Volume 12 | Issue 5 |

 Abstract

This research provides a detailed performance analysis of banking stocks, focusing on their market behavior, financial stability, and investment appeal. Using quantitative methods such as time-series analysis, and risk assessment models, we investigate the historical and current performance of selected banking stocks. Additionally, it evaluates the influence of macroeconomic factors and industry-specific elements on stock performance. The results reveal notable variations in the risk-return profiles of various banking stocks, offering essential insights for investors aiming to enhance their investment strategies. This research offers a comprehensive view of the determinants impacting the performance of banking stocks, serving as a valuable resource for investors, financial analysts, and policymakers engaged in the financial markets.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Performance analysis, banking stocks, financial stability, investment potential, time-series analysis, risk assessment, macroeconomic factors, industry-specific elements, risk-return profiles, investment strategies, financial markets.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Employee benefits and welfare provides wide ranges of offerings to the employees by their employers, beyond just their paycheck. These offerings are designed to make better the overall quality of work-life, promote employee health and wellness, and provide financial security. It ranges from the healthcare benefits to retirement plans, from flexible work arrangements to professional development opportunities, and a comprehensive benefit package plays a crucial role in attracting required talent, to retain employees, and a major role in increase in productivity

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Employee benefits , welfare programs

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

???????? ?? ???? '????' ????? ????? ?? ????? ?? ????? ??? ??? ???? ???? ??? ?? ???? ??????? ????? ??? ??? ???? ?? ????? ?? ?? ????? ?? ??? ????? ???? ?????? ????????? ???? ????? ?? ????? ?? ? ??? ?? ?????? ?? ???? ???? ????? ??? ?? ??? ???? ?????? ??? ???? ????? ?? ???????? ???? ????? ??? ??????? ?? ?????? ????? ??? ????? ?? ???? ?? ?????? , ??????? ??????? , ????? ????? ?? ?????????? , ?????? ???? , ??????? ?????? ???? ????? ?? ?????? ????????? , ??????? ?? ????? ?????, ??????? ???? ?? ???? ,???????? ?? ????? ?? ???? ?????? ????? ??? ????? ??? ???? ???? ???? ?????? ?? ??? ????? ???? ?? ?? ? ??????? ?? '????' ??? ??? ?????? ?? ??? ?? ??? ?? ?? ?????? ???? ?? ?? ???? ???? ????? ???? ?? ? ??? ???? ?? ?? ?????? ??? ?? ???? ??? ???? ??? ???? ?? ???? ?? ???? ?? ?? ????? ??? ?????? ???? ??? ???? ?? ? ??????? ?????? ??? ?? ???? ?? ??? ??? ?? ? ??? ?????? , ????? ,????????? ?? ???? ????? ?????? ??????? ????? ?? ????? ?? ????? ???? ?? ?? ???? ?? ?? ?????? ???????? ??? ??? ??????? ?? ? ????? ????? ?? ???????? ???? ?????? ,????? ????????? , ????? ????? , ?????? ???? ??? ???????? ?? ??? ????? ?????? ?? ???? ???? ???? ??????? ?? ? ??? ?? ???????? ??? ???? ?? ????? ???? ????? ????? ??? ???? ????? ?? ?

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

????? ?? ???? ?? ?????? , ??????? ???????, ??-????????, ??????? , ????????? ,??????? ???? ???? ?

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kota stone, a fine-grained limestone primarily found in Rajasthan's Kota, Jhalawar, and Chittorgarh districts, generates a significant amount of waste during its cutting and polishing processes. This waste, known as Kota stone slurry, presents environmental and health risks if not managed effectively. Recent studies have highlighted the potential of integrating Kota stone slurry into mortar mixes, offering substantial benefits. Initially, the incorporation of Kota stone slurry in mortar led to improvements in gradation and workability, particularly up to a 30% replacement level, resulting in significant water savings. Furthermore, enhancements in compressive strength, flexural strength, and tensile strength at 28 days were observed, reaching a peak at a 40% replacement threshold before experiencing a slight decline. It is noteworthy that the adhesive strength of mortar reached its maximum at the control mix but gradually decreased with increasing levels of Kota stone slurry replacement. In addition, mortars containing slurry exhibited higher water absorption rates compared to conventional river sand mortar, attributed to Kota stone slurry's superior water absorption capacity. In terms of durability, the mortar blend with a 40% sand replacement by Kota stone slurry demonstrated exceptional stability, making it suitable for construction in acidic or sulfate-rich soils and industrial areas with elevated pollutant levels. Overall, the findings underscore the potential of integrating Kota stone slurry into mortar mixes as a constructive solution, particularly in mitigating environmental impact and enhancing the performance of construction materials. Moreover, the potential use of marble powder as a sand substitute, up to 50% by volume, offers an alternative approach for environments not exposed to sulfate and acidic conditions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Kota Stone, Slurry, Durability, cement

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Heart Disease poses a major risk to humans, and late diagnosis often affects a patient's chances of survival. Factors such as age, gender, cholesterol levels, diabetes and heart rate can affect heart problems. However, due to the many variables, it is difficult for professionals to evaluate each patient individually. The medical industry produces a lot of information about patients and diseases every day. But doctors do not use this information well. Predicting heart disease requires complex and large amounts of data that cannot be processed and analyzed with modern tools. Today healthcare industry is rich in data but not using the data efficiently. There are many machine learning techniques and tools that can be used to extract useful information from data and use this data for accurate diagnosis and decision making. This study provides a solution that uses machine learning to predict heart disease in patients. "Heart Disease Prediction System" is based on predictive modeling and uses five algorithms to predict the occurrence of diseases based on user symptoms: KNN, Random Forest, Naive Bayes, Logistic regression, and decision tree classification algorithms. This cardiovascular disease prediction can improve health and reduce costs. This study highlights the importance of predictive models in measuring and reducing cardiovascular disease in the population.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

?? ??? ?? ????? ???????? ??? ????? ?? ????????????? ?? ?????? ????????? ?? ??????????? ?? ??? ????? ?? ????? ?? ??????? ?????? ?? ??? ?????????? ?? ?????? ?? ?????? ???? ??? ?????? ????????? ?? ??????? ?????? ???? ????????? ???????, ???????, ??????????, ???????-????????, ???? ?????? ?? ??????????? ?? ???????? ???? ??? ??? ?????? ??? ???? ??? ?? ?????? ????????? ?? ??????????? ?? ??? ????? ?? ????? ????? ???? ???????? ?? ??? ??????????? (6.23) ?????? (5.86) ?? ????? ??? ???? ???? ???? ??????? ????????? ?? ???????? ?? ????? ?? ???????? ?? ?? ?????? ??? ?? ?????? ?? ???????? ?? ??????????? ?? ???? ??? ?????????? ???? ???? ??????? ?????? ?? ??? ?????????? ??????? ?? ?????? ????????? ?? ??????????? ?? ????????? ??? ?? ???????? ???? ???, ????? ?? ???????? ??? ???? ?? ???? ?? ??????? ??? ?? ??? ?? ?????? ?? ???????? ???? ??????????, ???????? ?? ????????? ?? ?? ????? ??? ?????? ?? ???? ??? ?? ???? ??? ?? ??????? ?????????? ?????? ?? ?????? ????????? ?? ??????????? ?? ?????? ???? ??? ?????????? ?????? ???? ???? ????

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

NA

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research project focuses on evaluating the impact of incorporating silica fume and steel fiber into concrete to enhance its performance. Silica fume, a fine material, is added to improve the workability and strength of the concrete, while steel fiber is used as a partial replacement for cement. The study involves testing different concrete mixtures, including control and variation mixes, to assess the effects of silica fume and steel fiber on the compressive strength, flexural strength, and splitting tensile strength of the concrete. The research findings indicate that the inclusion of silica fume and steel fiber resulted in concrete with increased strength and durability compared to traditional concrete, suggesting potential benefits for both performance enhancement and environmental sustainability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Concrete, Silica Fume, Steel Fiber

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The project titled "Solar-Powered Wireless Charging System for Electric Vehicles" showcases a pioneering solution in the realm of sustainable transportation. In this innovative endeavor, a solar car has been meticulously designed, leveraging the abundant energy of the sun for both propulsion and extending support to other electric vehicles. The solar car is equipped with a 6V solar panel, charging a 4.5V battery, and driven by four 4V motors, controlled seamlessly by a standard RC transmitter and receiver. A key highlight of this project is the incorporation of a wireless charging system at the car's rear, capable of wirelessly transferring power to other electric vehicles in need. This functionality is particularly useful when encountering stranded or discharged electric cars on the road. In such situations, the solar car automatically identifies the needy vehicle, navigates to its location, and initiates wireless charging, effectively reviving the stranded vehicle and enabling it to resume its journey. This project not only represents a significant advancement in green technology but also addresses a critical challenge in the adoption of electric vehicles--range anxiety. By providing on-the-go wireless charging support, the solar car mitigates concerns about running out of power, thereby bolstering confidence in the viability of electric vehicles for everyday use. The research and development behind this project encompassed solar energy harnessing, efficient battery management, precise motor control through remote technology, and the implementation of cutting-edge wireless charging principles. Furthermore, the system's autonomous response to identifying and assisting other electric vehicles demonstrates the project's practical applicability and real-world impact

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AUTOMATED SOLAR PANEL CLEANING SYSTEM

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Professors play an important role in shaping a student's future. Hence it is important to take care of their mental health. The present research aims to study how different personality traits affect the perceived stress of university professors. For this purpose, data was collected from 53 university professors of West Bengal. The result indicates that the trait of neuroticism, extraversion and conscientiousness is significantly correlated with the perceived stress of professors. However no significant correlation was found between the traits of openness, agreeableness and perceived stress.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

University Professors, Perceived Stress, Big Five Personality Traits

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Cryptocurrency Price Prediction System uses AngularJS and Node.js to offer real-time data on Bitcoin, Ethereum, and Polkadot, providing predictions and recommendations. While data-driven, users should exercise caution in speculative markets.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cryptocurrency, Price Prediction, AngularJS, Node.js, Real-time Data, Bitcoin, Ethereum, Polkadot, Predictions, Recommendations, Speculative Markets, Caution

  License

Creative Commons Attribution 4.0 and The Open Definition