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Elastic circuit de-constructor: a pattern to enhance resiliency in microservices
Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel De-constructed state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging Availability, Latency and Error rate as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
ELCCFD: An Efficient and Enhanced Credit Card Fraud Detection using Enhanced Deep Learning Principle
Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud Detection (ELCCFD) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN's output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFD methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems. 2024 IEEE. -
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE. -
Elections and Their Results Uncertainty: Did It Induce Herding Behaviour in Indian Stock Markets? A Quantile Regression Analysis for the 2024 Indian Parliamentary Elections
This empirical work investigates the Indian investors herding mentality in the stock markets throughout the 2024 general election period, that is, the pre-election period, during and post-election period, and the whole election period. In addition, the investigation extends to explore, in particular, if the election outcome uncertainty induced any herding in the Indian equity markets on the day of the counting and result announcements of the 2024 general elections. A series of cross-sectional absolute deviation (CSAD) models and the quantile regression framework are employed to determine the existence of investors herding throughout the study period. The findings of the CSAD models show no signs of investors herding in the Indian stock markets for the pre-election season, during and post-election period, and the whole election period. In addition, the quantile regression analysis results also corroborated with the CSAD results by exhibiting adverse herding behaviour throughout the 2024 general election period. Furthermore, the study identified key psychological, macroeconomic and global factors driving the herding behaviour. Among them, only the global factor, that is, the RCBOE:VIXm,t, reported a significant impact on the herding behaviour in the during and post-elections period and whole election periods at the quantile level of 95%. The studys findings offer significant implications for market participants and market regulators regarding investment decision-making and policy formulation during seasons of political uncertainty. 2025 MDI -
ELECTRIC EROSION MACHINING BEHAVIOR OF TITANIUM DIOXIDE PARTICLES REINFORCED WITH AL ALLOY COMPOSITE USING TOPSIS APPROACH
This research work describes the optimization of parameters in electric discharge machining (EDM) of AlFeSi (AA8011) alloy composite using the technique for order preference by similarity to ideal solution (TOPSIS) method. Initially, the AA8011 matrix alloy was synthesized with the addition of 10 wt.% TiO2 particles through the stir casting route. Scanning electron microscopy (SEM) was used to examine the morphology of the synthesized composite, which revealed that the TiO2 particles were evenly disseminated within the alloy. The machining factors, such as peak current (Ip), pulse-on time (Ton and pulse-o time (Toff), were chosen as input, whereas the material removal rate (MRR), the surface roughness (SR), and the tool wear rate (TWR) were selected as the output responses. According to the L9 (33array, the machining experiments were conducted using a brass (Br) electrode. By employing the TOPSIS method, the optimum combination of variables was determined. Based on the analysis, the Ip of 10 amps, Ton of 200 s, and Toff of 30 s provide the highest MRR (0.2379 g/min) with lower SR (3.284 m), and TWR (0.0258 g/min). ANOVA ndings exhibited that Ton was found to be the primary noteworthy factor contributing 50.67%, next by Toff (32.98%) and Ip (13.12%), respectively. Finally, the conrmation trials were carried out using the optimal parameters, which veried the predicted results. 2026 World Scientific Publishing Company. -
Electric Vehicle Control and Driving Safety Systems: A Review
The relevance of Electric Vehicles (EVs) and the overall market demands of the respective control units is in a never before leap all around the globe as seen from the news, business studies, research trends and technological innovations today. Compared to earlier years, the relevance of driving safety in EVs also gains special attention due to the unforeseen surge in promoting EVs by National, State and City administrations for better environment and societal changes in future. For EV, the scenario broadens to a wider landscape beyond the earlier passive safety design features, to a highly comfortable and safer possible road travel. Safety enhancements can be experimented and implemented on EVs in a reliable way with higher end control of the dynamics, stability and optimised utilisation of individual vehicle characteristics and driver behaviours. In this paper, an attempt is made to scrutinise different control design approaches and possible solution paths experimented upon in the past and currently for EV as seen in the published literature. The quest is also to explore optimisation strategies in an organised way to ensure best possible driving safety along with passenger safety in EVs. 2023 IETE. -
