13th IEEE Integrated STEM Education Conference — 9 AM - 5 PM EDT, Saturday, March 11 Onsite Venue - Kossiakoff Center - 11100 Johns Hopkins Road, Laurel, Maryland
K-12 Poster Abstracts
Poster Session 1
Drone-Aided Sensor Networks for Soil Contamination Monitoring
Lizbeth He (USA)
With this newfound research, it is critical to accurately monitor soil contamination in farmlands before implementing any pollution treatments. This research project applies recent advancements in chemical sensors and computer networks. It implements drone-aided sensor networks to tackle this issue. A group of sensors can be deployed as a sensor network to cover a particular area. Data collected by each sensor are transmitted to a central node for storage, analysis, and further processing.
The proposed method includes two types of components in the network: chemical sensors and drones. The drone first deploys the available chemical sensors into farmland in a formation to maximize the coverage. The sensors will detect factors such as but not limited to the amount of heavy metals, petroleum hydrocarbons, and polychlorinated biphenyls (PCB). The drone then functions as the central node to collect data from each sensor later on. After data collection for a pre-scheduled duration, the drone will fly out to receive the data from each sensor. Scientists and agricultural professionals can use the collected data for analysis.
To evaluate performance of the drone-aided sensor network, a mathematical model is further proposed. It aims to use the least amount of resources while still providing enough monitoring readouts. Parameters in this model include sensor amount, sensor monitoring range, sensor battery life, detection power of the drone, distance of the drone flying above the ground, monitoring interval, as well as the required readout amount. They describe different characteristics of the sensor network. Typical values from sample cases are implemented to evaluate scenarios. Results show this proposal offers feasible guidelines of conducting soil contamination monitoring.
Detection of Lycorma delicatula using Thermal Imagery and UAVs
Joseph E Miller (PRISMS, USA)
Spotted Lanternfly often live on the trunks of trees and it has been difficult to estimate the complete population distribution through conventional means because of inaccessible locations and high elevations. This project describes a method to detect Spotted Lanternfly populations by using an Unmanned Aerial Vehicle equipped with a FLIR Vue Pro thermal camera to record heat signatures. Likely as a result of the Spotted Lanternfly's natural metabolism, body heat of adult Spotted Lanternfly can be detected using thermal infrared imagery if the difference between the lanternfly and background is significant enough, which occurs at an ambient temperature of approximately 50°F. The resulting footage is processed using object detection machine learning to provide an accurate estimation of colony size, location, and spread. The data produced by this study will be used to create a density map of the Spotted Lanternflies over a given area.
This will provide an easy way to count Spotted Lanternfly for observational studies and future research.
A Biomedical Device for Separating Fluids from Tissues - FluidXtractor
Arthur Yang (Marriotts Ridge High School, USA); Feng Ouyang (Johns Hopkins University / Applied Physics Lab, USA)
SOS.net: A Robust System Harnessing the Power of AI to Expedite Search and Rescue Missions
Nesara Shree (Portland State University, USA)
Current, drone-and-human-vision dependent systems in place are not only incredibly inefficient, but also tiresome for drone pilot operators, who carry out over 60 Search and Rescue (SAR) missions a year. Alternatively, thermal detection drones used are inaccurate and far too generalizing, picking up on unrelated, inanimate objects that radiate heat. This is where I saw Artificial Intelligence (AI), Machine Learning (ML), and their powerful Computer Vision (CV) capabilities coming into play. What is needed is a reliable system that can accurately locate and signal by recognizing visual indicators of human presence or distress, and AI's application is a crucial first step in being able to expedite SAR missions, relieving the strain on our SAR teams, and saving lives. The goal is to make human search missions much more refined and efficient by implementing RCNN's Resnet50 ML model methodologies. By open-sourcing SOS.net, meaning that all of the code, procedures, a snapshot of the trained model, and the option to access the entire dataset is available to the general public on Github to download and/or contribute towards its further improvement, enables SOS.net to be a dynamic, yet robust, AI system that has high potential for actual implementation and continued refinement as a tool.
Design Calculations of a Biochair for Patients Requiring Leg Rehabilitation
Pranav R Bellannagari (IntelliScience Institute & San Jose State University, USA)
Design and Testing of A Multifaceted DBD Plasma Torch
Karthik Hari (Santa Teresa High School & San Jose State University, USA); Krishnaveni Parvataneni (BASIS Independent Silicon Valley, USA)
Impact of Training/Testing data ratio on ML Model Accuracy in predicting Cardiac Patient's Mortality
Siddhartha Shibi (Washington High School & Intelliscience Training Institute, USA); Vaishali Jha (Evergreen Valley High School, USA)
MATLAB Based Meta-Analysis Code Providing Common Perspective by Synthesizing Data from Various Sources
Himani Jha (Intelliscience Research Institute, USA); Rina M Weaver (Intelliscience Institute & San Jose State University, USA); Ambika Palleti (Evergreen Valley High School, USA)
In the second part of the code, the concept of heterogeneity was addressed by identifying and quantifying it in effect sizes through the MATLAB models. As the observed variation in the estimated effect sizes include true variation and random error, there is a need to isolate true variance and then use it to create to identify various perspectives on the dispersion. To achieve this goal, the MATLAB model was further developed that determined the Q statistics (a measure of weighted square deviations), the results of a statistical based on Q (i.e., P), the between-studies variance (T2), the between-studies standard deviation (T), and the ratio of the true heterogeneity to total observed variation (I2). This analysis provides evidence of heterogeneity in the true effect size. The newly developed MATLAB code is open source and will be available for any user to process the data. Our poster will include details on the models adopted in the MATLAB code along with the examples results that were obtained by executing the code for the target data borrowed from literature.
Automating Conventional Intravenous Stands for Easier Hospital Infusion
Yihan Chen (PRISMS (School), USA)
The automated version of intravenous stands aims for a better infusion experience for both patients and medical staff. Furthermore, it should be easily adaptable to current hospitals.
I am trying to create a clip-on device for current intravenous stands. This device should enable the intravenous stands to free the patients' hands and help with their mobility to some extent, monitor the medicinal fluids, infusion frequency, and patients' status, and share all data of patients with medical staff.
The project is still in progress.
Building a Standing Mobility Device to Help Handicapped People
Yihan Chen (PRISMS (School), USA)
The Standing Mobility Device mainly focuses on helping people with lower body disabilities. As suggested in its name, this device enables people to get up and go around on the same plane.
The goal of the Standing Mobility Device is to make disabled people in wheelchairs able to move around more freely and let them have a more independent life. The whole device looks like a small-sized car that people can sit on. In this case, a pushing device is used to push the person on the device into a standing position, while wheels are attached at the bottom to move both the person and the device as a whole around the flat plane. As for the pushing device, we mainly used an electric putter for the main force input. Arduino is used to controlling the two functions, and buttons and a rocker are designed to let the person control the device. The overall structure of the Standing Mobility Device is built with Aluminum bars and Aluminum plates. A few testing experiments done on the device prove its feasibility.
Using human body tracking technology to analyze the double axel in figure skating
Wanyun Qu (HIgh School, USA)
Instrumentation and Control of a Fluidic Muscle-Based Exoskeleton Device for Leg Rehabilitation
Rishit Agrawal (Evergreen Valley High School & IntelliScience Training Institute and San Jose State University, USA); Sahana Chowlur (Silver Creek High School, USA & IntelliScience Institute, San Jose State University, USA)
Domestic Wind Power Apparatus
Man Kin Cheng (Bishop Hall Jubilee School & BHJS, Hong Kong); Andrew Wong (Secondary School & Bishop Hall Jubilee School, Hong Kong); Shing Chan (Secondary School & Bishop Hall Jubilee School, China); Christopher Tang (Bishop Hall Jubilee School, Hong Kong)
Furthermore, although there are vast non-renewable energy resources, our consumption speed of non-renewable energy is far greater than their formation, like petroleum. Therefore, we need to confront the energy crisis and develop renewable energy which is more stable.
