Miracle-dz/UniFAR-FTUs huggingface dataset:
THE COMPARATIVE STUDY OF TWO DRUGS OF CARBAMAZEPINE AND BETAHISTINE ON TINNITUS IMPROVEMENT

Background and Objective: Tinnitus is a common complaint among patients who refer with ear problem; according to multiple etiologies of tinnitus and lack of understanding of its indication mechanism, finding an effective treatment method is still a challenge. The purpose of this study was the study of carbamazepine and betahistine effect on tinnitus of referred patients to Ahvaz Imam Hospital. Materials and Methods: This was a clinical trial conducted on patients (23 to 65 years old) with non-pulsatile tinnitus from May 20015 to 2016 in Ahvaz Imam Hospital. A total of 73 patients were randomly divided into three groups: Two intervention groups received carbamazepine (200-400) and betahistine (16mg), and one placebo group who received placebo drug for 12 weeks. The measures were audiometric tests and Tinnitus Severity Index (TSI) that were performed preand post-intervention. Data were analyzed with SPSS statistical package. Results: Of the 73 patients, carbamazepine was superior to placebo and betahistine (P= 0.007), but Betahistine was not superior to placebo. Conclusion: Carbamazepine and betahistine drugs are effective in decreasing the patients’ tinnitus intensity, whereas betahistine is not significantly more effective than placebo.
Machine Learning based Access Control Framework for the Internet of Things

The main challenge facing the Internet of Things (IoT) in general, and IoT security in particular, is that humans have never handled such a huge amount of nodes and quantity of data. Fortunately, it turns out that Machine Learning (ML) systems are very effective in the presence of these two elements. However, can IoT devices support ML techniques? In this paper, we investigated this issue and proposed a twofold contribution: a thorough study of the IoT paradigm and its intersections with ML from a security perspective; then, we actually proposed a holistic ML-based framework for access control, which is the defense head of recent IT systems. In addition to learning techniques, this second pillar was based on the organization and attribute concepts to avoid role explosion problems and applied to a smart city case study to prove its effectiveness.
Initialization of Weights in Neural Networks

: When training and building a neural network, a number of subtle but important decisions needs to be taken. Zeroing down on the loss function to be used, the number of layers, kernel size, and the stride for each convolution layer, best-suited optimization algorithm for the network, etc. Compared to all these things, the choice of initialization of weights may seem trivial pre-training detail. But weight initialization contributes as a significant factor on the final quality of a network as well as its convergence rate. This paper discusses different approaches to weight initialization and compares their results on few datasets to find out the best technique that can be employed to achieve higher accuracy in relatively lower duration.
TRANSPORT PLANE [TP] SIMULATOR IN E-UTRAN LTE FOR CONTROL PLANE [CP]

LTE (Long Term Evolution) is a 4G wireless communications standard created by the third Generation Partnership Project (3GPP). It is intended to give up to ten times the speeds of 3G systems for cell phones, for example, smartphones, tablets, netbooks, and remote hotspots. LTE is developed from an existing 3GPP framework known as the Universal Mobile Telecommunication System (UMTS). In principle, LTE can be up to ten times speedier than 3G. In practice, the actual system speed will change when different components, for example, system load, and signal quality are taken into account. LTE is made out of numerous new advances compared with existing generations of mobile frameworks. LTE-Advanced fundamentally upgrades the previous LTE Release and supports much higher peak rates, higher throughput and scope, and lower latencies, bringing about a better client experience. In existing system, TP simulator establishes RRC connection and executes various scenarios for single or multiple RNTIs (users). There are some drawbacks in the existing system like we have to run/create initial cell setup before running any test case, deleting UE context manually, etc. Also, we have to integrate the code manually and after each change, we have to test all the scenarios manually which consumes more time and this integration process takes few months duration. The objective of this project is to develop Test Environment for E-UTRAN RRC (Radio Resource Control) layer and to enhance existing TP simulator/stub. The E-UTRAN consists of Wipro eNodeB (Base Station eNB), providing the E-UTRA user plane (PDCP/RLC/MAC/PHY) and control plane (RRC) protocol terminations towards the UE. The goal of this project is to reduce the manual activities and to automate the entire process of building and unit/integration testing using continuous integration (CI) with Jenkins.
An implicit-explicit discontinuous Galerkin scheme using a newton-free Picard iteration for a thin-film model

