Generative AI Text Detection: Strengths and Weaknesses

The advancement of AI since 2022 has led to increased usage in all aspects of the world around us. This introduces a new era of online threats due to misinformation. Using AI, bad actors can easily generate vast amounts of believable misinformation which can be used to manipulate public opinion. In this study, we evaluate various AI text detection models, along with circumvention techniques such as DFT fooler, complex paraphrasing, and humanizers which modify AI-generated text to circumvent detectors. We found that even advanced detection models such as GPTZero and ZeroGPT used by top universities were weak when challenged by DFT Fooler or humanizer models. While current detection methods are effective against simple texts, they need much improvement to face the challenges of real-world applications

Evaluating Corruption Defenses for Model Robustness

Within the last few years, deep learning models have seen increased usage in various computer vision domains and have attained high accuracy rates for several visual identification tasks. However, when faced with real data containing visible anomalies, their accuracy can be reduced significantly. In addition, adversarial examples, inputs with deliberate changes that induce misclassification, have further hindered their performance. The training methods used to minimize the effects of adversarial examples have also been found to negatively impact a model\u27s robustness on clean data as well as common noise and distortions, which may be an undesired trade-off. Adversarial robustness is a model\u27s ability to resist intentional deceptive inputs, while corruption robustness is its resilience to everyday noise and distortions. In our work, we adopt a technique that improves the neural network\u27s ability to generalize by training on augmented images of the dataset and evaluate its efficacy in the face of adversarial examples with several empirical metrics, comparing its performance with traditional adversarial training techniques

Competitive Pokemon: Underused Pokemon Viability

Pokemon is a video game with a battling system in which you bring a team of six pokemon to each game. Within this battling system, some pokemon are considered less useful and thereful being used less. This dilemma on whether or not an underused pokemon are a specific downgrade than another overused pokemon with similarly good typing effect the winrate in “best of three” matches in Pokemon Showdown. This leads me to my research question, to what extent does having the under-used pokemon, Zoroark-Hisui, change the results of a similar team with Flutter Mane in twenty five best of three matches each. Zoroark-Hisui (ZH) was chosen because it’s my favorite underused pokemon. ZH is an underused pokemon because of its frail bulk, or inability to take a hit. Flutter Mane, an overpowered pokemon (Uber), was chosen due to its dominance in the meta and it’s decent resemblance to ZH. It’s important to me and other competitive players to understand whether or not an underused pokemon can perform as well as an overused pokemon because it’s more fun to use a more diverse set of pokemon. The results (so far) dictate that Flutter Mane would perform better in a tournament setting with the same person piloting both teams due to having a win-rate of 50% to Zoroark-Hisui\u27s win-rate of 25%

Integrating a hybrid Machine Learning approach for stock price prediction and realistic modeling

The stock market’s increasing volatility makes predicting accurate trends more challenging. Since the stock market influences the economy, precise predictions help investors maximize profits or minimize losses. This paper proposed a hybrid approach to enhance stock market predictions with high accuracy by integrating multiple models, stock market indicators, stock options, realistic modeling, and news sentiments. It was hypothesized that this approach would yield low error margins and realistically model the stock market while minimizing computational intensity. The model achieved a mean absolute percentage error of 2.93%, demonstrating high prediction accuracy compared to actual prices. Data were sourced from Yahoo Finance, including stock prices, options, indicators, news, and other financial data. Monte Carlo simulations trained, tested, and validated machine learning models. Mathematical modeling techniques were also employed to ensure accurate predictions and disciplined modeling. A paired linear regression test was conducted to analyze prediction accuracy across training and testing datasets. Under a 95% confidence level, the p-value of 0.6047 was greater than the ��-value of 0.05, indicating the hybrid model architecture is a dependable, precise, and efficient alternative to conventional prediction models

Role of Stiffness Markers in Astrocytes in Glaucoma Pathology

Glaucoma is a disease that increases pressure within the eye which applies stress to the optic nerve, leading to blindness. Most research has focused on different cells in the optic nerve, but this research focuses on a glial cell called astrocytes, which stabilizes cell connection, maintains the eye’s immune status, and maintains nutrient levels. Previous research has shown that stiffness around the optic nerve can be indicated by proteins which get upregulated. This research focuses on these proteins (Fibronectin, Collagen IV, ɑ-Smooth Muscle Actin) in an astrocyte, therefore, this project focuses on trying to understand the role of these stiffness markers. It was hypothesized that the levels of these proteins will change in response to glaucoma injury. This was tested by culturing astrocytes from a human cadaver’s optic nerve for 3-5 days in Astrocyte Growth Media. Once the cells reached 90% confluency, they were placed into separate chamber slides, then starved with serum-less DMEM. Half of the slides were placed into hypoxic conditions while the rest underwent normal conditions. These cells were then observed under a microscope at 4h, 16h, and 24h. Immunocytochemistry was then done on these cells for the three markers. The preliminary results indicate that healthy astrocytes do express all three markers, indicating potential for glaucomatous studies. Unfortunately, astrocytes could not survive hypoxic conditions as they died even in 4 hours. These preliminary studies enhance our understanding of stiffness markers that may play a role in glaucoma as compared to healthy astrocytes