Wind power is the largest source of renewable electricity generation in the US, providing 10.2% of the country's electricity, and growing. The advantages of wind power include occupying tiny land and having a minimal environmental impact.
We aim to provide a sustainable energy supply and reduce pollution produced by the consumption of non-renewable energy. We want to alleviate the energy crisis worldwide by creating a wind power generator.
We faced some difficulties during the making process of the device. For example, we need to lower the voltage to 5V, the common voltage for daily uses. Also, we may need to design the custom printed circuit board (PCB) because there is no suitable designed PCB for us to use in the market and we may only design it ourselves.
We first surfed the Internet on how to build a wind turbine model. Then, we started to design it with devices like Rectifiers Diodes on our own-designed PCB to stabilize the voltage, then with a capacitor to store the electricity. After that, a 5V3ADC-DC converter module is for converting all the voltages into 5V. Lastly, we test all the models we made.
When the wind speed in front of the blade is greater than the wind speed behind the blade, the blade starts to turn which generates power from the movement of the blade. Then the energy flows onto the PCB. First, energy enters rectifiers diodes to make its voltage stabilized. Then, the stabilized energy passes through the capacitor, 5V3ADC-DC converter module, and lastly the battery. We can consume energy by charging batteries and phones with a USB plug.
The product meets the intended goals. The product can convert kinetic energy into electric energy for charging. It can generate electricity by wind power. If it were widely used, it would decrease the use of non-renewable energy.
Our device cannot face strong winds. We hope we can use tougher materials to make a new stand to overcome it. In addition, if we can improve it, we may enlarge the wind turbine model and blades to have better efficiency and the ability to generate more energy.
We hope that each household could own our device and put it in their garden or balcony to generate electricity for personal use to save energy.
Muscle-Inspired Home Automation System
Andrew Yuting Lu (Oyster River Middle School, USA); Femi Olugbon (University of New Hampshire, USA)
Immersive Experiences in the Omniverse Channel
Adrik Ray (Huber Street Elementary School, USA)
One area in which this technology can be particularly transformative is leveraging weather forecasts in predicting and planning our experiences. By incorporating weather data into augmented and virtual realities, users can experience the impacts of weather in a way that is not possible with traditional methods of data delivery. This allows for a more intuitive and engaging way to understand and prepare for changing weather conditions and plan for the best possible experiences.
The possibilities for immersive experiences are not limited to weather forecasts. Other areas such as sports, education, and entertainment can all be transformed by incorporating augmented and virtual realities. This can result in a more personalized, engaging and collaborative experience for users, with the potential to revolutionize the way we learn, play, and interact with the world.
The potential impact of the Metaverse and Omniverse goes beyond the realm of individual experience. They have the potential to create new opportunities for businesses and commerce, as well as to facilitate communication and collaboration on a global scale.
While there are still many technical and ethical challenges to overcome in the development of these new channels, the potential benefits are enormous. As we continue to push the boundaries of technology and explore new frontiers in human experiences, the Metaverse and Omniverse offer the promise of a truly transformative future.
My paper focuses on combining weather forecasts and augmented reality to create an immersive experience for visualizing a location on a future day and time. Though I have used weather forecasts to create this future immersive experience, this is not limiting. This approach can be expanded to include other elements such as crowd density, surrounding environment, augmentations with avatars etc for added effects. These experiences can be rendered in Metaverse and Omniverse channels, as well as existing channels like Mobile. Such experiences can enable better decision making, planning and satisfaction.
Modulation and Noise Effects in a Free-Space Optical Communication System
Joseph M Bailor (Johns Hopkins University Applied Physics Laboratory, USA); Jeremy Chung (Johns Hopkins University Applied Physics Laboratory & Winston Churchill High School, USA); Jonathan C Moses (Mount Saint Joseph High School & Johns Hopkins University Applied Physics Laboratory, USA); Jose Martinez Lopez, Jade Sim and Jony Teklemariyam (Johns Hopkins University Applied Physics Laboratory, USA)
Investigating the role of polyrhythmic music in attention-based neurological therapies using EEG Sensors
Sumanth Mahalingam (Evergreen Valley High School, USA)
In this paper, the neural processing involved in polyrhythmic music was investigated as a possible therapy for attention control. Polyrhythms involve the concurrence of two different rhythms simultaneously, such as a three-beat pattern superimposed on a four-beat pattern. In theory, entrainment models involving oscillators would involve adaptation to multiple simultaneous rhythms; thus, the additional overlay of rhythms involved in polyrhythms would create complexities in the rhythm that aid in restoring the balance between dopaminergic fulfillment and violation of cognitive expectations. Using electroencephalography (EEG) to measure neural responses and activity in the frontal and parieto-temporal regions, participants in one experiment were played a continuous 4:3 polyrhythmic melody with variances in tonal patterns. As the music was played, participants were instructed to copy a passage from a book, as a means of measuring the extent of the music's effects on motor coordination and attention. In another experiment, the same participants were instructed to copy down a similar-length passage while listening to a non-polyrhythmic melody with similar minor-scale tonal patterns as the polyrhythmic melody. In the final experiment, the same participants merely copied down a similar-length passage without music, to contrast neural activity during a motor task with no musical stimulus. Power-Spectral-Density analysis of the EEG results showed comparative increases in pre-frontal beta waves and decreases in pre-frontal theta waves when listening to polyrhythmic music, indicating an increase in focus while polyrhythmic music was played. This demonstrates that polyrhythmic music may be a viable avenue in exploring the extents of neural entrainment, providing insights into attentional therapies.
Detecting a system of Binary Black Holes using the Einstein Toolkit
Agneya D Pooleery (USA)
Black holes can be identified by jets and swirling masses of matter around them. They have an event horizon, plasma disk and a singularity in the center. The singularity of a black hole is an infinitely small point at the center where all its mass is concentrated. If you were to go inside a black hole and touch the singularity, you would instantly become part of the black hole. A black hole's event horizon is its perilous edge. Once something crosses the black hole's event horizon, it will never return. Since light cannot escape it, one would need to travel faster than the speed of light to escape it, which is impossible. A black hole also has an accretion disk. This is a disk of plasma orbiting around the black hole. The plasma may have been part of a star. The black hole's gravity is what keeps the plasma disk in place, and it can reach a stunning temperature of over 1,000,000 degrees Celsius! Also, the plasma is slowly spiraling into the black hole, making it smaller by the second.
An interesting phenomenon that has been observed by astronomers in recent years is the merging of two black holes, often called a Binary Black Hole (BBH) system. For many years detection of BBH systems was hard because of the nature of the black holes themselves and limited detection facilities available. More recently, it has been found that when black holes spin close to one another they can emit massive amounts of energy in the form of gravitational waves. These waves are about ten trillion times smaller than human hair and are incredibly hard to detect - but, they have distinctive waveforms and can be calculated using general relativity. When a BBH system reaches very high velocities, the amplitudes of gravitational waves reach its peak allowing them to be easily detected by laser interferometers.
My project aims to study and use a software platform designed by the astrophysics community - the Einstein Toolkit - which can be used to simulate the merging of black holes and study the gravitational waves emitted from them.
An Artificial Intelligence Approach to Fetal Health Risk Prediction
Vighnesh U Nair and Devika Gopakumar (Dougherty Valley High School & IntelliScience Training Institute, USA); Krishnaveni Parvataneni (BASIS Independent Silicon Valley, USA)
In conclusion, this study aims to demonstrate the effectiveness of using IBM Watson to predict fetal health based on factors like fetal movement and uterine contractions. By identifying patterns in these data, we hope to make more accurate predictions about fetal health and ultimately help to reduce maternal and infant mortality rates. Final poster will include all the information related to our research methodology, and IBM models that were developed in this work.
