Volume 12 | Issue 5 |

 Abstract

In this project, the turning of Steel SM45C, used for propeller shafts, will be performed on a lathe machine with a carbide insert tool. The goal is to optimize Surface Roughness and Material Removal Rate (MRR). Factors such as cutting speed, feed rate, and depth of cut are input parameters, while cutting force and tool vibration are responses. Experiments use 20 mm diameter and 100 mm length bars, based on an L9 orthogonal array design, with three process parameters. The Taguchi method and Analysis of Variance (ANOVA) are employed to analyse the data in MINITAB 21. This approach predicts optimal machining parameters and confirms results through a confirmatory test, highlighting the significance of these parameters on the response variables.

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 Keywords

Taguchi method, Orthogonal array, S/N Ratio, Process parameter, Surface roughness, Material Removal Rate (MRR), ANOVA (Analysis of Variance).

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 Abstract

The Vehicle Parking and Service Centers Locator app is a convenient solution designed to assist users in finding nearby parking facilities and automotive service centers. The app utilizes geolocation services to pinpoint the user's location and provides a comprehensive list of available parking lots and garages in the vicinity, along with relevant details. The app offers functionality to locate nearby service centers for vehicle maintenance and repairs. Users can search for service centers based on their specific requirements, such as car make, service type, and distance

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 Keywords

Vehicle, Parking, Service centres, Location.

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 Abstract

Heart disease is a significant global health challenge, and accurate prediction of cardiovascular disease is essential for effective clinical intervention. Leveraging machine learning (ML) techniques, we have developed a predictive model that incorporates advanced feature engineering. Through meticulous model tuning, we achieved a remarkable accuracy of approximately 92.7%. Our approach demonstrates the effectiveness of feature engineering and model tuning in enhancing the accuracy of heart disease prediction.

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 Keywords

Machine learning, heart disease prediction, feature engineering, feature selection, prediction model, classification algorithms, cardiovascular disease (CVD).

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A bio synthesis or Green synthesis of copper nanoparticles (CuNPs) is an interesting aspect in the field of these technology and is also inexpensive method for nanoparticles biosynthesis. But inrecently utilization of secondary metabolites from plant leafage excerption has emerged as a new technology for the synthesis of various nanoparticles. we are focused on green synthesis,the synthesis of copper nanoparticles and comparison the chemical process in CuNPs/peroxodisulfate (PDS) and CuNPs/peroxomonosulfate (PMS) process for degradation of Methyl Orange (MO). copper nanoparticles were synthesized by the Liquid leaf excerpt of Azadirachta indica (Neem). In Green synthesis Dye, initial pH and high temperature rapidly promoted the degradation of molecule. The degradation of MO in CuNPs/ Peroxosulfates system is sculptured as pseudo-first order mechanics and parameters were also determined. Sulfate radicals (SRs) (SO4%-) were also identified with copper as oxidative species using circumstantial alcohols. Moreover, nanoparticles of copper oxide are characterized through UV-Vis spectra, FTIR, XRD In the present study the formation and stability of the reduced CuNPs was monitored by absorbance spectra of UV-visible photometer at different stages during the synthesis process. Metal nanoparticles in the solution were monitored by UV-Vis spectro photometer analysis. Their peaks of CuNPs were determined with size and crystal shape and characterization. Apply green synthesis of CuNPs to enhance oxidation capacity of peroxosulfates for degradation of MO is a novel, efficient, promising and eco-friendly process and it does not require costly chemical agents.

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 Keywords

green synthesis, neem extract, azadirachta indica, CuNPs

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 Abstract

The main objectives was to analyze the working capital management and financial trend series with special reference to Tagros chemicals India limited. A efficient working capital management is imperative for sustaining competitiveness and ensuring financial stability in the chemical industry. This paper examines the intricacies of working capital management in the context of chemical companies, focusing on strategies to optimize liquidity, mitigate risks, and enhance profitability. Through a comprehensive review of literature, case studies, and industry insights, key factors influencing working capital management in the chemical sector are identified and analyzed. The study highlights the significance of effective inventory management, prudent credit policies, and streamlined cash flow management in maximizing operational efficiency and minimizing costs. By implementing tailored working capital management strategies, chemical companies can navigate the complexities of the industry landscape, capitalize on growth opportunities, and achieve enduring success.

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 Keywords

Working capital management, Inventory management, Financial stability, Cash flow management.

