bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease
Authors: Kar Shreyas , W Campbell Peter
Published: September 2023

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Since one of the earliest symptoms of PD are changes in voice patterns, we employed machine learning algorithms to detect these abnormalities. Using existing voice data from patients with PD and healthy controls, we created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN). The inputs for these algorithms were two statistical measures of voice patterns: Pitch Period Entropy (PPE) and Spread1. Both algorithms were successfully able to discriminate between PD patients and healthy controls with F2 scores > 0.93. Moreover, we found that the time from diagnosis had no impact on the performance of our models. Thus, we report the creation of two models that can reliably and accurately identify the voice patterns characteristic of PD. Our findings suggest that it is possible to diagnose PD by analyzing voice patterns, which would enable disease screening that is cheap, accessible, and accurate.
Monitoring the formation of polyurethane foams with an infrared camera: Classroom activity
Authors: Kerewanmas Napat , Kongchu Natthanan , Pewnim Thanit , Maneesan Janjira , Jenjob Ratchapol , Crespy Daniel
Published: September 2023

Foams prepared with polyurethanes (PU), a polymer with units in the main chain joined by urethane linkages, are commonly used in isolation panels, seals, automative seats, and bedding. The reaction causing the formation of polyurethane foams is a conventional, yet spectacular, way to attract the attention of polymer chemistry students. Infrared cameras are a relatively inexpensive apparatus that allow for semi-quantitatively temperature monitoring at the surface of objects. Because the formation of polyurethane is exothermic, the polymerization reaction and the expansion of the foam can be easily detected by an infrared camera. Information such as volume expansion, maximum temperature, temperature distribution in time and space, and reaction time can be retrieved from short videos. These data can be correlated and discussed in relationship with the reactivity of the components for synthesizing the foam. The primary goal of this activity was to investigate the exothermic reaction of polyurethane foam using an infrared camera which has been successfully used for monitoring the exothermic formation of polyurethane foams. These values can be discussed in term of reactivity of isocyanates (aromatic and aliphatic/cycloaliphatic) for a given formulation of polyols. The highly visual content of such experiments is attractive for children and students. Chemicals for performing the experiments can be donated either individually or as ready sets (“polyols”) from the industry. It is anticipated that the investment required for buying the infrared camera can be redeemed by using it for other types of exo- or endothermic reactions and for teaching heat transfer in physics class.
Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks
Authors: Mehta Anav , MehtaPhD Huzefa
Published: September 2023

Chess game position analysis is important in improving one’s game. One method requires entry of logged moves into a chess engine which is cumbersome and error prone. A quick and effective method for analysis is hence strongly desired. Our hypothesis was that a faster chess game entry method can be built using a combination of vision and machine learning techniques to be analyzed directly in a chess engine. To test the hypothesis, we developed the Augmented Reality Chess Analyzer (ARChessAnalyzer), a complete pipeline from a live image capture of a physical chess game, to board and piece recognition, to move analysis and finally to augmented reality overlay of the position and best move on the physical board. The chess position predictor is like a scene predictor - it is an ensemble of traditional image and vision techniques and image classifier for chess board recognition and Convolutional Neural Network (CNN) for chess piece recognition. ARChessAnalyzer was used - as the input mechanism - to compare against manual entry into StockFish which was also the chess engine used in the app. The results validate the hypothesis that both for sparse and dense chessboard populations, ARChessAnalyzer was faster than manual entry with p-value < 0.005. This app and technologies underneath will help chess learners improve their game and hopefully will be widely used in chess clubs.
LawCrypt: Secret Sharing for Attorney-Client Data in a Multi-Provider Cloud Architecture
Authors: Zhang Joseph , Taylor David
Published: September 2023

The accelerated employment of cloud computing among law firms is due to multiple benefits, including wide accessibility and inter-organizational information sharing. Nevertheless, the project’s preliminary case study revealed lawyers’ mass neglect of standard precautionary measures and, consequently, a high proportion of security breaches, putting confidential and important documents at risk. The goal of the multi-provider cloud secret sharing architecture was to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. After an exhaustive development phase involving considerable testing and optimizations, software assessments of the architecture indicate the low computational overhead of adding the secret-sharing approach to a multi-provider law firm sharing environment. The efficient combination of constructions satisfies the engineering criteria as ChaCha20-Poly1305 warrants authenticity and privacy, and secret sharing ensures availability and perfect privacy. Compared with AES in CFB mode, widely used for encrypting data in the Cloud today, the secret sharing implementation boasts almost a 40% improvement over all file sizes. The only possible way of compromising this system is if multiple cloud providers collude, which is still unlikely given that the documents are additionally encrypted.
Sepia bandensis ink inhibits polymerase chain reactions
Authors: Novoselov Anna , Espinosa Eric
Published: September 2023

