A tacit health care knowledge explication info-structure using contrived knowledge acquisition and representation approaches.

Projek ini telah menghasilkan suatu Info-Struktur Pengeksplikasian Pengetahuan Kesihatan Tersirat yang mampu mempero1ehi, menyimpan dan menyebarkan pengetahuan kesihatan tersirat untuk digunakan oleh para pakar dan doktor kesihatan supaya perkhidmatan kesihatan yang berkualiti dapat diberi secara berterusan. The project has produced a Tacit Health care Knowledge Explication Info Structure that is designed to acquire, store and disseminate tacit health care knowledge to be used by health care specialists, experts and practitioners to ensure the provision and continuation of expert-quality health care services

Model-Based Enterprise Capabilities Matrix

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Towards a maintenance semantic architecture.

International audienceTechnological and software progress with the evolution of processes within company have highlighted the need to evolve systems of maintenance process from autonomous systems to cooperative and sharing information system based on software platform. However, this need gives rise to various maintenance platforms. The first part of this study investigates the different types of existing industrial platforms and characterizes them compared to two criteria namely : information exchange and relationship intensity. This allowed identifying the e-maintenance architecture as the current most efficient architecture. despite its effectiveness, this latter can only guarantee technical interoperability between various components. Therefore, the second part of this study proposes a semantic-knowledge based architecture, thereby ensuring a higher level of semantic interoperability. To this end, specific maintenance ontology has been developed

Interrogating technology-led experiments in sustainability governance

Solutions to global sustainability challenges are increasingly technology‐intensive. Yet, technologies are neither developed nor applied to governance problems in a socio‐political vacuum. Despite aspirations to provide novel solutions to current sustainability governance challenges, many technology‐centred projects, pilots and plans remain implicated in longer‐standing global governance trends shaping the possibilities for success in often under‐recognized ways. This article identifies three overlapping contexts within which technology‐led efforts to address sustainability challenges are evolving, highlighting the growing roles of: (1) private actors; (2) experimentalism; and (3) informality. The confluence of these interconnected trends illuminates an important yet often under‐recognized paradox: that the use of technology in multi‐stakeholder initiatives tends to reduce rather than expand the set of actors, enhancing instead of reducing challenges to participation and transparency, and reinforcing rather than transforming existing forms of power relations. Without recognizing and attempting to address these limits, technology‐led multi‐stakeholder initiatives will remain less effective in addressing the complexity and uncertainty surrounding global sustainability governance. We provide pathways for interrogating the ways that novel technologies are being harnessed to address long‐standing global sustainability issues in manners that foreground key ethical, social and political considerations and the contexts in which they are evolving

Global disordering:Practices of reflexivity in global economic governance

In this article, I offer a reinterpretation of late 20th-century ‘neo-liberal’ transformations of global economic governance. My argumentative foil is a macro-institutional interpretation of the post-1980s period in which neo-liberalism appears as programmatic institutional form and disciplinary formation. I argue that a second, and complementary, dynamic also needs to be taken into account – namely, the emergence and operationalization of a set of critical technologies for embedding practices of reflexivity within the state. I suggest, moreover, that attention to this dimension of neo-liberalization provides a new perspective on the present. I offer an interpretation of the current moment of transition as one in which a similar repertoire of neo-liberal techniques of reflexivization is, in a second iteration, being trained on the architecture of global economic governance itself

Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review

In the realm of data protection, a striking disconnect prevails between traditional domains of doctrinal, legal, theoretical, and policy-based inquiries and a burgeoning body of empirical evidence. Much of the scholarly and regulatory discourse remains entrenched in abstract legal principles or normative frameworks, leaving the empirical landscape uncharted or minimally engaged. Since the birth of EU data protection law, a modest body of empirical evidence has been generated but remains widely scattered and unexamined. Such evidence offers vital insights into the perception, impact, clarity, and effects of data protection measures but languishes on the periphery, inadequately integrated into the broader conversation. To make a meaningful connection, we conduct a comprehensive review and synthesis of empirical research spanning nearly three decades (1995- March 2022), advocating for a more robust integration of empirical evidence into the evaluation and review of the GDPR, while laying a methodological foundation for future empirical research

Two-photon all-optical interrogation of mouse barrel cortex during sensory discrimination

