Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Cardiac Muscle Function Using Speckle Tracking in Non-ST-Elevation Myocardial infarction Patients

Alaa El-sayed Salama,Mohamed Hossam El-deenMohamed El-Shaer, Hisham Samir Roshdy Saleh, and Islam Abou Bakr Abd El Kader 1 Professor of Cardiology, Faculty of Medicine, Zagazig University. Assistant Professor of Cardiology, Faculty of Medicine, Zagazig University. Lecturer of Cardiology, Faculty of Medicine, Zagazig University. 4 Cardiology Resident at Elsahel Teaching Hospital Correspondingauthor:Islam Abou Bakr Abd El Kader Email:islamaboubakr90@gmail.com Abstract Background:Ischemic heart disease is the number one cause of mortality worldwide resulting in 7 million of the 53 million deaths in reported in 2010. Acute coronary syndrome is divided into ST-elevation myocardial infarction (STEMI), Non ST-elevation myocardial infarction (NSTEMI) and unstable angina. NSTEMI and unstable angina share similar pathophysiology and have similar clinical presentation with the only difference is positive cardiac biomarkers (cardiac Troponins and CK-MB) in the case of NSTEMI. Two dimensional (2D) speckle tracking echocardiography is a relatively new imaging technique. Like tissue Doppler imaging (TDI), it allows calculation of myocardial velocities and distortion parameters like strain and strain rate (SR) giving important information for assessment of systolic and diastolic function, particularly longitudinal strain, which both sensitive and specific in post myocardial. The main advantage of speckle tracking analysis is its ability to assess several planes (circumferential, radial and longitudinal) giving accurate data about LV dimensions and deformation with good correlation to tagged cMR and sonomicrometry as reference methods.The accuracy and sensitivity of detecting MI visually by detecting segmental wall motion abnormalities are moderate and operator dependent but improve greatly by using speckle analysis.
Bias Detection and Neutralization of Wikipedia Articles

Biased means the perceptual strong instinct or a judgment on something. Bias articles are introduced by unique offensive words or phrases during a declaration and must be excluded in order to form the objective declaration. Wikipedia focuses on the policy of the Rational Point of View that implies that Wikipedia data must be neutral. Due to sizable amount of articles on Wikipedia and operating guidelines with voluntary basis of Wikipedia editors, the quality assurance and Wikipedia guidelines can not be always followed. There has been many researchers to developed different techniques ideas to get rid of subjectivity during this paper we are that specialize in those techniques and briefly study about them. Keywords—Neutral Network, Random forest, Naive Bayes, Logistic Regression, Support Vector Machine, NLP.
From nonalcoholic fatty liver disease to metabolic dysfunction-associated fatty liver disease: more than a single-letter change in an acronym

Nonalcoholic fatty liver disease (NAFLD) is an increasingly recognized public health problem worldwide. To emphasize the close pathophysiological links between NAFLD and overweight/obesity, insulin resistance, and related metabolic comorbidities, a consensus statement of international experts in 2020 has recommended to replace the old acronym NAFLD with “metabolic dysfunction-associated fatty liver disease” (MAFLD). A set of “positive” criteria to diagnose MAFLD, regardless of daily alcohol consumption, has also been proposed. A “positive” definition of MAFLD and its special focus on the metabolic causative drivers of this liver disease is expected to reduce patient confusion on disease etiology, which can, in turn, improve the identification and awareness of this common and burdensome liver disease among both primary care physicians and specialists. However, the proposal to change the terminology from NAFLD to MAFLD is still under intense discussion, as also recently highlighted by a panel of international experts (led by Dr. Polyzos and Mantzoros), which is the main topic of discussion of this commentary. Further studies are required to better understand whether, and how, the proposed changes to the diagnostic criteria for MAFLD may impact on the risk of adverse hepatic and extrahepatic clinical outcomes.
VEHICLE NUMBER PLATE DETECTION, IDENTIFICATION AND MONITORING OF VEHICLES FOR A CRIMINAL RECORD

