Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise Vehicle Tracking Ways Exhausting Arduino

Everyday model for routing of vehicles during a fat space out of doors supported the GPS (Global Positioning System) is employed popular frequent apps in the real life environment. An absolute position for vehicle with help of the GPS technology. The supporting device GPS continually move with the vehicle and can reckon the coordinates of every location and once needed by the owner it may be communicated with the assistance of (Global system mobile communication) GSM electronic equipment that is put in in each Transmitter and receiver section. An order to trace the vehicle, the user should send SMS (Short Message Service) proceeding with SIM variety authorized to the GSM services. When the SMS is away to the device then authorization will check for user. Condition confirmed, the locality latitude and longitude coordinates of the occur vehicle is distributed to the user mobile variety with kind of SMS. The coordinates of particular received with SMS may be viewed on the user mobile that may be show the precise location of the vehicle in Google Maps with support received coordinates and GPRS (General Package radio service) technology of user mobile.
The Influence of Driving Axle Location on the Lateral Force of Vehicle

The lateral force acting on the tires produces a side slip angle that affects the directional stability of vehicle. This paper presents some research results of the influence of driving axle location on the lateral force of vehicle with 4x2 wheel formula when vehicle is turning. The single track dynamical models of FWD and RWD vehicle while cornering are created. Based on these two dynamical models, the system equations of motion built are enable to study the influence of driving axle location on the lateral force. Some calculated simulation results are shown for illustration.
Enhanced Fatigue Characteristics of Topologically Optimized Biomedical Titanium Porous Structure by Selective Laser Melting

Fatigue property is a critical consideration in the porous structures and most existing porous samples have unsatisfactory performance due to lack of structural optimization. This work finds that a topologically optimized structure of α-type commercial-purity titanium (CP-Ti) produced by selective laser melting presents excellent fatigue properties with an ultra-high normalized fatigue life of ~0.65 at 10<sup>6</sup> cycles at a low density of 1.3 g/cm<sup>3</sup>. All factors affecting fatigue have been studied, including material microstructure and porous structure stress analysis. The topologically optimized structure can effectively increase the fatigue life by reducing stress concentrations. The cyclic ratcheting effect plays a dominant role in fatigue crack initiation for the topologically optimized structure. As a results of twinning occurred during the fatigue process, the porous CP-Ti sample exhibits a higher ductility than the Ti-6Al-4V sample with the same structure, which delayed the fatigue crack initiation and therefore produced a higher fatigue life. In addition, the fatigue crack propagation rate was reduced significantly because of the large plastic zone ahead of the fatigue crack tip and the effect of fatigue crack deflection and bifurcation.
Microplastic is an Emerging Problem for Marine Life

Micro-plastic is an emerging problem for aquatic as well as terrestrial organisms. Plastic is a synthetic material which is produced from organic polymers including polyethylene, nylon and PVC. There are mainly two types of the MP 1st is primary micro-plastic and 2nd is secondary micro-plastic. The source of these micro-plastic are domestic and industrial products include sunscreens, cosmetic, scrubbers (facial scrubs and hand cleansers) personal care products, insect repellents and materials used in air-blasting, other industrial applications and some plastic are produce breaking of very large plastic by a different process such as chemical deposition, physical fragmentation, physical fragmentation and biological degradation in terrestrial or marine environments. Micro-plastic cause lethal effect on marine organisms includes invertebrates, vertebrates and other biotas of the marine organisms. Micro-plastic cause tissue and organ damages, the effect on respiration rates, reduction of feeding, the effect on digestive cavities, to impaired immune functions and physiological stress which influence the status of health and the behavior, accumulation within mussels cause cellular damage which response to oxidative stress and toxic reaction in all organisms of water. Micro-plastic in aquatic organisms not only kills the marine organisms but also disturb human health. Micro-plastic directly or indirectly enters into the human food chain and cause digestive problems, stomach disease and cancer in human. Micro-plastic also causes financial damage because fishes are the main source of income in many countries. Without immediate strong preventive measures about micro-plastic many problems will be world face in the future. In this article, we discuss the about the source of micro-plastic, transfer of micro-plastic, the effect on marine organisms as well as human, ingestion of micro-plastic and control measure of micro-plastic.
Toward a Formalization of BPEL 2.0 : An Algebra Approach