While cephalopods serve critical roles in ecosystems and are of significant interest in scientific studies of the nervous system, medicinal toxins, and evolutionary diversification. The absence of a genomic library and the lack of comprehensive gene analysis present challenges to conducting efficient and thorough research. One difficulty in advancing cephalopod genomics is the presence of inhibitors (such as ink) that impede the amplification of DNA samples with PCR. We tested the hypothesis that Sepia bandensis (dwarf cuttlefish) ink inhibits PCR by running PCR reactions with and without the back addition of ink to Turbo fluctuosus (marine sea snail) DNA with the inclusion of the appropriate positive and negative controls. The experimental results show that ink added to T. fluctuosus DNA extracted using two kit-based extraction methods or phenol chloroform extraction prevents the amplification of the cytochrome c oxidase subunit I (COI) mitochondrial gene. Also, while modern extraction methods like quaternary amine resin and silica column isolation failed to produce genomic products viable for PCR from S. bandensis, phenol chloroform extraction eliminated the inhibitors and resulted in successful amplification. The results of this investigation could further cephalopod genomic studies and serve as a model for experiments aiming to determine the cause of PCR inhibition.
The Effect of the Human MeCP2 gene on Drosophila melanogaster behavior and p53 inhibition as a model for Rett Syndrome
Authors: Ganga Chetana , Devitt Amy
Published: September 2023

The purpose of this study was to explore the effect of introducing the human methyl CPG binding protein 2 gene (MeCP2) into the Drosophila melanogaster genome, examine how it would affect protein levels of p53, and test how inhibiting p53 with pifithrin-alpha would affect behavioral and physical patterns in fruit flies. One goal was to observe if the symptoms of Rett Syndrome, a neurodegenerative disease in humans, are reflected in Drosophila melanogaster. This was achieved by differentiating the behavior and physical aspects of wild-type flies from flies expressing the full-length MeCP2 gene and the mutated MeCP2 gene (R106W). Differences in eye structure, climbing (the use of locomotor skills), geotaxis, as well as differences in the flies’ abilities to fly were analyzed. In addition, after these series of tests, the MeCP2 protein and the p53 protein, endogenous to Drosophila, were extracted and their levels were compared in each of the populations of flies. Then, after the Drosophila consumed pifithrin-alpha hydrobromide, their behavior, physical appearance, and protein levels were again monitored. After conducting these experiments, the effects of full-length and mutant MeCP2 on Drosophila melanogaster’s psychomotor skills were analyzed, along with their effects on how the treatment of pifithrin-alpha affected the level of MeCP2 and p53 in the various populations of flies. In conclusion, some of the Rett Syndrome symptoms were recapitulated in Drosophila, and a subset of those were partially ameliorated by the introduction of pifithrin-alpha.
The Effects of Barley Straw (Hordeum vulgare) Extract and Barley Straw Pellets on Algal Growth and Water Quality
Authors: McHargue Elaina , Gillan Chelle
Published: September 2023

In recent years, harmful algal blooms have increased in both frequency and intensity worldwide. This is a growing concern because many algae species can clog agricultural irrigation systems, make potable water unfit for consumption, and release toxins that can be dangerous to human and animal health. The purpose of this study was to test a natural solution for inhibiting algal growth that does not expose animals to potentially harmful chemicals. This experiment tests the effects of barley straw extract (BSE) and barley straw pellets (BSP) on algal growth and water quality. We predicted that both treatments would have significant effects on algal growth and water quality. We added BSE and BSP to containers of water placed them in a temporary greenhouse. Then, we conducted several different types of tests to determine the amount of algal growth as well as the quality of the water over a course of 25 days. The results showed some significant differences between the treatments on certain testing days with the transmittance, dissolved oxygen (DO), and carbonate tests; however, the results were not conclusive enough to reject the null hypothesis that neither treatment would have significant effects on algal growth and water quality. Research of this type has value because it is important to protect human and animal health by providing clean water sources.
High-throughput virtual screening of novel dihydropyrimidine monastrol analogs reveals robust structure-activity relationship to kinesin Eg5 binding thermodynamics
Authors: Shern Tyler , Rai Ansh , Premnath Krithikaa , Kwan Audrey , Kolala Ria , Ashok Ishani , Njoo Edward
Published: September 2023