1-6Department of Information Technology Engineering, D. Y. Patil College of Engineering, Akurdi, Pune-44, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Automatic Number Plate Recognition (ANPR) is a technology that uses Optical Character Recognition (OCR) to read the registered vehicle number plate. image processing technology which uses a number (license) plate to identify the vehicle. In this paper, we have introduced by using Optical Character Recognition (OCR) how the respective authority can monitor vehicles for a criminal record. In this system Vehicle Number plate first get detected, Identified and monitored of vehicles for criminal records. So basically, the system includes 4 Modules i.e Detection, Identification, Monitoring and Web application (for Notifying police) Respectively. The aim is to provide an efficient automatic vehicle identification system by using the vehicle number plate. The system can be implemented on residential societies, tolls, business complexes and parking spaces in India where crimes happen because of a lack of an automation system for vehicle monitoring.
Diet diversity among pregnant women and young children—Insights for improving malaria, family planning, and maternal and child health outcomes in northwestern Nigeria through social and behavior change programming

This research brief provides rigorous, evidence-based insights to policy-makers, implementers, and researchers of social and behavior change (SBC) programs on maternal and child nutrition during the 1,000-day period between a woman’s pregnancy and her child’s second birthday. It is one of a series of briefs presenting findings from a Breakthrough RESEARCH study that uniquely captures data on a wide range of psychosocial drivers of behavioral outcomes in family planning, malaria, and maternal, newborn and child health and nutrition (MNCH+N) in the three northwestern states of Nigeria.
A Machine Learning Approach to Traffic Flow Prediction Using CP Tensor Decomposition

This paper deals with the prediction of highway traffic flow based on historic data. The methodology is based on canonical polyadic (CP) tensor decompositions of traffic flow data. This step captures the regular elements of the traffic signal based on daily and weekly rhythms and typical geographical distributions of the traffic, while significantly reducing the amount of data required to describe these. The key factors are then extrapolated into the future, and the traffic data is reconstructed from the decomposition. Applied to traffic flow data from the M62 in the North of England in October 2019, this approach provides a surprisingly accurate prediction based on a very compact model, which is a distinct advantage compared to conventional machine learning approaches. Using 4 factors, the prediction captures 90% of the signal energy, which beats existing rolling average prediction techniques.
IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events HIERARCHICAL SOUND EVENT DETECTION

Environmental sound recognition in real-world conditions is a particularly challenging topic, since it requires significant efforts on both the feature extraction and the classification modeling parts in order to achieve satisfactory results. In the presented work we propose a multi-tier method that employs best of breed techniques at all relevant tasks; initially feature extraction takes place focusing on a broad range of audio features. Following feature extraction a Hierarchical Hidden Markov Model classifier scheme with explicit modeling of the finishing of the state to better detect transitions is developed. Finally, the best result is achieved when ensemble methods are added on top of the previous scheme. Specifically, a variation of Stacking using a Random Forest and the HHMM as level-1 classifiers and a second instance of HHMM as the metaclassifier is selected. Results indicate that this final method is on one hand able to deliver the best overall performance, as well as explore different tradeoffs between classes and metrics (e.g. emphasize on specific metrics or classes that are of higher importance).
Human Activity Recognition in Video Based on Histogram of Oriented Gradients and K-Nearest Neighbor

The human activity recognition system is automatically identified as human activity from the input video stream. It is a vital task in computer perception because it has many application areas such as healthcare, security, entertainment, and tactical scenarios. The system provides a way to automatically recognize human activity in the input video stream by distinguishing the Oriented Gradient Features (HOG) and K Nearest Neighbor (KNN) types. Functional features can be extracted from the input video frames with the HOG feature and can be organized to form an activity pattern. The empirical results and its accuracy indicate that the proposed system applies to recognition of human activity in real life. This system has been completed using the MATLAB programming software and the evaluation results for the system quality are measured by the confusion metric that is precision, recall and fitness measures.
MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING

1-3Department of Information Technology, Padmabhushan Vasantdada Patil College of Engineering, Mumbai -----------------------------------------------------------------------***-------------------------------------------------------------------Abstract Advance technology and rising use of Internet in today’s world stands on the fundamental rule i.e. user friendly, it has been achieving by trying to give higher user satisfaction Majority of applications consist of UI this leads to huge collection of information and sufficient resources to make datasets. Recommendation system helps in predicting personalized suggestions by using filtering methods on clustered data.
CARDIAC ARRHYTHMIA CLASSIFICATION USING SVM, KNN AND NAIVE BAYES ALGORITHMS

Assistant Professor, School of Engineering, Computer Science Department, Presidency University, Bangalore, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract -) A normal human being heart beat is somewhere between 60 to 100 beats per minute, any deviation from normal heart beat is a medical condition referred as Cardiac Arrhythmia. The purpose of our work is to classify Arrhythmia into 16 different types using three machine learning algorithms viz SVM, KNN and Naïve Bayes. This work considers UCI Arrhythmia dataset which has huge number of features which are reduced using feature selection technique. This work shows improvement in accuracy of 9.9 % in SVM model, 3.3 % in KNN model and 24.2 % in Naïve Bayes model when we consider relevant features only. This study performs multiclass classification considering all the original features in the dataset and also leaving out the irrelevant features and compares the performance of three algorithms using Accuracy and Kappa scores. The results show comparative performance among SVM (accuracy 71.4 %) and Naïve Bayes (accuracy 70.3 %) and least accuracy of 62.6 % for KNN algorithm after applying feature selection.
Carotid intima-media thickness and related factors in chronic kidney disease

DOI: 10.4328/ACAM.20629 Received: 2021-03-31 Accepted: 2021-04-20 Published Online: 2021-04-30 Printed: 2021-05-01 Ann Clin Anal Med 2021;12(5):558-562 Corresponding Author: Can Sevinc, Ataturk University Faculty of Medicine, Department of Nephrology, Erzurum, Turkey. E-mail: can.sevinc@atauni.edu. tr P: +90 534 6157812 Corresponding Author ORCID ID: https://orcid.org/0000-0002-4069-9181 Abstract Aim: Cardiovascular diseases are the most important cause of mortality. Evaluation of atherosclerosis is important to prevent the development and progression of cardiovascular disease. Carotid intima-media thickness (CIMT) is an indicator of generalized atherosclerosis. In this study, we aimed to examine CIMT and related factors in CKD. Material and Methods: The study was conducted with the patients who applied to the Nephrology outpatient clinic. The control group consisted of healthy volunteers. Patients over 18 years of age with a diagnosis of CKD, without active infection, without malignancy, without obesity, without lipid and uric acidlowering medication, without thyroid hormone replacement, and without uncontrolled hypertension were included in the study. The CIMT of the patients and their laboratory and demographic data were compared statistically. Results: Proteinuria in patients increased with increasing stages, and no significant difference was found between the CIMT values. The risk of atherosclerosis increased approximately 4 times over the age of 60, and the risk increased approximately 2.5 times in the presence of proteinuria above 500 mg/day. Discussion: Our study has shown that the presence of proteinuria, independent of GFR in CKD increases CIMT and thus cardiovascular mortality. We think that evaluation of patients with CIMT may be important for early diagnosis of cardiovascular events, especially in elderly patients with proteinuria.
Prevalence of Cataract and its Associated Factors among Adults Aged 40 Years and Above in Waghimra zone, Northeast Ethiopia

