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Organizational Conflicts Of Interest

Research & Economic Development Policies

Government and some industry sponsors that fund Georgia Southern University (University) research require an organizational conflict of interest (OCI) assessment to ensure objectivity in Federal contracting. The OCI policy described below is directed toward identifying and managing OCI in a manner consistent with Federal Acquisition Regulation (FAR) Subpart 9.5.

Search For Eccentric Black Hole Coalescences During The Third Observing Run Of Ligo And Virgo, A. G. Abac, R. Abbott, H. Abe, Teviet Creighton, Mario C. Diaz, Francisco Llamas Villarreal, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

Despite the growing number of binary black hole coalescences confidently observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include the effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that have already been identified with searches focusing on …

An Evaluation Of Cutting-Edge Ai Research Tools Using The React Framework, Susan Gardner Archambault, Jose Rincón

Librarian Publications & Presentations

As AI continues to revolutionize academic research, librarians must stay informed about the latest AI-powered tools and their potential applications. This article explores a range of cutting-edge AI research tools, evaluating their key features, benefits, and drawbacks using the REACT framework (Relevancy, Ease of Use, Assessing DEIA [diversity, equity, inclusion, and accessibility], Currency, Transparency & Accuracy). We focus on two categories of tools: citation-based literature mapping tools and text-extraction tools for literature reviews. The citation mapping tools are Litmaps, Connected Papers, and ResearchRabbit, which help researchers discover and visualize related academic literature. The text-extraction tools—Elicit, scite, and Consensus—assist in finding, …

Assessing Factors Of Adherence And Efficacy: A Randomized Controlled Trial Of A Fully Automated Self-Help A-Ebt Website, Leila K. Capel, Emily M. Bowers, Mckenzie R. Becker, Marisa P. Davis, Michael E. Levin, Michael P. Twohig

Psychology Faculty Publications

Treatment for trichotillomania is notably limited, preventing suffering individuals from having access to treatment. To address this need, researchers have developed and tested asynchronous online interventions for adults with trichotillomania. A factor that may impact the efficacy of these programs is the use of phone check-ins (or similar coaching support) to improve treatment adherence in website treatment delivery. In the current study we evaluated the role of check-ins on treatment adherence and efficacy of a website delivering acceptance and commitment therapy-enhanced behavior therapy (A-EBT). A sample of 101 adults with trichotillomania were randomly assigned to an A-EBT web-based intervention with …

A Content Analysis Of Counseling Psychology Literature: Resilience Against Oppression Among People Of Color, David C. Stanley Jr., Rawan H. Atari-Khan

Psychology Faculty Research and Publications

The researchers analyzed articles from two flagship counseling psychology journals (i.e., Journal of Counseling Psychology and The Counseling Psychologist) to examine current understandings of resilience. There were 54 articles included in the final analysis that spanned the years 1997–2022. The researchers conducted a content analysis to identify, analyze, and report patterns across counseling psychology journals with regard to how resilience has been defined, the racial/ethnic groups that were of focus, and the forms of oppression that were studied or addressed. Five themes were generated that are a direct representation of the topics within previous literature on resilience in the field …

Trends And Patterns In Hypertension-Related Deaths: A Comprehensive Analysis Using Center For Disease Control And Prevention's Wide-Ranging Online Data For Epidemiologic Research (Cdc Wonder) Data, Oyinlola O. Fasehun, Joycelyn Adjei-Mensah, Wisdom S. Ugorji, Victoria O. Titus, Oluwatobi O. Asade, Damilola A. Adeyemo, Okelue E. Okobi

School of Medicine Publications

Hypertension (HTN) is a leading cause of cardiovascular morbidity and mortality worldwide. Despite advances in treatment, including the development and use of vasodilator-β-blocker combination and treatment with antihypertensive agents, HTN-related deaths have shown concerning trends. As such, the objective of this study is to examine the trends, disparities, and demographic variations in HTN-related mortality over a decade and to identify key factors contributing to these patterns, including genetics, dietary habits, structural discrimination in access to healthcare, lifestyle choices, and secondary hypertension, which is due to underlying conditions like kidney disease, hormonal disorders, or certain medications. To attain this objective, this …

Evaluating The Efficacy Of And Preference For Interactive Computer Training With Student-Generated Examples, Sylvia C. Aquino, Stephanie Hood, Tara A. Fahmie, Richard Tanis

