Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Scientific Research in Mechanical and Materials Engineering

A review of mechanical properties of Kevlar and different artificial based fibers/fillers/particles or nanotubes reinforced composites forming polymer hybrid in different capacities was carried out. Most of the composites were prepared by hand layup, compression and injection molded techniques. Many of the review works has concentrated on using Scanning electron microscopic analysis to be followed in order to evaluate surface morphology of the composites respectively. Normally the relationship between theoretical and experimental values was figured out using rules of mixture in the present papers studied for review. Mainly out of the analytical results showed good agreement with the experimental results. From the review, in Kevlar based composites, it is observed that, hybrid composites showed good results in terms of Impact strength, tensile strength, flexure strength, fracture toughness good energy absorption capacity, and compression after impact properties. The use of surface treatment or coupling agents have helped in improving the strength, adhesive, high strain properties in the materials. The use of fillers, particles, nano-tubes or nano clays has really enhances the Kevlar based polymer materials properties.
Macroscopic Changes in Oral Mucosa and Hygiene Indicators in Smokers

Smoking is the most important factor affecting the oral cavity by components born in the tobacco combustion process and acting directly on the oral mucous membranes, dental arch and indirectly on the teeth support. Recent studies show the tobacco action on the oral cavity, manifestations in the form of gingivitis, bacterial plaque, dental plaque, papillary bleeding at drilling, periodontitis. Purpose of the study. In this study, we have set out to assess the macroscopic modifications of oral cavity on smokers. Materials and Methods. The participants in the study were divided into two groups, the first group of smokers with a smoking period over 5 years and the control group of nonsmokers. The patients in the two groups underwent a physical examination and an objective clinical examination, the resulting data being compared with the control group. Results. For the bacterial plaque indicatorin the smoker group there was obtained a mean value of 35.68±12.45, compared to a mean value of 16.32±6.61 for the nonsmoker group, the dental plaque indicatorfor the smoker group had a mean value of 2.24±1.02, higher than the one in the nonsmoker group, namely 0.94±0.68, and for the drilling bleeding indicator we obtained a mean value of 19.54±7.89 in the nonsmoker group, which is lower than that in the smoker group, namely 42.86±14.93. Conclusions. Smoking is a cause that maintains and aggravates the periodontal disease, including the risk of periodontitis, allowing the aggravation of gingivitis, considered a reversible surface inflammation of the gum mucosa which, by accumulation of dental plaque, the dental plaque accompanied by incorrect oral hygiene, favors the progression to periodontitis.
Association of homocysteine with metabolic syndrome risk factors in postmenopausal women

Background: The prevalence of cardio-metabolic disorders is highest among the elderly population and homocysteine (Hcy) is considered as a major risk factor. Vascular aging in women is exacerbated by reproductive aging in response to hormonal changes during the menopause. The present study assessed the levels of Hcy, Vitamin B12, folate, and examined their association with metabolic syndrome risk factors in menopausal women. Aim and Objectives: The objectives of the study were to examine the levels of Hcy, Vitamin B12, and folate and their association with various components of metabolic syndrome in menopausal women. Materials and Methods: We recruited a total of 300 women between 35 and 64 year from Bharati Hospital, Pune. They were classified into pre, peri, and post-menopausal groups according to their menstrual history. Results: Lower Vitamin B12 and folic acid and higher Hcy concentrations were found in peri and post-menopausal women as compared to premenopausal women. Women with metabolic syndrome showed lower micronutrients and higher Hcy compared to those without metabolic syndrome. An inverse association of Vitamin B12 with waist circumference (WC), diastolic blood pressure, triglycerides and HOMA-IR and inverse association of folate with fasting glucose and HOMA-IR were observed. A positive association of Hcy with WC, fasting glucose, systolic and diastolic blood pressure, triglycerides, and HOMA-IR was also seen. In a logistic regression model, metabolic syndrome was found to be independently associated with age and Hcy. Conclusion: This study reports elevated Hcy and low micronutrient levels in postmenopausal women. Hcy was found to be independently associated with metabolic syndrome risk in these women. Monitoring plasma Hcy concentrations with adequate B vitamin stores could be an effective strategy to minimize metabolic syndrome risk in middle aged women.
Discourse Maps — Feature Encoding for the Analysis of Verbatim Conversation Transcripts

