Miracle-dz/UniFAR-FTUs huggingface dataset:
National Hospital Ambulatory Medical Care Survey: 1997 emergency department summary.

OBJECTIVES This report describes ambulatory care visits to hospital emergency departments in the United States. Statistics are presented on selected patient and visit characteristics. METHODS The data presented in this report were collected from the 1997 National Hospital Ambulatory Medical Care Survey (NHAMCS). NHAMCS is part of the ambulatory care component of the National Health Care Survey that measures health care utilization across various types of providers. NHAMCS is a national probability survey of visits to hospital emergency and outpatient departments of non-Federal, short-stay, and general hospitals in the United States. Sample data were weighted to produce annual estimates. RESULTS During 1997, an estimated 94.9 million visits were made to hospital emergency departments (ED's) in the United States, about 35.6 visits per 100 persons. Persons 75 years and over had the highest rate of ED visits. There were an estimated 35.1 million injury-related ED visits during 1997, or 13.2 visits per 100 persons. Seventy percent of injury-related ED visits were made by persons under 45 years of age. Injury visit rates were higher for males than females in each age group under 45 years. According to ICD-9-CM classification, about four-fifths of injury visits were unintentional. Almost 72 percent of the ED visits involved medication therapy, with pain relief drugs accounting for almost 30 percent of the medications mentioned. Acute upper respiratory infection was the leading illness-related diagnosis at ED visits.
Twitter Data Analysis using Distill BERT and Graph Based Convolution Neural Network during Disaster

: TDS (Twitter Data Analysis) in Social media plays an essential role in spreading information during disasters and needy situations. May it be for seeking help or sharing and updating seriousness of situation or communicating the status or response to public in need social media plays a major role in case of emergencies. This research focuses on to prepare the data by performing cleansing and various transformations, to generate word embedding vectors using DistillBERT and generate a similarity matrix , to construct the (Graph based Convolution Network) GCN model to classify text sequences, to analyze the performance of the DistillBERT with GCN model in Text classification. To implement this, contextual word embedding’s introduced for generating word vectors. The contextual word embedding’s and concept of graph neural networks which has gained its importance to capture contextual relationship among the text which further improves the performance of classification. In this work proposed a semi supervised ML model to detect and classify Need and Availability of Resources tweets with accuracy of 98% as compared with state-of-the-art approaches.
ASSESSING THE INFLUENCE OF READINESS TOWARDS THE CONTINUOUS USE INTENTION OF FUND TRANSFER FINTECH IN INDONESIA

Financial Technology has become increasingly popular because it provides various solutions to its users. One of them is reducing transaction costs of basic banking and money transfer services. However, despite the advantage offered and the prominent market opportunity, many products in this market have been unsuccessful and ceased operation. This high rate of failure in the market prompts Fintech to know its customers better. As a fund transfer fintech, Flip must understand its customer's continuous use intention to ensure its success. Therefore, this research employs the TRAM model to analyze the factors that influence the continuous use intention of fund transfer applications. This study utilizes the Technological Readiness and Acceptance Model, which constructs includes Optimism, Innovativeness, Discomfort, Insecurity, Perceived Ease of Use, and Perceived Usefulness. Research results on 402 samples showed that Continuous Use Intention is positively and significantly influenced by Perceived Ease of Use and Perceived Usefulness. Optimism and Innovativeness are shown to positively and significantly affect Perceived Ease of Use. Perceived Ease of Use is positively and significantly influenced by Optimism but negatively influenced by Insecurity.
RESEARCH OF APPROACHES AND METHODS FOR ORGANIZATION OF COMPUTATIONAL PROCESSES IN THE CLOUD ENVIRONMENT

