12th IEEE Integrated STEM Education Conference (On Demand Recording available now)
Introduction
Welcome to ISEC 2022
Ashutosh Dutta (Johns Hopkins University Applied Physics Lab)
Session Chair
Ashutosh Dutta (Johns Hopkins University Applied Physics Lab)
Keynote Speaker 1: Sami Kahn
Another ‘M’ for STEM: Moral Considerations for Advancing STEM Literacy
Sami Kahn
Session Chair
Nagi Naganathan (Northrop Grumman)
Keynote Speaker 2: Steve O'Brien
Stories of Integrative-STEM, K-12 and HigherEd
Steve O’Brien
Session Chair
Roger Ding (US Navy)
Keynote Speaker 3: Sharnet Chavis
IEEE- Honoring Your Resilient Spirit
Sharnet Chavis
Session Chair
Ashutosh Dutta (Johns Hopkins University Applied Physics Lab)
Track 1 — Full Papers I
An interdisciplinary approach to high school curriculum development: Swarming Powered by Neuroscience
Elise Buckley (Johns Hopkins University Applied Physics Laboratory, USA); Joseph Monaco (Johns Hopkins University School of Medicine, USA); Kechen Zhang (Johns Hopkins University, USA); Kevin Schultz (JHU/APL, USA); Robert Chalmers and Armin Hadzic (Johns Hopkins University Applied Physics Laboratory, USA); Grace M Hwang (Johns Hopkins Applied Physics Laboratory & National Science Foundation, USA); M. Dwight Carr (Johns Hopkins University Applied Physics Laboratory, USA)
Extracurricular Student-Centered Projects to Learn Computer Programming
Wei Yu, William Haynes and Diane DiMassa (Massachusetts Maritime Academy, USA)
Design and Development of a Smart Cities General Education Online Course for Undergraduates
Mohammad U. Mahfuz (University of Wisconsin-Green Bay, USA)
Design and Development i-AVEN|GER as High-Tech Virtual Remote Teaching and Learning Platform with Experienced Based Learning and Self-regulated Learning Approaches in facilitating STEAM Education
Ken Nee Chee (Universiti Pendidikan Sultan Idris, Malaysia); Noraffandy Yahaya (Universiti Teknologi Malaysia, Malaysia); Mohd Hishamuddin Abdul Rahman (Universiti Pendidikan Sultan Idris, Malaysia); Rafiza Abdul Razak (University Of Malaya, Malaysia); Nor Hasniza Ibrahim (Universiti Teknologi Malaysia, Malaysia)
Automated Car applying Artificial Intelligence
Satyam Mishra (Vietnam National University - International School, Vietnam)
Session Chair
Mithun Mukherjee
Track 2 — Full Papers II
A Personality Types Research Study Based on Personal Values in an Ethics Course for the Engineering and Computer Sc. Undergraduates
Atma Sahu (Coppin State University, USA)
Motivating Potential of the ESP Course Themes at Russian University of Transport
Natalya V Matveeva (Russian University of Transport & Serpukhov College, Russia); Elena Fedotkina (Russian University of Transport, Russia)
Immersive-Experiential Business-Technology in Simulated Business Cases
Stephen Andriole (Villanova University, USA)
Management Information Systems (MIS) program. Some of the technologies include AI and machine learning, the Internet of Things (IOT), augmented and virtual reality, robotics, 3D modeling and manufacturing, edge and fog computing, blockchain, cryptocurrency and
quantum computing, among others. A technology adoption scenario - which ends with demonstrations of high impact technologies - guides students through an immersive-experiential due diligence process via a simulated business technology adoption case complete with roles, deliverables and outcomes. The adoption of emerging technology is a goal for most - if not all - corporations as they maneuver through The Fourth Industrial Revolution. At Villanova, we've delivered a course on Emerging Business Technologies for several years. But the course was delivered "traditionally" to undergraduates and graduate students. The course has been converted to an immersive-experiential course where students are expected to solve technology adoption problems through role-playing: they are immersed in the case and experience a range of digital technologies. These cases simulate how CIOs, CTOs and other technology leaders must decide how and where to invest in existing (and mostly) emerging business technologies.
Pandemic and Natural Disasters Driving the Need for AI Driven NEXTGen Medical Services
John Lamb (Pace University, USA); David Marimekala (Farmington High School, CT, USA)
Assurance of Learning in Technology Management by Curriculum Alignment to A Professional Body of Knowledge
Andres Fortino and Ming Cai (NYU, USA)
Higher education institutions prepare students with skills that better prepare them for a highly competitive labor market. Aligning the learning outcomes of a program to industry specified knowledge and skills is highly desirable. The recently developed American Management Association Certified Professional in Management (CPM) certification is an important source of industry-based knowledge for incumbent and aspiring managers. We based our assessment instrument on the AMA CPM Body of Knowledge for our assessment.
We researched several questions for this project. 1) do students of management-related majors graduating from a technology management graduate program have adequate management competencies and skills? 2) can an assessment instrument based on an industry derived standard be a useful tool to assist students in having a better understanding of their learning outcomes and progress, and thereby improve their learning? 3) how can such an assessment instrument assist faculty and administrators modify and improve their curriculum?
The curriculum was reviewed, and an assessment instrument created with topical course coverage pertinent to the AMA CPM BOK. The assessment was administered to capstone students, and results were analyzed.
Using the existing curriculum, we found that most students could not pass the assessment and identified many deficiencies in the knowledge domains. The assessment results were sufficiently granular to help faculty modify the curriculum and course content and improve students' acquisition of the required knowledge.
Session Chair
Mithun Mukherjee
Track 3 — Full Papers III
An Integrated Approach to Sustainability-focused Instruction in Undergraduate Engineering Curricula
Mohammad U. Mahfuz (University of Wisconsin-Green Bay, USA)
Acknowledging Perceptions, Behaviors, and Beliefs: Exploring What Faculty Need to Integrate Technology into Instruction in Qatar
Ibrahim M Karkouti (The American University in Cairo, Egypt)
Reading in the Dark - Radar Imaging Demonstrator for STEM Outreach of Autonomous Systems
Michael A Saville, Ryan Ball, Garrett Harris and Sarah Willenbrink-Sahin (Wright State University, USA)
The demo is portable and permits hands-on participation for workshop events. Participants create a radar target of their choosing using a set of custom-made reflectors. Primary school students typically create smiley faces and such and watch near real-time imaging much like a medical image. Older students and parents are encouraged to form alpha-numeric characters with the reflectors but to simulate a dark room by laying a shroud over the target. In addition to watching their character appear in the image, the visitor also witnesses the computer AI read the character. The system is described with examples of how the demo uses its different stages to support STEM outreach to a wide variety of students. Lastly, future developments to make the system read words and learn more complicated shapes are discussed.
