Digital Commons Network^™

Board Commitment, Continuous Education Program And Firm Performance: A Law And Governance Context, Ahmad Saiful Azlin Puteh Salin, Zubaidah Ismail, Malcolm Smith

Research outputs 2022 to 2026

The majority of worldwide corporate governance codes mandate that directors allocate an adequate amount of time to fulfil their responsibilities and consistently enhance their knowledge and abilities through continuous education programs. These two factors are crucial in propelling the organisation towards higher levels of success and satisfying the needs of the stakeholders. This research aims to analyse the impact of board commitment and their access to education on firm performance. After analysing the annual reports of Malaysian public listed companies, this study does not find any conclusive evidence to support earlier research that highlights the advantages of having a director …

Error In Jump Height Estimation Using The Flight Time Method: Simulation Of The Effect Of Ankle Position Between Takeoff And Landing, Carlos Gonçalves, Roberto Baptista, James Tufano, Anthony J. Blazevich, Amilton Vieira

Research outputs 2022 to 2026

During vertical jump evaluations in which jump height is estimated from flight time (FT), the jumper must maintain the same body posture between vertical takeoff and landing. As maintaining identical posture is rare during takeoff and landing between different jump attempts and in different individuals, we simulated the effect of changes in ankle position from takeoff to landing in vertical jumping to determine the range of errors that might occur in real-life scenarios. Our simulations account for changes in center of mass position during takeoff and landing, changes in ankle position, different subject statures (1.44–1.98 m), and poor to above-average …

Accounting-Based Variables As An Early Warning Indicator Of Financial Distress In Crisis And Non-Crisis Periods, Robert J. Powell, Dung V. Dinh, Nam T. Vu, Duc H. Vo

Research outputs 2022 to 2026

Financial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in-sample and out-of-sample approaches. This …

What's In A Name? Standardization Of Vernacular Names For Trichechus Manatus, A. A. Mignucci-Giannoni, Daniel Gonzalez-Socoloske, Anmari Alvarez-Alemán, Jamie Aquino, D. Caicedo-Herrera, Delma Nataly Castelblanco-Martínez, Diane Claridge, M. Fabiola Corona-Figueroa, Adolphe O. Debrot, Benoit De Thoisy, Carlos Espinoza-Marin, J. Galves, Eddy Garcia-Alfonso, Hector M. Guzman, Jalaludin Ahmad Khan, Jeremy J. Kiszka, Fabia De Oliveira Luna, Miriam Marmontel, Leon David Olivera-Gomez, Christine O'Sullivan, James A. Powell, Enrique Pugibet-Bobea, Indranee Roopsind, Carlos J. Silva

Faculty Publications

Trichechus manatus is one of 3 recognized extant species of manatees of the mammalian Order Sirenia. Currently, it is known by various vernacular names, none of which are descriptive of its biology and known distribution. This lack of standardization has led to confusion and miscommunication, hindering conservation efforts for this species. We propose standardizing the vernacular names used to refer to the species and its 2 subspecies based on appropriate geographical characterization in English, Spanish, French, Dutch, and Portuguese, respectively: “American Manatee”, “Manatí de las Américas”, “Lamantin d’Amérique”, “Amerikaanse Lamantijn”, and “Peixe-boi das Américas” for T. manatus; “Florida Manatee”, “Manatí …

Scat Dna As A Non-Invasive Method For Estimating The Abundance Of The Vulnerable Mala (Lagorchestes Hirsutus), Shannon Treloar, Cheryl Lohr, Anna J. Hopkins, Kym Ottewell, Shelley Mcarthur, Robert A. Davis

Research outputs 2022 to 2026

Context: Population-monitoring programs often use direct (e.g. live capture or spotlighting) or indirect (e.g. scats sightings) observations to estimate population abundance. Such methods, however, are often inadequate for rare, elusive, or cryptic species due to the difficulty in achieving sufficient encounters or detection rates. The mala (Lagorchestes hirsutus), a small native Australian macropod, listed as Vulnerable by the IUCN, is difficult to capture, susceptible to capture myopathy, and not easily sighted in their dense habitat; consequently, the population size cannot always be estimated. The use of molecular markers to identify individual genotypes from non-invasively collected samples is increasingly being used …