This paper provides a high-order numerical scheme for solving thin-film models of the form qt+ ( q2 − q3 ) x = − ( qqxxx ) x . The second term in this equation is of nonlinear hyperbolic type, while the right-hand side is of nonlinear parabolic type. The nonlinear hyperbolic term is discretized with the standard modal discontinuous Galerkin method, and the nonlinear parabolic term is discretized with the local discontinuous Galerkin method. Propagation in time is done with an implicit-explicit Runge-Kutta scheme so as to allow for larger time steps. The timestep restriction for these methods is determined by the hyperbolic wavespeed restriction, and is not limited by the nonlinear parabolic term. A novel aspect of this method is that the resulting nonlinear algebraic equations are solved with a Newton-free iteration specifically with a Picard iteration. The number of iterations required to converge is less than or equal to the estimated order of the method. We have demonstrated with this method up to third order convergence.
Gaps between competence and importance of employability skills: evidence from Malaysia

The purpose of this paper is to identify the gaps between the importance perceived and the competence gained by the hospitality students in terms of the employability skills. ,Questionnaires were distributed to 841 hospitality students in five regions in Malaysia. Descriptive and inferential statistics were used to analyze the data. ,The findings show that there are significant differences between importance perceived and competence attained in teamwork, leadership, basic, technical skills and ethical skills. Additionally, the finding of this study indicates that the culinary and bakery students perceive that they are not given adequate exposure to possess such skills. It is important to ensure that graduates are equipped with the twenty-first century skills such as problem solving and analytic, decision making, organization and time management, risk taking and communication. ,The findings provide insights to curriculum developers and policy makers on the appropriate interventions that need to be taken to improve the employability skills of the graduates. ,An original contribution is made by linking employability model to TVET sector.
In vitro antioxidant activity and total phenolic content of Sansevieria trifasciata (Snake plant) crude ethanolic and aqueous leaf extracts

Sansevieria trifasciata is a widely used ornamental plant in the Philippines with proven anti-inflammatory, thrombolytic, antipyretic, analgesic and anti-diabetic properties. However, its antioxidant potential has not been studied. This study determined the antioxidant property of S. trifasciata crude ethanolic and aqueous leaf extracts by 1,1-diphenyl-2picrylhydrazyl (DPPH) free radical scavenging assay and phosphomolybdenum method for reducing property as well as total phenolic content by Folin-Ciocalteu method. Results of the study revealed that both extracts exhibited antioxidant activity. The total phenolic content of the ethanolic extract (0.474 mg GAE/g) was found to be higher than that of the aqueous extract (0.285 mg GAE/g) and exhibited better reducing activity (2.417 mg AAE) than the aqueous extract (0.999 mg AAE) at 100 mg/mL. However, the aqueous leaf extract at 1000 μg/mL exhibited higher percentage inhibition (84.41 ± 0.49) than the ethanolic leaf extract (80.07 ± 0.65). Results of the study revealed the antioxidant potential of S. trifasciata with dose-dependent activity in higher
The Impact of DevOps Automation, Controls, and Visibility Practices on Software Continuous Deployment and Delivery

DevOps is a concept intended to blend development and operations processes to increase the efficiency of the software delivery process and early detection of software problems. The ultimate goal of any DevOps implementation is to achieve continuous delivery and deployment of software solutions and services. As DevOps concepts have not yet matured, it is not well understood among software practitioners. This research provides insights on the three elements of DevOps relevant to automation, visibility, and control practices to identify their impact on continuous deliver and deployment. The study found that automation does not have a significant impact in continuous delivery and deployment, while both visibility and control practices had a significant impact. Having that said, visibility practices have proven to have more positive impact in continuous delivery and deployment. When considering all three elements of DevOps visibility had the most impact, followed by automation practices second, and control practices last.
Hate Crime after the Brexit Vote:

