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Sökning: WFRF:(Thornadtsson J.)

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  • Högman, Marieann, et al. (författare)
  • Effects of growth and aging on the reference values of pulmonary nitric oxide dynamics in healthy subjects
  • 2017
  • Ingår i: Journal of Breath Research. - : IOP Publishing. - 1752-7155 .- 1752-7163. ; 11:4
  • Tidskriftsartikel (refereegranskat)abstract
    • The lung just like all other organs is affected by age. The lung matures by the age of 20 and age-related changes start around middle age, at 40-50 years. Exhaled nitric oxide (FENO) has been shown to be age, height and gender dependent. We hypothesize that the nitric oxide (NO) parameters alveolar NO (CANO), airway flux (JawNO), airway diffusing capacity (DawNO) and airway wall content (CawNO) will also demonstrate this dependence. Data from healthy subjects were gathered by the current authors from their earlier publications in which healthy individuals were included as control subjects. Healthy subjects (n = 433) ranged in age from 7 to 78 years. Age-stratified reference values of the NO parameters were significantly different. Gender differences were only observed in the 20-49 age group. The results from the multiple regression models in subjects older than 20 years revealed that age, height and gender interaction together explained 6% of variation in FENO at 50 ml s-1 (FENO50), 4% in JawNO, 16% in CawNO, 8% in DawNO and 12% in CANO. In conclusion, in this study we have generated reference values for NO parameters from an extended NO analysis of healthy subjects. This is important in order to be able to use these parameters in clinical practice.
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  • Singh, P., et al. (författare)
  • Using log analytics and process mining to enable self-healing in the Internet of Things
  • 2022
  • Ingår i: Environment Systems and Decisions. - : Springer. - 2194-5403 .- 2194-5411. ; 42:2, s. 234-250
  • Tidskriftsartikel (refereegranskat)abstract
    • The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing and industrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system, detecting and fixing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known as Process Mining, which—in combination with log-file analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physical systems. Issues addressed in this paper include generation of consistent Event Logs and definition of a roadmap toward effective Process Discovery and Conformance Checking to support Self-Healing in IoT.
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