Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug Sentiment Analysis using Machine Learning Classifiers

In recent times, one of the most emerging subdimensions of natural language processing is sentiment analysis which refers to analyzing opinion on a particular subject from plain text. Drug sentiment analysis has become very significant in present times as classifying medicines based on their effectiveness through analyzing reviews from users can assist potential future consumers in gaining knowledge and making better decisions about a particular drug. The objective of this proposed research is to measure the effectiveness level of a particular drug. Currently most of the text mining researches are based on unsupervised machine learning methods to cluster data. When supervised learning methods are used for text mining, the usual primary concern is to classify the data into two classes. Lack of technical terms in similar datasets make the categorization even more challenging. The proposed research focuses on finding out the keywords through tokenization and lemmatization so that better accuracy can be achieved for categorizing the drugs based on their effectiveness using different algorithms. Such categorization can be instrumental for treating illness as well as improve one’s health and well-being. Four machine learning algorithms have been applied for binary classification and one for multiclass classification on the drug review dataset acquired from the UCI machine learning repository. The machine learning algorithms used for binary classification are naive Bayes classifier, random forest, support vector classifier (SVC), and multilayer perceptron; among these machine learning algorithms, linear SVC was used for multiclass classification. Results obtained from these four classifier algorithms have been analyzed to evaluate their performances. The random forest has been proven to have the best performance among these four algorithms. However, multiclass classification was found to have low performance when applied to natural language processing. On the contrary, the applied linear SVC algorithm performed better for class 2 with AUC 0.82 in this research. Keywords—Machine Learning Algorithms; natural language processing; drugs sentiment analysis; text mining
METODOLOGÍA PARA EL DISEÑO DE UNA RED DE SENSORES INALÁMBRICOS

The increasing incorporation of electronic sensors in the industry has identified the need to propose methods to help reduce wiring costs as much as channeling. In these cases, wireless solutions are very convenient, however, there is no standard method to enable its implementation regardless of the owner to use technology. For this reason, in this research a methodology for the design and implementation of a wireless sensor network is proposed. This method has seven steps: detection measurement needs of the environment, environment characterization, study of available devices associated with wireless sensor networks , selecting the type of network to use , perform calculations, experimental trials with the devices individually deploy the network in half. The method was tested in a cement plant by measuring the temperature in four distant points -based sensors and wireless devices. The results were observed by internet from a central computer.
Adaptive DRL-Based Virtual Machine Consolidation in Energy-Efficient Cloud Data Center

The dramatic increasing of data and demands for computing capabilities may result in excessive use of resources in cloud data centers, which not only causes the raising of energy consumption, but also leads to the violation of Service Level Agreement (SLA). Dynamic consolidation of virtual machines (VMs) is proven to be an efficient way to tackle this issue. In this paper, we present an Adaptive Deep Reinforcement Learning (DRL)-based Virtual Machine Consolidation (ADVMC) framework for energy-efficient cloud data centers. ADVMC has two phases. In the first phase, Influence Coefficient is introduced to measure the impact of a VM on producing host overload, and a dynamic Influence Coefficient-based VM selection algorithm (ICVMS) is proposed to preferentially choose those VMs with the greatest impact for migration in order to remove the excessive workloads of the overloaded host quickly and accurately. In the second phase, a Prediction Aware DRL-based VM placement method (PADRL) is further proposed to automatically find suitable hosts for VMs to be migrated, in which a state prediction network is designed based on LSTM to provide DRL-based model more reasonable environment states so as to accelerate the convergence of DRL. Simulation experiments on the real-world workload provided by Google Cluster Trace have shown that our ADVMC approach can largely cut down system energy consumption and reduce SLA violation of users as compared to many other VM consolidation policies.
Governança Corporativa, Conselho De Administração E Redes De Empresas Corporate Governance, Board Of Directors And Company Networks