A New System for designing a 'Student Aide' Application
Ira Nath (JIS College of Engineering, India)
Project-Based Exploration of Cluster Computing and Parallelization Using Raspberry Pis
Taylor R Powell, Ayman Elmesalami and Soad Ibrahim (Old Dominion University, USA)
Session Chair
Mithun Mukherjee
Poster Session 1
Hamstring Injury Detection Using Body-Centric Nano Networks
Lawrence He (Princeton High School, USA)
The hamstring group consists of many muscles and tendons. It is critical to accurately detect the injured part before applying any treatment. In this research project, Body-Centric Nano Network (BCNN) is implemented for hamstring injury detection.
I first propose an architecture to support hamstring injury detection. My architecture includes two types of nodes to collect bio-parameters for injury detection: nano sensors and data collectors. The nano sensors enter the patient's circulatory system via either injection or drinking with solutions. The data collectors are wearable devices covering the hamstring group area. Bio-parameters, such as blood cells, muscle enzymes, swelling, bruising, and dislocation, are collected by the data collectors for diagnosis purposes. By using the nano sensors for in-body monitoring, accurate bio-parameters can be collected to determine the root reason.
Performance of my proposal is further analyzed by evaluating key elements in the architecture. These elements are nano sensors, the hamstring area, the blood circulatory speed, and the frequency of collecting bio-parameters. The goal is to minimize the deployed nano sensors while collecting enough information to detect the injury. A mathematical model is established to relate all of the aforementioned elements. My model is based on the nano-scale communication framework in the IEEE Standard 1906.1. Its enhancement relies on employing the hamstring specific parameters to relax a portion of the constrains and thus reducing the computational complexity. Practical values from the bio-research literature are implemented to compare multiple scenarios. Results show this model provides salient guidelines for hamstring injury detection.
Controlling Wildfires though Aerial Seeding
Mrinalini Suresh Kumar (USA)
According to my research, 90% of the wildfires are caused by humans, and 10% are caused by nature(although more are caused by humans, there is a greater impact when the fire is caused by nature). In 2017, 12,306 structures were burned, in 2018, 25,790 were burned, in 2019, 963 structures were burned, and lastly, in 2020, 17,904 structures were burned, all during wildfires. Forest Fires occur during various conditions: drought, heat, and wind participate in drying out the timber or other fuel, making it easier to ignite. Once a fire is burning, drought, heat, and wind all increase its intensity. The arrangement of the natural and physical features of the area also affects wildfire, which spreads quickly uphill and slowly downhill. Dried grass, leaves, and light branches are considered flash fuels, and fire spreads quickly in them, often creating enough heat to ignite heavier fuels such as tree stumps, heavy limbs, and the organic matter of the forest floor. Such fuels, ordinarily slow to kindle, are difficult to extinguish. Green fuels-growing vegetation-are not considered flammable, but an intense fire can dry out leaves and needles quickly enough to allow ready ignition. Green fuels sometimes carry a special danger: evergreens, such as pine, cedar, fir, and spruce, contain flammable oils that burst into flames when heated sufficiently by the drafts of a forest fire.
To help stop the spread of wildfires near human settlements, I recommend a solution through Aerial Seeding. Aerial Seeding is when either a plane, helicopter, or drone drops seeds onto land. We propose dropping seed balls instead of raw seeds as this will greatly and precisely land the balls on soil surface even in mountain ranges.
To carry this project to the next level, I want to collect historical data from the types of trees and shrubs which were most effective in the spread of wildfires in the past. So that in the future I can use this data to create predictive models and help state and federal agencies to manage wildfires effectively.
Understanding the Geographic and Temporal Evolution of Asian Hate Crimes in the United States
Arthur Wang (Canada)
Using Mycelium for the Packaging and Transportation of Fossils
Victor I Robila (Hunter College High School, USA)
Using K-Wave to Simulate Ultrasound for Optimal Intravascular Ultrasound Device Frequencies
Zewen Ha (Canada)
Intravascular ultrasound (IVUS) is a catheter-based diagnostic device using echo-pulsed ultrasound for imaging from within arteries. IVUS can be used as an adjunct of angiography and, in contrast to the latter, it is critical for detailed imaging by detecting the exact location and morphology of plaques. A conventional IVUS device employs a piezoelectric transducer for transmitting and receiving ultrasound waves with a single frequency. Although higher ultrasound frequencies enable greater image resolution, it suffers stronger attenuation within biological tissues, which diminishes the penetration depth of the ultrasound signal and thus limits the view of imaging. This means that a single frequency cannot account for all situations as plaques may have various morphologies and sizes, and employing an IVUS catheter with a suitable frequency is crucial for the accurate identification of atherosclerotic plaques. Therefore, a systematic study of the impacts of ultrasound frequency on IVUS imaging of atherosclerotic plaques is significant. The ideal method would be testing IVUS catheters with different frequencies for various plaques, however, the cost of IVUS catheter and plaque prop fabrication will be staggeringly large as ultrasound frequency has a very wide range.
To address this issue, we present a solution in this work: a numerical simulation platform to mimic IVUS imaging of atherosclerotic plaques. The platform is built off the K-Wave toolbox in Matlab which is designed for time-domain acoustic simulations in complex and tissue-realistic media. The ultrasound transmitter and sensor functions were combined to simulate a piezoelectric transducer, which was spatially scanned to mimic imaging implementation in IVUS. The frequency of the IVUS was made adjustable in the simulation, and IVUS imaging of the same plaque was achieved with varying frequencies. The evolution of image quality was quantitively characterized with edge spread function, indicating that high-frequency IVUS can produce images with higher axial resolution but less penetration depth while lower-frequencies have the inverse effect, which is consistent with the physical theory. IVUS images of plaques with various shapes, sizes, and mechanical properties were also demonstrated. As such, this simulation platform offers a tool for the study of IVUS imaging. Further steps on this project will be taken to create a graphical user interface that is more user-friendly for clinicians, so it is promising to be used in clinical scenarios to guide IVUS catheter selection for the identification of atherosclerotic plaques.
Fueling the Future: A Study of Nuclear Fusion Reactors and Their Modern Capabilities
Suzanne Keilson (Loyola University Maryland, USA); Dahlia Shafiq (River Hill High School, USA)
One aspect of this work is to assess what people know and are interested in learning about nuclear fusion. Another is to introduce and explain current efforts to develop fusion reactors on a global scale. A survey has been developed to assess attitudes towards nuclear fusion. In particular, the survey is being distributed to high school students as they will be the important future decision makers about sources of energy. We also looked at some global data of the distribution and development of energy sources, which show that developed countries are not necessarily changing their power sources, but new sources may be more likely to be implemented in other parts of the world. The effort for a sustainable fusion reaction remains very much an international effort. This poster serves to illustrate the background and general process of modern nuclear fusion reactors, as well as dissect the benefits (both numerical and psychological) and roadblocks our planet faces to integrate fusion into our energy grid. From these factors we determine that the benefits to fusion are far beyond the problems that serve as walls towards its implementation.
