Miracle-dz/UniFAR-FTUs huggingface dataset:
THE MARKERS OF INFLAMMATION AS PREDICTORS OF CARDIOVASCULAR EVENTS AFTER PRIMARY MYOCARDIAL INFARCTION

The aim of the study was to determine the potential of inflammatory processes such as interleukin6, interleukin-10, C-reactive protein, tumor necrosis factor alpha, as predictors of cardiovascular events after primary myocardial infarction. Резюме. целью нашего исследования было определение возможности воспалительынх маркерок таких как интерлейкин-6, интерлейкин-10, С-реактивный белок, фактор некроза опухоль альфа, как предикторов сердечно сосудистых событий после первичного инфаркта миокарда. Ключевые слова: инфаркт миокарда, воспаление, интерлейкин,С-реактивный протеин.
The effect of emotional labour on English teachers in Japan

This research investigates the extent to which emotional labour is experienced by nonJapanese teachers of English as a foreign language in Japan, what coping mechanisms are employed and how it impairs individual performance. Understanding these issues is pivotal in improving the competitive advantage of these organisations and the productivity of the many individuals that continue to work in the industry. The research has demonstrated that teachers carried out surface acting on a daily basis and several instructors acknowledged the need to perform deep acting over a sustained period of time. Whereas some teachers endured the working conditions, the most important coping mechanism involved engaging in camaraderie. Teachers had difficulties not being allowed to exhibit any form of creativity, having excessive workloads, dealing with students who were silent, had mental problems or were xenophobic. These factors made their working conditions stressful and emotionally draining.
Application of Twin Objective Function SVM in Sentiment Analysis

Classification modeling is one of the key issues in sentiment analysis. Support vector machine (SVM) has been widely used in classification as an effective machine learning method. Generally, a common SVM is only for decision-making that sacrifices the distribution of data. In practice, sentiment data are big and mazy, which results in the deficiency of accuracy and stability when common SVM is used. The study investigates sentiment analysis by applying the twin objective function SVM, including nonparallel SVM(NPSVM) and twin SVM (TWSVM). From the experiments, we concluded that twin objective function SVMs are superior to NB and single objective function SVM in accuracy and stability.
Wikidata: a new platform for collaborative data collection

This year, Wikimedia starts to build a new platform for the collaborative acquisition and maintenance of structured data: Wikidata. Wikidata's prime purpose is to be used within the other Wikimedia projects, like Wikipedia, to provide well-maintained, high-quality data. The nature and requirements of the Wikimedia projects require to develop a few novel, or at least unusual features for Wikidata: Wikidata will be a secondary database, i.e. instead of containing facts it will contain references for facts. It will be fully internationalized. It will contain inconsistent and contradictory facts, in order to represent the diversity of knowledge about a given entity.
Moving developmental research online: comparing in-lab and web-based studies of model-based reinforcement learning

For years, adult psychological research has benefitted from remote data collection via web-based experiments. There is growing interest in harnessing this approach to facilitate data collection from children and adolescents to address foundational questions about cognitive development in large and diverse participant samples. To date, however, few studies have directly tested whether findings from in-lab developmental psychology tasks can be replicated online, particularly in the domain of value-based learning and decision-making. These tasks, and the computational characterization of learning processes, typically require participants to make many, repeated decisions over a lengthy experimental session (30 - 60 minutes). To address whether we can obtain high-quality data in such tasks, we set up a pipeline for online data collection with children, adolescents, and adults, and conducted a replication of Decker et al. (2016). The original in-lab study employed a sequential decision-making paradigm to examine shifts in value-learning strategies from childhood to adulthood. Here, we employed the same paradigm and, in a sample of 151 children (N = 50; ages 8 - 12 years), adolescents (N = 50; ages 13 - 17 years), and adults (N = 51; ages 18 - 25 years) replicated the main finding that the use of a “model-based” learning strategy increases from childhood to adulthood. In addition, we adapted a new index of abstract reasoning (MaRs-IB; Chierchia, Furhmann et al. (2019)) for use online, and replicated a key result from Potter, Bryce et al. (2017), which found that age-related differences in abstract reasoning mediated the relation between age and model-based learning. Our re-analyses of Decker et al. (2016) and Potter, Bryce et al. (2017) alongside our new analysis of our online dataset revealed few qualitative differences across the in-lab and online task administrations. Taken together, these findings suggest that with appropriate precautions, researchers can effectively examine developmental differences in learning and decision-making computations through unmoderated, online experiments.
Comparative Study of Complementary and Alternative Medicine Use in Patients of Chronic Kidney Disease: Predialysis Versus Dialysis

