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Economic Growth, Automation and Environmental Degradation: An Empirical Evidence from Asian Countries
In the era of Industry 4.0 the increase in population as a result of environmental erosion is the prime concern in the global scenario, Asia as the biggest continent is very much applied to it. In this context assessment of the interrelation relationship between automation, financial development, environmental degradation, and per capita growth of 12 Asian Countries from 1995 to 2022 using the panel ARDL model, in addition to assessing the cause-effect relationship panel causality test also incorporated. As a part of ARDL PMG estimation results demonstrated that capital formation, import automation machinery, urban population growth, and ecological footprint positively impact per capita in the long term. But in this phenomenon, aggregate industrial value added negatively impacts per capita, because of automation labor displacement. Results from the causality test suggest that economic upswing, and urban population growth two-way causal relationship. However, capital formation, value-added, and ecological footprint positively impacted per capita growth. Regarding policy formulation need to formulate the necessary skill development program so that individuals can cope with the new decade of automation, in addition, ecological footprint as an indicator of environmental degradation positively impacts per capita growth, so the government needs to make a strategy at the societal level toward sustainable ecofriendly behavior. 2024 IEEE. -
EEG Signals Acquisition and Processing of Mental Tasks for Controlling Smart Systems
With the help of this biosignal, we can create various kinds of interfaces. These interfaces were especially used for paralyzed individuals who have problems in the normal bio-sensor channels. Electroencephalogram (EEG) signals play a vital role in semi-paralyzed and fully paralyzed individuals to make communication with their caretakers. EEG signals are used not only for paralyzed individuals, nowadays most of home appliances are designed due to their reliability and accuracy. EEG based Smart devices are very easy to operate because signals are generated by the human brain automatically whenever the task is performed by humans. Through this research, we discussed the basic methodology needed for interfacing the EEG signals with smart devices. From this paper, most of the researchers will be going to know how to connect the EEG signals with the smart systems to control the external devices and induce the researchers to apply the EEG signals in real time. 2023 IEEE. -
Effect of basalt fiber hybridization on mechanical properties of silk fiber reinforced epoxy composites
Poor mechanical properties and constraints on production presently limit the utilization of bio-based reinforcing agents to non-structural and structural automotive elements. The conjugation of natural fibre with volcanic rock fibre provides a way to improve the mechanical properties of composites over natural fibre alone. In this study, physico-mechanical properties of hybrid fibre (silk and basalt) reinforced epoxy composites were found by experimentation following acceptable ASTM standards. Hybrid composites were produced by combining silk/basalt fibres in the ratio of 50:0, 25:25 and 30:20, whereas overall weight fraction was maintained at 0.5. The experimental results showed that the performance of combined fibres were superior compared to that of silk fibre bolstered epoxy composites. Among the 2 varieties of hybrids, the silk/basalt (25:25 by weight ratio) combination offered the very best hardness, strength, modulus, and toughness to the epoxy matrix owing to the similar modulus and synergistic interaction between the two reinforcing fibres. The results also steered that the morphology and surface adhesion affected the strength of the hybrid composites. These observations give insight into the advantages of various fibre reinforcements to the mechanical performance of epoxy matrix which is considered to be brittle. The failure mechanisms and the adhesion between fibres and matrix were studied by analysing the photomicrographs of broken coupons. 2020 Elsevier Ltd. -
Effect of calcium sulfoaluminate additive on linear deformation at different humidity and strength of cement mortars
The effect of calcium Sulfoaluminate additives (CSA) on the compression and bending strength of mortar, as well as linear deformation of prism samples at different environmental humidity was studied. Test results indicate that bending strength of mortars with CSA and the referent at the age of 28 days are practically equal. Compressive strength of mortars with CSA reduced by 20... 23% for all dosages of CSA. Relative linear deformations depend on the humidity of the environment. At a humidity of 100%, the relative linear deformations are positive and the expansion increases with increasing dosage of the expanding additive. When hardening in dry air at a humidity of 55%, the greatest shrinkage deformations were observed for mortars with CSA. We can conclude that the expanding effect of CSA is fully manifested at high humidity, i.e. under construction conditions, this means very high-quality moisture care for concrete structures. The Authors 2020. -