I investigate the change in hate crime targeting the victim's race or religion after the Brexit vote. The vote represents a public information shock about society's attitude regarding immigrants. My results reveal a substantial and transitory increase in such crime following the vote. The central focus of my analysis is the considerable spatial heterogeneity of this increase. Areas with a greater increase in hate crime are characterized by both a greater immigrant share, and higher income proxies. Di erences in unemployment rates do not signi cantly contribute to the observed variance. More speci cally, parsimonious linear prediction models show the shares of recent immigrants and people with formal quali cations as key predictors of the hate crime increase. My ndings are consistent with treating the Brexit vote as an update of expected social sanctions to hate o enders. Issues of multiple hypothesis testing and model selection limit the use of classic methods; therefore I apply and adapt recent machine learning methods as well. JEL Classi cation: J15, K42, C31
RELIABILITY ANALYSIS FOR TESTING INFORMATION ON TWITTER:SURVEY

: The reliability of information on Twitter has been a subject of interest among researchers in both the computer and computer fields Social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter is increasingly possible to provide virtually real-time data transfer in a cost-effective manner. It is now used as a news source among a wide range of users around the world. The beauty of this platform is that they deliver content in a timely manner the way users are able to get news related to their topics of interest. Thus, the development techniques that can verify information from Twitter are a difficult and necessary task. In this system of reliability analysis to assess the reliability of information on Twitter to prevent the spread of counterfeiting or Malicious information. This system consists of four integrated components: a reputation-based component, a Drive reliability credibility, component user experience, algorithm ranking features. The components work together in an algorithmic form to analyze and evaluate the reliability of Twitter tweets and users. We tested the performance of our system on two different data sets of 489,330 unique Twitter accounts. We applied cross-checking 10 times through four automatic learning algorithms. The results reveal that a significant balance was achieved between the recall and accuracy of the set of data tested.
Chronic Inflammatory HCV Hepatitis: Clinical Pictures and Immunopathogenesis

HCV is a RNA virus that is characterized by structural regions (E1, E2 and NS1) located at the 5' end, which encode the core and envelope proteins, and non-structural regions (NS2, NS3 and NS5) located at the 3' end, involved in viral replication and in the lysis of infected cells (1). The HCV virus is extremely heterogeneous and presents a high mutation rate in its variable E2 regions. Six HCV virus genotypes are known and some of them are divided into subtypes (2). Genotype 1 is the most widespread in industrialized countries, including Italy (over 50% of cases). The series of mutations (including the mutation of only one amino acid) justifies the quasi species concept that expresses the extreme genomic variability of the virus, important molecular basis of the failed eradication and thus of a "chronic state". The HCV infection is widespread throughout the world and its prevalence varies between 1.5% in the US and 6-7% in Africa and Southeast Asia. The main risk factors (3) of HCV infections are expressed in Table I: HCV infections have a higher tendency to become chronic than HBV infections and their incubation period can vary from 6 to 12 weeks. Almost always the acute forms are asymptomatics: in over 90% of cases the primary infection develops asymptomatically. It can be diagnosed by using an immunoenzymatic method (ELISA) to search for anti-HCV antibodies (AbHCV) at least 7-8 weeks after contagion. Considering the relatively delayed appearance of these antibodies, in suspect cases an early diagnosis can be made by using genetic amplification methods to search for the HCV RNA. In acute forms that tend to solve spontaneously the HCV RNA is inclined to disappear, while non-neutralizing anti HVC antibodies still remain. The natural history of HCV foresees different patterns (4): complete healing with the normalization of transaminases, disappearance of the HCV RNA and later the disappearance of HCV antigens (20-25% of cases); chronic development of the infection, with a more or less rapid development of chronic hepatitis (high ALT and more or less high levels of HCV RNA) (70-75% of cases). The transformation of chronic hepatitis in cirrhosis triggers a series of clinical signs that are common to other cirrhosis cases too. Besides, the causes of the transition from chronic HCV hepatitis to hepatocarcinoma (HCC), mediated or not by the degree of cirrhosis are not entirely known. Special importance has been given to the virus' possible carcinogenic role and, according to recent mouse models, to the imbalance between the function of the apoptotic genes (Fas) and the apoptotic ones (Bcl-2): an over expression of the bcl-2 genes seems to exercise an important role in the induction of carcinogenesis. IFN-alpha is the choice treatment in chronic HCV hepatitis (5). Meta-analysis studies have proved that generally, the best treatment specifies at
Lap sleeve gastrectomy vs . lap gastric bypass after gastric band failure in morbid obese patient