Fight-Pd: A Feasibility Study Of Periodized Boxing Training For Parkinson Disease, David J. Blacker, Raimondo Fazio, Claire Tucak, Phillip Beranek, Connor Pollard, Tegan Shelley, Sanathraj Rajandran, Georgina Holbeche, Mitchell Turner, Travis Cruickshank

Research outputs 2022 to 2026

Background: Boxing training has become a popular form of exercise for people with Parkinson disease (PD). There is a dearth of high-quality feasibility, safety, and efficacy data on boxing training for PD. Feasibility of Instituting Graduated High-intensity Training (FIGHT-PD) aimed to examine these features in a periodized boxing training program featuring high-intensity physical and cognitive demands. Objective: To conduct a feasibility study, aiming to address deficiencies in the current knowledge base and to provide data for future studies. Design: Single-arm, open-label feasibility. Setting: University department and medical research institute. Participants: Ten people with early stage PD without contraindications to intense …

The Stability Of The Deadlift Three Repetition Maximum, Stuart N. Guppy, Tsuyoshi Nagatani, Wayne C. K. Poon, Kristina L. Kendall, Jason P. Lake, G. Gregory Haff

Research outputs 2022 to 2026

This study investigated the stability of three repetition maximum (3RM) strength during the deadlift. Eleven participants performed four testing sessions comprising a one repetition maximum test and 3RM tests separated by 48 h. Preparedness was assessed before each testing session using countermovement jumps and by measuring barbell velocity during each set of deadlifts. Trivial statistically significant differences were determined for the 3RM between T1 and both T2 (p = 0.012; ES [95% CI] = −0.1 [−0.58, 0.41]) and T3 (p = 0.027; ES [95% CI] = −0.09 [−0.57, −0.43]). No significant differences were noted between T2 and T3 …

Assessing The Transformative Influence Of Chatgpt On Research Practices Among Scholars In Pakistan, Nayab Arshad, Mehran Ullah Baber, Adnan Ullah

Mesopotamian Journal of Big Data

This article investigates the transformative impact of ChatGPT on research practices within the scholarly community in Pakistan. ChatGPT, a powerful AI language model, has added significant consideration for its possibility of improving academic research. Survey data was gathered via a structured questionnaire distributed to researchers in Pakistan. A total of 278 questionnaires were distributed for the randomly chosen sample, of which 223 were returned. For calculating descriptive statistics, SPSS was utilized.  Results of the study indicated that 90% of scholars are familiar with the practice of ChatGPT in research activities. 86% of scholars used 3.5 (Basic version) of ChatGPT for …

Generalized Time Domain Prediction Model For Motor Imagery-Based Wheelchair Movement Control, Z. T. Al-Qaysi, M. S. Suzani, Nazre Bin Abdul Rashid, Reem D. Ismail, M. A. Ahmed, Rasha A. Aljanabi, Veronica Gil-Costa

Mesopotamian Journal of Big Data

Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research's goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system. This GPRM has the advantage of having a model that is applicable to unknown …

Healthcare Intelligence And Decision Making: Big Data’S Role In Predictive Analytics For Clinical Decision-Making, Sahar Yousif Mohammed, Noora Saleem Jumaah, Estabraq Abbas, Haifaa Mhammad Ali, Mohammad Aljanabi

Mesopotamian Journal of Big Data

Technology and data are transforming healthcare systems. The use of big data in predictive analytics to anticipate healthcare outcomes, make accurate diagnoses, and improve care is a major advance. Predictive modeling analyzes patient data from EHRs, genetic data, and wearable devices to improve early diagnosis, targeted treatment, and efficiency. For example, predictors of chronic diseases like diabetes and heart disease can identify high-risk groups and treat them early, improving outcomes and saving money. A backpack matched to the patient's genetics and surroundings is also used. Privacy, system integration, and algorithmic transparency remain major issues. Predictive analytics may transform healthcare and …

