Paper Title

THERMAL BARRIER COATINGS AND THEIR EFFECT ON TURBINE BLADE PERFORMANCE

Abstract

On the turbine blade, thermal barrier coatings (TBCs) are applied to lower the temperature of the underlying substrate and offer defense against hot corrosion and oxidation caused by hightemperature gases. The performance and effectiveness of the coatings can be enhanced by the blade\'s optimal ceramic top-coat thickness distribution. Because the goals of high thermal insulation performance, long operation durability, and low fabrication cost conflict, designing the coatings\' thickness is a multi-objective optimization problem. This study created a process for designing the gas turbine blade\'s TBC thickness distribution, which ranges from 100 μm to 500 μm. Nickel alloy is used to create the base material for the blade geometry, and partially stabilized zirconia is chosen as the coating material. The multi-objective optimization problem in this case was solved using a weighted-sum approach after three-dimensional finite element models were constructed with CATIA and examined with ANSYS WORKBENCH. A suitable multi-region top-coat thickness distribution scheme was created while taking fabrication cost, productivity, and manufacturing accuracy into account.
KEYWORDS : Thermal Barrier Coatings; Oxidation; corrosion; Ceramic Top-coat thickness

Paper Title

ANALYSIS OF NUMERICAL METHODS TO INCLUDE DYNAMIC CONSTRAIN IN TSCOPF MODELS

Abstract

Transient Stability Constrained Optimal Power Flow (TSCOPF) models effectively solve the optimization of power system operation, including steady state and dynamic constraints. TSCOPF studies incorporate the electromechanical oscillations of synchronous machines into well-known optimal power flow models. The discretized differential equations that depict the system dynamics in the optimization model are one of the primary methods used in TSCOPF studies. This study examines the effects of the integration time step and various implicit and explicit numerical integration techniques on the solution of a TSCOPF model. The impact on power dispatch, the overall cost of generation, the precision of the computation of electromechanical oscillations between machines, and the magnitude of the optimization problem are specifically examined and the computational time.
KEYWORDS : power system transient stability; economic dispatch; numerical integration methods; non-linear programming; optimal power flow

Paper Title

TRANSFORMATION OF BIG DATA THROUGH MACHINE LEARNING AND IT’S APPLICATIONS

Abstract

Both Sciences and Industry are towards a data revolution. And this has led to a complete data of new formats and unparalleled data bases. Such an increase in huge amount of data have given rise to an opportunity for Machine Learning and Bigdata to come concurrently and to develop Machine Learning methods that have the capability to hold present data types and for navigation of large amount of information with minimal or no human intervention. By implementing fast and effective algorithms and information driven models for processing of data, Machine Learning is capable to give faultless results. Today Machine Learning is being vigorously utilized in a wide range of areas than we anticipate. A pure Machine Learning process, the more data provided to the system, the more it can learn from it, returning the results that are looking for, and that’s why it works well with Bigdata. Without it, the Machine Learning can\'t keep running at its at most level and this is because of the way that with less information, the machine has less examples to gain from, and subsequently its results may be influenced. This paper gives the survey on applications and challenges of Machine Learning techniques, advanced learning methods towards Bigdata.
KEYWORDS : Machine Learning, Bigdata, Deep Learning, Neural Networks.

Paper Title

IMPROVING SECURE MULTI-PARTY COMPUTATION PERFORMANCE USING AI-DRIVEN OPTIMIZATION TECHNIQUES

Abstract

This study presents a performance of the theoretical and practical aspects of SMPC protocols. Secure Multi-Party Computation (SMPC) is a cryptographic framework enabling multiple parties to collaboratively compute a function over their private inputs while ensuring the confidentiality of those inputs. This work proposes an innovative approach to optimize SMPC performance using AI-driven techniques. By leveraging machine learning and optimization algorithms, we identify and exploit opportunities to improve SMPC efficiency, reducing computational latency and enhancing scalability. Specifically, we start by demonstrating the underlying concepts of SMPC, including its security requirements and basic construction techniques. Our AI-driven optimization framework enables adaptive protocol selection, optimized computation scheduling, and intelligent resource allocation. Then, we present the research advances regarding construction techniques for generic SMPC protocols, and also the cutting-edge approaches to cloud-assisted SMPC protocols. Collaborative machine learning enables multiple organizations to train models on combined datasets without compromising data privacy. This study explores the use of secure multi-party computation (SMPC) to ensure data confidentiality during collaborative training. This has occurred mainly because, as a generic tool for computing on private data, SMPC has a natural advantage in solving security and privacy issues in these areas.
KEYWORDS : SMPC protocols, privacy-preserving, cryptographic framework, optimization algorithms, AI-driven optimization.