As cancer continues to take millions of lives worldwide, the need to create effective therapeutics for the disease persists. The kinesin Eg5 assembly motor protein is a promising target for cancer therapeutics as inhibition of this protein leads to cell cycle arrest. Monastrol, a small dihydropyrimidine-based molecule capable of inhibiting the kinesin Eg5 function, has attracted the attention of medicinal chemists with its potency, affinity, and specificity to the highly targeted loop5/α2/α3 allosteric binding pocket. In this work, we employed high-throughput virtual screening (HTVS) to identify potential small molecule Eg5 inhibitors from a designed set of novel dihydropyrimidine analogs structurally similar to monastrol. Density functional theory (DFT) calculations and protein-ligand docking experiments revealed that the analogs with geranyl ester substitutions exhibited the greatest binding affinities to the allosteric binding pocket of kinesin Eg5. In-depth analysis of the binding pocket amino acid residues and calculations of the cLogP value for each compound demonstrated qualitatively and quantitatively that strong hydrophobic interactions of the ester functionality with kinesin Eg5 are of great significance in the improved binding of dihydropyrimidine analogs. This establishment of a quantitative structure-activity relationship to kinesin Eg5 binding thermodynamics using HTVS revealed the discovery of improved dihydropyrimidine-based inhibitors capable of advancing society’s progress in the fight against cancer.
Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke in silico
Authors: Vinay Samhita , Yalamanchili Keertana , Vinay Sowmya
Published: September 2023

Ischemic stroke occurs when blood flow to the brain is interrupted, causing brain damage. There is evidence that reactive oxygen species, ROS, are produced by the enzyme family NADPH oxidase (NOX) following ischemic stroke, which leads to further brain injury. This study investigated the effectiveness of different NOX inhibitors as treatments for ischemic stroke in silico. The ADMET (absorption, distribution, metabolism, excretion, and toxicity) profile of each NOX inhibitor was taken, in which four classifications, namely applicability domain, human intestinal absorption, blood-brain barrier, and human oral bioavailability, were observed. The profile was used to determine the properties of each inhibitor in order to examine the extent to which it will work as a drug candidate. Then, AutoDock Vina was used to model the docking of the inhibitors: VAS2870, GSK2795039, apocynin, and AEBSF to NOX2, an isoform of the NOX family. We hypothesized that VAS2870 would be the most effective inhibitor in silico due to its potency to NOX2, not present in the other inhibitors. The binding affinities of each of the inhibitors to NOX2 were recorded, and the value was used to calculate the Ki value of each inhibitor. VAS2870 and apocynin were the most potent NOX2 inhibitors, and all four inhibitors had favorable ADMET profiles. This study helps corroborate previous in vivo and in vitro studies in an in silico format, and can be used towards developing drugs to treat ischemic stroke.
Open Source RNN designed for text generation is capable of composing music similar to Baroque composers
Authors: Goel Zoya , Lebron Cristi
Published: September 2023

Recurrent neural networks (RNNs) are useful for text generation since they can generate outputs in the context of previous ones. Baroque music and language are similar, as every word or note exists in context with others, and they both follow strict rules. We hypothesized that if we represent music in a text format, an RNN designed to generate language could train on it and create music structurally similar to Bach’s. We used an RNN designed for text generation and trained it on 107 Bach pieces. Unlike previously implemented algorithms, such as Bachbot, this algorithm trained on all types of Bach’s music-both his instrumental/orchestral pieces and chorales. Since the dataset used in this study is more diverse than that of previous algorithms, we predicted that the present algorithm’s output will have a structure that is more reflective of all of Bach’s music. We compared the Attacks/Spaces and the Spaces/ (Attacks+Spaces) ratios of both Bachbot’s and this experiment’s outputs and found that Bachbot’s Attacks/Spaces ratios were significantly lower than this experiment’s outputs (p < 0.001), and their Spaces/ (Attacks+Spaces) ratios were significantly higher (p < 0.001). Overall, we found that the music generated by our RNN shared a similar structure with Bach’s music in the input dataset, while Bachbot’s outputs are significantly different from this experiment’s outputs and thus are less similar to Bach’s repertoire compared to our algorithm. Therefore, it is plausible that an RNN designed for text generation could create Baroque music.
Comparing the Biodegradability of Petroleum-based Plastic with a Novel, Sustainable Bio-plastic Alternative
Authors: Van Note Lana , Wnek John
Published: September 2023

The convenience and affordability of single-use plastic products makes them an attractive option for consumers. The toxic constituents of these traditional plastics, however, are known to cause a variety of health issues in thousands of species. These environmental hazards, along with the issue of white plastic and microplastic pollution, causes increased interest in biodegradable alternatives to petroleum-based hydrocarbons. In this research, a novel bioplastic inclusive of bamboo tannins and chitosan is selected from more than 60 trial formula variations based on resulting strength, fatigue, and transparency attributes. The biodegradability of the finalized bioplastic is compared to that of conventional polyethylene, in addition to investigating its solubility and water absorbance. Biodegradation rates of the bio-based plastic exceeded that of the petroleum-based formula, as determined with Fourier Transform Infrared Spectroscopy (FTIRATR) analysis. The behavior of the experimental product in water deviated from the initial hypothesis, with substantial weight increase of approximately 193% after 60 minutes. A cost analysis displayed a difference of $0.0016 between the two products, with the natural additives of the experimental being more expensive. This research displays the potential of a legitimate, fully biodegradable plastic alternative to current marketplace bioplastics.
Repurposing citrus peel waste and its positive effects on our health and communities
Authors: Kim Andrew , Om Joon
Published: September 2023