Psychology Faculty Research and Publications

Designing effective and preferred teaching practices for undergraduate students are common goals in behavior analytic training programs. A preliminary study by Nava et al. (2019) showed that undergraduate students generally rated peer-generated examples of the principles of behavior analysis as more preferred, relatable, and culturally responsive than traditional textbook examples. However, peer-generated examples did not result in any improvement in performance on concept knowledge assessments. The current study extended the study by Nava et al. by embedding peer-generated examples within interactive computer training (ICT) to provide opportunities for active responding, prompt fading, automated feedback, and practice with examples and nonexamples. …

Rules, Privacy, And Ethics: Challenges In Creating Author Name Change Guidelines, Angela Yon, Emily Baldoni, Eric Willey

Faculty and Staff Publications – Milner Library

People change their names for a variety of reasons, including, but not limited to, gender transition, change in marital status, and religious conversion. Guidance on the metadata management of author name changes for the myriad resources in a library’s discovery system is elusive and absent. Executing metadata for name changes creates many challenges in a rapidly advancing infrastructure of emerging discovery technologies, aggregators with shared metadata in multiple schemas, and numerous formats in varied platforms. Moreover, it is difficult to find a balanced approach to ethically apply evolving cataloging and name authority control rules to suit the linked data environment. …

Evaluating The Impact Of Spot Position Errors On Dose Distribution: A Comparison Between Spot-Scanning Arc Therapy (Sparc) And Intensity-Modulated Proton Therapy (Impt), Peilin Liu, Lewei Zhao, Gang Liu, Xiaoda Cong, Xiaoqiang Li, Xuanfeng Ding

Conference Presentation Abstracts

Purpose: To quantitatively investigate the impact of spot position error (PE) on the dose distribution in (Spot-scanning arc therapy) SPArc plans versus Intensity-Modulated Proton Therapy (IMPT).

Methods:

Four representative disease sites, including brain, lung, liver, and prostate cancers, were retrospectively selected. Spot position errors were simulated during dynamic SPArc treatment delivery. Two types of errors were generated, including randomized error and systematic error. For each random error scenario, they were further examined across four sub-scenarios (25%, 50%, 75%, and 100% of spots affected) at 1 mm and 2 mm deviations.The randomized errors used two categories with and without Gaussian distribution …

The Internist's Post V.1 N.8, The University Of Texas Rio Grande Valley. School Of Medicine, Edgar Dorsey Trevino, Diana Othon Martinez, Ramon Elizondo Romo, Carlos Alejos Aguero, Elizabeth Mills-Reyes, Roque Mifuji, Martha Solis, Cesar Gutierrez

School of Medicine Publications

No abstract provided.

Response To Letter To The Editor Concerning The Article "The Clinical And Economic Impact Of Extended Battery Longevity Of A Substernal Extravascular Implantable Cardioverter Defibrillator"., Bradley P Knight, Nicolas Clémenty, Anish Amin, Ulrika Maria Birgersdotter-Green, Henri Roukoz, Reece Holbrook, Jaimie Manlucu

Heart and Vascular Articles

No abstract provided.

Impact Of Functional Recovery On Patients Having Heart Surgery., Richard J Snow, Lauren Mckown, Geoffrey Blossom, Karen Vogel, Amy Creighton, Jason Shriver, Linda Will, Katie Lentz, Elizabeth Snow, Teresa Caulin-Glaser

Heart and Vascular Articles

OBJECTIVE: To describe the results of a program developed to manage institutional postacute care (IPAC) (postacute skilled nursing, inpatient rehabilitation facility, and long-term acute care) in a CMS Bundled Payments for Care Improvement (BPCI) project for coronary artery bypass graft (CABG) surgery.

STUDY DESIGN: We compared pre- and postutilization patterns during a 3-year period by evaluating risk-adjusted national, state, and other BPCI participant comparisons using a difference-in-differences (DID) analysis in a large urban community tertiary center with a CABG surgery program. Included in the analysis were all Medicare patients receiving CABG surgery at the institution (n = 504), across the …

Performance Evaluation Of Ai-Based Healthcare Systems In The United Arab Emirates: A Comparative Analysis Of Diagnostic Accuracy And Predictive Capability Using Spss, Hamad Ismail

Theses

This research investigates the opinions and experiences of United Arab Emirates (UAE) healthcare personnel on artificial intelligence (AI)-based technology. Successful application and integration depend on a knowledge of the viewpoints of people at the front-line of patient care as artificial intelligence technology progressively enter healthcare contexts. Combining advanced statistical analysis with quantitative questionnaires using a mixed-methods approach, the study presents a whole picture of artificial intelligence opinions across several healthcare environments and UAE geographical areas. The study revealed a topography of opinions on artificial intelligence in the healthcare sector that is complicated. Though views varied widely based on their employment, …

Data Ownership And Control: Ethical Perspectives In The Digital Age, Roya Ebadi

Theses

The rapid advancement of machine learning (ML) technologies has significantly impacted various sectors, with healthcare standing out as a prominent area for transformation. Machine learning holds the potential to revolutionize healthcare, particularly in the areas of diagnosis, treatment plans, drug development, and personalized medicine. However, with the growing integration of ML into healthcare systems, ethical concerns regarding data privacy, security, and fairness have emerged, requiring comprehensive governance frameworks to ensure the responsible use of these technologies. This research investigates the ethical considerations associated with the application of machine learning in healthcare, focusing on data governance practices that are crucial for …

Predicting Consumer Churn In Telecommunications, Amna Yousef Almatrooshi

Theses

The telecommunications industry is highly competitive, with consumer churn being a significant concern due to market saturation. Churn, or customer attrition, refers to customers leaving a service provider due to dissatisfaction or better offers from competitors. This phenomenon is particularly impactful in the telecom sector, where retaining customers is more cost-effective than acquiring new ones. To address this issue, telecom companies increasingly rely on machine learning techniques to predict customer churn and implement strategies to retain at-risk customers. The study highlights the importance of understanding the factors contributing to customer churn, including service quality, pricing, customer service interactions, and competition. …

Assessing The Effectiveness Of Law Enforcement Strategies In Combating E-Crime Using Ai, Mohammad Ahmad Alkhazraji

Theses

The rise of e-crime, particularly international online scams, has presented significant challenges to law enforcement agencies around the world. Scams have become transnational, exploiting the internet's interconnectivity to deceive victims from different countries. Law enforcement agencies have a severe uphill battle in trying to combat these types of crimes because of technological and jurisdictional limitations and resource accessibility. This research has analyzed the effectiveness of current policing against international online scams, focusing on the challenges encountered thus far and possible solutions. The critical research objectives revolve around assessing law enforcement agencies' present strategies, identifying the main barriers that these agencies …

Chemometric Processing Of Ecstasy Tablet Thermal Profiles Using Machine Learning Methods, Ali Arif Ibrahim

Theses

Ecstasy is an amphetamine-type substance scientifically known as MDMA (3,4- methylenedioxymethamphetamine) that belongs to a group of illicit drugs called “club drugs” that are widespread and distributed illegally (Maione et al., 2016). Its ever-growing popularity and variety in multiple forms, shapes, and colors complicates the identification and tracking process to law enforcement and forensic analysts. The current study focused on integrating data analytics and advanced chemometric methods to Differential Scanning Calorimetry (DSC) to analyze the thermal profiles of ecstasy tablets. This approach aims to discriminate between tablets linked to different suspects and origins through thermal profiling, which will provide law …

Exploring Ngc 2287: An Investigation Of Open Cluster Membership, Rotation, And A Curiously Bifurcated Main Sequence, Benjamin J. Ramsey

Theses

The study of stellar clusters is integral to our understanding of many areas of astrophysics because of the role they play as astrophysical benchmarks. Thanks to shared characteristics (in particular, age and chemical composition) of their members, open star clusters represent essential subjects to address contemporary problems in stellar structure, formation, and evolution. The rich open cluster NGC 2287 (the Little Beehive) is young (age~200 Myr), relatively nearby (D~735 pc), and suffers little intervening extinction, making it ripe for further study. The cluster is particularly noteworthy for displaying a rotationally bifurcated upper main sequence. We are investigating the membership and …

Stable Isotope Analysis Of Antipatharian Diets From The Northwestern Gulf Of Mexico, Keir J. Macartney, Victoria Salinas, Zachary Rivera, David Hicks

School of Earth, Environmental, & Marine Sciences Faculty Publications

Antipatharians, or black corals, form a key component of the benthic fauna found throughout the Gulf of Mexico (GOM). In the northwestern GOM, they are a common member of the communities found on the natural relic coral—algal banks known as the South Texas Banks as well as on artificial reefs. They are known to provide important habitat for many ecologically and economically important species in the area but are relatively understudied. Here we present a study on their trophic ecology using stable isotope analysis of carbon and nitrogen from 2 species, Antipathes atlantica and Stichopathes luetkeni from a mesophotic artificial …

Accreditation Update, Georgia Southern University

Institutional Assessment & Accreditation

No abstract provided.

The George-Anne Daily, Georgia Southern University

George-Anne Media Group: Newsletters & Magazines

No abstract provided.