Geometry and Origami
Rishi Balaji (RJGrey Junior High School, USA)
In this case, math and origami meet, involving numerous geometric concepts. This poster paper will cover some proofs and explanations of different geometric techniques used in various origami models. For example, the folding of a square into any number of divisions using diagonals involves similar triangles, while folding a strip of paper to make equilateral triangles uses 30-60-90 triangles.
Plantis: Floating Greenhouse
Simeon Wan To Suen, Ka Lun Tang, Hoi Ching Leung and Zi You Jasmine Siaw (Bishop Hall Jubilee School, Hong Kong); Man Kin Cheng (Bishop Hall Jubilee School & BHJS, Hong Kong)
On the other hand, the demand of crops around the globe is increasing with the rising population growth. Together with the disruption of supply chain and logistics, the food prices skyrocket recently. In addition, the situation is worsening with tightening of geopolitics such as Russo-Ukrainian War. Therefore, due to the inadequate supply of agricultural land, we have to explore new farmland with minimal transportation. Hence we propose the use Plantis, a floating greenhouse, to adapt to such change given in this era.
Plantis aims to make use of the inundated area to plant crops. The system will first absorb seawater through a cotton wick that's made from old clothes. And then, we can obtain fresh water from the sea simply by distillation that made use of the natural heat source, sun, and room temperature. Which does not require any additional artificial energy sources. Besides, we have applied new technology like the ESP32 camera, water level, humidity, and temperature sensors, so as an electric valve. With the use of such IoT (Internet of Things), farmers can monitor their crops remotely and control the amount of water inflow, so that the crops will not be flooded. And these devices are all powered by the solar panel above to achieve zero carbon emission.
To observe the effectiveness of the system, we have monitored the growth of Dazzling Blue Kale and the seedlings of Lacinato Dinosaur Kale inside the greenhouse for 8 days. We can see that both species grow significantly. This means that the salinity of seawater does not affect the growth of vegetation. Instead, it seems that fresh water has been successfully obtained and supports their growth. In other words, Plantis succeeded in providing a suitable environment for the growth of plants.
The Importance of Experiential Learning
Yingyi Wei (China)
Simulation of Basketball Shooting Process and Investigation of the Optimal Shooting Speed and Angle Using Mathematical Models
Enze Danny Zhang (Beijing 80 High School, China); Rui Wang and Haoran Zhang (China)
Local Teachers' Satisfaction with and Perceptions of Voluntary Teaching Programs and Their Instructional Practices in Rural China
Siyu Liu (Shenzhen College of International Education, China)
Do volunteer teaching programs affect local teachers' satisfaction with these programs?
How do local teachers perceive volunteer teachers' teaching quality (i.e., classroom management, teaching content)?
Do volunteer teaching programs affect local teachers' relationship with students (i.e., perception of students' closeness with volunteer teachers and local teachers)?
Do volunteer teaching programs affect local teachers' instructional practices (i.e., teaching schedule; class contents; effectiveness of communication)?
Online questionnaires were delivered to local teachers who have participated in voluntary teaching programs. Participants were reached by convenience sampling and were asked to respond to multiple choice questions, questions with rating scales, and open-ended questions. The data include demographic information of local teachers, their satisfaction with volunteers and programs, their perception on the relationship between local students and volunteers, as well as the degree of disruptions on their teaching schedules. I analyzed data descriptively using mean and standard deviations in Stata.
I found that there were both positive and negative impacts of volunteer teaching programs on local teachers. Local teachers were generally satisfied with volunteers, their class management ability, and the voluntary teaching programs. Moreover, local teachers felt that their communication with volunteers was efficient. However, it was also reported that there should be more frequent communication between local teachers and volunteers, and local teachers' teaching schedules were disrupted, which brought inconvenience for their teaching progress.
Taken together, this study has the following policy implications. First, volunteers should contact local teachers before voluntary teaching programs, such as online meetings to have a better understanding of the real circumstances of local education and prepare for class contents. Second, frequent communication between local teachers and volunteers is needed before and during the voluntary teaching programs. Lastly, further research is needed to reach a larger sample size, increasing generalizability.
Performance Improvement of Table Tennis Server and Intelligent Training System
Lijia Shen (High School, China)
Man Hin Cheung, Hoi Lam Wong, Ka Yip Li and Anson Ngan (Hong Kong); Man Kin Cheng (Bishop Hall Jubilee School & BHJS, Hong Kong)
For the motors, we decided to use servo motors as they are light, and their degree of turning can be easily controlled and limited by coding. We are using an Arduino board for our prototype, but we may change this due to its size.
In order to control movement, we tested buttons, microswitches, slide switches, and toggle switches. Slide switches are not favorable to us as it is hard to activate. They are not convenient to be used to control the servo motors. Moreover, we would like to let the servo motors have different degrees of movement. Hence, switches that can only send on-off signals are not favorable. Rotary Switches and rotary resistors seem like our only choice. However, they are bulky and we would like them to be portable.
We are using different body parts to control the movement of servo motors. Since we read a few sci-fi books, we decided to control the movement of servos by less-used body parts such as the jaw, toes, and eye muscles. To fulfill our imagination, we used stress sensors on those body parts for signals to control the motors. However, our school does not have stress sensors, so we made our own to test them out. We tried using a pressure-sensitive conductor sheet (velostat) to create a pressure sensor, which worked very well. However, it is expensive and unstable as we ordered it from Taobao (the change of resistance differs greatly for each sensor). After knowing how velostat works (Wikipedia: Velostat, also known as Linqstat, is a packaging material made of a polymeric foil (polyolefins) impregnated with carbon black to make it electrically conductive.), we decided to try coating paper with carbon by pencil lead (12B which conducts electricity better than other pencils lead) and it also works well. We found a cheaper version of a homemade stress sensor. The stress sensor consists of two copper or foil strips and a pencil coated paper as the sensor is bent, the resistance of the strip will decrease and hence, we can use the change of resistance to control the movements. The board will read the change and hence, cause the servos to contact. We would also like to add a mini-game to let it play rock paper and scissors, pushing buttons to demonstrate its movements.
In the future, we would also make a whole arm. We would like to improve the design and help more people as it's the aim of this project. We would also like to try using EMG to control it, but we have to conduct more research. We are unsure whether people born without arms can have those neuron motors.
Improving chess player skills with studying tactics comparison between chess Grandmaster and chess engines
Jinshang Li (PRISMS High School, USA)
Effective Methods of Detection and Prevention of Falling Over by Using AI
Qinuo He (PRISMS High School, USA)
Designing a Sensor Embedded Tracksuit using Arduino MCUs and Accelerometers to Model Kinesiology of Athletes
Shaunak M Marathe (JHU APL, USA)
Today, in modern times, every athlete yearns the opportunity to become a better and more stronger version of themselves with immediate hopes of improving their unprecedented skills and multitude of fitness levels. Many have the challenge of how or what they specifically have to improve on and because of this, they fail to understand their full potential by becoming figuratively frozen in the process. Not knowing how to improve and what areas they are stronger/weaker in can be a detrimental factor for every athlete. Whether it be in sports that require immense stamina, skills, or patience, perfecting the specified sport form and understanding the in-depth statistics will improve any athlete's performance and game confidence exponentially, making this one of the hardest problems in athletes today.
Solution: With cutting edge, modern wearable technology, every athlete can benefit from information honed around improving their technique and physical movements in their designated sports area. In order to tackle the given problem, I decided to create a tracksuit model/prototype that would use sensors attached to a person's limbs (arms and legs) to imitate the physical motion and proper technique of designated exercise such as a workout routine, high endurance game, or just a simple walk.
