Blockchain technology is on the cusp of revolutionizing the way we handle healthcare data, in term of storage and utilization. The main goal is to empower patients to be the center of their own health record so that, the patient doesn't have to rely on different institutions or hospitals they might visit. Blockchain technology and smart contracts provide an interesting and innovative way to keep track of Electronic Health Records (EHRs). This technology could help the patients to have better control of their own data. Health professionals and institutions, such as hospitals, could have access to patient’s data owned by other institutions. In the present article, we discuss how blockchain technologies can be used to handle EHR while improving the efficiency of operations through streamlining processes and transparency. We propose an architecture to manage and share healthcare data among different organizations. The proposed work could significantly reduce the time needed to share patient data among different health organizations and reduce the overall cost.

The first confirmed case of COVID-19 in the United States was January 20, 2020 in Washington, while the first globally confirmed cases were in China in December 2019. The CDC's Influenza-like Illness Surveillance Network is used to track the amount of people who seek medical attention for influenza-like illnesses, along with the illness cause. The metric rILI- is used to assess the amount of people who test negative for influenza or any other specific cause. To assess the evidence of COVID-19 presence in the US in late December 2019 or early January 2020, rILI- data from 2010 to mid-March 2020 was used to perform three types of analysis. First, we forecast prediction intervals using data until mid-November 2019 and compared the predictions with observed values for the subsequent 16 weeks. Second, we performed residual hypothesis testing by removing the trend and seasonality in order to compare residuals from before and after November 17, 2019. Third, we used changepoint analysis to identify major changes in trend and seasonality. This study provides strong evidence of COVID-19 presence in the US in late December 2019 or early January 2020. Combined with the knowledge that COVID-19 was spreading across other parts of the world, anomalous patterns in ILINet data should have been a warning sign that COVID-19 was already spreading in the US. The purpose of the study was not to identify specific states, but South Dakota has the strongest evidence of any US state, followed by California, Delaware, Maine, and New Mexico.

Twitter has become a medium through which a substantial percentage of the global population communicates their feelings and reactions to current events. Emotion mining from text aims to capture these emotions by using a series of algorithms to evaluate the contents of each tweet. In this study, tweets that expressed at least one of seven basic emotions were collected. The resulting dataset was a corpus of 42,000 tweets with a balanced presence of each emotion. From this corpus a lexicon of roughly 40,000 words, each associated with a weighted vector corresponding to one of the emotions, was created. Next, different methods of identifying emotion in these ‘cleaned’ tweets were performed and evaluated. These methods included both lexically-based classification and supervised machine learning-based classification. Finally, an ensemble method involving several multi-class classifiers trained on unigram features of the lexicon was evaluated. This evaluation revealed that the ensemble method outperformed all other tested methods when tested on existing datasets as well as on the dataset created for this study.

The sinh-Gordon equation is simply the classical wave equation with a nonlinear sinh source term. It arises in diverse scientific applications including differential geometry theory, integrable quantum field theory, fluid dynamics, kink dynamics, and statistical mechanics. It can be used to describe generic properties of string dynamics for strings and multi-strings in constant curvature space. In the present paper, we study a generalized sinh-Gordon equation with variable coefficients with the goal of obtaining analytical traveling wave solutions. Our results show that the traveling waves of the variable coefficient sinh-Gordon equation can be derived from the known solutions of the standard sinh-Gordon equation under a specific selection of a choice of the variable coefficients. These solutions include some real single and multi-solitons, periodic waves, breaking kink waves, singular waves, periodic singular waves, and compactons. These solutions might be valuable when scientists model some real-life phenomena using the sinh-Gordon equation where the balance between dispersion and nonlinearity is perturbed.

This work aims to expand the knowledge of the area of data analysis through both persistence homology, as well as representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical data set. By testing the data, we tried to find the agglomerative hierarchical clustering method that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we came with a conclusion that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with random forest are the most beneficial approaches when working with categorical variables. All these tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, for the business area, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators.