| 1. |
- Aad, G, et al.
(författare)
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- 2015
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swepub:Mat__t
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| 2. |
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| 3. |
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| 5. |
- Zumla, A, et al.
(författare)
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Towards host-directed therapies for tuberculosis
- 2015
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Ingår i: Nature reviews. Drug discovery. - : Springer Science and Business Media LLC. - 1474-1784 .- 1474-1776. ; 14:8, s. 511-
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Tidskriftsartikel (refereegranskat)
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| 6. |
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| 7. |
- Kumar, Uday, et al.
(författare)
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Maintenance performance metrics : a state of the art review
- 2011
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Ingår i: MPMM 2011: Maintenance Performance Measurement & Management. - Luleå : Luleå tekniska universitet. - 9789174393798 ; , s. 3-34
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Konferensbidrag (refereegranskat)abstract
- This paper provides an overview of research and developments in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in, and references for, the measurement of maintenance performance.The main focus is to determine how value can be created for organizations by measuring maintenance performance, looking at such maintenance strategies as condition based maintenance, reliability centered maintenance, e-maintenance etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.The paper asks the following research questions:- What approaches and techniques are used for Maintenance Performance Measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies?- In general, how can MPM create value for organizations, and more specifically, which system of measurement is best for which maintenance strategy?The body of knowledge on maintenance performance is both quantitative and qualitative based. Quantitative approaches include economic and technical ratios, value-based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst others. Qualitative-based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.
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| 9. |
- Palmié, M., et al.
(författare)
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The evolution of the digital service ecosystem and digital business model innovation in retail : The emergence of meta-ecosystems and the value of physical interactions
- 2022
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Ingår i: Technological forecasting & social change. - : Elsevier. - 0040-1625 .- 1873-5509. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- As e-commerce has increasingly gained traction in the retail market, many traditional “brick-and-mortar” retailers are innovating their business models and making the transition towards digital business models. While scholars have started to examine the influence of digitalization on various business model elements, they have so far paid little attention to its implications on the external relationships in which firms engage for value creation. Building on a qualitative analysis of seventeen interviews, this study develops a two-stage framework for the transition to digital business models. In Stage 1, retailers collaborate with specialized service providers to implement a digital business model. As firms from the retail ecosystem collaborate with firms from the digital-service ecosystem to create a value proposition for end-customers, a meta-ecosystem emerges. In Stage 2, firms (retailers) seek to differentiate themselves from their competitors in the meta-ecosystem. Physical interactions with the digital service providers, the product suppliers, and the customers are a primary means towards this end. Thus, digitalization does not make physical interactions and close personal ties obsolete. Our study has substantial implications for the academic literature and management practice.
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| 10. |
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| 11. |
- Parida, Aditya, et al.
(författare)
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Decision support systems in maintenance : Fusion of business data and physical data
- 2011
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Ingår i: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. - Longborough, Glos : Coxmoor Publishing Co.. - 9781901892369 ; , s. 227-239
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Konferensbidrag (refereegranskat)abstract
- Maintenance optimization is critical for enhancing the effectiveness of asset management system. Maintenance effectiveness depends on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of information and communication technologies (lCT) in organizations conceptualization and implementation of e-maintenance is turning into a reality. While e-maintenance shows a lot of promise, seamless integration of ICT into the industrial environment remains a challenge. Computer Maintenance Management Systems (CMMS) are the core of traditional maintenance record-keeping practices and often facilitate the usage of textual descriptions of faults and actions performed on a vehicle. Recently developed Condition Monitoring Systems (CM) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been fairly limited in their approach and scalability. In this article, we present our approach for addressing this predicament. We argue that understanding the requirements and constraints in conjunction - from maintenance and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful e-maintenance providing a new theoretical process for integrating the two disparate data types
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| 12. |
- Parida, SK, et al.
(författare)
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T-Cell Therapy: Options for Infectious Diseases
- 2015
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Ingår i: Clinical infectious diseases : an official publication of the Infectious Diseases Society of America. - : Oxford University Press (OUP). - 1537-6591. ; 6161Suppl 3, s. S217-S224
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Tidskriftsartikel (refereegranskat)
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| 13. |
- Poznanski, R. R., et al.
(författare)
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Intentionality for better communication in minimally conscious AI design
- 2023
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Ingår i: Journal of Multiscale Neuroscience. - : Neural Press. - 2653-4983. ; 3:1, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Consciousness is the ability to have intentionality, which is a process that operates at various temporal scales. To qualify as conscious, an artificial device must express functionality capable of solving the Intrinsicality problem, where experienceable form or syntax gives rise to understanding 'meaning' as a noncontextual dynamic prior to language. This is suggestive of replacing the Hard Problem of consciousness to build conscious artificial intelligence (AI). Developing model emulations and exploring fundamental mechanisms of how machines understand meaning is central to the development of minimally conscious AI. It has been shown by Alemdar and colleagues [New insights into holonomic brain theory: implications for active consciousness. Journal of Multiscale Neuroscience 2(2023), 159-168] that a framework for advancing artificial systems through understanding uncertainty derived from negentropic action to create intentional systems entails quantum-thermal fluctuations through informational channels instead of recognizing (cf., introspection) sensory cues through perceptual channels. Improving communication in conscious AI requires both software and hardware implementation. The software can be developed through the brain-machine interface of multiscale temporal processing, while hardware implementation can be done by creating energy flow using dipole-like hydrogen ion (proton) interactions in an artificial 'wetwire' protonic filament. Machine understanding can be achieved through memristors implemented in the protonic 'wetwire' filament embedded in a real-world device. This report presents a blueprint for the process, but it does not cover the algorithms or engineering aspects, which need to be conceptualized before minimally conscious AI can become operational.
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