This poster will cover the results and experiences of using Tiktok as a form of peer-to-peer outreach to share research tips with other college students. Specific examples, learned best practices, and future plans for improvement will be discussed. It was presented at the Association of College and Research Libraries (ACRL) Distance and Online Learning Virtual Poster Session held April 26-30, 2021.

This poster explores the process and resources used to create subject metadata for The Chronicles of Oklahoma collection in The Gateway to Oklahoma History. The Chronicles of Oklahoma is a regional publication from the Oklahoma Historical Society that has been published since 1921. The UNT Digital Library is digitizing the back issues and making them available to the public through the digital library, The Gateway to Oklahoma History. It was presented at the UNT Libraries' 2023 Student Snapshots Symposium held in Denton, Texas and at the 2023 Texas Conference on Digital Libraries (TCDL) held May 16-18, 2023 in Austin, Texas.

This poster highlighting the development of machine learning model to automate part of the process of digitizing and archiving documents from the Texas State Depository Program. This particular part of the process is the sorting of documents to facilitate metadata creation. It was presented at the 2023 Texas Conference on Digital Libraries (TCDL) held May 16-18, 2023 in Austin, Texas.

Poster examining the association of acculturation with test performance on a neuropsychological battery. It was presented at the 9th Annual Psychology Research Fair held at the University of North Texas in 2019.

Poster examines the association of acculturation with test performance on a neuropsychological battery. It was presented at the 9th Annual Psychology Research Fair held at the University of North Texas in 2019.

The title compound, C₂₆H₂₁Cl₂NO, crystallizes with two symmetry-independent molecules in the asymmetric unit.

This article describes the crystal structure of the title compound, and examines application as an agent for locating cancer cells with two-photon excited fluorescence as as a potential agent for a photodynamic treatment of cancer. This article includes supporting information with computing details of the study.

In the title molecule, C₂₀H₁₇Cl₂NO, the central heterocyclic ring adopts a flattened boat conformation.

This book chapter reviews the electrodeposition of Cu-Ni incorporated with nano- to microparticles to produce metal matrix composites.

This article discusses validation of the potential of an extraction and characterization approach via application to ancient bone proteins, as part of a larger method-development project for innovation and improvement of liquid chromatography - mass spectrometry analysis of protein residues from cooking pottery.

This article aims to examine the link between low-level arsenic exposure and cognitive functioning, and the potential role of a single nucleotide polymorphism (SNP A35991G, rs10748835) of the AS3MT gene in modifying this link.

This article discusses the isolation of five novel phages, which were used to established the cluster BF, using Streptomyces griseus subsp. griseus strain ATcC 10137 as the host.

This article discusses the use of metabolic engineering to generate raffinose-family oligosaccharides (RFOs) at the inception of the translocation stream of Arabidopsis thaliana, which loads from the apoplasm and transports predominantly sucrose, and the fate of the sugars through the plant determined.

This book chapter reviews experimental results of observed room temperature ferromagnetism in transition metal doped group II-VI semiconductors.

This article assesses what Togolese educated women know about cervical cancer and the accuracy of their knowledge.

Poster for the 2017 Texas Conference on Digital Libraries. This poster discusses results from an analysis of UNT Electronic Theses and Dissertations citing URLs to items from the UNT Digital Library and The Portal to Texas History.

Poster for the 2017 Texas Conference on Digital Libraries. This poster discusses metadata remediation issues within the UNT Libraries TRAIL collection focused on identifying and measuring issues related to incomplete records and creation date discrepancies.

This poster explores data gathered from the UNT Libraries' reference analytics from questions submitted to the libraries. It was presented at the UNT Libraries' 2023 Student Snapshots Symposium held in Denton, Texas.

This article discusses the test of the function of FIT2 in plant cells by ectopically expressing mouse (Mus musculus) FIT2 in Nicotiana tabacum suspension-cultured cells, Nicotiana benthamiana leaves and Arabidopsis thaliana plants.

Understanding trends is helpful to identify future behaviors in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering introduced in Behpour et al. (2021) study. The developed and freely available web application enables users to detect well defined trending topics in financial journals by experimenting with various levels of temporal bias - from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

This workshop is presented by members of a University of North Texas research team. First, the team will overview their experience as members of the research team and share experience in areas such as trust formation, team roles, productivity, work-life balance, faculty-students interaction, peer and faculty mentorship, dissertation preparation, and job seeking. Second, the workshop will discuss and brainstorm how this format can be implemented for organizations both with faculty-student teams and with peer-directed teams. Finally, successes and challenges are openly discussed with audience.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.

Article exploring phonon dynamics in mechanically exfoliated two-dimensional WSe₂ using temperature-dependent and laser-power-dependent Raman and photoluminescence (PL) spectroscopy. The work reported sheds fundamental insights into the evolution of phonon dynamics in WSe₂ and should help pave the way for designing high-performance electronic, optoelectronic and thermoelectric devices in the future.