Every year, more than 30% of food products go to waste. This is approximately 1.3 billion tons of food, which is equivalent to 1.3 trillion U.S. dollars. While conventional solid waste treatments and fertilization of food waste are common, citrus fruit peels require secondary applications and advanced disposal management due to their low pH values and high antimicrobial characteristics. Since citrus fruits are well-known sources of vitamin C and antioxidants, we hypothesized that their peels also contain high amounts of vitamin C and antioxidants. In our study, five common citrus peels including grapefruit, lemon, lime, orange, and tangerine, were used to determine the amounts of vitamin C and total soluble antioxidants. Experimental results showed that lemon peels contain the highest vitamin C concentration (39.68 mg/g sample), closely followed by tangerine peels (37.23 mg/g sample). Lower vitamin C concentrations were extracted from the grapefruit, lime, and orange peels within a 3-hour reaction period with the average concentrations of 26.36 mg/g, 25.20 mg/g, and 24.53 mg/g sample, respectively. Despite containing a relatively low level of vitamin C, grapefruit peels demonstrated the highest antioxidant capacity (48.142 µmol Trolox/g peel sample), followed by orange, lemon, tangerine, and lime. Based on our results, if we were able to repurpose fruit peels and utilize them as a nutrient source, each U.S. individual would be able to meet the daily amount of the recommended vitamin C with 2-3 teaspoons of citrus peels reducing 4.8 pounds of the carbon dioxide (CO2) equivalent annually at the same time.
Computational Structure-Activity Relationship (SAR) of Berberine Analogs in Double-Stranded and G-Quadruplex DNA Binding Reveals Both Position and Target Dependence
Authors: Sun Stephanie , Ashok Bhavesh , Su Andrew , Hamid Saira , Indran Karthikha Sri , Shah Aashi , Su Sarah , Sakhrani Simrun , Njoo Edward
Published: September 2023

Berberine, a natural product alkaloid, and its analogs have a wide range of medicinal properties, including antibacterial and anticancer effects. Previous studies showed that berberine and its analogs intercalate into DNA, thereby inhibiting DNA replication. Berberine has also been studied as a photosensitizer and was shown to generate reactive singlet oxygen in situ, and this has applications in photodynamic therapy. Various groups have synthesized berberine analogs that have comparable or improved biological activity; however, an exhaustive structure-activity relationship of the free energy of binding of berberine analogs with substitution at C-8, C-12, and C-13 to DNA have not been previously reported. High throughput virtual screening (HTVS) allows for efficient analysis of compound libraries to identify lead compounds as possible pharmaceutical agents. Here, we employed HTVS towards a library of alkyl or aryl berberine analogs on C-8, C-12, and C-13 to probe binding to double-stranded and G-quadruplex DNA. Predicted free energies of binding to double-stranded DNA and G-quadruplex DNA were generated via molecular docking. The excited state electronic structure calculations were conducted via time-dependent density functional theory to probe the potential photosensitizing activity of each compound, and the potential G-quadruplex stabilizing abilities of key berberine analogs were probed through molecular dynamics simulations on a 4.0 nanosecond timescale. We determined that the nature of the substituent, the position of the substituent, and the nucleic acid target affect the free energy of binding of berberine analogs to DNA and G-quadruplex DNA, however berberine analogs did not result in net stabilization of G-quadruplex DNA.
Propagation of representation bias in machine learning
Authors: Dass-Vattam Upamanyu , MacGregor Elizabeth
Published: September 2023

Transfer learning is an emerging paradigm in machine learning that involves reusing existing pretrained models to develop new machine learning applications. As machine learning grows in importance, keeping new models and applications clear of biases is of paramount importance. While previous works focus on the development of bias free models, they fail to address the mechanism of bias propagation in transfer learning. Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.
Phytochemical analysis of Annona Reticulata extract and an in-vitro study on its anti-proliferative effects
Authors: Tadiparti Rishi , Kasture Pooja , Umrao Ankita , Rao Jyothsna , Rao Gururaj
Published: September 2023