Genomic Curation for Improved Marine Mammal eDNA Classification
Christopher Li (The Johns Hopkins University Applied Physics Laboratory, USA); Olive J Lara (Johns Hopkins University Applied Physics Laboratory, USA); William Joseph Ross III (John Hopkins Applied Physics Laboratory)
Precision Medicine in Lung Cancer
Yuchen Ye (China)
Flow of beads in a viscous film on vertical fibers
Leonardo Dobrinsky (USA)
This setup highlights the interesting behavior of beads in a viscous film sliding down thin strings. The experiment shows that even a relatively simple system can exhibit complex behavior. For example, beads can slide in unexpected ways. By observing the experiment, I hope that you will find the joy and beauty in physics.
Academic Stress, Parental Expectations, and Sleep: A Daily Diary Study Among Adolescents
Melinda Yu (USA)
Methods: Thirty high school students (aged between 15 and 17, 77% female) were asked to participate in a 3-day daily diary survey in which they reported their daily academic stress and sleep. Academic stress was measured by daily study time (in hours), number of school problems, whether participants had testing (yes/no), and overwhelmingness of workloads (1 (not at all) to 5 (extremely)). The current study calculated the average score of academic stress and sleep across the three days to indicate participants' experiences during the study period. The standard deviation of sleep quality was calculated to represent the fluctuations in sleep.
Results: On average, participants self-rated their sleep quality as 2.63 (possible range = 0-5), representing a relatively poor sleep quality. Their sleep length on average was 6.95 hours (range = 4.95-11.53 hours). Multiple regression showed that longer study time and a more overwhelming workload on a certain day were associated with poorer sleep quality. Facing more school problems was associated with shorter sleep. The study also found that higher parental expectation was associated with poorer sleep quality. Yet parental expectations did not moderate the effects of academic stress on sleep, meaning that academic stress has an adverse effect on sleep regardless of parental expectations. None of the academic stress indicators were associated with sleep fluctuations.
Discussion: The findings suggest that adolescents may face problems related to sleep quality and sleep length, especially when they perceive large academic stress and higher parental expectations on their developmental outcomes. Academic stress and parental expectations act dependently to influence sleep, such that adolescents with higher parental expectations also experienced more overwhelmed schoolwork. In short, the current study highlights the importance of both school and family environments on adolescents' daily activities and health, as well as provides implications for more integrative perspectives of adolescents' development.
Conclusion: Higher academic stress had an adverse effect on adolescents' sleep quality and length. The strength of such associations does not vary on parental expectations, yet higher parental expectations itself also had a negative effect on sleep quality.
Advancing Knee Arthroscopic Surgeries with Endoscopic and B Mode Ultrasound Imaging
Catherine Ren (Havergal College, Canada); Yining Zhang (University of Toronto Schools, Canada)
To solve this issue, combining ultrasound imaging in conjunction with the traditional optical modality has been investigated. An external ultrasound can allow the surgeon to track the whereabouts of the surgical instruments inside the knee, while an ultrasound arthroscope using a modified intravenous ultrasound can allow the surgeon to receive depth-resolved information, such as evaluating the properties and integrity of cartilage and tissue around the joint in three-dimension (3D) structure. While current studies in this field focus on either using an external ultrasound or an adapted intravenous ultrasound to be used inside the knee cavity, the specific combination of both an external and arthroscopic ultrasound offers great promises for improving knee arthroscopy procedures with comprehensive information. However, designing and fabricating an internal ultrasound imaging device for this specific purpose is resource-intensive and costly.
In this work, we developed a simulation program to mimic ultrasound arthroscopy implementations for optimizing the design of ultrasound arthroscopy devices for knee surgery. The simulation was built in MATLAB on the basis of an ultrasound propagation simulation tool-box (k-wave). A series of new functions were developed to build ultrasound transducers for imaging from inside the knee. A 3D model of the human knee was developed in SketchUp. The 2D cross-sectional images were captured from this model and then uploaded into the simulation program as imaging targets for ultrasound imaging tests. Ultrasound images with high consistency to targets were received in the simulation. The simulation program can be easily modified to fit specific needs and correct small problems, which allows researchers to test the effectiveness of the proposed designs without spending large amounts of money to create a genuine working model. The optimized combination of using endoscopic (internal) and B-mode (external) ultrasound imaging is a new design that can create positive outcomes for many different stakeholders. Patients will benefit from an improved surgery procedure which ensures less error and a better outcome. It is anticipated that this study will serve as a stepping stone for future research and eventually test trials that employ the dual ultrasound devices method in clinical settings.
Toward an Energy Saving Smart Campus - IoT Smart Light Switch
Tsz-Him Ma and Yuen-Ning Poon (Cognitio College Kowloon, Hong Kong)
We also introduce the details of the design and construction of the IoT Smart Light Switch. It was designed for an environment with abundant natural light, such as a corridor or semi-open areas. It is a WIFI-based smart switch that senses the presence of a passerby, the ambient light level of the surrounding environment, and the present time. It was built with a passive infrared sensor (PIR sensor), ambient light sensor, and WIFI-enabled ESP32 microcontroller, shielded by an outer case made of acrylic plates. The IoT Smart Light Switch connects with the Smart Relay and the cloud server via WIFI, and it transmits the switching command to the Smart Relay to turn on/off the light. The light control is achieved through three parameters: ambient light level, time of the day, and human presence detection. For example, lights are turned off when the ambient light is low outside working hours with no human detected. The IoT Smart Light Switch also periodically uploads data to the cloud server, such as light level, passerby presence, and light power status. An end-user can access the cloud server to retrieve data, access the dashboard, and control the IoT Smart Light Switch through their mobile devices.
Finally, the poster includes the results and future work of the project. Since the project is still a work in progress, we present an estimation of the energy and electricity bills that could be saved by adopting this prototype. Future plans include (i) conducting a trial in the school corridor or podium to compare the trial result with the estimation of electricity use, (ii) improving the IoT Smart Lighting Switch based on the trial result, and (iii) exploring other potential applications.
Can Deep Learning Models Trained on Small and Imbalanced Ultrasound Image Samples Detect Polycystic Ovary Syndrome (PCOS)?
Sophia Y Liu (Cherry Hill High School East, USA)
Several studies have applied deep learning methods to diagnose PCOS through analyzing ultrasound images. Recently, transfer learning has become increasingly popular for enhancing the performance of deep learning models. Transfer learning uses deep learning models pretrained on data from other domains to solve a problem in a new domain. This approach can be more effective because the pretrained model may store knowledge and information that could help solve the new problem. Last year, researchers began investigating the use of transfer learning in the diagnosis of PCOS based on large and moderate sized training sets (Suha and Islam, 2022 and Hosain et al., 2022).
My research aims to help develop a more thorough understanding of the effectiveness and efficiency of using deep learning to identify PCOS from ultrasound images. Specifically, the purpose was to shed light on the following questions: How do small and imbalanced training samples affect transfer learning? Will a transfer learning approach outperform a non-transfer learning approach? Will deeper neural networks result in higher accuracies?
I use four classical deep learning architectures, ResNet50, ResNet101, VGG16BN, and VGG19BN. The ResNet architectures have over 20 million parameters and the VGG architectures have over 130 million parameters. I implement a transfer learning model and a non-transfer learning model of each architecture. The transfer learning models were pretrained on the ImageNet which has over 16 million natural images but no medical images. The PCOS dataset has about 2000 images. All models are prepared using training and validation sets, and tested on the same test sets.
The experiments based on ResNet and VGG models generated consistent results. First, small and imbalanced training sets have minimal impacts on the performance of transfer learning models. Training sets of different sizes are used. The smallest, most imbalanced training samples only have 6 PCOS positive images and 60 negative images. All transfer learning models trained on these sets achieved average accuracies >99% on the test set of over 1500 images. Second, transfer learning models are more accurate and reliable, and have shorter and smoother training processes. Third, models with more layers don't perform better on this problem.
This is the first research that demonstrates transfer learning's capability of detecting PCOS using small and imbalanced samples of ultrasound images.
Suha, S.A. and Islam, M.N., 2022. An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image. Scientific Reports, 12(1), p.17123.
Hosain, A.S., Mehedi, M.H.K. and Kabir, I.E., 2022, October. PCONet: A convolutional neural network architecture to detect polycystic ovary syndrome (PCOS) from ovarian ultrasound images. In 2022 International Conference on Engineering and Emerging Technologies (pp. 1-6).
General Optical Properties of Two-Dimentional Materials & Applications in Optoelectronics
ZiRui Yu (High School, China)
Deciphering the Indus Script: Decoding Missing and Unclear Indus signs and Identifying Anomalous Indus texts from West Asia using Markov Chain Language Models
Varun Venkatesh (USA)
First, we analyzed patterns and concordances of the signs, pairs, triplets, and other n-grams and discovered how the signs behave with respect to their positions in the Indus texts. We then did statistical analyses focused on text length and sign positional distribution and built a positional probability model. With the understanding of sign behavior, we built Markov chain language models based on n-grams, augmented with the positional probabilities of the signs. We also devised and implemented an effective sign fill-in algorithm on top of these Markov chain language models using model scores of snippets of n-grams. We find that a group of three signs in a cluster capture a lot of information when the signs appear in the middle of a text. Signs appearing in the leftmost positions are the most difficult to predict. Using the language models and the sign fill-in algorithm, we identified missing single signs in the test dataset and tuned our parameters to improve the accuracy to about 63%. Then we filled in the actual unclear texts with our predicted signs and published our predictions in the order of probabilities. This adds a wealth of information that was previously missing to the Indus script corpus.
Our results also show that the language model perplexity was high for several Indus texts that were found in the West Asian region in the contemporary bronze age civilizations of Sumer, Dilmun, and Elam. Some of these texts did not fit in well with the language model built with Indus texts from just the Indian subcontinent. From this, we conclude that the language in several West Asian Indus texts is quite different from the language used in the Indus script from the Indian subcontinent.
We believe that the sophisticated language models and algorithms that we developed give a better understanding of how the Indus Script behaves, add more complete texts to the Indus text corpus by filling in the missing signs, and postulate that the Indus script encodes multiple languages that varied by geography. We think these are significant advancements toward deciphering the Indus script.
Enhancing STEM Education to Communities with Low Access to STEM Resources
Christine DiMenna (Gilman School & QuarkNet, USA); Arya Kazemnia, Aman Garg, Leo Leo Wang, Abraham Karikkineth and Daniel Koldobskiy (Gilman School, USA)
Mathematics Model of Honey Bee Colony
Qingyuan Yao (China)
In the model, we drew upon a lot of academic papers and created a conclusion formula which displays the whole quantity of a colony. This model contains several factors which can affect the whole population. Apart from that, we considered about special winter behavior of honeybees, which can remarkably affect the final result.
Reimagining Seawalls: Exploring Shoreline Protection Methods with Minimal Surface Inspired Seawalls
Alex Yang and Michael Wen (USA)
With rising sea levels, waves and the abrasive energy that they bring disrupts the flow of everyday life. Waves erode shorelines which crawl closer and closer to vulnerable parts of these cities, causing insurmountable damage to the population and infrastructure. Traditional seawall designs are not only expensive and outdated, but also ecologically harmful by blocking much of the areas seashore organisms inhabit.
To help combat this problem, this study focuses on exploring more efficient seawall designs. Porous structures such as Triply Periodic Minimal Surface (TPMS) based seawalls have been chosen due to their mathematical simplicity, mechanical strength, cost effectiveness, accessibility, and ecological friendliness. This research will compare the seawalls made using different types of TPMS structures. TPMS structures are first created using MathMod and Blender using a mathematically defined equation. Computational Fluid Dynamics (CFD) tools such as Ansys Discovery and Fluent are used to investigate the potential performance of a new seawall design (water flow speed reduction, wave height reduction, etc.) with respect to engineering parameters of the seawall such as porosity, slope and spatial frequency. The results will help recommend the best paths to take in engineering the next generation of seawalls.
Evaluating the Effectiveness of Design Processes in Mechanical Engineering Applications
Diana N Omar (Johns Hopkins University Applied Physics Laboratory, USA)
Integration of Quantum Computing with Deep Learning
Amin Boukari (Caesar Rodney High School, USA)
Machine Learning Predictive Model to Reduce the Harmful Environmental Effects of Pesticide Usage in Agriculture
Kareem Boukari (Caesar Rodney High School & Delaware State University, USA)
In agriculture, Crop and food production are necessary to provide supply, avoid hunger and inflation. To protect their crops, farmers need to use insecticides, pesticides, and nutrients. However, these chemicals are harmful to our health and ecosystem, as they pollute the environment. In agriculture, this is a trade-off between increasing crop production and reducing necessary land treatments. Reducing too much of these chemicals may lead to less production of food.
To address this challenge and avoid unnecessary excessive use of pollutants, I propose to build new predictive supervised machine learning algorithms based on decision trees, Support Vector Machine and Random Forest that will assist farmers to continue to use pesticides in a way that benefits both them and the environment. Using this tool, the farmers can reduce chemical and frequency usage based on the predicted outcome on their crop health ahead of time by setting some experiments where they minimize the environmentally harmful chemical usage as much as possible while keeping good and sustainable crop productivity.
The publicly available dataset used in this project is a three-class labelled dataset that contains the quantity of insecticides, pesticides, nutrients, and the soil category, frequency, season, and type of crop.
To choose the best model, I ran multiple experiments and compared different models using different parameters using Python scikit-learn library. For training, k-fold cross validation was used to split the data into training and testing sets.
Using Bayesian Classifier, we obtained an accuracy of 82%. The SVM was not able to separate the classes very well especially because we obtained null precision and recall for two imbalanced classes. The decision tree classifier led to 83% accuracy. We also conducted multiple experiments for each random forest using 200, 500, 700, and 1000 trees at different depths. The best average accuracy result of 89% accuracy was obtained using the XGBoost Random Forest with 1000 trees of depth 12. However, precision and recall for the 2 imbalanced classes were low. To overcome the data imbalance, further work needs to be done using data augmentation or under sampling.
In conclusion, the proposed method can be used as a strategy to convince farmers to reduce the harmful chemicals for the environment while keeping good crop productivity. Findings will provide novel insights to farmers about the extent to which crops can be exposed to pesticides before having a major crop damage and how they can be reduced while keeping good crop productivity.
Simulating Quantum Magnetism on Noisy Quantum Computers: An Analysis of Trotter-Suzuki and qDRIFT
Peter C Seelman (Johns Hopkins University Applied Physics Laboratory & Glenelg Country School, USA); Taohan Lin (Johns Hopkins University Applied Physics Laboratory & Thomas Jefferson High School for Science and Technology, USA); Milan Tenn and Samuel N Manolis (Johns Hopkins University Applied Physics Laboratory, USA)
Novel Medical Sensor Design For Mass Casualty Triage and Trauma Care
Diya Sharma (Johns Hopkins University Applied Physics Laboratory, USA)
Quantum Noise Mitigation Via Randomized Compiling Abstract
Harry Rathbun (Johns Hopkins University Applied Physics Laboratory, USA); Alex J Zhang (Johns Hopkins Applied Physics Laboratory, USA); Colin La and Kenji Ishi (Johns Hopkins University Applied Physics Laboratory, USA)
Mentored by: Tom Gilliss, Gregory Quiroz, Paraj Titum, Leigh Norris
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, 20723, USA
Quantum computers harness properties of quantum mechanics to make complex calculations that classical computers cannot. Thus, quantum computers have the potential to solve problems that today's best supercomputers cannot, such as problems in drug development, computational biology, prime factorization, and optimization. However, current quantum computers are greatly hindered by error, also known as noise.
