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Designing an aircraft to fly at supersonic/hypersonic speeds comes with many challenges. These are mainly attributed to the shockwaves formed at those speeds, which create unique flow conditions at these speeds. These challenges lead to unique design considerations in the three pillars of aerospace engineering: propulsion, structures, and aerodynamics. This study is a preliminary design study that explores aerodynamic considerations and provides aerodynamic analysis. It aims to create an optimal aircraft design for a passenger aircraft at Mach 5 cruise speed with 50 passengers. The design study focuses on the aerodynamic impact (lift and drag) of changing the specifications of the wing, where it explores the effects of changing the wingspan, root incidence angle, and root chord length. Using OpenVSP, a parametric aircraft modeling software, multiple wing iterations are created based on the changing variables listed above. VSPaero, a vortex lattice solver, is then employed to evaluate the wings’ aerodynamic performance based on the variables listed above. This design study explores the flight performance metrics of lift-to-drag ratio, skin friction coefficient, wave drag coefficient, induced drag coefficient, and lift coefficient. Using the data gathered from these simulations, this design study establishes a relationship between the design variables and their effect on the aerodynamic performance metrics listed above. These relationships and models are then utilized to define an optimization problem that yields the optimal 50-passenger supersonic aircraft that maximizes lift-to-drag ratio at Mach 5 cruise speed.

aerospace engineering, mechanical design & simulation, sensors, signal processing, machine learning

Aerodynamic Optimization of a 50-Passenger Aircraft for Mach 5 Flight

Pedro is writing & producing a podcast about the US prison system and its flaws. He will be paying specific attention to solitary confinement and the controversies surrounding it. For example, he plans on exploring the psychological consequences of being placed in solitary. He will interview people who are experts on these topics so that he can develop skills in interviewing. He will also be looking at how other countries' prison systems work and how they compare to the US. 

mental health counseling, trauma and resiliency, race-related stress and racial trauma, gender-affirming mental health treatment, qualitative research skills,

How US Prison Systems Can Be Fixed - A Podcast 

Aanya's project will include a 3 piece blog series focusing on key mental health issues impacting youth populations. Her blog series will highlight evidence informed rates of psychological issues related to impacts from school related stressors, how to cope with the stress of exam season, and impacts of sleeping on mental health performance. We will work together to develop a blog that describes prevalence rates as urgent concerns and then introduce strategies to minimize harmful impacts and increase normalization of experiences among youth. Her key audience will be youth populations, and her goals are to maximize her learning and understanding of mental health among youth, while also promoting evidence-informed prevention strategies to others in her age group.

Community psychology, social policy, statistics, quantitative and qualitative research

A blog series on key mental health issues impacting youth populations

Shyamak's project involves analyzing driver behavior at intersections; namely, understanding when a driver is showing signs of atypical driving (such as being stopped while at a green light). 
The first step to solving this problem with this input is to detect the traffic lights and states automatically. Shyamak will use machine learning to approach this problem. 

Artificial Intelligence, Computer Vision, Machine Learning, Autonomous Vehicles, Human-Robot (and Human-Computer) Interaction, Music, Intelligent Systems, Image Processing

Detecting Anomalous Driving At Intersections

The project will consist of literature review research paper on neurodegenerative disorders. Specifically, it will focus on Alzheimer's, Parkinson's, and Huntington's diseases. The project will include background information, etiologies, and the behavioral aspect of symptoms for each disease. In addition, it will analyze current treatments for each disease as well as look into future possible treatments and directions of research. There will also be a brief comparison of similarities and differences between each of the three neurodegenerative diseases.
 The purpose of the paper is to serve as a basic source of information and help in further research on these disorders. For this purpose, around 17 research articles were picked for the review paper. In the conclusion, suggestions for future research were included, from what areas can be targeted in the quest for finding a cure for these disorders. The table of similarities and differences serves as a summary of the paper while also suggests how a comparative and holistic approach can be taken for these disorders. A major focus has been given on the behavioral aspects of Alzheimer's, Parkinson's and Huntington's disease, as they highlight how each of these disorders take away the quality of lives of patients who suffer from these disorders. They also show how the behavioral symptoms are more or less similar in these three disorders, therefore comparative studies can be really helpful for further research. The paper doesn't get too technical, so that even students can benefit from this review while doing projects related to neurodegenerative disorders.

