Volume 12 | Issue 6 |

 Abstract

The aim of the study was to examine the relationship between socio-economic status and risk-taking ability of college female students, those who were studying in various colleges of Chandigarh. To achieve the aim of the study six hundred (N=600) female students (120 students from each stream) were selected randomly from different colleges i.e., P.G.G.C., Sector-11 Chandigarh, P.G.G.C., Sector-42 Chandigarh, PGGC, Sector-46 Chandigarh, S.G.G.S. College, Chandigarh and G.G.D.S.D. College, Chandigarh. The age of the subjects was ranged between 18-22 years. The selected subjects were from five different streams i.e., B.A., B. Com, B.Sc. Medical, B.Sc. Non-Medical and B.C.A. The analysis of the data was done by using Product Moment Correlation. The results of the study revealed that significant positive relationship between Socio Economic Status and Risk-Taking Ability (r= 0.124) in case of total samples. This relationship was found to be significant as the corresponding p-value (=0.002) was less than 0.01 level of significance. Significant positive correlation was found between socio-economic status and risk-taking ability in B. Com and B.C.A streams. On the other hand, non-significant correlation was observed between socio-economic status and risk-taking ability in case of B.A., B. Sc non-medical and B. Sc medical streams.

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 Keywords

Socio-Economic Status, Risk-Taking Ability, College, Female And Students

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 Abstract

--With the development of technology, online shopping, known as e-shopping, has grown exponentially worldwide today. Advances in electronic shopping have led to a revolution in shopping that allows consumers to shop anytime, anywhere. Although e-shopping has many advantages, its main disadvantage is that it is difficult for online shoppers to try on products, especially clothes. Therefore, the virtual room using AR is proposed to be created as a web application that allows buyers to see how the shirt will look and whether it will suit their body without putting the shirt on. This is done to improve the market for buyers and allow them to try on clothes for size, fit or style. In this development, AR, human body detection and motion tracking are combined, the user's body movements are tracked through the camera, and clothing patterns are adapted to the user's body. Use predictive modeling to identify, analyze and track the position of the skeleton. In addition, Body measurements and garments are analyzed and interpreted to help virtual garments fit the body perfectly[1] . Retailers are looking for better options to improve their e-commerce business amid the spread of the COVID-19 virus. This project will help them achieve their best work with the help of the internet and its extensive resources. This web-based application helps users try on clothes online while sitting at home[3]

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 Keywords

Human body detection, AR, skeleton-joint position, virtually analyzed

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 Abstract

The purpose of this study was to establish the reliability and validity of Tamil version of the Early Adolescent Temperament Questionnaire-Revised (EATQ-R) Parent Report. The original version of the Early Adolescent Temperament Questionnaire-Revised Parent Report which has got 62 questions was translated into Tamil through forward and backward translation. This questionnaire was then sent to experts and their reviews were used to establish the content validity. The Tamil version of (EATQ-R) Parent Report was then administered to parents of adolescents of age 9 to 15. The data collected was used to establish its internal consistency. The internal consistency reliability was determined using Cronbach's alpha. The value ranged from 0.45 to 0.88 with eight out of ten items having a value more than 0.60 showing an acceptable internal consistency. The content validity was established by taking the reviews of experts. It was quantified using content validity index (CVI). Its value was 0.822 indicating a good content validity. The Tamil version of Early Adolescent Temperament Questionnaire- Revised (EATQ-R) Parent Report has acceptable internal consistency and good content validity.

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 Keywords

Temperament, Activation Control, Affiliation, Attention, Fear, Frustration, Inhibitory Control, Shyness, Surgency, Depressive mood, Aggression, Cronbach's alpha, Content Validity Index (CVI).

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Abstract: Culture is the set of social traditions and customs. The form of culture is very extensive and the world is so large that all the population cannot come into contact with each other at one time. Different societies are affected differently by different beliefs, customs, and traditions. Culture includes scientific investigations, achievements, literature and its kind such as drama, poetry, short story, novel etc. Customs, traditions, law, habits, methodology, fashion, festivals are also an integral part of culture along with art, music, dance, sculptures, paintings, religions, moral values, human beliefs, patterns of behaviour, etc. Socially relevant cultural ideas may be classified into four broad categories such as belief, values, norms and technologies. Cultural believes are statements that are assumed to express, fundamental truths, about reality and human life. Cultural values are shared conceptions of what is desirable and undesirable in social life. Cultural norms are shared standards regarding acceptable and unacceptable social actions. Technology consists of knowledge about techniques using material objects or social actions to achieve desired objectives .