There are two primary types of noise: coherent and stochastic. Coherent noise is error created by the environment and systemic flaws. Some causes of coherent noise are detuning, calibration errors, and crosstalk (qubits interacting with one another in an uncontrolled way). Stochastic noise is random error. It is often caused by fluctuating fields in the environment or interactions with other systems. Errors caused by certain types of stochastic noise can be corrected by quantum error correction (QEC), a technique that uses redundancy to protect the information stored in a quantum computer. The same is not true for coherent noise, which generally leads to the highest error rates under QEC.
Randomized Compiling (RC), which was introduced by Wallman in 2016, transforms coherent noise into a type of correctable stochastic noise . Our objective is to study the effectiveness of RC on both coherent noise and stochastic noise. RC effectively alters the noise by inserting random gates into a quantum circuit. Since RC is random, it can produce the desired outcome after averaging over many circuit evaluations. Importantly, RC keeps the circuit logically equivalent and does not extend the circuit length. Unlike other error mitigation methods, it can be adapted to many different types of quantum circuits.
Using the Python library Qiskit, which is an open-source software development kit for working with quantum computers, and IBM's free online quantum computers, we simulated RC on coherent and stochastic noise. Our experiments showed that RC reduced the overall error. We found that the error for the bare circuit without RC grew exponentially as circuit depth increased, while the error for the RC circuit grew linearly. The standard deviation of outcomes was greater for the RC circuit due to the randomness. Despite this, the RC circuit clearly showed superior performance to the bare circuit.
 J. J. Wallman and J. Emerson, Physical Review A 94, 052325 (2016).
First Ever Whole Genome Sequencing and De Novo Assembly of the Freshwater Angelfish Pterophyllum Scalare
Indeever Madireddy (USA)
With the MinION MK1B device, 6.94 million sequencing reads and an estimated 10.1 gigabases at a 3.24 kb N50 read length were collected. Two flow cells were used to collect this sequencing data, and the flow cells were run for 72 hours each. The reads collected had a mean read quality of 15.06 and a median read quality of 14.58, corresponding to an estimated 97% sequencing accuracy. Reads were collected at an average translocation speed of 220 bases per second.
Collected reads were then screened to identify potential contaminant organisms in the sequencing data. The kraken2 tool identified that Pseudomonas aeruginosa, a common opportunistic aquatic pathogen, was the largest contaminant of the sequencing reads.
The mitochondrial genome of the angelfish was assembled from the sequencing reads. All 37 conserved mitochondrial genes, including 2 rRNAs, 13 genes, and 22 tRNAs common to eukaryotic organisms, were identified, indicating a complete and robust assembly. This new assembly was 25 bp longer than the reference mitochondrial assembly, with a 99.1% similarity.
The final nuclear genome assembly consisted of 15,486 contigs totaling 734.79 Mb with a final BUSCO score of 86.5% and a 41% GC content (Simão et al., 2019). The genome size and GC content is similar to other fish species, such as the Asian seabass and the Nile tilapia. The N50 contig length of the assembled genome was 96,962 bp, and the longest contig was 543,394 bp. Repeatmasker masked 12.47% of the genome containing simple repeat sequences.
NCBI blastp (ver. 2.12.0) performed functional annotation of the genome through the GenSAS platform. 24,247 unique protein-coding sequences orthologous to other species were identified in the angelfish genome against the refseq vertebrate-other database. Most genes, 59%, were orthologous to Archocentrus centrarchus, a closely related South American cichlid. Timetree suggests that A. centrarchus and P. scalare diverged between 28.7 to 72.4 million years ago.
Future work would involve RNA sequencing of the angelfish to build an appropriate transcriptome of the organism. Illumina sequencing could also be performed to improve the current assembly.
Chat Bot Implementation on Mattermost Servers Using APIs
Taylor Ann Benning (Johns Hopkins University Applied Physics Laboratory, USA)
Min-Max Optimal Matching
Yibo Cheng (USA)
The strategy formation process of publicly listed firms under the "Double Reduction" Policy - a pilot study of factors impacting firm survival
Leming Liu (China); Lufan Wang (Florida International University, USA)
By collecting data 71 publicly listed firms registered in mainland China under category of education, we found the following statements. We found 87.5% turned their K12 business into non-profit, and 10% of firms entered the market which is not relevant to general education marketing. Second, 30.7% of publicly listed firms claimed bankruptcy after a year of policy release. Third, 75% are still pivoting their potential sustainable business model, as well as dealing with customers' refund. Fourth, 15% have finished their navigation phase and entered alternative profit-seeking market.
By collecting, coding, and analyzing the data about firms' pivoting behaviors during the transition, as well as firms' demographic characteristics, we found that firms' accumulated assets and revenue model diversity both positively correlated to the possibility of firms' survival.
In theory, the work contributes to the literature regime of strategy formation under unexpected shock. It provided an extreme case of how market-wide publicly listed firms pivot their surviving strategies giving a very short period of time. In practice, it is the first academic analysis providing insights to Chinese policymakers and public firm stakeholders about the impact of the "Double Reduction" policy. It alerted the big firm's higher managing teams to be aware of potential political shocks.
Paving the on-ramp to AI learning in the classroom
James Murray (Holy Ghost Preparatory School, USA)
Using hands-on physical projects we were able to perform various tasks related to autonomous driving to establish a driving baseline for current capabilities for driving with code and object detection with frame recognition. These included cautious driving, screenshots from various reference points, color detection, and buzzer noises. We've applied Python programming skills to navigate many different virtual and physical challenges and have also designed custom challenges to create a fun learning process. All of these challenges are highly competitive among classmates as the title of the best programmer/driver in the class is always on the line. In the future we plan to further developing these projects using AI to focus on making the car self-sufficient so that it can demonstrate making decisions completely on its own without any human input.
Low-cost, High Accuracy Smart Parking Solution for Urban Areas
Vivek Pragada (Central Bucks South High School, USA)
Vehicle presence detection is a fundamental aspect of intelligent parking systems - systems that would inform users about parking spot occupancy throughout their area in order to minimize wasted search time. This can only be efficiently accomplished by smart parking sensors that can convey real-time information about parking spot occupancy. One of the key requirements for smart parking sensors is the high accuracy detection capability of parking slot occupancy for various automobile makes and models, including recent electronic vehicles (EVs), under a multitude of practical parking events. Also crucial are long battery life, easy installation, and low maintenance, all of which need to be met under the strict constraints of low cost, high durability, and operation under numerous environmental conditions.
While several approaches are proposed by recent studies, most are either unreasonably expensive, require considerably high power consumption, or cannot provide the accuracy necessary for most practical parking scenarios. There is an urgent need for low-cost smart parking sensors that can provide high accuracy in almost any environmental conditions.
In this paper, we propose a smart parking sensor that consists of a magnetometer and a low-power wide area (LPWA) connectivity module. Unlike other state-of-the-art approaches, data from multiple parking sensors in adjacent parking spots are synthesized, dramatically increasing accuracy of detection. The accuracy of parking spot occupancy increases especially when the magnetometers are distributed evenly across parking spaces, fitting nicely with typical parking lot deployments. Our research shows that this technique helps in determining parking spot occupancy significantly better than independent sensors for practical parking events, including front-park, reverse-park, pass-through-park, double-park, and drive-by events, as well as various automobile makes and models, including EVs.
To implement this multi-sensor approach efficiently, the magnetometers cannot continuously broadcast its readings, and are configured with specific thresholds, in both measured magnitude and duration, that determine when to upload information. When the configured thresholds are met, the magnetometer in the smart parking sensor triggers an event to its corresponding LWPA LoRa communication module, via a simple microcontroller, to the LoRa base station, which then sends it to the smart parking server in the cloud. The smart parking server synthesizes event data received from multiple smart parking sensors. Since the smart parking server is aware of each specific deployment, a reliable algorithm can be implemented to accurately determine parking spot occupancy changes due to the new parking event.
