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Synthesis of high temperature (1150 C) resistant materials after extraction of oxides of Al and Mg from Aluminum dross
Aluminum Dross (Al-dross) is a well-known Industrial waste generated in an Aluminium industry from the melting of the metal itself. It gets made yearly in hundreds of thousands of tons worldwide, due to the wide use and demand of Aluminum in almost every industry. However, Al-dross is not completely a waste as it contains two compounds of interest, namely Aluminum Oxide (Al2O3) and Magnesium Aluminate (MgAl2O4). They are the basic compounds present in any refractory which are products featuring low thermal conductivity and high temperature shock characteristics in the order of 1000 C+. Thus, Aluminum Dross becomes a vital candidate to be considered for the extraction of the two of the aforementioned compounds. Recent studies have shown that Al-dross indeed can be used to extract Al2O3 and MgAl2O4. However, Al-dross also contains Aluminum Nitride (AlN) a compound that exhibits the exact opposite properties demonstrated by refractories. In addition to being technically unsuitable for use as refractory material, AlN also possesses another huge issue. When Al-dross is dumped into landfills, the AlN present in the dross combines with the moisture in the soil and is energized by geothermal heat which leads into an exothermic reaction, thereby releases highly toxic and health hazardous gases. Keeping the above techno-environment challenges in mind, prior to utilizing the beneficiated Al-dross in any industrial application, it is important to leach out the AlN from the dross in an environment friendly manner. This paper deals with the successive leaching of AlN from the Al-dross using two laboratory procedures. Sintered (to be added) pellets made out of the processed powder in the lab were subjected to analysis of structural phases and chemical constituents by employing XRD and EDS. Cyclic thermal shock test cycles were also carried out by subjecting the pellets to 1150 C and quenching in air alternately, to study the refractory characteristics. 2019 Elsevier Ltd. All rights reserved. -
Synthesis of Magnetorheological fluid Compositions for Valve Mode Operation
Smart materials such as Magnetorheological Fluids (MRF) have become sought-after material in wide ranging applications due to the ability to change properties in a controlled manner under application of stimulation such as a variable current, magnetization, heat, force, stress and deformation. Magnetorheological fluids in the rheological fluid domain has found use due to its ability to change its shear strength based on the applied magnetic field. Magnetorheological fluids are composed of magnetizable micron sized iron particles and a non-magnetizable base/carrier fluid. The shear strength of commercially available MRF varies from 0 to 100kPa under the effect of the magnetic field. In a valve mode, the Magnetorheological damper (MR Damper or MRD), the MR fluid flows between two-fixed poles, which are parallel to each other. When the fluid flows between them, due to the applied magnetic field the magnetic particles align themselves in a chain form (on state) which is easily reversible when the field is removed (off state). Physical change of the fluid from liquid to semi-solid is controlled by the magnetic field, which makes the fluid a reliable member in active vibration control applications. In this study, two types of magnetizable particles (Carbonyl iron (CI) and Electrolytic iron (EI)) are taken and characterized using an Anton Paar MCR 702 rheometer set-up, in on and off states. To overcome issues like sedimentation, agglomeration and corrosion of the MR fluid, the iron particles are coated with natural gum like guar and xanthan, to the carrier fluid grease and other thixotropic additives are added. The addition of grease and thixotropic additives will inhibit the microbiological degradation of natural gum over an extended period. These engineered MR fluids are then used to analyze the performance of designed and developed stand-alone MR damper, which is tested using an electro-dynamic shaker. The response and damping performance of the MR Damper is analyzed with controlled changes in variables including percentage of additives in MR fluid & magnetization values 2019 Elsevier Ltd. -
Synthesis of Online Criminal User Behaviours Disseminating Bengali Fake News Using Sentiment Analysis