Today, in the context of a constant increase in the number of solved scientific and applied problems and a significant increase in the load on computing systems, cluster systems, grid systems and cloud systems are widely used. Providers of network, information and computing services, relying on large consolidated data centers, began to pay special attention to improving methods of organizing the computing process, which includes planning and allocating suitable resources to meet the resource needs of users, as well as the discipline of servicing tasks with computing resources. An overview of modern approaches and methods of organizing the computational process in distributed systems is given. Based on the results of the analysis of the current situation in the studied area, the formulation of the research problem. The essence and mathematical models of the organization of the computational process are proposed.
FEATURE SELECTION AND EXTRACTION USING DECOMPOSITION TECHNIQUES IN BIOMETRIC AUTHENTICATION

Pattern recognition is a very significant and the most prominent emerging technology which aims at analysis or investigation and construction of pattern. It is highly complex phenomena. Vector logic would provide better strategies and yield results for this problem. Generally, in several phases of human beings life, accurate uniqueness validation seems to be critical. Before the emergence of computing revolution, the issue security is for individual ensured after checking the someone in person and also with the help of the signature. It is observed that conventional methods of authentication are unproductive because any person can impress as a real person with the help of therapeutic procedure and through spoofing. At present, authentication is made through offline and or online mode taking the distinctive features like biometrics of a person. The main use of the distinctive or unique features is that no one can duplicate the features of original human being. In the event of processing the biometric qualities, it is a complex process to derive authentication. In order to enhance accuracy, researchers have proposed diverse types of algorithms. During this process, finger and face traits of a person are considered and also , and applications of Kronecker Product(KP) such as Khatri Rao Product are used. And then, two multimodal authentication systems using AT&T, FERET and Yale data sets are implemented in MATLAB
Trusted Fine-Grained Image Classiﬁcation through Hierarchical Evidence Fusion

Fine-Grained Image Classiﬁcation (FGIC) aims to classify images into speciﬁc subordinate classes of a superclass. Due to insufﬁcient training data and confusing data samples, FGIC may produce uncertain classiﬁcation results that are untrusted for data applications. In fact, FGIC can be viewed as a hierarchical classiﬁcation process and the multilayer information facilitates to reduce uncertainty and improve the reliability of FGIC. In this paper, we adopt the evidence theory to measure uncertainty and conﬁdence in hierarchical classiﬁcation process and propose a trusted FGIC method through fusing multilayer classiﬁcation evidence. Comparing with traditional approaches, the trusted FGIC method not only generates accurate classiﬁcation results but also reduces the uncertainty of ﬁne-grained classiﬁcation. Speciﬁcally, we construct an evidence extractor at each classiﬁcation layer and extract multilayer (multi-grained) evidence in hierarchical classiﬁcation. To fuse the extracted multi-grained evidence from coarse to ﬁne, we formulate evidence fusion with the Dirichlet hyper probability distribution and thereby hierarchically decompose the evidence of coarse-grained classes into ﬁne-grained classes to enhance the performance of FGIC and reduce uncertainty. The ablation experiments validate that the hierarchical evidence fusion can improve the precision and also reduce the uncertainty of ﬁne-grained classiﬁcation. Comparison with the state-of-the-art FGIC methods shows that the proposed method achieves competitive performance.
WILL TIKTOK USERS TAKE ADVANTAGE OF THE LIVE-SHOPPING FEATURE IN ONLINE SHOPPING?

Live shopping is a breakthrough and has become a new trend in recent years. Many applications add live shopping features to be able to compete with their competitors and increase transactions made by users. TikTok is one of those apps with live streaming functionality. The researcher conducted this research to study the elements that influence the Buy Intention of consumers shopping directly on TikTok because this function is popularly discussed and used. This research was conducted using an online questionnaire containing 18 questions with a scale of 1-5. The results of the questionnaire obtained 110 valid data. After the data is collected, the research is continued using PLS-SEM based on the TAM model which has seven research hypotheses. The results of the analysis using the SmartPLS software show that Trust, information quality, convenience, usability, and Attitude have a positive effect on Purchase Intentions. After conducting the analysis, the researchers also offered suggestions and feedback to the TikTok business or application, especially for the live streaming function, to improve the quality of the feature according to the analysis findings. Some of the suggestions and feedback offered are: 1) Improving the security of personal data, transaction processing, and conformity of product information. 2) Improving the accuracy of information and conformity of information. 3) Improving convenience and usability by making the application interface more straightforward but with complete information. 4) Improving the quality of information by checking the quality of information.
TRACKING PEOPLE IN CLOSED SPACES TO LINK WITH METAVERSE