Predicting Patient Hospital Admission for Triage with Machine Learning: An Analysis of Emergency Service Index Data
Rishi Mulchandani (Johns Hopkins University Applied Physics Laboratory, USA); Soma S Hebbar (Johns Hopkins University Applied Physics Laboratory (JHUAPL), USA); Jayant Maheshwari (Johns Hopkins University Applied Physics Lab (JHUAPL), USA)
In recent years, the use of artificial intelligence and machine learning techniques in healthcare has become increasingly crucial due to the vast amounts of data and the difficulty of manual analysis. The focus of this study is to use supervised machine learning models to accurately predict patient hospital admission based on Emergency Service Index (ESI) data. The ESI index classifies emergency room patients into five risk severity levels, with Level 1 being the most severe and Level 5 being the least severe. The importance of the ESI index lies in its ability to allocate resources efficiently and accurately in critical care situations.
Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), we developed a machine learning framework to predict admission and critical care outcomes in patients presenting to emergency departments. Our objective was to identify the socio-demographic and clinical factors associated with admission and critical care outcomes, and to achieve high accuracy in our predictions. By utilizing 76 numerical features and 9 categorical features from the NHAMCS dataset, we trained and validated our models using logistic regression (LR), random forest (RF), and XGBoost algorithms. The categorical features are one-hot encoded and combined with the numerical features to form the complete feature set. The data is then split into training, testing and validation sets using the train_test_split method with a 20% test size and a random number generation seed of 1234. Cross-validation is performed using StratifiedKFold with N_Folds set to 10.
The study aims to achieve high accuracy in predicting patient hospital admission based on ESI data. The results of this study highlight the potential of machine learning in healthcare and the usefulness of XGBoost as the best performing model in this study.
This study demonstrates the significance of using machine learning techniques in healthcare, particularly in the prediction of patient hospital admission. The results of this study show the potential of XGBoost as a powerful tool in healthcare and emphasize the importance of accurate patient classification during the triage process for the benefit of patients. Future studies could aim to expand the dataset and evaluate the models on a larger scale, as well as investigate the use of unsupervised learning techniques in healthcare prediction.
Wearable ultrasound devices for blood pressure measurement: a simulation study
King Ho Guo (UWC CSC Chang Shu College, Japan)
Cliffs is commonly used for measuring blood pressure in hospital; however, it is challenging to use such a device for real-time monitoring. ECG is another method to measure blood pressure, however, the need to carry the ECG machine in 24 hours makes it unrealistic. In contrast, a light sensor that is more portable and real-time is highly desired for practical and daily use.
Wearable ultrasound devices have been studied to address this challenge. A recent study reported a design of an ultrasound array that can be easily wore and can measure the blood pressure by characterizing the distance between two blood vessels. Due to high portability and small size, this wearable ultrasound device can provide real-time, 24 hour monitoring of blood pressure. Since this device uses ultrasound penetration, the sensitivity of the device is very important to ensure the accuracy of measurement in deep tissue. The reported device employed a piezoelectric element array distributed in a 4 by 4 grid to generate and receive ultrasound and managed to measure blood pressure at a depth up to (find the value in literature) cm. Changing the distribution of the array is promising to further improve the sensitivity and hence the depth of use, however, fabricating a series of such devices is very resource-intensity.
In this work, we designed an ultrasound simulation program to mimic a wearable ultrasound device for optimising the array design. The ultrasound transmission and detection were achieved with K-wave, a MATLAB toolbox for mimicking ultrasound propagation in various media. Blood vessels, blood, and surrounding tissues were mimicked by setting different medium density and the speed of sound. The simulated devices use ultrasound to calculate the time taken to travel and bounce back between the two blood vessels. With the pre-knowledge of the speed that ultrasound travels in blood, the distance between two blood vessels was calculated and correspondingly, the blood pressure can be read out. We designed and tested different array shapes and distributions to maximize the signal-to-noise ratio of the ultrasound signal, which provided the highest sensitivity in blood pressure measurement. Limited by computing power, the current simulation is under a 2D model instead of a 3D model. In future works, 3D model will be setup and tested on a more powerful workstation to further test and improve the design of wearable ultrasound device. In future work, a wearable ultrasound device will be fabricated under the guidance of the simulation results, which could increase the effectiveness and the scope of the use of wearable ultrasound devices.
Engineering Kits to Prevent Summer Learning Loss
Anna R Rosner (Albemarle High School, USA)
Commercial truck parking conceptual design
Trung Q Tchiong (Upper Darby School District, USA); Nelson Dennis (Main Author, USA)
The Ethics and Privacy Risks of Artificial Intelligence in Education: Balancing the Benefits and Concerns with More AI
Cynthia C Zhang (Canada)
A subfield of AI, Educational Data Mining (EDM), specifically focuses on the application of AI to educational data. This refers to the process of using data mining analytics to interpret data from educational systems to improve student outcomes. EDM applies machine learning (ML), neural networks, and statistical methods to educational data to uncover patterns, trends and relationships. Specifically, ML algorithms are used to build predictive models based on educational data. For example, a decision tree algorithm can predict student exam performance based on factors such as prior grades, attendance, and demographic information. This, coupled with Neural networks- a type of ML algorithm inspired by the structure of the human brain used to model relationships such as student behaviour or demographic correlation - allows EDM to perform the following:
-Student performance prediction: Predict student performance on assessments and courses based on their demographics and learning behaviour.
-Adaptive learning: Personalizing the learning experience for individual students based on their performance and preferences.
-Student behaviour analysis: Understanding how students interact with educational technology and what factors influence learning.
-Early warning systems: Identifying at-risk students early on and providing targeted interventions.
However, there are several privacy and ethical risks associated with EDM:
-Data collection: EDM involves collecting large amounts of sensitive data.
-Data sharing: Sharing of educational data between different stakeholders, such as schools, government agencies, and companies, can increase the risk of data breaches and unauthorized access to information.
-Data security: Storing and managing large amounts of student data presents a risk of data breaches, hacking, and theft.
-Profiling and discrimination: EDM algorithms can be used to create profiles of students based on their data, which could lead to biased decisions and discrimination.
-Student rights: EDM may infringe on students' rights to control their own personal information.
This is only one of the many examples of AI systems and their moral implications, particularly if they are designed or used in ways that discriminate against certain groups of people. As a result, there is a growing need for a robust and comprehensive framework for privacy and ethics in AI which could address the various challenges and benefits posed by AI, as well as the need to provide guidance on how to build and utilize AI in a manner that considers both privacy and ethics. This document provides an overview of the key privacy and ethical issues in educational AI and discusses the possibility of a framework to address these challenges in order to balance AI's potential benefits and concerns.
A Novel Pre-Hospital Indoor Rescue Drone For Locating Cardiac Arrest Patients at Home Instantly and Delivering Emergency Medication Under Surveillance Before an EMS Arrives
Max Du (Canada)
In this project, a novel Pre-Hospital Indoor Rescue Drone is designed and constructed to solve the two challenges and save more lives by aiming to start rescue faster and to witness more patients, including those home alone, in the first critical minutes before EMS arrives. The drone system is designed for indoor use, like personal home drone standby. It has four design features: 1) auto-activate the drone and locate the patient instantly after receiving wireless alert from the patient; 2) live-video surveil the patient with an EMS; 3) deliver patient's prescribed emergency medication under surveillance; 4) open room doors if necessary. A drone homebase is designed to enable auto-activation of the drone and keep it on power standby 24/7, by using ESP32 C3 M5 Stamp wireless communication, and a pulley mechanism driven by a stepper motor. A web server is created for EMS to activate remote surveillance and phone calls with the patient. Through a low-cost Android phone mounted on the drone with a screen mirroring app, first responders can know the patient's situation and specific position in the house instantly. An auto injector is designed which consists of a modified linear lift system and an intramuscular needleless injector; a web server is created for EMS to remote control the speed and direction of motors on the auto injector; and a medicine pill box is designed and attached on the top of the drone beside the auto injector. A door-mounted servo system is designed to open a room door through a wirelessly controlled gripper.