Even though research on artificial intelligence (AI) is still in its early phases, the field is growing in popularity. We created a hybrid machine learning model to better understand the pattern of results connected to illegal user behaviour. Then, after identifying the components of illegal user activity, we created a theory for forecasting criminal user behaviour that explains the patterns and results. Our study focuses on offenders spreading misleading information online and makes use of a Bengali dataset. Sentiment analysis is a modern technology that can help us understand how individuals feel in different scenarios during their everyday lives. To comprehend the pattern behind this agenda, machine learning and deep learning techniques will be applied throughout the categorization process. To determine the possible attitudes driving criminal conduct that spreads misleading information, sentiment levels on social media may be monitored or studied. This study examines the use of several artificial intelligence approaches to assess sentiment in social media data in order to identify criminal user activity occurring throughout the world. The hybrid model CNN with Adam optimizer exhibits higher precision levels while doing sentiment analysis. In addition to identifying solutions to the issues that people currently face in the modern world, we also propose a new categorization system for illicit user activity. In our analysis of the research's shortcomings, we make recommendations for a broader research agenda on illicit user conduct and how one can forecast the criminal user behaviour on psychological aspects. Our model was thus able to draw 87.33% accuracy in determining criminal behaviour patterns. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Synthesis of UWB Pulse Shaper for Efficient Pulse Propagation in Human Tissue
In this paper, a filter based pulse shaper is proposed for efficient Ultra-wideband (UWB) pulse transmission through human tissues. A bandpass Finite Impulse Response (FIR) filter is synthesized and its closed form expression for the impulse response coefficients is obtained. The filter shapes the basic UWB pulse, to closely fit the desired Federal Communication Commission (FCC) mask specifications, to achieve high spectral utilization efficiency. In this approach, the effects due to Gibb's phenomenon are minimized thereby resulting in lower dominant sidelobe of the resultant UWB pulse. The interference between adjacent pulses of the UWB data stream is minimized thus it allows shorter duration UWB pulses to be synthesized leading to higher data rate transmission compared to some techniques in literature. 2020 IEEE. -
Synthesis of Value Added Refractories from Aluminium Dross and Zirconia Composites
Results of a developmental study on the potential to synthesize industrial grade refractories from aluminum dross with un-stabilized zirconia are reported. The merit of the developed product to perform as refractories suitable for use at or above 1000C was assessed by studying the thermo-physical behavior as per guidelines of ASTM and IS. Aluminum dross, an industrial waste (slag) is generated in several millions of tons in the production of Aluminum and is dumped into landfills, which releases poisonous gases like methane and ammonia upon contact with moisture present in the land and the heat generated by the earth, warranting stringent mitigation efforts. Rich in aluminum metal (?15%), ?-Al2O3 (7-15%), MgAl2O4 (10-15%) and AlN (20-30%), the general prime dross composition draws interest due to its abundance and presence of ?-Al2O3 and MgAl2O4, for the production of refractories with insulating, shock resistance and stability at high temperature (?1000C and above) characteristics. Nevertheless, presence of AlN, a good thermal conductor acts as a deterrent in the production of refractories. Aluminium dross (after leaching out AlN) was processed with un-stabilized zirconia (monoclinic ZrO2) to synthesize the refractory composites. Conventional process (calcination, ball milling, compaction and sintering (1550C/6 hrs)) was employed. Characterization involved thermal shock cycling (air quench at furnace ambient of 1000C and room temperature) to determine the number of shock cycles endured before failure. Structural phase analyses at various stages of processing were carried out by via XRD. Magnesia present in dross did not appear to stabilize either the tetragonal or cubic ZrO2. Microstructural and chemical composition studies were carried out via SEM and EDS. The favourable results confirm the viability of the process methodology. 2019 Elsevier Ltd. -
Synthesis of Yttria-Stabilized Zirconia Nano Powders for Plasma Sprayed Nano Coatings