Continuous-Release Mist Diffusion of Essential Oils For Varroa Control: A Field Study
Kaitlyn N Culbert (Toms River High School North, USA)
Evaluating the Safety of COVID-19 mRNA Vaccines by Comparing Its Side Effects to Conventional Vaccines'
Shichun Zhang (Carlucci American International School of Lisbon, Portugal)
A review of bionics for bird flight and potentially applicable mechanisms
Shu Zhitao (The Second High School Attached to Beijing Normal University, China); Lufan Wang (Florida International University, USA)
The Impact of Blurb Sentiments on Crowdfunding Success
Siyuan Liu (Beijing 101 High School, China)
Session Chair
Weihsing Wang
Track 10 — Works-In-Progress I
Low Cost and Lightweight Boat for Water Lake Cleaning: A Work in Progress
Rodrigo Alberto Cordero (Turing Lab, Guatemala); Erick Petersen (Universidad Galileo, Guatemala); Oscar Rodas (Universidad Galileo & Tesla Lab, Guatemala)
Junior High-School study of RoboPhysics
Ofer Danino (Technion, Israel); Gideon Kaplan and Itamar Feldman (Ministry of Education, Israel); Joseph Shapira (Consultant, Israel)
Exploring Coding Attitudes of Chinese Elementary Students: A Preliminary Study
Shuhan Zhang and Gary K. W. Wong (The University of Hong Kong, Hong Kong); Xiaojing Sun (Weifang Dongming School, China)
A3Sat: Using CubeSats to Inspire the Next Generation STEM Professionals
John Moore (Institute for Earth Observations, USA & NASA GLOBE Mission Earth, USA); Sriram Elango and Maxwell Friedman (Institute for Earth Observations, USA); Jin Kang (United States Naval Academy, USA)
constructed to both strengthen existing curriculum taught in class and incorporate topics commonly missed. Incorporating a wide variety of fields simply in its construction, domains such as computer science, mechanical engineering, spatial structures, electrical engineering, and material science are embedded within it, allowing students to explore these fields and build vital technical skills. The rapid development of CubeSats over the past two decades (1999-present), from research to significant mission integration, has
occurred. The capabilities of CubeSats continue to expand and are being deployed in a wide range of sophisticated scientific and commercial missions,
demonstrating that CubeSats have earned a legitimate place in the Aerospace Enterprise. Extending outward from these topics, the nature of satellites and their close intricate ties to big data is what will further thrust the topics learned to a higher, more advanced level. In this model, the collection of data from environmental aspects allows students to further understand such topics
as chemical compounds and concentrations, atmospheric phenomena, geographical data sets, imagery, and other physical science topics integral in both foundational and advanced knowledge of the scientific world. As this data is collected, students gain the ability to map, plot, deeply analyze and interpret the data, catalyzing the process of scientific thinking and experimentation.
The A3 Sat, not only intelligent in its design, serves as a gateway for students to immerse themselves in STEM fields far out of reach - developing schools and minds alike with the processes and methodologies utilized by the world's leading
scientists, and further establishing a foundation for the next generation to build upon.
Why The Trans Programmer?
Skye Kychenthal (USA)
A Unified Aviation STEM Program
Lyndsay Digneo (Federal Aviation Administration, USA); Holly M Cyrus (Research & Development & FAA, USA); Somil Shah (Federal Aviation Administration, USA)
Session Chair
Jay Roy
Track 15 — Workshops
Coding with Tinkercad and the Use of Sustainable Printing Material
Aditya Dutt (Middlesex County Academy, NJ, USA)
In this workshop, we will learn how CAD and 3D printing is changing the world of innovation. We will learn how to design a Snowflake/Mandala using pattern modeling with coding. This workshop can be run remotely as well as in-person.
Details:
Tinkercad offers an environment for coding similar to the professional software "OpenSCAD", where drag-and-drop coding is used to create 3D models.
In this workshop we learn:
-Basics of CAD and its effect on innovation and our earth
-The various coding blocks that are in the Tinkercad environment
-Basic programming such as sequential coding, loops, and functions
-Basic pattern modeling with coding to make a snowflake or mandala
Prerequisites:
Tinkercad knowledge is not required. Workshop starts with some introduction to Tinkercad.
National Science Foundation Graduate Research Fellowship Program
Anni Leming (Professional Management Consulting Services, USA)
This session will provide the audience a brief overview of the NSF-GRFP, such as its goal, eligibility requirements, application process, and timeline. Additionally, the session will include information on the NSF-GRFP's efforts to increase representation of women and members of groups historically underrepresented in STEM with a follow-up discussion on how the audience can encourage women and members of groups historically underrepresented in STEM to apply for the program or become reviewers. The last part of the session will be reserved for Q & A. We strongly encourage interaction among the audience and presenters.
To boldly go where no young mind has gone before…
Jose L Lopez (Seton Hall University, USA)
Create life-long learners with practical hands-on interdisciplinary STEAM kits and programs
Shubhendu Das (STEAM Works)
Spark an interest with some practical interesting projects and see what spectacular ideas they come up with. Also, make sure to add art as a powerful tool into everything you create as that brings in elements of design thinking, creativity and personalized fun. Those are the ideas that remain sticky for a long time.
In this workshop we will display STEAM WORKS STUDIO kits, program ideas that we have been creating and sharing with global schools & communities everyday. In addition to making a curious life long learner, practical excellence, equity in education, direct measurable engagement, keeping youth out of trouble, nurturing an entrepreneurial mind set, providing a ray of hope and joy in some conflicted and troubled parts of the world are all meaningful outcomes from such an approach.
Session Chair
Ralph Tillinghast
Track 4 — Full Papers IV
A Semantic Text Processing System for Free-Write English Papers
Ryan DePascale and Stefan Robila (Montclair State University, USA)
Building Student Engagement in Mathematics with Interdisciplinary Study of Voting Systems
Teresa Piliouras (TCR, Inc., USA); Aaron Kershenbam (University of Pennsylvania, USA); Robert Schiaffino (Iona College, USA); Steffi Crasto (TCR, Inc., USA)
Impact of Student Research in Engineering: Case Study of a Non-Doctoral University in the Arab World
Sawsan Samir El-Zahr (Lebanese American University, Lebanon)
projects with the collaboration of undergraduate and graduate students. In this work, we investigate in the field of Engineering, the current status of student research and their impact on the quantity and quality of research in non-doctoral universities in the Arab world. Results show that publications with student collaboration are mostly high-quality journal articles or conference proceedings. Moreover, departments with higher student contribution have higher amount of research output. Finally, the h-index of faculty members is found to be positively correlated with the number of students involved in research.
