When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will ...newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions. For this reason, the detection of emerging infectious disease pattern is critical. However, as the epidemic spread of infectious disease occurs sporadically and rapidly, it is not easy to predict whether an infectious disease will emerge or not. Furthermore, accumulating data related to a specific infectious disease is not easy. For these reasons, finding useful data and building a prediction model with these data is required. The Internet press releases numerous articles every day that rapidly reflect currently pending issues. Thus, in this research, we accumulated Internet articles from Medisys that were related to infectious disease, to see if news data could be used to predict infectious disease outbreak. Articles related to infectious disease from January to December 2019 were collected. In this study, we evaluated if newly emerging infectious diseases could be detected using the news article data. Support Vector Machine (SVM), Semi-supervised Learning (SSL), and Deep Neural Network (DNN) were used for prediction to examine the use of information embedded in the web articles: and to detect the pattern of emerging infectious disease.
Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. However, as the epidemic spread of influenza occurs ...sporadically and rapidly, it is not easy to estimate the future variance of influenza virus infection. Furthermore, accumulating influenza related data is not easy, because the type of data that is associated with influenza is very limited. For these reasons, identifying useful data and building a prediction model with these data are necessary steps toward predicting if the number of patients will increase or decrease. On the Internet, numerous press releases are published every day that reflect currently pending issues.
In this research, we collected Internet articles related to infectious diseases from the Centre for Health Protection (CHP), which is maintained the by Hong Kong Department of Health, to see if news text data could be used to predict the spread of influenza. In total, 7769 articles related to infectious diseases published from 2004 January to 2018 January were collected. We evaluated the predictive ability of article text data from the period of 2013-2018 for each of the weekly time horizons. The support vector machine (SVM) model was used for prediction in order to examine the use of information embedded in the web articles and detect the pattern of influenza spread variance. The prediction result using news text data with SVM exhibited a mean accuracy of 86.7 % on predicting whether weekly ILI patient ratio would increase or decrease, and a root mean square error of 0.611 on estimating the weekly ILI patient ratio.
In order to remedy the problems of conventional data, using news articles can be a suitable choice, because they can help estimate if ILI patient ratio will increase or decrease as well as how many patients will be affected, as shown in the result of research. Thus, advancements in research on using news articles for influenza prediction should continue to be pursed, as the result showed acceptable performance as compared to existing influenza prediction researches.
To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018-2019 seasonal influenza ...outbreak in the U.S., we collected the surveillance data of 164 countries using the FluNet database, search queries from Google Trends, and temperature from 2010 to 2018. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. We identified the time lag between two time-series which were weekly surveillances for ILI, total influenza (Total INF), influenza A (INF A), and influenza B (INF B) viruses between two countries using cross-correlation analysis. In order to forecast ILI, Total INF, INF A, and INF B of next season (after 26 weeks) in the U.S., we developed prediction models using linear regression, auto regressive integrated moving average, and an artificial neural network (ANN). As a result of cross-correlation analysis between the countries located in northern and southern hemisphere, the seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of ANN models for ILI for validation set in 2015-2019 was 0.758 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation between seasonal influenza patterns in the U.S., Australia, and Chile could be used to forecast the next seasonal influenza pattern, which can help to determine influenza vaccine strategy approximately six months ahead in the U.S.
The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations ...and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.
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To meet the increasing demand for wearable electronics today, fabrication of the stretchable devices becomes crucial. In this respect, a stretchable conductor is an essential ...component for achieving stretchability of the device. Herein, a stretchable and transparent conductor unit, Au-metallized PVP (Au@PVP) nanofiber network on a kirigami-structured PDMS substrate, was newly developed. By a series of comparative studies, the effectiveness of our strategies to the invariant electrical conductivity and high stretchability is convincingly demonstrated. Our stretchable conductor showed high stretchability of 110% without significant change in resistance, ∼50% increase. It also exhibited ∼80% transparency, as well as excellent durability. To point out its applicability, we fabricated a transparent and stretchable photodetector having the same geometry. ZnO nanorod, the 1D transparent metal oxide nanostructure, is used as a sensing material owing to its high sensitivity for UV light and large surface to volume ratio. The resulting device showed outstanding on/off ratio of 1020 at its original state and 440 under 80% strain. Its fast response/reset time, high transparency and stable performance indicate the feasibility of the stretchable and transparent optoelectronic device.
This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on ...learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success.
As most of patients with Myasthenia Gravis have limitations in their physical functioning, many experience changes in psychological states and often have depression. The objective of the current ...study was to examine the roles of communication with medical professionals, patients' loneliness, and patients' depression, in relation to their effects on the patients' quality of life.
For 120 patients with MG of 18 years and older, demographic variables, along with communication with medical professionals, loneliness, depression, and quality of life were measured.
As a result, people suffering from MG experienced lower quality of life when their career has changed due to the illness. At the same time, depression was a significant predictor of their quality of life, both in physical and mental domains.
The implications for clinical settings and the suggestions for future research are discussed.
Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically ...down to a scalar activation function of a weighted sum of inputs. Here we emulate more biologically realistic neurons by learning canonical activation functions with two input arguments, analogous to basal and apical dendrites. We use a network-in-network architecture where each neuron is modeled as a multilayer perceptron with two inputs and a single output. This inner perceptron is shared by all units in the outer network. Remarkably, the resultant nonlinearities often produce soft XOR functions, consistent with recent experimental observations about interactions between inputs in human cortical neurons. When hyperparameters are optimized, networks with these nonlinearities learn faster and perform better than conventional ReLU nonlinearities with matched parameter counts, and they are more robust to natural and adversarial perturbations.
Urban areas play a crucial role in carbon absorption, while also producing a considerable amount of carbon emissions. However, there has been a lack of research that has systematically examined the ...carbon storage and sequestration in green spaces located within urban environments, at a spatial scale. This study analyzes carbon storage and sequestration in Yurim Park, Daejeon, South Korea on a grid basis to fill the research gap. The research compares the variation in sequestration capacity across different grids and provides insights into the development of sustainable urban parks in urban planning. The classification of grids is based on specific site characteristics, such as land cover, tree distribution, type, and density. This results in a total of seven distinct types. The study employs a combination of the I-tree eco model, drone-based modeling, and on-site surveys to estimate carbon storage and sequestration in urban parks. The results show that the average carbon storage per unit area in the entire park was 15.3 tons of carbon per hectare, ranging from a minimum of 5.0 to a maximum of 21.4 tons per hectare. For the planted area, the average carbon storage was 8.6 tons per hectare. Grids with green areas dominated by broad-leaved trees and closed canopy cover had the highest carbon sequestration and storage values. The planting area ratio and the type of trees planted were found to directly influence the carbon sequestration capacity per unit area of urban parks. This study stands out from previous research by conducting a detailed area-based comparison and analysis of carbon sequestration capacity in urban parks using sophisticated measurement techniques. The findings offer direct insights into strategies and policies for securing future urban carbon sinks and can be of practical use in this regard.
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