Research Participants' Perspectives On Precision Diagnostics For Alzheimer's Disease, Hadley Stevens Smith, Jill O Robinson, Ariel Levchenko, Stacey Pereira, Belen Pascual, Kathleen Bradbury, Victoria Arbones, Jamie Fong, Joshua M Shulman, Amy L Mcguire, Joseph Masdeu

Faculty, Staff and Students Publications

BACKGROUND: Understanding research participants' responses to learning Alzheimer's disease (AD) risk information is important to inform clinical implementation of precision diagnostics given rapid advances in disease modifying therapies.

OBJECTIVE: We assessed participants' perspectives on the meaning of their amyloid positron emission tomography (PET) imaging results for their health, self-efficacy to understand their results, psychological impact of learning their results, experience receiving their results from the clinical team, and interest in genetic testing for AD risk.

METHODS: We surveyed individuals who were being clinically evaluated for AD and received PET imaging six weeks after the return of results. We analyzed responses …

Effective Land Use Classification Through Hybrid Transformer Using Remote Sensing Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ulhaq, David Blake, Naeem Janjua

Research outputs 2022 to 2026

Recent advances in deep learning for hyperspectral image (HSI) classification have shown exceptional performance in resource management and environmental planning through land use classification. Despite these successes, challenges continue to persist in land use classification due to the complex topology of natural and man-made structures. The uneven distribution of land cover introduces spectral-spatial variability, causing inter- and intra-class similarity. To address this issue, this study adopts a hybrid approach that combines convolutional neural networks (CNNs) and a transformer model. The technique comprises three key components: a spectral-spatial convolutional module (SSCM), a spatial attention module (SAM), and a transformer module. Each …

Risk Factors Of Under-Five And Infant Mortality: An Umbrella Review Of Systematic Reviews And Meta-Analyses, Bereket Kefale, Jonine Jancey, Amanuel T. Gebremedhin, Sylvester Dodzi Nyadanu, Daniel G. Belay, Gavin Pereira, Gizachew A. Tessema

Research outputs 2022 to 2026

Background: Ensuring child survival is a critical global challenge, requiring a robust and comprehensive understanding of the risk factors contributing to under-five mortality (U5M). We aimed to synthesise and summarise the current available evidence on risk factors of U5M and infant mortality worldwide to inform global child health programmes.

Methods: We searched six major databases (Embase, Medline, Scopus, CINAHL, Web of Science, and Global Health) and repositories of systematic reviews, as well as grey literature sources to identify systematic reviews and meta-analyses that examined the associations between risk factors of U5M and infant mortality between 1 January 1990 and 4 …

Racial Diversity, Interracial Trust, And Mental Distress In Post-Apartheid South Africa, Isaac Koomson, Obed Adonteng-Kissi, Desmond Tutu Ayentimi, Uchechukwu Levi Osuagwu

Research outputs 2022 to 2026

The emerging difficulties and tensions in establishing inclusive and multicultural societies in the contemporary globalised world have necessitated the generation of ample empirical evidence in support of the socioeconomic and health benefits of racial diversity. This study contributes to the scholarly and policy discourses by examining the effect of racial diversity on mental distress in post-apartheid South Africa after several decades of racial segregation. We used all five waves (2008, 2010, 2012, 2014, and 2017) of the National Income Dynamics Survey (NIDS). After addressing the endogeneity problem in the racial diversity–mental distress nexus, our findings show that an increase in …

Digital Technologies For Environmental Sustainability, Raimundo Macêdo, Marcelo Pias, Liwan Liyanage, David M. Cook, Yuko Murayama, Otthein Herzog, Gülgün Kayakutlu, David Kreps