Annona Reticulata (Ramphal or custard apple) has been a plant of interest in the traditional medicinal system for the prevention and treatment of various inflammatory disease conditions, including cancer. But there has always been a requirement of continuous efforts for the efficient extraction of potential secondary metabolites that are used for treatment. This study is concerned with the anti-cancerous secondary metabolites present in the leaves of the Annona Reticulata plant (RL) and its anti-proliferative properties. Many extracts were tested on HeLa cells using a variety of solvents. The anti-proliferative effect of the combination of methanol dry extract and wet extract in acetone was observed to contain the greatest range of dissolved secondary metabolites. At concentrations over 2.5%, weight over volume, a sharp decrease in viability of below 50% for both 24 hours and 48 hour time duration. This result suggests that the leaves may have potent anti-proliferative and possibly anti-cancer properties.
Analysis of the effects of positive ions and boundary layer temperature at various hypersonic speeds on boundary layer density
Authors: Selvakumar Akash , Palanisamy Sridhar
Published: September 2023

Vehicles traveling at hypersonic speeds ionize the air around them. This ionization leads to the possibility of utilizing an induced positive surface charge to lower the density of the boundary layer flow electrostatically. Reducing the boundary layer's density would have several positive effects, such as lowering the drag and heat transfer. The study's goal was to identify the Mach numbers for which the electrostatic drag and heat transfer manipulation would be most applicable inside the stratosphere. We hypothesized that the potential to use induced positive surface charge repulsion to lower boundary layer density would increase with speed, and there will be greater (>10%) potential to decrease boundary layer density at a lower bound estimate of Mach 18-19. We also explored the extent to which extrapolation through a quadratic model based on Dr. Jesse R. Maxwell's data in Morphing Waveriders for Atmospheric Entry could be used to approximate maximum boundary layer temperature values. The experiments were conducted using computational fluid dynamics software. The study demonstrated that, on average, higher Mach speeds resulted in a considerably higher potential decrease in density. Also, as predicted by the hypothesis, a significant potential reduction in density occurred near Mach 19. The study also supported using the quadratic model based on Maxwell's data for approximating the maximum boundary layer temperature up to at least Mach 26. This study highlights that further research on the surface charge method is warranted as we seek to explore higher hypersonic speeds within the stratosphere.
Caffeine: Does Drinking Coffee Alter Performance and RPE Levels of a Teenage Athlete in both Aerobic and Anaerobic Exercises?
Authors: Gopalan Srinidhi , Gopalan Jaysree
Published: September 2023

The purpose of the study is to determine if ingesting caffeine in the form of coffee prior to workouts can enhance performance and alter Borg Rating of Perceived Exertion (RPE) of teenage athletes for both endurance (aerobic) and strength (anaerobic) exercises. Our hypotheses are that caffeine lowers RPE for both endurance and strength exercises and that it enhances performance in endurance, but not strength, exercises.To answer this question, we analyzed a 16-year-old high school athlete (myself) for a total of 12 days: 3 days consuming a cup of coffee prior to running, 3 days consuming a cup of coffee 1 hour prior to performing strength exercises, and 3 days without consuming a cup of coffee prior to exercise for each exercise type. For the endurance test, the athlete ran two miles as fast as they could. Average heart rate and total time of the run were analyzed for three days with coffee and three days without, as well as RPE levels. For the strength test, the athlete performed as many push-ups as possible in a one-minute time frame. The results showed that caffeine ingestion prior to exercise enhanced endurance, but had no impact on strength. Additionally, though RPE was lower for the endurance test, RPE remained unchanged for the strength test. Thus, the effect of drinking coffee in aerobic and anaerobic exercises were evaluated through several measures in this study, providing support that caffeine has an effect on aerobic exercises as opposed to anaerobic exercises.
Effects of Various Environmental Factors on Stomatal Density, Area, and Potential Conductance Index
Authors: Kim Lina , Balani Sonia , Edelberg Martin , Macke Natalie
Published: September 2023

Stomata, microscopic pores on a leaf flanked with flexible guard cells that open and close the stomatal opening, account for 95% of terrestrial movement of water vapor and carbon essential to the survival of plants. The climate crisis is challenging plants with elevated CO2, drought, varying soil salinity, varying soil acidity, and increasing temperature. While research has been done on how stomata respond to elevated CO2 alone, markedly less research has been done on the effect of elevated CO2 in combination with other environmental factors. Thus, the purpose of this study was to determine the effects of elevated CO2 in combination with other environmental factors on stomatal density, size, and conductance in radish, barley, tomato, and buckwheat. A controlled experiment with these plants and six conditions (Control, Elevated CO2, Elevated CO2 + Salinity, Elevated CO2 + Acidity, Elevated CO2 + Temperature, and Elevated CO2 + Drought) was conducted, and data was collected. The results trend towards a decrease in stomatal density, stomatal area, and potential conductance index (PCI) in the elevated CO2 conditions compared to the control conditions. Additionally, our results suggest that the other four conditions do not amplify the effect of elevated CO2 levels alone. Overall, results showed variation in data among the tested plants, suggesting that making generalizations about the impact of CO2in combination with other environmental factors is risky. Thus, further research on the effects of multiple environmental conditions on stomatal characteristics is warranted to determine the impact on agriculture adaptation and water management strategies.
Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance
Authors: Gupta Ananya , Gupta Meghna
Published: September 2023