O presente estudo tem como objetivo investigar o fenomeno de board interlocking (lacos entre ﬁrmas) e sua inﬂuencia sobre o desempenho empresarial das empresas listadas no Indice de Governanca Corporativa. Fich e White (2001) afirmam que o fato do mesmo individuo possuir relacionamentos em mais de uma empresa faz com que se estabeleca uma ligacao entre elas (FICH e WHITE, 2001). Para construcao desse trabalho a amostra da pesquisa foram as empresas listadas no Indice de Governanca Corporativa – Novo Mercado, da BM&FBOVESPA (IGC-NM). O indice e composto por 184 empresas. Para verificar se o board interlocking influencia no valor das empresas, utilizou-se a metodologia de dados em painel. As variaveis utilizadas neste trabalho foram baseadas nos estudos de Mendes-Da-Silva et al. (2008) e Santos e Silveira (2007), contudo somente algumas variaveis foram utilizadas. Como resultados, a pesquisa aponta que cerca de 85% das empresas do IGC estao ligadas entre si atraves de seus conselheiros. Observou-se que seis das empresas da amostra (3%) apresentam uma densidade relativa forte na rede, enquanto 15% das companhias possuiam conselheiros sem relacao com as demais empresas avaliadas. Estes resultados sao superiores aqueles encontrados por Santos e Silveira (2007) para empresas componentes do Ibovespa, sugerindo que o fenomeno de Board Interlocking aumenta caso seja analisadas apenas empresas com maior padrao de governanca corporativa. Palavras-chave: Board Interlocking . Conselheiros. Novo Mercado. ABSTRACT The present study aims to investigate the board interlocking phenomenon (firms ties) and their influence on the business performance of companies listed in the Corporate Governance Index. Fich &White (2001) state that the fact that the same individual has relationships in more than one company makes it establishes a link between them (FISH & WHITE, 2001). Construction of this work the research sample were companies listed in the Corporate Governance Index –New Market of the BM & FBOVESPA (IGC-NM). The index is composed of 184 companies. To verify that the board interlocking influences the value of the companies used the panel data methodology. The variables used in this work were based on studies of Mendes-Da-Silva et al. (2008) and Santos & Silveira (2007), yet only some variables were used. As a result, the research shows that about 85% of PMI companies are linked together through their advisors. It was observed that six of the companies in the sample (3%) have a strong relative density on the network, while 15% of companies had directors unrelated to other companies evaluated. These results are superior to those found by Santos & Silveira (2007) for components of Ibovespa companies, suggesting that the Board Interlocking phenomenon increases if analyzed only companies with the highest standards of corporate governance. Keywords : Board Interlocking. Board Of Directors. New Market.
Kinematically Aligned Total Knee Arthroplasty

National registries demonstrate a 7-fold increase in total knee arthroplasties (TKAs) over the last 13 years. In the early implementation of TKAs in the 1960s, most surgeries were performed to treat lower extremity deformity caused by rheumatoid arthritis. In the present era, patients primarily undergo TKA when osteoarthritic pain limits their activity. Despite advances in technology and materials, up to 20% of patients remain unsatisfied after TKA. Malalignment of the femoral and tibial components is one reason for these poor results. The current standard for aligning the femoral and tibial components is to position the components perpendicular to the mechanical axis. This technique of mechanically aligning the knee is applied irrespective of the patient’s individual anatomy, often requiring soft tissue balancing to accommodate for the artificial alignment created during surgery. Kinematically aligned TKA strives to restore the native joint line, thereby preserving the soft tissue envelope to permit normal knee motion. This book explains the basic science and goals of kinematic alignment, provides clinical evidence, and details the surgical technique of kinematically aligned TKA.
Human orthopedic research articles convey information better than veterinary orthopedic research articles: Evidence-based manuscript writing guidelines