Electric Vehicle Traction Motor Hardware in Loop (HIL) Regulation for Adaptive Cruise Control Scenario
This paper aims at developing a adaptive cruise control system using model predictive algorithm which operates on a Software-in- loop system. The vehicle modelling performed in IPG Car Maker operates with a Matlab based Model Predictive Controller at the back end. The Model Predictive Controller works on the relative distance between the leader vehicle and the ego vehicle. The primary focus is on optimizing the ACC performance to enhance energy efficiency, taking into account the specific dynamics of electric power trains. The study places particular emphasis on the integration of IPG Car Maker software to provide a realistic and dynamic simulation environment, enabling the evaluation of the proposed ACC-MPC system under an urban driving scenario and environmental conditions. 2024 IEEE. -
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799 -
Electrical and mechanical properties of vapour grown gallium monotelluride crystals
The physical vapour deposition (PVD) of gallium monotelluride (GaTe) in different crystalline habits was established in the growth ampoule, strongly depending on the temperature gradient. Proper control on the temperatures of source and growth zones in an indigenously fabricated dual zone furnace could yield the crystals in the form of whiskers and spherulites. Optical and electron microscopic images were examined to predict the growth mechanism of morphologies. The structural parameters of the grown spherulites were determined by X-ray powder diffraction (XRD). The stoichiometric composition of these crystals was confirmed using energy dispersive analysis by X-rays (EDAX). The type and nature of electrical conductivity were identified by the conventional hot probe and two probe methods, respectively. The mechanical parameters, such as Vickers microhardness, work hardening index, and yield strength, were deduced from microindentation measurements. The results show that the vapour grown p-GaTe crystals exhibit novel physical properties, which make them suitable for device applications. 2013 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
Electrical transport and magnetoresistance studies on the magnetic moment compensated Mn2V1-xCoxZ (Z=Ga, Al; x=0, 0.25, 0.5, 0.75, 1) Heusler alloys
We report the electrical resistivity and magnetoresistance properties of arc-melted Mn2V1-xCoxZ (Z=Ga, Al; x =0, 0.25, 0.5, 0.75, 1) alloys, which possess compensated ferrimagnetic behaviour with high TC when x=0.5. Apart from metallicity, the alloys in the Ga series with x= 0, 0.75, 1 composition showed a positive to negative crossover in the magnetoresistance versus temperature curves. This crossover was absent for Mn2V0.75Co0.25Ga and the fully compensated ferrimagnet Mn2V0.5Co0.5Ga. In contrast to this, Co-substituted Mn2VAl exhibits distinctly different resistive behaviour. While the alloys Mn2VAl and Mn2CoAl exhibit metallic and semiconducting behaviour respectively, the intermediate compositions show a gradual metallic to semiconducting transition as the Co concentration increases. The compensated ferrimagnet Mn2V0.5Co0.5Al showed a mixed transport behaviour of metallic and semiconducting nature with a resistivity minimum at 140 K. In contrast to this mixed response of the arc-melted bulk sample, the Mn2V0.5Co0.5Al melt-spun ribbon shows a clear semiconducting nature throughout the temperature range, indicating that the sample preparation methods could highly influence the electrical properties of the investigated compensated ferrimagnets. 2024 Elsevier B.V. -
Electrical transport and magnetoresistance studies on the magnetic moment compensated Mn2V1-xCoxZ (Z=Ga, Al; x=0, 0.25, 0.5, 0.75, 1) Heusler alloys
We report the electrical resistivity and magnetoresistance properties of arc-melted Mn2V1-xCoxZ (Z=Ga, Al; x =0, 0.25, 0.5, 0.75, 1) alloys, which possess compensated ferrimagnetic behaviour with high TC when x=0.5. Apart from metallicity, the alloys in the Ga series with x= 0, 0.75, 1 composition showed a positive to negative crossover in the magnetoresistance versus temperature curves. This crossover was absent for Mn2V0.75Co0.25Ga and the fully compensated ferrimagnet Mn2V0.5Co0.5Ga. In contrast to this, Co-substituted Mn2VAl exhibits distinctly different resistive behaviour. While the alloys Mn2VAl and Mn2CoAl exhibit metallic and semiconducting behaviour respectively, the intermediate compositions show a gradual metallic to semiconducting transition as the Co concentration increases. The compensated ferrimagnet Mn2V0.5Co0.5Al showed a mixed transport behaviour of metallic and semiconducting nature with a resistivity minimum at 140 K. In contrast to this mixed response of the arc-melted bulk sample, the Mn2V0.5Co0.5Al melt-spun ribbon shows a clear semiconducting nature throughout the temperature range, indicating that the sample preparation methods could highly influence the electrical properties of the investigated compensated ferrimagnets. 2024 Elsevier B.V. -
Electrically small S-band antenna for cubesat applications