Quality, Quantity and Impact in Engineering Research: Case Study of a Non-Doctoral University in the Arab World
Sawsan Samir El-Zahr (Lebanese American University, Lebanon)
involved in. The case of universities in the Arab world is not studied previously especially for non-doctoral institutions. Universities in developing countries have limited funding and hence a relatively low research output. This work investigates the associations between quality, quantity and impact of research in the field of Engineering for faculty members in a non-doctoral university in the Arab world. Results show that the Field-Weighted Citation Impact (FWCI) is more correlated with quality than quantity of publications while the h-index is more correlated with quantity than quality. Also, a positive correlation is reported between the quantity and quality of publications for the Engineering field. Finally, faculty members with a higher credit-load have relatively lower quality of publications.
Synchronous Online Army Educational Research Program for High School Students
Anitha Sarah Subburaj (West Texas A&M Univerisity, USA); Ilham Osman (Design Release Engineer, Electrification Power Conversion Release, General Motors, Michigan, USA); Gail Alleyne Bayne and Stephen Bayne (Texas Tech University, USA)
Session Chair
Mithun Mukherjee
Track 5 — Full Papers V
Identifying Students' Progress and Mobility Patterns in Higher Education Through Open-Source Visualization
Ali Oran (Brigham and Women's Hospital, USA); Andrew Martin, Michael Klymkowsky and Robert Stubbs (University of Colorado, Boulder, USA)
academic programs. Depending on the goals and the resources of the institution, these revisions can focus only on an analysis of retention-graduation rates of different entry cohorts over the years, or survey results measuring
students level of satisfaction in their programs. They can also be more comprehensive requiring an analysis of the content, scope, and alignment of a program's curricula, for improving academic excellence. The revisions require
the academic units to collaborate with university's data experts, commonly the Institutional Research Office, to gather the needed information. The information should be highly informative yet easily interpretable, so that the review committee can quickly notice areas of improvement and take actions afterwards. In this study, we discuss the development and practical use of a visual
that was developed with these key points in mind. The visuals, referred by us as "Students' Progress Visuals", are based on the Sankey diagram and provide
information on students' progress and mobility patterns in an academic unit over time in an easily understandable format. They were developed using open
source software, and recently began to be used by several departments of our research intensive higher-ed institution for academic units' review processes. Our discussion includes questions these visuals can address in Higher-Ed, other relevant studies, the data requirements for their development, comparisons with other reporting methods, and how they were used in actual practice with actual case studies.
Blockchain-based Electronic Voting System for Modern Democracy: A Review
Dylan Weiss and Jacob Wolmer (Tenafly High School, USA); Avimanyou K Vatsa (Fairleigh Dickinson University, Teaneck, USA)
Understanding Natural Disasters Through Participatory Simulations: A Pilot Study
Patricia M. Davies (Prince Mohammad Bin Fahd University, Saudi Arabia)
Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT
Luca Carlone (MIT, USA); Kasra Khosoussi (CSIRO, Australia); Vasileios Tzoumas (University of Michigan, USA); Golnaz Habibi (University of Oklahoma, USA); Markus Ryll (Technical University of Munich, Germany); Rajat Talak, Jingnan Shi and Pasquale Antonante (MIT, USA)
Educating educators on social engineering: Experiences developing and implementing a social engineering workshop for all education levels
Katorah N. Williams, Rachel Bleiman and Aunshul Rege (Temple University, USA)
Session Chair
Eman Hammad
Track 6 — Full Papers VI
Identification of Important Factors in Digital Citizenship Learning Curriculum
Alex Budiyanto (University of Indonesia & Indonesia Cloud Computing Association, Indonesia)
Challenges and good practices in STEM: a systematic review and implications for higher education institutions
Eirini Christou (CUT, Cyprus); Antigoni Parmaxi (Cyprus University of Technology, Cyprus); Anastasios A. Economides and Maria Perifanou (University of Macedonia, Greece); Maryna Manchenko and Jelena Mazaj (CESIE, Italy)
Understanding Obstacles in the STEM Career Pipeline through System Dynamics Modeling
Daniel C Appel (US Air Force Research Laboratory, Kirtland AFB, NM & AEgis Technologies Group Inc., USA); Carla Winsor (University of Wisconsin-Madison, USA); Ralph Tillinghast (US Army & CCDC Armaments Center, Picatinny Arsenal, NJ, USA); Mo Mansouri (Stevens Institute of Technology & University of South-Eastern Norway, USA)
Adapting a STEM Robotics Program to the Covid-19 Pandemic - a validation of the proposal presented at ISEC in 2021
Neville E. Jacobs (IEEE Baltimore Section, USA); Eric V Sudano (Eric V. Sudano System Solutions LLC, USA)
Developing surveillance applications with Raspberry Pi, Django, and cloud services
Ravi Rao (Fairleigh Dickinson University)
Though there are multiple tools available, we chose an all-Python-based workflow for the sake of simplicity. We describe the use of Django, a Python-based open-source web framework. We created a surveillance application where a camera attached to a Raspberry Pi collects images and transmits them to a cloud-based service. We used the Google Cloud Platform for its cost and simplicity.
Educational institutions and students will benefit from the design and implementation of our system. Though we describe the process for image data, any sensory stream can be used in a similar manner. Our framework can also be used for any remote monitoring application.
Session Chair
Eman Hammad
Poster Session 2
Applying Face Recognition on Smart Family Album Player
Eckart M Schneider (Poolesville High School & John's Hopkins Applied Physics Laboratory, USA); Shunguang Wu (Johns Hopkins: Applied Physics Laboratory, USA)
In this poster, we present the design and function of a GUI that would work on many different devices with little set-up. At this moment, the GUI is programmed with C++ and the facial recognition engine is programmed with Python. The graphical-user interface was already programmed in C++ on a Windows device from a previous project before being used for this one. Python was the chosen programming language for facial recognition software as there are large sources of troubleshooting information and it can be implemented in a C++ program without much difficulty. The program as a whole would work through the user specifying a folder of images they have and the program would go through each file in the folder, including files inside other folders, and cluster them based on the people identified. For instance, the folder chosen contains photos of a family of a dad, mom, and a child. From there, each photo is clustered together under the respective person. The libraries used in both the GUI and facial recognition program will be shown and explained on the poster.