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 Abstract

Sugarcane cultivation in India faces challenges that impact farmers' economic viability and sustainability. This study examines issues such as labour shortages, lack of high-yielding varieties and fertilizers, stagnant yields, and pest problems. Using Stratified Random Sampling, 640 farmers from Krishna District, Andhra Pradesh, were surveyed. Data analysis shows significant challenges including labour shortages and poor access to quality inputs. Solutions include modernizing farming practices, improving input availability, implementing pest management, and promoting diversification. Supportive policies, better market access, and credit facilities are also crucial for enhancing farmers' economic conditions and sustainable agricultural development.

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 Keywords

Sugarcane cultivation, problems, labour shortages, high-yielding varieties

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 Abstract

In this paper, we introduce a Sanitization Robot Vehicle with UV light prototype using Arduino technology. This project outlines the changing landscape of robotics within healthcare and related fields, focusing on addressing the challenges posed by the novel corona virus disease. The primary objective of these robots is to reduce interpersonal contact while enhancing cleaning, sterilization, and overall support in medical settings like hospitals and quarantine facilities. By doing so, they play a crucial role in safeguarding medical personnel and actively contributing to pandemic management, thereby minimizing risks to their lives. The robot is outfitted with a range of sensors, UV-C light and disinfectant sprayers that enable it to detect high-touch surfaces and implement sanitation procedures efficiently.

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 Keywords

Arduino, DC Pump, Disinfectant sprayers, Sanitization robot, UV-C light.

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 Abstract

Premchand ka shaitya ,shahityatik visheshtaye ev jivan mulyo ka vishleshnatmak adhayan

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Premchand ka shaitya ,shahityatik visheshtaye ev jivan mulyo ka vishleshnatmak adhayan

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Creative Commons Attribution 4.0 and The Open Definition

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In the world of meteorological forecasting and atmospheric analysis, the integration of deep learning techniques has ushered in a transformative era, particularly in the analysis of satellite images. This study focuses on leveraging cutting-edge neural network architectures, such as Convolutional Neural Networks (CNNs), to extract intricate patterns, spectral data, and spatial features from satellite imagery. The primary goal is to facilitate precise estimation and forecasting of various weather parameters, including cloud cover, precipitation, and temperature fluctuations. The proposed system follows a systematic approach, encompassing phases such as image uploading, pre-processing, image processing, feature extraction, classification, similarity measurement, and weather prediction. Utilizing the Waterfall Model in the Software Development Life Cycle (SDLC), the project emphasizes rigorous testing, deployment, and maintenance for robust and reliable performance.

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 Keywords

Weather Prediction, Cloud classification, Content Based Image Retrieval, Convolutional Neural Network, Deep Learning, Data Preprocessing, Forecasting

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 Abstract

To evaluate the effects of Bergia capensis and Acalypha communis extracts on rats with diabetes brought on by streptozocin. The Euphorbiaceae family member Acalypha communis has historically been used in the treatment and/or management of a wide range of illnesses, including diabetes, jaundice, hypertension, fever, liver inflammation, dysentery, and respiratory issues. Bergia capensis is a tiny, annual herb that thrives in moist environments. It is a member of the Elatinaceae plant family, which is frequently found in seasonally flooded parts of tropical and subtropical countries. It is utilised in traditional medicine and folk rites. 42 male albino rats, weighing between 250 and 300 gms, were allocated into seven groups at random (n = 6). Both the aqueous and the ethanol extracts were diluted in tween 80 and then administered orally for a total of 14 days. On days 1, 7, and 14 of the treatment plan, blood samples will be taken by puncturing the retro orbital plexus while under mild ether anaesthesia. The estimation of glucose was done using plasma, while the estimation of the lipid profile, SGOT, SGPT, ALT, DB, TB, and creatinine was done using serum. The mean SE was used to express every piece of experimental data. The one-way analysis of variance (ANOVA) was used to examine the data. P values at or below the 0.05 threshold were deemed statistically significant. Liver enzymes such ALT, AST, ALP, and bilirubin were assessed in albino rats to examine the role of liver markers in diabetes mellitus (DM). The liver indicators SGOT and SGPT are strongly elevated in diabetic control.

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 Keywords

Acalypha communis, Bergia capensis, diabetes, and liver markers

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Creative Commons Attribution 4.0 and The Open Definition