This research paper deals with the design and development of a circularly polarized S-band rectangular patch antenna providing performance suitable for application in CubeSat. A CubeSat is a type of miniaturized satellite used primarily by university research groups for demonstration of technology. They are low earth orbiting sun-synchronous (LEOSS) type of satellites. The design protocol specifies maximum outer dimensions equal to 100 mm00 mm00 mm and weighing a mass between 1.3-6 kg. However, being small in size, they pose some challenges such as low profile antenna, possibility for cross-link communication with other similar satellites and high reliability of communication in a swarm without the prior knowledge of their positions. Additionally CubeSats dictate the space limitation for placing the antenna within it. With all these, it also requires small antenna with high gain and wide directivity. The most suitable antennas that address most of the aforementioned challenges are planar antennas. The design and simulation of the proposed design of electrically small sband antenna for CubeSat achieves gain of 5.01 dBi with a narrow bandwidth of 100 MHz. The analysis is performed using MATLAB and HFSS (High Frequency Structural Simulator). 2017 IEEE. -
Electricity Demand Prediction: An Analytical Comparison of ARIMA and Artificial Neural Network
Electricity plays a dominant role globally, especially in the economies of India. Accurately projecting its consumption is crucial for energy planning. This study focuses on forecasting electricity consumption across distinct sectors using Autoregressive Integrate Moving Average (ARIMA) and Artificial Neural Network (ANN). The efficacy of the models is evaluated via various error metrics and compared, demonstrating the superior performance of the ANN model over ARIMA model. 2025 IEEE. -
Electro fabrication of molecularly imprinted sensor based on Pd nanoparticles decorated poly-(3 thiophene acetic acid) for progesterone detection
In recent years, scientific community has witnessed substantial interest in the design and engineering of electrodes as sensing platforms towards sensitive and selective detection of hormones. An electrochemical strategy for the detection of progesterone was proposed by generating a composite film comprising of palladium nanoparticles with 3-thiophene acetic acid (3-TAA) coupled with molecular imprinting technology. Progesterone molecule was employed as the template while generating molecular imprints by electropolymerization on the surface of the Carbon Fibre Paper (CFP) electrode. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry were used to analyse the various modified working electrodes (CV). Characterization methods included field emission scanning microscopy, energy dispersive X-ray spectrometry, optical profilometry, and X-ray photon electron spectroscopy. Pd nanoparticles resulted in enhanced sensitivity and molecular imprinting technology contributed to its specificity. Because of the molecular cavities created on the removal of the template molecule, Nyquist plots data showed that the MIP/Pd/CFP electrode had the lowest charge transfer resistance compared to other control electrodes. 2022 Elsevier Ltd -
Electro fabrication of molecularly imprinted sensor based on Pd nanoparticles decorated poly-(3 thiophene acetic acid) for progesterone detection /
Electrochimica Acta, Vol.408, ISSN No: 0013-4686.
In recent years, scientific community has witnessed substantial interest in the design and engineering of electrodes as sensing platforms towards sensitive and selective detection of hormones. An electrochemical strategy for the detection of progesterone was proposed by generating a composite film comprising of palladium nanoparticles with 3-thiophene acetic acid (3-TAA) coupled with molecular imprinting technology. Progesterone molecule was employed as the template while generating molecular imprints by electropolymerization on the surface of the Carbon Fibre Paper (CFP) electrode. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry were used to analyse the various modified working electrodes (CV). -
Electro-osmotic effect on the three-layer flow of Binary nanoliquid between two concentric cylinders
The three-layer flow of an immiscible nanoliquid in composite annulus with an electro-kinetic effect is analyzed using Buongiornos model. This model helps in analyzing the impact of two major phenomena, namely thermophoresis and Brownian motion. In this model, an interfacial layer is formed between the liquids due to the immiscibility of the base liquids. The use of a multilayer model especially in cooling systems brings more applications in many industries such as nuclear, biomedical, and solar. Different from the earlier studies on multilayer channel flow, this paper explains the three-layer flow between two concentric cylinders in the presence of cross-diffusion which makes the work unique. Further, the middle region is assumed to be porous and heat source or sink is applied to the entire system. Also, the flux conservation condition for nanoparticle volume fraction is considered. The equations governing the problem are simplified and are solved using the differential transform method. The results indicate that the electroosmotic parameter enhances the velocity but reduces the electrostatic potential. Further, the diffusion ratio improves the temperature and decreases the solute concentration of the fluid. 2022, Akadiai Kiad Budapest, Hungary. -
Electro-osmotic peristaltic streaming of a fractional second-grade viscoelastic nanofluid with single and multi-walled carbon nanotubes in a ciliated tube
