Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of 2-4 Mixed-Logic Line Decoders for unsupervised learning

—Recent advancement in CMOS technology involve materials progress which led to new design structures and new design techniques in nanoscale circuits. The 90nm design rules uses CMOS circuits and materials for metal interconnects, allow the design of basic circuits with low power consumption, low delay and less transistor numbers. In this paper, mixed-logic implementation of 2-4 line decoders, combining pass transistors, static CMOS and transmission gates. Comparative spice simulations show that the circuits present a significant improvement in power and delay, outperforming CMOS in almost all cases. Unsupervised learning is used for decoding which can be further used in many applications.
CHOLERETIC AND ANTIOXIDANT EFFECT OF EUPATORIUM BUNIIFOLIUM. Gorzalczany

Eupatorium buniifolium is widely distributed in Argentina and it is used in folk medicine as digestive and in hepato-biliary complaints. The aqueous extract of the aerial parts has been tested in rats for its choleretic, antioxidant activity and acute toxicity effects. The extract showed significant choleretic effect (35%, i.v. and 50%, p.o.) after both oral (500 mg/kg) and intravenous (250 mg/kg) administration without changes in the bile acids output. Measure of Total Reactive Antioxidant Potential (TRAP) and the Total Antioxidant Reactivity (TAR) indicate that the extract has antioxidants of high reactivity antioxidant capacity. It may be due to the presence of antioxidants such as phenolic and flavonoid compound, extensively reported as free radicals scavengers. Toxicity signs were not observed until oral dose of 3000 mg/kg.
Application of Feature Selection Techniques on Bioinformatics Data

: Bioinformatics so far have become an integral part of the research and various other applications in big data. Determining the clinical implications through varied experiments in biomedical research is increasing rapidly. Selection of certain attributes on which the research will rely is yet another crucial task, as the outlook of the algorithm depicts how the information extracted is useful in the given field. Feature Selection so far has proved to be efficacious in this meadow of research, as it selects a subset of features for avoiding the issue of ‘curse of dimensionality’. Multi-attribute feature selection is chosen which involves the selection of relevant attributes from multi-attributed datasets, results of which improve accuracy. Our aim is to imitate the survival probability of the diabetic patients and identify the associated risk factors along with it. The analysis depicts the factors which contribute to a shorter survival span of diabetes mellitus patients. The factors include glucose levels, BMI, age, insulin, blood pressure, skin thickness and pregnancies.
Sentiment Analysis Using Ensemble Learners and Gini Index

: Sentiment analysis is a part of natural language processing for evaluation of opinions from data. In this paper sentiment analysis has been performed on movie reviews. This paper explores the sentiment evaluation using gini index feature selection method. Furthermore the ensemble learners method has been used for classification. Finally the results have been obtained from the confusion matrix.
ISOLATED DIESEL GENERATOR INTEGRATED WITH PV-DSTATCOM

In this paper, isolated Diesel generator set is integrated with static synchronous compensator (DSTATCOM) for harmonics elimination, reactive power compensation and injection of active power. The control of scheme of DSTATCOM is derived from instantaneous reactive power theory. The dc bus voltage is regulated to constant value with proportional integral (PI) controller of DSTATCOM. The controlling pulses DSTATCOM is carried out with hysteresis controller. The overall system consisting of DG set, DSTATCOM and nonlinear load simulation is carried out with MATLAB environment using Simulink and power system block-set toolboxes. The MATLAB modeling is performed for a synchronous generator coupled to a diesel engine, along with the three-leg VSC working as a PV-DSTATCOM. The simulated responses of PV-DSTATCOM with DG set are presented to verify the effectiveness of the control of PV-DSTATCOM for the compensation harmonics, reactive power and active injection without separate DSTATCOM. IndexTerms: DSTATCOM, MPPT,harmonics, reactive power.
An Autonomous Wireless Implementation of IOT based Healthcare Applications

: Internet of things (IOT) is a wireless network which interconnects devices for sharing data and information and producing a new information which is stored and can be analysed in future whenever required. TheIOT plays a vital role in medical field in providing services to patients and doctors. The idea of this project is to send the health status of patient in which includes blood pressure(BP),heart rate, and ECG directly to the doctor without attending the hospital. This model consists of a system which communicates between network connected systems, Applications and devices that helps the doctors and patients to monitor, track and record patient’s vital data and medical information. We are implementing this model o the Raspberry pi zero which uses ARM1176JZF-S ARM core processor and software includes Python which is needed to program Raspberry pi zero which was used to upload our final code of maintaining a database. By using this system doctor can examine his/her patient’s current health status and full medical information from anywhere and anytime.
HIGH ACCURACY METHODS FOR FOAM INJECTION-EXPANSION SIMULATION G. François, Transvalor Company, Mougins, France L. Ville, Transvalor Company, Mougins, France

