Search results for: Ana Rubi L. Sereño

Authors: Rex C. Apillanes, Ana Rubi L. Sereño, Ellen Joy L. Palangan

Abstract:

The main objective of this action research is to know the effectiveness of the compendium on Contextualized Story Word Problem in Mathematics used as an intervention material to enhance the comprehension and problem-solving skills of Grade 4 pupils. This also addresses the competencies outlined in the curriculum guide while, at the same time, providing instructional material which the pupils can work on and practice solving word problems. The twelve randomly selected grade four pupils of Mantuyom Elementary School have been chosen as respondents for this action research in consideration of their consent and approval. A Pre-Test and a Post-test have been given to the pupils to determine their baseline proficiency level in four fundamental operations. The data has been statistically treated using a T-test to determine their difference. At a mean score of 13.42 and 16.83 for pre and post-tests, respectively, the p-value of 0.000620816 reflects a highly significant difference for the pre-test and post-test. This is lesser than the 0.05 level of significance (p≤0.05). Therefore, it is found that the compendium of contextualized story word problems is an efficient instructional material for Mathematics 4, yet; it is recommended that a Parents’ User Guide shall be developed to assist the parents in the conduct of the Remediation, Reinforcement and Enhancement (RRE).

Keywords: action research, compendium, contextualized, story, word problem, research, intervention

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Authors: Rubi Alvarez-Rodriguez

Abstract:

Traditionally, eco-centric and anthropocentric forms of environmentalism have been seen as mutually exclusive. While eco-centrism focuses on global environmental issues, anthropocentrism is concerned with local ones. The objective of this paper is to characterize the relationship between eco-centric and anthropocentric attitudes across 43 countries. This study analysed secondary data from the 2005 World Values Survey, using a standard linear regression approach. It is shown that eco-centric and anthropocentric attitudes are not mutually exclusive and that the predominance of one over the other is best predicted by a country’s level of wealth.

Keywords: anthropocentrism, eco-centrism, pro-environmental attitudes, wealth

Procedia PDF Downloads 336

Authors: Yawen Xia, Rubi Yang, Jing Zhao

Abstract:

By using the Reputation Risk Index (RRI) to measure company environmental, social, and governance (ESG) activities, this research studies firms’ ESG comovement with their industry and local peers. This comovement is attenuated during the Covid-19 pandemic. Further analysis shows that corporate governance plays an important role in comovement decrease. We classify companies by region (city, state, region) and industry and calculate the average RRI of companies of the same type. We run separate regressions to test 1) industry comovement; 2) local comovement; 3) Covid-19 pandemic and reputation risk comovement; 4) corporate governance and reputation risk comovement. Our findings are consistent with previous literature that companies follow their industry and local counterparts in engaging in irresponsible activities and reducing ESG engagement. We speculate Covid shock led to a reduction in social activities and information sharing among enterprise managers, and comovement between enterprises, as a result, decreased during the pandemic.

Keywords: ESG, Covid, peer pressure, local comovement, corporate governance

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Authors: Min Chen, Rubi Gupta

Abstract:

Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Luz del Carmen Rubí Félix Peña, Jose Adan Felix-Ortiz, Ely Sara Lopez-Alvarez, Wenceslao Valenzuela-Quiñonez

Abstract:

On a global scale, Mexico is the sixth largest producer of farmed white shrimp (Penaeus vannamei). The activity suffered significant economic losses due to acute hepatopancreatic necrosis (AHPND) caused by a strain of Vibrio parahaemolyticus. For control, the first option is the application of antibiotics in food, causing changes in the environment and bacterial communities, which has produced greater virulence and resistance of pathogenic bacteria. An alternative treatment is silver nanoparticles (AgNPs) generated by green synthesis, which have shown an antibacterial capacity by destroying the cell membrane or denaturing the cell. However, the doses at which these are effective are still unknown. The aim is to calculate the minimum inhibitory concentration (MIC) using the Gompertz, Richard, and Logistic model of biosynthesized AgNPs against a strain of V. parahaemolyticus. Through the testing of different formulations of AgNPs synthesized from Euphorbia prostrate (Ep) extracts against V. parahaemolyticus causing AHPND in white shrimp. Aqueous and ethanol extracts were obtained, and the concentration of phenols and flavonoids was quantified. In the antibiograms, AgNPs were formulated in ethanol extracts of Ep (20 and 30%). The inhibition halo at well dilution test were 18±1.7 and 17.67±2.1 mm against V. parahaemolyticus. A broth microdilution was performed with the inhibitory agents (aqueous and ethanolic extracts and AgNPs) and 20 μL of the inoculum of V. parahaemolyticus. The MIC for AgNPs was 6.2-9.3 μg/mL and for ethanol extract of 49-73 mg/mL. The Akaike index (AIC) was used to choose the Gompertz model for ethanol extracts of Ep as the best data descriptor (AIC=204.8, 10%; 45.5, 20%, and 204.8, 30%). The Richards model was at AgNPs ethanol extract with AIC=-9.3 (10%), -17.5 (20 and 30%). The MIC calculated for EP extracts with the modified Gompertz model were 20 mg/mL (10% and 20% extract) and 40 mg/mL at 30%, while Richard was winner for AgNPs-synthesized it was 5 μg/mL (10% and 20%) and 8 μg/mL (30%). The solver tool Excel was used for the calculations of the models and inhibition curves against V.parahaemolyticus.

Keywords: green synthesis, euphorbia prostata, phenols, flavonoids, bactericide

Procedia PDF Downloads 70