Investigating the Motivational Differences for Healthy Eating in Men and Women

The study aimed to measure the differing levels of intrinsic and extrinsic motivation for healthy eating behaviors in men and women. Through social media outreach, a sample of 57 participants (n=57), aged 18-69, living across the United States, primarily in the midwestern area, completed an online survey. The Motivation for Healthy Eating Scale (MHES) assessed different subgroups of internal and external motivation for healthy eating. Five of the six subgroups were used in the online survey sent to participants (intrinsic motivation, integrated regulation, identified regulation, introjected regulation, and external regulation). An independent samples t-test was performed to assess the differing MHES intrinsic and extrinsic motivation results between the male and female participants. Results indicated no statistical significance between gender in four of the five MHES subgroups: intrinsic motivation (p = .163), integrated regulation (p = .866), identified regulation (p = .309), and introjected regulation (p = .151). Extrinsic regulation was the only subgroup with significant results (p = .035). A paired samples t-test was also performed to evaluate the MHES results within men and women separately. Both tests indicated no statistical significance between the differing types of motivation in men and women (p = .122, p = .140, respectively). The present study suggests that there are mostly no significant motivational differences for healthy eating between and within men and women. However, the study does suggest that there is significance in differing levels of external motivation between men and women for healthy eating. Further studies conducted on this subject should consider focusing on a young adult population in order to account for social media internal and external influences on healthy eating motivation

The Impact of Social Media on Mental Health: A Systematic Review

Social media has become an integral part of modern society, especially among young people. However, its effects on mental health are still unclear and controversial. This paper aims to provide a comprehensive and critical overview of the existing literature on the impact of social media on mental health. We conducted a systematic review of studies published from 2010 to 2020, using the PRISMA guidelines. We searched six databases and identified 45 relevant articles that met our inclusion criteria. We categorized the studies into four themes: (1) the association between social media use and mental health outcomes, (2) the mediating and moderating factors that influence this association, (3) the potential mechanisms that explain how social media affects mental health, and (4) the interventions that target social media use to improve mental health. We found mixed and inconsistent evidence on the relationship between social media use and mental health, depending on the type, frequency, duration, and quality of social media use, as well as the individual and contextual factors that shape this relationship. We also identified several psychological and neurobiological processes that may underlie the impact of social media on mental health, such as social comparison, self-esteem, social support, cyberbullying, dopamine reward system, and sleep quality. Pressure Washing Salem Oregon Finally, we discussed the challenges and limitations of the current research, and suggested some directions for future studies and implications for practice

Autodocking Studies of Oxygenated Fullerenes as Inhibitors of The HIV Protease

In a previous study, several oxygenated fullerene compounds were produced by ozonation of C60 (Buckminsterfullerene) and were identified by elemental analysis and by SALI (surface analysis by laser ionization). Some of these compounds, especially a batch, SK-5, a mixture of C60O1-8, were shown to inhibit activities of HIV-Protease with IC50 (concentration for 50% inhibition) of 1 mg/mol in the in-vitro studies. The oxygenated fullerenes were shown to have epoxide, ketones, and hydroxyl functionalities. As expected, C60 interacted with ozone with alkene functionality and not as an aromatic compound. It was postulated that C60O had epoxide functionality, as a product of ozonation of one of the double bonds of C60; C60O2 had ketone functionalities by Ozonolysis of one of the double bonds of C60; C60O4, C60O6, C60O8 had ketone functionalities by Ozonolysis of successive double bonds of C60. The hydroxyl functionality was likely produced due to interaction of water and the oxygenated fullerenes. In the present study, the interactions of oxygenated fullerenes (small molecules) with HIV-Protease (macromolecules) at the molecular levels were studied via blind docking using AutoDock Vina software to elucidate the mechanism of these interactions. The structures of C60O, C60O2, C60O4, C60O6, C60O8 were generated using Spartan software and were docked with a crystal structure of the HIV-Protease (PR) obtained from The Brookhaven Protein Data Bank using AutoDock Vina. These docking studies showed that the oxygenated fullerenes bound to multiple sites of the HIV-PR with high binding affinities (-10.4 to -8.1 kcal/mol). Nine docking poses were generated for each structure, and the models of each conformation were studied using PyMOL software. The docking models in PyMOL suggest that the high binding affinity is a result of the abundance of strong hydrogen bonds ~2.3Å long across the various C60OX structures. The presence of dipole-dipole and van der waals interactions were also found to have played a significant role in binding, with several conformations exhibiting strong binding (≥ -9.8 kcal/mol) with no hydrogen bonds. Results indicate a strong correlation between the ability of C60OX to produce hydrogen bonds/strong dipole interaction with the HIV-PR and inhibition efficacy

Faculty University Service Roster - 2023-2024

A list distributed by Faculty Senate of members of the governance body and affiliated committees

