Poster Sessions

Session Poster-1

Poster Session 1

11:30 AM — 12:30 PM EDT
Mar 26 Sat, 11:30 AM — 12:30 PM EDT

Hamstring Injury Detection Using Body-Centric Nano Networks

Lawrence He (Princeton High School, USA)

Hamstring injury includes any strain or tear of any of the muscles or tendons within the hamstring group. It occurs frequently among athletes of all types of sports. As a Division I athlete, I suffered from a hamstring injury a couple of times. Hamstring injury impacted my results in several national tournaments.

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)

In recent years through the news I've seen many wildfire incidents which had harmed wildlife, human-beings and structures. Initially I thought wildfires needed to be prevented completely, but later through research I realized wildfires help a lot in continuing the forest's cycle. Now I am proposing a solution without breaking this cycle, while protecting the people. We found an effective approach which not just can be used for reforestation but also to control and contain the wildfires.

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)

Though Asian immigrants have settled in the United States from the last century, the Asian communities are still viewed as foreign and vulnerable among many residents and have often been accused of stealing job opportunities from the rest of the American population. The initial breakup of Covid-19 started in Wuhan, China, consequently leading to the national hate crimes towards the Asian communities in the United States. Criminal activities have evolved from online harassment to severe physical attacks and violence, threatening the life and possession safety of the Asian community. However, up to now, most reports about Asian hate crimes are from public media, and we lack a deeper understanding of the impact and extent of this social phenomenon on the civil life of US residents. In this study, using data gathered from Police Departments in the major American cities, such as New York, as well as data from the FBI dating back to the 1990s, we manage to identify how Asian-hate-related crimes evolved and their occurrence frequency compared with other similar racial crimes, e.g., anti-black crimes. We found that though the number of Asian hate crimes surged during the pandemic compared to the past, the occurrence frequency is still much less than other racial crimes during the same period. We also plot the geographic distribution of Asian hate crimes and the country's racial crime rate over time. We found the propagation of hate crimes from more populated regions to less populated regions. We conclude that though the media heavily reported the Asian hate crimes during the pandemic, the situation may not be as bad as what the public, especially Asian communities, perceives. This study portrays the hate crime across the US during the pandemic time, highlights the severeness of the discriminative crimes against minorities. We hope to use this work to provide more data-driven, rational insights to raise awareness of the problems and release the frustration among Asian communities.

Using Mycelium for the Packaging and Transportation of Fossils

Victor I Robila (Hunter College High School, USA)

Despite surviving hundreds of thousands or even millions of years, fossils could be very brittle once unearthed. Insulation against outside elements is needed in order to protect the fossil on long trips from remote locations. Unchanged for decades, current fossil packing approaches rely on simple materials such as Plaster of Paris, toilet paper, and water, all in large amounts. While easily accessible, such materials also result in significant waste, are difficult to apply to the fossil, and also become difficult to remove. In this project, an alternative eco-friendly packing approach based on mycelium is proposed and evaluated. Mycelium is a part of a fungus that helps it absorb nutrients from its environment and usually comes in a thread-like mass. The mass is found in various forms and can be quite strong depending on the species. Unlike paper or Plaster of Paris, mycelium is a renewable material that has been shown sturdy enough to replace bricks in home construction. The experiment was conducted using 4 cups of Plaster of Paris and 4 cups of mycelium with a bone embedded into one of the cups from both materials to simulate the fossil. The mycelium cup was left for two weeks to grow and the materials were then evaluated in terms of their physical aspects. The weight, density, volume, and water resistance were experimentally determined. The experiment showed that while taking longer to create, the mycelium was lighter and less permeable than the Plaster of Paris. The use of mycelium both in fossil transportation and other applications could have great benefit for the climate, and could also be used as an affordable substitute for packing materials.

Using K-Wave to Simulate Ultrasound for Optimal Intravascular Ultrasound Device Frequencies

Zewen Ha (Canada)

Cardiovascular diseases are the leading cause of death worldwide; they accounted for 17.9 million deaths or an estimated 32% of all deaths in 2019. The rupture of vulnerable atherosclerotic plaques is a major cause of cardiovascular diseases. Atherosclerotic plaques are characterized by the thickening or hardening of blood vessel walls due to the build-up of fats, cholesterol, and other materials, between the intima and adventitia. Angiography is commonly used for the diagnosis of cardiovascular diseases. However, it is more for vessel stenosis detection with a general overview of vessel structure; it is challenging to apply angiography for meticulous examination of plaques.
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)

In light of current global issues concerning the depletion of non-renewable energy sources and degradation of our atmosphere, there has been an increased interest in the topic of nuclear fusion. Fusion, a process that was discovered in the early 20th century, began development internationally in the 1930s, and continues to be studied today. As the nature of the atomic nucleus was being explored, it was hypothesized early on that fusion was the process behind the power generated by the sun and stars. This quickly led to the idea of harnessing that power, but it seemed as if the problems would not be overcome. Within the past 20 years, the intensity of fusion research has increased as a result of the growing sustainable energy problem the Earth faces. There are a number of practical engineering problems that implementing functional fusion reactors face. Two of the most daunting have been creating strong enough magnetic fields and maintaining the necessary conditions of temperature, pressure, density for a long enough period of time to generate sufficient amounts of energy to be competitive with current power plants. In addition, politically fusion has often been conflated with an anti-nuclear (bomb and fission) movement. Due to the frequent comparisons between the two, there may be stigma against the construction of fusion reactors. As the climate and energy crisis have changed public thinking, however, it is possible that the public opinion has changed. There are a number of important positive aspects to exploiting nuclear fusion. It does not produce greenhouse gasses. It has an abundant supply of initial fuel (which are isotopes of hydrogen). It does not generate dangerous radioactive waste. And unlike wind and solar power it is not an intermittent source of energy and can be integrated more easily into the existing power distribution grid.

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)

Honey bee (Apis mellifera) pollination is responsible for approximately 80% of all cultivated crops. Unfortunately, reports suggest losses of 30-50% of all bee colonies in the US. The greatest single contributor to the decline of bee health is the Varroa mite. Synthetic chemicals are currently used to control Varroa, but the mites are developing resistance. Essential oils may be a viable alternative. Essential oils are cheaper, environmentally-friendly, pose fewer health risks to bees and consumers, and most importantly Varroa have not developed resistance to essential oils. Its' shortcoming is the limitation of exposure. Humidity and temperature affect the rate of evaporation and therefore the mites' exposure to essential oils. Currently, all commercially available thymol-centered systems are gel-based and work only by direct contact with the mite. Following the laboratory investigation (Part I), a field study (Part II) examined the use of thymol-based essential oils, dispersed via battery-operated mist diffusers, to provide effective miticide efficacy without causing harm to honey bees in the hive environment. The use of the mist diffusers effectively eliminated fluctuations in temperature and humidity. Miticide efficacy was recorded as follows: thyme>oregano>rosemary > control (glycerin). Across all tested essential oils, the highest miticide activity occurred during the first two weeks of treatment. The safety of the tested essential oils to honey bees was found to be comparable to the vegetable glycerin control. Furthermore, a brief cost analysis demonstrates the use of mist diffusers was more cost-effective than commercially available thymol-based systems (US$3.20 versus US$15-$18 per application). Continuous-release mist diffusion permits the disbursement of essential oils throughout the entire hive and effectively provided early elimination of mites as they emerged from the brood cell, while remaining safe for honey bees.

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)

mRNA vaccine is one of the newest vaccines during the COVID-19 pandemic. However, the hesitancy of vaccination has always been one of the main problems that impact the extent of vaccine coverage. The largest concern that leads to hesitancy is the lack of knowledge on its safety and side effects. Therefore, our work aims to investigate the safety and side effects of the COVID-19 mRNA vaccine, compare that with the conventional vaccine through literature review, and propose potential causes of those side effects. The hypothesis is that the side effects after receiving the mRNA vaccine are similar to conventional vaccines. We chose studies on Pfizer mRNA and conventional influenza vaccines in this work. Systematic reactions such as fever, muscle aches, headache, and fatigue occurred among recipients in both studies. We found that the percentage of mRNA recipients who experienced systematic or adverse reactions could be close to or less than the percentage of people experiencing the same reactions in the conventional vaccine. These results support the previously stated hypothesis, and our work will help reduce public concern about mRNA vaccines by sharing these results. Future work will focus on a detailed study on potential causes of the side effects.