Research outputs 2022 to 2026

Recognizing the immense challenges posed by climate change and aligning with the UN Sustainable Development Goals (SDGs), particularly SDG 13, which focuses on climate action, the International Federation for Information Processing (IFIP) established the Digital Technology and Climate Change Task Force in December 2022. The Task Force aims to identify opportunities to leverage digital technologies effectively in combating climate change and to provide recommendations for professionals and relevant institutions. Since its inception, the Task Force has met regularly. After conducting a scoping exercise and defining a taxonomy, a comprehensive questionnaire was distributed twice among IFIP stakeholders, namely, technical committees and …

Two Decades Of Seagrass Monitoring Data Show Drivers Include Enso, Climate Warming And Local Stressors, Chanelle Webster, Kathryn Mcmahon, Claire Ross, Eben Afrifa-Yamoah, Nicole Said, Renae Hovey, Belinda Martin, Simone Strydom

Research outputs 2022 to 2026

The drivers contributing to the trajectories of seagrass ecosystems is a key knowledge gap which limits our effectiveness in preventing their decline. Here, we present the key findings from the regional assessment of seagrass condition (shoot density) over time, which was undertaken to inform environmental impact assessment and monitoring as well as mitigation for the Westport program. To assess regional trends and drivers of these trends, data was collated from five regional seagrass monitoring programs, including the Cockburn Sound State Environmental Policy Seagrass Monitoring Program and marine park monitoring programs conducted by the Department of Biodiversity, Conservation and Attractions. These …

Experiences Of The Change Process During Emotion Focused Group Therapy, Jason M. Sharbanee, Kirra Rauchelle, Marina Nelson, Eyal Gringart

Research outputs 2022 to 2026

Emotion Focused Group Therapy has demonstrated preliminary efficacy for multiple syndromes. The groups involve individual chair-work in the context of the group, followed by interpersonal processing of the aroused emotions. So far there has been limited investigation of the mechanisms of change, including participants’ experiences of the change process. This study aimed to qualitatively examine participants’ experiences of the change process during Emotion Focused Group Therapy. Sixteen participants were interviewed about their experiences of the change process during the therapy group. The interview was structured to move from overarching reflections on the process of change, to specific experiences of their …

Work-Integrated Learning In The Humanities, Arts And Social Sciences: Where To From Here?, Denise Jackson, Elizabeth Cook

Research outputs 2022 to 2026

Amidst increasing pressures on Humanities, Arts, and Social Sciences (HASS) disciplines regarding graduate career readiness, this study examines HASS student participation in work-integrated learning (WIL) and its impacts on employability and short-term employment outcomes, using data from the Australian Graduate Outcomes Survey from 2020 to 2023. The research examined the extent of participation across WIL types (work-based, non-workplace and global) and its influence on graduates’ perceived employability and employment outcomes (full-time employment and perceived overqualification), while investigating demographic differences using descriptive analysis and logistic regression models. Results indicated rising WIL participation yet identified disparities by activity type and among student …

Alzheimer's Disease Genetic Risk And Changes In Brain Atrophy And White Matter Hyperintensities In Cognitively Unimpaired Adults, Anja Soldan, Jiangxia Wang, Corinne Pettigrew, Christos Davatzikos, Guray Erus, Timothy J. Hohman, Logan Dumitrescu, Murat Bilgel, Susan M. Resnick, Leonardo A. Rivera-Rivera, Rebecca Langhough, Sterling C. Johnson, Tammie Benzinger, John C. Morris, Simon M. Laws, Jurgen Fripp, Colin L. Masters, Marilyn S. Albert

Research outputs 2022 to 2026

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE- ε4, APOE-ε2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged …

Can Frontline Employees Help Consumers Improve Their Financial Planning Behavior? Implications From Triadic Analysis, Vida Siahtiri, Welf Hermann Weiger, Christian Tetteh-Afi, Tobias Kraemer

