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A model to predict the influence of inconsistencies in Thermal Barrier Coating ( TBC) thicknesses in pistons of IC engines /
Materials Today Proceedings, Vol.5, Issue 5, Part 2, pp.12623-12631 -
A Model to Predict the Influence of Inconsistencies in Thermal Barrier Coating (TBC) Thicknesses in Pistons of IC Engines
LHR (Low heat Rejection) engines comprise of components that are modified with ceramic Thermal Barrier Coatings (TBC) to derive improvements in performance, fuel efficiency, combustion characteristics and life. In addition to engine parameters, the ability of TBCs (250 - 300?m thick) to function favorably depends on materials technology related factors such as surface-connected porosity, coating surface roughness, uniformity and consistency in coating thickness [1]. Right since the nineties, emphasis has been placed on the complexity of piston contours from a coating processing standpoint because the piston bowl geometry although appears simple, is actually quite complex. Robotic plasma gun manipulation programs have been developed to obtain uniform coating properties and thicknesses which are highly classified information. Thicker coatings offer better thermal insulation characteristics but in thickness deficient regions, TBCs may be as thin as ?30 microns. Applied via the 'line of sight' process, in the Atmospheric Plasma Spray System the coating thickness does not get developed adequately if the components comprise of contours with shadow regions. Thus the coating quality of a LHR engine heavily depends upon the shape of the engine components. This affects the barrier effects offered by the TBC and is reflected via generation of unwanted thermal gradients in the combustion chamber and on the external piston walls that adversely influence the engine performance. Extensive diesel engine cycle simulation and finite-element analysis of the coatings have been conducted to understand their effects on (a) diesel engine performance and (b) stress state in the coating and underlying metal substructure. Research work presented here involves the need and developmental efforts made via Computational Fluid Dynamics (CFD) to generate a model via ANSYS - Fluent simulation software that predicts the temperature gradient across TBCs of various ceramics and coating thicknesses. The geometric model was developed using the dimensions obtained using a CMM (Coordinate Measuring Machine) in Solidworks and the mesh was developed in Altair Hypermesh. The generated mesh consists of 221938 elements. Interfaces were created between the piston-bond coat-top coat surfaces. The Ansys-FLUENT CFD code solves the energy equation to find out the temperature drop in the piston for different combustion temperatures. Although most of the cavities presented are not rectangular, incompressible and steady laminar flow was assumed. The Semi-Implicit Method for Pressure-linked Equations (SIMPLE) was used to model the interaction between pressure and velocity. The energy variables were solved using the second order upwind scheme. In addition, the CFD program uses the Standard scheme to find the pressure values at the cell faces. Convergence was determined by checking the scaled residuals and ensuring that they were less than 10-6 for all variables. Two cases with combustion temperatures varying between 700 and 800 K were developed in Ansys FLUENT, wherein the thickness was deficient in the 'shadow' region. The model was validated via experimentation involving thermal shock cycle tests in prototype burner rig facility and measuring the temperature drop across the TBC as well. Non uniform coatings, leading to non-uniform drop in temperature across the thickness are most likely to affect the lubrication system of the engine and therefore the performance. Substantial efforts must be directed towards development of consistent and uniformly thick coatings for optimum performance of the LHR engine. 2017 Elsevier Ltd. All rights reserved. -
A modern approach of swarm intelligence analysis in big data: Methods, tools, and applications
Swarm intelligence is one of the most modern and less discovered artificial intelligence types. Until now it has been proven that the most comprehensive method to solve complex problems is using behaviours of swarms. Big data analysis plays a beneficial role in decision making, education domain, innovations, and healthcare in this digitally growing world. To synchronize and make decisions by analysing such a big amount of data may not be possible by the traditional methods. Traditional model-based methods may fail because of problem varieties such as volume, dynamic changes, noise, and so forth. Because of the above varieties, the traditional data processing approach will become inefficient. On the basis of the combination of swarm intelligence and data mining techniques, we can have better understanding of big data analytics, so utilizing swarm intelligence to analyse big data will give massive results. By utilizing existing information about this domain, more efficient algorithm can be designed to solve real-life problems. 2023, IGI Global. All rights reserved. -
A modified approach for extraction and association of triplets
In this paper we present an enhanced algorithm with modified approach to extricate various Triplets i.e. subject-predicate-object from Natural language sentences. The Treebank Structure and the Typed Dependencies obtained from Stanford Parser are used to elicit multiple triplets from English Sentences. Typed Dependencies represents grammatical connections among the words of any sentence and represents how triplets are associated. The intended interpretation behind the extraction of Triplets is that the subject is acting on the object in a way described by the predicate. In graphical form it can be considered that subject and object will be acting as nodes i.e. entities and predicate as edges i.e. relationship. The resulting triplets and relations can be useful for building and analysis of a social network graph and for generating communication pattern and Information retrieval. 2015 IEEE. -
