund-of-Funds (FoFs) portfolios. First, we separated the style risk factors and stock-specific sources held by each individual fund. Stock-specific sources, referred to as principal alpha portfolios, are extracted through principal component analysis, where the sources are utilized for risk parity in the alpha division. As the parity portfolio was integrated into both the alpha and style factor divisions, we used a Basin-Hopping two-phase optimization technique, which can mitigate the local optimal trap by exploring the surroundings of the sequential quadratic programming solution secondarily. Through this, a more stable integrated risk parity portfolio can be realized. Finally, the suggested integrated risk parity portfolios were simulated with a global fund dataset. The simulation results from 2006 through June 2022 show a more stable risk-return profile than an independently constructed strategy using style risk factors or principal alpha sources, especially in high volatility and down-market periods, such as a global financial crisis or unexpected events like COVID-19. This study can be applied to various areas covering other FoFs and asset allocation strategies by integrating alpha and factor divisions. © 2023 Elsevier Inc.

Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests.

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.

Technological, healthcare and consumer funds efficiency: influence of COVID-19

This paper aims to analyze the efficiency of the funds in technological, healthcare, and consumer cyclical sectors based on the U.S. News & World Report rankings. We employed a Principal Component Analysis to select the indicators to explain efficiency. Then, we have used an alternative approach that combines Data Envelopment Analysis (DEA) with Multiple Criteria Decision Aiding, the Value-Based DEA, to assess the efficiency of funds for 1 year (2020), 3 years (2018–2020), and 5 years (2016–2020). The results highlight that in 2020 the number of efficient funds is much smaller than in previous periods and this can be justified by the effect of the COVID-19 pandemic crisis. The sectors with the most efficient funds are technology and healthcare. The factors that determine the efficiency of funds in the health sector and the technology sector are quite different, although they have not undergone major changes in the three periods considered. For managers, health funds are seen as low risk and hardly consider the return factors in all analyzed periods, which is often considered as benchmarks for inefficient funds. In the technology sector, Beta and Alpha are generally the indicators with the greatest weight in fund efficiency, showing that these funds beat the market in terms of returns and are less risky than the benchmark. This study seeks to complete the scarce existing literature on the subject, namely in the sectors under analysis, seeking to identify the indicators that fund managers ponder most to consider a fund as efficient. As far as we know, the joint efficiency analysis of these sectors and the impact they suffered from the COVID-19 pandemic are new in the literature.

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Non-Academic Factors Analysis Impacting Students' Online Learning During the COVID-19 Pandemic in Universitas Logistic and Bisnis Internasional

In education, online learning with an e-learning system is an irreplaceable need. Many argue that online learning is the current educational crisis. Several studies show how complicated the handling of COVID-19 for universities is, especially in online learning (e-learning) outcomes. The variables influencing online learning during the COVID-19 epidemic have been shown in numerous studies. However, the influence of several other factors still needs to be investigated. Therefore, this study aims to determine non-academic factors that affect online learning during the COVID-19 pandemic. With data collected from the International University of Logistics and Business (ULBI), this study uses Cronbach's-Alpha analysis, Bayesian Exploratory Factor Analysis (BEFA), Principal Component Analysis (PCA), and Multivariate Regression Analysis. The evaluation of the research scale shows 20 observed variables. The test results prove that three non-academic factors influence students' online learning outcomes during the COVID-19 pandemic: education cost policy (H1), communication quality (H2), and student support (H3). Each factor has p - value < 0.001, p - value = 0.029, and p - value = 0.004, respectively. Meanwhile, family circumstances do not affect students' online learning outcomes during the COVID-19 pandemic (H4 rejected) because the p-value is 0.152. An example case in the questionnaire shows that most students say family income can adapt to changes during the COVID-19 pandemic. © 2022 IEEE.

Policy Analysis of Low-Carbon Energy Transition in Senegal Using a Multi-Criteria Decision Approach Based on Principal Component Analysis

Senegal has been investing in the development of its energy sector for decades. By using a novel multi-criteria decision analysis (MCDA) based on the principal component analysis (PCA) method, this paper develops an approach to determine the effectiveness of Senegal's policies in supporting low-carbon development. This was determined using six criteria (C1 to C6) and 17 policies selected from the review of Senegal's energy system. In order to determine the optimal weighting of the six criteria, a PCA is performed. In our approach, the best weighted factor is the normalized version of the best linear combination of the initial criteria with the maximum summarized information. Proper weighted factors are determined through the percentage of the information provided by the six criteria kept by the principal components. The percentage of information is statistically a fit of goodness of a principal component. The higher it is, the more statistically important the corresponding principal component is. Among the six principal components obtained, the first principal component (comp1) best summarizes the values of criteria C1 to C6 for each policy. It contains 81.15% of the information on energy policies presented by the six criteria and was used to rank the policies. Future research should take into account that when the number of criteria is high, the share of information explained by the first principal component could be lower (less than 50% of the total variance). In this case, the use of a single principal component would be detrimental to the analysis. For such cases, we recommend a higher dimensional visualization (using two or three components), or a new PCA should be performed on the principal components. This approach presented in our study can serve as an important benchmark for energy projects and policy evaluation.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Applications of alignment-free sequence descriptors in the characterization of sequences in the age of big data: a case study with Zika virus, SARS, MERS, and COVID-19

This chapter gives a detailed presentation of the theoretical background and computational approaches to the utility of alignment-free sequence descriptors and multidimensional variable reduction methods in the characterization and visualization of biological sequence data. The utility of such novel methods developed by the authors of this chapter is shown using data on case studies of severe acute respiratory syndrome, Middle East respiratory syndrome, Coronavirus disease-2019, and Zika viruses. © 2023 Elsevier Inc. All rights reserved.

