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Using data science along with machine learning to determine the ARIMA model’s ability to adjust to irregularities in the dataset

Choudhary et al. | Jul 26, 2021

Using data science along with machine learning to determine the ARIMA model’s ability to adjust to irregularities in the dataset

Auto-Regressive Integrated Moving Average (ARIMA) models are known for their influence and application on time series data. This statistical analysis model uses time series data to depict future trends or values: a key contributor to crime mapping algorithms. However, the models may not function to their true potential when analyzing data with many different patterns. In order to determine the potential of ARIMA models, our research will test the model on irregularities in the data. Our team hypothesizes that the ARIMA model will be able to adapt to the different irregularities in the data that do not correspond to a certain trend or pattern. Using crime theft data and an ARIMA model, we determined the results of the ARIMA model’s forecast and how the accuracy differed on different days with irregularities in crime.

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Error mitigation of quantum teleportation on IBM quantum computers

Chen et al. | May 15, 2023

Error mitigation of quantum teleportation on IBM quantum computers

Quantum computers can perform computational tasks beyond the capability of classical computers, such as simulating quantum systems in materials science and chemistry. Quantum teleportation is the transfer of quantum information across distances, relying on entangled states generated by quantum computing. We sought to mitigate the error of quantum teleportation which was simulated on IBM cloud quantum computers.

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The effects of social media on STEM identity in adolescent girls

Sreekanth et al. | Mar 11, 2024

The effects of social media on STEM identity in adolescent girls
Image credit: Diane Serik

Social media is widely used and easily accessible for adolescents, it has the potential to increase STEM (Science, Technology, Engineering, and Math) identity in girls. We aimed to investigate the effects of exposure to counter-stereotypical portrayals of women in STEM on social media on the STEM identity of adolescent girls. The study concluded that social media alone may not be an effective tool to increase STEM identity in girls. Social media can still be used as a complementary tool to support and encourage women in STEM, but it should not be relied upon solely to address the gender disparity in STEM fields.

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Can the Growth Mindset Encourage Girls to Pursue “Male” Careers?

Lateef et al. | Oct 03, 2021

Can the Growth Mindset Encourage Girls to Pursue “Male” Careers?

Despite major advances in gender equality, men still far outnumber women in science, technology, engineering and math (STEM) professions. The purpose of this project was to determine whether mindset could affect a student’s future career choices and whether this effect differed based on gender. When looking within the gender groups, 86% of females who had a growth mindset were likely to consider a “male” career, whereas only 16% of females with fixed mindset would likely to consider a “male” career. Especially for girls, cultivating a growth mindset may be a great strategy to address the problem of fewer girls picking STEM careers.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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Varying Growth Hormone Levels in Chondrocytes Increases Proliferation Rate and Collagen Production by a Direct Pathway

Bennett et al. | Sep 03, 2019

Varying Growth Hormone Levels in Chondrocytes Increases Proliferation Rate and Collagen Production by a Direct Pathway

Bennett and Joykutty test whether growth hormone directly or indirectly affected the rate at which cartilage renewed itself. Growth hormone could exert a direct effect on cartilage or chondrocytes by modifying the expression of different genes, whereas an indirect effect would come from growth hormone stimulating insulin-like growth factor. The results from this research support the hypothesis that growth hormone increases proliferation rate using the direct pathway. This research can be used in the medical sciences for people who suffer from joint damage and other cartilage-related diseases, since the results demonstrated conditions that lead to increased proliferation of chondrocytes. These combined results could be applied in a clinical setting with the goal of allowing patient cartilage to renew itself at a faster pace, therefore keeping those patients out of pain from these chondrocyte-related diseases.

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Changing public opinions on genetically modified organisms through access to educational resources

Klein et al. | Jul 26, 2022

Changing public opinions on genetically modified organisms through access to educational resources

Genetically modified organisms (GMOs) are crops or animals that have been genetically engineered to express a certain physical or biological characteristic and have various benefits that have made them become increasingly popular. However, the public has had mixed reactions to the use of GMOs, with some skeptical of their safety. The purpose of this study was to evaluate how opinions on genetically modified foods can change from exposure to small amounts of information

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Population Forecasting by Population Growth Models based on MATLAB Simulation

Li et al. | Aug 31, 2020

Population Forecasting by Population Growth Models based on MATLAB Simulation

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

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Quantitative definition of chemical synthetic pathway complexity of organic compounds

Baranwal et al. | Jun 19, 2023

Quantitative definition of chemical synthetic pathway complexity of organic compounds

Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.

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