Machine learning and deep learning algorithms are rapidly becoming integrated into everyday life. Whether it is in your face-ID to unlock your phone or the detection of deadly diseases like melanoma, neural networks have been traditionally designed to work in isolation to achieve amazing tasks once thought impossible by computers. However, these algorithms are trained to be able to solve extremely specific tasks. Models have to be rebuilt from scratch once the source and target domains change and the required task changes. Transfer learning is defined as a field that leverages learnings and weights from one task for related tasks. This process is quite smooth if one has enough data and the task is similar to the previous, already learnt task. However, research on when these two conditions are not met is scarce. The purpose of this research is to investigate how fine-tuning a pre-trained image classification model will affect accuracy for a binary image classification task. Image classification is widely used, and when only a small dataset is available, transfer learning becomes an important asset. Convolutional neural networks and the VGG-16 model trained on Imagenet will be used. Through this study, I am investigating whether there are specific trends in how fine-tuning affects accuracy when used for a small dataset which is dissimilar from Imagenet. This will allow for the beginning of investigating quantifiable methods to train a model when using Transfer Learning techniques.
Comparison of the ease of use and accuracy of two machine learning algorithms – forestry case study
Authors: Bhatia Bhavya , Michalski Jakub , Shebin Jegath
Published: September 2023

With the availability of massive amounts of data and cheap computing, machine learning has become increasingly viable to create extensive multivariate mathematical models of natural phenomena to help predict accurate future trends that would have been impossible for humans to accomplish by themselves. There is a wide variety of different machine learning algorithms available, and it is not always known which one will perform best for a given dataset. This can be determined after training and evaluating the different models and comparing them. In this case study, logistic regression and random forest models were compared in terms of accuracy and ease of use. We hypothesized that logistic regression would yield a higher accuracy and be easier to set up in a comparable scenario for a given dataset compared to random forest. Both algorithms used the same forestry dataset to see which one would outperform the other. Initially, logistic regression looked like the better choice, however, after a variety of comparisons, random forest yielded higher performance in both accuracy measurements (accuracy=0.9722, Fbeta=0.9722 for random forest vs. accuracy=0.7141, Fbeta=0.6990 for logistic regression) and did not require as much detailed tuning as logistic regression did.
The Effect of Poverty on Mosquito-borne Illness Across the United States
Authors: Kar Kavita , Low Russanne
Published: September 2023

Mosquito-borne diseases are a major issue across the world, and the objective for this project was to determine the characteristics that make some communities more susceptible to these diseases than others. We hypothesized that low-income communities are more susceptible to mosquito-borne diseases. To test this hypothesis, we identified and studied characteristics that make communities susceptible to mosquito-borne diseases, including water in square miles, average temperature, population, population density, and poverty rates per county. We used regression analysis to understand the relationship between the above variables and the total number of mosquito-borne disease cases. We found that the population of a county is the best indicator of the prevalence of mosquito-borne diseases. Rate of poverty in the county was not found to be a significant indicator of mosquito-borne diseases. This may be because poverty aggregated at county level does not describe income level variation within the county. In this research, both rural and urban counties were analyzed. Although rural counties have high rates of poverty, the population of rural counties are spread out, so they are at a lower risk of disease. Therefore, we conclude that population data needs to be studied in conjunction with poverty levels to truly understand the relationship between poverty and mosquito-borne diseases.
Thermoelectric Power Generation: Harnessing Solar Thermal Energy to Power an Air Conditioner
Authors: Lew Jennifer , Fong Derek
Published: September 2023

We researched the feasibility of using thermoelectric modules as a power source and as an air conditioner to decrease reliance on fossil fuels. Thermoelectric modules have a “hot side” and a “cold side.” Based on the Seebeck Effect, when the hot side is heated, and the cold side is cooled, the module generates a voltage. Based on the Peltier Effect, when a voltage is applied to a module, the hot side becomes hot, and the cold side becomes cold. We constructed a “battery” using thermoelectric modules operating in the Seebeck Effect: solar thermal energy was used to heat the hot side, and tap water was used to cool the cold side to generate a voltage. The battery was then used to power a “thermoelectric air conditioner” made of one thermoelectric module operating in the Peltier Effect, where the cold side of the module absorbs ambient heat like a traditional air conditioner. We hypothesized that the battery would generate more power per square inch than a solar panel and that the thermoelectric air conditioner would operate without voltage regulation. The results showed that, at its peak, the battery generated 27% more power – in watts per square inch – than a solar panel, and the thermoelectric air conditioner operated despite an unsteady input voltage. The battery has incredible potential, especially if its peak power output can be maintained. However, the thermoelectric air conditioner is a viable alternative to commercial air conditioners only when space is an extreme limiting factor.
Low environmental pH inhibits phagosome formation and motility of Tetrahymena pyriformis
Authors: Chung Thea , Weissleder Oliver , Lillis Elizabeth
Published: September 2023

