Browse Items (5543 total)
Sort by:
-
Effect of heat treated HNT on physico-mechanical properties of epoxy nanocomposites
Halloysite Nano Tubes (HNTs) are naturally occurring Kaolite group minerals having an aluminosilicate-layer in the form of nanotubes which are known to enhance the properties of the polymer matrix composites when effectively dispersed in the epoxy matrix phase. In this regard, the present work is carried out to fabricate the composite specimens by polymer stir casting techniques and evaluate the basic properties viz., density, hardness, tensile strength, flexural strength, impact strength and the microstructure using Transmission electron microscopy (TEM) for better morphological studies of the dispersions in the nanocomposites with a filler content of 0, 5 and 10 wt% of HNT's that are effectively treated at three temperature conditions viz., Room temperature (RT), 50 C and 70 C according to specified ASTM test methods selected after thorough investigations and review of literature. As per the experimental investigation, the mechanical properties of the nanocomposite increases by the incorporation of heat treated HNT. Further, the study revealed that the nano composite with a filler content of 10 Wt.% of HNT preheat treated at 50 C shows superior tensile and flexural strength, However the critical observation of the results reveal that the impact strength is maximum for Nano composites with a filler content of 5 Wt.% HNT pre heat treated at 70 C. The study of TEM images gives an overview of uniform dispersion of HNTs in the matrix phase owing to varying pre-treatment conditions. It is evident that the properties of the nanocomposite depends on the quantity of functional filler present and temperature of heat treatment. 2019 Elsevier Ltd -
Biodegradation studies of polyhydroxyalkanoates extracted from Bacillus subtilis NCDC 0671
The major characteristic feature that distinguishes polyhydroxyalkanoates (PHAs) from its synthetic counterparts is its biodegradability. PHAs are the only class of biopolymers reported to be 100% degradable under both aerobic and anaerobic conditions without production of any toxic residues. The biodegradability of PHAs is influenced by several factors like moisture, temperature, pH, surface area and molecular weight of the polymer. The rate of biodegradation varies greatly depending on the environment. Biodegradation studies were carried out using plating method and direct inoculation method using selected Bacillus strains. Fungal degradation of PHA sheets was assessed using Penicillium chrysogenum. Biodegradation of PHA sheets in different soil types like river valley, agricultural land and garden soil was investigated. The degree of PHA degradation in aqueous environment was studied by incubating the sheets in distilled water, sea water, fish tank water and pond water. The highest degradation rate was observed with agriculture land soil (35.47 0.13%) and fish tank soil (36.93 0.13%). The non-toxic nature of the soil incubated with PHA sheets was ensured using plant growth test. 2019, World Research Association. All rights reserved. -
A stochastic propagation model to the energy dependent rapid temporal behaviour of Cygnus X-1 as observed by AstroSat in the hard state
We report the results from analysis of six observations of Cygnus X-1 by Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) onboard AstroSat, when the source was in the hard spectral state as revealed by the broad-band spectra. The spectra obtained from all the observations can be described by a single-temperature Comptonizing region with disc and reflection components. The event mode data from LAXPC provides unprecedented energy dependent fractional root mean square (rms) and time-lag at different frequencies which we fit with empirical functions.We invoke a fluctuation propagation model for a simple geometry of a truncated disc with a hot inner region. Unlike other propagation models, the hard X-ray emission (>4 keV) is assumed to be from the hot inner disc by a single-temperature thermal Comptonization process. The fluctuations first cause a variation in the temperature of the truncated disc and then the temperature of the inner disc after a frequency dependent time delay.We find that the model can explain the energy dependent rms and time-lag at different frequencies. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Analysis of machining parameters for face milling of inconel 718 using response surface methodology
The machining of Inconel 718 which is a nickel based super alloy has become a material of great importance mainly in the aerospace industry. Reason being the materials possesses properties of increase in strength at elevated temperature, high resilience to chemical reaction and high wear resistance. Gaining optimum machining parameters have become a great concern in the manufacturing industry, where economy of machining plays a very important key role in the market. This paper gives an overview of the experimentation conducted on the basis of Response Surface Methodology (RSM). Regression equations have been developed for surface roughness, by taking into consideration the machining parameters like cutting speed, feed rate and depth of cut for face milling operation performed in CNC machine. RSM analysis was carried out with the help of Mini Tab 18 software. The Mathematical equation developed after regression analysis shows to be very efficient. BEIESP. -