The need to know the location and track people has become an important aspect of our daily lives, and although the global position system (GPS) has dominated outdoor location, in terms of indoor signal it is blocked and distorted by buildings, which affects its performance. Much of the technology that has emerged makes use of location, such are the applications that you have to do with the internet of things (IoT). In this work, an indoor people tracking system is proposed using the inertial navigation method to access different areas. Through an IMU device, the values of its sensors are obtained, which are transmitted to a computer in a virtual world, and the location of the person inside a building is disclosed. Thus, communication is established between the real world and a metaverse. Where movements of a person are represented in a virtual world.
TSGA10, a new player in regulating autophagy and apoptosis

Testis speci�c gene antigen 10 (TSGA10) overexpression could inhibit angiogenesis and tumor cell proliferation, migration and invasion through inhibiting HIF-1α. The exact role of TSGA10 in autophagy and apoptosis is not clear. Present study was conducted to investigate the potential effects of TSGA10 overexpression on regulation of autophagy and apoptosis. To do so, TSGA10-containing vector (pcDNA3.1-TSGA10 vector) was designed for stable and transient transfections in HeLa cells, and clonal selection was applied. Expression of autophagy and apoptosis-related genes was assessed by real-time RT-PCR in TSGA10-overexpressing cells compared with HeLa cells transfected by empty vector. Rate of autophagy and cell viability were assessed by acridine orange and MTT assays, respectively. Our �ndings showed that overexpression of TSGA10 would induce autophagy under normoxic and hypoxic conditions in the presence or absence of autophagy inducers. Expression levels of autophagy and apoptosis-related genes increased signi�cantly in TSGA10-overexpressing cells compared with control cells. In addition, HeLa cells overexpressing TSGA-10 exhibited lower expression of BCL-2 and survival rates in comparison with control cells. Molecular docking results showed that interaction between TSGA10 and BCL-2 could inhibit anti-apoptotic activity of BCL-2. According to our �ndings, TSGA10 overexpression has a main role in regulating autophagy and apoptosis through inhibition of BCL-2. Therefore, TSGA10 could be considered as a new regulator of autophagy and apoptosis in future studies.
Outlier Detection And Denoising By The Measures Of Dispersion With Naïve Bayes To Predict Soil Fertility

: Farming is one of the essential segments of Indian Economy and it funds about 17% of Gross Domestic Product (GDB). Growing crops more than millennia without thinking often about renewing has prompted consumption and depletion of soil supplements bringing about their low usefulness. To work on production, compound composts are added. Adequate measure of manures should be added for the improvement of good yield and simultaneously the normal nature of soil stays perfect. The soil should be identified as fertile or not fertile to carry out the process of manuring. Methods/Statistical analysis: It is essential to remove redundant data, replace missing values and outliers from the soil dataset. Removing duplicates and replacing missing values are performed using Discretized Naïve Bayes (DNBayes) and Outliers are detected and replaced using Discretized Naïve Bayes with Quartiles (DNBQ). Findings: The performance of the proposed system is analyzed in terms of different types of errors like KS, MAE, RMSE, RAE and RRSE along with TPR, FPR, Precision, Recall, F1-Score, ROC and classification accuracy. Prediction of soil fertility based on DNBayes and DNBQ achieved 85% and 87% of classification accuracy. Novelty/Applications: The proposed approaches will benefit soil scientists in decision making to help farmers in predicting fertile soil for sugarcane cultivation.
A HIERARCHICAL ROUTING TREE TOPOLOGY BASED ON PROBABILISTIC SELECTION OF CLUSTER HEAD AND SUPER CLUSTER HEAD TO OVERCOME INTER-CLUSTER AND INTRA-CLUSTER ROUTING PROBLEMS IN DTN NETWORKS