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 Keywords

Keywords- Consumer, Indology, Marketing, Behaviour, Culture, Traditions, Motivation, Values etc.

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 Abstract

One area that significantly affects people's quality of life and financial situation is agriculture. Poor management causes agricultural goods to be lost. This method uses a deep neural network, an alternative to a regular neural network, to identify leaf diseases. This makes it simple to identify leaf disease. The user can upload a picture of a leaf first, and the image will be uploaded on the screen. Next, click the button to analyse the image. A leaf's state of health can be determined by the disease and shown. The leaf image shows the presence of the illness. Utilizing an accessible dataset of 1500 images of solid and ill plants, this work characterizes crop species and determines the disease status of three different groups using deep convolutional systems and semi-supervised approaches.

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Image analysis, Semi-Supervised Approaches, Crop Species Classification, Disease Identification.

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This paper presents an enhanced approach for detecting and assessing the severity of COVID-19 from chest X-ray (CXR) images using a modified VGG16 architecture integrated with an attention mechanism. The attention layer, placed strategically after the Conv4_3 layer, enables the network to focus on critical regions, improving sensitivity and specificity. The model was trained on a combined dataset, achieving an accuracy of 94% and a Root Mean Square Error (RMSE) of 0.95 for severity prediction. These results highlight the model's superior performance in distinguishing between COVID-19 infected and normal cases and in providing accurate severity assessments. Comparative analyses demonstrate the model's efficacy over other retrained models, ensuring minimal false positives and accurate COVID-19 detection. This modified VGG16 model represents a significant advancement in medical image analysis, offering a reliable tool for real-time clinical applications in diagnosing and managing COVID-19.

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 Keywords

COVID-19 Detection, Chest X-ray (CXR) Analysis, VGG16 Architecture, Attention Mechanism, Severity Assessment

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Upgrading pictures in low light is a troublesome undertaking that has earned a great deal of revenue. Low-light photography every now and again delivers pictures with poor visual quality. We approach the low-light upgrade as a leftover learning issue, i.e., assessing the lingering between photographs taken in low-and typical light circumstances. We present a one of a kind Deep Lightening Network (DLN) in this review, which profits by the ongoing advances in Convolutional Neural Networks (CNNs). There are numerous Lightning Back Projection (LBP) blocks in the proposed DLN. The LBPs gain proficiency with the lingering for ordinary light gauges by iteratively performing lighting up and obscuring activities. Furthermore, we present a Feature Aggregation (FA) block that adaptively blends the results of a few LBPs to utilize the nearby and worldwide highlights ideally. We test the recommended approach utilizing a few datasets. In view of goal and emotional markers, our recommended DLN philosophy performs better compared to elective methodologies, as per mathematical information.

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Low-Light Image Enhancement, Deep Learning, Residual Learning, Deep Lightening Network (DLN).

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 Abstract

The Laplace transform is very efficient method for solving ordinary differential equations. There are many mathematical models arising in engineering in the form of ordinary differential equations. The motive of this paper is that a scientific review on solving some ordinary differential equations using Laplace transform method.

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Laplace Transform, Linear Ordinary Differential Equations, Initial Value Problem

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Peripheral ossifying fibroma is a benign neoplastic inflammatory hyperplasia that is a reactive proliferative lesion of periodontal tissues that arises mostly from interdental papillae of gingiva in response to chronic mechanical irritation..This case report was focused on a case of peripheral ossifying fibroma involving interdental papilla between 21 & 22 on the labial aspect in a 12 year old female patient.

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Gingiva, Peripheral ossifying fibroma, Reactive proliferative lesion.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This agricultural project leverages advanced sensor nodes, including BME280 and soil moisture sensors, to revolutionize farming practices. Integration of LoRa technology establishes a robust communication backbone for real-time data transmission. The BME280 sensor provides essential weather data, while soil moisture sensors optimize irrigation. The LoRa-enabled system, with a centralized hub, ensures efficient data collection and management. Remote monitoring through a web application empowers farmers, and the ESP8266 enhances connectivity for remote data storage. The incorporation of a weather API enhances environmental data accuracy, offering a holistic approach to precision agriculture.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

LoRa, BME280, ARDUINO, Weather API

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Creative Commons Attribution 4.0 and The Open Definition