Paper Title

A COMPREHENSIVE STUDY ON THE VALIDATION OF ACTIVE COMPOUNDS IN MULTI-COMPONENT DRUG FORMULATIONS

Abstract

The drug discovery and development industry has aimed at identifying single components with a clear mechanism of action as desirable candidates for potential drugs. To-date modern drug research has focused on the discovery and synthesis of single active substances. This may explain the frequently observed pleiotropic bioactivity spectra of these compounds, which may also suggest that they possess novel therapeutic opportunities. Interestingly, considerable bioactivity properties are exhibited not only by remedies that contain high doses of phytochemicals with prominent pharmaceutical efficacy, but also preparations that lack a sole active principle component. Despite that each individual substance within these multi-components has a low molar fraction, the therapeutic activity of these substances is established via a potentializing of their effects through combined and simultaneous attacks on multiple molecular targets. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the “single standard to determine multiple components (SSDMC)” approach. In the present study, a comprehensive survey on contamination profiles, occurrence, removals, temporal variation and ecological risk of multiclass multiresidue PhACs, such as antibiotics, non-steroidal anti-inflammatories, lipid regulators and phsychiatrics, was performed in wastewaters from the WWTP of Ioannina University hospital along one year period on a monthly sampling basis.
KEYWORDS : drug discovery, multi-components, phsychiatrics, compounds, non-steroidal anti-inflammatories,

Paper Title

A STUDY ON THE IMPACT OF DATA SELECTION AND CLEANING TECHNIQUES

Abstract

Data cleansing offers a better data quality which will be a great help for the organization to make sure their data is ready for the analysing phase. However, the amount of data collected by the organizations has been increasing every year, which is making most of the existing methods no longer suitable for big data. The accuracy and integrity of data are important for the implementation of construction waste treatment. Abnormal detection and incomplete filling occur when traditional cleaning algorithms are used. Data quality affects machine learning (ML) model performances, and data scientists spend considerable amount of time on data cleaning before model training. Data collected from the various resources are dirty and this will affect the accuracy of prediction result. However, to date, there does not exist a rigorous study on how exactly cleaning affects ML — ML community usually focuses on developing ML algorithms that are robust to some particular noise types of certain distributions. To improve the cleaning of construction waste data, a data cleaning algorithm based on multi-type construction waste was presented in this study. Thereafter, a natural language data cleaning model was proposed, and the spatial location data were separated from the general data through the content separation mechanism to effectively frame the area to be cleaned. Data cleansing process mainly consists of identifying the errors, detecting the errors and corrects them.
KEYWORDS : Data cleansing, Data Integration, Data Mining, machine learning (ML) model, Data collected, ML community.

Paper Title

DEVELOPMENT OF FUNCTIONAL NANOSTRUCTURES FOR SENSITIVE DETECTION OF ANALYTES IN COMPLEX FLUIDS

Abstract

The development of functional nanostructured materials, which show unique and versatile characteristics, is highly desirable for important applications, such as catalysis and solar cells. In this review, we first summarize our recent studies on the synthesis of nanohybrid catalysts (such as bimetallic and binary metal oxide nanostructures) and their catalytic behavior in diverse catalytic reactions. We then present our recent developments on plasmonic nanostructures (Au and Ag), and demonstrate and discuss how they may be explored for enhancing photocatalysis and solar cell performance. Subsequently, we describe our work on the synthesis of semiconductor nanocrystals, also known as quantum dots, and their application in solar cells. Besides traditional wet chemical method, we also introduce an alternative, physical method, pulsed laser ablation, toward synthesizing these nanostructures with a unique “bare and clean” surface, highly relevant to catalytic, plasmonic and photovoltaic applications. Finally, perspectives on future advances of nanostructured catalytic and plasmonic materials as well as quantum dots are outlined. Hormones, which are complex biomolecules, play a vital role in various biochemical pathways and the growth of animals. Recently, we have developed a novel family of functionalized nanostructures that exhibit liquid-like behavior in the absence of solvents and preserve their nanostructure in the liquid state. The gallery of nanostructures developed so far includes functionalized silica and magnetic iron oxide nanoparticles.
KEYWORDS : functional nanostructured, semiconductor nanocrystals, nanostructured catalytic, nanohybrid catalysts

Paper Title

DATA MINING AND ENSEMBLE TECHNIQUES FOR IMPROVED DIABETIC CLASSIFICATION WITH MACHINE LEARNING

Abstract

Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Naive Bayes, and XGBoost) to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique.
KEYWORDS : Diabetic retinopathy, Machine Learning (ML), k-nearest Neighbour, image processing, ML model.