MD simulation and MM/PBSA identifies phytochemicals as bifunctional inhibitors of SARS-CoV-2.

The global spread of SARS-CoV-2 has resulted in millions of fatalities worldwide, making it crucial to identify potent antiviral therapeutics to combat this virus. We employed structure-assisted virtual screening to identify phytochemicals that can target the two proteases which are essential for SARS-CoV-2 replication and transcription, the main protease and papain-like protease. Using virtual screening and molecular dynamics, we discovered new phytochemicals with inhibitory activity against the two proteases. Isoginkgetin, kaempferol-3-robinobioside, methyl amentoflavone, bianthraquinone, podocarpusflavone A, and albanin F were shown to have the best affinity and inhibitory potential among the compounds, and can be explored clinically for use as inhibitors of novel coronavirus SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Research on user satisfaction of sports and healthcare integration system in post-pandemic era

After COVID-19, some initiatives such as Healthy China, and Smart Living have been widely mentioned. This study explored the factors influencing user satisfaction in sports and healthcare integration services to help system builders and interaction designers better seek opportunities and directions for systems construction. Based on grounded theory method, conducted semi-structured interviews with people who have home exercise needs, and then summarised the influencing factors after coding the raw information level by level. It applied the user experience honeycomb to classify potential variables, used principal component analysis (PCA) to extract representative evaluation indicators as observed variables, and followed the construction of a theoretical model of the satisfaction factors. The structural equation model (SEM) was validated and analyzed to prove its scientific validity and reasonableness. Research showed that the core factors affecting the user experience of sports and healthcare integration system include usefulness, interactivity, usability, credibility, and findability, all of which have a positive and significant impact on user satisfaction. According to the results of empirical analysis, A multidimensional design strategy for sports and healthcare integration system is proposed to provide a reference for improving user satisfaction. © 2023 - IOS Press. All rights reserved.

Exploiting the co-crystal ligands shape, features and structure-based approaches for identification of SARS-CoV-2 Mpro inhibitors.

SARS-CoV-2 enters the host cell through the ACE2 receptor and replicates its genome using an RNA-Dependent RNA Polymerase (RDRP). The functional RDRP is released from pro-protein pp1ab by the proteolytic activity of Main protease (Mpro) which is encoded within the viral genome. Due to its vital role in proteolysis of viral polyprotein chains, it has become an attractive potential drug target. We employed a hierarchical virtual screening approach to identify small synthetic protease inhibitors. Statistically optimized molecular shape and color-based features (various functional groups) from co-crystal ligands were used to screen different databases through various scoring schemes. Then, the electrostatic complementarity of screened compounds was matched with the most active molecule to further reduce the hit molecules' size. Finally, five hundred eighty-seven molecules were docked in Mpro catalytic binding site, out of which 29 common best hits were selected based on Glide and FRED scores. Five best-fitting compounds in complex with Mpro were subjected to MD simulations to analyze their structural stability and binding affinities with Mpro using MM/GB(PB)SA models. Modeling results suggest that identified hits can act as the lead compounds for designing better active Mpro inhibitors to enhance the chemical space to combat COVID-19.Communicated by Ramaswamy H. Sarma.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals.

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Deep Learning Algorithms with LIME and Similarity Distance Analysis on COVID-19 Chest X-ray Dataset.

In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep learning algorithm from the standpoint of feature space and similarity analysis. Firstly, we utilized Local Interpretable Model-agnostic Explanations (LIME) to justify the necessity of the region of interest (ROI) process and further prepared ROI via U-Net segmentation that masked out non-lung areas of images to prevent the classifier from being distracted by irrelevant features. The experimental results were promising, with detection performance reaching an overall accuracy of 95.5%, a sensitivity of 98.4%, a precision of 94.7%, and an F1 score of 96.5% on the COVID-19 category. Secondly, we applied similarity analysis to identify outliers and further provided an objective confidence reference specific to the similarity distance to centers or boundaries of clusters while inferring. Finally, the experimental results suggested putting more effort into enhancing the low-accuracy subspace locally, which is identified by the similarity distance to the centers. The experimental results were promising, and based on those perspectives, our approach could be more flexible to deploy dedicated classifiers specific to different subspaces instead of one rigid end-to-end black box model for all feature space.

Extraction of Antioxidant Compounds from Brazilian Green Propolis Using Ultrasound-Assisted Associated with Low- and High-Pressure Extraction Methods.

The demand for bee products has been growing, especially regarding their application in complementary medicine. Apis mellifera bees using Baccharis dracunculifolia D.C. (Asteraceae) as substrate produce green propolis. Among the examples of bioactivity of this matrix are antioxidant, antimicrobial, and antiviral actions. This work aimed to verify the impact of the experimental conditions applied in low- and high-pressure extractions of green propolis, using sonication (60 kHz) as pretreatment to determine the antioxidant profile in the extracts. Total flavonoid content (18.82 ± 1.15-50.47 ± 0.77 mgQE·g-1), total phenolic compounds (194.12 ± 3.40-439.05 ± 0.90 mgGAE·g-1) and antioxidant capacity by DPPH (33.86 ± 1.99-201.29 ± 0.31 µg·mL-1) of the twelve green propolis extracts were determined. By means of HPLC-DAD, it was possible to quantify nine of the fifteen compounds analyzed. The results highlighted formononetin (4.76 ± 0.16-14.80 ± 0.02 mg·g-1) and p-coumaric acid (