International Journal of Pharmaceutical Sciences Review and Research International Journal of Pharmaceutical Sciences Review and Research Available online at www.globalresearchonline. net ©Copyright protected. Unauthorised republication, reproduction, distribution, dissemination and copying of this document in whole or in part is strictly prohibited. Available online at www.globalresearchonline. net 136 Chinmay Guralwar1, Dr. Smita Sontakke*2, Dr. Charulata Bawankule3, Tanmay Bhanuse1, Dr. V.M. Motghare4, Dr. Mrunalini Kalikar2 1) Final year MBBS Student GMC Nagpur, M.S., India. 2) Associate Professor Dept of Pharmacology GMC Nagpur, M.S., India. 3) Prof & Head Dept of Nephrology GMC and Superspeciality Hospital Nagpur, M.S., India. 4) Prof. & Head Dept of Pharmacology GMC Nagpur, M.S., India. *Corresponding author’s E-mail: smitaavanti@yahoo.co.in
Use of the theory of Euclidean distance in testing for multivariate normality with application to breast cancer diagnostic data

This paper presents an adaptive technique for assessing multivariate normality (MVN). It is shown that the squared L2¬ norm otherwise known as the squared Euclidean distance of a standard d-variate normal distribution is chi-squared distributed with d degrees of freedom. Based on this, an adaptive test for MVN was proposed as the sum of squared differences between the ordered set of the squared normalized L2 norms of the observation vectors and the set of the population pth quantiles from the chi-squared distribution with d degrees of freedom. The critical values of the test were evaluated for different sample sizes and different number of random variables contained in the multivariate data through extensive simulations. For some selected sample sizes and number of random variables, the empirical power of the proposed test was compared with those of some other widely used techniques for assessing multivariate normality. The results showed that the test can be recommended as a good tool for testing MVN of a dataset especially for large sample cases. The test was applied to a data set extracted from the Wisconsin breast cancer diagnostic data and the result showed that the data set was not multivariate normal.
Pattern-based Classification Using Entropy Coding For MRI Data Classification

Dealing with artifacts in order to segment medical images stills a challenge. In this paper we propose new appropriate features for Magnetic Resonance Images (MRIs) data that allow enhancing tissue segmentation, regardless levels of the inherent artifacts, namely noise and intensity non uniformity (INU). We show that using features based on spatial entropy of intensities with different classifiers has significantly enhanced the brain matter segmentation. In addition to their powerful discrimination, the proposed features are computationally low. Experimentation was conducted using a brain web database, and the obtained results have allowed us to conclude that the proposed new features are well suited to represent MRI data for image segmentation.
Price-Setting in the Foreign Exchange Swap Market: Evidence from Order Flow

This paper investigates price discovery in foreign exchange (FX) swaps. Using data on inter-dealer transactions, we find a 1 standard deviation increase in order flow (i.e. net pressure to obtain USD through FX swaps) increases the cost of dollar funding by up to 4 basis points after the 2008 crisis. This is explained by increased dispersion in dollar funding costs and quarter-end periods. We find central bank swap lines reduced the order flow to obtain USD through FX swaps, subsequently affecting the forward rate. In contrast, during quarter-ends and monetary announcements we observe high frequency adjustment of the forward rate.
Effect of processing variables on the performance of electrochemical aptasensor for determination of aflatoxin M1

A novel aptasensor based on electrospun carbon nanofiber (ECNF) mat modified with gold naoparticles (AuNPs) and aptamer was developed to detect aflatoxin M1 (AFM1) in the milk. The electrospinning and heat treatment technique were firstly used to construct the ECNF mat electrode. Then, this platform was electrodeposited by AuNPs and immobilized with a thiol-modified single stranded DNA (ss-HSDNA). The effect of processing parameters such as concentration of HAuCl4 and ss-HSDNA in the electrolyte, incubation time of aptamer and AFM1, pH and temperature of the electrolyte on the performance of aptasensor was investigated using cyclic voltammetry (CV) experiments in the [Fe(CN)6]-3/-4 solution. The results showed that the optimum concentration of HAuCl4 in the electrolyte was 4mM. An increment in ss-HSDNA aptamer of electrolyte led to improvement of the electrochemical current response. Furthermore, the peak currents of CV enhanced with increasing incubation time of aptamer or AFM1. By increasing temperature of the electrolyte, the CV peak currents increased and then decreased. This trend was also observed in pH of electrolyte.
Factors associated with glycated albumin in adults without diabetes