Plasma sprayed Yttria Stabilized Zirconia (YSZ) coatings, with few microns sized microstructure/grain morphology has been well researched, reported and established as an industrial Thermal Barrier Coatings (TBC) material/system. However, nano structured YSZ coatings possess improved characteristics when compared with their micron sized counterparts. However, due to their nano sizes, light weight, and low density, plasma spray coating process of nano powders suffers from flowability issues due to lack of nano powder inertia/momentum, leading to poor deposition/uneven coating thickness. In this research work, nano structured YSZ coatings were synthesized by using an Atmospheric Spray Coating (APS) facility. Nano powders of YSZ were used as the starting materials to prepare micron sized plasma sprayable powders. 80?m thick NiCrAlY bond coat (commercial) and 200?m thick YSZ top coat with nano microstructure (lab synthesized) were built on steel substrates. The starting nano crystalline (YSZ) powders, measuring 30-70 nanometers (nm) were synthesized in the laboratory via chemical method (sol-gel) by employing zirconium oxy chloride hexa-hydrate and yttrium nitrate as precursors, citric acid as chelating agent and ethylene glycol for the diversification reaction followed by calcination @ 1000C. They were then re-constituted into micron sized (53-106 ?m) plasma sprayable powders by agglomerating with polyvinyl alcohol (PVA) binders. The nano crystallite morphology of powders and coatings were analyzed by Scanning Electron Microscope (SEM), chemical composition by Energy Dispersive spectroscopy (EDS) and crystal structural phase by X-ray diffraction (XRD). The influence of calcination temperature of 1150C on nano crystallite morphology was also studied. 2019 Elsevier Ltd. -
System Design for Financial and Economic Monitoring Using Big Data Clustering
Economic data executives are becoming increasingly important for the longevity and improvement of ventures due to the constant expansion in the influence of data innovation. This study lays out an undertaking economic data the executive's structure for the intricate internal undertaking economic data the board business. It also includes the application of web-based big data technology to understand the fairness, reliability, and security of system database calculations, mainly to improve office capabilities and solve daily project management problems. used in the project. The aim is to evaluate the suitability of transfer clustering computation (DCA) for managing large amounts of data in energy systems and the suitability of data economics dispatch methods for harnessing new energies. Then, combine day-ahead shipping plans with continuous shipping plans to create a multi-period, data-economic shipping model. Consider how the calculations are performed using a case study on the use of new energies. This will enable new energy in multi-period data economics shipping models while meeting his DR requirements on the customer side. 2023 IEEE. -
Systematic Contemplate Paradigm on Diabetes Mellitus using different Machine Learning Predictive Techniques
As the foodies love fast food, from micro to combined families across the world the ratio of family members 1:4 is affected with silent killer named as diabetes. A very high blood glucose levels, metabolism, improper carbohydrate, damaged hormone insulin alleviating a human body disability leading to the silent killer of the body parts is the diabetes. An estimated 425 million of people around the globe suffering with diabetes up to 108 million to 1.7 trillion will be affected with diabetes. Therefore millennium, the universe ubiquity suffering with diabetes has next to quadrupled, growing from 9 percent and above among the people. As the eating habits of people in this trendy 21st century is dramatically devastating to the risk of overweight or obese. The silent killer diabetes consequences include kidney failure, Diabetic retinopathy, Heart attack, Stiffness of body muscles, Nerves stroke and lower limb amputation leads to type I and type II diabetes. As the researchers across the globe are using the machine learning algorithms as the reliable problem solver, The complications still continue. The purpose of this percu is to help with the apt selection of features garnishing with machine learning paradigm techniques in selecting the accurate attributes for each person to be properly diagnosed. In this archetype survey paper, we have done a systematic review chronologically a decade research which will help the researchers to explore and get the contemplate on various tangible and intangible data sets they can adopt in diagnosing the mellitus diabetes. Grenze Scientific Society, 2023. -
Systematic Literature Review on Industry Revolution 4.0 to Enhance Supply Chain Operation Performance
Industry 4.0 is a notion in which industries automate systems and processes, innovate digitally, and share information. It aims to obtain a smart factory in an attempt to lessen required time in responding to consumer demand or unexpected circumstances and to enhance organizational productivity. The integration of Industry 4.0 and supply chain management (SCM) ensures immense development opportunities for manufacturing firms. This article provides a systematic literature review and formulation of the existing research on Industry 4.0 in SCM, resulting in some intriguing analyses that will be useful to academics and industry, particularly top managers. The content of the article is classified into three categories: exploratory vs. confirmatory, qualitative vs. quantitative, and management level vs. technology level. The findings will benefit managers in understanding the significance of Industry 4.0 and its relationship with SCM. The formation of clusters and their affiliations has resulted in the emergence of new areas requiring managerial attention. The article concludes by examining the possibilities of the present and future research. 2022 ACM. -
Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry
Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. 2023 IEEE. -
Systematic Review on Decentralised Artificial Intelligence and Its Applications
Initially, Artificial Intelligence (AI) models were centralized. This resulted in various challenges. To overcome this challenge, the decentralized or distributed frameworks were developed. Recent advancements in blockchain technology and cryptography have accelerated the decentralization process. Decentralized Artificial Intelligence (DAI) is gaining a significant research attention in recent times. This study reviews various DAI techniques such as Decentralized machine learning frameworks, Federated Learning and Distributed AI marketplaces. In particular, this study focuses on reviewing the recent developments in DAI by analyzing its potential advantages and challenges. 2023 IEEE. -
Systematic Review on Humanizing Machine Intelligence and Artificial Intelligence
In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now. 2023 IEEE. -
Talent acquisition-artificial intelligence to manage recruitment
The research aims to examine the awareness of Artificial Intelligence among the HR managers and Talent Acquisition managers in the process of Talent Acquisition, Investigating the factors influencing the adoption and usage of Assisted Intelligence, and evaluating the impact of Artificial Intelligence on Talent Management. Multi-Stage sampling method was adopted to collect the responses from the 384 customers across the HR and TA managers working across the IT companies situated in Bangalore, Mysore, Pune, and Chennai & Hyderabad. SAS was applied to perform the Simple Percentage Analysis, Correlation Analysis, Multiple Linear Regression Analysis to validate the hypothesis. The demographic & construct variables considered were Adoption, Actual usage, Perceived usefulness, Perceived Ease of Use, & Talent Management. Awareness of the Artificial Intelligence technology and its adoption in managing Talent Acquisition has the positive and high correlation and followed by its actual usage. Candidate experience is the most influencing variable from the first factor, Competency and Easy to use is the most influencing variable from the second factor, Effectiveness in the adoption and actual usage of Artificial Intelligence in Talent Acquisition. Talent Management is the highest predictor of using the technology and its adoption is the most influencing predictor in the effective implementation of the technology among the Information Technology Companies. The Authors, published by EDP Sciences. -
Talent retention, job involvement satisfaction, and commitment towards the organization in the IT sector
Even if there is presently much need for improvement, the information technology (IT) sector plays a key role in the nation's financial development. With enormous growth potential, India's IT sector is up against fierce competition. Numerous participants are competing with one another for resources and jobs inside the company. The direction of events and the manageability of the IT industry depend on capable employees and their responsibilities and participation. Additionally, there is a grouping of the representatives who possess the capacity. Between duty and association and ability maintenance, work fulfilment plays a crucial guiding role. The goal of the current study is to comprehend the effects of talent retention, job satisfaction, and organizational commitment in the IT industry. In this research, we looked at the variables factor analysis. In Bangalore, we chose to survey workers in the IT industry. To understand the results of Talent Retention, Job Involvement, and Commitment for IT Sector Employees, we collected the data using a questionnaire (Likert-scale), which we then analyzed using spss26. 2023 Author(s). -
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. 2023 IEEE. -
Taming theComplexity ofDistributed Lag Models: A Practical Approach toMulticollinearity, Outliers, andAuto-Correlation inFinance