Mathematical modeling of carbon nanotubes (CNTs) in biological fluids is essential for drug delivery, biosensing, and targeted therapy. This study explores the transport dynamics of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) based nanofluids under electro-osmotic peristaltic flow influenced by ciliary motion. A microfluidic channel lined with cilia, hair-like structures found in human airways and reproductive tracts, is considered. The coordinated beating of cilia generates a wavelike motion that propels the surrounding biological fluid. When an electric field is applied across the channel, electro-osmotic forces further modify the flow, affecting velocity and temperature distribution. A nanofluid, consisting of CNTs suspended in a base fluid, flows through this cilia-driven microchannel. The transport process is governed by electro-osmosis, heat transfer, and thermal radiation effects, with simplifications based on long-wavelength and low Reynolds number assumptions. The Caputo fractional model and DebyeHkel linearization are used to analyze the interaction between electro-osmotic forces and thermal-mechanical effects. The results reveal that the negative Helmholtz-Smoluchowski parameter (Uhs) reduces the axial velocity in the core whereas it increases in the periphery of the channel, while the opposite trend is observed for positive Uhs. Longer cilia (?) and higher electro-osmotic parameter (m) slow the core flow while accelerating peripheral transport. Thermal effects indicate that an increased heat source (B) raises temperature and axial velocity, whereas a higher nanotube volume fraction (?) enhances axial velocity but reduces temperature. Notably, MWCNTsexhibit superior axial velocity and temperature enhancement compared to SWCNTs. These outcomes provide valuable insights into electro-osmotic cilia-driven nanofluid transport, offering a theoretical foundation for optimizing microfluidic and biomedical applications. 2025 -
Electro-osmotically modulated viscoelastic SWCNT-blood flow in symmetric/nonsymmetric stenosed arteries with heat generation using a fractional second grade model
This study examines the electro-osmotically modulated viscoelastic blood flow in arteries with both symmetric and nonsymmetric stenosis, accounting for heat generation and thermal buoyancy effects. Blood is modeled as a fractional second-grade fluid to more accurately capture its viscoelastic and memory-dependent behavior. The DebyeHkel linearization is applied to analyze the electro-osmotic effects. The governing partial differential equations are reduced to a system of ordinary differential equations using appropriate scaling transformations. Analytical solutions are derived for the resulting nondimensional boundary value problem. Key flow characteristics, such as axial velocity, temperature distribution, electric potential, volumetric flow rate, and wall shear stress, are computed and illustrated graphically using the Mathematica software. The computations reveal that axial velocity decreases near the arterial walls but increases in the core region for both symmetric (n = 2) and nonsymmetric (n = 6) stenoses with rising HelmholtzSmoluchowski velocity (UHs), CNT volume fraction (?), Debye length parameter (m), and stenosis height (e). Heat generation (? > 0) further enhances both velocity and temperature. Increasing ? reduces temperature and wall shear stress (?w), while higher flow rate (Q) and stenosis height (e) are elevated (?w). Nonsymmetric stenoses yield higher temperatures than symmetric ones. Trapping boluses grow in size and number with increasing Q for both stenosis types. These findings underscore the significant role of electro-osmotic and viscoelastic effects in hemodynamic regulation, with potential biomedical applications. World Scientific Publishing Europe Ltd. -
Electro-sprayed Quaternary Composite of Poly(aniline-co-pyrrole), Graphene Oxide, and Iron Oxide as an Efficient Electrode for Hybrid Supercapacitor Application
Abstract: A novel quaternary nanocomposite has been developed using a cost-effective and user-friendly method called electro-spraying. This composite consists of poly(aniline-co-pyrrole), Graphene Oxide (GO), and Iron Oxide (Fe3O4), aimed at achieving improved electrochemical stability and performance. The composite electrodes displayed an impressive specific capacitance of 950 Fg1 at a current density of 0.5 Ag1 when tested in a 1 M H2SO4 solution. Furthermore, even after 2000 cycles at a current density of 1 Ag1, the electrode exhibited an outstanding capacitance retention rate of 91%, showcasing its remarkable stability and long-lasting performance. These exceptional properties can be attributed to the synergistic effects arising from the combination of the conducting polymer, metal oxide, and graphene oxide components within the electrode material. Additionally, significant advancements in other electrochemical properties make this nanocomposite a promising candidate for use as an electrode material in supercapacitors. Pleiades Publishing, Ltd. 2024. -
Electrocatalytic oxidation and determination of morin at a poly (2,5-dimercapto-1,3,4-thiadiazole) modified carbon fiber paper electrode /
Journal Of The Electrochemical Society, Vol.163, Issue 8, ISSN:0013-4651 (print) 1945-7111 (web).