The WS-BPEL 2.0 (Web Service Business Process Execution Language) has been the dominant standard to describe Web Services (WS) orchestration approach. It is a rich and expressive language that provides interesting features, among them we find four mechanisms to deal with abnormal situations (Event, Fault, Compensation and Termination) handlers, EFCT- handlers. However, WS-BPEL is not rigorously defined as a formal language making EFCT- handlers complicated and ambiguous. This paper aims to remove ambiguities from EFCT-handlers by formalizing their semantics.
An Extension of Calderón Transfer Principle and its Application to Ergodic Maximal Function

We first prove that the well known transfer principle of A. P. Calderon can be extended to the vector-valued setting and then we apply this extension to vector-valued inequalities for the Hardy-Littlewood maximal function to prove the vector-valued strong type $L^p$ norm inequalities for $1<p<\infty$ and the vector-valued weak type $(1,1)$ inequality for ergodic maximal function.
Docking and Antiproliferative Effect of 4-T-Butylbenzoyl-3-Allylthiourea on MCF-7 Breast Cancer Cells With/Without Her-2 Overexpression

Breast cancer is currently one of the most common causes of death in women all over the world, with 30 percent cases caused by overexpression of HER-2 and poor prognosis. HER-2 plays an important role in cell proliferation, thereby, making it a promising strategy in the treatment of breast cancer. This research, therefore, uses 4-t-butylbenzoyl-3-allylthiourea a category of the thiourea group which acts as a needed pharmacophore for cytotoxic activity. Its aim is to dock and investigate the antiproliferative effect of 4-t-butylbenzoyl-3-allylthiourea on MCF-7/HER-2 and MCF-7 parented breast cancer cells. Virtual screening was conducted by docking the interaction study of the compounds into the binding site of EGFR and/or HER-2, with PBD code: 1M17 and 3PP0 to predict their affinity. Furthermore, it enabled the extraction of RS -91.0665 kcal/mol value on the EGFR receptor (1M17.pdb) and RS -91.0365 kcal/mol value on the HER-2 receptor (3PP0.pdb). The Anti-proliferative effect of the compound on MCF-7 and MCF-7/HER-2 cell line was observed by MTT assay, while 4-t-butylbenzoyl-3-allylthiourea exhibited its effect on MCF-7/HER-2 cells using IC50 values of 0.15±6.48 mM higher than MCF-7 cells with IC50 values of 0.26±2.48 mM. In conclusion, 4-t-butylbenzoyl-3-allylthiourea is potentially developed as an anti-breast cancer agent with overexpression of HER-2.
The Technology Nexus of AI: How Narratives Serve as a Lock-in

Artificial intelligence, which is created by applying machine learning techniques to big data, is a new general and dual-purpose technology with disruptive potential for industry and military alike. It disrupts in multiple dimensions, in particular: <br><br>(a) by introducing a new and irreducible form of information asymmetry which creates existential risks for information have-nots when this is evaluated in an evolutionary context; <br><br>(b) by introducing a new form of productive capital – machine knowledge capital – which changes income distribution within societies; <br><br>(c) by changing the source of comparative advantage in innovation across nations; <br><br>(d) by putting in play new internationally contestable rents, which drive strategic behavior; <br><br>(e) by creating new national security vulnerabilities; and <br><br>(f) through the enhancement of prediction, serving as the handmaiden of quantitative social psychology, which entails risk of social and political manipulation. <br><br>Further, as it is emerging in a context of accelerated change, conflict has pre-empted negotiation in dividing the benefits; narrative construction has been accordingly dedicated to suit adversarial rather than constructive purposes. The analysis suggests that the narrative framework for international relations that had evolved during the postwar era, as adapted to advance the interests of the digital economy powers, risks becoming a barrier to a cost-minimizing solution to the current trade and technology wars in which the technology nexus of AI/ML/data play a pivotal role.
Endothelialized TiO 2- and Ti45Nb-Coated PMP, PDMS and PES/PVP Membranes for Biohybrid Lung Support

