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A Comparative Study of Body Image Issues in Young Adult Women and Middle-aged Women

This study delves into the body image issues among women as they progress in their age. Body image is an evolving research field. Body image is the subjective view of one’s appearance. With the intervention of social media and mass media, body image concerns among people are growing and has significantly contributed to behaviors like social comparisons, public avoidance, and causes an array of physical and psychological ailments. The study aims to provide a comparative insight on what factors affect body image concerns among women of different age groups, particularly young adult (18-25 years) and middle-aged (45-59 years) women. The study particularly considers these two age groups because priorities are often different among the two age groups. It would therefore be interesting to investigate what specific body image related concerns exist for each group, what factors influence their concerns, how they differently respond to them, and what can be done to help them having a better body image. The study requires basic knowledge of psychology and would be of help to not only academicians but industry too. The beauty, cosmetic and personal care industry is expected to have the maximum advantage of the study as its findings may help them to devise their marketing mix strategies. The study is based entirely on secondary data, and involves a comprehensive reading of journal articles, magazines, newspaper articles, books, and other sources of online data. A systematic review of available literature through PRISMA would be conducted. Research in this domain is generally constrained to studying a particular age group in men and women. Hence, the study would attempt to fill the existing literary gap.


Breast cancer prediction with survival analysis

Breast cancer is a significant health concern worldwide. Its prognosis and survival rate are greatly dependent on timely detection and accurate prediction of the progression. Many prediction models have been developed which take into consideration genomics, racial disparities, and tumor characteristics. However most of them focus on short-term outcomes. Long-term follow-up studies that assess breast cancer recurrence, late-stage complications, and survival beyond the initial treatment phase are essential for providing a more comprehensive picture of patient outcomes.

This study first reviews critical research which has been conducted in the past on breast cancer prediction and identifies their shortcomings. It also identiies the distribution pattern and risk factors. Then it uses two existing breast cancer datasets with over 1000 observations each, containing important variables such as demographics, tumor size, omics data, mutation count, cancer type, duration of treatment, among others. Survival analysis is applied to identify independent predictors of breast cancer survival, considering factors such as tumor characteristics, treatment modalities, and patient demographics. Furthermore, machine learning algorithms are employed to enhance predictive accuracy. Python software is used.


Politics and human rights issues in state formation on religion and the Divine Right

Theoretically, the divine rights theory does not apply to the modern concept of state formation. The reason is, that it appears more convincing to believe that the state has formed as an outcome of class struggle. In this, people of similar interests have united together to form a state.  So, there is no place to believe that the state is a result of any religious process or that any sort of state-related leadership is influenced by divine instructions.

However, some political leaders and state heads still apply this divine theory and convince their followers to believe their beliefs and political activities are religiously justified. Thus, the study will draw upon the divine right theory of state formation and use case studies of selected countries to understand how religion is still important in state formation in the 20th and 21st centuries and how it impacts the people and their rights in those states.


Breaking boundaries: multidisciplinary approaches to thesis and dissertation writing

Multidisciplinary research holds paramount importance in today's universities. It is seen to break down traditional disciplinary silos by fostering collaborations between researchers from different disciplines. As our ecosystem becomes increasingly complex with issues like climate change, public health crises, and global recessions, the demand for innovative technological solutions with insights from diverse fields is rising. Multidisciplinary research has become a catalyst for holistic exploration, allowing researchers to integrate varied perspectives and methodologies.
This study provides a streamlined yet comprehensive roadmap for effective thesis and dissertation writing for multidisciplinary research. It starts with guidance on how to select a research topic, reviews groundbreaking past multidisciplinary studies using case studies, dissects challenges faced by researchers in conducting multidisciplinary research, and ends with guidance for researchers looking to pursue it professionally or applying it in real-world settings. Whether a novice or experienced researcher, individuals will find valuable strategies and insights to enrich their multidisciplinary research journey.


Understanding stock reactions to quarterly financial results announcements

This study delves into the dynamics of stock price movements in response to financial results announcements. The objective is to investigate how stock prices of companies are affected on the announcement day and the subsequent seven days following the release of financial results. Using historical stock price data and financial results announcement dates, this study examines whether there is a discernible pattern in how stock prices react to different types of financial results.

The study includes banking and financial services, pharmaceutical, and power, healthcare, and FMCG sector stocks listed on the Bombay Stock Exchange and Nifty-50 indexes. Period of the data is April 2018 to March 2023.  Further, event window considered is announcement day (T), two days preceding the announcement day (T-1 and T-2), and 7 days following the announcement day (T+1, T+2, T+3, T+4, T+5, T+6, and T+7). Multiple statistical analysis methods are applied, such as trend analysis, T-Test and Anova using Python.


Research and Publication Essentials

In today's world, research competency is required to properly assess articles, cite relevant sources, generate high-quality work, and comprehend publication requirements. This module aims to provide learners with the necessary skills and knowledge to succeed in their research and publication activities. The study topic chosen for examination is "How do modern Indian authors portray the emotional experiences of disabled individuals?". Participants will improve their academic skills and scholarly impact by learning the fundamentals of research and publication.


Improving learning outcomes using systems thinking

Systems around the world have become complex and dynamic, meaning that they are made of many components and are changing continuously. The education system is complex as it is made up of multiple interconnected elements like industrial expectations, government regulations, technological innovation, and teaching methods. It is also dynamic because these factors are constantly changing, affecting the whole system and ultimately, learning outcomes.

But learning outcomes are facing a massive challenge in the form of high dropout rates, poor academic performance, and a widening gap between industrial requirements and skills available. This study proposes the concept of systems thinking as a solution to these problems by recommending a non-linear and holistic approach to addressing problems in the education system. It explores case studies of educational institutions that have applied systems thinking in the past and uses the findings to suggest models which can be implemented in order to improve learning outcomes for all higher education institutions.


Performance optimization of MANET networks through comparison of Routing protocols

MANET (Mobile Ad Hoc networks) are the wireless networks which work without any specific infrastructure. MANET routes make use of nodes without a central access point. These nodes witness constant movement and sometimes the information flows from multiple nodes, causing congestion in networks. Therefore, to control the congestion issue and reduce unpredictability from MANET networks, there is a need to build an efficient routing protocol.

Many routing protocols have been built and applied over decades. They were either proactive or reactive. However as the complexity of networks grew, hybrid networks started being developed. Today there is a vast array of hybrid routing protocols in MANETs. Academic research has compared proactive and reactive routing protocols to find out which one is most efficient for communication. However, there has been little attention given to hybrid routing protocols. Such a comparison is warranted to assess different routing protocols' limitations and identify a more advanced method of addressing mobile sensor network traffic issues. The protocols could be simulated using a Network simulator (NS3 or NS2).

The purpose of this module is to compare hybrid, proactive and reactive routing protocols in MANET. The performance metrics for comparison of the routing protocols will be throughput, packet delivery ratio, packet loss, delay, drop packet, and routing overhead. The module focuses on a selection of optimal protocol based on the criteria that there is a rise in throughput performance, routing overhead and packet delivery ratio; and a reduction in packet loss, drop packet and delay. The packet delivery ratio should be at least 85%. Routing protocol fulfilling the performance metrics requirement will tend to have the best performance and major role in the transmission of data effectively.


Understanding the profitability problem of businesses due to poor inventory management

Businesses serve as the backbone of the economy yet all of them bear the persistent challenge of deriving profitability which jeopardises their long-term viability. In FY 22, about 10 startup unicorns witnessed huge losses with the highest-making unicorn being Bharatpe with a $726 million loss. Some other unicorns were Flipkart, Meesho, Swiggy, Unacademy, Paytm, Udaan, Phonepe, verse, and Sharechat. The key issue which led to this profitability problem was the lack of insights available for assessing the customer retention rate, costs, product differentiation status, or the medium providing sales to the businesses. Apart from these, factors like lack of cash flow management, less knowledge of technology, operational inefficiencies, and lack of ability to manage business also hinder businesses profitability prospect. Among these issues, a lack of focus on market dynamics emerges as the major concern for businesses. 

This module focuses on the case of Bigbasket company. With millions of consumers relying on the company for daily essentials, the need of Bigbasket has been constantly managing its inventory effectively. As there are many perishable products included in the product line, the procedure of managing inventory becomes more complex and challenging. The process becomes more complicated due to a lack of infrastructure availability for preserving food. Poor inventory management results in many a time having out-of-stock situations, overstocking of less demanded products, and delivery of expired or damaged products to the customer. This results in the failure of the company to secure consumer demand timely and remain competitive in the market.

The objective of this module is to address the issue of poor inventory management by assessing consumer demand patterns and making relevant predictions about the change in demand. The study will use basic and advanced Excel as the tool of analysis for fulfilling the two-fold objective of the case. For this, five datasets were considered:

  1. Market status-based data for a company wherein information about a product sold, its sub-category, quantity sold, region of sale, and profit are present.
  2. Financial data including income statement, cash flow statement and balance sheet of the company.
  3. Different investment options available with BigBasket for improving their performance.
  4. Weekly data about the sold products and their inventory details.
  5. Consumer behaviour wherein the data was collected using a survey.

We use a mix of business market analysis, descriptive analysis, financial statement analysis, regression analysis, sensitivity analysis, and optimization to recommend for empowering businesses to thrive in a competitive business market.


Illegal transactions detection model to prevent money laundering

Money laundering is a multi-billion-dollar issue. Detection of laundering is very difficult. Banks and regulatory authorities struggle to identify these illegal transactions. Not only does it cost them billions in unpaid taxes, but it also promotes crime at the cost of socioeconomic health of a country.

There exist many automated algorithms which aim to detect illegal transactions but most of them have a high false positive rate: legitimate transactions are incorrectly flagged as laundering. The converse is also a major problem --false negatives, i.e. undetected laundering transactions. Naturally, criminals work hard to cover their tracks.

The aim of this module is to utilise IBM’s synthetic dataset of over 10 lakh financial transactions to create a deep learning model for fraud detection. The dataset, which is available on the Kaggle repository, is based on a virtual world inhabited by individuals, companies, and banks. Individuals interact with other individuals and companies. Likewise, companies interact with other companies and with individuals. These interactions can take many forms, e.g. purchase of consumer goods and services, purchase orders for industrial supplies, payment of salaries, repayment of loans, and more. These financial transactions are generally conducted via banks. Using a combination of supervised learning, deep learning, and GridSearchCV assisted models, this module will aim to achieve at least 90% accuracy in identifying illegal transactions.


Beyond the filter: does influencer marketing affect consumer perception?

‘Influencers’ are individuals with a significant online following. Companies leverage their popularity to promote their brand, product or service on social media like Instagram, TikTok, Twitter and Facebook. Social media has emerged as a powerful platform influencing consumer perception because of its role in building connections and sharing information. In this context, ‘perception’ refers to brand image, product trust and credibility, purchase intention, consumer engagement, perceived value, and skepticism. Influencers tie up with brands to create content that discusses its product features via sponsored posts, product reviews, tutorials, and ads.

Survey and interview methods are used to map consumers’ perception to understand how consumers view a brand and whether  they are more like to purchase its product after seeing its recommendations from social media influencers. We use a pre-existing scale to determine the effect. The selected industries are cosmetics, food, clothing and consumer electronics. The findings will help to give marketers insightful information about how to improve influencer marketing campaigns' authenticity and, consequently, strengthen positive consumer perceptions in the dynamic digital age.



Enhancing Specificity and Delivery in Cancer Treatment: A Study on CRISPR/Cas9 Gene Editing Applications

Today cancer is considered one of the top ten most evil diseases in the world. In earlier days chemotherapy, radiation therapy and surgery were the most orthodox treatments for cancer. Nevertheless, although such procedures have recovered countless lives, they all tend to be harsh and severe in their side effects as well as in their lack of specificity. Not only do they destroy normal cells and tissues, but they also often harm patients ' health and quality of life. The relatively recent advent of the revolutionary technology of CRISPR-Cas9 has created a new era of personalized treatment, especially immunotherapy for cancer. However, it has a restricted range of use because of issues of specificity, off-target effects and target-cell delivery.

The first goal of this study is to develop better accuracy and delivery mechanisms for cancer treatments using the powerful gene editing tool of CRISPR-Cas9. The investigation of new gene-editing technologies and delivery systems aims to lower the risks of current methods and fill an important gap in the application of CRISPR technology in oncology.  The second goal is to optimise the delivery system for CRISPR-Cas9 in cancer therapy.

The ultimate objective is to arrive at a more specific, less invasive and immensely more effective cancer treatment regimen. Through improving the accuracy and delivery of CRISPR-Cas9, we aim to give those who suffer from cancer hope in combating this disease with greater precision and fewer side effects.


Study on reducing the AT&C losses of Indian Public Power systems using Smart Grid Infrastructure

India has made commendable strides in enhancing its electrical distribution systems by reducing AT&C (Aggregate Technical and commercial) losses from 23.72% in 2016 to 17% in 2021. However, it is much higher than in countries like the USA, Japan, and South Korea. Technical factors that lead to AT&C losses include aging infrastructure, overloading of transformers and lines, and voltage fluctuations. On the other hand, non-technical factors include theft, inaccurate metering and billing, and lack of billing infrastructure. Its operational factors include inefficient collection processes, inadequate load management, unmetered and unbilled connections,  and voltage instabilities.

The primary objectives of this research are twofold: firstly, to comprehensively review existing methods for monitoring AT&C losses in distribution systems in India, and secondly, to implement a predictive model aimed at mitigating AT&C losses. This model aims to reduce AT&C losses due to theft and pilferage, inaccurate billing, operational inefficiencies, and inefficient collection processes. This model attempts to improve the ‘smart grid infrastructure’ which India has been actively working on for many years. Finally, this study proposes a method to integrate the predictive model into components of the power distribution system in India.


Integrated Design and Simulation of Semiconductor Devices in Atlas TCAD and Cadence Virtuoso

The semiconductor industry continually faces challenges in achieving efficient and accurate design and simulation of semiconductor devices. While Atlas TCAD offers a robust platform for semiconductor device simulation, there is a need to seamlessly integrate this simulation environment with the Cadence Virtuoso design tool. The current lack of integration poses limitations on the efficient translation of simulated device characteristics to the design phase. Therefore, the primary problem addressed by this study is the absence of a streamlined process for linking semiconductor devices simulated in Atlas TCAD to Cadence Virtuoso using a Verilog-a model.

This study aims to develop and demonstrate an integrated approach that not only designs and simulates semiconductor devices accurately in Atlas TCAD but also establishes a seamless connection to Cadence Virtuoso through the implementation of a Verilog-a model. The successful resolution of this problem will enhance the overall design and simulation workflow in semiconductor device development, ultimately contributing to improved efficiency and accuracy in the semiconductor industry.


Making Chatbots more personalized to improve customer loyalty

Making chatbots more personalised to improve consumer loyalty


In a world where businesses are driven by abundance and competition, consumer loyalty has become an invaluable tool for survival. Not only does it ensure continuity in sales, but it also reduces cost of acquiring new customers via word of mouth. However consumer personalities are constantly evolving. Numerous personality models have emerged in recent years to navigate the complexity of personality traits, especially to understand their influence on consumer behaviour.

This study takes into account the 16 personality factors model to understand how chatbots influence consumer loyalty in modern businesses. Preliminary investigation shows that chatbots affect consumers’ purchase behaviour and overall experience with a brand. In this relationship, personality factors act as the mediating variable which invariably influences the outcome of their interaction with chatbots. Therefore personalising chatbots is expected to offer companies immense benefits by maximising consumer loyalty. A recommendation model for personalising chatbots is therefore proposed.

Tags: consumer loyalty, personality factors, literature review, systematic review, marketing, sem analysis, mediating variable, mediation analysis, spss amos, research methodology, survey analysis


Analysis of the connectivity between the banking and other financial service companies

In today’s complex financial world, it is important to have strong knowledge of “spillover effect” of macroeconomic events, i.e., how they affect the banking sector. “Banking sector” in this context includes not only traditional banks but also financial subsidiaries such as insurance, non-banking financial institutions and microfinance organisations. This knowledge helps them make sound risk management decisions. This study aims to detect the spillover effect of macroeconomic events on the India’s banking sector and its subsidiaries by considering stocks listed on the Bombay Stock Exchange (BSE). For this, the first goal is to review existing research and critically appraise previous studies’ findings to find critical knowledge gaps.

The second goal is to assess banking and other financial subsidiaries’ interconnectedness using the TVP-VAR model. Collecting the data from BSE for 1st January 2005 to 31st December 2023, and for variables like opening price, closing price, macroeconomic indicators (inflation, GDP, or exchange rate), and financial ratios (P/E or ROI), the study initially determines the spillover effect among banking sector companies. Following this, the interconnectedness between banking and other financial subsidiaries too will be examined using R software. The findings of the study will reveal the company that held a dominant position in the banking sector during major macroeconomic events from 2005-2023 (like the oil crisis of 2007, the environmental crisis of 2011, COVID-19, or the Russia-Ukraine war). It also identifies the financial sector (banking or any other financial subsidiaries) which drives the financial status of India amid the crisis.


Recruitment and retention challenge for meeting workplace diversity and inclusivity laws in India

Contemporary businesses are evolving at a pace that has never been witnessed before, and one of the most critical changes taking place is diversity in the workplace. Organizational HR policies promoting inclusivity and diversity aim to engage a diverse workforce, opitimise their productivity and maximise their commitment towards the company. However,  organisations encounter several challenges in recruiting, retaining, and managing a diverse workforce. It thus becomes pertinent to identify strategies that have been implemented around the world in recent years to this effect.

Some countries like India have also introduced government policies to promote workplace diversity. In India several workplace laws protecting the rights of minority sections like women, LGBTQ+, persons with disabilities, and persons belonging to protected categories have been introduced in recent years. However, the problem is that medium and small-scale companies are unacquainted with such laws, while large-scale companies have reported difficulties in meeting the goals. This study aims to understand the recent HR policies introduced by India’s large and medium scale companies to adhere to the recent diversity laws and the challenges faced in this regard.


Impact of Digitalisation on Dubai's retail business due to COVID-19

Technological advancements, diffusion of technology among the population, competitive pressures, and evolving consumer behaviours and attitudes have disrupted the food retail business.  There has been a shift from the conventional business module that impacts all stakeholders in the industry. Some of the ill impacts of virtualization have been identified to comprise businesses facing losses leading to economic imbalances and job cuts. As Dubai is also going through a rapid transformation based on the adoption of technologies amidst the pandemic of COVID19, this study attempts to investigate food retailers ‘condition during and post the pandemic. It also aims to understand the extent of digital disruption in the retail industry and the problem with stakeholders in the transition to virtualization and to sustain the market.

The current study is significant in understanding the overall impact of digital transformation and organizations' perspectives on dealing with it. We will survey customers and supervisors of food retail stores across Dubai. We intend to find out the extent of digitalisation adopted by these stores and the impact it has had on customers' shopping experience. Based on the results, recommendations will be made to allow firms to navigate through digital disruption successfully and attain business success.