This chapter reports on work that extracts information about linguistic features from political deliberations in order to make the deliberative quality of political dialog measurable.1 With Discourse Maps, a dynamic visualization that is tailored to both the requirements of the data and the theoretical framework on measuring deliberative quality as articulated within political science Gold et al. (2016), we showcase how Visual Analytics can combine theory-driven (top-down) analysis with a data-driven (bottom-up) view on the data. Unlike the Zhao et al. (this volume) paper, we do not work solely on the basis of discourse relations, but extact a plethora of relevant linguistic features and visualize these according to their type and contribution to the deliberative nature of the dialog. Our system has the potential to provide political scientists, linguists, stakeholders in the debate or the general public with a visualized rep-
A NOVEL TWO-STEP DOPPLER COMPENSATION SCHEME FOR CODED OFDM UNDERWATER ACOUSTIC COMMUNICATION SYSTEMS

The performance of orthogonal frequency division multiplexing (OFDM) is gravely affected by the Doppler induced symbol dilation. Thus, it is important to compensate for this effect before detection. In this paper, we propose a combined Doppler Effect estimation scheme for coded OFDM underwater acoustic communication systems. This scheme contains two steps; the first step is using the Fractional Fourier Transform to estimate the chirp rate change of the linear frequency modulation (LFM) signal, which is used as synchronisation probe to obtain an initial Doppler Effect estimate using the chirp rate change. In the second step, cyclic prefix (CP) correlation is used to estimate the fine Doppler factor. We compare the proposed approach with state-of-the-art, time and frequency based resampling methods. The presented numerical results show that the proposed scheme can estimate the Doppler factor precisely after the two steps and match the performance of the resampling-based approaches. However, the advantage of the twostep approach does not require the whole packet to be received, which in turn reduces the storage requirements and hardware resources, and thus, it is more suitable for the realtime implementation.
A Research on EM ̄EEG Discrimination Based on CNN ̄GRU Fusion Network

Aim at the low recognition of emotion EEG signals(EM ̄EEG)identification method. Based on the analysis of EM ̄EEG feature distribution and the characteristics of single convolutional neural network(CNN)ꎬcombined with gated recurrent unit (GRU)ꎬthe CNN ̄GRU fusion network is proposed for EM ̄EEG discrimination research. The constructed CNN ̄GRU network integrates the EM ̄EEG frequency domain and spatial feature extracted by CNN with the EM ̄EEG time domain feature extracted by GRUꎬand carries out emotional state identification in the time ̄frequency ̄ spatial feature space of the constructed EM ̄EEG. In additionꎬthe self ̄built data acquisition platform is used to design emotion induction experiments to generate new emotional state data sets. After experimental explorationꎬthe results show that the recognition rates of the proposed network model trained on the SEED IV data set and the self ̄acquired data set are 89.2% and 83.76% respectivelyꎬboth of which can effectively distinguish the four different emotions and verify the good generalization ability of the network. The prediction experiment of the model trained by SEED IV data set on the self ̄acquired emotion data set verified the strong robustness of the network.
Uncoupling Inequality: Reflections on the Ethics of Benchmarks for Digital Media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion.
CREATeV: An Evaluation of Solar Charging Optimization for Ultra-Long Endurance Flight

Over the past four years the Clean Renewable Energy Aerial Test Vehicle (CREATeV) solar-powered UAV has completed a total of 23 hours of flight testing. This testing has identified a need for a theoretical model to predict the solar power intake of the aircraft. Using this data and the power required, different loiter circuits can be evaluated to select the most beneficial path. This model analyzes each flightpath by evaluating when the aircraft’s batteries are charging or discharging. The model also takes into consideration the effects of wind, heading, date, and time of day. Experimental data collected during a 10 hour flight conducted in November 2020 is used to develop an understanding of the vehicle’s level flight power required. This model will be used to maximize the net power gained from the sun during daylight hours, and improve the chances of completing future multi-day flight tests with the CREATeV vehicle.
HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints

Protein engineering has become an important field in biomedicine with application in therapeutics, diagnostics and synthetic biology. Due to the complexity of protein structure de novo computational design remains a difficult problem. As helices are an abundant structural feature and play a vital role in determination of the protein structure, full atom de novo computational design for helices would be an important step. Here, we apply Wasserstein bi-directional Generative Adversarial Networks to generate full atom helical structures. To design for structure or function, we allow the design according to structural constraints and introduce a novel Markov Chain Monte Carlo search mechanism with the encoder such that the generated helices match target "hotspot" residues structures. Our model generates helices matching well to the target hotpots (within 3 Å RMSD) and with near-native geometries for a large fraction of the test cases. We demonstrate that our approach is able to quickly generate structurally plausible solutions, bringing us closer to the final goal of full atom computational protein design.
Explanatory Factors for Severe Acute Malnutrition in 6 - 59 months of age Patients Admitted in the Therapeutic Nutritional Unit of N’Djamena, Chad