Lacking hemocompatibility of extracorporeal membrane oxygenation led to a biohybrid lung concept, in which gas exchange membranes are seeded with endothelial cells to form long-term stable surfaces. However, extensive surface modification of large oxygenator membranes is required for cell adhesion. In this study, polymethylpentene (PMP), polydimethylsiloxane (PDMS) and polyethersulfone/polyvinylpyrrolidone (PES/PVP) membranes were functionalized with thin films of titanium dioxide (TiO 2 ) or titanium-niobium (Ti45Nb), and characterized by field emission scanning electron microscopy, ellipsometry, X-ray photoelectron spectroscopy and gas permeability measurements. Endothelialized membranes were cultured under static conditions to evaluate cell coverage. Cell retention was investigated in a bioreactor system under flow conditions with a wall shear stress of 0.5 Pa, and cells were stained for endothelial markers CD31 and von Willebrand factor (vWf). Film thicknesses of 3.3±0.2 nm and 5.5±0.3 nm were determined for TiO<i>2</i> and Ti45Nb coatings, respectively. With the exception of a slight decrease for PDMS+Ti45Nb, gas permeabilities of coated PMP and PDMS membranes did not deteriorate significantly compared to uncoated membranes. The microporous structure and the high hydrophilicity of PES/PVP prevented permeability measurements and subsequent dynamic culture due to membrane wetting. However, mean cell density increased substantially for all coated membranes compared to uncoated membranes. Although endothelialized PDMS+Ti45Nb showed minor cell layer defects, all coated PMP and PDMS membranes demonstrated integral cell layers after dynamic culture that stained positive for CD31 and vWf. This study has shown the suitability of thin TiO<sub>2</sub> and Ti45Nb films for flow-stable endothelialization of gas exchange membranes for application in a biohybrid lung.
A Deep Learning Approach to Recognize Handwritten Telugu Character Using Convolution Neural Networks

Automated character recognition is one of the most vital components which enable a data processor to distinguish letters and digits possibly using contextual data. Various attempts at resolving this problem using different selections of classifiers and features have been established and still the problem remains challenging. In the proposed work, we suggest and evaluate a classic Convolutional Neural Network (CNN) for the identification of online Telugu characters. The network comprises of 4 layers, one with 5 X 5 and remaining with 3 X 3 kernels and ReLU, softmax activation functions, followed by max pooling and two dense layers. The final layer has 168 outputs, matching to the classes considered: vowels, consonants. To train and estimate the CNN, we preowned 45,133 images written by many telugu writers. A qualified analysis proved the efficiency of the proposed CNN against previous methods in an interesting dataset. On test dataset, the classification technique provided 92.4% accuracy. The conclusion is improved than some recently proposed literature used for the identification of online handwritten Telugu characters.
The Video-Microscopy Linked Electrochemical Cell – An Innovative Method Allowing for a More Accurate Interpretation of Electrochemical Measurements of Magnesium Degradation

An innovative miniature optical-electrochemical cell was developed and tested that allows for the conducting of electrochemical corrosion measurements and simultaneous microscopic observations over a small, well-defined surface area of corroding magnesium samples. The setup consists of a miniature electrochemical cell that is clamped onto the magnesium sample before being filled with electrolyte, bringing a sample surface area of about 0.8 mm<sup>2</sup> in contact with the electrolyte. The miniature cell comprises afferent/efferent electrolyte ducts as well as a connection to the Mini Cell System (MCS), a combination counter and reference electrode that itself is connected to a potentiostat. The transparent roof of the electrolytic cell allows optical microscopic access to the corroding magnesium surface. Consequently, all measured and induced currents and voltages refer to the same small area corroding completely within the field of view of the microscope, thus allowing for real time observation and linking of surface phenomena such as hydrogen evolution and oxide deposition to electrochemical data. The experimental setup was tested on commercial purity (cp) and extra high purity (XHP) magnesium (Mg) samples using open circuit potential and cyclic voltammetry methods under static and flowing conditions. The measurements and observations with this new method pave the way for a more detailed understanding of magnesium corrosion mechanisms, thus improving predictive power of electrochemical corrosion measurements on newly developed magnesium alloys and modifications for use as absorbable stents or orthopedic implants. Different electrochemical test can be run under various conditions, while being easy to set up and reproduce as well as being minimally destructive to the sample.
Implementation of SDC-SDF Architecture for Radix-4 FFT