Research outputs 2022 to 2026

Purpose: As consumer debt can substantially impair subjective well-being, it is crucial for research to gain insights into how consumers can be motivated to improve financial planning. This paper aims to investigate how frontline employees in financial services can help consumers regulate their financial planning behaviors and how financial service providers can effectively support their frontline employees in this effort through leadership and organizational climate. Design/methodology/approach: We incorporate regulatory focus theory and conservation of resource theory to develop a conceptual model that we test in a triadic study with a unique dataset collected from consumers, frontline employees, and managers in …

Associations Between Flavonoid Intake And Subclinical Atherosclerosis: The Mesa, Nicola P. Bondonno, Benjamin H. Parmenter, Kevin Murray, Catherine P. Bondonno, Lauren C. Blekkenhorst, Alexis C. Wood, Wendy S. Post, Matthew Allison, Michael H. Criqui, Joshua R. Lewis, Jonathan M. Hodgson

Research outputs 2022 to 2026

Background: Flavonoids may play a role in mitigating atherosclerotic cardiovascular diseases, with evidence suggesting effects may differ between vascular beds. Studies examining associations with subclinical markers of atherosclerosis between subpopulations with different underlying risks of atherosclerosis are lacking. METHODS: Among 5599 participants from the MESA (Multi-Ethnic Study of Atherosclerosis), associations between dietary flavonoid intakes (estimated from a food frequency questionnaire) and subclinical measures of atherosclerosis (ankle-brachial index, carotid plaques and intima-media thickness, and coronary artery calcification) were examined using repeated measures models. Exposures and outcomes were measured at exam 1 (2000-2002) and exam 5 (2010-2011). Stratified analyses and interaction terms …

White Lies And Safety Nets: The Perceptions Of Nurses On The Use Of Early Warning Systems And The Development Of Higher-Order Thinking Skills, Deb Massey, Tracy Flenady, Amy Louise Byrne, Justine Connor, Danielle Le Lagadec

Research outputs 2022 to 2026

Background: Algorithmic tools such as early warning systems (EWSs) have been embedded into clinical practice globally to facilitate the early recognition of patient deterioration and to guide the escalation of care. Concerns have been raised that the mandated use of these EWS tools may impact the development of nurses' higher-order thinking. However, the relationship between EWS tools and the development of higher-order thinking is poorly understood.

Objectives: This paper provides the qualitative results of a larger study that sought to explore the impact of EWS tools on the development of nurses' higher-order thinking. The objective of this component of the …

Classification Of Vaginal Cleanliness Grades Through Surface-Enhanced Raman Spectral Analysis Via The Deep-Learning Variational Autoencoder: Long Short-Term Memory Model, Jia Wei Tang, Xin Ru Wen, Hui Min Chen, Jie Chen, Kun Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

Research outputs 2022 to 2026

In this study, it is aimed to establish a novel method based on a deep-learning-guided surface-enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled with a deep learning algorithm long short-term memory (LSTM) neural network to analyze SERS spectra produced by vaginal secretions. The performance of various machine learning (ML) algorithms is assessed using multiple evaluation metrics. Finally, the reliability of the optimal model is tested using blind test data (N = 10/group for each cleanliness level). The data quality of the …

Density And Abundance Estimation Of West Indian Manatee, Trichechus Manatus, Between The States Of Ceará And Piauí, Northeast Brazil, Using Active Acoustics, Katherine Fiedler Choi Lima, Denis Moledo De Sousa Abessa, Daniel Gonzalez-Socoloske, Renata Sousa-Lima, Ana Carolina Oliveira De Meirelles, Cristine Pereira Negrão Silva

Faculty Publications

The West Indian manatee is one of the most threatened species in Brazil. The species has currently a patchy distribution from the state of Alagoas to the state of Amapá. The difficulty of observing manatees, especially in estuarine waters, is a challenge for conservation. Therefore, it is necessary to use new methodologies and technologies to solve manatee detection problems in their natural habitats. The goal of this study was to use an active acoustic method of detection to estimate manatee density and abundance in the estuarine complex of the Timonha and Ubatuba rivers, between the states of Ceará and Piauí, …