Effect of different base isolation techniques in multistoried rc regular and irregular building
Base confinement system for a structure is acquainted to decouple the building structure from possible movement incited by the movement of the seismic tremor, keeping the building superstructures from retaining the quake vitality. Base isolator increases the regular time period of the general structure and diminishes its shear increasing speed reaction to the seismic movement. In this explanatory examination, a ten-storey reinforced concrete (RC) building with lead elastic bearing, high damping elastic bearing and triple-contact pendulum framework bearing is acquainted with the structures, and correlation is made between fixed base and the base-secluded structures. Demonstrating and investigation are conveyed utilizing ETABS 2015 v15.2.2. The investigation examination is performed to check the ampleness of the working against the lateral displacement, inter-storey drift, story shear and story acceleration. It is found from the investigation that reaction of working to lateral load diminishes, while modular period is expanded in both X and Y bearings. Furthermore, it was reasoned that triple grating pendulum bearing is increasingly compelling in examination of different direction utilized in this investigation. Springer Nature Singapore Pte Ltd 2021. -
Effect of Doping in Aluminium Nitride (AlN) Nanomaterials: A Review
Piezoelectric materials can generate electrical charges when subjected to mechanical pressure through the piezoelectric effect. In addition to generating electricity from environmental vibrations, they are also used as nano energy generators for micro electro mechanical systems (MEMS). Aluminum Nitride (AlN) with a doping element exhibits unique physical and chemical properties. It is used to manufacture many electromechanical devices. They are ideal candidates for many applications, including MEMS resonators and microwave filters, due to their large piezoelectric coefficient and low resistance. A number of material properties led to its selection, including high thermal conductivity, good mechanical strength, high resistance, corrosion resistance, and the largest piezoelectric coefficient. A piezoelectric coefficient d33 characterizes the piezoelectric response of AlN thin films. By doping this material, a wide range of applications have been explored. The Electrochemical Society -
Effect of fiber types, shape, aspect ratio and volume fraction on properties of geopolymer concrete A review
Researchers have emphasized on sustainable construction with utilization of industrial wastes or byproducts in production of concrete. Geopolymer concrete is one of the popular construction materials which has shown promising results and potential to substitute conventional energy intensive materials such as Portland cement concrete. Further, the use of fibers has shown potential to overcome various deficiencies of geopolymer concrete. However, there are limited studies which explore the benefits of fiber reinforced geopolymer concrete and its applications. The development of fiber reinforced geopolymer concrete is relatively new construction material and has to be experimentally validated in order to increase its usage in the construction industry. As a result, this review paper is an attempt to discuss the effect of shape, type, aspect ratio and volume fraction of fibers on strength and durability properties of geopolymer concrete. From this detailed review it can be concluded that fiber reinforced geopolymer concrete enhances ductile behavior, tensile strength, toughness & energy absorption capacities. 2022 -
Effect of Halloysite Nanotubes on Physico-Mechanical Properties of Silk/Basalt Fabric Reinforced Epoxy Composites
Natural fiber reinforced polymer composites have become more attractive due to their high specific strength, light weight and environmental concern. However, some limitations such as low modulus and poor moisture resistance were reported. This paper presents the role of halloysite nanotubes (HNTs) on physico-mechanical properties of bidirectional silk and basalt fiber reinforced epoxy (SF-BF/Ep) hybrid composites. Vacuum bagging and ultra-sonication method were used for the fabrication of hybrid composite slabs. The effect of HNT loadings (1.5, 3 and 4.5 wt. %) on physico-mechanical characteristics like density, hardness, flexural and impact properties of SF-BF/Ep composites were determined according to ASTM standards. Experimental results revealed that the incorporation of HNTs improves the mechanical properties. The impact strength of SF-BF/Ep is predominant at 3 wt. % HNT loading where the impact strength surges to 568.67 J/m, which may render HNT filled SF-BF/Ep desirable for various toughness-critical structural applications. The test results demonstrated that SF-BF/Ep-3HNT coded composites exhibited improved mechanical properties among the all composites. 2022 Trans Tech Publications Ltd, Switzerland. -
Effect of Heat Treatment on Fatigue Characteristics of En8 Steel
