Information System Articulated Logistics and Supply Chains and their Spatial-Temporal Modelling and Management

The logistics and supply chains have affected worldwide businesses, particularly in pandemic locations and periods, unsettling supply and demand pursuits. Logistics of global markets have been irregular, with poorly aligned superstore outlets in different geographies. Businesses and their alliances have affected spatial-temporal dimensions. The research explores the significance of Information System (IS) articulations and how IS artefacts can motivate connecting global companies and boost market values. The study aims to improve the Logistics and Supply Chains (LSC) between companies and organizations through evaluable IS artefacts in industry scenarios. The use and reuse of IS articulations are investigated in spatial-temporal dimensions for LSC knowledge management. Further, the IS artefacts are assessed through the logistics performance in geographic dimensions using the Attribute Journey Mapping and Modelling (AJMM) method. The ability to track and trace the supply chain consignments is inferred proportional to attributes of quality of trade and transport systems

Digital Web Ecosystem Development for Managing Social Network Data Science

The World Wide Web (WWW) unfolds with diverse domains and associated data sources, complicating the network data science. In addition, heterogeneity and multidimensionality can make data management, documentation, and even integration more challenging. The WWW emerges as a complex digital ecosystem on Big Data scale, and we conceptualize the web network as a Digital Web Ecosystem (DWE) in an analytical space. The purpose of the research is to develop a framework, explore the association between attributes of social networks and assess their strengths. We have experimented network users and usability attributes of social networks and tools, including misgivings. We construe new insights from data views of DWE metadata. For leveraging the usability and popularity-sentiment attribute relationships, we compute map views and several regressions between instances of technology and society dimensions, interpreting their strengths and weaknesses. Visual analytics adds values to the DWE meta-knowledge, establishing cognitive data usability in the WWW

Data geo-Science Approach for Modelling Unconventional Petroleum Ecosystems and their Visual Analytics

Storage, integration and interoperability are critical challenges in the unconventional exploration data management. With a quest to explore unconventional hydrocarbons, in particular, shale gas from fractured shales, we aim at investigating new petroleum data geoscience approaches. The data geo-science describes the integration of geoscience-domain expertise, collaborating mathematical concepts, computing algorithms, machine learning tools, including data and business analytics. Further, to strengthen data-science services among producing companies, we propose an integrated multidimensional repository system, for which factual instances are acquired on gas shales, to store, process and deliver fractured-data views in new knowledge domains. Data dimensions are categorized to examine their suitability in the integrated prototype articulations that use fracture-networks and attribute dimension model descriptions. The factual instances are typically from seismic attributes, seismically interpreted geological structures and reservoirs, well log, including production data entities. For designing and developing multidimensional repository systems, we create various artefacts, describing conceptual, logical and physical models. For exploring the connectivity between seismic and geology entities, multidimensional ontology models are construed using fracture network attribute dimensions and their instances. Different data warehousing and mining are added support to the management of ontologies that can bring the data instances of fractured shales, to unify and explore the associativity between high-dense fractured shales and their orientations. The models depicting collaboration of geology, geophysics, reservoir engineering and geo-mechanics entities and their dimensions can substantially reduce the risk and uncertainty involved in modelling and interpreting shale- and tight-gas reservoirs, including traps associated with Coal Bed Methane (CBM). Anisotropy, Poisson's ratio and Young's modulus properties corroborate the interpretation of stress images from the 3D acoustic characterization of shale reservoirs. The statistical analysis of data-views, their correlations and patterns further facilitate us to visualize and interpret geoscientific metadata meticulously. Data geo-science guided integrated methodology can be applied in any basin, including frontier basins

Digital Web Ecosystem Development for Managing Social Network Data Science

The World Wide Web (WWW) unfolds with diverse domains and associated data sources, complicating the network data science. In addition, heterogeneity and multidimensionality can make data management, documentation, and even integration more challenging. The WWW emerges as a complex digital ecosystem on Big Data scale, and we conceptualize the web network as a Digital Web Ecosystem (DWE) in an analytical space. The purpose of the research is to develop a framework, explore the association between attributes of social networks and assess their strengths. We have experimented network users and usability attributes of social networks and tools, including misgivings. We construe new insights from data views of DWE metadata. For leveraging the usability and popularity-sentiment attribute relationships, we compute map views and several regressions between instances of technology and society dimensions, interpreting their strengths and weaknesses. Visual analytics adds values to the DWE meta-knowledge, establishing cognitive data usability in the WWW