Background: Globally, lens opacity is a foremost cause of treatable blindness. Its paramount burden is found in unindustrialized countries. Hence, estimating the magnitude and delineating the risk factors would be critical in planning strategies for early detection and treatment. Therefore, this study aimed to measure the prevalence of cataract and its contributing factors among adults aged 40 years and above in Waghimra Zone, Amhara, Ethiopia. Methods: A cross-sectional study was commenced among 528 adults living in Waghimra Zone. To select a representative sample, multi-stage random sampling was used. An interviewer-administered questionnaire, Snellen’s chart, and penlight torch were used to collect data and screen the study subjects. To enter and analyze the data, Epidata version 3.1, and SPSS version 21 were used, respectively. Frequencies, percentages, and median were used to describe the variables. Bivariate analysis using logistic regression were carried out to select candidate variables. An adjusted odds ratio (AOR) with a 95% CI and a p-value of < 0.05 were used to declare a statistically significant association after running multivariable logistic regression analysis. Result: The prevalence of cataracts among the study subjects was 20.1% (95% CI: 16.87, 23.32). In this study, a year increment in age was associated with a 1.05 (95% CI: 1.01 1.08) times occurrence of cataract. The odds of cataract was 6.2 (AOR = 6.2, 95% CI: 1.60, 23.9), and 2.46 times higher (AOR = 2.46, 95% CI: 1.10, 5.48) among those who were single, and divorced compared with those who had married, respectively. Conclusion: The study revealed that burden of cataract in the study area was huge. Besides, the role of increasing age, and being single, divorced, widowed pointing towards a multi-factorial association. Hence, the concerned bodies should strengthen further screening and treatment of cataract patients who are targeted groups such as the aged population as early as possible.
THE LANGUAGE OF THE HYBRID: VERBAL MANIFESTATIONS OF IDENTITY AND ALTERITY IN G. B. SHAW’S JOHN BULL’S OTHER ISLAND

This paper presents the discursive construction of ethnic identities in the verbal behaviour of the three major characters of G. B. Shaw’s John Bull’s Other Island (1904): the English protagonist Thomas Broadbent, and two ethnic Irish characters, Larry Doyle and Father Keegan. The play is approached with the help of micro-sociolinguistic methods, focusing on face-to-face conversational interactions between characters, as ethnic stereotypes. The main linguistic means of expressing ethnic sameness and difference (deictic ‘we’ vs. ‘they’, politeness strategies, markers of power and solidarity, etc.) are analysed as conversational strategies that foreground the relationship among these characters and their attitude towards their own and the other’s ethnicity and home country. The analysis of these strategies reveal an unusual result: the reversal of ethnic roles which – as a technique of character treatment – proves Shaw to be wellahead his age, at the same time constructing and subverting ethnic stereotypes.
Classification of Alcohols in Cosmetic Production

Different chemical structures of alcohols are chemical founds in cosmetic products such as lotions, shampoos, and conditioners. The correct usage of alcohol in cosmetic production is the important factor for product quality. If experts know the correct name and compound of the alcohol in short time, they can easily applied this alcohol in good quality cosmetic production. Machine learning algorithms are less costly way to classify alcohols according their attributes obtained by QCM sensors so that an informed decision about cosmetic products can be made automatically. In this paper, Support Vector Machine (SVM) learning algorithm is performed to classify type of alcohols based on their features obtained from QCM sensor for cosmetic production development. The QCM sensor dataset for alcohol classification is used in this paper and the five labels of alcohols are 1-octanol alcohols, 1-propanol alcohols, 2butanol alcohols, 2-propanol alcohols and 1-isobutanol alcohols. Keywords—alcohols classification, machine learning algorithms, QCM sensors, Support Vector Machine
An Investigation into the Relationship between Nomophobia and Social and Emotional Loneliness of Turkish University Students

Article History: Received 01.12. 2020 Received in revised form 17.02. 2021 Accepted 28.03. 2021 Available online 21.04. 2021 This research was conducted to measure the relationship and effects of social and emotional loneliness levels of university students to nomophobia levels. The sample of this study consisted of 692 university students who attended Trakya University in Edirne province of Turkey. The findings obtained in this study showed that it was observed that there was a significant relationship between the level of nomophobia of the university students and the loneliness in the familial relations, one of the sub-dimensions of social and emotional loneliness Scale. In addition, no significant relationship with nomophobia was observed regarding loneliness in social and emotional relations. In conclusion, an increase in the sense of loneliness experienced by university students in familial relationships may suggest that it increases the risk of nomophobia. © 2021 IJPES. All rights reserved
Review: State of Charge Estimation of Lithium Ion Battery

