PolyGene

Advertisements affect cultures' social norms significantly, but also have an effect on certain cultures' cultural values, and this can help to identify similarities and differences between separate cultures.Using the Schwartz Theory of Basic Values as a framework, this paper analyzes how hidden messages within advertisements reflect and portray values such as conformity, tradition, achievement, and self-direction, based on the messages hidden within skin lightening advertisements and portrayals of female models in advertisements.By performing a cross-cultural analysis with countries such as Japan, China, India, The Philippines, and The United States, the research emphasizes how skin lightening advertisements enforce colorism while associating lighter skin with success, beauty, and social mobility.Furthermore, this paper additionally studies the development of women's portrayal in advertisements over the years, focusing on sexualization, stereotyping, and the emergence of "femvertising".By comparing Western and Eastern advertising practices, the research finds that, while some beauty ideals are universal, cultural values have a significant role in shaping how women and skin tone are portrayed in advertisements.Altogether, this research highlights the role of advertisements as cultural agents, which both reflect and influence societal values, which contributes to the persistent beauty standards and gender norms across cultures.

This project examines whether humans or robots are better therapists for children with varying degrees of Autism by comparing the Standard Human Treatment (hereafter, SHT) to Robotic Enhanced Therapy (hereafter, RET).An analysis of the standard deviation of eye gaze data from the DREAM Dataset (i.e., a pre-existing experiment that recorded changes in ADOS scores in children based on scripts used in both Human and Robotic intervention methods) revealed that Robotic Enhanced Therapy had a higher standard deviation in eye gaze, showing that the child was more engaged with (and therefore paid more attention to) the robotic therapist in comparison to the human counterpart.This conclusion ultimately serves as an important step forward in the process of finding the most effective intervention method to help children with Autism Spectrum Disorder (hereafter, ASD) manage the associated symptoms.

Foldable solar panels are a promising design for space applications, where minimizing stowed volume and maximizing deployed area are essential.Current solar panel systems are limited by their bulky stowage volume, complex deployment mechanisms, and vulnerability to mechanical failure during deployment or operation.This work explores origami-inspired folding patterns to enhance spatial efficiency along with their simple integrated deployment mechanisms suited for CubeSats.We evaluate structural integrity through CAD simulations and paper prototypes.By combining computational modeling and physical testing, the research aims to determine an optimal design that improves deployment efficiency and durability, contributing to more effective energy solutions for space environments.Thermal testing validates the design for survivability in space.

This study conducts a comprehensive comparison of traditional machine learning, deep learning, and nave baseline models for daily stock close predictions, using twelve publicly traded equities from various sectors within the S&P 500 index, including Tesla ($TSLA), JPMorgan Chase ($JPM), NVIDIA ($NVDA), UnitedHealth Group ($UNH), Alphabet ($GOOGL), General Electric ($GE), The Coca-Cola Company ($KO), ExxonMobil ($XOM), Duke Energy ($DUK), American Tower ($AMT), and Linde plc ($LIN) and the SPDR S&P 500 ETF Trust ($SPY).The SPDR S&P 500 ETF Trust -hereafter referred to by SPY -is used as a highly liquid proxy for the S&P 500 index, closely mirroring its price movements.Five models, Linear Regression, Lasso Regression, Random Forest, XGBoost and Long Short-Term Memory (LSTM) neural networks, are trained on identical historical market data with engineered technical features.These are then evaluated against common baselines, including previous close, previous open, and midpoint estimators.The predictive accuracy of each model is assessed using the average Mean Squared Error (MSE) and directional accuracy of each model on all 12 equities, with additional analysis via permutation feature importance to determine key drivers of model performance.Results reveal that, despite the complexity of advanced models, like the LSTM and XGBoost, simple linear and decision-tree based approaches often outperform deep learning in this domain, while all models struggle to consistently surpass basic baselines due to the efficient and noisy nature of financial markets.This research highlights the practical strengths and limitations of various modeling approaches for financial time series, offering guidance for future research and practitioners interested in predictive modeling for asset prices.

Amblyopia, often called "lazy eye," impacts about 2-3% of people, making it the top reason for vision problems in one eye among children.Amblyopia occurs when one eye has weaker vision than the other, usually because it didn't receive enough visual stimulation during early childhood, causing the brain to favor the stronger eye.Traditional treatments like eye patches can be uncomfortable and hard to stick with, especially for older kids.This paper explores a new treatment that uses artificial intelligence (AI) and virtual reality (VR) to personalize therapy for amblyopia.The method involves supervised machine learning, which adjusts the VR exercises based on individual patient data like eye strength, eye movement, and personal background information.This personalized VR therapy aims to make treatment more effective, enjoyable, and easier to follow.Compared to traditional methods, AI-driven VR therapy offers a more engaging experience, improving adherence and effectiveness by directly responding to each patient's unique visual needs.The paper will look closely at current treatments, review important research, clearly explain the new AI-based VR method, discuss its advantages and possible challenges, and suggest directions for future studies.

With the ever-changing face of the music industry, an insight into what drives popularity among tracks would be a golden ace in the hands of any producer, artist, or marketer.In this work, an in-depth analysis of the most meaningful characteristics related to the popularity of songs concerning different genres is made, including pop, rock, hip-hop, and country genres.Using the Spotify API, it develops a dataset comprising 1000 tracks compiled from popular songs of various genres.The features of tempo, energy, danceability, acousticness, and valence are analyzed by the use of statistical methods such as t-tests and correlation analysis.In this respect, the results indicated that, across genres, song popularity was significantly related to tempo, energy, and valence, though with significant genre-specific variations.