Background: Laparoscopic gastric band had gained a great popularity as a bariatric operation due to its simplicity and good short-term outcomes, while its long term out comes have been shown to be not that favorable. This study aimed to compare laparoscopic sleeve gastrectomy (LSG) with laparoscopic gastric bypass (LRYGB) as a revisional procedure for treatment of gastric band failure. Methods: From June 2013 to November 2016, 29 patients (19 females and 10 males) who had either LSG or LRYGB for failed gastric banding at bariatric center, Elite Hospital, Riyadh, KSA were enrolled to our study. Patients were divided into 2 groups, group (A) included 16 patients (10 females and 6 males), where LSG was performed and group (B) included 13 patients (9 females and 4 males), where LRYGB was done. Patient demographics, indications for revision, operative and postoperative data were recorded. Perioperative and clinical outcomes were compared between both groups. Results: 29 patients with gastric band failure were candied for revisional bariatric surgery, divided into 2 groups. In group (A) (n = 16), LSG were performed in concurrence with the band removal as a one-stage operation in 13 (81.3%), while 3 (18.7%) patients were performed in two-stages. while in group (B) (n = 13), one-stage operation were done for 11 (84.6%) patients and only 2 (15.4%) patients underwent two-stages surgery. Mean age for group (A) was 37.3±15.3 years, while it was 36.9±17.21 for group (B). Mean BMI was 44.5±10.2 kg/m2 for group (A), and 45.5±16.31 kg/m2 for group (B). Mean operative time for Group (A) was143.6±25.12 minutes, while it was 216±56.36 minutes for Group (B) with a significant statistical difference. Mean length of hospital stay were 2.9±1.3 days and 3.1±0.8 for group (A) and (B) respectively. One case (7%) in group (B) had leakage from the gastro-jejunal anastomosis that needed reoperation. The excess weight loss (%EWL) at 3, 6, 12 and 18 months were 29.7±10.2%, 43.3±16.21%, 50.4±17.6% and 48.9±16.83% respectively for group (A), while in group (B), it was 30.8±9.41%, 45.3±8.62%, 51.32±14.31% and 50.52±13.42 % respectively without a significant statistical difference. Conclusion: conversion to LRYGB as well asLSG, after gastric band failure, is feasible and safe with a significant advantage in terms of further weight lossafter surgery. [Nasser Ahmed Abdelnazer and Rabbah k. Abdellatif. Lap sleeve gastrectomy vs. lap gastric bypass after gastric band failure in morbid obese patient. J Am Sci 2017;13(2):86-94]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience. org. 12. doi:10.7537/marsjas130217.12.
Islet Amyloid Polypeptide: A Partner in Crime With Aβ in the Pathology of Alzheimer's Disease