Background and aims Glycated albumin is a glycemic marker useful in short-term monitoring and in situations when a glycated hemoglobin test is not reliable. This study aims to evaluate glycated albumin levels and its associated factors in normoglycemic adults from Southern Brazil. Method 136 individuals, without diabetes or pre-diabetes, were included in this cross-sectional study. Levels of glycated albumin, glycated hemoglobin, and other biochemical markers were measured. Results Glycated albumin levels ranged from 11.1% to 17.5% (2.5th and 97.5th percentiles). Glycated albumin/glycated hemoglobin ratio was 2.8±0.2. Glycated albumin did not differ according to gender and age groups. However, in overweight individuals, levels of glycated albumin and glycated albumin/glycated hemoglobin ratio were lower and weakly and negatively correlated with body mass index. Conclusions Glycated albumin levels in Brazilians were similar to those previously described in other populations. Glycated albumin seems to be irrespective of gender or age, but weakly correlated with weight. These aspects should be taken into account in the interpretation of glycated albumin results.
Comparative Analysis of the Duodenum Microbial Communities of Zhedong White Goose (Anser cygnoides) at Different Reproductive Stages by High-Throughput Sequencing

Wanqiu Zhao1,2, Taoyan Yuan1, Li Chen2, Xue Du2, Weihu Chen4, Yan Fu1, Dong Niu3,* and Lizhi Lu2,* 1College of Animal Sciences, Zhejiang University, Hangzhou 310058, China 2Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China 3College of Animal Science and Technology, Zhejiang A&F University, Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Hangzhou 311300, China 4Xiangshan County Agricultural and Rural Bureau, Ningbo 315700, China Article Information Received 01 November 2019 Revised 01 March 2020 Accepted 14 March 2020 Available online 10 December 2020
Clinicopathological study and management of infective swellings of neck in pediatric age group patients

The broad spectrum of etiology of neck swelling includes Congenital, Infective and neoplastic. Infective etiology accounts for the majority of neck swelling in pediatric age group. The severity of infective neck swelling may vary from mild reactive lymphadenopathy to life threatening Ludwig’s angina which may require immediate airway resuscitation. Majority of the time, anamnesis and physical examination is sufficient to establish the diagnosis, but when diagnosis can’t be made additional FNAC and radiology of swelling are required. Judicious use of investigations may avoid the unnecessary stress and anxiety for the treating doctor and parents. Treatment of infective neck swelling can be done by conservative method and/or surgical methods.
A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

Due to the launch of new applications the behavior of Internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively.
Civil-Military Coordination (CIMIC) Model in Natural Disasters in Iran

Objective: The present study aimed at codifying a native model of civil-military coordination (CIMIC) in natural disasters in Iran. Methods: This manuscript is a part of a larger study. The present cross-sectional study was conducted in 2019 using a two-stage Delphi technique. The factors confirmed by the technique were prioritized via a pairwise questionnaire. In doing so, 24 elites and experts in civil-military coordination were presented with the indicators in the course of classic Delphi technique and pairwise comparison. At the end, the nationalized model was finalized by sending the model to ten experts and asking their ideas. Results: The results obtained from the two rounds of Delphi indicated that 36 coordination factors could be classified into three primary classes of staff, stuff, and system. All factors were confirmed by the experts. Considering the weight of each class, “staff” and “stuff” classes were considered to be the highest and lowest priorities, respectively. Conclusion: Application of the coordination factors in the context of military and civil organizations leads to a better response to natural disasters. The organizations in charge of responding to disasters should be obliged to apply this model in the highest organizational commitment level as the final goals of disaster management. The results of the present study can be applied for codification of a comprehensive plan for assessing the civil-military coordination in natural disasters.
Are Kuwaiti Stock Returns Affected by Fluctuations in Oil Prices?

This paper examines the impact of fluctuations in the price of oil on Kuwaiti stock market returns for the month-to-month period of 2000 to 2020. The Augmented Dickey-Fuller (ADF) test for stationarity, the error correction model (ECM), and various cointegration test techniques were used to examine the estimated model. In an oil-based economy like Kuwait, the exposure to oil prices seems to affect the performance of the country’s stock market. Our main findings related to the long run showed that the price of oil is cointegrated with stock market returns. Interestingly, our ECM examination confirmed that changes in Kuwaiti stock market returns are only affected by oil price fluctuations in the short run. Further strategies are needed to better stabilize Kuwait’s capital market. This equilibrium can be achieved by pursuing more stability in other macroeconomic factors and providing a solid legal independence for the country’s financial market.
A Learning oriented DLP System based on Classification Model