Biology, Neuroscience, Epidemiology, Neuroepidemiology, Social and Behavioral Epidemiology, Developmental Psychology, Behavioral Psychology

Neurodegenerative Disorders: Today and Tomorrow

Bhavik is working on a project to predict disulfide bond placement in protein structure. He is using PyRosetta, the premier protein folding software, to get started. After, he will be applying PyRosetta to PDB files of his choice for prediction. He will also hopefully get to use machine learning to optimize the disulfide bond location prediction.

Case reports in medicine, computational protein design, cryo-EM image analysis, computational genomics, telemedicine, AI in medicine, biochemistry, web development, data journalism

Working with pyrosetta to predict disulfide bonds

Mannya is working on a review paper summarizing research on relationships between SES, mental health, and brain structure. She is thinking she will focus on anxiety and the amygdala. She plans to break the paper up into two sections. The first will summarize research on relationships between SES and anxiety in adolescents/teens. The second will focus on relationships between SES and the amygdala (e.g., amygdala volume). She will also have an introduction and conclusion.

Neuropsychology; Clinical psychology; Aging; Alzheimer’s disease; Dementia; neuroimaging

Effects of socioeconomic status on mental health issues and brain structure

In the last few decades, usage of aquatic herbicides has significantly increased, causing rising concern about its potential effects on the health of the treated ecosystems. The purpose of this paper was to determine if there is a significant relationship between the application of aquatic herbicides and the health of *Micropterus nigricans* (largemouth bass) populations. To accomplish this, bass length and weight data was gathered from lake records and analyzed together. The lakes analyzed in this study were Lake Winnipesaukee (treated), Big Squam Lake (untreated), and Spofford Lake (untreated). All of these lakes, especially Lake Winnipesaukee, are of great economic importance to the surrounding communities.  When comparing the population characteristics of the lakes on an individual basis, the lakes are ranked highest to lowest as follows: Lake Winnipesaukee, Spofford Lake, and Big Squam Lake. When comparing based on treatment status, the treated group had higher mean weight and length compared to the untreated group. The results of the Student’s t-test showed a significant difference between the means of the untreated group and the treated group. An over time comparison showed similar historical patterns among all three lakes, with fluctuations appearing to be caused by weather events. Big Squam Lake appears to be impacted less by these factors. These results indicate that while herbicide treatment may increase the size of bass, it may come at the cost of the fishery’s resilience to outside factors such as extreme weather events by interfering with the natural regulatory processes of its ecosystem.

coral biology, environmental changes, marine ecosystems, applications of math to ecology, how ocean dynamics affect marine life, how organisms specialize for their environments, how hybridization affects the potential of species, using models to connect to real-world systems, interactions of social factors and the environment, spatial statistics projects, symbiosis, landscape genetics, microbial interactions

ON THE IMPACT OF HERBICIDE TREATMENT ON THE POPULATION CHARACTERISTICS OF MICROPTERUS NIGRICANS AND A LAKE’S RESILIENCE TO ABIOTIC FACTORS

Social media is widely prevalent in our daily lives as it has become one of the most widespread mediums for publicizing news and information. Due to its extensive usage, millions of different news outlets have sprouted in the past few decades with the purpose of influencing the public with targeted information. However, in the masses, some sources take advantage of the gullible public to further private agendas by altering the facts. To prevent the public from falling into the bait, detecting fake news and altering the public is essential. In the envisioning of my project, I aim to create a synthesis that compares and contrasts different articles on detecting fake news to determine the best possible way to catch incorrect information before it becomes available to the broader public. Over the years, others have attempted to tackle the same issue with various results. My intention is to summarize all the different methods, algorithms, and structures and derive a combination of them that works best. The final result will be a paper that is broken down into three different parts: an analysis of previous fake news detection techniques, details of a newly proposed method to detect fake news, and the steps it took to create and validate the said model. When it comes to sources and data sets the project will be assessing a lot of the same material used by previous studies as well as any new additional sources needed for research purposes. Most of the data will be large quantities of sample sets of fake and real news to train machines. The final takeaway from the project will be not only to find a tangible solution to detecting fake news but to also kick off a wider impact for the future. As mentioned before, social media is only growing more popular day by day so being able to stimulate factual information while still keeping up the right to free speech is a crucial step in humanity’s progression.