The current prototype of the Pre-Hospital Indoor Rescue Drone is constructed, automated, and operational. As tested in a residential setting to simulate a real scenario for a random patient, the design is valid for 1) drone auto activation and approaching patients instantly, as tested 55 seconds flying upstairs; 2) patient live video surveillance and phone call communication from 11km away; 3) delivering emergency medication smoothly with a pillbox and intramuscular auto-injector 3-4 centimeters close to patients; 4) room door opening through remote control. This innovation is the first indoor prehospital rescue drone to help save cardiac arrest patients, which can be affordably integrated into the existing EMS rescue process to help survival chances, shorten recovery time, and reduce healthcare costs.
Wireless Networked Motion Planning Control for a QBOT2
Saami Ali (Cold Spring Harbor High School, USA)
The "Rock Candy Approach for Lithium Extraction"
Qixiang Feng (USA); Zhiyong Ren (Princeton University, USA); Qiang Chen (Princeton International School of Math and Science, USA)
Beauty or the Beast: Understanding the Durability of Nail Polishes
Anwita Wadekar (St. Bernadette School, USA)
I chose three nail polishes from Sally Hansen; Xtreme Wear which contains two plasticizers, Complete Manicure which contains one plasticizer, and Good Kind Pure Vegan which has zero plasticizers. I painted five fake nails with each of these three nail polishes and attached them to fake hands. I then put these fake hands through rough, moderate, and light-use conditions. The rough use experiment was rubbing sandpaper against each nail and counting the number of rubs until the nail polish started to chip. The moderate use experiment mimicked dishwashing. I put dish soap into a bucket of water and rubbed a sponge gently across the nails and measured the time it took for the nail polish to fade. The last experiment was the light use experiment which simulated handwashing. I put hand soap and water into a bucket and moved the hand around while tracking the time taken for the nail polish to fade. I found that the Xtreme Wear nail polish takes longer to fade and chip compared to the Complete Manicure, which takes longer than the Pure Vegan nail polish.
I then studied the harmful effects of the two plasticizers, Ethyl Tosylamide and Triphenyl Phosphate, which are used in the Xtreme Wear nail polish. Using the Skin Deep Database from the Environmental Working Group I found that Ethyl Tosylamide is not extremely toxic but it still can affect the endocrine and hormonal system causing cancers and birth defects along with some allergic reactions. Triphenyl Phosphate also known as TPHP, is more toxic than Ethyl Tosylamide. It causes reproductive issues and a couple of animal studies revealed neurodevelopmental effects with small doses. Some human case studies showed disruption to the endocrine system too. It is used in the manufacturing of plastics and as a flame retardant. TPHP is also an environmental toxin. When nail polishes are thrown away the remaining polish from the bottles can diffuse into the soil and water, coming into contact with other species and disrupting their bodies.
Through this project, I have learned that many cosmetics and personal care products can contain chemicals that can cause short-term and long-term health problems. I plan to use this knowledge to raise awareness amongst my friends and in my community about toxic chemicals found in everyday cosmetics. I would also like to advocate for a law that prevents the use of such toxic chemicals in cosmetics. In California, legislation was signed to ban toxic chemicals in cosmetics back in 2020. 24 toxic chemicals were banned and California was the first state to stop using these perilous ingredients. I hope to do the same in Massachusetts and inform the community how toxic and harmful some cosmetic products can be.
Federated Learning with Prioritized Data Sample Selection
Rebekah Wang (West Windsor-Plainsboro High School South, USA)
However, when using federated learning to predict user preferences, not all data samples are equally useful. For example, when predicting what videos a user might want to watch next, a user's more recent watch history would be more useful than a user's older watch history. In this case, data samples with a small age could be more useful, where the age of a data sample is defined as the amount of time that has passed since the data sample was generated. Training the model with a higher number of useful data samples would allow the model to make more relevant and accurate predictions. Additionally, when aggregating model updates from the clients, a weighted average should be used, where a heavier weight is given to a model that was trained with more useful data.
Thus, a new federated learning approach with priority-based data sample selection and weighted model aggregation is proposed. Priority-based data sample selection works as follows: when the client devices train their local models, each device should deploy a data sample selection process that prioritizes useful data. The essential idea is to give useful data samples a higher priority or probability, while maintaining the randomness in selected data samples for each training round. Then, when the server uses priority-based weighted model aggregation, the local models from special clients (clients that used a higher percentage of useful data samples during training) will be assigned a heavier weight. This way, the global model will make more relevant predictions as it is more influenced by useful data.
To assess the proposed approach, federated learning using the proposed approach was compared to benchmark federated learning (i.e., FedAvg). After each training round, the accuracy of each global model was tested to graph a global model convergence chart. Three trials were conducted, each with different weights. In the results, as the weights for special clients increased, the global model accuracy increased faster and more dramatically. Even when all the clients had the same weights, the global model still achieved a higher accuracy than the benchmark's. There are a few areas for further study to improve the effectiveness of this approach. For example, the server could adaptively assign weights for each client independently.
Study on Projects of Natural Restoration of Rivers in Korea and Other Countries
Sahng-Won Lee (Seoul International School, Korea (South)); Richard Kyung (CRG-NJ, USA)
The goals of river restoration include improving water quality, restoring habitat for native species, and enhancing recreational opportunities. Effective river restoration requires careful planning, monitoring, and collaboration among various stakeholders, including local communities, government agencies, and scientific experts.
This study addresses recent river restoration projects in progress both internationally and in Korea and introduces relevant study cases and reviews. Since river restoration is a complex subject that involves more than simply environmental protection, the perspectives of communities living near and sometimes dependent on a river are considered and discussed in the presented study.
The natural sciences and engineering may effectively resolve the technical issues, but cooperation with experts in the social sciences and humanities is required to achieve lasting solutions.
Modeling atmospheric ablation of iron meteors undergoing thermal decomposition
Jonathan Wu (Applied Physics Lab, USA)
Study on the Electron Carriers in the Active Layers to Improve Photocurrent in Polymer Solar Cells
Geonwoo Bae (Choate Rosemary Hall, USA); Richard Kyung (CRG-NJ, USA)
In this paper, the active layer of the cell which contains an electron-rich material and an electron-deficient material was theoretically and computationally studied to enhance the efficiency of conduction in the unit. The properties of the polymers in the photoactive layers, such as the optimized energy, electron distributions, bandgap energy, and electron mobility, were found or discussed to determine the efficiency of the unit.
The objectives of this research are to develop new potential materials to improve the performance of the photoactive layer and increase the overall efficiency of solar cells.
Study on Hospitability Industry Trends and Changing Demands
Keonha Bae (Choate Rosemary Hall, USA); Richard Kyung (CRG-NJ, USA)
The insights gained from hospitality research can inform decision-making and improve overall business performance by conducting surveys, focus groups, market analysis, and other methods. In the hospitality industry, there are a few factors to consider for successful management. Personalization: offering customized experiences to guests, such as tailored recommendations and services.
Technology: using technology to improve guest experiences, such as mobile check-in, smart room technology, and virtual assistants. Wellness: providing guests with health and wellness experiences, such as fitness centers, healthy food options, and spa services. Lastly, unique and experiential accommodations are important since they offer unusual or distinctive accommodation options, such as treehouses, yurts, and tiny homes.
In this paper, these factors and trends are studied to shape the future of the hospitality industry so that many hotels and resorts can incorporate them into their operations to stay competitive and meet the changing demands of guests.
The Advanced & Automated Pill Tracking & Dispensing System
Archishma Marrapu (Thomas Jefferson High School for Science and Technology in Northern Virginia)
Weihsing Wang (PRISMS)