Exploring Students Perceptions of Learning During and after the COVID-19 Pandemic

The coronavirus pandemic (COVID-19) has caused an immediate transition to remote learning among all academic institutions (Doorn et al., 2022). According to Madrigal and Blevins nearly 32% of students feel as if an online learning environment was not sufficient for their learning experience (2022). This review of literature aims examines learning perceptions of college undergraduate and graduate level students during and after the COVID-19 pandemic. The work also explores student perceptions of learning in regards to meeting expected learning outcomes in multiple modalities, as well as the possible environmental influences impacting student learning outcomes

FundRaiser

FundRaiser website is an online network that helps individuals and organizations to help the nonprofit technology sector grow. FundRaiser website used for personal fundraising to help cover medical, tuition expenses or any other worthy cause. The project will go through the whole phases of Software Development Life Cycle (SDLC), to create a website with high quality and low cost in the shortest time, the CDLC phases are Requirement analysis, Planning, Software design such as architectural design, Software development, Testing and Deployment. The website design will include 3 user roles: the users who start campaigns need to set up an account with the website. And donors who have access to the fund-raising page and administrator who manages users and campaigns. May earn a commission. The website will be created using ASP.NET framework using C#

Classification of Online Toxic Comments Using Machine Learning Algorithms

This application classification of online toxic comments using machine learning algorithms project was implemented for the Graduate Capstone Seminar Project for the Master of Science Degree with a Major in Computer Science. In this project, different Machine learning algorithms are used to find toxic comments. In discussions, toxic comments are disrespectful and abusive which makes other people leave the discussion. So, many social networking sites difficult to promote discussions effectively. The main aim of the project is to examine the data of online harassment and classify it into different labels to find toxicity correctly. In this project, we are going to use six machine learning algorithms, apply them to our data and find which algorithm is best by analyzing evaluation metrics for toxic comments classification. We will aim to examine the toxicity with high accuracy to limit its adverse effects and help organizations take the necessary steps

Evaluating the Effectiveness of Cyber Security Regulations

The rapid advancement of technology has led to an increase in the volume and sensitivity of personal and professional data stored and shared online. As a result, there is a growing need for effective cyber security regulations to protect against data breaches and ensure the confidentiality and integrity of sensitive information. The Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI DSS), and General Data Protection Regulation (GDPR) are three such regulations that have been implemented to address this need. This thesis aims to evaluate the effectiveness of these regulations in protecting sensitive information and preventing data breaches. A comprehensive literature review of existing research on the topic is conducted, and case studies of the implementation and enforcement of these regulations are analyzed. The study finds that while these regulations have been successful in raising awareness and establishing standards for cyber security, there is still room for improvement in their implementation and enforcement. Additionally, the study identifies the challenges and limitations in evaluating the effectiveness of cyber security regulations. Finally, recommendations for future research are provided in this area. The study concludes that while these regulations are important steps towards improving cyber security, more research is needed to fully understand their effectiveness and potential for improvement

Profiles of Academic Majors - 2023

The Profiles of Academic Majors (PAM) is an annual publication that provides a ten-year longitudinal snapshot of enrollments, degrees awarded and student demographics arranged by fall term or academic year as appropriat

Computer Vision for Robot Using AI

Robotics is a multidisciplinary field that involves the design, construction, programming, and operation of robots. Robots are mechanical or electromechanical devices that are outfitted with sensors, processors, and actuators to carry out tasks autonomously or under the direction of a human. Robotics offers several benefits and uses in a variety of fields and industries. Robots can carry out repetitive activities with great accuracy and reliability. They can produce products with consistency in quality by performing operations with extreme accuracy. Our anticipated approach merges traditional methodologies with forward-thinking innovations. Central to our design philosophy is the integration of tools such as Android Studio, GitHub, Git, and the Java programming language. We propose a robot with a constrained starting volume of 18x18x18 inches and a material flexibility of up to 0.25 inches. Integral to its design will be a web camera to enhance navigation capabilities, with the inclusion of April Tags for a comprehensive understanding of the game field\u27s layout. The control system, a cornerstone of our design, is expected to feature an Android device interlinked with two team controllers, a driver station, and a Wi-Fi-enabled robot controller, with potential interfacing through the REV Robotics Expansion Hub or the REV Robotics Control Hub. Our strategy envisions a robot capable of navigating the competition\u27s multifaceted challenges, including a 30-second autonomous period, a two-minute driver-operated phase, and a climactic 30-second endgame. For robot manipulation learning, we provide a vision-based architectural search method that identifies relationships between high-dimensional visual inputs and low-dimensional action inputs. Our method automatically creates structures as it learns the task, coming up with fresh ways to associate and attend picture feature representations with actions and features from earlier levels. Comparing the obtained new architectures to a current, high performing baseline, they show superior task success rates, sometimes by a significant margin