Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications

Abstract-Dynamic decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -aka quality properties-and the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-time. In this paper we develop further our ideas on re-assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

THE PRIMITIVE COGNITIVE NETWORK PROCESS: COMPARISONS WITH THE ANALYTIC HIERARCHY PROCESS

This research proposes primitive cognitive network process (P-CNP), which comprises five decision processes, as an alternative of analytic hierarchy process (AHP). Two published cases using AHP are revised by P-CNP, and the validity and applicability of P-CNP are demonstrated. The comparison results indicate that AHP produces questionable results due to the ill-defined axioms of the perception of the paired difference, and suggest that the proposed P-CNP performs better than AHP in multicriteria decision-making problems.Decision analysis, analytic hierarchy process (AHP), pairwise comparison, multicriteria decision-making, optimization