Diabetes affects hundreds of millions of patients worldwide. Despite the advances in understanding the disease and therapeutic options, it remains a leading cause of death and of comorbidities globally. Islet amyloid polypeptide (IAPP), or amylin, is a hormone produced by pancreatic β-cells. It contributes to the maintenance of glucose physiological levels namely by inhibiting insulin and glucagon secretion as well as controlling adiposity and satiation. IAPP is a highly amyloidogenic polypeptide forming intracellular aggregates and amyloid structures that are associated with β-cell death. Data also suggest the relevance of unprocessed IAPP forms as seeding for amyloid buildup. Besides the known consequences of hyperamylinemia in the pancreas, evidence has also pointed out that IAPP has a pathological role in cognitive function. More specifically, IAPP was shown to impair the blood–brain barrier; it was also seen to interact and co-deposit with amyloid beta peptide (Aß), and possibly with Tau, within the brain of Alzheimer's disease (AD) patients, thereby contributing to diabetes-associated dementia. In fact, it has been suggested that AD results from a metabolic dysfunction in the brain, leading to its proposed designation as type 3 diabetes. Here, we have first provided a brief perspective on the IAPP amyloidogenic process and its role in diabetes and AD. We have then discussed the potential interventions for modulating IAPP proteotoxicity that can be explored for therapeutics. Finally, we have proposed the concept of a “diabetes brain phenotype” hypothesis in AD, which may help design future IAPP-centered drug developmentstrategies against AD.
An Efficient Surface Remeshing Algorithm Based on Centroidal Power Diagram

We propose an efficient algorithm for remeshing models under the framework of Centroidal Voronoi tessellation (CVT). Instead of using the Voronoi diagram, we use a power diagram to optimize the movement of sites on the surface to avoid the intersection computation between the Voronoi cell and the input surface. Given a set of sites on the surface and their normal, paired sites are computed by offsetting the surface sites along their normal, a power diagram is constructed from the two sets. According to the paired sites, tangent planes, extracted from the power diagram, are used to constrain the movement of sites on the surface. We do a series of experiments on different models and show that the proposed algorithm is fast while the mesh quality is comparable.
A Survey on Computation Offloading for Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a promising new paradigm that has received a lot of attention recently. Computation Offloading (CO) can migrate computing tasks to the network edge of VEC, which is critical for mobile applications that are sensitive to computation power. However, the dynamicity and randomness of Internet of Vehicles (IoV) lead to new features and challenges in vehicular computation offloading. Therefore, we focus on the CO in VEC. This paper depicts a broad methodical literature analysis of CO scheme and CO methods in VEC domains and divides the current works of CO into various categories. The methodical analysis of this research will help researchers to find the important characteristics of CO and select the most appropriate algorithm for computing tasks. Challenges and research directions have also been suggested in this paper.
Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network

In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural brain imaging and DL can be used for predicting a physical trait that is of significant clinical relevance—the body mass index (BMI) of the individual. We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about age and sex. Localization maps computed for the CNN highlighted several brain structures that strongly contributed to BMI prediction, including the caudate nucleus and the amygdala. Comparison to the results obtained via a standard automatic brain segmentation method revealed that the CNN-based visualization approach yielded complementary evidence regarding the relationship between brain structure and BMI. Taken together, our results imply that predicting BMI from structural brain scans using DL represents a promising approach to investigate the relationship between brain morphological variability and individual differences in body weight and provide a new scope for future investigations regarding the potential clinical utility of brain-predicted BMI.
P- Q Control Using Cascaded Two-Level Inverter-Based Multilevel Power Flow Controller in a Distribution Level

This paper discuss with power quality improvement of system by reducing the Total harmonic distortion (THD) in the grid current. In this system static compensator (STATCOM) is used to injecting currents by regulating dc link voltages in the system which consists of two standard inverters are associated in cascaded to obtain eleven level output. In present system dc link voltages are controlled by PI controller. By this method, when the fault occurrence in the system then time response of PI controller will be slow, Overshoot problems are raised. To rectify this problem, PID and fuzzy controller are using. By using PID and fuzzy controller, dc link voltages are synchronized and a dq reference frame theory is used in this system for Statcom to recompense the current harmonics. The case study has been performed using MATLAB-Simulink software.