Video Editing For Video Retrieval, Bin Zhu, Kevin Flanagan, Adriano Fragomeni, Michael Wray, Dima Damen

Research Collection School Of Computing and Information Systems

Though pre-training vision-language models have demonstrated significant benefits in boosting video-text retrieval performance from large-scale web videos, fine-tuning still plays a critical role with manually annotated clips with start and end times, which requires considerable human effort. To address this issue, we explore an alternative cheaper source of annotations, single timestamps, for video-text retrieval. We initialise clips from timestamps in a heuristic way to warm up a retrieval model. Then a video clip editing method is proposed to refine the initial rough boundaries to improve retrieval performance. A student-teacher network is introduced for video clip editing: the teacher model is …

Stagedvulbert: Multi-Granular Vulnerability Detection With A Novel Pre-Trained Code Model, Yuan Jiang, Yujian Zhang, Xiaohong Su, Christoph Treude, Tiantian Wang

Research Collection School Of Computing and Information Systems

The emergence of pre-trained model-based vulnerability detection methods has significantly advanced the field of automated vulnerability detection. However, these methods still face several challenges, such as difficulty in learning effective feature representations of statements for fine-grained predictions and struggling to process overly long code sequences. To address these issues, this study introduces StagedVulBERT, a novel vulnerability detection framework that leverages a pre-trained code language model and employs a coarse-to-fine strategy. The key innovation and contribution of our research lies in the development of the CodeBERT-HLS component within our framework, specialized in hierarchical, layered, and semantic encoding. This component is designed …

Kpiroot: Efficient Monitoring Metric-Based Root Cause Localization In Large-Scale Cloud Systems, Wenwei Gu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

To ensure the reliability of cloud systems, their run-time status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …

Ocapo: Fine-Grained Occupancy-Aware, Empirically-Driven Pdc Control In Open-Plan, Shared Workspaces, Ravi Anuradha, Dulaj Sanjaya Weerakoon, Archan Misra

Research Collection School Of Computing and Information Systems

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle …

Retrofitting A Legacy Cutlery Washing Machine Using Computer Vision, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed andutilization. With the existing operational constraints of …

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History: News & Publications

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Evaluation Of The Proposed Strategy For The Iraqi Media Network For The Period (2024-2028), Field Research, Aous Mahmood Ibrahim, Yousif Aftan Abdullah, Muhammad Umar

Journal of Economics and Administrative Sciences

The current research aims to study a crucial phase of strategic management, which is strategic evaluation, to assess the proposed strategy through data analysis, ensuring its implementation with minimal deviation from the planned course. The research focuses on studying and evaluating the proposed strategy for the Iraqi media network for the years 2024-2028, examining its effectiveness and alignment with the state's directions. This involves understanding the realism of strategic visions, plans, and programs and building a suitable conceptualization. The research takes into consideration the environmental conditions and relies on scientific principles and steps to formulate a comprehensive and realistic strategy. …

Foss: Towards Fine-Grained Unknown Class Detection Against The Open-Set Attack Spectrum With Variable Legitimate Traffic, Ziming Zhao, Zhaoxuan Li, Xiaofei Xie, Jiongchi Yu, Fan Zhang, Rui Zhang, Binbin Chen, Xiangyang Luo, Ming Hu, Wenrui Ma

Research Collection School Of Computing and Information Systems

Anomaly-based network intrusion detection systems (NIDSs) are essential for ensuring cybersecurity. However, the security communities realize some limitations when they put most existing proposals into practice. The challenges are mainly concerned with (i) fine-grained unknown attack detection and (ii) ever-changing legitimate traffic adaptation. To tackle these problem, we present three key design norms. The core idea is to construct a model to split the data distribution hyperplane and leverage the concept of isolation, as well as advance the incremental model update. We utilize the isolation tree as the backbone to design our model, named FOSS, to echo back three norms. …

Themis: Automatic And Efficient Deep Learning System Testing With Strong Fault Detection Capability, Dong Huang, Tsz On Li, Xiaofei Xie, Heming Cui

Research Collection School Of Computing and Information Systems

Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has incorrect outputs (i.e., faults). DLS testing techniques (e.g., DeepXplore) detect such faults by generating perturbed inputs to explore data flows that induce faults. Since a DLS often has infinitely many data flows, existing techniques require developers to manually specify a set of activation values in a DLS’s neurons for exploring fault-inducing data flows. Unfortunately, recent studies show that such manual effort is tedious and can detect only a tiny proportion …