From 1990, lithium-ion battery application becomes most popular in electronics devices like mobile phones, laptops, tablets, photovoltaic system, smart grid application as well as in an electric vehicle, hybrid electrical vehicles etc. The use of such lithium-ion battery is increased day by day due to their good properties like high cell voltage ability, lightweight, high energy density, long lifetime, low maintenance cost, small memory effect and low self-discharge rate of the battery. But every battery has its charging and discharging number of cycles. But improper charging system or use may degrade their number of charging and discharging cycles i.e. the life of the battery reduces. For, proper supply system to battery, there is the use of battery management system (BMS) which supply within specified limits. BMS is responsible for the estimation of an accurate state of charge (SOC). Hence, SOC indicates the remaining charges present in the battery. This paper review different approaches for SOC estimation of the lithium-ion battery. This paper is present a Neural network, Recurrent Neural Network, Deep feed-forward neural network, Grunwald-Letnikov approach, discrete-time nonlinear observer (DNLO), Kalman filter (EKF) and discretetime sliding mode observer (DSMO) approaches to discuss their limitations and advantages. This paper will be very much useful in future for students and researcher working on the estimation of SOC of Lithium-Ion battery for different applications.
Detailed Derivation of the Update Rules for the Contrastive-NMF (C-NMF)

W, H, Sa ≥ 0 ‖hk:‖2 = 1, ‖sk:‖2 = 1. (1) where X ∈ RM×N + is the magnitude spectrogram of the mixture, the columns of W ∈ RM×K + are interpreted as non-negative audio spectral patterns expected to correspond to different sources and the rows of H ∈ RK×N + as their activations. M represents the number of frequency bins, N the number short-time Fourier transform frames and K the number of spectral patterns.
Performance Enhancement of Single Phase Voltage Source Inverter used for Solar PV System

This paper presents solar PV system based converter topology. Solar PV modules generate dc voltage at low level and it is required to convert at high voltage level for utilization. Conversion process consists of two stages dc-dc and dc-ac. harmonic minimization is main problem associated with the power inverters and reduction of size of the system. In this paper for minimization of harmonics filter circuit is used and for minimization of size of dc link capacitor voltage control method for dc-dc boost converter is proposed. Proposed controlling scheme minimizes system size, cost, harmonics content at the output and increases efficiency of the conversion system. Simulation results using MATLAB/SIMULINK are also presented.
Characterization of Two α-l-Arabinofuranosidases from Acetivibrio mesophilus and Their Synergistic Effect in Degradation of Arabinose-Containing Substrates

Arabinofuranosidases are important accessory enzymes involved in the degradation of arabinose-containing poly- and oligosaccharides. Two arabinofuranosidases from the recently described novel anaerobic cellulolytic bacterium Acetivibrio mesophilus, designated AmAraf51 and AmAraf43, were heterologously expressed in Escherichia coli and biochemically characterized. AmAraf51 not only removed arabinose moieties at O-3, O-2 and terminal O-5 positions of arabinose-containing oligosaccharides, but also exhibited exo-β-xylosidase side activity. In comparison, AmAraf43 preferably cleaved 1,3-linkages from arabinosyl disubstitutions. AmAraf51 and AmAraf43 demonstrated maximum activity at 70 °C and 57 °C, respectively. Judging from the genetic context and substrate specificity, AmAraf51 may decompose internalized arabino/xylo-oligosaccharides. The embedding of the AmAraf43 gene between genes for several putative xylanolytic enzymes, along with its enzymatic properties suggests that AmAraf43 cleaves arabinose decorations from heteroxylans extracellularly. The enzymes revealed completely converse activity profiles towards arabinan/arabinoxylan: AmAraf51 displayed strong activity on arabinan, while AmAraf43 prefers arabinoxylan. AmAraf51 dramatically stimulated the saccharification level of wheat arabinoxylan (WAX-RS) and sugar beet arabinan when administered along with xylanase M_Xyn10 or arabinanase PpAbn43, respectively. For WAX-RS degradation, the yield of arabinose and xylose was boosted 13.77-fold and 4.96-fold, respectively. The bifunctional activity, thermostability and high catalytic efficiency make AmAraf51 an interesting candidate for industrial applications.