This research investigates the application of robust estimators within the finite distributed lag model (DLM), a critical framework in finance research capturing temporal dependencies between lagged explanatory variables and a response variable. Traditional Ordinary Least Squares (OLS) estimation faces challenges when dealing with high lag counts, multicollinearity, and outliers, potentially compromising parameter estimates and model reliability. Employing real-world data from the RBI, spanning the years 20222023 encompassing budgetary and economic variables of Indian states and Union Territories, the study demonstrates that the MMS estimator emerges as the most efficient estimator, showcasing enhanced robustness against outliers and multicollinearity. Additionally, the study reveals positive autocorrelation in residuals, underscoring the importance of robust methods in addressing such issues in financial modeling. This research contributes valuable insights to financial analysts and offers a more accurate understanding of dynamic relationships in financial systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Teaching Learning-Based Optimization with Learning EnthusiasmMechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals
This study presents the optimal placement and operation of distributed generation (DG) sources in a distribution system embedded with utility-owned DG sources. Cost minimization and technical improvement of the network are the key objectives of the distribution company (DisCo). With the increasing popularity for renewable energy sources, DisCos are installing their own DGs to fulfill their electricity demand partially. When DisCos are the DG owners, the technical and economic considerations overlap. A novel method is proposed in this paper based on the recent variant of the teaching learning-based optimization (TLBO) algorithm and learning enthusiasm-based TLBO (LebTLBO) to optimize locations, sizes, and operational power factors of DGs in a distribution system with DisCo-owned DGs. A multi-objective function to improve voltage stability, reduce distribution losses, and reduce energy costs has been considered for solving the problem. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Technologies Driving Digital Payments in India: Present and Future
The payments market in India has been witnessing a significant transformation in recent years. The Indian payments market has robustly and consistently been moving towards digitization due to enhanced digital infrastructure, favourable government policies, and initiatives, availability of new technologies, disruptive innovations, and changes in the mindset of the customers. India tops in the worlds real-time digital payments with 20.5billion transactions in the year 2020 despite the adverse effect of the COVID-19 pandemic. This article deals with the growth of the Indian digital payments market and the technologies that drive the digital payments space at present and in the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Technologies in Transportation Engineering
Deteriorating quality of the air, traffic congestions, and rising accident rates have all resulted from an ever-increasing number of vehicles in Indian cities. As a result of a variety of issues, current public transit systems often fall short or are considered unreliable. The present paper deals with multiple ITS architecture and to be specific four major parts of the ITS. These four major parts are Advanced Public Transportation System (APTS), Advanced Traveler Information System (ATIS), Advanced Traffic Management System (ATMS), and Emergency Management System (EMS). Thus, the framework and produced models of four key divisions of ITS have been evaluated in order to conduct a comparative study of the many models currently being developed in respective investigations. 2022 IEEE. -
Template based speech enhancement of disordered speech
In this paper, we have taken Electro-Larynx (EL) speech and have improved the speech quality, electro-larynx speech was improved in terms of naturalness and intelligibility by introducing variations in the F0-contour and template matching with correlation coefficient. Initially, we introduced two different speech signals, the first speech signal introduced was healthy speech signal and the second speech signal introduced was disordered speech signal. Here, the second speech signal, the disordered speech is taken as the EL speech. The fundamental frequency or pitch was extracted first from the two inputed speech signals, then the contour of each fundamental frequency was extracted from the two input speech signals. Using these extracted features of fundamental frequency the gender classification by K-means algorithm was instigated. The same process was implemented with F0 contour features which was extracted using K-NN algorithm. EL speech contains directly radiated electrolarynx noise (DREL). The noise was filtered out using spectral subtraction algorithm. Once DREL noise is removed from EL speech, the quality of the speech was greatly improved. Then EL enhanced speech signal is compared and mapped with healthy speech signal using template matching algorithm with the help of correlation coefficient, this improves the overall quality, that is the naturalness and intelligibity of the introduced disordered speech signal. This technique helps solve the major problem of speech faced by differently abled persons with larynx disorder. 2016 IEEE.