Any topic in data science (machine learning, applied math, statistics), web development, or chemistry.

What is the most efficient way of detecting fake news on a text base level?

Sidd is working on a film analysis project, specifically looking at how color is used in film to add depth to the story. He will analyze 3 films, examining how color highlights changes and arcs in the plot and reveals specific character traits. He will then compare and contrast the use of color between movies. The final outcome will be 5-10 minute audio-video piece that will show the results of the analysis and highlight key insights for the audience. The goal is to help other artists apply color more considerately in their own films or photography to enhance the story.

Graphic design, data science, sustainability, participatory design, information design, sound design, film making, photography, creative writing, user experience design

Color In Film

Ideas had changed from when I started with the website, toward the ending of it. In the beginning, I had possible ideas of using it to teach the public about how to understand these disorders, but as I have progressed, my main goal has changed as it Is reading towards educating a research audience about some of these disorders, and about how deadly they are. Each genetic disorder is deadly in its own way, but my main argument stood out as in the past, technology was not so advanced and detailed as it is today, and this gives us room to advance further and develop better technologies to combat this disease. For example, CRISPER CAS-9 has just come into the industry and has already been making big advancements in the field, as it is supporting to cure of many types of Genetic Disorders ranging from a single gene to polygene disorders at any level of complexity. Techniques like these are what can make a change in our world, and we need to be as advanced as we can be with AI and Technology, so we can cure many of these disorders. Another common technique used in Gene Therapy, where scientists use vectors to transport the goo version of a gene. For example, Viruses are common examples that are used because they have the natural ability to deliver genetic material into cells, as they transport their RNA into the Cell. This might seem dangerous, but the infectious part of the virus is removed, and therefore is safe to use. This is also an advancing technology that is commonly used by scientists. Similar to these, more effective technologies should arrive for disorders like severe asthma, and Huntington's which have been cured yet properly. Severe Asthma is so complete that conducting research on that is very difficult. Solving these issues is what we should aim for with technology in this field.

Molecular and Cellular Biology, Genetics & Genomics, Plant Biology, Immunology, Environmental Biology, Microbiology

The research question I am currently tackling, is what are the similarities of certain genetic disorders, and how can we use that understanding to help us figure out possible solutions to them?

Cryptographic hash functions take some input and uniquely map it to a protected output. The speciality of these functions is that they are not easily reversible so that it is practically impossible to get back the input given the output. There are several methods already in place for creating a secure cryptographic hash function. I would like to focus on the using MFCCs and NSSCs from the human voice to create a hash function. MFCCs and NSSCs are coefficients that succinctly represent the envelope of the human voice by looking at energies from different parts of the vocal tract. This voice data is processed through a BiGAN network that convolves this data and produces a unique vector. This vector represents the unique cryptographic key. To improve the performance, especially since the GAN can have several parameters, I would like to implement a quantum GAN instead of a classical GAN. However, this assumes an ideal case where there is no noise between quantum gates. This project researches and compares how different noise-adaptive qubit mapping methods minimize error due to unwanted environmental or qubit interactions in this quantum GAN, allowing us to produce a highly secure cryptographic hash function.

Computer Science (Data structures, algorithms, Data science, ML, AI, Backend, Databases, AWS, etc)

Which noise-adaptive qubit mapping method is best for minimal error in a cryptographic hashing function?

Parkinson's Disease (PD) is a prevalent and challenging neurodegenerative disorder, affecting nearly 10 million people globally. Current diagnostic methods rely on observations of symptoms and medical history but have proven to be fallible, with misdiagnosis rates ranging from nearly 10%-20%. Furthermore, a lack of understanding in the field of PD poses a great hurdle in determining a well-fitted treatment for each patient. As of now, PD has no cure, making early detection critical for appropriate intervention to slow its progression. Electroencephalograms (EEGs) are a novel approach in the field of neurodegenerative diseases that provide crucial information on a patient's brain’s electrical activity, which can be crystalized into new observable symptoms of PD, as well as serve in diagnosing the disease earlier and more accurately. In this project, we verify the applicability of EEG biomarkers to characterize and predict PD using a cycle-by-cycle analysis of neural oscillations. We segment patient recordings into individual cycles, which offer a more accurate measure than conventional methods that operate on a sinusoidal basis, and compute relevant features. A statistically significant difference (p<0.05) between PD and Control in amplitude, period, and symmetry suggests that EEGs are able to differentiate the two groups successfully. To verify these findings, we implement a sequential neural network model to classify a segmented filtered reading, which attained a 96.15% accuracy, well outperforming current methodologies. These results indicate that electro-neurophysiological data has strong potential in effectively predicting PD earlier, ultimately improving treatment outcomes for millions worldwide.