Gender Difference On Internal Equity: Evidence From The Hospitality Industry In Ghana, Patricia Peprah

Faculty Publications

It is a hypothetical position that employees in the hospitality industry in Ghana experience salary disparities based on the gender of the employee. Pay disparities exist because of unconscious biases in areas such as hiring, promotions, performance reviews, and salary decisions. In ensuring equal pay for equal work to fulfill the International Labor Organization (ILO) convention, this study investigated the internal equity pay of the employees in Ghana's hospitality industry based on equal work. This quantitative association study adopted a self-constructed research instrument to measure internal equity pay and randomly selected 350 respondents in the hospitality industry in Ghana to …

Managing Differences In The Family, Alina M. Baltazar

Faculty Publications

No abstract provided.

Machine Learning Helps In Quickly Diagnosis Cases Of "New Corona", Maad M. Mijwil, Ioannis Adamopoulos, Pramila Pudasaini

Mesopotamian Journal of Artificial Intelligence in Healthcare

Machine learning is considered one of the most significant techniques that play a vital role in diagnosing the Coronavirus. It is a set of advanced algorithms capable of analysing medical data and identifying patterns and behaviours of diseases. It is used to interpret medical images, giving details of each image with high accuracy and efficiency, such as chest X-ray images. These algorithms are trained on a large set of images to recognise patterns that indicate the presence of infection with the Coronavirus (COVID-19). This article will provide a brief overview of the importance of machine learning in diagnosing COVID-19 by …

Benchmarking Generative Ai: A Call For Establishing A Comprehensive Framework And A Generative Aiq Test, Malik Sallam, Roaa Khalil, Mohammed Sallam

Mesopotamian Journal of Artificial Intelligence in Healthcare

The introduction and rapid evolution of generative artificial intelligence (genAI) models necessitates a refined understanding for the concept of “intelligence”. The genAI tools are known for its capability to produce complex, creative, and contextually relevant output. Nevertheless, the deployment of genAI models in healthcare should be accompanied appropriate and rigorous performance evaluation tools. In this rapid communication, we emphasizes the urgent need to develop a “Generative AIQ Test” as a novel tailored tool for comprehensive benchmarking of genAI models against multiple human-like intelligence attributes. A preliminary framework is proposed in this communication. This framework incorporates miscellaneous performance metrics including accuracy, …

Assigning Medical Professionals: Chatgpt's Contributions To Medical Education And Health Prediction, Maad M. Mijwil, Mostafa Abotaleb, Guma Ali, Klodian Dhoska

Mesopotamian Journal of Artificial Intelligence in Healthcare

Artificial intelligence is increasingly present in many applications that help humans accomplish many tasks. It can support improved results, increased productivity, and high efficiency in providing services. Systems developers seek to integrate improved artificial intelligence models into the development of healthcare services and make a significant qualitative shift in the medical field. One recently invented tool is ChatGPT, which deserves extensive advertising space. This tool provides sample answers that help both healthcare workers and patients answer all their questions. It is an important tool in medical education, increasing knowledge in making decisions that can improve the performance of medical professionals. …

Machine Learning Techniques To Predictive In Healthcare: Hepatitis C Diagnosis, Hussein Alkattan, Bashar Talib Al-Nuaimi, Alhumaima Ali Subhi

Mesopotamian Journal of Artificial Intelligence in Healthcare

The accurate prediction and classification of medical data, such as Hepatitis C, play a significant role in upgrading symptomatic accuracy and treatment planning. This study utilized progressed machine learning algorithms, including RF, SVR, and Gradient Boosting, to analyze features and predict outcomes viably. By coordination robust preprocessing strategies and feature engineering, the models tended to missing values and categorical transformations, enabling exact predictions from complex datasets. The models accomplished high accuracy and unwavering quality in execution, as prove by comparative measurements and validation results. These discoveries highlight the potential of machine learning in increasing clinical decision-making and emphasize the require …