Managing change, growth and transformation: Case studies of organizations in an emerging economy
Purpose: In view of dynamic and widespread economic transformation in emerging economies, managing organizational change and growth in this context deserves more research attention. The purpose of this paper is to examine how three organizations in different industries manage change, growth and transformation in their organizational ecosystem. Design/methodology/approach: The authors conducted in-depth interviews with the leadership of three organizations in different economic sectors in India, a country representing an emerging economy. The authors also reviewed historical data from these organizations. Three case studies illustrating the evolution of these organizations were developed from the data collected. Findings: Lessons and implications from the three case studies suggest the following key elements of effective organizational change mechanisms in an emerging economy: visionary entrepreneurial leadership; program quality excellence; scale growth and scope expansion; network capabilities; and sustainable stakeholders engagement. At the same time, this study also shows how these organizations manage change, growth and transformation in the context of a society with strong traditions and cultural norms. Research limitations/implications: Results and conclusions may be limited by the fact that the study is based on three case studies. Additional studies from a variety of industries with large numbers of participants will be helpful in more fully understanding the ways in which change, growth and transformation can best be developed and deployed in different organizational settings. Practical implications: The proposed model of organizational change in an emerging economy may assist organizational leadership in designing and sustaining their change efforts. Social implications: This study highlights the role of visionary entrepreneurial leadership and the impact of organizational growth mechanisms on organizational value delivery capabilities and organizational reputation. Originality/value: Lessons and implications of five growth steps of outstanding organizations in an emerging economy context provide valuable insight for organizational change, growth and transformation in other emerging contexts. 2019, Emerald Publishing Limited. -
Signature based key exchange for securing data and user from web data stealing attacks
Due to the immense technological growth, web and its related applications are becoming a major part of everyday life. The growth of the internet and technology not only increases the positive benefits but also increases negative activities such as data theft. As web applications are used frequently for many online services, it is the most common and valuable target for the adversary to host any web vulnerabilities. Data theft or data stealing attacks are quite common in the web and the internet with severe consequences. The private data are generally stored on the system which gives an opportunity for the attacker to steal the data from the storage or during transit. However, apart from stealing the critical data from the user, the attacker also steals the sensitive data from the web applications. This type of attack takes several forms for stealing perilous information from the user and web application. Unfortunately, these attacks are easy to execute as the attacker needs only the internet connection, a web server and technical knowledge which are readily available. Several prevention strategies exist to thwart the user and the application from the web attacks, however, they do not provide the complete solution. This paper presents the signature based key exchange to prevent the user as well as the web application from several variations of data stealing attacks through mutual attestation. The experimental results show that the proposed method prevents the user and application from data theft than any other existing methods. BEIESP. -
An efficient scheme for water leakage detection using support vector machines (SVM)-Zig
Water is one of the most essential and valuable resources for all living beings, yet in the present day, there is a scarcity of it. Half of the water loss in large cities and industries is due to leaks and illegal lines. 10%-20% of water loss can be reduced by detecting leaks but without the presence of advanced monitoring systems, this problem is typically worsened. Monitoring the consumption and leak detection for such large areas is a challenging task. To overcome this issue a small prototype is prepared called Zig. Zig is designed for both household and industrial purposes. Its main aim is to monitor the flow and consumption of water at different levels of a building like a first-floor and so on which may represent some industrial and household situation. This work focuses on pressure/flow monitoring method to reduce the operational cost and also to detect leakage. One of the machine learning algorithms, Support Vector Machines (SVM) has been applied to detect the leakage and it is compared with Random Forest algorithm to show that proposed scheme is detecting water leakage better. BEIESP. -
