Journal Articles

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    Non-negative Matrix Factorization: A Survey
    (Oxford University Press on behalf of the British Computer Society, 2021-07-01) Gan J; Liu T; Li L; Zhang J
    Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.
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    Behavioral transition: A framework for the construction conflict - Tension relationship
    (1/08/2007) Yiu TW; Cheung SO
    Conflicts are inevitable in construction projects. One of the reasons is that all construction projects involve complex human interactions. Previous studies have shown that behavioral states can respond dynamically as the magnitude of a conflict increases. This has been empirically demonstrated using a catastrophe-theory-based, three-variable system involving the level of construction conflict, the level of tension, and the amount of behavioral flexibility (Yiu and Cheung, 2006). This paper reports on a study that builds on the above-mentioned study by Yiu and Cheung, and examines the application of moderated multiple regression (MMR) to the three-variable system. It was found that not all MMR models display a significant moderating effect. Two out of six MMR models were found to be significant in their effect. These models affirm that the nature of the relationship between the degree of uncertainty and adversarial attitudes (or mistrust level) varies, depending on the behavioral flexibility of the parties. Disordinal interactions were also found, suggesting that the interaction between behavioral flexibility and the conflict-tension relationship can change radically. Critical points for the degree of uncertainty were also able to be calculated. Beyond these points, even a flexible individual may find difficulty in minimizing or resolving construction conflicts. As such, it is suggested that such radical changes could be prevented by minimizing the degree of uncertainty in construction projects. © 2007 IEEE.
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    The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma
    (IEEE, 17/08/2021) Perera AD; Jayamaha NP; Grigg NP; Tunnicliffe M; Singh A
    Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.
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    A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench
    (BioMed Central Ltd, 14/12/2020) Ahmed N; Barczak ALC; Susnjak T; Rashid MA
    Big Data analytics for storing, processing, and analyzing large-scale datasets has become an essential tool for the industry. The advent of distributed computing frameworks such as Hadoop and Spark offers efficient solutions to analyze vast amounts of data. Due to the application programming interface (API) availability and its performance, Spark becomes very popular, even more popular than the MapReduce framework. Both these frameworks have more than 150 parameters, and the combination of these parameters has a massive impact on cluster performance. The default system parameters help the system administrator deploy their system applications without much effort, and they can measure their specific cluster performance with factory-set parameters. However, an open question remains: can new parameter selection improve cluster performance for large datasets? In this regard, this study investigates the most impacting parameters, under resource utilization, input splits, and shuffle, to compare the performance between Hadoop and Spark, using an implemented cluster in our laboratory. We used a trial-and-error approach for tuning these parameters based on a large number of experiments. In order to evaluate the frameworks of comparative analysis, we select two workloads: WordCount and TeraSort. The performance metrics are carried out based on three criteria: execution time, throughput, and speedup. Our experimental results revealed that both system performances heavily depends on input data size and correct parameter selection. The analysis of the results shows that Spark has better performance as compared to Hadoop when data sets are small, achieving up to two times speedup in WordCount workloads and up to 14 times in TeraSort workloads when default parameter values are reconfigured.
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    Functional development of the adult ovine mammary gland--insights from gene expression profiling.