Fatigue failure is an important factor in most of the engineering applications, especially in steel materials, and among the steel materials, it is an important phenomena in medium carbon steels like EN8, which is very commonly used in components like shaft, gears etc., since it is prone to fatigue failure. Hence, without changing the composition, an attempt is made to enhance the fatigue strength by different heat treatment techniques. In this study, the investigation is carried out on heat treatment of EN8 steel material. Various kinds of heat treatment techniques like quench and temper, normalizing and annealing are performed on EN8 steel. After exposure to the heat treatment, the EN 8 steel material specimens are machined as per the ASTM standards and are subjected to RR MOORE test and SN-curves are plotted from the obtained results; the obtained results from the fatigue tests are further analyzed with the help of ANSYS software. Fatigue life and Factor of Safety (FOS) comparisons for EN 8 steel material is made with the structural steel material and it is found from the comparisons, that the heat treatment process enhances the fatigue strength and endurance limit. Published under licence by IOP Publishing Ltd. -
Effect of MWCNT concentration on microstructures, mechanical properties and sintering behaviour of spark plasma sintered AA2219-MWCNT composites
Uniform dispersion of nano tubes without any structural damage is still a challenge in processing of metal matrix nano composites. Effective dispersion of MWCNT (0, 0.5, 0.75, 1, 2 wt. %) in AA 2219 alloy powder has achieved with a combined effect of premixing process and ball milling. An effort is done using spark plasma sintering (SPS) to consolidate the composites and to investigate the effect of MWCNT concentrations on enhancement of the properties of the composites. Particle boundary clustering was observed on consolidated composites even after a uniform distribution is achieved in alloy powder. Significant improvement in mechanical property is observed by reinforcing with MWCNT. Preferable level of MWCNT for bulk sampling was selected as 0.75 wt. % and 1 wt.%. Addition beyond the limit will cause agglomeration and will act like a lubricant during ball milling. 2019 Elsevier Ltd. -
Effect of pH on the structural and optical properties of cobalt oxide nanoparticles synthesized by hydrothermal method
The paper focuses on the synthesis and characterization of cobalt oxide nanoparticles synthesized under different alkaline pH of the precursor solution by hydrothermal method. Cubic spinel Co3O4 crystallites were observed by X-ray diffraction pattern (XRD) and Raman spectrum. The crystallite size decreases as the pH value increases. The absorption spectrum exhibited two broad bands which are in good agreement with the cobalt oxide band structure. The change in bandgap was observed with pH of the precursor solution in agreement with size effects. Photoluminescence (PL) spectra consist of a broad emission with different peaks which are due to point defects. 2022 -
Effect of phonon-substrate scattering on lattice thermal conductivity of monolayer MoS2
The effect of phonon-substrate scattering on lattice thermal conductivity (LTC) of supported MoS2 MLs is investigated over a wide temperature range (1 -
Effect of salt spray parameters on TiC reinforced aluminium based in-situ metal matrix composites
This paper aims attention at characteristics of corrosion of reinforced primary and secondary processed Al6061 based composites along TiC particles. Using potassium hexaflourotitanate (K2TiF6) and potassium tetrafluoroborate (KB4) halide salts, the synthesis of composites was done utilizing in-situ technique using stir casting route at temperature 850 Celsius. Open die forging was subjected upon in-situ composites of cast aluminium alloy at a temperature 500C. Both microstructure studies and salt spray test were subjected upon to forged and cast alloy 6061 and its in-situ composites. In accordance to ASTM B117 standard test procedure, salt spray test was conducted utilizing 5% NaCl test solution. The results impart that, the alloy forged, and respective in-situ composites exhibited enhanced corrosion resistance comparatively. 2019 Elsevier Ltd. All rights reserved. -
Effect of sonication in enhancing the uniformity of MWCNT distribution in aluminium alloy AA2219 matrix
The present paper investigates the effect of premixing process on the distribution of 0, 0.5, 0.75, 1 and 2 wt.% multiwall carbon nanotubes (MWCNTs) and resultant properties of aluminium alloy AA2219 matrix. Premixing process consists of ultrasonication, magnetic stirring and mechanical stirring. FESEM was used for characterizing the distribution of reinforcement in the matrix. Ball milling with premixing was found to be effective in achieving better uniform distribution of the reinforcement than mere ball milling. Hardness testing of the composite revealed reinforcement of MWCNT enhances the matrix hardness. The thermal stability of composite as evidenced by DTA analysis proved the presence of MWCNT without any structural damages. 2019 Elsevier Ltd. All rights reserved. -
Effect of Temperature on Electrical Properties of Reduced Graphene Oxide (rGO)/Li-ion Embedded Flexible Solid Polymer Electrolyte Films