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)

Researchers from different disciplines have been learning from bird flight and were inspired to design and optimize novel flying machines. Though lots of bird bionics have emerged, the mechanisms of different bird flight capabilities still remain unknown. By studying the mechanisms of some birds' special flying abilities, researchers can design new flying machines easier and better. The advancement of studies on bird flight bionics in recent years is fast. Rich discoveries have been reported by scholars about the mechanisms of bird flight from different disciplines. However, there is a lack of review efforts that combine bionics for bird flight and potentially applicable mechanisms. Therefore, a review of recent publications reporting bird flight bionics and the underlying mechanisms of bird flight is needed. Here we reviewed 41 recent studies about bionics for bird flight. We analyzed these bird flight bionics from three perspectives - energy efficiency, material characteristics and signal control. We found that energy consumption per mile of bionic machines is still higher than birds in nature and the energy efficiency can be improved by optimizing aerodynamics. The studies about materials characteristics mainly investigated how to mimic the feather of birds, while how to use shape memory materials to bionic the muscle of birds is a new direction. Finally, we found very few studies bionic the signal processing systems of birds. With the increasing knowledge of neuroscience, if we can mimic the signal control circuits in bird's brain, it will help us a lot on designing more agile flying machines. We also summarized 20 studies about the mechanisms of bird flight, three main categories of research have been identified, i.e., flight agility bionics, long-distance flight bionics and stabilizer bionics. Some special species give us good examples to imitate in these three categories, such as bee hummingbirds, ibises and pigeons. These potential mechanisms may provide us new direction for future bionics for bird flight. By summarizing these bird flight bionics and predicting applicable mechanisms, this review identified new directions to advance the current status quo in the bionics domain, which will eventually benefit our real life, such as controlling drones to fly in groups for shipments or achieving stable hovering in the air for aerial photography.

The Impact of Blurb Sentiments on Crowdfunding Success

Siyuan Liu (Beijing 101 High School, China)

Crowdfunding is a newly emerged fundraising method in domains such as art, filmmaking, and product design. Prior research studied many factors (e.g., demographic and project type) predicting the success rate of crowdfunding projects. However, limited studies focused on exploring whether language use can impact the success rate of a crowdfunding project. In this research, we use scraped data from Kickstarter, the largest crowdfunding platform, to explore the impact of blurbs on project success rate. In a crowdfunding project, a blurb is a short introductory paragraph under the project's title. Blurb might affect the success of a project because it is one of the most important ways potential funders look specifically into the project. Therefore, in this study, we aim to leverage natural language processing to measure the positivity and subjectivity of blurbs, and find the relationship between blurb sentiments and the project's success rate. Specifically, we focus on technology products, art products, and game products. We conducted three Ordinary Least Squares (OLS) regressions analyses for technology, art, and game respectively, to get the estimated coefficients of blurbs' subjectivity and positivity, controlling variables such as demographic location, time, funding duration, etc. We found a significant positive correlation between blurb sentiment and success rate for game and technology categories, but an insignificant relationship for art projects. Similar coefficients between game and technology are found, indicating the similarity between these two types. A possible explanation is provided and discussed, that artworks are more subjective on their appearance, but game and technology are not. This work extends the understanding of current literature about factors impacting crowdfunding success. The result of this study will help future funders to understand how their language use can affect their fund amount, therefore selecting the optimal blurb writing method to maximize their profit.

Session Chair

Weihsing Wang

Session Poster-2

Poster Session 2

1:30 PM — 3:00 PM EDT
Mar 26 Sat, 1:30 PM — 3:00 PM EDT

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)

The past decade has seen large advances in technology allowing for technologies only available to governments and large scale companies to now be accessed by anyone with a phone. One such example is the access to facial recognition software with apps like Snapchat, Instagram, and TikTok identifying faces and applying filters based on the user's request. These apps make it as simple as swiping a screen and immediately the user's face is recognized and an additional layer is added on top. Facial recognition has capabilities beyond applying filters as it allows people to quickly sort and group images based on a person. Moreover, facial recognition has the flexibility of being added to any previous programming project from beforehand. Therefore, to give users an easy method of sorting images, we intend to create a simple graphical-user interface (GUI) that can sort through hundreds of images without human input.

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)

Skin cancer is a common form of cancer that affects many. Melanoma, the deadliest type of skin cancer, takes thousands of lives every year. However, if Melanoma is diagnosed early, treatment can be provided to prevent further harm. Lesions can specifically recognize melanoma on the skin. Skin with lesions can be visually surveyed to identify Melanoma.
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)

I identified this project by researching related papers and discussing with my mentor. I conducted all the experiments and analyzed all results under the advisory of my mentor. The goal was to identify small molecules for cancer immunotherapy treatment.

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)

Fruit flies not only cause unsightly scenes, but also carry food borne pathogens that can pose severe health risks. They are common in food service facilities, and can also infest regular households, particularly when a family composts food waste. Although these small pests are a nuisance, there is still no established ready-to-use method to control fruit fly infestations at home, and most people resort to using homemade fly traps from everyday items such as vinegar and dish soap. To address this issue, in this study, a thyme oil encapsulating chitosan product was developed so that it can be used in household compost bins to help control fruit fly infestations through the repelling and killing of fruit flies. The experiment was conducted in two steps. First, a 2-choice fruit fly repelling assay was established and 18 different essential oils were selected through literature research for reported insect repelling effect and tested in the assay. A separate toxicity assay was also conducted through recording the number of fruit flies killed by the essential oil. Thyme oil was selected for further experimental design because it showed the highest repelling effect, and it is safe and environmentally friendly for home-use. Second, to prevent fast depletion and reduce the need for repeated application, the thyme oil was encapsulated into a chitosan matrix for controlled release. Chitosan was selected due to its biodegradability and unique property of turning into semi-solid form through ionization gelation. After testing different experimental conditions of encapsulated thyme oil concentration, type of surfactant, and surfactant concentration for optimal encapsulation efficiency and loading capacity, a prototype product was generated using 2% chitosan, 2% thyme oil and 1% Tween 80. The prototype demonstrated 100% fruit fly repelling effect and 65% fruit fly killing effect after 24 hours in the 2-choice repelling assay and the toxicity assay, respectively. Encapsulation efficiency and loading capacity, measured by thyme oil absorbance at 270 nm, were 65.1±3.5% and 61.2±3.3%, respectively. No significant loss of thyme oil content or insect repelling capability was observed after six weeks. This study created a prototype product suitable for household use to help control fruit fly infestations and may provide useful information to guide encapsulation of a wider range of pesticides to replace insect sprays that are of very short effect duration. Further studies will be conducted to determine the long term stability of the product, and more materials (e.g., other natural polymers such as agar, gelatin, and alginate) will be investigated to determine the optimal encapsulation with affordable price and promising fruit fly repelling efficiency.

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)

Malignant melanoma is an exceptionally dangerous skin cancer due to its ability to spread if not treated promptly. While early detection of malignant melanoma is essential in increasing the likelihood of curing skin cancer, diagnosis is often difficult due to the suboptimal quality of images used in dermoscopy practices. The hazy boundaries, artifacts, and noises make accurate and timely detection using skin lesion images difficult. This paper applies the GAN (Generative Adversarial Networks) to preprocess skin lesion images to better use classification algorithms. GAN will produce images absent of artifacts with clear borders by attributing our dataset of skin lesion images into the Generator of the algorithm.
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)

Modern soccer games are exciting to watch not only for individual techniques of stylish players but also for managers' sharp and creative tactics. As if playing chess, managers set the number of strikers and defenders and manage the game through various formations and flexible tactical changes. Since tactics have become a key factor in winning, soccer clubs hire high-quality managers for tactical knowledge and data scientists for better data analysis. Sports data scientists analyze previous game videos to identify past games' problems and analyze opponents' game styles. By combining scientists' data and the data of the players collected through the wearable tracker, the manager predicts the opponent's tactic and prepares the tactic of the next game.
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)

Since quantum computers were proposed in the 1980's, quantum computing has attracted widespread interest as it appears to be more powerful than classical computing, especially for certain types of problems. One such example of quantum computing's power is Grover's quantum algorithm for unstructured searches.