    (5/10/2015) Paten AM; Duncan EJ; Pain SJ; Peterson SW; Kenyon PR; Blair HT; Dearden PK
    BACKGROUND: The mammary gland is a dynamic organ that undergoes dramatic physiological adaptations during the transition from late pregnancy to lactation. Investigation of the molecular basis of mammary development and function will provide fundamental insights into tissue remodelling as well as a better understanding of milk production and mammary disease. This is important to livestock production systems and human health. Here we use RNA-seq to identify differences in gene expression in the ovine mammary gland between late pregnancy and lactation. RESULTS: Between late pregnancy (135 days of gestation ± 2.4 SD) and lactation (15 days post partum ± 1.27 SD) 13 % of genes in the sheep genome were differentially expressed in the ovine mammary gland. In late pregnancy, cell proliferation, beta-oxidation of fatty acids and translation were identified as key biological processes. During lactation, high levels of milk fat synthesis were mirrored by enrichment of genes associated with fatty acid biosynthesis, transport and lipogenesis. Protein processing in the endoplasmic reticulum was enriched during lactation, likely in support of active milk protein synthesis. Hormone and growth factor signalling and activation of signal transduction pathways, including the JAK-STAT and PPAR pathways, were also differently regulated, indicating key roles for these pathways in functional development of the ovine mammary gland. Changes in the expression of epigenetic regulators, particularly chromatin remodellers, indicate a possible role in coordinating the large-scale transcriptional changes that appear to be required to switch mammary processes from growth and development during late pregnancy to synthesis and secretion of milk during lactation. CONCLUSIONS: Coordinated transcriptional regulation of large numbers of genes is required to switch between mammary tissue establishment during late pregnancy, and activation and maintenance of milk production during lactation. Our findings indicate the remarkable plasticity of the mammary gland, and the coordinated regulation of multiple genes and pathways to begin milk production. Genes and pathways identified by the present study may be important for managing milk production and mammary development, and may inform studies of diseases affecting the mammary gland.
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    A Typology of Patients Based on Decision-Making Styles: Cross-Sectional Survey Study
    (JMIR Publications, 20/11/2019) FitzPatrick MA; Hess AC; Sudbury-Riley L; Schulz PJ
    Background: Although previous research shows broad differences in the impact of online health information on patient-practitioner decision making, specific research is required to identify and conceptualize patient decision-making styles related to the use of online health information and to differentiate segments according to the influence of online information on patient decision making and interactions with health professionals. Objective: This study aimed to investigate patients’ decision making in relation to online health information and interactions with health care practitioners. We also aimed to present a typology of patients based on significant differences in their decision making. Methods: We applied a large-scale cross-sectional research design using a survey. Data, generated using a questionnaire that was administered by companies specializing in providing online panels, were collected from random samples of baby boomers in the United Kingdom, the United States, and New Zealand. The total sample comprised 996 baby boomers born between 1946 and 1964, who had used the internet in the previous 6 months to search for and share health-related information. Data were analyzed using hierarchical cluster analysis and confirmatory factor analysis, as well as one-way analysis of variance, chi-square tests, and paired sample t tests. Results: Analyses identified 3 key decision-making styles that served as the base for 4 unique and stable segments of patients with distinctive decision-making styles: the Collaborators (229/996, 23.0%), the Autonomous-Collaborators (385/996, 38.7%), the Assertive-Collaborators (111/996, 11.1%), and the Passives (271/996, 27.2%). Profiles were further developed for these segments according to key differences in the online health information behavior, demographics, and interactional behaviors of patients. The typology demonstrates that collaborative decision making is dominant among patients either in its pure form or in combination with autonomous or assertive decision making. In other words, most patients (725/996, 72.8%) show significant collaboration in their decision making with health care professionals. However, at times, patients in the combination Autonomous-Collaborative segment prefer to exercise individual autonomy in their decision making, and those in the combination Assertive-Collaborative segment prefer to be assertive with health professionals. Finally, this study shows that a substantial number of patients adopt a distinctly passive decision-making style (271/996, 27.2%). Conclusions: The patient typology provides a framework for distinguishing practice-relevant and addressable segments with important implications for health care practitioners, including better-targeted communication programs for patients and more successful outcomes for health care services in the long term.