At the current stage, the facial recognition software is mostly complete as it is able to identify and recognize faces if given training data. In short, the program works by analyzing the commonalities of the training images and determining if the other images match with the data. Currently, the software is being worked on to allow for unsupervised machine learning, meaning the user doesn't need to give training data from beforehand. Furthermore, integrating the current Python program into the GUI is being looked at. With this project, we hope that any user will find it simple and efficient to sort their memorable family photos without spending any of their own precious time.
Effect of Cycle GAN in Melanoma Classification
Tyler R Jan (USA); Ava Miller (Tenafly High School, Tenafly, NJ, USA); Q'Andre Small (Bergen County Technical High School, Teterboro, NJ, USA); Ayushi Kumar (Monroe Township High School, Monroe Township, NJ, USA); Avimanyou K Vatsa (Fairleigh Dickinson University, Teaneck, USA)
In this paper, we explore a method to alter skin images to make it easier to identify whether a person has Melanoma. For example, if an image of a mole has a great deal of hair throughout the image that obscures the area, this method would increase the visibility of the mole by removing the hair. By doing so, Melanoma can be diagnosed more quickly and then receive medical care. For this, we will use the deep learning technique, Cycle GAN. Cycle GAN is trained through unpaired images. So, accessing data to train the algorithm will not be as problematic as finding paired images. Cycle GAN functions by translating an image into a new domain and translating an image again to recreate the original image. We are planning to implement this method onto data. We expect it to translate an image to make the image clearer such that performance metrics of classification methods (CNN, RNN, and XG-Boost) may be improved.
Using Computational Methods to Identify Small Molecules for Cancer Immunotherapy
Nicole Liang (USA)
One immunotherapy treatment against cancer activates the body's immune response by inhibiting interactions between cancer cells and T-cells (cells that fight against foreign substances or in this case, cancer). On the surface of T-cells lie immune checkpoint proteins, such as programmed cell death 1 (PD-1), that, by interacting with proteins on foreign cells, signal to the T-cells whether or not to attack. Cancer bypasses this system by presenting proteins, like programmed cell death ligand 2 (PD-L2), that could bind with PD-1 and thus not elicit an immune response.
Small molecule drugs aim to inhibit such PD-1/PD-L2 interactions and increase the immune response against cancer. None are FDA-approved yet, but research has been done with success, analyzing similar interactions between PD-1 and PD-L1 (another ligand found on cancer cells). Other inhibitors have also been researched, including IgG and monoclonal antibodies. Small molecule treatments are advantageous in that they have oral bioavailability, lower costs, better tissue penetration, and a shorter half-life.
Computational methods are highly effective ways that can be employed to screen a large number of small molecules to help identify potential candidates for drugs. Four steps are taken in this research project.
The first experiment determines whether possible binding sites exist on PD-1 for small molecules. A geometric method was used to find binding sites with the correct size. Next, an energy-based method was used to identify sites with a reasonable binding energy level.
The second step involves virtual screening of small molecules using Pocketquery (Pocketquery) to find ones that match the amino acid clusters on PD-L2 that bind with previously identified PD-1 binding sites. A series of screening tests were conducted in search of the optimal RMSD scores, indicating a good fit.
The next experiment conducts the molecular docking of the selected small molecules from the second experiment and quantifies the energy of the interactions with Swissdock (Swissdock) to ensure that the corresponding molecule can successfully bind to the protein.
Lastly, the small molecules with the right docking energy were inputted into SwissADME (SwissADME) and checked for any violations of Lipinski's rules, which ensure the drugs perform well when taken by a human.
Despite issues with database codes, unsuccessful file procedures, and many failed results, revised techniques eventually produced successful outcomes after applying computational screening to a large candidate pool.
16 small molecules have been identified to inhibit PD-1/PD-L2 binding, including one with an especially promising result. Since all experiments were conducted entirely with virtual tools, the next step would be to verify the bindings in a lab with actual compounds. With further physical screening, these molecules may be eligible for drugs for future use in patients.
Development of chitosan encapsulated thyme essential oil as an alternative fruit fly repellent for household use
Vivian Wu (Palo Alto High School, USA)
GAN Assistance in Diagnosis of Melanoma
Ava Miller (Tenafly High School, Tenafly, NJ, USA); Tyler R Jan (USA); Q'Andre Small (Bergen County Technical High School, Teterboro, NJ, USA); Ayushi Kumar (Monroe Township High School, Monroe Township, NJ, USA); Avimanyou K Vatsa (Fairleigh Dickinson University, Teaneck, USA)
Moreover, in order to classify skin images among benign or malignant or other types of skin cancer, the skin images are used for accurate testing and prediction by trained classification algorithms. These data-centric-based deep learning methods may detect skin cancers accurately and in the early stages and delineate them properly from healthy tissue. These methods use multiple layers of nonlinear models to recognize correct patterns. Therefore, The GAN's outcome will be processed through classification algorithms (CNN, RNN, and XG-Boost) and measured their six performance metrics - loss, accuracy, precision, recall, F1 score, and ROC - and compared against the performance metrics of original images of the dataset.
Tactic classification of broadcast soccer videos by using AI
Jioh In (Princeton International School of Mathematics and Science)
However, data scientists' game video analysis has problems. Data scientists directly review the game and add labels to the video hand by hand, which is a time-consuming task and makes it impossible to analyze the game in real-time. In addition, wearable trackers are expensive and heavy, which can be a price burden for amateur clubs and affect players' performance. This research aims to extract tactics from broadcasting soccer game videos automatically. Since it analyzes soccer broadcast videos, I expect that not only coaches and data scientists but also ordinary soccer fans can easily analyze tactics.
There are two ways to do this research. The first method is detecting the players and the ball in the video with convolutional neural network(CNN)-based object detection. Teams and player names are assigned to each player after object detection. The objects are tracked by using the deepSORT algorithm. The detected players and ball location information and interactions such as passes, shots, and tackles between players and players or between players and ball are quantified in each game. Using the quantified information from videos as inputs, the tactics are finally classified with supervised machine learning algorithms. The second method is to train and classify preprocessed frames with the deep neural network model without going through the detection process.
I am currently working on player identification and object tracking. Broadcasting soccer video datasets are preprocessed with action labels and video labels. I used You Look Only Once(YOLO) v5, which is CNN-based object detection algorithm, to detect players, balls, and referees in preprocessed videos.
A Quantum Optimization Algorithm for Single Machine Total Weighted Tardiness Minimization
Youhao S. Wang (Union County Magnet High School, USA); Julian Cheng (University of British Columbia, Canada)
Grover's search algorithm uses quantum mechanics principles to search an unstructured list, in which items are arranged in a completely random manner and no knowledge about the structure of the solution is assumed or used. The algorithm identifies the item in the list satisfying a given condition as the solution. For the unstructured search problem, while the computational complexity of classical algorithms grows at least at the order of the list size, the computational complexity of Grover's quantum search algorithm only grows at most at the order of the square root of the list size. For this type of problem, quantum computing is more efficient than classical computing.