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)

Fantasy Sports, a continuously rising field of entertainment, allows sports fans to manage a virtual team of athletes and compete with friends based on their players' real life performances. Fantasy Premier League (FPL) ( is a fantasy soccer game based on the English Premier League that is played by managers worldwide. This season, over eight million managers play the virtual sport. To improve, managers will consider factors such as their own player and team preferences, sports pundit insight, and social media opinions. While these sources will suffice for an average manager, they hold many subjective biases that humans are susceptible to. Therefore, we have used statistical and data analysis to gain an objective understanding of Fantasy Premier League and how to create optimal Fantasy lineups. By doing so, we were able to go beyond conventional methods of Fantasy sports prediction and utilize the plethora of data available to make optimal managerial decisions.

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) (, 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)

Rationale: The D-Wave Quantum Annealer is a machine that uses qubits in place of bits since they have special behaviors desirable for solving optimization problems. These machines are not able to natively replicate logical gates (AND gate). Logical gates process binary information in order to output processed information in binary too. Without the basic function of these gates, larger circuits, such as a multiplication circuit, are impossible to replicate.

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 (

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)

With modern-day technological advances, creating and developing innovations that greatly improve the standards of life, it is also important to consider their environmental impact. Side-effects of these innovations are costly to the environment. A method to monitor environmental sustainability is to use birds as a bioindicator of an area. The greater variety of bird species present in an area suggests a superior habitat quality. People can take pictures or set up cameras to capture photographs of birds to record the species present. However, to capture an identifiable photo is difficult birds are erratic and sensitive to movement. In contrast, the sounds of birds travel far distances and are easily recorded. Therefore, we propose to develop a program to identify bird species using audio recordings.

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

Session Poster-3

Poster Session 3

3:00 PM — 4:45 PM EDT
Mar 26 Sat, 3:00 PM — 4:45 PM EDT

Analyzing the environmental effect of Chlorophyta using Convolutional Neural Network


My research is about building a program to identify whether a specific type of species will become an invasive species or not when entering a specific type of ecosystem and using a special kind of Artificial Intelligence to analyze the effect that a certain type of plant has on the environment around it, or them. In this case, Chlorophytes. This type of organism has a very clear effect on the environment, when the amount of Chlorophyta in a certain environment is below a certain threshold, it can benefit the environment by producing more oxygen and recycling the waste produced by other large organisms. However, when the amount of Chlorophyta grows beyond the threshold, there could be many problems that would be triggered by it. The program to do is process images of ecosystems with a certain amount of chlorophytes and try to predict whether the chlorophytes in this ecosystem would become an invasive species within a certain amount of time. A broader purpose of this research is to build this program to detect an invasive species in a certain type of environment by imputing the parameters of the animals and environment. How do we know whether an ecosystem is being destroyed by chlorophytes or not? The method I am currently using is first, to find some databases that are about all aspects of many species, for example, databases about species and their habits. By implying some data in it to make a little education program, and then, maybe import more data so it could be applied for more difficult and complex problems. What I am currently doing is that I am training my program that can process the image and predict the growth of chlorophytes in the ecosystem, by building this camera near the area that we want to detect, and letting it take a picture once in a certain amount of time and let the image being processed by my program to do what I asked it to do, along with other detectors to monitor the environment. For example, oxygen and CO2 sensor, light sensor, and so on. And we can build this whole system that can run on itself to monitor the growth of chlorophytes and give a warning to the control center when it has the trend to grow to an invasive species. Furthermore, this program would be a base of a bigger program, which is a whole system of invasive species forecasts. This system can not only be used to protect the environment but also be able to protect endangered species.

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)

High stress and anxiety levels are often cited as a cause for acne outbreaks and spotty skin. General anxiety disorder, known as GAD, elevates stress levels, making it a potential cause of problematic skin. However, anxiety may be the cause for not only more temporary malformations like acne but also chronic skin conditions. This research project explores this correlation through the use of the CDC NHANES questionnaire databases. The exact questions used in this project asked: "Is [the surveyor] worried, tense, or anxious a lot more than most?" and "Has [the surveyor] been diagnosed with eczema, dermatitis, or a persistent rash?"

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)

Plastic consumption is one of the main causes of pollution and climate change in our world today. Over time, pollution has filled up our oceans and hurt animal and plant life, in water in addition to on land. Plastics do not biodegrade efficiently and they can stay on our planet for over 500 years after being disposed of by a person. As more plastic gets piled up on land and in oceans, the chances of finding a solution get smaller. However, after this issue became more recognized, there have been many interesting ideas on how to create a solution. One of these ideas is known as molecular gastronomy, or food science. Molecular gastronomy can be utilized to create edible replacements for products that lead to high plastic consumption, making it a viable solution. This has been implemented in the last few years into reducing the need for plastic water bottles and using water bubbles made from food grade calcium lactate and sodium alginate. This will assist in creating a brighter future for us and the environment.

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)

The human body has over 200 different types of cells, each of which has a distinct function. Cures for many illnesses, such as cancer and neurological/cardiovascular diseases, involve killing specific dysfunctional cell groups. In the field of cancer research and immunotherapy treatment, scientists must identify harmful cancer cells and healthy immune cells in a patient sample. Current identification methods are inefficient because a researcher must manually set conditions, or a 'threshold value', to identify each cell type independently. Implementing machine learning image-recognition technologies enables researchers to identify cancer and immune cells faster than with the existing approach because human intervention is not required. We are using Faster R-CNN implemented by Keras Tensorflow, one of the best one-stage object detection models proven to work well with dense, small-scale objects such as cells. The model learns to recognize patterns in the data using JPEG images, CSV renditions, and XML annotations to train. Once training is completed and the accuracy is calculated, adjustments are made to the model or dataset. This training cycle repeats until the model achieves the highest possible accuracy. The subsequent validation process confirms that the model's inferences regarding the cell type are accurate. This engineering project shows that our model has the capacity to identify cancer and immune cells significantly faster than the current approach. Further experimentation includes not only cancer and immune cell identification, but also differentiating between the two cell types within one data sample. Applying this technology will allow researchers to improve upon current immunotherapy treatments.

Chess4Girls - Empowering Girls through Chess

Nesara Shree (Portland State University, USA)

The World Chess Federation's report in July 2019 lists 1643 male Grandmasters against only 37 females. This is approximately 44 men for every woman- an unsettling gender gap that is one of the largest observed amongst other male-dominated domains in STEM. A former world champion, Garry Kasparov, stated in a 1989 that "There is real Chess and women's Chess…Women are weaker fighters." These belittling beliefs induce what is known as a stereotype threat, playing into the performance gap. The underlying idea is that minorities underperform solely because they are aware of a convention that people of their kind are anticipated to do worse. This mindset leads to significant confidence decline, waning of interest, and a cycle of self-depreciation- notably in young girls. Bolstered by the stereotype threats surrounding women in Chess, female participation drops off due to deterioration of support and motivation.

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)

The New Jersey wine industry is growing due to good harvesting conditions and the successful production of a variety of grapes. However, a particular invasive species, the spotted lanternfly (SLF), has targeted many crops, one of its top being grapevines. Thus, in my research, I am visualizing the overlap between NJ's most suitable grape growing territories and spotted lanternfly quarantine zones(zones where higher precautions have to be taken to prevent further spread of the insect) so key areas of overlap can be discovered. This analysis will help point to counties that are at higher risk of infestation and preventative measures can be taken by residents and farmers. Based on the maps I generated in the ArcGIS software using vineyard data from the Rutgers NJ Agricultural Experiment Station, it can be noted that there is a strong correlation/overlap between the insects' infestation locations and grapevines(especially in West Jersey). In addition, it can also be noted that certain counties, such as Burlington and Salem, may be critical areas with vineyards at a greater risk of SLF spreading and damage. The next step is to process multi-spectral satellite imagery to assess the damage over time and use that to create a predictive model.

Color Melting Ice

Bela Sameep Sanghavi (1312 Ashton Falls Drive & O'Fallon Township High School, USA)

Color is an aspect everyone sees every day in their lives. From big cities to the country side, color surrounds all environments and objects. Even though to the eye, color is just a simple pigment, it is actually much more complicated than that. Color is part of the electromagnetic magnetic spectrum and takes form of waves in the visible light portion of this spectrum. Each color, like its appearance, is different and unique. All of them have a different amount of energy calculated with the frequency of the waves (ν) and Planck's constant (h) or 6.626 X 10-34 or
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.