A modified fuzzy approach to prioritize project activities
Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model. 2018 Authors. -
A modified invasive weed optimization for MPPT of PV based water pumping system driven by induction motor
A novel approach called Modified Invasive Weed Optimization (MIWO) technique has been developed and combined with the Perturb and Observes (P&O) algorithm to enhance the extraction of maximum power from photovoltaic (PV) panels in the presence of partial shading conditions. The conventional P&O algorithm falls short in extracting the maximum power from PV systems under partial shading conditions due to the existence of multiple maximum points. In such scenarios, optimization techniques can be employed to search for the global maximum point. The proposed MIWO-based P&O algorithm updates the reference voltage to ensure that the PV system operates at the Maximum Power Point (MPP) based on the prevailing weather conditions. This MIWO based PV system is further fed to water pumping system. A PV-based water pumping system is utilized for both irrigation and domestic purposes. Additionally, a sensorless vector control-based induction motor is employed in this study to drive the pump. The objective of this research is to demonstrate the achievement of an efficient PV-based water pumping system without the need for battery storage. Various results based on MIWO are compared with PSO and GWO. The results are presented based on various water pumping applications and the availability of solar irradiance during rapid climate changes. MATLAB/Simulink simulations, along with hardware-based experiments, are provided to validate the effectiveness of the proposed method under both transient and steady-state conditions. 2024 IOP Publishing Ltd. -
A Modified Seven-Level Inverter with Inverted Sine Wave Carrier for PWM Control
The conventional multilevel inverter necessitates more active switching devices and high dc-link voltages. To minimalize the employment of switching devices and dc-link voltages, a novel topology has been proposed. In this paper, a novel minimum switch multilevel inverter is established using six switches and two dc-link voltages in the proportion of 1: 2. In addition, the proposed topology is proficient in making seven-level voltages by appropriate gate signals. The PWM signals were produced using several inverted sine carriers and a single trapezoidal reference. When compared to other existing inverters, this configuration needs fewer components, as well as fewer gate drives. Furthermore, this module can generate a negative level without the use of a supplementary circuit such as an H-Bridge. As a result, overall cost and complexity are greatly reduced. The proposed minimum switch multilevel inverter operation is validated through simulations followed by experimental results of a prototype. 2022 Arun Vijayakumar et al. -
A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery
SARS-CoV-2, the causative virus of the Corona virus disease that was first recorded in 2019 (COVID-19), has already affected over 110 million people across the world with no clear targeted drug therapy that can be efficiently administered to the wide spread victims. This study tries to discover a novel potential inhibitor to the main protease of the virus, by computer aided drug discovery where various major active phytochemicals of the plant Boerhavia diffusa Linn. namely 2-3-4 beta-Ecdysone, Bioquercetin, Biorobin, Boeravinone J, Boerhavisterol, kaempferol, Liriodendrin, quercetin and trans-caftaric acid were docked to SAR-CoV-2 Main Protease using Molecular docking server. The ligands that showed the least binding energy were Biorobin with ? 8.17kcal/mol, Bioquercetin with ? 7.97kcal/mol and Boerhavisterol with ? 6.77kcal/mol. These binding energies were found to be favorable for an efficient docking and resultant inhibition of the viral main protease. The graphical illustrations and visualizations of the docking were obtained along with inhibition constant, intermolecular energy (total and degenerate), interaction surfaces and HB Plot for all the successfully docked conditions of all the 9 ligands mentioned. Additionally the druglikeness of the top 3 hits namely Bioquercetin, Biorobin and Boeravisterol were tested by ADME studies and Boeravisterol was found to be a suitable candidate obeying the Lipinskys rule. Since the main protease of SARS has been reported to possess structural similarity with the main protease of MERS, comparative docking of these ligands were also carried out on the MERS Mpro, however the binding energies for this target was found to be unfavorable for spontaneous binding. From these results, it was concluded that Boerhavia diffusa possess potential therapeutic properties against COVID-19. 2021, Indian Virological Society. -
A molecular QCA based UV lamp for water purification /
Patent Number: 201731011405, Applicant: Dr.Paramartha Dutta. -
A Multi Objective Artificial Eco-System Based Optimization Technique Integrating Solar Photovoltaic System In Distribution Network
Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder 'Racheruvu 11kV agricultural feeder' Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system. 2022 IEEE. -
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A multi-cognitive approach to empowering secondary school teachers' self-efficacy and practices related to education for sustainable development