In this paper, we propose a set of advanced numerical methods in order to simulate foam injection-expansion processes in a single step. Our approach is based on a set of stabilized solvers for each governing equation. These solvers are coupled with an advanced global stabilization algorithm and a multi-criteria adaptive meshing technology. These new technologies provide realistic results. We present several analytical and technical examples to demonstrate the overall robustness.
Deposition rate effect on microstructure and perpendicular magnetic anisotropy of iron films prepared by ion-beam-assisted deposition

Thin films of iron with different thickness (⁓40–300 nm) were deposited on quartz and silicon substrates by using ion beam assisted deposition (IBAD) technique. The influence of film thickness and deposition rate on microstructure, magnetic phase composition and perpendicular magnetic anisotropy (PMA) of iron films were studied by electron microscopy, X-ray diffraction, Mössbauer spectroscopy, vibrating-sample magnetometry and differential thermomagnetic analysis. Shown, that phase composition and magnetic properties of films depend substantially on the deposition rate, and, in fact, don't depend on both film thickness and the type of substrate. The strong PMA is observed in iron films formed at low (~3 nm/min) and medium (~10 nm/min) deposition rates only. The microstructure is characterized by the presence of nanocrystalline grains of α-Fe phase, as well as a significant fraction of disordered iron phase and nonmagnetic oxide phase of FeO (up to 20%). The FeO phase occurs due to the reaction of iron atoms with the residual atmosphere of the vacuum chamber. Moreover, significant positive deformation (about 3%) of crystal lattice of α-Fe grains was observed. The effect of deposition rate on the crystallinity, phase composition and PMA in thin iron films we associate with the features of
Evaluation of an InfiniBand Switch: Choose Latency or Bandwidth, but Not Both

Today’s cloud datacenters feature a large number of concurrently executing applications with diverse intradatacenter latency and bandwidth requirements. To remove the network as a potential performance bottleneck, datacenter operators have begun deploying high-end HPC-grade networks, such as InfiniBand (IB), which offer fully offloaded network stacks, remote direct memory access (RDMA) capability, and non-discarding links. While known to provide both low latency and high bandwidth for a single application, it is not clear how well such networks accommodate a mix of latencyand bandwidth-sensitive traffic that is likely in a real-world deployment. As a step toward answering this question, we develop a performance measurement tool for RDMA-based networks, RPerf, that is capable of precisely measuring the IB switch performance without hardware support. Using RPerf, we benchmark a rack-scale IB cluster in isolated and mixedtraffic scenarios. Our key finding is that the evaluated switch can provide either low latency or high bandwidth, but not both simultaneously in a mixed-traffic scenario. We evaluate several options to improve the latency-bandwidth trade-off and demonstrate that none are ideal.
Fracture behavior of prototype steel-GRP (hybrid) riser under internal fluid pressure

This study describes the fracture behavior of prototype steel-glass reinforced plastic (SGRP) riser under internal fluid pressure. Subsea marine riser comprising of hybrid metal composite materials has recently drawn keen industrial attention. In comparison to metallic riser, this kind of composite riser have already proven to be cost effective comparing the specific strength. The hybrid riser structure used in the study consists of a steel liner and composite layer. The composite layers have been manufactured from symmetric layers of four harness satin weave configuration. The prototype specimens were prepared according to DNV-RP-F202. A static burst test is carried out to determine the critical burst pressure of the hybrid riser. The failure mode of the fractured specimen is investigated and analyzed using scanning electron microscopy (SEM). The improved burst pressure results show that, hybrid riser developed in this study is indeed a feasible alternative over the conventional metallic riser.
General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.
An urban expressway forwarding scheme for cognitive Internet of vehicles

