Synthesis of Electrically Conductive Polylactic Acid Composites for 3D Printing

3D printing technology is a process of synthesizing an object by slicing a three-dimensional object into two-dimensional layers. It is making its mark as it reshapes product development and manufacturing industry in which everyone can participate in the process of 3D printing.The demand for various types of materials is increasing as customers become more innovative with designs. The overarching goal of this project was to create innovative 3D printing materials for a conventional 3D printer. Conductive filaments allow us to 3D print electrically conductive components using almost any commercially available desktop 3D printer. The electrically conductive filament for this research was made with carbon black and clear polylactic acid (PLA) pellets. Two different experimental methods were carried out to produce electrically conductive filaments. The first method involved a heating treatment of PLA pellets whereas the second method included a plasticizer to ease the process. Resistance measurements were taken for samples produced with both methods. The resistance increased as the length of the sample increased while the width was held constant. The resistance measurements were inconsistent which may be due to the non-uniform surface. The surface and cross sections of a 3D sample were studied under scanning electron microscope (SEM) machine. Certain features of the composite as well as the thermoplastic were observed with the SEM images

A Novel Pseudo Nearest Neighbor Classification Method Using Local Harmonic Mean Distance

In the realm of machine learning, the KNN classification algorithm is widely recognized for its simplicity and efficiency. However, its sensitivity to the K value poses challenges, especially with small sample sizes or outliers, impacting classification performance. This article introduces a novel KNN-based classifier called LMPHNN (Novel Pseudo Nearest Neighbor Classification Method Using Local Harmonic Mean Distance). LMPHNN leverages harmonic mean distance (HMD) to improve classification performance based on LMPNN rules and HMD. The classifier begins by identifying k nearest neighbors for each class and generates distinct local vectors as prototypes. Pseudo nearest neighbors (PNNs) are then created based on the local mean for each class, determined by comparing the HMD of the sample with the initial k group. Classification is determined by calculating the Euclidean distance between the query sample and PNNs, based on the local mean of these categories. Extensive experiments on various real UCI datasets and combined datasets compare LMPHNN with seven KNN-based classifiers, using precision, recall, accuracy, and F1 as evaluation metrics. LMPHNN achieves an average precision of 97%, surpassing other methods by 14%. The average recall improves by 12%, with an average accuracy enhancement of 5%. Additionally, LMPHNN demonstrates a 13% higher average F1 value compared to other methods. In summary, LMPHNN outperforms other classifiers, showcasing lower sensitivity with small sample sizes

Reaching the Unreached: Day 1 Conference Summary (IMTFI Blog)

This is Part 3 of a 6-part blog series documenting the Mobile Money Payments Conference in Ghana March 12-13th, 2013 hosted by Ghana Telecom University College in Accra, Ghana and in partnership with scholars Cliff Mensah, Richard Zhixin Kang and Vivian Dzokoto.The conference brought together relevant stakeholders in the mobile money industry in Ghana together to deliberate on the barriers to the adoption of mobile money and the strategies to mitigate the barriers to promote awareness and enhance mobile money uptake.Blogposts1 - Announcement: Mobile Money Payments Conference in Ghana March 12-13th, 2013 (1/24/2013)2 - Mobile Money Adoption in Ghana: Why So Long? (3/12/2013)3 - Reaching the Unreached: Day 1 Conference Summary (3/14/2013)4 - Trains at Different Stations: The Ghanaian-Kenyan Mobile Money Discourse (11/26/2013)5 - Mobile Money Payments in Ghana: Part One, Private Intervention (3/10/2014)6 - Mobile Money Payments in Ghana: Part Two, Public Intervention (4/7/2014

Mobile Money Adoption in Ghana: Why So Long? (IMTFI Blog)

This is Part 2 of a 6-part blog series documenting the Mobile Money Payments Conference in Ghana March 12-13th, 2013 hosted by Ghana Telecom University College in Accra, Ghana and in partnership with scholars Cliff Mensah, Richard Zhixin Kang and Vivian Dzokoto.The conference brought together relevant stakeholders in the mobile money industry in Ghana together to deliberate on the barriers to the adoption of mobile money and the strategies to mitigate the barriers to promote awareness and enhance mobile money uptake.Blogposts1 - Announcement: Mobile Money Payments Conference in Ghana March 12-13th, 2013 (1/24/2013)2 - Mobile Money Adoption in Ghana: Why So Long? (3/12/2013)3 - Reaching the Unreached: Day 1 Conference Summary (3/14/2013)4 - Trains at Different Stations: The Ghanaian-Kenyan Mobile Money Discourse (11/26/2013)5 - Mobile Money Payments in Ghana: Part One, Private Intervention (3/10/2014)6 - Mobile Money Payments in Ghana: Part Two, Public Intervention (4/7/2014

Announcement: Mobile Money Payments Conference in Ghana March 12-13th, 2013 (IMTFI Blog)