Creating an app using AI to analyze eye movements to screen for Neurological Disorders

Srihithaa Vaidya (USA)

Like many other medical imaging and diagnostic techniques, Magnetic Resonance Imaging (MRI) and clinical evaluation are costly, particularly when identifying and diagnosing neurological disorders. Moreover, little research has been done to find alternatives for this issue. The problem here is that many individuals go undiagnosed with neurological disorders, such as autism, ADHD, dementia, Parkinson's, and Multiple Sclerosis because they cannot afford diagnostic testing. This is a significant issue because it causes social complications, physical problems, and even death. Our goal is to assist those individuals by creating an app that serves as an alternative to high-cost diagnostic exams/imaging. The app, Braineyefy, would analyze eye movements to diagnose individuals with neurological disorders using Artificial Intelligence. Benefits of Braineyefy include early diagnosis, widespread availability, and the potential of helping doctors as a portable screening tool. To design and build the app, we are using open source libraries and frameworks available in the Python software ecosystem such as Python PyGaze (eye-tracking software), Kivy (publish app on multiple devices), and Scikit Learn/PyTorch (build AI modeling algorithm to make a trained AI model). The app will consist of a login page, a testing page, and a results page (where the patient may send test results to the individual's doctor). Continuing the project, we will obtain research participants who will take the app's diagnostic exam to collect data that will be used to train the app algorithm; the trained app algorithm will be deployed into the app. Braineyefy would identify abnormalities through the way our eyes move/reflect neurological responses when perceiving visual stimuli, accurately matching them with neurological disorders and serving as a tool for early diagnosis.

Using Unmanned Aerial Vehicles to Test Water Quality

Arnav Machavarapu (Westwood High School, USA); Aadet Samant and Zubin Chhabra (USA)

The Indian water crisis, which resurfaced in 2018, threatens the lives of millions and shows the disastrous effects of water pollution. This is due to a lack of awareness that people have in regards to the cleanliness of their water. Countless lives could be saved if rural citizens of developing countries in regions such as South East Asia and Africa knew how unclean their water was. Pollutants have made water sources unsafe to consume which has forced people to expand their search to find clean water sources. Additionally, the usage of water with a high pH factor has been shown to negatively impact agricultural output and affect livelihoods. The first step to fix this issue is to measure the level of contamination in the water, which can be difficult due to a lack of easy access to sources of water and affordable technology. To address these two issues, our team aims to build an Unmanned Aerial Vehicle (UAV) that incorporates water quality testing equipment so that water testing can be done remotely. This empowers people in rural areas to make informed decisions about the cleanliness of their water. We will use multiple probes, along with a small water sample to obtain a value on the Water Quality Index (WQI). This number can then be used to understand the sanitation level of the water and how suitable it is for household and agricultural use. As part of a technology demonstration, we will build one system which consists of the following parts. The UAV will be in the form of a quadcopter along with housing for Vernier probes for pH, dissolved oxygen and temperature sensors. This housing will be an enclosed box apart from a small gap where the sensors will be exposed. Once the drone lowers to the water, the water quality probes will collect data and transmit data using the UAV's basic radio transmission technology in order for the users to understand the data in a user-friendly manner. Through simple calculations using data collected, the user will have a number on the Water Quality Index which will demonstrate the cleanliness of their water. Our proposed system is cost-effective and eliminates the need for extensive lab equipment which would require a large water sample and multiple rounds of tests. This more user-friendly and efficient way of testing water will allow much faster data return rates. Our goal is to build this system in a manner that can easily access remote locations and is affordable for mass-market adoption. Data collected from the device will be stored on the cloud to drive deeper data analytics capabilities for future decision-making.

How to Design Single-Sheet Origami Models

Qi Ao (Princeton Academy of the Sacred Heart, USA)

This presentation is a tutorial on how to design an origami model using a single sheet of paper. I will provide step-by-step instructions on how to divide the paper, assign sections of the paper to each part of the model, and use key points to determine the model's proportions. Once the basic model has been folded, I will demonstrate how to shape and customize it to make it uniquely yours.

I began folding origami when I was 9 years old, following instructions from books and watching YouTube videos. Within a year, I was experimenting and modifying different models. I learned how various parts were formed by playing around with paper and gradually started creating my own designs.

My goal is to share my passion by teaching others how much fun it is to fold origami and how much more fun it is to create original designs.

Session Chair

Weihsing Wang

Session Poster-4

Poster Session 4

3:00 PM — 4:45 PM EDT
Mar 26 Sat, 3:00 PM — 4:45 PM EDT

Automatic Clothes Finder for the Colorblind

Bao T To (USA)

One challenge for people who are colorblind is finding clothes that match or are according to their needs. This system serves as a solution to that problem by providing an automated clothes finder system based on user input of the preferred color. The paper discusses the design of a color-based sensing system using modules to help colorblind people determine the color of their clothes. The paper justifies why each component is selected based on their tested performances. The paper also documents the software and hardware modifications in the design process. The test results show that the device can accurately detect multiple colors and coordinate the mechanical system to present the user with the clothing item with the correct color.

Stem project-Straw roller coaster

Juliette Hancock, Jeanine Hancock, Julia Rodriguez, Alex Albiter and Carina Tullo (Goetz Middle School Jackson, NJ USA, USA)

Straw roller coaster

• Introduction:
We will be experimenting how forces act on a roller coaster. We are also manipulating the slope of an inclined plane to observe the effects of height on an object's speed. We will then learn about how gravity and normal force affects objects on an inclined plane.

• Procedures:
We will be designing a roller coaster similar to the Batman coaster at Six Flags Great Adventure. We will be working on the following:
• The height of the coaster
• 90˚ turns with drops
• Keeping the ping pong ball on the track
• Speed

We are using a cardboard box top as our base, glue guns to anchor the straws to the base, and we're going to be gluing straws together to create the tracks. We will also be testing each section as we build it.

We will be doing the following:
Calculating the force of gravity (Mass * acceleration)
• Mass of the ping pong ball
• Acceleration due to gravity
• The force of gravity
We will be using rulers and protractors to measure the track and angles.

We will consider the following:
• With higher heights, the acceleration will increase, which causes the time to decrease.
• As the angle increases, acceleration increases causing the time to decrease.
• Roller Coasters build up momentum and accelerate on straight tracks. Longer track lengths before turning gives roller coasters more times to accelerate before they have to decelerate before turning
• We will be calculating the average speed of the Roller Coaster, equal to the total length of the track divided by the time it takes to complete the ride, the shorter it takes the higher the average speed.

The result should be a working coaster that looks similar to the Batman coaster at Six Flags.

Teachers can use this as a Stem project for their classrooms.

Detecting Anomalies in IoT Device Communication Based on MUD Profiles With Zeek and Python

Rohan Nunugonda (Peddie School & Carnegie Mellon University CyLab, USA); Vyas Sekar (Carnegie Mellon University, USA)

The threat of Internet of Things (IoT) vulnerabilities remains highly persistent today, with IoT devices responsible for a third of all infections in mobile networks by the end of 2020. Consumers are now more worried about their personal data being compromised or collected by unauthorized personnel. Run by Dr. Vyas Sekar, the [email protected] Initiative at Carnegie Mellon University's CyLab focuses on creating technologies that address different IoT issues by creating a network that can independently protect devices from potential threats and miscommunications. In this research, we analyze device communications with the use of Python to generate a Zeek script that uses device protocol and IP address information from the device MUD profile. Via string manipulation, the Python script grabs the allowed destination IP addresses from the MUD profile. This list is passed into the Zeek script, which checks the addresses in the list against destination addresses in a PCAP file that contains the device communications. Next steps include testing of the Zeek script against pre-existing test data to tune the scripts to assess real-time data flow.

Project NARWHAL (Nautical Autonomous Robot for Wire Hunting through Analysis and Localization) Poster Abstract

Thomas Edwards (Johns Hopkins University Applied Physics Laboratory, USA)

NARWHAL (Nautical Autonomous Robot for Wire Hunting through Analysis and Localization) is a project I led at my internship with the Johns Hopkins Applied Physics Laboratory. This project seeks to design an autonomous underwater vehicle (AUV) to chart subsea power and communication cables. A survey of existing research into the magnetic fields and geometric properties utilized by other researchers to localize cables based off magnetometer readings informed the selection of sensors and data fusion algorithms for NARWHAL. Conversations with JHUAPL ocean engineering and undersea warfare subject matter experts informed the selection of an underwater glider platform. This project also included the construction of a SeaGlide AUV, the prototyping of a sensor rig including barometers, magnetometers, and accelerometers, and the design of a filtering algorithm to estimate cable position from sensor measurements. Efforts to date include the implementation and testing of an above-water, single-magnetometer algorithm and significant milestones in the construction of the AUV. Future work includes finalization of the prototype, augmentation with additional control surfaces for navigation, fusion of multiple magnetometer readings to expedite localization, design of an intuitive user interface for increased accessibility, and testing of underwater cable localization.