The Internet of vehicles is an essential component for building smart cities that can improve traffic safety and provide multimedia entertainment services. The cognitive radio–enabled Internet of vehicles was proposed to resolve the conflict between the increasing demand of Internet of vehicles applications and the limited spectrum resources. The multi-hop transmission is one of the most important issues in cognitive radio–enabled Internet of vehicles networks. Nevertheless, most existing forwarding solutions designed for the cognitive radio–enabled Internet of vehicles did not consider the urban expressway scenario, where primary base stations are densely installed with small coverage areas. In this case, it is difficult to ensure that the sender and the receiver of the same cognitive radio link have similar channel availability statistics, which makes cognitive radio links more likely to be interrupted. To address this challenge, we develop a multi-hop forwarding scheme to minimize the end-to-end delay for such networks. We first formulate the delay minimization problem as a non-linear integer optimization problem. Then, we propose an approach to select the relay candidates by jointly considering the high mobility of vehicles and the unique cognitive radio spectrum usage distributions in urban expressway scenarios. Finally, we propose the low-latency forwarding strategies by considering the channel availability and the delay cost of different situations of relay candidates. Simulations show the advantages of our proposed scheme, compared with state-of-art methods.
Sentiment Analysis and Features Extraction of Reviews using Latent Dirichlet Allocation

- More recently, the Social Media has become extremely popular to share the user’s viewpoints to their friends by using various social networking platforms. It creates obligatory for the users to post their reviews for other users to learn about the quality of the products. But there is much information overloading problems. So to address this problem we propose, A Sentiment Analysis and Features Extraction of Reviews Using Latent Dirichlet Allocation Method used to extract product features, to deal with incorrect sentiment estimation, improve the precision of sentiment mapping, enhance the sentiment dictionary and to calculate the sentiment score and improve prediction accuracy and reliability in the original recommender systems. As the result, it helps to boost the recommendation performance.
Indoor positioning-based mobile resource movement data management system for smart factory operations management

The primary role of modern information and communication technology infrastructures in smart factory is to collect a wide range of digital data from manufacturing resources; however, many manufacturing companies still have significant trouble in collecting data relevant for operations management. Especially, it is difficult to collect data about movement of mobile resources, such as human operators and material handling equipment. Moreover, managers of manufacturing companies often have significant troubles in utilizing the raw data collected by information and communication technology infrastructures due to its complexities and vast amount. To fill these gaps, this article proposes indoor positioning-based mobile resource movement data management system, which can be used to collect and process the mobile resource movement data flexibly. The indoor positioning technologies enable to track the positions of physical objects in real time; however, they generate time series data for 3D coordinates of object position not suitable for practical use. Therefore, this article aims to integrate the indoor positioning technology with a specialized user application, which allows the users to define what kinds of data should be collected and how the raw data should be transformed.
Introduction of Computational Thin-Layer Chromatography

Rapid analysis of various heavy metal ions from the environmental sample has been carried out using the Super-Saragraphy-831(SS-831) software (© SW-10774/2018 India). The separation was carried out on a thin film of aminoplast polymer. The identification with retention factor value was carried out using Super-Saragraphy 831 software. The proposed method was successfully applied for identification and separation of heavy metals from river water, industrial wastewater as well as from bore well water.
ORTHOTROPIC RHEOLOGY OF GLASS-MAT- REINFORCED THERMOPLASTICS (GMT): LUBRICATED SQUEEZING FLOW

The rheological behaviour of Glass-Mat Reinforced Thermoplastics (GMT) was studied under isothermal compression moulding conditions on a universal test machine. The anisotropic behaviour of the lubricated squeezing flow is modelled using three nonNewtonian extensional viscosities. Previous work modelled the material as transversely isotropic, resulting in two in-plane extensional viscosities. In that work, the main assumption was that the material had the same viscosity in one of the in-plane directions and through thickness. Consideration of three independent viscosities is considered necessary as a result of an extensive research study of the fibre structure and orientation of the material, which shows that the material has different fibre structures in three different directions.
Distribution and New Records of Lymexylidae (Coleoptera: Lymexyloidea) in Guatemala and Mexico

Abstract Lymexylidae is a small family of beetles with worldwide distribution. Only two species are recorded from Guatemala and Mexico: Atractocerus brasiliensis Lepeletier and Audinet-Serville and Melittomma brasiliensis (Laporte). New distributional records from Guatemala and Mexico are presented for these species. A distribution map and comments about a phoretic mite interaction are provided.
First Description of the Female of the Rare Megasoma lecontei Hardy (Coleoptera: Scarabaeidae: Dynastinae: Dynastini)

Abstract Megasoma lecontei Hardy is the most rarely collected of all species of Megasoma Kirby, and the female remained undescribed. A description and illustrations of the female of M. lecontei are provided for the first time based on several specimens from near Cabo San Lucas, Baja California Sur, Mexico. A revised key to the Megasoma species occurring in Baja California Sur, Mexico is presented.
Driving maneuver detection using knowledge distillation networks