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    Using information communication technology in models of integrated community-based primary health care: learning from the iCOACH case studies
    (BioMed Central Limited, 26/06/2018) Steele Gray C; Barnsley J; Gagnon D; Belzile L; Kenealy T; Shaw J; Sheridan N; Wankah Nji P; Wodchis WP
    BACKGROUND: Information communication technology (ICT) is a critical enabler of integrated models of community-based primary health care; however, little is known about how existing technologies have been used to support new models of integrated care. To address this gap, we draw on data from an international study of integrated models, exploring how ICT is used to support activities of integrated care and the organizational and environmental barriers and enablers to its adoption. METHODS: We take an embedded comparative multiple-case study approach using data from a study of implementation of nine models of integrated community-based primary health care, the Implementing Integrated Care for Older Adults with Complex Health Needs (iCOACH) study. Six cases from Canada, three each in Ontario and Quebec, and three in New Zealand, were studied. As part of the case studies, interviews were conducted with managers and front-line health care providers from February 2015 to March 2017. A qualitative descriptive approach was used to code data from 137 interviews and generate word tables to guide analysis. RESULTS: Despite different models and contexts, we found strikingly similar accounts of the types of activities supported through ICT systems in each of the cases. ICT systems were used most frequently to support activities like care coordination by inter-professional teams through information sharing. However, providers were limited in their ability to efficiently share patient data due to data access issues across organizational and professional boundaries and due to system functionality limitations, such as a lack of interoperability. CONCLUSIONS: Even in innovative models of care, managers and providers in our cases mainly use technology to enable traditional ways of working. Technology limitations prevent more innovative uses of technology that could support disruption necessary to improve care delivery. We argue the barriers to more innovative use of technology are linked to three factors: (1) information access barriers, (2) limited functionality of available technology, and (3) organizational and provider inertia.
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    Gender Diversity Population Simulations in an Extended Game of Life Context
    (IEEE, 20/06/2019) Mathrani A; Scogings C; Mathrani S
    Cellular automata studies have been instrumental in computational and biological studies for simulating life contours based on simple rule-based strategies. Game of Life (GoL) presented us with one of the earliest automata studies that led the way in exemplifying non-linear spatial representations, such as large-scale population evolution scenarios depicting species dominance, species equilibrium, and species extinction. However, the GoL was driven by interactions among vegetative entities comprising live and die states only. This paper extends GoL to gendered-GoL (g-GoL) in which male phenotypes and female phenotypes interact in an extended world to procreate. Using the g-GoL, we have demonstrated many evolution contours by applying gender-based dependence rules. Evolution scenarios have been simulated with skewed gender ratios that favor the birth of male offspring. Preference for a male child is common in certain cultures; therefore, empirical data realized with skewed gender settings in g-GoL can reveal the long-term impact of non-egalitarian gender societal structures. Our model provides a tool for the study of emergent life contours and brings awareness on current gender imbalances to strengthen multi-disciplinary research inquiry in the areas of social practices, mathematical modeling, and use of computational technologies.
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    Bayesian networks in healthcare: Distribution by medical condition
    (2020-07) McLachlan, S; Dube, K; Hitman, GA; Fenton, NE; Kyrimi, E
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    Effects of eHealth literacy on general practitioner consultations: A mediation analysis
    (JMIR Publications, 16/05/2017) Schulz PJ; Fitzpatrick MA; Hess AC; Sudbury-Riley L; Hartung U
    Objective: We propose and test two potential mediators of the negative effect of eHealth literacy on health care utilization: (1) health information seeking and (2) gain in empowerment by information seeking. Methods: Data were collected in New Zealand, the United Kingdom, and the United States using a Web-based survey administered by a company specialized on providing online panels. Combined, the three samples resulted in a total of 996 baby boomers born between 1946 and 1965 who had used the Internet to search for and share health information in the previous 6 months. Measured variables include eHealth literacy, Internet health information seeking, the self-perceived gain in empowerment by that information, and the number of consultations with one’s general practitioner (GP). Path analysis was employed for data analysis. Results: We found a bundle of indirect effect paths showing a positive relationship between health literacy and health care utilization: via health information seeking (Path 1), via gain in empowerment (Path 2), and via both (Path 3). In addition to the emergence of these indirect effects, the direct effect of health literacy on health care utilization disappeared. Conclusions: The indirect paths from health literacy via information seeking and empowerment to GP consultations can be interpreted as a dynamic process and an expression of the ability to find, process, and understand relevant information when that is necessary.