Erode Arts and Science College

This study aimed to optimize the photodegradation of methyl orange dye (MO) using reduced graphene oxide-supported zirconium oxide (ZrO2/rGO) nanocomposites. The morphological and structural properties of the synthesized samples were analyzed using various instruments, including X-ray diffraction, TEM, FTIR, Raman, BET, and FTIR. The inclusion of reduced graphene oxide (rGO) effectively constrained the substantial band gap of ZrO2, facilitating visible light absorption for photocatalytic applications. The photocatalytic degradation of MO dye was assessed using synthesized ZrO2/rGO nanocomposites under UV illumination. The photocatalytic activity was found to be satisfactory under optimal conditions, with a degradation percentage rate of 99% under ultraviolet light. Factors contributing to the improved efficiency include the oxygen-deficient metal oxide phase, increased surface area, and increased number of hydroxyl groups.

The effect weave parameters such as CFF (crossing over firmness factor), FYF (floating yarn factor), FFF (fabric firmness factor) and fabric geometrical properties, namely, areal density, thickness and porosity on air resistance, is examined. A series of woven fabrics differing only in weave structures and having the common count and fabric sett was produced from three types of doubled yarns, namely, compact/compact, conventional/conventional and compact/conventional. The hybrid combination was produced principally to reduce the cost of manufacturing doubled yarns. Essential data for the fabrics such as CFF, FYF, FFF, mass per unit area, thickness and porosity were determined. Air resistance was determined by performing the standard test KES-F8 API. Analysis of the results with respect to fabrics produced from the compact/compact, conventional/conventional and conventional/compact yarns shows a strong correlation between the air resistance, porosity, CFF, FYF, FFF, mass per unit area and thickness ( R = conventional/conventional 0.97, compact/compact 0.861, conventional/compact 0.974). Thus, in addition to the CFF, FYF and FFF, the thickness should also be taken into consideration for predicting the air resistance of fabrics. It is concluded that in order to predict air resistance, a number of parameters are needed and any conclusion which has been drawn using only CFF and FYF should be treated with caution.

Researchers have been leveraging various data analytics methods for Diabetes mellitus (DM) diagnosis, prognosis and management. The data analytics paradigm has become advanced and automated with the emergence of machine learning (ML) and deep learning (DL) algorithms. With new techniques, the prediction accuracy of ML models for various real-world problems has increased significantly. In our previous work, we introduced and investigated the Improved K-Means with Adaptive Divergence Weight Binary Bat Algorithm to create an innovative diagnosis system. Across several problem scenarios, the performance of this algorithm is much better in terms of speed. However, this algorithm's accuracy of data categorization comes below expectations. To achieve high classification accuracy, the objective of this study work is to concentrate on methods and strategies. This aim is fulfilled through a Support Vector Machine (SVM) with an Enhanced Levy Flight-based Fruitfly Optimization model. This novel model improves diabetes prediction accuracy and can be applied to regressions, classifications, and other tasks. The nearest training data points? distances should be greater as this can lower classifiers? generalization errors. Missing values in datasets are retrieved using the Adaptive Neuro Fuzzy Inference System (ANFIS). A new algorithm called the Enhanced Inertia Weight Binary Bat Algorithm (EIWBBA) is introduced to optimize feature spaces and eliminate unimportant aspects. Further on, a novel feature selection technique is introduced by using the Enhanced Generalized Lambda Distribution Independent Component Analysis (EGLD-ICA). The classification uses a Support Vector Machine with an Enhanced Levy flight-based Fruitfly Optimization Algorithm (SVM-ELFFOA). The SVM-ELFFOA classification techniques are implemented using MATLsoftware. It is evident that the discussed IKM-EIWBBA+SVM-ELFFOA classifier produces much better values of the accuracy of 93.50%, while the available IKM-EIWBBA+SVM yields 91.87%, IKM-ADWFA+LR renders 90.50%, and IKM+LR renders just 85.00%. From the simulation experiment, the proposed classification techniques implemented in MATLsoftware and according to comparative data, this suggested model has a higher prediction accuracy of 93.50% compared to existing classification methods.

The online food delivery service is one of the fastest growing businesses in India today. The increased mobile phone usage and development in the internet and communication technology have boosted the growth of such business in India. On-line food ordering is a simple, convenient and easy way for consumers to buy and have food delivered at their doorstep without wasting the time in restaurant. This method is convenient, safe, and reliable and is revolutionizing the present restaurant industry. At this background, this study is an attempt to identify the factors inuencing the consumers to prefer online food delivery services in Erode District of Tamil Nadu. The primary data of this study have been collected from 100 sample respondents using convenient sampling technique and analyzed with the help of Statistical Tool like Garret Ranking Technique. The study concludes that the Door Step Delivery is the primary factor inuencing the consumer to prefer online food delivery services and suggests that prompt delivery of food at the doorstep of consumer at a reasonable price can still inuence the consumers towards preferring online food delivery services.