Adaptive uplink scheduling model for WiMAX network using evolutionary computing model
The increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using Multi-Objective Genetic Algorithm (MOGA). The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
On the Mass Accretion Rate and Infrared Excess in Herbig Ae/Be Stars
The present study makes use of the unprecedented capability of the Gaia mission to obtain the stellar parameters such as distance, age, and mass of HAeBe stars. The accuracy of Gaia DR2 astrometry is demonstrated from the comparison of the Gaia DR2 distances of 131 HAeBe stars with the previously estimated values from the literature. This is one of the initial studies to estimate the age and mass of a confirmed sample of HAeBe stars using both the photometry and distance from the Gaia mission. Mass accretion rates are calculated from H? line flux measurements of 106 HAeBe stars. Since we used distances and the stellar masses derived from the Gaia DR2 data in the calculation of the mass accretion rate, our estimates are more accurate than previous studies. The mass accretion rate is found to decay exponentially with age, from which we estimated a disk dissipation timescale of 1.9 0.1 Myr. The mass accretion rate and stellar mass exhibit a power-law relation of the form . From the distinct distribution in the values of the infrared spectral index, n2-4.6, we suggest the possibility of difference in the disk structure between Herbig Be and Herbig Ae stars. 2019. The American Astronomical Society. All rights reserved.. -
Benefits of cross training: Scale development and validity
Studies related to benefits of cross - training were mainly done either in the context of qualitative research or as comprehension of desk research. The literature scarcely covered the measurement issues, and thus, it became vital to quantify and develop a scale to measure the benefits of cross - training (BCT). Cross -training means training that covers multiple tasks within a department This training technique keeps employees prepared to handle more than a single Job for which they have been Initially hired. This concept Is also called 'worker multlfunctlonallty'. The study aimed to propose and validate an Instrument to measure BCT. The nrst section of the study was exploratory factor analysis (EFA) establishing the benefits of cross training through four dimensions namely Job Stability, Career Advancement, Networking, and Idle lime Management. Confirmatory factor analysis (CFA) was used in the second section to verify the factor structure of the observed variables. The results indicated that cross training the employees in an organization could help practitioners to adopt the same as a strategy in retaining the employees by saving on the costs of recruitment, selection, and staffing. The findings also suggested that cross training helped in securing a job, progressing in one's career, enabling better interaction among the employees, and efficiently managing the idle time in the organization. 2019, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
MHD flow of SWCNT and MWCNT nanoliquids past a rotating stretchable disk with thermal and exponential space dependent heat source
The main purpose of this investigation is to analyze the impacts of a novel exponential space dependent heat source on MHD slip flow of carbon nanoliquids past a stretchable rotating disk. The flow is created due to rotation and stretching of the disk. Aspects of the convective condition and cross-diffusion (Soret and Dufour effects) are also accounted. A comparative study of nanofluids made up of SWCNTs (single-walled carbon nanotube) and MWCNTs (multi-walled carbon nanotube) is presented. The governing partial differential equations system is reduced to nonlinear ordinary boundary value problem. The RungeKuttaFehlberg is utilized for numerical simulations. Embedded dimensionless parameters on the flow fields are examined via graphical illustrations. The rate of heat mass transfer can be controlled by cross-diffusion, exponential space-based heat source and thermal-based heat source effects. It is also proved that q( ) (? ) x q x SWCNT nanoliquid MWCNT nanoliquid -. A novel idea of the exponential space dependent heat source is implemented in the investigation of the slip flow over a rotating deformable disk under the effects of cross-diffusion, temperature based heat source and magnetic field for the first time. A comparison between two different fluids namely SWCNT-H2O nanoliquid and MWCNT-H2O nanoliquid are studied. 2019 IOP Publishing Ltd Printed in the UK. -
Credit card fraud detection using ANN