The neocortex supports a rich repertoire of cognitive and behavioural functions, yet the rules, or neural ‘codes’, that determine how patterns of cortical activity drive perceptual processes remain enigmatic. Experimental neuroscientists study these codes through measuring and manipulating neuronal activity in awake behaving subjects, which allows links to be identified between patterns of neural activity and ongoing behaviour functions. In this thesis, I detail the application of novel optical techniques for simultaneously recording and manipulating neurons with cellular resolution to examine how tactile signals are processed in sparse neuronal ensembles in mouse somatosensory ‘barrel’ cortex. To do this, I designed a whisker-based perceptual decision-making task for head-fixed mice, that allows precise control over sensory input and interpretable readout of perceptual choice. Through several complementary experimental approaches, I show that task performance is exquisitely coupled to barrel cortical activity. Using two- photon calcium imaging to simultaneously record from populations of barrel cortex neurons, I demonstrate that different subpopulations of neurons in layer 2/3 (L2/3) show selectivity for contralateral and ipsilateral whisker input during behaviour. To directly test whether these stimulus-tuned groups of neurons differentially impact perceptual decision-making I performed patterned photostimulation experiments to selectively activate these functionally defined sets of neurons and assessed the resulting impact on behaviour and the local cortical network in layer 2/3. In contrast with the expected results, stimulation of sensory-coding neurons appeared to have little perceptual impact on task performance. However, activation of non- stimulus coding neurons did drive decision biases. These results challenge the conventional view that strongly sensory responsive neurons carry more perceptual weight than non-responsive sensory neurons during perceptual decision-making. Furthermore, patterned photostimulation revealed and imposed potent surround suppression in L2/3, which points to strong lateral inhibition playing a dominant role in shaping spatiotemporally sparse activity patterns. These results showcase the utility of combined patterned photostimulation methods and population calcium imaging for revealing and testing neural circuit function during sensorimotor behaviour and provide new perspectives on sensory coding in barrel cortex

Information regimes in government bureaucracies and 'digital decompression'

In all bureaucracies how information is acquired, stored, re-accessed and analysed creates an ‘information regime’ of crucial importance for the rational or efficient conduct of business. Government departments and agencies use a wide range of information practices that can seem simply heterogenous, highly specific or hard to characterize. Yet an essential move in all pre-digital forms of organization has been data compression, using administrative routines to reduce complex realities to data and information in formats and quantities that can be classified, indexed, filed and re-found when needed. Three conventional information regimes can be distinguished by their level and mode of compression. ‘Lossy’ data compression via drastic data selection and radical simplification, especially using open or gated-access forms, predominated in machine bureaucracies with hierarchic morphologies. By contrast, professional bureaucracies developed ‘lossless text/narrative/verbatim’compression for mission-critical tasks, relying on professional language, socialization, and knowledge development to summarize cases or events in more fully recoverable forms. With the advent of new public management and late twentieth century computerization/automation, hybrid forms of machine/professional bureaucracy developed, focusing on metrics-based compression (using pre-fixed statistics, key performance indicators and similar data) in a central governance role. In the current digital era governance wave technologies facilitating big data, artificial intelligence and data science approaches have made feasible a new information regime of ‘lossless’ uncompressed data and expanded data science, opening a potential for bureaucratic operations to alter in fundamental ways. Full digital data gathering or recording of interactions at the initial stage plus complete storing, organic indexing and new analytic capabilities can obviate much of the earlier need for data compression, and foster forms of post hoc knowledge development, e.g., via machine learning and algorithmic governance. This development will change most government bureaucracies somewhat, but how far still remains unclear

Policy and Place: A Spatial Data Science Framework for Research and Decision-Making

abstract: A major challenge in health-related policy and program evaluation research is attributing underlying causal relationships where complicated processes may exist in natural or quasi-experimental settings. Spatial interaction and heterogeneity between units at individual or group levels can violate both components of the Stable-Unit-Treatment-Value-Assumption (SUTVA) that are core to the counterfactual framework, making treatment effects difficult to assess. New approaches are needed in health studies to develop spatially dynamic causal modeling methods to both derive insights from data that are sensitive to spatial differences and dependencies, and also be able to rely on a more robust, dynamic technical infrastructure needed for decision-making. To address this gap with a focus on causal applications theoretically, methodologically and technologically, I (1) develop a theoretical spatial framework (within single-level panel econometric methodology) that extends existing theories and methods of causal inference, which tend to ignore spatial dynamics; (2) demonstrate how this spatial framework can be applied in empirical research; and (3) implement a new spatial infrastructure framework that integrates and manages the required data for health systems evaluation. The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.Dissertation/ThesisDoctoral Dissertation Geography 201