Comparing the Gains of Bipolar Junction Transistor's (BJT) using the IV characteristics of a Transistor

Roshan S Natarajan (Georgetown Day School, USA)

The bipolar junction transistor was invented by William Shockley, Walter Brattain and John Bardeen at AT&T Bell Laboratories. This three-terminal-device can function as a switch or a signal amplifier. The name "bipolar" comes from the fact that there are two types of semiconductor materials, one positive(p) and one negative(n), through which a current flows. BJT's are created by layering p-type and n-type silicon to form three layers. The three layers are the base, with which a small starting current is attached, the emitter, which emits electrons, and the collector, which collects electrons. There are two types of configurations: an NPN (Negative-Positive-Negative) and a PNP(Positive-Negative-Positive). An NPN switches on when a current flows through the base while a PNP switches on when there is no current through the base.

The objective of this experiment is to compare the gain (href) of an NPN to a PNP transistor by finding the Current vs Voltage (IV) characteristics of each type of transistor. The data collected from the IV characteristics will be used to extract the gain.

To find the IV characteristics of a transistor two schematics will be made: one for a NPN and another for a PNP transistor. The PASCO Science Workshop 750 will be used to collect data. Since the PASCO 750 only has voltage sensors, the voltage over the collector resistor will be taken and then the current will be extracted using Ohm's Law. Then the current gain will be calculated by dividing the base current by the collector current. The gain for the NPN transistor will then be compared to the PNP transistor to see which transistor has a stronger amplification.

Unsupervised GAN for Melanoma

Q'Andre Small (Bergen County Technical High School, Teterboro, NJ, USA); Avimanyou K Vatsa (Fairleigh Dickinson University, Teaneck, USA); Tyler R Jan (USA); Ava Miller (Tenafly High School, Tenafly, NJ, USA); Ayushi Kumar (Monroe Township High School, Monroe Township, NJ, USA)

Computer vision technology is being used more often as time progresses in the medical imaging world. The neural network-based classification methods play a better role in Melanoma pattern identification to recognize an exact pattern in images. However, images need to be preprocessed such that artifacts, highlight shadows, and noises may be removed. The GAN (Generative Adversarial Network) is being applied to Melanoma's deadliest form of skin cancer images. The GAN helps preprocess images so that finding correct patterns and focusing on identifying and distinguishing malignant and benign melanoma lesions becomes easy. For that reason, our contribution is to build the unsupervised GANs and make it easy and seamless early identification in order that patients may be diagnosed in the early stage of Melanoma. First, the publicly accessible ISIC (International Skin Imaging Collections) dataset will be preprocessed using unsupervised GAN. After that, the preprocessing images will be classified using deep neural network-based algorithms - CNN, RNN, and XG-Boost. The performance of these algorithms will be measured using six performance metrics - loss, accuracy, recall, precision, ROC, and F1 score. Finally, these performance metrics will be compared with those without preprocessed images. The outcome of this work will be helpful for dermatologists, geneticists, and health care scientists. Also, people can be diagnosed with Melanoma earlier and receive treatment before cancer becomes lethal.

Girls teaching Girls: Mentoring Middle School students in Mathematics

Sowmya Natarajan (Whittle School and Studios, USA)

Recently, I have been teaching mathematics to two 6th graders. In March of 2021, I started tutoring two fifth-grade twin girls. I met person A and person B through a family friend, and I have been tutoring them ever since then. Where the three of us started emotionally and academically compared to now is a huge improvement. I had to take many steps to get where I am now. I had to learn how to build their trust, form a relationship, be seen as a mentor, figure out the ways they learn best to ensure there understood everything. They also had to take many steps to adjust to learning with me. They had to learn to be comfortable with me, trust me, become accustomed to the way I teach, become more familiar with me as a tutor trying to help them, and they had to be open to learning. After several months of taking these steps and slowly accomplishing each and every one of them, we are at a completely different place than where we started with our relationship. It has changed and grown for the better over the months as well as their understanding of math.

Minimizing Weight in High Performing Gaming Mice

Judah Lerman (Community Park Elementary School, Princeton NJ, USA)

What does it take to design an ultra-light, high performing gaming mouse specifically for drag clicking and double clicking? What can be improved upon from the best gaming mice already on the market? This project will be focused on reducing weight in the mechanical components and the outer shell. A high performing gaming mouse would need a certain circuit board, but would also need to weigh under 60 grams. By taking parts from many already-existing gaming mice along with other components, a gaming mouse with a good shape, light weight, responsive switches, and low debounce time would be possible. I plan to use a material unlike standard coated plastic used in most gaming mice, that has a light weight, and is also sturdy. I'll explore using different types of switches. Even though I'm focusing on weight, it is important that the mouse has a debounce time of zero in its software, or else it won't be able to double click and drag click effectively in games like Minecraft.

ATmega328P-Based Portable Heartrate Monitoring System That Can Transmit SMS Messages in Case of a Cardiac Arrest

Eugene John (Korea (South)); Joseph Matthew Y. Espinas and Ramon Carmelo Y. Calimbahin (Philippines); Randy O. Zebroff (Canada)

Ischemic Heart Disease is a leading cause of death. In the Philippines, ischemic heart diseases have claimed 105,114 lives in the year 2020 alone (PSA, 2021). This study focuses on the creation of a device that utilizes an ATmega328P microcontroller for a Heart Monitoring System. The device works with an Android app that can be downloaded on a smartphone. The heart rate is recorded by the Heartrate Acquisition Bluetooth Segment (HABS) and sent to the app via Bluetooth Low Energy. The Heartrate Monitoring App Segment (HMAS) processes and determines if calling an emergency service is needed based on the heart rate. The HMAS is preprogrammed to send an SMS to any emergency responders inputted by the user. A total of seven experiments were done to test the accuracy, data transfer, SMS transmission, location pinpoint, storage capability, latency, battery life, and portability of the device. A mixed methodology with an experimental case study, one-shot case studies, and a survey were utilized. The functionality test demonstrates that the HMS can transfer information, grab the user's locations, and send an SMS message when the user is having a cardiac arrest. The HABS was able to record the heart rate without any incorrect or missing data. Through the latency test, an overall average of 29ms was recorded to send data from the HABS to the HMAS.

Facial Recognition for Deepfake Detection

Firaol Desta (JHU/APL Intern, USA)

Facial recognition is a tool utilized by social media companies to assist in tagging people in photos. Facial recognition's ability to detect modified images is useful for these companies to prevent privacy violations, or even online impersonation. For example, deepfakes are very realistic pictures and/or videos that are created by pasting a face onto someone else's body, which are of concern to social media companies. Deepfakes are very dangerous media that could be used to hijack someone else's identity for malicious purposes. In order to prevent this, facial recognition must be accurate in recognizing modified faces. By using the Python facial recognition library, we researched if facial recognition can be used to recognize when an image has been modified. To start off, we first checked if facial recognition can identify an alternate photo of someone, like if they are in a different position, wearing sunglasses, or of a different age. We did this by creating a folder of known celebrity faces, and an unknown folder with the alternate photos. Once we could do it manually, we automated the code to cycle through a folder of unknown images and compare each of them to a known target image, and count how many times a match was found in the folder. We are ultimately trying to use this library to check the accuracy of facial recognition in recognizing original and modified faces. Understanding the current effectiveness of facial recognition technologies against modified faces will enable us to improve these technologies, and in turn allow us to defend better against deepfakes, and even greater threats against privacy and security.

Session Chair

Weihsing Wang

Session Poster-5

Poster Session 5

3:00 PM — 4:45 PM EDT
Mar 26 Sat, 3:00 PM — 4:45 PM EDT

Darwin's Finches Population - Will it Thrive or Dwindle under Climate Change?