This is Part 1 of a 6-part blog series documenting the Mobile Money Payments Conference in Ghana March 12-13th, 2013 hosted by Ghana Telecom University College in Accra, Ghana and in partnership with scholars Cliff Mensah, Richard Zhixin Kang and Vivian Dzokoto.The conference brought together relevant stakeholders in the mobile money industry in Ghana together to deliberate on the barriers to the adoption of mobile money and the strategies to mitigate the barriers to promote awareness and enhance mobile money uptake.Blogposts1 - Announcement: Mobile Money Payments Conference in Ghana March 12-13th, 2013 (1/24/2013)2 - Mobile Money Adoption in Ghana: Why So Long? (3/12/2013)3 - Reaching the Unreached: Day 1 Conference Summary (3/14/2013)4 - Trains at Different Stations: The Ghanaian-Kenyan Mobile Money Discourse (11/26/2013)5 - Mobile Money Payments in Ghana: Part One, Private Intervention (3/10/2014)6 - Mobile Money Payments in Ghana: Part Two, Public Intervention (4/7/2014

Application of Biostatistics and Bioinformatics Tools to Identify Putative Transcription Factor- Gene Regulatory Network of Ankylosing Spondylitis and Sarcoidosis

Transcription factors and corresponding cis-regulatory elements are considered key components in gene regulation. We combined biostatistics and bioinformatics tools to streamline identification of putative transcription factor-gene regulatory networks unique for two immune-mediated diseases, ankylosing spondylitis and sarcoidosis. After identifying differentially expressed genes from microarrays, we employed tightCluster to find tight clusters of potentially co-regulated genes. By subsequently applying bioinformatics tools to search for common cis-regulatory elements, putative transcription factor-gene regulatory networks were found. Recognition of these networks by applying this methodology could pave the way for new insights into disease pathogenesis

A Comparison of Alternative Forecast Models of REIT Volatility

This study compares the relative performance of several well-known models in the forecasting of REIT volatility. Overall our results suggest that long-memory models (ARFIMA & FIGARCH) provide the best forecasts. Using either a large sample or some statistically justified small subsamples, we find that long memory models consistently outperform their short-memory counterparts (GARCH & Stochastic Volatility models) over a variety of forecast horizons. We also find that asymmetric models (EGARCH & FIEGARCH) are the worst performers among all models. Our study complements and extends a recent study of Cotter and Stevenson (2008) which demonstrates the usefulness of long-memory models in modeling REIT volatility. We conclude that in addition to modeling REIT volatility, long-memory models should also be adopted to forecast REIT volatility

Long Term Dependence of Popular and Neglected Stocks

In this study, we establish a connection between the levels of market attentions of a stock with its long memory features. We construct two portfolios of US equities based on Doyle et al’s (2006) criteria for neglected and popular stocks and measure the degrees of persistence for their daily returns from January 1, 2003 to December 31, 2007. We find that all stocks except for one display anti-persistence in the neglect portfolio; while the popular portfolio stocks uniformly display random walk returns. This suggests that there is a connection between the persistence features of stock return series and the levels of “neglect” of stocks. We use book to market ratio, analyst coverage, and transaction frictions to classify the levels of market neglect of stocks. Based on our study, while these criteria combined appear to contribute to the long memory features of daily returns of stocks, we also suspect the presence of other factors driving the persistence of stock returns

Retention and Student Success: A Study of First Time, Full Time First-Generation Students at UNC Pembroke

First generation students face unique challenges in retention and graduation in colleges and universities. Recently, EAB released a study indicating that 90% of low-income, first generation students did not graduate on time within six years, particularly underrepresented minority students. UNCP is the top diverse institution in the South and had low retention rates and graduation rates compared with other UNC institutions in the past. For instance, 59% of our students were underrepresented minority students; and more than 33% of Freshmen were identified as first generation in Fall 2015. In addition, among students who graduated in May 2015, 39% were identified as first generation.The purpose of this study is to find how well our first generation students have done compared to other racial groups at UNCP, particularly Fall-to-Fall retention and six-year graduation. The results may help us to improve our services and add/adjust some programs to better serve the student body in the future.The study will focus on first-time, full-time (FTFT) Freshmen who were identified as first-generation in FAFSA application at UNCP. The cohorts included the FTFT Freshmen from Fall 2008 to Fall 2010

A Multi-Country Study Exploring Relationship of Lifestyles to Ethnocentrism

In this paper, we study consumer market segments in four Latin American countries and one U.S. territory by using lifestyle patterns and ethnocentrism. We partition consumer ethnocentrism into low, medium, and high levels, and then investigate the relationship between each level of consumer ethnocentrism and lifestyles. Furthermore, the impacts of gender, age, and marital status on the relationship between ethnocentrism and life style are explored. Data for the study was collected through self-survey in major cities in these countries. The results reveal distinct ethnocentrism- lifestyle relationship patterns at different levels of consumer ethnocentrism among the five Latin American regions. Especially, at the high ethnocentrism level, consumer lifestyles have significant influence on the consumers‘ ethnocentric tendencies. In addition, we found that the older consumers at the high ethnocentrism level exhibit significant relationship to their lifestyle. These findings have considerable implications for marketers in that, it opens up more opportunities for them in comparison to what others have been exposed to through extant research. Secondly, for marketers who are already operating in a global environment, our analysis offers them ideas in market segmentation, environmental scanning and opportunity analysis