DTN networks are environments that are characterized by a dynamic topology and which can change randomly and unpredictably due to high node mobility, frequent node disconnections, low node density and energy failures. All these factors create a set of routing problems namely: a very long delivery delay, a very low delivery rate and a significant increase in the resources consumed in the network. With all these problems, the hierarchical topologies proposed by the researchers are ineffective in terms of coordinating communications and data exchanges between the nodes of the different network clusters and are characterized by the absence of any kind of forecast against the premature death of cluster-Heads (CHs) and message ferries (MF). Therefore, it is necessary to develop hierarchical routing topologies adapted to routing problems in DTN networks to encourage the cooperation of nodes in the same cluster and also to coordinate communications between different clusters in the network. In this article, we will discuss routing improvement in DTNs by exploiting three factors: (a) hierarchical routing topology, (b) the probabilistic election of Cluster Head (CH) and the optimal election of Super Cluster Head (SCH), (c) inter-cluster and intra-cluster routing. We propose a hierarchical routing tree topology (HRTT) based on a probabilistic approach for the election of Cluster Head (CH) and Super Cluster Head (SCH) on the basis of three criteria: The probability of contact of the Message Ferry (MF), the residual energy and the node mobility model. The results of the simulations carried out show the efficiency of the topology (HRTT) with a considerable increase in the delivery rate and a minimization of the average latency in the DTN networks.
A REVIEW ON TOOLS AND TECHNIQUES FOR FAMILY TREE DATA VISUALIZATION

Visualization is an important and helpful ways to support the exploration of large data sets. The leading benefit of visualization is that it does not only provide graphical representation of data but also allows changing of form, omitting what is not required and browsing deeper to get further details. This paper reviews some of the previous research related to family tree (or sometimes known as genealogy) data visualization. It focuses on existing techniques and applications that are currently available to address family tree visualization issues. The content of this paper is divided into several sections such as visualization usability, family tree visualization, graph theory of kinship network and graph visualization. Visualization gives opportunity to approach huge network type of data and makes it easily comprehensible. In order to gain the full benefit of family tree data, a proper understanding about the current visualization tool or techniques that is used to represent these type of data in a fully interactive environment will be highly beneficial.
The effects of ultraviolet‐b radiation on the toxicity of fire‐fighting chemicals

The interactive effects of ultraviolet (UV) and fire‐retardant chemicals were evaluated by exposing rainbow trout (Oncorhyncus mykiss) juveniles and tadpoles of southern leopard frogs (Rana sphenocephala) to six fire‐retardant formulations with and without sodium ferrocyanide (yellow prussiate of soda [YPS]) and to YPS alone under three simulated UV light treatments. Yellow prussiate of soda is used as a corrosion inhibitor in some of the fire‐retardant chemical formulations. The underwater UV intensities measured were about 2 to 10% of surface irradiance measured in various aquatic habitats and were within tolerance limits for the species tested. Mortality of trout and tadpoles exposed to Fire‐Trolr̀ GTS‐R, Fire‐Trol 300‐F, Fire‐Trol LCA‐R, and Fire‐Trol LCA‐F was significantly increased in the presence of UV radiation when YPS was present in the formulation. The boreal toad (Bufo boreas), listed as endangered by the state of Colorado (USA), and southern leopard frog were similar in their sensitivity to these chemicals. Photoenhancement of fire‐retardant chemicals can occur in a range of aquatic habitats and may be of concern even when optical clarity of water is low; however, other habitat characteristics can also reduce fire retardant toxicity.
Dual Speci�city Phosphatase 7 Drives the Formation of Cardiac Mesoderm in Mouse Embryonic Stem Cells