Tetrahymena pyriformis, single-celled protozoans, populate ponds, lakes, and streams. As 2.57 million tons of carbon dioxide enter the atmosphere every second, Earth’s bodies of water acidify rapidly, creating harmful habitats for organisms such as ciliates at the bottom of the food chain, like T. pyriformis. Investigating the ability of T. pyriformis to feed in acidic pHs presents a deeper understanding of the short-term ramifications of carbon dioxide emissions on freshwater ecological communities. In this experiment, we varied pH from 4.5 to 7.0 by diluting carbonated water. To observe T. pyriformis food vesicle formation, we counted the number of phagosomes located in iodine-fixed T. pyriformis after a 10-minute feeding period. We hypothesized that increased suspension acidity would reduce T. pyriformis’ consumption of food vesicles due to inhibited motility functions and phagosome formation during phagocytosis. Our data suggests T. pyriformis best generate phagosomes within a pH range of 6.0 to 7.0. Data displayed a low average vesicle count of 0.60 ± 0.16 at a pH of 4.5 and a high average vesicle count of 3.73 ± 0.18 at a pH of 7.0. At a pH of 6.0, average vesicle count plateaued as its rate of increase slowed. We posit that as pH levels decrease, T. pyriformis lose feeding competence due to three probable mechanisms: increased membrane density, weakened myosin necessary for vesicle transport, and inhibition of ciliary movement –– all components necessary to initiate and complete phagocytosis.
Detergent pollutants decrease nutrient availability in soil
Authors: Ryu Sarah , Spuller Kerri
Published: September 2023

Household detergents contain potentially harmful surfactants that can alter microbial activity and interfere with biogeochemical cycles. Surfactants have previously been found to inhibit enzyme activity and plant growth and alter the physical properties of soil. Because detergent pollution poses a threat to ecosystems but has not been extensively studied, this study investigated the short-term effects of detergent pollution on pH and nutrient availability in soil by utilizing silty clay loam soil. Here we observed pH, phosphorus, nitrogen, and potassium levels in soil polluted with either a plant-based green detergent or a less eco-friendly detergent. Nitrogen and potassium availability levels decreased in the polluted groups. These findings support the idea that although green detergents are considered an environmentally safer choice, even these eco-friendly products have an effect on nutrient availability when introduced into soil. Overall, this study indicates that proper disposal of household products is crucial, as pollutants released into the environment can have an impact on nutrient cycling in ecosystems even in a short time period.
Correlation between shutdowns and CO levels across the United States.
Authors: Gupta Arnav , Dudek Daniel
Published: September 2023

We conducted research in Summer 2020 to analyze the effect of the shutdown orders due to the COVID-19 pandemic on the carbon monoxide (CO) levels across the United States. The effects of these lockdowns have not yet been entirely studied, but they have caused wide-spread lifestyle changes. Since the shutdown orders during the pandemic prevented most public places from conducting any kind of business, we hypothesized that CO levels would drop as there was no commute, fewer buildings being used, and fewer people traveling as much as they previously would. We collected publicly available data from the EPA’s Daily Air Quality Data and analyzed in R. We selected the 15 states based on the greatest number of coronavirus cases on August 20th, 2020. Each state had a mean value of CO concentration for each date that the data was available in the sites that was an average of multiple recorded values across counties in the state. In almost every state, the CO levels went down starting from February, with the lowest CO levels during the shutdown, indicating that the shutdown likely could have led to a decrease in CO levels. Since some states saw CO levels start to drop before the shutdown orders were enacted, the absence of people on roads and the lack of human activity due to concerns over the spread of the virus in general could have also been a contributing factor. As some states opened and restrictions eased, the CO level started rising then fluctuated again, similar to before the shutdowns.
Understanding the movement of professional and high school soccer players
Authors: Andrews Stephen , Zhang Lisa
Published: September 2023