Fraud on its own was and is devastating a lot of businesses, be them small or large. Particularly in the field of finance where we can see constant attacks on both individuals and enterprises alike. As such, credit cards are the most targeted as they are linked to both personal information and accounts. It is also evident to say that credit card fraud detection research is very much needed to deter and mitigate the impact of fraud on the financial field in general. It is important to identify frauds before it is too late so that the stolen credit card cannot be used for fraudulent transactions. To effectively detect these fraud transactions, we use a data consisting of fraudulent and non-fraudulent transactions to create a model that classifies these transactions with a high accuracy based on a machine learning technique. We used Artificial Neural Network with Logistic Regression to measure and in order to achieve high accuracy, we refined the parameters using the algorithms Back-propagation which has proved to have a high accuracy rate giving the model the ability to distinguish a fraudulent transaction from a normal one. BEIESP. -
Evaluating the performance of machine learning using feature selection methods on dengue dataset
Dengue fever is a mosquito-borne disease transmitted by the bite of an Aedes mosquito infected with a dengue virus. The bites of an infected female Aedes mosquito which gets the virus while feeding on the infected persons blood, transmits the virus to others. Dengue transmission is climate sensitive for several reasons such as temperature, humidity, rainfall, etc. Areas having higher vapor pressure and rainfall rate are most vulnerable to the spreading of the dengue disease. So to find the important features responsible for spreading the dengue we have used the classification algorithms. Machine learning is one of the key methods used in modern day analysis. Many algorithms have been used for medical purposes. Dengue disease is one of the serious contagious diseases. To find the features related to spreading of dengue disease, we have used popular machine learning algorithms. This proposed work focuses on evaluating the performances of the various machine learning techniques like-Random Forest Classifier (RFC), Decision Tree Classifier (DTC) and Linear Support Vector Machine (LSVM). Predictive Mean Matching is applied for preprocessing of the data and percentage split is applied for resampling of the data. Information gain values for each of the attributes are calculated. The attributes are sorted on the basis of information gain values. Feature selection methods (FSMs) such as Forward Selection (FS) and Backward Elimination (BE) are applied to choose the finest subset of the attributes, so that the algorithm runs more efficiently with a lower run time. It also results in the improvement of the accuracy. The attributes selected by the Feature Selection Methods are the main attributes which results in the probable effects of global weather change on human healthiness. BEIESP. -
Community based open source geographical classical data analysis
The traditional Geographical Information Systems (GIS) have to be migrated to the internet eventually much like every other software today. The article has explored ways of utilizing the Open Geospatial Consortium (OGC) standards to come up with ways of achieving a workflow for the development of a service-based implementation of a customized Web Processing Service (WPS). The proposed concept has explored multiple workflows using various combinations of the publishing and development options and the simplest and the least resource intensive one has been identified as the outcome of this project. The workflow identified was then split into two section to make it even more simplify and adaptable, aiding development from the WPS that has to publish. The development process used for the final workflow is done without the use of a resource intensive IDE keeping in mind the major aim of the proposed model is to reduce the dependency on resource intensive software and services. The proposed model is built solely on open source platforms which are in tandem with second stipulation of proposed model is promoting community-based development. The proposed system provides the better execution time and retrieval time. The execution time is compared with similar system, open source Geographical system provide less execution time. The retrieval time is also reduced this indicated Quality of Service is increased. BEIESP. -
A method to secure FIR system using blockchain
In India, we can see that technology has touched in every aspect of our life. There exist technology in all the fields e.g. education, agricultural, business, government etc. and we can also understand how beneficial it is, as it saves the time, money and human power. In spite of being technologically advanced, the system lacks in security perspective. When we talk about today, India has moved to the era of digitalization after the launch of the campaign Digital India, the Indian Police Department has replaced the manual system with the centralized online process to register the complaint. The main objective of this paper is to provide a method to secure the FIR system using blockchain technology. This introduces to the essential principal of blockchain technology and its future in the police department of India. Blockchain technology will also explain to protect the FIR from the malfeasance. BEIESP. -
Implementing artificial intelligence agent within connect 4 using unity3d and machine learning concepts