Objective.The objective of this study was to compare the brevity and clarity of human orthopedic articles to veterinary orthopedic articles. The goal is to provide authors and reviewers with objective, evidence-based guidelines to critically evaluate the brevity and clarity of veterinary manuscripts during the writing phase of research and the review process. Study design.We used word counts and the percent of active voice sentences to assess the brevity and clarity of the introduction sections and discussion sections in 15 randomly chosen veterinary orthopedic clinical trial articles and 15 randomly chosen human orthopedic clinical trial articles. Results.Veterinary introduction sections were on average 193 words longer than human introduction sections (p=0.001). Veterinary discussion sections were on average 370 words longer than human discussion sections. Veterinary introduction sections had on average 14.4 percent fewer active voice sentences than human introduction sections (p=0.003). Veterinary discussion sections had on average 8.3 percent fewer active voice sentences than human discussion sections. Conclusion.Human orthopedic clinical trial articles are more concise and direct than veterinary clinical trial articles.
Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network

After drilling a borehole in the ground and in a rocky environment, the materials around the borehole are crushed and separated in layers from the borehole wall; this causes the borehole cross section to lose its original circular shape, which redistributes stresses and further failure. This type of episodic failure, which occurs symmetrically and V-shaped on both sides of the borehole and along the minor principal stress, is called breakout. The dimensions of breakout, i.e., its depth and width, are two important indicators that have recently been used in estimating in situ stresses; however, the dimensions of the breakout area depend on the in situ stresses and mechanical properties of the rock, which have not been well addressed so far. This paper presents a comprehensive investigation of breakout dimensions using finite element numerical analysis. The proposed numerical model is based on the equations governing the two-dimensional breakout phenomenon under nonisotropic in situ stresses and plane strain condition. According to the results, increasing the failure function of the area around the breakout tip causes the breakout to expand, until the failure function is less than 1 for all points around the breakout tip, at which point the breakout expansion is stopped and breakout reaches stability. In the other part of the article, using 121 datasets obtained from numerical analysis, an artificial neural network is trained to predict breakout dimensions based on the input parameters of the problem. The main finding of this section is a model that shows that among the parameters affecting the borehole breakout, the internal friction angle of the rock has the greatest impact on the dimensions of the breakout.
Augmented Reality: Prototype for the Teaching-Learning Process in Peru

In recent years, augmented reality is playing an important role in the world of mobile technology, since it is a way to facilitate the teaching-learning processes, this easy teachinglearning process generates a great contribution in companies, either creating opportunities or changing the way in which companies approach and interact with their end customers, this can mean a remarkable growth of the organization, that is why in this work an augmented reality prototype has been made using the methodology Scrum at the University of Sciences and Humanities of Lima-Peru, but with a focus on the nursing career. Knowing that the problem is the limited learning that students acquire in the classrooms, for which, we want to make use of augmented reality, so that this improves the form of education that is provided to university students. The result obtained, from developing the case study, was an augmented reality prototype for the improvement of education at the University of Sciences and Humanities of Lima-Peru, which shows a virtual model (it depends on the image shown), able to interact with the user, making it attractive and motivating for the student, this prototype was achieved, using Unity (3D development platform, Vuforia (augmented reality software development kit), Microsoft Visual Studio (integrated development environment), the Scrum Methodology (Scrum Pillars, Product Backlog, Product Backlog Estimation, Speed, Backlog Prioritization and Sprint Planning) and the C# Language (C Sharp). Keywords—Augmented reality; teaching; education; scrum; unity
Atemmuster- und Schlaf Stadienerkennung mit neuronalen Netzen bei Säuglingen

In der Polysomnographie wird der physiologische Zustand des Menschen während des Schlafes überwacht. Dazu zeichnet man verschiedene Größen der Atmung, die wichtigsten Blutgaswerte und elektrophysiologische Signale auf. Die Auswertung dieser bis zu zehn Stunden dauernden Messungen erfordert vom untersuchenden Mediziner einen erheblichen Zeitaufwand, der mit Hilfe einer rechnergestützten Analyse reduziert werden kann. Dieser Beitrag stellt das automatische Erkennen von Atemregulationsstörungen, insbesondere Atempausen, und die Einteilung des Schlafverlaufes in verschiedene Schlafstadien mit Hilfe neuronaler Netze vor.
The evolution of the association between teacher technology integration and its influencing factors over time