In this thesis, we examine the current state of Advanced Driving Assistance Systems (ADAS) and their relation to maneuver prediction in the literature. We then attempt to solve the problem of variable inter-driver behavior by applying a novel distillation learning system using RoadLab data on tracked driver cephalo-ocular gaze behavior in tandem with high-resolution CANbus data. Current training-based methods in maneuver prediction are potentially subject to underfitting as drivers may exhibit different behavior when preparing to maneuver, but it has been shown that drivers can be grouped into at least two distinct behavior models. We use this information to personalize a deep neural network ensemble by distilling knowledge from a larger ”teacher” network to a smaller ”student” network. We change the networks’ input data to a subset of that data during training. Various groupings of driving sequence data are tested for prediction accuracy within this system, particularly against a validation driving sequence belonging to a specific driver group.
A Study on Adaptive Image Interpolation Algorithms for Multimedia Applications

Image interpolation enhances the quality of images by introducing new pixels in the given image. The new pixels are created by using old pixels. Non-adaptive and adaptive interpolations are the two major categories of image interpolation which are widely used in multimedia applications. Non-adaptive image interpolation performs the same operation throughout the entire interpolation process. But the adaptive image interpolation performs differently based on the characteristics of the surrounding pixels of the interpolation point. Adaptive image interpolation performs complex operations to improve the quality of image and it provides high-quality interpolation as compared with conventional non-adaptive interpolation. This work evaluates the characteristics of different adaptive image interpolation in terms of their quantitative measures. Both peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) are considered for this evaluation. Based on the detailed review, it is identified that the adaptive image interpolation using edge-orientation outperforms other adaptive interpolation schemes. Further, it affords that the quality of interpolation can also be improved by using optimized filters and by splitting the edge-regions by higher order.
Automatic Bug Triage with Data Reduction

: Bug triage means to transfer a new bug to expertise developer. The manual bug triage is opulent in time and poor in accuracy, there is a need to automatize the bug triage process. In order to automate the bug triage process, text classification techniques are applied using stopword removal and stemming. In our proposed work we have used NB-Classifiers to predict the developers. The data reduction techniques like instance selection and keyword selection are used to obtain bug report and words. This will help the system to predict only those developers who are expertise in solving the assigned bug. We will also provide the change of status of bug report i.e. if the bug is solved then the bug report will be updated. If a particular developer fails to solve the bug then the bug will go back to another developer.
Detection of Similar Identities in XML Documents

 Abstract — Duplicate detection is an important part of data cleaning; it is the process of detecting multiple representations of a same real-world object in the data sources. Numbers of solutions are available for detecting duplicates in XML data. One of the novel methods for XML duplicate detection is XMLDup. XMLDup makes use of a Bayesian network to evaluate the probability of two XML elements are duplicates. In addition a network pruning strategy is also used for improving the evaluation of the Bayesian network. A DOM tree construction algorithm for constructing the tree of the input XML data is proposed. It is seen that by using DOM tree construction algorithm higher efficiency is achieved for detection of similar identities in XML Documents .
Experimentally Determining the Onset Conditions of the Faraday Instability with Electrostatic Forcing

he Faraday instability has been studied since 1831 when Faraday accelerated a fluid periodically perpendicular to its interface and observed that after a certain frequency threshold was reached, the fluid interface formed distinct patterns, known as modes, which oscillated at one half of the system frequency [1]. The patterns resulted from the combination of the gravitational force, the shaking force, and the interfacial forces. Researchers like Benjamin and Ursell [2] and Kumar and Tuckerman [3] have developed theoretical models for the prediction of these patterns in mechanically shaken systems. Although the mechanically shaken Faraday instability has been studied extensively, there are many varieties of Faraday instability that have not received attention. This paper will focus on the experimental determination of the onset conditions for the instability while the system is contained inside an electric field as suggested by Yih [3]. Modes and onset conditions were mapped by utilizing an electrostatic levitator apparatus located at the Japanese Aerospace Exploration Agency (JAXA) laboratories in Tsukuba, Japan. This device has a reliable frequency range of 1 Hz-15 MHz and peak-to-peak amplitude range of 0-20 kV. The inviscid theory that has been suggested by Yih depends on a free-slip boundary condition. The free-slip boundary condition implies that the interaction between the sidewall and the fluid is stress-free. Since the theory strongly depends on a free-slip boundary condition, the fluids that are chosen to represent the theory are critical to the success of the experiment. Once selection of the fluids was completed, the Faraday instability was observed consistently across a range of frequencies.