Very large scale integration and Digital signal processing are the very crucial technologies from the last few decades. DSP applications require high performance, low area and low power VLSI circuits. This paper is discussing about FFT which is one of the vital component in the digital signal processing. In this Paper, we propose a single path delay commutator–feedback (SDC-SDF) Architecture for Radix-4 FFT and presented its simulation and synthesis results. The Radix-4 FFT architecture consists of log4 N-1 SDC Stages and 1 SDF stage. Previously, the radix-2 SDC-SDF (Single path delay commutator-feedback) FFT architecture was includes log2 N-1 SDC Stages and 1 SDF stage. The proposed Radix-4 SDC-SDF architecture reduces the number of multiplications and additions as well as number of stages which achieves reduced area and low power. The resultant architecture is simulated using Modelsim, design verification and synthesis results are done using Xilinx ISE. The proposed architecture is compared with Radix-2 SDC-SDF FFT and it can achieve less area as well as low power consumption.
Improved Multinomial Naïve Bayes Approach for Sentiment Analysis on Social Media

Due to a substantial growth in amount of internet users over the years, there has been a large growth in amount social media users. Every person likes to voice their opinion on some matter that concerns them directly or indirectly. With the ease of expressing our views to millions of people in just a matter of few seconds by a few strokes of keyboard, social media has become a platform that can be efficiently utilized to extract valuable information about people. Comments made by users can be extracted using some API and then be fed to an algorithm that determines whether it is positive or not, which is the sentiment of the user. One such proposed algorithm is Multinomial Naïve Bayes Classifier which after certain steps of linguistic preprocessing gives an appreciable result when compared to more sophisticated algorithms on previously unseen data even on a very small training set containing less than 1000 training examples. We observe how generative models like Naïve Bayes usually perform better than discriminative models like SVM and Decision Trees for the task of Sentiment Analysis on micro blogging websites where the corpus is small.
Improvement in Validation Process of Embedded Software using Regression Testing

Validation process is very important in software development life cycle to adhere the development standards and the user's requirements. This paper brings out approach to improve the performance of verification and validation (V&amp;V) of embedded software. The Paper explores latest techniques in the direction of improving the quality and overall performance of the software design. The proposed approach introduces essential modifications in the conventional V&amp;V process which leads to improvement in the validation activities and provides a way to remove defects to save the time in design efforts of the projects and improvise the reliability of the embedded system.
International Conference on Advancements in Computing & Management (ICACM-2019)

In this study, a CPV-TEG system has been assessed by developing a thermodynamic model. Seebeck effect, jointly with Thomson effect, Fourier heat conductive and Joule effects have been considered and simulated in MATLAB. The temperature of the photovoltaic module and the colder and hotter junctions of the thermoelectric generator have been determined analytically by solving energy balance equations iteratively. Observations regarding the effect of concentration ratio, the figure of merit, thermocouple numbers, irradiance, on the power at the output of the system have been made. The efficiencies of the photovoltaic module, thermoelectric generator, and the hybrid system have been measured. Considering Thomson effect in TEG, it can be observed that the power obtained at the output of CPV-TEG is reduced by some percentage at CR=1 and CR=5 respectively. The reduction of power at the output of the TEG module is more than the hybrid system. This analysis provides insight for designing of non-theoretical CPV-TEG systems © 2019ICACM. Hosting by SSRN. All rights reserved. Peer review under responsibility of International Conference on Advancements in Computing & Management.
Study of Effect of Crack on Natural Frequencies of Euler Bernoulli Cantilever Beam