Managing Embedded Digital Ecosystems in Pandemic Big Data Contexts

The embeddedness of ecosystems interpreted as the connectivity between data sources has been the research focus of ecosystem service providers. Heterogeneity of data sources,linked with embedded systems,is challenging in the ecosystem integration process. Big data is an added motivation in the ecosystem integration process. The purpose of the research is to provide an improved understanding of ecosystem inherent connectivity by integrating multiple ecosystems through their big data in a multidimensional repository system, with a focus on data analytics. We need an architecture to drive the composite congruence existing between disease-human-environment-business systems.We propose an Embedded Digital Ecosystem Architecture (EDEA), from which the associations hidden among big data sources of multiple ecosystems are analysed in new knowledge domains. We construe in our research that pandemic-related disease ecologies have connectivity with the human, environment and economic ecosystems,ascertaining the potential benefits of data science in embedded digital ecosystems’ research

Role of Governance in e-Business IS Designs and their Evaluations

The role of governance is undervalued in e-business information system design and development. The transparency, integrity and ethical values are disregarded while articulating e-business artefacts in different organizations. E-Governance can affect e-business growth, including motivations for technology development, compromising the product and service qualities. Corruption, ineffective governance, political instability, flawed regulations, and violation of rules of law, unaccountability are linked attribute dimensions affecting the business alignments, including technology implementations. We need architecture to explore the role of governance indicators in e-business design strategies. The research aims to develop an Information System (IS) architecture with artefacts to connect the governance attribute dimensions with interpreted information management, organization strategies and e-business needs. Based on empirical research and governance attribute modelling done for several governments, we infer e-government and e-business objectives are connectable and accomplishable through successful implementation of IS framework in business environments through improved governance and transparency

Social Informatics guided Social Intelligence Management and its Analysis in the Asia-Pacific Contexts

The research is aimed at investigating knowledge-based social informatics solutions. Socio-economic development relies on technology use in education and employment sectors. To explore such challenges, we examine the existing indicators of socio-economic development, such as gender equalities, employment, and education and population growth attribute dimensions. To understand them precisely, we analyse large-size human ecosystems and their data analytics. Social-informatics and -intelligence analysis are proposed with the design of logical and physical data schemas in diverse socio-economic contexts and their interoperability in varied geographies. We compute predictive models for different attribute dimensions, usable by technology developers and policy-makers. We interpret the data views of digital human ecosystems in the form of various graphs, tables, and polynomial regressions to envisage the influence of technology on societal collisions. The polynomial regressions suggest a strong positive relationship between different socio-economic attributes, cognizing the social intelligence and its knowledge management in Asia-Pacific contexts

Baseline Xpert MTB/RIF ct values predict sputum conversion during the intensive phase of anti-TB treatment in HIV infected patients in Kampala, Uganda: a retrospective study

BACKGROUND In resource-limited settings, sputum smear conversion is used to document treatment response. Many People living with HIV (PLHIV) are smear-negative at baseline. The Xpert MTB/RIF test can indirectly measure bacterial load through cycle threshold (ct) values. This study aimed to determine if baseline Xpert MTB/RIF could predict time to culture negativity in PLHIV with newly diagnosed TB. METHODS A subset of 138 PLHIV from the 'SOUTH' study on outcomes related to TB and antiretroviral drug concentrations were included. Bacterial load was estimated by Mycobacterium Growth Indicator Tubes (MGIT) culture time-to-positivity (TTP) and Lowenstein Jensen (LJ) colony counts. Changes in TTP and colony counts were analyzed with Poisson Generalised Estimating Equations (GEE) and multilevel ordered logistic regression models, respectively, while time to culture negativity analysed with Cox proportional hazard models. ROC curves were used to explore the accuracy of the ct value in predicting culture negativity. RESULTS A total of 81 patients (58.7%) were males, median age 34 (IQR 29  ̶ 40) years, median CD4 cell count of 180 (IQR 68  ̶ 345) cells/μL and 77.5% were ART naive. The median baseline ct value was 25.1 (IQR 21.0  ̶ 30.1). A unit Increase in the ct value was associated with a 5% (IRR = 1.05 95% CI 1.04  ̶ 1.06) and 3% (IRR = 1.03 95% CI 1.03  ̶ 1.04) increase in TTP at week 2 and 4 respectively. With LJ culture, a patient's colony grade was reduced by 0.86 times (0R = 0.86 95% CI 0.74  ̶ 0.97) at week 2 and 0.84 times (OR = 0.84 95% CI 0.79  ̶ 0.95 P = 0.002) at week 4 for every unit increase in the baseline ct value. There was a 3% higher likelihood of earlier conversion to negativity for every unit increase in the ct value. A ct cut point ≥28 best predicted culture negativity at week 4 with a sensitivity of 91. 7% & specificity 53.7% while a cut point ≥23 best predicted culture negativity at week 8. CONCLUSION Baseline Xpert MTB/RIF ct values predict sputum conversion in PLHIV on anti-TB treatment. Surrogate biomarkers for sputum conversion in PLHIV are still a research priority