Furthermore, there is another class of search problems in which quantum computing excels, and this class is called the combinatorial search problem or combinatorial optimization problem. In these problems, a cost value is associated with each item in the searching list and the goal is to find the item associated with the minimum (or maximum) cost value. This type of problem is NP-hard and has no known solution using classical computers that has computational complexity increasing in a polynomial relationship to the searching list size. While multiple suboptimal classical algorithms were developed based on classical computers, hoping to find suboptimal solutions with polynomial computational complexity, Trugenberger's quantum optimization algorithm was proposed for unconstrained combinatorial search problems based on quantum mechanics principles. Its idea, like that of Grover's quantum search algorithm, is to manipulate quantum parallelism so that the desired solution can be measured with a higher probability compared with nonsolutions. The computational complexity of this quantum optimization algorithm is independent of the list size.
However, combinatorial optimization problems with constraints occur in certain practical applications. For example, the total weighted tardiness (TWT) minimization problem, which is a well-known NP-hard problem, can be found in operational planning. This problem requires the construction of a schedule for a single machine with a fixed start time and multiple tasks with various due times that minimizes the sum of weighted tardiness of tasks relative to their respective due times. The problem can be formulated as a constrained combinatorial optimization problem.
To solve the TWT minimization problem, we propose a novel efficient quantum optimization algorithm based on Grover's quantum search algorithm and Trugenberger's quantum optimization algorithm to ensure that the desired solution satisfying the searching constraints and showing the minimal TWT value in the searching list will be measured with the highest probability. In the proposed quantum optimization algorithm, a more powerful cost function normalization method is also proposed.
Statistical Analyses for Fantasy Sports
Zachary Wu (Johns Hopkins University Applied Physics Laboratory, USA); Vince Pulido (Johns Hopkins University Applied Physics Laboratory)
In this poster, we present the steps it took for our FPL lineup, from data collection to mathematical analysis. Initially, for data collection, Premier League data CSV and XLS files from the past five years were acquired to make our analysis. However, since these data files did not include live data, it was difficult to achieve our task of continuous and live FPL predictions. Therefore, we then resorted to using the official FPL application programming interface (API) (https://fantasy.premierleague.com/api/bootstrap-static/), in order to request and pull live data from their database. Our work then moved on to player analysis: determining how much opponent difficulty impacted certain player performances. To do this, we created a Points vs. Fixture scatterplot for Premier League soccer players. The "r-squared" value indicated how much a difficult fixture impacted a player's performance. Looking at these scatterplots, it was evident which players needed to be targeted in order to optimize fantasy points. Since an athlete's FPL points are significantly boosted when they score a goal, we then looked at which factors most influenced goals scored over the past season. Correlation matrices were created through Python which allowed us to see that assists and fixture difficulty were largely correlated with goals scored. Finally, in order to draft our optimal team, a Python program was created using Pandas dataframes. Using the previous factors that were deemed significant (player goals scored and opponent difficulty) an optimal lineup was drafted.
Our team_picker program shows that statistics, math, and computer science can all be fused to make objective FPL decisions that are smarter than the average human. Currently, only opponent difficulty and goals scored were considered with our model. Given sufficient data, it is able to go use the API and go through the dataframe and draft a team. In the future, dozens of other factors could be considered (Ex. Home/Away Status, Game Time, Defensive Stats, etc.) to further optimize this drafting algorithm. Our project is not limited to FPL and we hope others will do the same with other Fantasy Sports such as Baseball, Cricket, Football, and even in the real world field of using data-driven decisions for professional sports management.
Coding Classical Logic Gates on the D-Wave Quantum Annealer
Naren K Sathishkumar (American High School, USA)
Objectives: This project was conducted in order to build and write a program which would allow the quantum annealer to replicate the behavior of classical logic gates. Given a certain input, the quantum computer should be able to realize the correct outputs based on the behavior of the gate that is being encoded.
Methods: The technical report "Boosting Integer Factoring Performance via Quantum Annealing Offsets" was used in order to formulate a binary quadratic model which replicates the behavior of the AND gate, and the half- and full-adder circuits. These models use the Ising penalty model, where constraints are set in order to achieve the wanted behavior; these models are encoded using binary, or in this case, the Ising model. This project took place over the course of 5 months, under the mentorship of Professor Terrill Frantz and Alex Khan from Harrisburg University.
Results: In the report, the binary formulation is used to encode the constraints. In practice, it is more practical to use the Ising formulation, which encodes 0 as -1, and 1 as 1. In this binary quadratic model, two dictionaries are required as inputs: the linear dictionary (for the values of qubits) and the quadratic dictionary (for the connections between these qubits). These values were taken from the charts and graphics from the "Boosting" report, and then implemented in a Python program. The results showed inputs and outputs that correspond with the expected values from an AND gate, and the half- and full-adders. The results of the project were posted in full detail in an article on Medium.com (https://tinyurl.com/yj55fc7m).
Conclusion: The program successfully replicates the AND gate and other small circuits (the half- and full-adders). Continuing on the success in encoding logical gates (such as the AND gate), more complex and useful circuits can be created, to solve problems never done before on a quantum annealer.
Bird Audio Recognition Using Convolutional Neural Networks
Kelsey H Lo (Johns Hopkins University Applied Physics Laboratory, USA)
In this poster, we present our method to identify the species of birds through analyzing audio profiles in convolutional neural networks (CNN). The first part visualizes the audio profile, using a MatLab program. The CNN algorithm analyzes two-dimensional images instead of one-dimensional audio files. Therefore, the MatLab program applies the Short-Time Fourier Transform onto the waveform audio file to create a spectrogram. It visualizes an audio profile by representing the magnitude of the signal frequencies in relation to time. The spectrograms are cropped to four seconds segments to increase the amount of training data and unify the spectrogram dimensions. Then, the next section inputs the spectrograms into a CNN algorithm. The CNN algorithm analyzes the spectrograms to extract the distinct features present throughout the spectrograms of one species. Since each bird has a distinct vocal sound, it has a distinct pattern in the spectrogram. This two-part process is reiterated for various species to introduce variety in the database as well as test the accuracy of the algorithm.
In the current stage, the CNN algorithm is trained to identify a few local species in Maryland. The algorithm requires additional fine-tuning to ensure accurate identification for every input. To create a program that promotes user convenience, integrating the components of the MatLab program and CNN algorithm into a streamlined program is in development. Moreover, once the CNN algorithm is finalized, we plan to expand the database of recognized birds by training the algorithm with more data. With this program, we aspire to encourage environmental sustainability by using birds as bioindicators. People can be a part of monitoring the welfare of their environment by recording nature's music.