Background: Dual speci�city phosphatase 7 (DUSP7) is a protein belonging to a broad group of phosphatases that can dephosphorylate phosphoserine/phosphothreonine as well as phosphotyrosine residues within the same substrate. DUSP7 has been linked to the negative regulation of mitogen activated protein kinases (MAPK), and in particular to the regulation of extracellular signal-regulated kinases 1 and 2 (ERK1/2). MAPKs play an important role in embryonic development, where their duration, magnitude, and spatiotemporal activity must be strictly controlled by other proteins, among others by DUSPs. Methods: In this study, we focused on the effect of DUSP7 depletion on the in vitro differentiation of mouse embryonic stem (ES) cells. Undifferentiated stem cells as well as cells at various days of in vitro differentiation protocol were used for analysis using mainly qPCR, western blotting and immunohistochemistry. Data are expressed as mean ± standard deviation (SD). Statistical analysis was assessed by t-test and by one-or two-way ANOVA, and by Bonferroni’s Multiple Comparison post hoc test. Values of P < 0.05 were considered to be statistically signi�cant ( ∗ p < 0.05). Results: We showed that even though DUSP7 knock-out ES cells do retain some of their basic characteristics, when it comes to differentiation, they preferentially differentiate towards neural cells, while the formation of early cardiac mesoderm is repressed. Conclusions: Our data indicate that DUSP7 is necessary for the correct formation of neuroectoderm and cardiac mesoderm during the in vitro differentiation of ES cells.
Deep learning and rules-based hybrid approach to improve the accuracy of early detection of skin cancer

: Skin cancer is one of the most dangerous diseases in the world. As per the American Cancer Society, about 99,780 new melanomas will be diagnosed in the United States and about 7,650 people are expected to die of melanoma. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. AI can be of use for the early detection of skin cancer. For example, the use of deep convolutional neural networks can help to develop a system to evaluate images of the skin to diagnose skin cancer. However, there is likelihood of errors and it depends on a lot of factors, including the amount and quality of the data used to train the algorithms, the environment in which machine learning operates may itself evolve or differ from what the algorithms were developed to face, the complexity of the overall systems it’s embedded in. While, in the deep learning approach the knowledge is developed based on sample picture, there are rule based traditional approach which has a business logic built in the application. Business logics could be based on teat results, skin colors, etc. This paper presents a comprehensive hybrid approach combining the AI methodology along with the color pigment analysis to reduce the errors and improve the accuracy and a discussion on how it can be implemented in the field of diagnosing skin cancer. We reviewed the
Asking Questions: an Innovative Way to Interact with Oral History Archives

The paper describes our initial effort to use Transformer-based neural networks for understanding and presenting oral history archives. Such archives of interviews often contain large passages of the interviewee’s speech. Our approach automatically generates relevant questions, which enrich such monotonous parts and allows the listener to better orient in the interview. The generated questions also allow for finding interesting parts of the interview without changing the original meaning of the testimony. We present our working pipeline consisting of a Wav2Vec speech recognizer, BERT-based punctuation detection, T5 asking questions model and BERT-based semantic continuity model.
The re-production of

In this qualitative research, we use one year of participant observation and 12 in-depth interviews of the men on a highly-ranked university rugby team in England in order to nuance the theoretical understandings of the re-production of homosexually-themed discourse in organised sport. Data collected through this ethnographic investigation is used to inductively theorise the complex relationship between language, homosocial masculine relationships and organised sport. In examining the political, intentional and inadvertent effects of these men’s discourses, we define and discuss the notion of gay discourse as a form of heteronormativity that is distinct from the well-established traditional use of homophobic discourse. Highlighting that homosexually-themed discourse is best understood as a continuum, we stress the importance of context in interpreting the meaning and effect of discourse when used in men’s teamsport settings.