The Impact of the pH on the Concentration of the Released Ogremorphin-Mimic Dye from Double-Network Hydrogels for Glioblastoma Localized Treatment

Glioblastoma (GBM), a highly aggressive brain cancer, poses significant treatment challenges due to its complex microenvironment and low therapeutic efficacy of current approaches. Localized drug delivery systems hold promise for improving therapeutic outcomes. This study investigated the release of an Ogremorphin (OGM)-mimic dye from a double-network hydrogel (DNH) designed for GBM therapy, hypothesizing that the hydrogel would have a steady release of the mimic drug at a lower pH. A polyacrylamide alginate DNH was synthesized, and its swelling behavior, drug-loading efficiency, and release dynamics were evaluated. The hydrogel’s swelling degree was calculated after 72 hours of immersion in distilled water. Drug loading efficiency was determined by UV-Vis spectroscopy, measuring the residual concentration of a fluorescein solution. Dye release was assessed under various pH conditions from 4-8, simulating the tumor microenvironment (TME), with cumulative release profiles analyzed over 228 hours. The hydrogel exhibited a swelling degree of 135.8% and a drug loading efficiency of 87.4%. Drug release was pH-dependent, with minimal release at acidic pH (4 and 5) due to a compact polymer network and an increased release at alkaline pH 8, driven by network swelling. Statistical results from the one-way ANOVA test showed that F(4, 85) = 17.72, p \u3c .001. A post-hoc Tukey test showed a significant difference between the different pH levels. Optimized formulations at pH 4- 6 could minimize burst release, sustain therapeutic levels, and improve patient outcomes. These findings demonstrated the hydrogel’s pH-responsive properties made it a strong candidate for GBM therapy

Comparing Bioplastics made from Arrowroot Starch vs Orange Peels and Banana Peels to Determine which has the Greatest Durability and Water Resistance

Plastic is an essential part of daily life. However, due to the composition of traditional plastics, it is extremely toxic to the environment. In an attempt to mitigate this issue, bio-based plastics -namely, bioplastics- were created.. There are many types of bioplastics including cellulose-based, bacterial-based, and starch-based bioplastics. Starch-based bioplastics can be made of many different components including basic starches and fruits. Therefore, the purpose of this study was to understand the application of bioplastics through certain conditions such as force and water. It was hypothesized that in comparison to orange peels, banana peels would be the most effective bioplastic due to their higher starch concentration. In order to test this, three different types of bioplastics were created. First is arrowroot starch, which was treated as the control. This is followed by bioplastics made from banana peels and orange peels, which contain an abundant amount of starch. Due to their makeup, the methods of creating the bioplastics differed slightly between the banana and orange peels. However, both bioplastics required the use of glycerin, a plasticizer. The results were statistically significant. The mean differences overall for the bioplastics was highly significant, with a F(2,82) =47.6, p=\u3c .00001. The hypothesis was partially supported

Evaluation of Racket Head Weight Augmentation Through Lead Tape Application on Accuracy in Tennis First-Serve Performance

The purpose of this study was to determine if adding weight to the head of a tennis racket affects first-serve accuracy. It was hypothesized that the addition of weight to the head of the racket with lead tape would increase first-serve accuracy due to increased racket stability and a reduction in the effects of off-center hits. To test this hypothesis, an experimental design was employed using a Yonex EZONE 98 tennis racket with varying amounts of lead tape applied to the racket head (0g, 6g, 12g, and 18g). Fifty serves were executed for each weight condition, divided between the deuce and ad sides of the court, at the Spring Valley High School tennis courts under controlled environmental conditions. The accuracy was recorded and analyzed using a camera on the receiving side of the court. A chi-square test of independence was used to evaluate the influence of racket head weight on serve accuracy. The results showed that there was no significant relationship between racket weight and serve accuracy, χ² (3, N = 200) = 4.32, p = 0.228, with an effect size (Cramér\u27s V) of 0.11. While trends show that serve accuracy peaked at 66% with 6g of weight, it decreases for heavier weights, reaching 48% at 18g. These results indicate that the weight of the racket head does not significantly affect first-serve accuracy within the conditions tested. This study demonstrates the complexity of tennis performance and tests the idea that increasing racket inertia uniformly benefits all aspects of serve performance