Session Chair
Weihsing Wang
Track 11 — Works-In-Progress II
Retrieval of Data from the Database of a BCT-Voting System
Jacob Wolmer and Dylan Weiss (Tenafly High School, USA); Avimanyou K Vatsa (Fairleigh Dickinson University, Teaneck, USA)
Developing MATLAB Data Acquisition and Control Functions for the LABVOLT Electromechanical Training System
Hussein Abdeltawab (Burke Center & Penn State Behrend, USA); Keven Rall (Penn State Behrend, USA); Sohail Anwar (Penn State Altoona, USA); Mesude Bayrakci Boz (Penn State Hazelton, USA)
Engineering Project Activities Designed to Promote STEM Engagement
Zachary Dickinson, Tyler Seelnacht and Ramakrishnan Sundaram (Gannon University, USA)
Study of the eruption mechanism of Saturn's moon Enceladus plume using the mathematical model of a geyser (periodic bubbling spring)
Hiroyuki Kagami (Tokuyama University, Japan)
AI-Based Recipient Blood Type Matching Blood Transfusion Medical Device Design System
Atma Sahu (Coppin State University, USA)
Design and Implementation of an Educational Suit
Seyed Ebrahim Esmaeili, Abrar Aleidan, Aishah Almajedi, Abdulazziz Alqattan, Fatmah Alramezi and Amal Alateyah (American University of Kuwait, Kuwait)
Session Chair
Eric Sudano
Track 13 — Works-In-Progress IV
Credibility based Teaching Assessment in Smart Campus
Ruizhi Liao, Qianyu Ou, Wenjun Zheng and Zhan Shi (The Chinese University of Hong Kong, Shenzhen, China); Shuzhen Li (Nankai University & Binhai College, China)
Virtual summer research camp for incoming Freshmen students in STEM
Sanish Rai (West Virginia University Institute of Engineering, USA)
An Integrated Project-based Learning Approach in Engineering Technology Undergraduate Curricula
Mohammad U. Mahfuz (University of Wisconsin-Green Bay, USA)
Creating an Appropriate Computer Science and Computational Thinking Graduate Curriculum for K-12 Teachers: Context and Initial Results
Katherine Herbert (1 Normal Ave & Montclair State University, USA); Sumi Hagiwara (Montclair State University, USA); Elizabeth M Rogacki (Mount Saint Dominic Academy, USA); Thomas J Marlowe (Seton Hall University, USA)
Let the sunshine: learning about solar energy in equatorial Africa to facilitate the use of educational technology
Jorge Santiago-Aviles (University of Pennsylvania, USA); Geraldine Light (Walden University, USA)
Parallel Programming with Pictures - A Second Path (WIP)
Liam J Davis-Wallace and Wu-chun Feng (Virginia Tech, USA)
Utilizing parallelism in the Snap! block-based coding language, we can program with parallelism and utilize multiple cores of a machine at once. By teaching about multi-core technology, students will be more aptly prepared for the future of computer science and computing technology. Expanding on this with lessons to target different audiences can help broaden the appeal of introductory programming.
Session Chair
Roger Ding
Track 7 — Full Papers VII
Integrating Theory and Practice in Undergraduate Education through the Design and Implementation of Pin-Based Multi-Robot Manipulators
Kyle Pichney, Andrew Romero and Yancy Diaz-Mercado (University of Maryland, USA)
Role of Interdisciplinarity and Collaboration in Engineering Design Curriculum
Olivia Hall and Deeksha Seth (Villanova University, USA)
A survey of student motivations for enrolling in engineering and technology undergraduate programs
Ravi Rao (Datavani, USA)
We need detailed information at the micro-level such as student surveys across multiple institutions to probe student motivations and ensure that their expectations are met and nurtured. We present the results of conducting a survey among 32 STEMs students enrolled in an introductory engineering course at Fairleigh Dickinson University. This is the first semester after the pandemic that courses were taught in person.
We found significant differences along gender and racial lines. For the male students, 38% chose their STEM major due to parental or social influence, whereas for females it was 12.5%. For whites, parental/social influence accounted for 28% of STEM choices, whereas for African Americans, it was 0%. Across all students, 50% chose a STEM major due to an early interest in the field, or due to self-realization that they were good at STEM-related activities such as problem-solving.
Our results indicate the importance of hands-on STEM exposure to students at the K-12 level and the role of mentors. Due to the recency of the data collected, we expect our findings to be valuable to the STEM education community.
Closed Loop Digitally Controlled Power Supply Analysis and Design with Register Level Coding Emphasis
John Tsinetakes (Drexel University & Lockheed Martin, USA)
Analysis and Design of the digital closed loop control using a microcontroller is the focus of the course presented in the paper. Many digital control and microcontroller programming courses are not giving the student the tools to program the digital control from scratch. Previous courses use code generating tools or pre-made libraries to handle a lot of the programming functions. This teaching method is fine for an overall example, but it leaves the student with the skill to only repeat the course example and not develop a digital control program on their own. The student is left with a steep learning curve to use a microcontroller to control different power supplies or use the digital control in another application. This course will teach microcontroller register level programming integrated with the design of a digital control loop for a power supply.
Undergraduate research: Cyclostationary plot classification using machine learning
Sanish Rai (West Virginia University Institute of Engineering, USA)
Session Chair
Eman Hammad
Track 8 — Full Papers VIII
Integrating Animation and Game-making in Teaching JavaScript
Shuting Xu, Shuhua Lai and Lissa Pollacia (Georgia Gwinnett College, USA)
Influence of GFP GAN on Melanoma Classification
Ayushi Kumar (Monroe Township High School, Monroe Township, NJ, USA); Avimanyou K Vatsa (Fairleigh Dickinson University, Teaneck, USA)
Consequently, inspired and motivated by our previous study outcome when applying classification methods (CNN, RNN, and XG-Boost) on Melanomas' images dataset, we found that the border detection and feature extraction for classification methods was challenging. Therefore, we applied Generative Facial Prior (GFP) Generative Adversarial Network (GAN) method to preprocess the Melanoma images. We also changed a few architectures and optimization methods for these classification algorithms. Finally, an extensive evaluation of the validation dataset is conducted. After that, it is compared with the values of performance metrics with previous results. This outcome impacts the dermatologist, scientific, and medical community. As a result, it is an excellent service to humanity to cure the deadliest form of skin cancer - Melanoma.