Abstract Teacher technology integration is an evolutionary process, and its relation with various personal and contextual variables may change over time. This study collected questionnaire responses from 429 Chinese K-12 teachers at two time points, and examined how the associations of technology-related beliefs, pedagogical practices and perceived contextual barriers with student-centered technology use and teacher-centered technology use changed over time. PLS path modeling analysis revealed that value belief about technology use became less important and TPACK self-efficacy became more important for both types of technology use over time. The relationship of pedagogical practices with technology use decreased over time too as teachers developed greater flexibility toward technology use. The study further revealed that the evolution of the association varied for student-centered technology use and teacher-centered technology use. The findings advocate a dynamic and differentiated approach to understanding and supporting teacher technology use.
Comparison of individual based approaches using RAPD markers for identifying genetic relationships in Erodium (Geranaiceae)

Genetic diversity research is required to understand the conservation and management of plant resources in all ecosystems. In Iran, there are 15 different Erodium species. Erodium genetic diversity has not been studied extensively using Random Amplified Polymorphic DNA (RAPD). As a result, six species were gathered and learned from six different Iranian locales. A total of 82 plant specimens were collected. Our objectives were to assess Erodium species genetic diversity and to identify Erodium species genetic diversity. 2) Is there a link between a species's genetic makeup and geographic location? 3) Population and taxon genetic structure. Erodium species were separated into two groups using an unweighted pair group technique with arithmetic mean and principal component analysis. In Erodium, gene flow (Nm) was relatively modest (0.33). The Mantel test revealed a link between genetic and geographical distances (r = 0.77, p=0.0001). We identified many genetic variations in the Erodium species, which indicates that it can adapt to changing circumstances. According to recent findings, RAPD markers and morphometry approaches may investigate Erodium species genetic diversity.
Hybrid Deep Neural Network Model for Detection of Security Attacks in IoT Enabled Environment

The extensive use of Internet of Things (IoT) appliances has greatly contributed in the growth of smart cities. Moreover, the smart city deploys IoT-enabled applications, communications, and technologies to improve the quality of life, people’s wellbeing, quality of services for the service providers and increase the operational efficiency. Nevertheless, the expansion of smart city network has become the utmost hazard due to increased cyber security attacks and threats. Consequently, it is more significant to develop the system models for preventing the attacks and also to protect the IoT devices from hazards. This paper aims to present a novel deep hybrid attack detection method. The input data is subjected for preprocessing phase. Here, data normalization process is carried out. From the preprocessed data, the statistical and higher order statistical features are extracted. Finally, the extracted features are subjected to hybrid deep learning model for detecting the presence of attack. The proposed hybrid classifier combines the models like Convolution Neural Network (CNN) and Deep Belief Network (DBN). To make the detection more precise and accurate, the training of CNN and DBN is carried out by using Seagull Adopted Elephant Herding optimization (SAEHO) model by tuning the optimal weights. Keywords—Internet of things; deep learning; optimization; convolutional neural network; security attack detection
Effects of Ion Adsorption on Graphene Oxide Films and Interfacial Water Structure: A Molecular-Scale Description