Computational Neuroscience, mental health, neuroimaging, public health or translational work, and topics regarding racism, sexism, etc.

Electroencephalography Biomarkers to Characterize and Predict Parkinson’s Disease using Cycle-By-Cycle Analysis of Neural Oscillations and a Deep Learning Model

Dissociative identity disorder is most often singnaturised by the existence of plural consciousnesses within a single person and the recurrent memory gaps caused by said consciousnesses. These alternative consciousnesses(alters) often cause what is referred to as interidentity amnesia as [each identity withholds the memories that they experience from other identities], causing other identities to be supposedly amnesic to information they themselves did not learn or events they did not experience. Within the community of interidentity amnesia research, there is debate surrounding whether these alternative consciousnesses are legitimately amnesic or, rather, have repressed the memories. In this review, we will summarise the research that has been done to support both sides of this debate.

Neuroscience, Psychiatrics, Anxiety, PTSD, Endocrinology, Sensory Systems, Anatomy, Down Syndrome

Repressed Memory Versus Dissociative Amnesia in Patients with Dissociative Identity Disorder

Civil/environmental engineering, urban planning

The project is a podcast and interactive blog that explores the debatable and perplexing questions posed by different literature and film. In a unique relatable voice, this project will focus on the methodology of the posed questions and the implications they pose. Fostering a creative, safe atmosphere in which the conversation between reader and listener is constantly open.

Middle Eastern Studies, Arabic Literature, LGBTQ Literature, Gender and Sexuality Studies, Creative Writing: Poetry, Popular Literature, Fandom Studies

Is Rome a Part of Italy?

Innovative technology has the potential to solve pressing environmental issues like water scarcity. Specifically, developments in nanotechnology have emerged as promising ways to achieve fresh water. Such novel water treatment devices use nanoscopic materials such as carbon nanotubes, alumni fibers, and zeolites. Some materials on the nanoscale can detect contaminants while others can purify them. Nanotechnological approaches have promising performance and implications for energy efficiency and sustainability. This paper will explain the opportunities for nanotechnology to improve water purification with examples of current technology and its methods and efforts. It will inform and motivate a broad audience towards nanotechnology for sustainable use of valuable resources.

Resource recovery, water and wastewater treatment, renewable energy, air pollution control in environmental engineering, data analysis using R programming, engineering for developing communities (WASH), environmental sustainability, nanotechnology

How nanoscience benefits the process of water purification

Prenatal environmental maternal stress has been positively correlated with social and emotional development of the infant, which creates the foundation for future mental and emotional health. These stress factors have also been implicated with increased rates of anxiety, depression, and other mental disorders. Autism has also been linked to these environmental stress factors. Specifically, maternal stress has been shown to possibly alter the DNA methylation of genes, which links to autism. One particular gene that has been linked to changes in mental health is NR3C1. NR3C1, usually associated with long-term development of anxiety and depression, is a glucocorticoid receptor whose activity may also be correlated with autism. Furthermore, the DNA methylation of NR3C1 has hereditary properties with a higher correlation with infants of the male sex. This coincides with the higher rates of autism in males than females. Other genes are likely involved in the biological mechanism behind the connection between maternal stress and development of autism. 

There are numerous explanations behind maternal stress that emphasize societal norms. For example, an increased risk of abuse and societal expectations of womanhood/motherhood may generate different kinds of stress, such as traumatic stress. These explanations are bolstered by the higher rates of post traumatic stress disorder, depression, and suicide ideation in women. These potential explanations establish a link between autism and existing social constructs, suggesting that reform in societal norms may reduce the risk of autism development in future infants. In this review paper, I will utilize primary research articles and literature. Specifically, I intend to gather research and reviews from both medical and non-medical publication sources. 
(265 words)

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