Data analytics plays an increasingly visible role in the sports industry. Professional soccer utilizes data analytics to gain competitive advantage. It is intuitive that basic metrics like speed and passing matter, but the difference they make in the ability to score a goal is an open question. In this project, we were interested in discovering the statistical evidence that differentiates goal-scoring from no-goal-scoring sequences. Using a trajectory data set from professional games, we hypothesized that goal and no-goal sequences would be statistically different. Indeed, we found evidence that faster, longer possessions with more passes were more successful. From the same data set, we also investigated Long Ball, an offensive strategy that pushes the ball to the target goal via a big kick. We hypothesized that Long Ball was effective for goal scoring but found evidence to the contrary. In addition, we were interested in how professional and student players move differently. Comparing a student trajectory data set against the professional data set, we hypothesized that the movement directions of the two groups would be different. Indeed, we found evidence that professional players tended to run where the ball is going whereas students moved towards the ball’s current location more often than professionals. Throughout our study, we applied the 2-sample Kolmogorov–Smirnov test on the cumulative distribution function of each metric to calculate statistical differences between sequences. Our findings support the importance of basic skills in soccer, and this is useful for players at all levels. Additionally, by pinpointing a weakness in student movement, our findings could help students to move more like professional soccer players.
The Effects of Atmospheric Attenuation on Cosmic Ray Muons: How is Surface Level Cosmic Ray Muon Flux Affected by Atmospheric Attenuation?
Authors: Sun Daniel , Sun Steve , OByrne Michael
Published: September 2023

Cosmic rays are high-energy astronomical particles originating from various sources across the universe. After undergoing nuclear reactions in the upper atmosphere, the primary form they take on the Earth’s surface is in the form of muons. Here, we sought to understand how surface-level cosmic-ray muon flux is affected by atmospheric attenuation by measuring the variation in relative muon-flux rate relative to zenith angle, testing the hypothesis that muons follow an exponential attenuation model. Using a QuarkNet cosmic ray muon detector (CRMD), we collected relative muon-flux data while varying the rotational orientation of the CRMD with respect to the magnetic east-west axis. We then calculated a sensitivity function for the detector with respect to muon entry angle. We parameterized a relative muon angular-density function based on the model of exponential attenuation through the Earth’s atmosphere. Then, we convolved these functions and fitted parameters to experimental data using least-squares regression. The attenuation model predicts an attenuation length of 6.3 km. This result implies that only a maximum of 24% of muons can reach the Earth’s surface, due to both decay and atmospheric interactions. The agreement between data and model (χ2=0.351) provides evidence of exponential muon attenuation through the atmosphere.
The Development of a Superhydrophobic Surface Using Electrolytic Deposition & Polymer Chains Precipitation
Authors: Kim Heeyun , Woo Katelyn , Maxfield MacRae
Published: September 2023

The useful life of infrastructure metals is limited by prolonged exposure to water and deposition of insoluble minerals. Advances in surface treatment suggest that both problems can be alleviated through the formation of surfaces that are hydrophobic and therefore self-cleaning. In nature, the surface of a lotus leaf displays superhydrophobicity, containing microbumps on the surface with non-polar nanofibers on the bumps. Here, we describe a process that mimics this topography. The process includes brief electrodeposition of zinc from aqueous Zn(NO3)2 followed by drying and spray-coating of a xylene silicone solution. Our results indicate that zinc coated steel has a contact angle of 130° and a sliding angle of 16°, displaying it has high hydrophobicity and self-cleaning properties. Copper yielded similar results, indicating that this method can be applied to other metals. These results suggest that a Cassie-Baxter state, the ideal droplet to surface interaction, was formed on these metal surfaces. However, further development should be done regarding the precipitation of nanofibers to maintain the created topography. Such hydrophobic surfaces would improve the longevity of metal infrastructure since its anti-rusting characteristics limits the surface’s exposure to water.
Exploring Unconventional Growing Methods to Promote Healthy Growth in Common Household Plants: Tagetes patula L. and Lepidium sativum
Authors: Nguyen Andrew , Nguyen Ryan , Williams Erika
Published: September 2023

Chemical fertilizers have been used in increasing quantities for household gardening and commercial agriculture worldwide since their advent during the Green Revolution. Such fertilizers have detrimental impacts on the environment, contributing to nutrient runoff and aquatic dead zones. At the same time, water consumption for agricultural needs has

skyrocketed. Alternative growing methods are urgently needed to reduce the impacts of plant cultivation. This study focused on finding more sustainable growing methods that reduce chemical fertilizer or water usage and can be used at the household level for garden plants. We hypothesized that the alternative growing methods would better encourage healthy plant growth as compared to a control. Several marigold (Tagetes patula L.) and garden cress (Lepidium sativum) plants were observed over a 13-week period. Metrics for healthy plant growth were height at first bloom, growing time, and survival rate. The results indicated that the treatments did not have a statistically significant effect on marigold and garden cress growth times in addition to marigold heights. However, the Deep Water Culture (DWC) treatment for garden cress plants significantly increased the height at first bloom compared to the control group. For rates of surviving plants, the treatments had little effect on garden cress, but the Eggshell Grounds, Wick System, and DWC system groups outperformed the control group for marigolds.