Kavin S Sankar (Enloe High School, USA)

Kavin Sankar1, Laura Wendelberger2
1 - Junior, Enloe High School, Raleigh
2- PhD Student, Department of Statistics, North Carolina State University, Raleigh

The Galapagos Islands are famed for their beauty and ecological diversity. They are also known for playing an integral part in the formation of Charles Darwin's theories of evolution and natural selection. The Galapagos Islands even have their own group of finches known as Darwin's Finches, whose beak dimensions were foundational to Darwin's theory of evolution, recorded in The Origin of Species. The Galapagos Islands have a tropical climate, and frequently experience El Nino and La Nina oscillations. This kind of climate comes with high variability on temperature and precipitation, which can affect the reproductivity of the Darwinian Finches. Studies have shown that increased rainfall during El-Nino results in hatching success up to some extent, but after a threshold rainfall, it starts decreasing. A factor that could affect rainfall is climate change, which can also very seriously diminish or increase the reproductivity of Darwin's Finches depending on the range of projected rainfall. This is because climate change can alter temperature and precipitation in the tropical area and change the frequency of El Nino and La Nina cycles, which in turn will affect the reproductive rates of the Finches.
This paper explores the relationship between finch reproductivity and environmental factors to predict the reproductivity of the Darwin Finches in the short and long term under scenarios of climate change. We have acquired some data on the Darwinian Finches in the Galapagos Islands from the book 40 Years of Evolution: Darwin's Finches on Daphne Major Island by Drs. Peter and Rosemary Grant. The book contains data on various factors such as the climate and rainfall of the islands, as well as the phenotypes and reproductivity rates of different species of Darwinian Finches. Using some of the data from this book we created a quadratic model that relates rainfall to the hatchling rates for the Geospiza fortis species (R2 = 0.6 and p-value - 0.006). Using climate data, particularly rainfall, from CMIP6 models, this study predicts finches' reproductive rates in 2050 and 2100. Based on this, a quantitative assessment of how finches' population may thrive or dwindle due to climate change will be provided under rainfall projections developed under different emission pathways.

Investigation of the physics of creating fire with a plasma lighter

Juliet Lopez (William F. Halloran School No. 22, USA); Jose L Lopez (Seton Hall University, USA)

Fire has impacted the evolution of humanity in many important ways. It allowed humans to cook their food and make it more digestible, which also has lengthened human lifespan. Fire has had a significant influence on the settlement and socialization of humans. As humanity evolved, the methods of creating fire have also evolved. Early humans learned to make fire initially through the process of friction, by which they rubbed combustible materials together, therefore increasing the kindling temperature. The kindling temperature is the temperature at which oxygen will rapidly ignite the fuel source such as wood. Later on, humanity learned to start fire by percussion, in which people struck objects such as rocks to create sparks. In the last few years, humanity has learned to start fire by using plasmas. A plasma lighter is a small, hand-held device that consists of two electrodes that charge and ionize the air through the creation of a plasma arc, which is at a very high temperature. The plasma arc can be used to create a fire that will burn different materials. This research investigates the physics of how plasma lighters are able to start fires. This study focuses on investigating how the plasma arc is formed through the analysis of its electrical properties and temperature.

The Ethics of Artificial Intelligence: How to Avoid Bias in Machine Learning Systems

Maya Lerman (Princeton High School, USA)

AI systems use data to create predictive models used to make automated decisions. This includes systems like social media predicting which advertisements would appeal to your preferences, healthcare systems deciding treatment methods, and even machines used in warfare or police targeting individuals for violence. A problem arises when these algorithms contain the same biases as humans, especially when making life changing (or life ending) decisions that could unfairly target marginalized groups. In this project, I aim to detail the root cause of biases in AI systems and propose solutions so that the future development of automated technology can proceed ethically and equitably.

The STEM of Origami

Rishi Balaji (Gates Elementary School, USA)

You may be asking "Why origami? What use is art in the elements of STEM?" and you are not wrong to question that. After all, origami might seem like just a form of art, but it is much more. For starters, origami is an art of paper folding. Origami can be made as decorations - just something to showcase. But if you take it a step further, you can unlock a new world of creativity, especially in the world of STEM. I got interested in origami and started making models as a hobby. One part of origami that I found beneficial was that it can help with understanding the concepts of MATH related subjects such as Geometry. I started exploring origami and its uses and found out that it has been an inspiration for many STEM models. In this poster, I am going to discuss my experience exploring some origami models made by other artists, and their uses in STEM.

Fortune Teller Game

Anish Chaganti (JP Stevens High School, USA)

My program is a fortune teller or a psychic game. I coded this game in C# using the Unity game engine and Visual Studio development environment. The purpose of my program is to have users play a fortune telling game in which they receive a specific fortune based on their birth month. To make it more interesting, I added audio and a new background color representing the season for the selected month.

The sequencing is very important in the part of the code that selects the season. The order in which the months are tested is critical to identifying the season. I implemented code that minimizes the number of tests that have to be made. For example, I eliminated the winter months of Jan, Feb, Mar on my first test, so my second test for the spring season checks for Apr, May, and Jun with a single test for the first six months of the year, and similarly for the summer and fall seasons. Iteration in this function helps me save storage because I loop on one-second audio clips to get the duration I want. I had difficulty implementing the loop in the same function because of Unity's limitations.

Overall, the game is very fun and interactive to the user.

How Climate Change Effects the Ocean's Reefs

Skylar E Glass (Visitation School, USA)

The world's oceans cover about 70% of Earth's surface, and while they influence the climate, the rising global temperatures have a significant impact on the marine environment. The greenhouse gases produced from human activities such as burning fossil fuels for electricity, transportation, and heat are causing mass coral bleachings across the oceans. This can cause infectious coral diseases that are detrimental to the marine ecosystem. While the cause of coral diseases is still unknown, many scientists believe that rising sea temperatures and human pollution are the main reasons for the deteriorating reefs. The United States Environmental Protection Agency (EWA) established the Federal Water Pollution Control Act in 1948. In 1972, the act was renamed the Clean Water Act (CWA). The goal is to regulate the release of pollutants into the oceans. Wastewater standards have been set by the EWA to control the amount of waste released by industries. Under the Clean Water Act, a permit must be attained in order to release pollutants into navigable waters. The United Nations declared 2021-2030 the Decade on Ecosystem Restoration. The objective is to remove 26 gigatons of greenhouse gases from the atmosphere, therefore decreasing the rising sea levels and temperatures. The ocean's reefs are fundamental for not just marine life, but for human life. Coral reefs are the most productive ecosystems on the planet. They reduce wave energy and protect coastlines from harm and due to biological diversity found on reefs, there is potential for biopharmaceuticals. The future of the reefs depends on human efforts to prevent global warming.

Historical Re-creation of Marconi's Wireless Telegraph

Stella C Firmenich and Richard Wegmann (Community Project Studios, Princeton University, USA)

In 1896, Guglielmo Marconi invented the first successful wireless telegraph. This invention would be replicated around the world for years to come. These telegraphs were one of the first successful methods of long-distance communication, sending morse code across entire oceans. Marconi's ideas have been expanded upon ever since and many physicists have worked to improve upon his communication device.
The transmitter of the wireless telegraph uses a circuit, a solenoid and a spark gap to create an electric spark. This electricity is then sent from the transmitter's antenna to the receiver's antenna in the form of an electromagnetic wave. The receiver's antenna brings the signal to a coherer. The coherer-a glass tube with metal filling in it-becomes a conductor allowing the signal to continue to a polarized relay. When the signal reaches the relay, the "arm" attached to the relay strikes a bell and the coherer at the same time. This both produces audible noise and resets the coherer, preparing it for the next signal. The wireless telegraph combines the topics of make and break circuits, electromagnetism, radio waves, and early technological devices.
The goal of this project is to research and recreate a Marconi wireless telegraph from the early 20th century using similar methods and materials that were available to scientists then. This will enable students to not only study the history of the device, but also the exact parts and materials which were used in the invention. This allows a more developed understanding of wireless communication, make and break circuits, and other concepts that are key to the wireless telegraph.
Creating a model of the wireless telegraph is a great teacher for people who wish to fully understand how these devices functioned and were built. The model allows a visual as to how improvements in electricity and technology were pioneered over time. This develops a deep understanding of the electrical and technological concepts. While the building of a wireless telegraph can be an intensive process, the simple learning of models and study of them can help students further their studies.
We are working on this project under the guidance of Professor Littman of Princeton University, Harini Fredrickson, and Nathan Yates-a Princeton University senior. The project is part of a program spearheaded by Professor Littmann called the Community Project Studios.