In this paper study of the effect of transverse open crack on natural frequencies of Euler Bernoulli cantilever beam has been done. Mathematical modeling has been done using flexibility approach. Flexibility at crack location is calculated using fracture mechanics approach. Natural frequencies for first six modes are obtained for different crack locations and crack sizes. It is observed that when crack size increases for a given crack location, the natural frequencies for the beam decrease. For a crack size with variable crack location, natural frequency has less effect near the node.
A Comparative Study on Disease Classification using Machine Learning Algorithms

This paper is based on studying the classification methods on the datasets of five diseases. The data which was recorded on the patients complaining of heart disease, thyroid disease, and diabetes, are taken from the online repository. Both, the parametric and non-parametric machine learning algorithms are chosen and their ability to classify is analyzed. Support Vector Machine, Naïve Bayesian Classifier, K-Nearest Neighbors Classifier, Multilayer Perceptron Classifier, and Decision Tree Classifier are used to compare their performances on the clinical datasets. A combination of two feature selection approaches is taken to compare the performance of the model so developed. Firstly, filter method approach is used to select the features and then wrapper method approach is applied to assess the performance of the classifier built on the combination of those features. The Fisher Discriminant Ratio value is used to sort the features in order of their relevance in the binary classification. Three performance measures: F1-Measure, Precision and Accuracy are chosen to get the effects of adding more number of features into the classification model. The results are counterintuitive and that makes this work significant.
Study and Performance Analysis of Two Input Universal Gates (NAND & NOR) Designed Using Ten Different Logic Styles at 45nm Technology

Any digital gate can be designed using various types of logic structures. And two of the most important digital gates are the NAND and NOR gates since these are the universal gates which can be used to design any complex circuit. Hence in this paper we have designed and analyzed two input NAND and NOR gates using ten different logic styles at 45nm technology.
Real Time Face Recognition based on Convolution Neural Network

: Face recognition is an active research area with many successful traditional and deep learning methods. In our research, we are introducing a Real Time Face Recognition based on Convolution Neural Network (CNN) method for face recognition and to maintain a high accuracy which is running in real time. <br><br>Face represents multidimensional, complex, meaningful visualization and developing a real time face recognition computational model for face recognition is not easy. <br><br>In present time, real time face recognition is a challenging problem. The main challenge of face recognition is that how to improve the image recognition performance of the system when it is affected by the variability of non-linear effects. In this paper, 3-layer CNN architecture is proposed for solving the face recognition problem, CNN gives a solution which is capable of solving the facial images related problems that contains varying illumination, poses, occlusions and facial expressions. <br><br>The test on 115 subject of Essex University database achieve an accuracy of 98.5% which is similar range of performance as the best result of previous works. Meaningfully, our system of real time face recognition based on CNN completes the facial recognition process in less than 0.019 seconds. <br>
Classification of Batik Motifs Using Convolutional Neural Networks

Batik is Indonesia’s traditional cultural heritage which is a cloth-making technique using canting and malam. Each region of Indonesia usually produces their own batik motifs. This paper proposes the convolutional neural networks to recognize the batik motifs from images. The dataset consists of batik motifs from Lasem, Solo, and Yogyakarta region. In total, there are 13 classes of batik motif in the dataset. Experimental results show that the model performance is not sufficient enough to accurately detect most of the batik motifs.
Identity & Access Management System Based on Blockchain

Identity and access management plays an important role in our day to day life. Most of the existing IAM Solution are centralized which raises many issues among which privacy is a critical concern. This paper proposes a privacy protecting, decentralized IAM Solution. Blockchain serves as the backbone of our system which is inherently decentralized and secure. In our solution, none of the sensitive identity information about the user are stored on a centralized server rather is stored on the mobile device assigned to the user. This serves two purposes, protection of privacy of the user and giving the user control of his own identity information.