Data is the key asset for organizations and data sharing is lifeline for organization growth; which may lead to data loss. Data leakage is the most critical issue being faced by organizations. In order to mitigate the data leakage issues data leakage prevention systems (DLPSs) are deployed at various levels by the organizations. DLPSs are capable to protect all kind of data i.e. DAR, DIM/DIT, DIU. Statistical analysis, regular expression, data fingerprinting are common approaches exercised in DLP system. Out of these techniques; statistical analysis approach is most appropriate for proposed DLP model of data security. This paper defines a statistical DLP model for document classification. Model uses various statistical approaches like TF-IDF (Term Frequency- Inverse Document Frequency) a renowned term count/weighing function, Vectorization, Gradient boosting document classification etc. to classify the documents before allowing any access to it. Machine learning is used to test and train the model. Proposed model also introduces an extremely efficient and more accurate approach; IGBCA (Improvised Gradient Boosting Classification Algorithm); for document classification, to prevent them from possible data leakage. Results depicts that proposed model can classify documents with high accuracy and on basis of which data can be prevented from being loss.
Enhancement in iris recognition system using FPGA

The growth of using the iris recognition over the globe for identification and for verification and the problem that faces the iris recognition from noise like eyelash and eyelid. This paper focus on choosing the right pattern to collect the traits. The algorithm of this paper is searching and working on different rectangle iris template to spotting the ultimate traits that lies within rectangle iris templates. The Ridge Energy Direction (RED) is used as algorithm to spot the features that lies within the template. The overall iris system is design, implemented and tested on the Field Programmable gate Area (FPGA).
Analysis of the Usability of a Semiautomatic Pistol: the Perception of Public Safety's Agents

Regarding to state criminal and police enforcement officers, there is a number of reported cases of serious failings in the armaments they carry, such as accidental firing of the gun still in the holster, additional non-intentional firing, and even explosions, showing the possible disregard for the usability or design flaws in the manufacturing process of certain types of weapons distributed to the country's security forces (Tribunal de Justiça do Distrito Federal e dos Territórios, 2016; Leal, 2014; Instituto Defesa, 2013).
Behavioral Classification Using Feature Selection in the Micro Activity Retrieval Task

Human activity recognition has an important role in helping computers to understand human activity. Among them, micro activity recognition is required for computers to understand more detailed action. In this research, we proposed a micro activity retrieval task method to prevent over-learning for data sets with a small number of data and many feature dimensions. As the feature selection method, we use Super-LCC. Super-LCC is fast and has low information entropy loss. The proposed method reduces the feature size from 3108 dimensions to 20.75 dimensions on average. The result of the mAP evaluation of the proposed method was 0.71707. We succeeded in the task with a technique that requires fewer feature dimensions to be entered.
Nucleus Technetium-99 m Radiopharmaceuticals : A Review on Basic and Applied Aspects

Technetium-99m (Tc) usage is increasing worldwide at a rate of 32% per annum. Enriched Uranium235 is irradiated at nuclear reactor and subsequent process produces Molybdenum-99 (Mo) which decays to Tc, and Tc converts to Tc. Organic molecules are used as Tc carriers, e.g., Tcmercaptoactyltriglycine. Tc is excreted from body with feces and urine. It is estimated that 0.22%– 38.41% of Tc remains in needles and rest is injected to the patient. Forty generators per week are supplied to medical centers in Pakistan and 1.72 x10 Bq/y Tc is returned as radioactive waste. Every used Mo/Tc generator contains Tc ~ 83.3 Bq. Tc radioactive waste is increasing world-wide, as its global use is ~4.5×10 Bq/week. No satisfactory method exists for Tc immobilization although incorporation of Tc into Fe(III) or Sn(IV) oxide matrix before glass immobilization is suggested. The present review covers all aspect of Tc radiopharmaceutical life-cycle and suggests options for Tc radioactive waste management.
Comparative Osteointegration Testing of Titanium Implants in Relation to Gender After One Month of Implantation in Rabbit Femur

The biologic material used in this study is represented by 6 rabbits, 3 males and 3 females, aged 11 months and having an average weight of 4 kg. After general anaesthesia was performed, 2 mm diameter titanium implants were surgically inserted in the femur. Postoperative care of the animals included daily observation. After 4 weeks the animals were euthanized according to the guidelines. The femurs were collected along with the implants and processed for histological examination. The samples were placed in Stieve mixture for fixation, dehydrated with ethylic alcohol, clarified with 1-Butanol, and followed by paraffin embedding. 5 micrometers sections were cut using a microtome, and then stained using the Goldner trichrome staining method and were examined using an Olympus BX41 microscope with an attached digital camera for image capturing. In all animals, the implant was well tolerated by the bone and soft tissue implantation bed. The osteointegration process took place through bone formation around the implant, with variations in thickness and structure throughout the bone-implant interface. Except for the accelerated speed of osteointegration steps, observed in males, the osteointegration process follows certain stages and no gender related differences. The rapid progress of osteointegration in males resulted in a faster process in this gender compared to females. This aspect is clinically relevant for the appropriate evaluation of waiting periods from implant placement to prosthetic mounting, which according to this study is different in males and females.