Faraday's Ice Pail Experiment

Tvisha K Faria (20 Rowland drive, USA)

IFor the 2022 IEEE Conference, I have decided to present Faraday's ice pail. My essential goal for this presentation is to teach individuals about the key concepts of attraction, repulsion and electrostatic induction. This experiment will demonstrate the foundations of electricity, specifically the relationship between charges and their effects.

Michael Faraday, was an English scientist during the 19th century, greatly known for studying electromagnetism and electrochemistry. For his experiment, he acquired an ice pail (hence the name), to act as a metal container, an electroscope to measure the electricity, and a charged metal ball. By placing the metal ball into the pail without touching the sides, the free electrons around the metal pail repel the negatively charged sphere, leaving positive charges on the outside of the ice pal and negative charges on the inside. Whereas, if the metal sphere were to touch the pail, the electron charges would exchange with one another, and become neutral, without any charge.

My poster board will include information about the history, methodology, and takeaways of the ice pail experiment.

Modeling Historical Devices Using Computer Aided Design (CAD)

Vanisha S Nagali (Allentown High School, USA); Rohan Deb (Hillsborough High School, USA)

I constructed two different homopolar motors based on the original papers of Michael Faraday. That was followed by CAD models of the original designs of Lord Kelvin's water dropper and Arago's Disk. As my CAD skills improved, I designed a foolproof device that allows users to create perfect coils for the DIY homopolar motor and printed it using a 3D printer.

Using Fusion 360, I digitally recreated Joule's original design and modified it by adding a block-and-tackle system to allow a greater change in temperature. The next step was to animate the motion of the paddles caused by the falling weight. Since Fusion 360's animation feature is fairly basic, I researched alternative methods to simulate the movements. I wrote a script in Python to incrementally change parameters in Fusion 360, took frame-by-frame screenshots, and stitched them into a video. The result was a smooth stop-motion animation of the apparatus.

Bridging the Economic Divide through Blockchain

Jaden H Bethel (NONE, USA)

My presentation on the economic implications of blockchain aims to demystify the use of blockchain and other decentralized networks. While many may view blockchain as a form of investment for the uber-wealthy, countries with sizable GDPs - like El Salvador - have adopted Bitcoin as an acceptable form of currency. This success has been backed by years of research touting Bitcoin as an opportunity to combat economic divide. My research will provide an overview of the fundamentals of cryptocurrency, weigh both the benefits and risks of its implementation, and show why blockchain - alongside its many concepts including zero-knowledge-proofs - can be an equitable step in upholding financial unity.

Session Chair

Ashutosh Dutta (Johns Hopkins University Applied Physics Lab)

Session Poster-6

Poster Session 6

3:00 PM — 4:45 PM EDT
Mar 26 Sat, 3:00 PM — 4:45 PM EDT

Data Science and Analytics for Esports

Arjun Agrawal (Peddie School, USA)

The use of analytics in professional sports is widespread and rapidly increasing. Similarly, there is a need for analytics in the emerging area of esports, or professional video gaming. Counter-Strike: Global Offensive, widely known as CS: GO, is one of the most popular esports with over forty million copies sold, yet it has lacking analytics. This impedes simple and efficient evaluation of competitive CS: GO matches, player performance, and team performance, which is critical to teams, bettors, media, and fans.

The data for each CS: GO match, which includes both player actions and non-player events, is stored in a demofile. A demofile is a recording of the match generated by CS: GO that stores the data as a text of sequential sets of events with no contextualization of information. In order to perform analytics on the stored data, it must be modeled into organized data structures. The data parser developed by Dr. Claudio Silva and Peter Xenopoulos parses the data into Pandas DataFrames, which are spreadsheet-like data structures with rows and columns. Using the data parser, we introduce an analytics package consisting of (1) generalized functions to allow for the efficient filtering and aggregation of CS: GO data; and (2) specialized functions to allow for the efficient calculation of CS: GO statistics. The analytics package has been incorporated into a public software library and commercialized, with professionals and the worldwide CS: GO community currently using it.

Art and Science Education United

Helena Rittenhouse (Princeton University EPICS, USA)

As a society, we view art and science as two totally unrelated subjects, when in reality they have a lot of crossover. Art is often seen as a luxury pass time, something that is innately human and beautiful but does not have much room in modern society, which must make room for advances in the world of science - whether that be new medical discoveries, technological discoveries, and new ways of designing and building infrastructure. This separation is completely flawed; science and art intersect in numerous ways, and neither would be possible without the other.

Consider Leonardo DaVinci, creator of the Mona Lisa, among the most valuable paintings in the world. While he is a renowned artist, he was also a very successful engineer and architect. In fact, he designed a bridge in 1502 that would have been the longest bridge in the world at the time. It was dismissed as impossible to build, but in 2019, MIT engineers determined that the form of the bridge would have been structurally stable. Karly Bast, a recent MIT grad, herself said that it is a good example of how design and engineering are so closely linked.

Somewhere along the line, we started considering kids who are good at math and science as "smarter" as kids who pursue artwork. This is wildly off-base, and schools need to begin emphasizing art programs just as much as they do science programs. While science and math are traditionally thought of as rigid subjects, they must be seen with a creative eye, which requires artistic education as well!

Non-existence of the Algorithm that can Obtain the Optimal Solution for a Few Given Options of Investment in Constructive Mathematics

Jiahong Toby Sun (USA)

Constructive mathematics is simply a mathematical process of computational procedures. Constructive Mathematical Analysis is the incarnation of constructive mathematics. Constructive Analysis is according to constructive logic where the computation is built-in. Therefore, it is only based on the part of principles of classical analysis and constructive mathematics. In this paper, I apply constructive mathematical analysis to prove that there could not be an algorithm that always chooses the optimal investment from a few given options in constructive mathematical economics. The main tools are constructive real number, constructive function, and the existence of partially defined unextendible algorithms. Applying the definition of the constructive real number and the algorithm can express the profits of the companies. Comparing the profit among different companies based on the output of the algorithm cannot show the optimal solution for investment. Eventually, I use the limitation of the inextendible algorithm to prove the non-existence of the algorithm.

Using Social Media to Predict Stock Market Prices

Samuel T. Ghezae ( & Johns Hopkin's Applied Physics Laboratory, USA); Alexandru Cara ( & John Hopkins Applied Physics Laboratory, USA); Nicholas P Farber ( & Johns Hopkins Applied Physics Laboratory, USA)

In January 2021, history was made when a group on Reddit called r/WallStreetBets exposed how hedge funds excessively shorted GameStop stock (GME). Members of the community garnered attention by posting and coordinating buying of GME stock via platforms such as Reddit, Twitter, and Youtube, which led to a massive spike in the stock price by more than 500% in less than a week (Yahoo Finance). A spike this dramatic has never been seen before.
These stories caught our attention which led us to wonder how strong of an impact social media had on the stock market, and if it can be used to predict the increase in prices in the future. We are examining data from social media and looking at any possible correlation to GME stock data.
If there is a predictable relationship between social media activity and stock price, people could make better stock purchases based on online data. This can encourage people to share what they believe are good stock choices with others and share conversations on the topic.

Using Machine Learning to Identify Gender Bias in Screenplays

Faith Comising, Hanna E Wosenu, Jason H Kang and Irene Shijo (Johns Hopkins University Applied Physics Laboratory, USA)

In 1985, Alison Bechdel published a comic strip inspired by her friend's standard for avoiding movies with extreme male bias. Years later, the standard in her comic strip resurfaced and was adopted as the "Bechdel Test." The Bechdel Test for fictional works has three criteria: 1) the story includes at least two female characters, 2) the female characters talk to each other, and 3) the conversation revolves around something other than a man. While the criteria seem simple enough to meet, many of the most beloved movies do not pass the test, such as Star Wars (1977) and The Little Mermaid (1989). The Bechdel Test has been growing in popularity in the past couple of decades as recent studies have been bringing to light alarming findings about inadequate female representation in the film industry.

In 2015, Apoorv Agarwal of Columbia University and his team created a methodology that automates the task of determining whether a movie passes or fails the Bechdel Test. By categorizing each line of a screenplay into one of five tags ("N" for scene description, "C" for character name, "D" for dialogue, etc.), the machine learning algorithm finds the pertinent lines of the script and efficiently analyzes whether the movie meets the three criteria. Our group will be applying Agarwal's automated Bechdel Test methodology to recent movies in order to test the robustness of the proposed methodology.