Purpose Education for Sustainable Development (ESD) is vital for addressing global sustainability goals. However, integration in Indian schools faces challenges, particularly due to gaps in teacher preparedness. This study aimed to evaluate the effectiveness of a multi-cognitive approach (MCA) in empowering secondary school teachers' self-efficacy and ESD integration. Design/methodology/approach A quasi-experimental, one-group pretestposttest design was employed with 50 secondary school teachers from marginalized communities in Kerala, India. Participants with over 6years of experience but no prior ESD training underwent a 3-month MCA-based transformative learning program. The intervention addressed content, perspectives, processes and design. Teacher self-efficacy and ESD practices were measured pre- and immediately post-intervention, and three months later, using structured questionnaires. Findings Teachers' self-efficacy significantly improved post-intervention (52.707.61) and was sustained at three months (56.604.59), compared to baseline (49.069.69) (p<0.001). ESD-related practices also improved post-intervention (47.487.16), with further gains at three months (51.863.96), compared to pre-intervention (41.905.91). Research limitations/implications These results support incorporating the MCA into teacher training and professional development programs to foster sustainable education practices. The approach aligns with SDG 4.7 and can guide policy reforms in integrating ESD into mainstream education. Practical implications The study also presents a professional development model for schools, particularly beneficial in resource-constrained contexts, that enables teachers to embed sustainability in their practices. Furthermore, it offers policy guidance for embedding MCA-informed ESD into teacher education and national curricula, supporting Sustainable Development Goal 4.7 and NEP 2020 vision, promoting systemic education reform in sustainability. Social implications This study empirically validates an MCA as an effective framework for ESD. It highlights those engaging teachers across the cognitive, reflective, procedural and design dimensions, simultaneously enhancing their self-efficacy and sustaining ESD practices. The findings extend existing theories by showing that self-efficacy in sustainability is teachable and durable with the right interventions. Originality/value This study highlights MCA as a promising model for building teacher capacity in ESD and recommends future research on its impact on student outcomes. Emerald Publishing Limited -
A Multi-criteria Decision-Making Approach for Prioritising Customer Churn Factors in OTT Video Platforms
Over-the-top (OTT) platforms have revolutionised media consumption by providing on-demand streaming services. Despite their growing popularity, customer churn remains a significant challenge for the platform. This research paper analyses the factors affecting customer churn in OTT video platforms. The factors are identified through unstructured interviews with industry experts and an extensive literature review. This research paper employs a novel approach to prioritising customer churn factors by incorporating multi-criteria decision-making (MCDM) techniques like AHP and fuzzy AHP. The importance of each customer churn factor is measured based on the analytic hierarchy process (AHP) and fuzzy AHP to develop a hierarchy of churn factors. The MCDM analysis results indicated that the content variety and recommendation system, video streaming issues, and high subscription prices are the most significant factors that cause customer churn. Through comprehensive analysis, the study aims to provide insights for OTT service providers to enhance customer retention and mitigate churn rates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A multi-criteria decision-making approach for prioritising factors influencing customer switching intention in OTT video platforms
The rapid expansion of Over-the-Top (OTT) video platforms has transformed the media industry by providing diverse content and reshaping consumer preferences. This study explores factors influencing customer switching intentions using Multi-Criteria Decision-Making (MCDM) techniques like AHP, fuzzy AHP, and ANP. The findings of this study highlight that content variety and richness are the most critical determinants, followed by customer experience, with factors like the attractiveness of alternatives, switching costs and service quality having secondary importance in influencing customer switching intention. Content diversity, quality, and exclusivity drive retention, while intuitive and user-friendly interfaces enhance satisfaction. The study also emphasizes the influence of competing platforms. While brand familiarity and social influence play minor roles, prioritising rich content and seamless customer experience emerges as the key to loyalty. The integrated MCDM approach empowers OTT providers to improve customer retention in a competitive market. 2025, IGI Global Scientific Publishing. -
A Multi-Dimensional Analysis of NIFTY50's Strategic Integration and Performance on the United Nations' Sustainable Development Goals
In 2015, the United Nations introduced 2030 agenda for Sustainable Development focusing on Sustainable Development Goals (SDGs) and 167 specific targets which are adopted by 193 member countries. The goals serve as a global blueprint for achieving inclusive, equitable and sustainable growth. The present study evaluates the sustainability performance of leading companies listed on the NIFTY50 index to assess how effectively for top performing firms have integrated SDG principles into their strategic planning, disclosure practices and operational frameworks. The resulting scores provide a quantifiable measure of companys alignment with global SDG agenda. Also, the study analyzes the financial performance indicators specifically for stock returns and volatility using NIFTY50 as benchmark. It reveals a positive relationship between higher SDG scores and improved stock performance as well as a negative correlation for volatility suggesting that companies with stronger sustainability engagement tend to offer better risk- adjusted returns. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
A multi-frequency study of the candidate doubledouble radio galaxy J2349?0003 with a possible misalignment