One Degree of Freedom Copter Design and Control using Smart and Simple PID Controller
Zeyad A. Karam, Zaid Shafeeq Bakr, and Elaf Saeed (Al-Nahrain University, Iraq)
Wireless Body Area Networks (WBANs) Applications Necessity in Real Time Healthcare
Reza Khalilian (Islamic Azad University of Majlesi, Iran & Dr Vita Company, Iran); Abdalhossein Rezai (University of Science and Culture, Iran)
Keywords
Wireless Body Area Network (WBAN), Real Time Healthcare, Health Monitoring, Healthy Advancement, World Health Organization (WHO), Health and Hygienic Networks, Applications
Noise Removal of ECG Signal Using Multi-Techniques
Heyam A. Marzog (Al-Furat Al-Awsat University, Iraq); Aws Zuheer Yonis (Ninevah University, Iraq)
Session Chair
Eman Hammad
Track 9 — Full Papers IX
Training-Free Lane Tracking for 1/10th Scale Autonomous Vehicle Using Inverse Perspective Mapping and Probabilistic Hough Transforms
Mihir Rao (Chatham High School, USA); Laura Paulino (Montclair State University, USA); Victor I Robila (Hunter College High School, USA); Iris Li (Milburn High School, USA); Michelle M. Zhu and Weitian Wang (Montclair State University, USA)
Tracking Technology Trends using Text Data Mining
Andres Fortino and Yiying You (NYU, USA)
FedNet: Federated Implementation of CNNs for Facial Expression Recognition
Md. Saiful Bari Siddiqui (BRAC University & Bangladesh University of Engineering & Technology, Bangladesh); Sanjida Ali Shusmita and Shareea Sabreen (BRAC University, Bangladesh)
Segmentation Techniques in Iris Recognition Systems
Ruaa Waleed and Mayada Faris Ghanim (University of Mosul, Iraq)
Quantitative Study on the Anxiety Level of High School Students in Pandemic Life
Mofei Shen (USA)
Session Chair
Nagi Naganathan (Northrop Grumman)
Track 10 — Full Papers X
Transmission Line Fault Detection Using Wavelet Transform & ANN Approach
Jivitesh Nitin Chavan and Atul Kale (A C Patil College of Engineering, Khargar, Navi Mumbai, India); S Deore (University of Mumbai, India)
Performance Analysis of Kalman Filter as an Equalizer in a non-Gaussian environment
Ly Vu (International University, Vietnam)
Artificial Intelligence (AI) as a computerized decision aid for selection of candidates in higher education
Ravi Kumar V v (Symbiosis Institute of Business Management, Pune, India & Symbiosis International, Deemed University, Pune, India); Ramakrishnan Raman (Symbiosis Institute of Business Management Pune & Symbiosis International University India, India)
Student Perceptions on Artificial Intelligence (AI) in higher education
Ravi Kumar V v (Symbiosis Institute of Business Management, Pune, India & Symbiosis International, Deemed University, Pune, India); Ramakrishnan Raman (Symbiosis Institute of Business Management Pune & Symbiosis International University India, India)
Network Communication Intrusion Detection and Classification Security Techniques
Aws Zuheer Yonis (Ninevah University, Iraq)
Session Chair
Nagi Naganathan (Northrop Grumman)
Track 11 — Full Papers XI
Using Statistical Decision Making for a University Examination Scenario
Vijayakumar Bharathi. S (Symbiosis Centre for Information Technology (SCIT) & Symbiosis International University, India); Dhanya Pramod (Symbiosis Centre for Information Technology, India); Ramakrishnan Raman (Symbiosis Institute of Business Management Pune & Symbiosis International University India, India)
Competitive study on public and private key usage in Voice over Internet Protocol
Aws Naser Jaber Al-Zarqawee (Pilestredet 35, Norway & Oslo Metropolitan University (OsloMet), Norway)
Session Chair
Nagi Naganathan (Northrop Grumman)
Poster Session 3
Analyzing the environmental effect of Chlorophyta using Convolutional Neural Network
Heyu Li (PRISMS, USA)
Author: Henry Li
School: Princeton International School of Mathematics and Science
Determining a Correlation Between Common Skin Conditions and Anxiety
Janice Chao (High Technology High School, USA); Ching-yu Huang (Kean University, USA)
The datasets associated with the survey results were then converted to .csv files in order to load the data sets into the MySQL database. Structured Query Language (SQL) was used to create two tables. Table DEQ contains skin disorder information and it contains the column DED071, which indicates the skin disorder information with possible values 1, 2, and nan. Table CIQGAD contains anxiety information and it contains the column CIQG06, which indicates the presence of anxiety with possible values 1, 2, and nan.
SQL SELECT, INNER JOIN, and GROUP BY statements were used to efficiently join the datasets and generate the 2x2 matrix based on the categorical variables. From the dermatology dataset, 3140 people partook in the survey and the GAD dataset had 850 surveyors. Between the two datasets, a total of 850 common surveyors were found, 55 of which presented valid results in both surveys. A surveyor was excluded from the statistical analysis if it is missing information. A chi-square test based on the two variables was then performed on the resulting data to calculate the p-value of 0.038. This proves the existence of a significant correlation between anxiety and chronic skin problems within this dataset.
Reducing Plastic Consumption with Molecular Gastronomy
Shreya Dutt (MCVTS, USA)
Rapid and Automated Detection of Cancer and Immune Cells Using Novel Machine Learning Recognition Algorithms
Nesara Shree (Portland State University, USA); Eva Vu-Stern (Catlin Gabel School, USA)
Chess4Girls - Empowering Girls through Chess
Nesara Shree (Portland State University, USA)
As a girl with a thriving passion for Chess and a top-ranked female scholastic Chess player in my grade in the State, I have been frequently bothered by this underrepresentation, which neglects to provide aspiring girls with role models. Many of the friends I started with stopped competing through Elementary/Middle School. I believe that the Chess community is responsible for bridging this gap and motivating girls in the sport to pursue their interest.
My idea to stem this pipeline is by organizing All-Girls Chess Tournaments and tutoring Chess lessons for other girls in an effort to encourage, bring together and create the much-needed space for young female chess players. These events are held online, rated, incentivizing participation, and are regularly ongoing to continually foster girls' Chess careers. I want to help bring to light the valuable life skills that Chess can afford- critical thinking, planning ahead, strategizing, visualization, calculation and tactical analysis.
Visualizing Territorial Overlap of New Jersey Grapevines and Spotted Lanternflies using GIS
Sreya Jonnalagadda (Princeton International School of Matematics and Science, USA)
Color Melting Ice
Bela Sameep Sanghavi (1312 Ashton Falls Drive & O'Fallon Township High School, USA)
E = hν
with red color waves having the lowest energy and violet or purple waves having the highest.
During this experiment, ½ cup of ice will be dyed different colors-red, green, yellow, and blue-with food dye to see which one will melt the fastest. This will determine whether the amount of energy in each color wave will affect how fast the ice melts. 1 teaspoon of salt will be added to the ice to act as a catalyst for the melting process. The ice will be monitored the entire time to note any changes in melting.