Nowadays, we come across games that have unbelievably realistic graphics that it usually becomes hard to distinguish between reality and the virtual world when we are exposed to a virtual reality gaming console. Implementing the concepts of Artificial Intelligence (AI) and Machine-Learning (ML) makes the game self-sustainable and way too intelligent on its own, by making use of self-learning methodologies which can give the user a better gaming experience. The use of AI and ML in games can give a better dimension to the gaming experience in general as the virtual world can behave unpredictably, thus improving the overall stigma of the game. In this paper, we have implemented Connect-4, a multiplayer game, using ML concepts in Unity3D. The machine learning toolkit ML-Agents, which depends on Reinforcement Learning (RL) technique, is provided using Unity3D. This toolkit is used for training the game agent which can distinguish its good moves and mistakes while training, so that the agent will not go for same mistakes over and over during actual game with human player. With this paper, authors have increased intelligence of game agent of Connect 4 using Reinforcement Learning, Unity3D and ML-Agents toolkit. BEIESP. -
Inhibiting extracellular cathepsin d reduces hepatic steatosis in spraguedawley rats y
Dietary and lifestyle changes are leading to an increased occurrence of non-alcoholic fatty liver disease (NAFLD). Using a hyperlipidemic murine model for non-alcoholic steatohepatitis (NASH), we have previously demonstrated that the lysosomal protease cathepsin D (CTSD) is involved with lipid dysregulation and inflammation. However, despite identifying CTSD as a major player in NAFLD pathogenesis, the specific role of extracellular CTSD in NAFLD has not yet been investigated. Given that inhibition of intracellular CTSD is highly unfavorable due to its fundamental physiological function, we here investigated the impact of a highly specific and potent small-molecule inhibitor of extracellular CTSD (CTD-002) in the context of NAFLD. Treatment of bone marrow-derived macrophages with CTD-002, and incubation of hepatic HepG2 cells with a conditioned medium derived from CTD-002-treated macrophages, resulted in reduced levels of inflammation and improved cholesterol metabolism. Treatment with CTD-002 improved hepatic steatosis in high fat diet-fed rats. Additionally, plasma levels of insulin and hepatic transaminases were significantly reduced upon CTD-002 administration. Collectively, our findings demonstrate for the first time that modulation of extracellular CTSD can serve as a novel therapeutic modality for NAFLD. 2019 by the authors. -
Skin cancer classification using machine learning for dermoscopy image
Skin cancer is highly ambiguous and difficult to identify and cure in the last stage. To increase the survival rate, it is important to recognize the stages of skin cancer for effective treatment. The main aim of the paper is to classify the various stages of skin cancer using dermoscopy images from the data repository of ISIC and PH2. The data is pre -processed with the help of median filter and wiener filter for removing the noise. Segmentation is processed using Watershed and Morphological. After the segmentation, features were extracted using Grey Level Co-occurrence Matrix (GLCM), Color, Geometrical shapes in order to improve the accuracy of dermoscopy image. Finally, the dataset is classified with some popular methods like KNN with 89%, Ensemble with 84% and SVM works better than the other two methods by giving the highest accuracy of 92%. BEIESP. -
Opinion mining on newspaper headlines using SVM and NLP
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Development of perceived prenatal maternal stress scale
Background: Pregnancy is a state, which is often associated with extreme joy and happiness. Women undergo a number of physiological and psychological changes during pregnancy, which are often stressful if aligned with other adverse life events, compromising their health and well-being. However, there exists no comprehensive psychological instruments for measuring this stress. Objectives: The study was conducted to develop a multidimensional scale to assess prenatal maternal stress (PNMS) comprehensively. Methods: The initial phase of the study focuses on developing items and assessing the content validity of these items. The second phase focuses on pilot-testing and field-testing the newly developed perceived PNMS scale (PPNMSS) among 356 pregnant women belonging to different parity and trimester from November 2015 to October 2016. Results: The underlying factor structure of the 28-item PPNMSS had explored using exploratory factor analysis. The final scale is retained with 15 items having considerable item loading under four major factors as follows: perceived social support, pregnancy-specific concerns, intimate partner relations, and financial concerns. Reliability of each of these dimensions was assessed using Cronbach's alpha. Convergent and divergent validity of the scale was assessed by correlating the scores with perceived stress scale and the World Health Organization (five) well-being index (1998 version). Conclusions: As a comprehensive scale, PPNMSS is efficient to measure PNMS, which facilitates an early detection of stress and depression among pregnant women and timely intervention by health care professionals.