Introduction: Globally Severe acute malnutrition(SAM) affects more than 29 million children under the age of five. Demographic and Health Survey and Multiple Indicators in Chad (EDS-MICS) of 2014-2015 survey found an acute malnutrition level of 13% out of which 9% was moderate and 4% was severe. The Causes of SAMS are multifactorial, relating to multiple domains such as society, culture, environment, education, livelihoods, agriculture, water, hygiene, etc. Despite this established evidence, the evidence of parental knowledge is unknown. Objective: The objective of our study was to determine the explanatory factors of SAM and its level of knowledge, understanding by the parents. Materials and Methods: A prospective descriptive study was conducted in 238 children aged 6-59 months admitted to therapeutic nutritional unit Alerte Santé of Ndjamena from November 15 to December 30, 2017. Results: During the study, the male sex (58.8%), the age group of 12-17 months (26.9%) and 6-8 months (24.4%) were the most represented and are the majorities in the urban area (72.3%). We were able to identify the various explanatory factors among which the level of education of mothers (62.2%), exclusive breastfeeding (2.3%), the food taboo (81%) are not insignificant. Malnutrition would be decompensated by the breakdown of food access (59%) and digestive diseases (74.8%) and respiratory diseases (73.5%). We found that parents have poor knowledge of malnutrition (87%). Conclusion: A good understanding of these main factors of malnutrition by parents might help to effectively combat severe acute malnutrition.
Tutorial on Causal Inference for Natural Language Processing

Causal inference is becoming an increasingly important topic in deep learning, with the potential to deal with critical deep learning problems such as representation learning, model robustness, interpretability, and fairness. In addition, causality is naturally widely used in various disciplines of science, to discover causal relationships among variables and estimate causal effects of interest. In this tutorial, we introduce the recent advances in causal inference and causal discovery to the NLP audience, provide an overview of the state-of-the-art causal treatments of NLP problems, and aim to further inspire novel approaches to NLP. We will cover a general introduction to basics of causal discovery and inference, how to use causality to improve NLP models, and how to use causality to improve discovery of linguistic theories and NLP for social applications. This tutorial is inclusive to a variety of audience and is expected to facilitate the community’s developments in formulating and addressing new, important NLP problems in light of the emerging causal principles and methodologies.
Estimation of Quercetin and Rutin Content in Hyouttunia cordata and Centella asiatica Plant Extracts Using UV-Spectrophotometer

Flavonoids are secondary plant metabolites normally found as pigmented compounds in plants. Quercetin and rutin are two important and commonly found flavonoids in nature and exhibit wide pharmacological effects such as antioxidant, anticarcinogenic, antiviral, anti-inflammatory, antidiabetic, and hepatoprotective activities as well as antimicrobial activity. In this study, quercetin and rutin content is being quantified in the plant extracts of Centella asiatica and Houttuynia cordata and considerable amounts of these two flavonoids were depicted. A single beam UV – Spectrophotometer was used to measure the absorbance of the standard as well as test solutions. Calibration curves were constructed for standard quercetin and rutin in such a way that x-axis denotes concentration and the y-axis denotes the absorbance. The calibration curves showed linearity at concentrations 5-25 ? g /ml of quercetin and rutin respectively with a good correlation coefficient (r) of 0.99 for both the curves. The absorbance of the two test extracts was obtained from the calibration curve and respective concentrations of quercetin and rutin for the two extracts were calculated. The amount of quercetin and rutin present was expressed as Total Flavonoid Content (TFC) i.e. the amount of the flavonoid in ? g present per mg of the respective plant extract. The quercetin content in both the plant extracts was found to be more (315.8 in Houttuynia cordata; 487.6 in Centella asiatica) than the content of rutin (152.2 in Houttuynia cordata; 171.0 in Centella asiatica).
Development of the International Cardiac Rehabilitation Registry Including Variable Selection and Definition Process