Our project ultimately aims to contribute to the work being done on the Bechdel Test. Although the test is not an objective measure of whether movies are "sexist" or not, it is important to be cognizant that gender representations in the media, or the lack thereof, have the power to subconsciously enforce harmful gender stereotypes.

Distraction Osteogenesis Rign System (DORS)

Trung Q Tchiong (Upper Darby School District, USA)

Upper Darby High School (UDHS) Computer-aided Design (CAD) students have interests in the Bioengineering of the Distraction Osteogenesis Ring System (DORS) design and rapid prototyping technology.

The reversed engineering of the Synthes' DORS ( provided CAD students various challenging learning opportunities such as AutoDesk Fusion 360 3-D parametric solid modeling and static stress analysis software, the Lulzbot TAZ 6 3-D printer (rapid prototyping technology), and a large scale poster printer operations.

CAD students discovered an alternate assembled rings' locking that provides an improvement to the ring system. They also gained an insight into the components of Team Dynamics, win-win negotiation, and the urgency of deadlines that real-life industries are facing.

The large-scale DORS poster shows names and titles of all members, the Synthes-DORS system, the engineering hands-written notes, the 3-D Lulzbot assembled parts, the G-Codes, and one basic Fusion 360 static stress analysis result.

Multiverse Network Management System: Developing an IP-Based Network Discovery Agent

Shane E Jayasundera (Montgomery Blair High School, USA)

As newer networking technologies and architectures including Named Data Networking (NDN) and quantum networks are created, there is a need for network management systems that can handle multiple universes. In order to implement such a management system, a discovery agent must collect network data and information for telemetry. We built the agent using Python and specific libraries and tested on a SONiC network simulation. Constructed in three parts, the project first dealt with collecting the unstructured data, then parsing the data into JSON format, and finally sending the data to the multiverse controller. A dockerized, functional discovery agent was produced, with the independent capability to be implemented with the multiverse controller. The initial completion of the discovery agent has promised to be a step closer to being fully prepared for the application of new network architectures in the modern world.

Developing a Noise Canceling Device for Ranged Sound Suppression

Jason J Lai and Joseph Townsend (Gwinnett School of Math, Science, and Technology, USA)

The purpose of our project was to develop and test a device that was intended to reduce or eliminate noise within an open area. This would be achieved through the use of sound wave interference, by producing the inverse of the sound in order to destructively interfere with the original noise. Our noise canceling device consists of a speaker and microphone built into a small enclosure which was attached to an Arduino, an open-source electronic prototyping platform enabling users to create interactive electronic objects. The Arduino is programmed to analyze the surrounding noise inputted through the microphone before outputting the same noise through the speaker 180 degrees out of phase to achieve destructive interference. Code was developed to obtain the volume of noise before conducting a Fourier transform upon this input to calculate the needed outputted frequency. We hypothesized that the noise canceling device would reduce the noise levels within a defined area, the null hypothesis would then be that no decrease in noise is measured.

Our experimentation materials consisted of the noise cancelling device, a sound source, a preset field with consistent distance increments between the device and source, and a decibel meter which measures the sound's volume. Testing was done by placing the device between the noise source - where a smartphone was used to generate three notes, (G4 (392 hertz), A4 (440 hertz), and B4 (494 hertz)) - and the target area where the decibel meter recorded volume levels. Data from the experiment was consisted of the volume levels in the target after a control test was taken so that the ambient volume of the testing environment was taken into consideration. The data from the testing was averaged out and the results are as follows: The two lower notes, G4 and A4, revealed a decrease in volume from 67.8 dB to 64.3dB and 66.8 dB to 63.7 dB respectively, while the highest note B4 had a slight increase in volume from 67.8 dB to 68.0 dB. Using a t-test to conduct data analysis, the volume change for notes G4 and A4 were found to be statistically significant, rejecting the null hypothesis. The note B4 on the other hand did not have a statistically significant decrease, thus accepting the null hypothesis.

By analyzing the results, we concluded that the device performed better at lower frequencies than higher frequencies. This may be influenced by the testing environment, where higher frequency noises are more susceptible to interference, affecting the device's ability to reduce the noise. Furthermore, the irregularity could be due to higher frequency noises having shorter wavelengths which are harder to position out phase with. Overall, the device did accomplish our project's goal of reducing low to medium pitched noise within an area, with future goals to extend the device's effectiveness to higher pitched noises.

Dynamic Fused Deposition Modeling for Rapid Development of Aerial Swarms

Ryan A Ellis (Johns Hopkins Applied Physics Laboratory, USA)

Extrusion-based 3D printing has become an increasingly reliable manufacturing method. Though slower than Injection Molding, it enables rapid transition from design to product. Developments in materials and methodologies enable 3D printing to enhance prototyping in the field of aerial swarm robotics. Where the high density of traditional plastics makes them impractical for small fixed-wing applications, light-weight polylactic acid (LW-PLA) filament provides a solution. When printed at high temperatures, blowing agents in the plastic transform it into a lightweight foam. It expands by up to 300% after exiting the nozzle, reducing density at the expense of rigidity, with the secondary advantage of faster printing speeds. This material is difficult to configure due to its complex thermally modulated properties, and as such required significant testing to develop optimal printing profiles for airframe applications. In this testing, LW-PLA offered a great amount of customizability in its mechanical features. When printed at lower temperatures, it has similar properties to standard plastic filaments. This allows for hardness to be modulated at different points in the same print, enabling separate rigid and lightweight areas with custom machine control code. Through this hardening, wing spars or ribs can be printed in place with the rest of the part, allowing for structural advantages over traditional foam construction. Though many 3D printers are limited by build area for construction of full aircraft, hardened interlocking systems enable wing and body sections to be attached together with a high level of durability. These separately printed parts can be put together rapidly. Compared to similar handmade foam prototypes, assembly takes a fraction of the time. This is instrumental for the testing of fixed-wing UAV swarms, where many replicas of the same drone must be manufactured for each testing cycle. Waiting times and high costs for injection molds make it impractical for prototyping, and manufacturing many foam airframes by hand is imprecise and time-consuming. 3D printed airframes offer minimal difference between models, and close adherence to even the most complex CAD models. This improves the ability to add custom equipment mounts to both the interior and exterior of airframes, enabling increased modularity and greatly improved ergonomics compared to handmade airframes. Pairing FDM manufacturing with advanced printing materials and novel innovations in model design and slicing software makes it an ideal technology for aiding the development of fixed-wing UAV swarms.

The Fading Of Chinese Ethnic Minority Culture - A Case Study Of Five Inner Mongolia Novels

Hao Wen (High School, China)

Culture fading among ethnic minority population is critical to the social diversity, and it may cause social problems like reduced national identity and cultural confidence. China, experiencing fast economic development, is confronted with challenge of losing its splendid ethnic minority culture inheriting from thousands of years ago. Though government is calling for attentions and made laws to help protect the minority culture in China and built museums. but the young generation of Chinese people tend to migrate to urban areas for career development, where innovation, technology and efficiencies are more valued than ethnic diversity. This paper builds upon "Boundless Grassland", "The Believer's Last Word", "JunMa, CangLang, GuXiang", "Fu Qin Yu You Er Qu", "Da Sheng Kui Shang Hao", providing evidence of culture fading by analyzing the frequency of appearance of Mongolian Culture related images in those novel. The reasons of choosing novels as the data sources are the popularity of the novels is high enough, those novels are all written by Mongolian writers in China, and the the concentrated description or implication about the status quo of Mongolian Culture that are presented along the action of the characters in the novels. Based on Edward W Said's Orientalism, deep literature analysis methods of content analyses were used to ground change of Mongolian culture from the images presented in the novels that falls into 6 sectors: symbols, language, beliefs, values, and artifacts. I found that first, the fading of Chinese ethnic minority culture starts when modernization starts. Second, internal Orientalism caused culture fade and posed a strong threat to the preservation of Ethnic minority culture. Third, the process of objectification and internal Orientalism and their impact can be generalized to other culture. This work contributes to the literature of Orientalism, where China's data and state-of-art is rarely documented. Mongolian culture is one of the most influential culture on the world that impacted over half of the world throughout its history. The conservation of Mongolian culture is vital for the ethnic Mongolians, and will serve as a road map for the later preservation of the Culture of Ethnic Minority.

Session Chair

Ashutosh Dutta (Johns Hopkins University Applied Physics Lab)

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