Reduced graphene oxide (rGO) was synthesized from graphite powder by modified Hummers method. The rGO is emerged with Polystyrene sulfonic acid/Lithium phosphate to prepare PL-rGO solid polymer electrolyte films. The electrical properties of Polystyrene sulfonic acid/Lithium phosphate/reduced graphene oxide composites were analyzed, which is an essential property to obtain the performance, reliability and lifetime of battery with respect to temperature. The mass and charge transfer process that takes place at the interface of electrode and electrolyte was obtained by Impedance analyzer. The Nyquist plots were plotted in the frequency range 1 Hz-35 MHz at different temperatures (30-100OC). The ionic conductivity of PL-rGO polymer electrolyte is 1.4x10-3 S/c.m has been observed for the composition PSSA/Li3PO4/rGO::50:45:05 wt%. The conductivity of PL-rGO composites is directly related to temperature. The hopping of the ions in the PL-rGO is observed by using dc conductivity which follows the Arrhenius relationship. 2019 Elsevier Ltd. -
Effect of VR Technological Development in the Age of AI on Business Human Resource Management
Human resource management (HRM) strategies are increasingly using AI and other AI-based technologies for managing employees in both local and foreign enterprises. An exciting new field of study has emerged in the last decade on topics like the media interaction of AI and robotics, the possessions of AI acceptance on independence and consequences, and the evaluation of AI-enabled HRM practices due to the proliferation of AI-based implementations in the HRM function. The use of these technologies has influenced the way work is organized in both domestic and global corporations, presenting new possibilities for better resource management, faster decision-making, and more creative issue resolution. Research on AI-based solutions for HRM is scarce and dispersed, despite a growing interest in academia. Human resource management (HRM) roles and human-AI interactions in major multinational corporations disseminating such advances need more study. As computing and networking infrastructure has advanced rapidly, so has the era of artificial intelligence. Now that in the age of AI, virtual reality technology has found many applications beyond gaming. Human resource management has emerged as a hot topic, with interest coming from both large businesses and government agencies. Many studies have been conducted on HRM in the business world, but in order to stay up with the trends, HRM must be constantly updated. This article does a demand analysis, and sets up and tests a fully-featured VR business human resource management system, all against the backdrop of the age of artificial intelligence and the present popularity of VR technology. 2023 IEEE. -
Effective Emoticon Based Framework for Sentimental Analysis of Web Data
The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job. 2019, Springer Nature Singapore Pte Ltd. -
Effective ML Techniques to Predict Customer Churn
Customer churn is one of the most challenging problems that affects revenue and growth strategy of a company. According to a recent Gartner Tech Marketing survey, 91% of C-level respondents rate customer churn as one of their top concerns. However, only 43% have invested in additional resources to support customer expansion. Hence, retaining existing customers is of paramount importance to a company's growth. Many authors in the past have presented different versions of models to predict customer churn using machine learning techniques. The aim of this paper is to study some of the most important machine learning techniques used by researchers in the recent years. The paper also summarizes the prediction techniques, datasets used and performance achieved in these studies for a deeper understanding of the domain. The analysis shows that although hybrid and ensemble methods have been widely successful in improving model performance, there is a need for well-defined guidelines on appropriate model evaluation measures. While most approaches used are quantitative in nature, there is lack of research that focuses on information-rich content in customer company interaction instances, like emails, phone calls or customer support chat records. The information presented in the paper will not only help to increase awareness in industry about emerging trends in machine learning algorithms used in churn prediction, but also help new or existing researchers position their research activity appropriately. 2021 IEEE. -
Effective Techniques Non-linear Dynamic Model Calibration using CNN
This paper proposes an efficient method to estimate nonlinear dynamic models using convolutional neural networks (CNNs). The proposed method combines the power of statistical optimization and machine learning to obtain more accurate and efficient estimates of complex models by training CNNs to recognize maps featuring input models and between results, thereby reducing the computational cost of measurements and then using the trained CNN to generate surrogate models -The method can determine accuracy for a range of exposed cases in various nonlinear dynamic models, including differential equation model of chemical reactor and stochastic model of biological systems The results show that the proposed methods are effective for measuring these models, if at most with such accuracy and reducing the computational cost in terms of both frequency and magnitude, the proposed method represents a promising method for estimating nonlinear dynamic models, offering significant advantages in terms of accuracy, efficiency and in scalability 2024 IEEE. -
Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification
Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG.