Published Papers

Calendar Anomalies are directly related to the stock market, as, at different times throughout the year the returns of the stock market either increase by a lot or they crash. They are the reasons which affect the investors' behavior to change from time to time. They tend to occur at turning points in time such as during the different weathers, the start of a year, a month, or a week. The January effect, Day of the Week effect, Sell in May effect are some of the most famous calendar anomalies. This paper has been divided into multiple sections which will help us understand the “Sell in May” or the “Halloween Effect'' in a little more detail. In Section II, we will be discussing the calendar anomaly in itself; in Section III we discuss the consistency of this effect over the time; in Section IV we talk about how this phenomenon is a bug puzzle and how it is different from the others; in section V we talk about the possible causes or explanations to this effect; and at last in section VI we discuss about behavioral finance and the psychology behind this effect as well as the other calendar anomalies.

Spinal Cord injury is a devastating neurological trauma/disorder caused by damage to the nerves in the spinal cord, which may result in major motor, sensory, and autonomic dysfunction, along with damage to the functioning of the other organ systems. Due to the limitation of current treatments for SCI, many streams are being persuaded for a possible solution such as neural prosthetics, and brain-computer interface devices, and one of the foremost lines of research is cell-based therapies which comprises of stem cells. A number of clinical trials on stem cell-based treatments for SCI are being conducted across the world. This paper uses the method of systematic review to summarize and review data on the topic and provide accurate fact-based summaries of the information collected. It provides an overview of three trials conducted each at different stages of secondary injury(acute, sub-acute, and chronic), two of them use bone marrow-derived stem cells, and the other used mesenchymal stem cells. All three of them have been declared successful with two out of three having significant, distinct positive results.

Hydroponic farming has garnered considerable interest as an effective and environmentally-friendly approach to cultivate a range of crops, such as spinach (Spinacia oleracea). The concentration of Total Dissolved Solids (TDS) in hydroponic systems plays a vital role in delivering necessary nutrients to plants. This study seeks to examine the impacts of varying TDS levels on the development, productivity, and nutrient composition of hydroponic spinach. By comparing three TDS levels—low, moderate, and high—the aim is to identify the ideal TDS range for optimal growth and yield in spinach cultivation through hydroponics.

The question ‘To what extent is procrastination an emotion regulation strategy’ has left me wondering how procrastination and emotion regulation are even connected to each other! In this paper we will first discuss what procrastination is, then what we mean by emotion regulation strategy, how it is related to procrastination and, at last we will be talking about how we regulate our emotions with the help of procrastination every day without even knowing.

Emotion detection in virtual assistants and chatbots is pivotal for enhancing user experiences and strengthening the bond between humans and AI. This research focuses on infusing emotional intelligence into AI systems, encompassing robust emotion recognition models, contextual response generation, empathetic interaction strategies, ethical guidelines, and user evaluation. Diverse datasets and human evaluators will address potential validity threats. Success will be measured through objective metrics and user assessments, advancing AI technology. Future work may explore multi-modal emotion recognition, long-term user relationships, and ongoing ethical refinements.

Various games can have multiple outcomes, based on the decisions of the various stakeholders or players. In this paper, we created a program (in Python) that finds the type of dominant strategy in 2-player games, using the potential payoffs or utility returns for each of the possible moves a player plays. Specifically, the code was ideally made for a game where each of the players has 2 moves thus 4 total possible outcomes for each player with their own payoffs. We were able to process the inputted data for each of the 2 players through the use of a 2:2 matrix. By comparing payoffs or specific values in the matrix using logical steps and conditions, we were able to determine what move would be most beneficial and effective for each player, along with the type of dominance that move has.