Introduction: The International Council of Cardiovascular Prevention and Rehabilitation (ICCPR) is developing a registry (ICRR) specifically for low-resource settings, where the burden of cardiovascular diseases is greatest and the need for program development highest. Herein we describe the development process, including the variable selection process. Method: Following a literature search on registry best practices, a stepwise model for ICRR development was identified. Then, based on recommendations by Core Outcome Set-STAndards for Development (COS-STAD), we underwent a process to identify variables. All available CR registries were contacted to request their data dictionaries, reviewed CR quality indicators and guideline recommendations, and searched for common data elements and core outcome sets; 35 unique variables (including patient-reported outcomes) were selected for potential inclusion. Twenty-one purposively-identified stakeholders and experts agreed to serve on a Delphi panel. Panelists rated the variables in an online survey, and suggested potential additional variables; A webcall was held to reach consensus on which to include/exclude. Next, panelists provided input to finalize each variable definition, and rated which associated indicators should be used for benchmarking in registry dashboards and a patient lay summary; a second consensus call was held. A 1-month public comment period ensued. Results: First, registry objectives and governance were approved by ICCPR, including data quality and access policies. The protocol was developed, for public posting. For variable selection, the overall mean rating was 6.1 ± 0.3/7; 12 were excluded, some of which were moved to a program survey, and others were revised. Two variables were added in an annual follow-up, resulting in 13 program and 16 patient-reported variables. Legal advice was sought to finalize ICRR agreements. Ethics approvals were obtained. Usability testing is now being initiated. Conclusion: It is hoped this will serve to harmonize CR assessment internationally and enable quality improvement in CR delivery in low-resource settings.
Prediction of Heart Disease using Machine Learning Techniques

Heart attacks and strokes account for 85 percent of these fatalities. Unhealthy food, lack of physical exercise, cigarette smoking, and excessive alcohol use are all major behavioral risk factors for CVDs. These variables can lead to high blood pressure, high blood glucose, high blood cholesterol, and obesity. It is critical to identify cardiac illness as soon as possible, as well as swiftly and correctly as possible. Complex medical data is analyzed by various data mining and machine learning techniques in literature. The findings of the in-depth examination of these research articles are extremely convincing and accurate, but the future scope of these papers reflects the need for more significant characteristics and abundant standardized data, as well as the employment of different algorithms to achieve better accuracy rates. This research paper compares Random Forest algorithm with nearest neighbor (KNN) and Naïve Bayes on standard datasets from Cleveland database and Statlog Heart Disease of University of California Irvine (UCI) repository. The major goal of the research study is to get meaningful outcomes. With only 13 characteristics, we were able to get some extremely encouraging outcomes. The results validate Random Forest Classifier with accuracy of 93.02 %, significantly outperformed Naive Bayes and KNN which have accuracy of 83.72% and 90.69% respectively.
Responses of Larix principis-rupprechtii Radial Growth to Climatic Factors at Different Elevations on Guancen Mountain, North-Central China

Larix principis-rupprechtii is an important afforestation tree species in the North China alpine coniferous forest belt. Studying the correlations and response relationships between Larix principis-rupprechtii radial growth and climatic factors at different elevations is helpful for understanding the growth trends of L. principis-rupprechtiind its long-term sensitivity and adaptability to climate change. Pearson correlation, redundancy (RDA), and sliding analysis were performed to study the correlations and dynamic relationships between radial growth and climatic factors. The main conclusions are as follows: (1) The three-elevation standard chronologies all exhibited high characteristic values, contained rich climate information and were suitable for tree-ring climatological analyses. (2) Both temperature and precipitation restricted low-elevation L. principis-rupprechtii radial growth, while monthly maximum temperatures mainly affected mid-high-elevation L. principis-rupprechtii radial growth. (3) Mid-elevation L. principis-rupprechtii radial growth responded to climate factors with a “lag effect” and was not restricted by spring and early summer drought. (4) Long-term sliding analysis showed that spring temperatures and winter precipitation were the main climatic factors restricting L. principis-rupprechtii growth under warming and drying climate trends at different elevations. The tree-ring width index and Palmer drought severity index (PDSI) were positively correlated, indicating that L. principis-rupprechtii growth is somewhat restricted by drought. These results provide a reference and guidance for L. principis-rupprechtii management and sustainable development in different regions under warming and drying background climate trends.
Asymptotic and Finite-Time Semistability for Nonlinear Discrete-Time Systems With Application to Network Consensus

This article focuses on semistability and finite-time semistability analysis and synthesis of discrete-time dynamical systems having a continuum of equilibria. Semistability is the property whereby the solutions of a dynamical system converge to Lyapunov stable equilibrium points determined by the system initial conditions. In this article, we build on the theories of semistability and finite-time semistability for continuous-time dynamical systems to develop a rigorous framework for discrete semistability and discrete finite-time semistability. Specifically, Lyapunov and converse Lyapunov theorems for semistability and finite-time semistability are developed, and the regularity properties of the Lyapunov function establishing finite-time semistability are shown to be related to the settling time function capturing the finite settling time behavior of the dynamical system. These results are then used to develop a general framework for designing semistable and finite-time semistable consensus protocols for discrete dynamical networks for achieving multiagent coordination tasks asymptotically and in finite time. The proposed controller architectures involve the exchange of generalized energy state information between agents guaranteeing that the closed-loop dynamical network is semistable to an equipartitioned equilibrium representing a state of consensus consistent with basic thermodynamic principles.