In recent decades, there has been a lot of interest in novel high-performance active electrode materials for green energy storage devices. This is the first instance of a 3D mesoporous flower-like CuCo2S4@CuCo2O4/rGO heterostructure that has been developed and constructed with suitable nanoscale interfaces and a controllable chemical composition. The electrode has large electrochemical surface area, enhanced conductivity, short ion diffusion path, and rapid electron and ion transport rate due to its unique architecture and components. These features combine to give the electrode its exceptional electrochemical properties, including specific capacitance of 1545 Fg⁻¹ at 1 Ag⁻¹, excellent cycling stability, and high rate capability. The increased active surface area of the nanostructure, the potent synergistic interaction between CuCo2S4 and CuCo2O4, and the superior conductivity of rGO are all responsible for the enhanced electrochemical performance. These special nanoflowers serve as the cathode and activated carbon (AC) serves as the anode in an asymmetric supercapacitor device that offers significant power density (487 Wkg⁻¹), long-term stability (83% capacitance retention after 10,000 cycles), and energy density (33.5 Whkg⁻¹). These findings suggest that high-performance supercapacitors could find extensive use.

To ensure sustainable growth, emerging nations must provide diverse university services, particularly through educational libraries that meet user needs.This study examines the services and facilities influencing user satisfaction at Madurai Kamaraj University College Library. Primary data were collected from library users, focusing on resources such as scanning, printing, reading desks, personal computers, and electronic brochures. Statistical analysis revealed that printing and scanning significantly impact user satisfaction. An impact model was developed to measure these factors,offering insights for enhancing service quality and customer loyalty at the library.

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This study delves into the development and characterization of MoS2 nanoparticles, employing a hydrothermal approach. The synthesized MoS2 nanoparticles underwent comprehensive analysis utilizing various analytical techniques such as X-ray Diffraction (XRD), Raman spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray spectroscopy (EDX), UV-Visible spectroscopy, and BET surface analysis. XRD analysis revealed the presence of a hexagonal phase structure with a crystallite size of 13 nm, indicating the nanoscale nature of the synthesized material. RAMAN spectroscopy confirmed the presence of characteristic peaks corresponding to Mo and S, validating the composition of the composites. FESEM images shows that the formation of flake like morphology and EDX affirmed the presence of Mo and S elements with the absence of other impurities, ensuring the purity of the MoS2 nanoparticles. UV-visible spectroscopy exhibited an energy band gap of 2.37 eV, suggesting potential applications in optoelectronic devices. BET surface area analysis revealed a surface area of 80 m²/g, indicative of the high surface area of the composites, which may enhance their reactivity and performance in various applications. These findings contribute to the understanding of MoS2 nanoparticles and their potential utilization in fields such as solar cell, catalysis, sensing, and energy storage.

The health insurance facilities for the general public protect our lives and our family members, the public is affected by changes in food culture in society and it causes many diseases and people get the treatment of diseases from private hospitals due to the right financial position of the family. The people can get the treatment under this scheme and the approved hospitals should not provide service for them at the right time right people. This study focused on the following objectives to Identify Counseling by Doctors and To Study Counseling by Specialists to investigate the Treatment Present Technologies identify the Treatment for Diseases and study provide guidelines to Medicine and To study the Toll Free Service Facilities and the Sample simple size is 600 patients are selected for the study in East Krishnagiri districts in Tamilnadu.

Nanoparticles enhance the heat transmission by recovering energy that was dissipated due to increased thermal conductivity. Engine oil, commonly known as a lubricant, facilitates the movement of energy between various substances. The purpose of this analysis is to investigate the impact of the Maxwell fluid and entropy generation on the movement of ternary hybrid nanoparticles as they flow past a Riga plate under convective boundary conditions. The ternary nanoparticles AA7072, AA7075, and CoFe 2 O 4 are combined with engine oil (EO) as the primary liquid. The heat transfer analysis involves the examination of heat sink/source and thermal radiation. The use of a similarity transformation is employed to convert partial differential equations (PDEs) into a dimensionless form. The altered equation is subsequently solved with the homo-topy analysis method (HAM), a robust analytical technique. The current analysis is marked by convergence, which denotes the answer. The impact of flow properties, such as the Maxwell parameter, modified Hartmann number, thermal radiation, and Biot number, on the velocity distribution and temperature profile. Higher values of the Maxwell fluid parameter have been shown to result in an increase in the velocity curve. The skin friction and heat transmission variances are elucidated through the utilization of tables and figures. Moreover, the findings suggest that the thermal layer of the tri hybrid nanoliquid is strengthened in reaction to higher thermal radiation and Biot number. At the same time, the thermal layer is also strengthened.

The swift advancement of heat transfer technologies can be attributed to the growing need for effective heating and cooling systems in various sectors, including the automotive, chemical, and aerospace industries. This work aims to examine the impact of radiation on the behavior of Casson hybrid nanoparticles (Al2O3-CuO) mixed convective flow in three distinct scenarios. The physical properties of copper oxide (CuO) and aluminum oxide (Al2O3) nanoparticles are utilized when mixed with CMC-water as the solvent. This paper aims to analyze the influence of mixed convective flow on the thermal integrity of hybrid nanoparticles when subjected to a wedge, cone, and plate. The analysis of chemical reactions and the existence of a permeable substance is also incorporated. The partial differential systems are appropriately transformed into a system of ordinary differential equations (ODEs). In addition, the calculation of this system of ODEs is carried out using the analytical technique known as the homotopy analysis approach (HAM). The study examines potential resolutions for flow issues in three distinct configurations: wedge, cone, and plate. A comprehensive examination and record of the impacts of various physical characteristics is carried out. The concepts of wall friction, Nusselt number, and Sherwood number, among others, are explained through the utilization of graphical representations. The porosity and Casson fluid characteristics cause a decrease in the performance of the velocity profile. Hybrid nanofluids have superior heat transfer efficiency compared to conventional nanofluids.