We present a multi-frequency analysis of the candidate doubledouble radio galaxy (DDRG) J2349?0003, exhibiting a possible lobe misalignment. High-resolution uGMRT observations at Bands 3 and 4 reveal a complex radio morphology featuring a pair of inner and outer lobes, and the radio core, while the Band 5 image detects the core and the compact components. The positioning of both pairs of lobes with the central core supports its classification as a DDRG. Spectral age estimates for the inner and outer lobes indicate two distinct episodes of active galactic nucleus (AGN) activity interspaced by a short quiescent phase. The possible compact steep-spectrum nature of the core, together with its concave spectral curvature, suggests ongoing or recent jet activity, suggesting the possibility that J2349?0003 may be a candidate triple-double radio galaxy. With a projected linear size of 1.08 Mpc, J2349?0003 is classified as a giant radio galaxy (GRG), although its moderate radio power (?1024 WHz-1) suggests a sparse surrounding environment. Arm-length (R?) and flux density ratios (RS) indicate environmental influences on source symmetry. The observed lobe misalignment and the presence of nearby galaxies in the optical image suggest that merger-driven processes may have played a key role in shaping the sources evolution. Indian Academy of Sciences 2025. -
A Multi-Layer Complex Adaptive System Framework for AI-Driven Robo-Advisory Services
The rapid integration of Artificial Intelligence (AI) into investment advisory services has changed financial decision-making, giving rise to adaptive robo-advisory systems capable of real-time analysis, personal recommendations, and autonomous portfolio optimization. Existing research evaluates these systems primarily through technological performance or investor adoption, overlooking the complex feedback-driven interactions that emerge when AI analytics, data environments, and human behavior operate together. This study addresses this gap by conceptualizing AI-enabled robo-advisors as a multi-layered Complex Adaptive System comprising historical data, real-time data, AI analytics, investor perception, and decision-making layers. A simulation model grounded in machine learning dynamics, behavioral finance, and complexity theory is developed to capture nonlinear interactions, adaptive learning, and emergent investor responses. Results show that historical data acts as a stabilizing memory, real-time data amplifies short-term volatility, AI analytics self-organize toward performance equilibrium, and investor perception evolves through nonlinear trust thresholds that ultimately drive decision lock-in. Complexity measures reveal that adaptive intelligence is concentrated in the historical and perception layers, while the decision layer becomes increasingly deterministic as feedback loops strengthen. The findings provide a unified system-level understanding of robo-advisory ecosystems and highlight the need for governance structures that incorporate transparency, behavioral dynamics, and adaptive model monitoring. This framework offers a foundation for designing more resilient, trustworthy, and sustainable AI-driven financial advisory systems. 2026 Binghamton University Libraries. All rights reserved. -
A multi-layer memory enriched staticmemory system /
Patent Number: 202141039100, Applicant: Debarka Mukhopadhyay.
The present invention is configured with the generation of multiprocessor core of each 15 bit CAM cell and a section works at a lower supply area which will drive a part to work at a more prominent induced voltage for the CAM core processor. The present device is configured in such a way so that no level converters are required. The system operates avoiding the power overhead and low supply area as illustrated in Fig1. This process is used assorting voltage with less area interface inside the CAM cells, related by the applying product on D-FF. The match line indicates the search word and stored word are indistinguishable (the match case) or are extraordinary (a confounding case, or miss) of the CAM cell. -
A multi-layer memory enriched staticmemory system /
Patent Number: 202141039100, Applicant: Debarka Mukhopadhyay.The present invention is configured with the generation of multiprocessor core of each 15 bit CAM cell and a section works at a lower supply area which will drive a part to work at a more prominent induced voltage for the CAM core processor. The present device is configured in such a way so that no level converters are required. The system operates avoiding the power overhead and low supply area as illustrated in Fig1. This process is used assorting voltage with less area interface inside the CAM cells, related by the applying product on D-FF. The match line indicates the search word and stored word are indistinguishable (the match case) or are extraordinary (a confounding case, or miss) of the CAM cell. -
A Multi-Layer Security Framework for Adversarial SQL Injection in Machine Learning Systems
Adversarial machine learning (AML) is a field that works with attacks from hackers that deliberately cause machine learning systems to work incorrectly or identify data wrongly. Modern day machine learning systems grow in a very fast manner. This often introduces new threats and vulnerabilities that are above the capacity of the traditional cyber security measures. These attacks can in turn affect the trustworthiness and security of artificial systems across many domains like healthcare, education, finance, etc. This paper introduces a multi-layer security framework. It focuses on modelling and defending against SQL injection based attacks in machine learning. The paper emphasizes technical defences, governance and collaboration across various domains. By combining the risks with the existing cybersecurity frameworks such as NIST, MITRE ATLAS, and the EU AI Act, the framework provides a way to develop resilient, ethical and secure AI systems. 2025 IEEE.