Graphene oxide is a promising, emerging separation material, as it is durable, dispensable in water, and has naturally forming functional groups. Bulk studies using graphene oxide flakes have demonstrated impressive metal adsorption. However, little interfacial information about metal adsorption on graphene oxide is available and inferring interfacial structure from bulk experiments is usually not possible. A mechanistic understanding of ion adsorption on graphene oxide films is critical toward advanced separations, including improved sorption efficiency and membrane regeneration. In this paper, we study metal ion adsorption onto graphene oxide films formed at the air/water interface using x-ray reflectivity (XR), x-ray fluorescence near total reflection (XFNTR), and vibrational sum frequency generation spectroscopy (SFG). These interface-specific techniques provide the electron density profile normal to the interface, number of adsorbed ions, and information about the orientational ordering and hydrogen-bonding network of interfacial water, respectively. Via XFNTR and SFG, we find that trivalent yttrium ions preferentially adsorb to graphene oxide compared to divalent strontium and monovalent cesium ions. These trivalent ions affect the graphene oxide film structure significantly. The SFG data show that at least two different interfacial water populations can be described, based on their hydrogen bonding strength, and the adsorbed ions affect these populations differently. We demonstrate that ion adsorption onto graphene oxide is more complex than simple electrostatics and requires thorough interfacial investigation. These results pave the way toward improved soft-scaffold graphene oxide membranes and applications and provide fundamental information about the ion adsorption mechanism at the interface.
Factors Affecting the Jordanian Students' Selection Decision Among Private Universities

This study aims at defining the role of the marketing factors in affecting the Jordanian student choice of a private university. Through asking a random sample consists of 25 females and 25 males about the factors according to their point of view, the financial cost has the most significant effect on their selection of the universities, but between females this factor was not significant word of mouth and university branding has higher degree in influencing the students choice of the university among females.
Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation Using Error-Aware Unified Semisupervised and Active Learning

As the coronavirusdisease 2019 (COVID-19) pandemic continues, fast and automatic COVID-19-related pneumonia lesion segmentation method is in an urgent need. The current state-of-the-art methods for segmentation generally require sufficient amounts of annotated data for training. However, human expert annotation of such lesion on chest computed tomography (CT) scans is time-consuming and labor-intensive due to its heterogeneous appearance, ambiguous boundary, and large number of slices in 3-D CT images. Therefore, the purpose of this study is to present a novel annotation-efficient learning method for COVID-19 pneumonia lesion segmentation on CT. To make the best use of limited human expert annotation resources, we propose an error-aware unified semisupervised and active learning method. A novel error estimation network is proposed to estimate a voxelwise segmentation loss map, which is used to guide learning from unlabeled data for semisupervised learning and choose the most informative images to annotate next for active learning. Validation is carried out on segmenting pneumonia lesions in 110 chest CT scans. The experimental result demonstrates that the proposed method significantly boosts the segmentation accuracy given limited amount of human annotation, compared with a conventional fully supervised baseline (60.9% Dice to 72.0% at 30% labeled data). The performance is also competitive compared with other state-of-the-art annotation-efficient segmentation methods. The proposed method can significantly reduce the annotation effort needed to achieve accurate COVID-19 pneumonia lesion segmentation.
Gain and offset analysis of comparator using the bisection theorem and a balanced method

ABSTRACT Gain and offset represent two important measures to determine the accuracy of a comparator. Thus, analysis on these parameters is very important as they offer designers better understanding of the circuit and allow exploring trade-offs during design. In this paper, two methods were presented to derive a set of design equations that describe the gain, sensitivity, offset, and systematic mismatches observed in typical comparator circuits. A three-stage, fast complementary metal-oxide semiconductor (CMOS) comparator structure is analysed and simulated in order to validate the proposed methods. A 0.13 μm CMOS technology is used for simulations with 1.5 V supply voltage. Bisection theorem was used for gain and sensitivity analysis. Simulation results show that high gain improvement can be possible by using the design equations. The input offset voltage, due to mismatch in the width of the metal oxide semiconductor field-effect transistors (MOSFET) (W) and mismatches in the threshold voltages of the N and P type MOSFETs (VTHN, VTHP), is analysed using a proposed balanced method. The same comparator structure is used for the input offset voltage analysis. Simulations show that an offset improvement can be achieved following the design equations found through the proposed method.