Control Theoretical Modeling of Trust-Based Decision Making in Food-Energy-Water Management

We propose a hybrid Human-Machine decision making to manage Food-Energy-Water resources. In our system trust among human actors during decision making is measured and managed. Furthermore, such trust is used to pressure human actors to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. We model the trust-based loops in decision making by using control theory. In this system, the feedback information is the trust pressure that actors receive from peers. Using control theory, we studied the dynamics of the trust of an actor. Then, we presented the modeling of the change of solution distances. In both scenarios, we also calculated the settling times and the stability using the transfer functions and their Z-transforms as the number of rounds to show whether and when the decision making is finalized

How does study quality affect the results of a diagnostic meta-analysis?

Background: The use of systematic literature review to inform evidence based practice in diagnostics is rapidly expanding. Although the primary diagnostic literature is extensive, studies are often of low methodological quality or poorly reported. There has been no rigorously evaluated, evidence based tool to assess the methodological quality of diagnostic studies. The primary objective of this study was to determine the extent to which variations in the quality of primary studies impact the results of a diagnostic meta-analysis and whether this differs with diagnostic test type. A secondary objective was to contribute to the evaluation of QUADAS, an evidence-based tool for the assessment of quality in diagnostic accuracy studies. Methods: This study was conducted as part of large systematic review of tests used in the diagnosis and further investigation of urinary tract infection (UTI) in children. All studies included in this review were assessed using QUADAS, an evidence-based tool for the assessment of quality in systematic reviews of diagnostic accuracy studies. The impact of individual components of QUADAS on a summary measure of diagnostic accuracy was investigated using regression analysis. The review divided the diagnosis and further investigation of UTI into the following three clinical stages: diagnosis of UTI, localisation of infection, and further investigation of the UTI. Each stage used different types of diagnostic test, which were considered to involve different quality concerns. Results: Many of the studies included in our review were poorly reported. The proportion of QUADAS items fulfilled was similar for studies in different sections of the review. However, as might be expected, the individual items fulfilled differed between the three clinical stages. Regression analysis found that different items showed a strong association with test performance for the different tests evaluated. These differences were observed both within and between the three clinical stages assessed by the review. The results of regression analyses were also affected by whether or not a weighting (by sample size) was applied. Our analysis was severely limited by the completeness of reporting and the differences between the index tests evaluated and the reference standards used to confirm diagnoses in the primary studies. Few tests were evaluated by sufficient studies to allow meaningful use of meta-analytic pooling and investigation of heterogeneity. This meant that further analysis to investigate heterogeneity could only be undertaken using a subset of studies, and that the findings are open to various interpretations. Conclusion: Further work is needed to investigate the influence of methodological quality on the results of diagnostic meta-analyses. Large data sets of well-reported primary studies are needed to address this question. Without significant improvements in the completeness of reporting of primary studies, progress in this area will be limited

α1Proteinase Inhibitor Regulates CD4+ Lymphocyte Levels and Is Rate Limiting in HIV-1 Disease

Background: The regulation of adult stem cell migration through human hematopoietic tissue involves the chemokine CXCL12 (SDF-1) and its receptor CXCR4 (CD184). In addition, human leukocyte elastase (HLE) plays a key role. When HLE is located on the cell surface (HLE CS), it acts not as a proteinase, but as a receptor for a 1proteinase inhibitor (a 1PI, a 1antitrypsin, SerpinA1). Binding of a1PI to HLECS forms a motogenic complex. We previously demonstrated that a1PI deficiency attends HIV-1 disease and that a1PI augmentation produces increased numbers of immunocompetent circulating CD4 + lymphocytes. Herein we investigated the mechanism underlying the a 1PI deficiency that attends HIV-1 infection. Methods and Findings: Active a 1PI in HIV-1 subjects (median 17 mM, n = 35) was significantly below normal (median 36 mM, p,0.001, n = 30). In HIV-1 uninfected subjects, CD4 + lymphocytes were correlated with the combined factors a1PI, HLECS + lymphocytes, and CXCR4 + lymphocytes (r 2 = 0.91, p,0.001, n = 30), but not CXCL12. In contrast, in HIV-1 subjects with.220 CD4 cells/ml, CD4 + lymphocytes were correlated solely with active a 1PI (r 2 =0.93,p,0.0001, n = 26). The monoclonal anti-HIV-1 gp120 antibody 3F5 present in HIV-1 patient blood is shown to bind and inactivate human a 1PI. Chimpanzee a 1PI differs from human a1PI by a single amino acid within the 3F5-binding epitope. Unlike human a1PI, chimpanzee a1PI did not bind 3F5 or become depleted following HIV-1 challenge, consistent with the normal CD4 + lymphocyte levels and benign syndrome of HIV-1 infected chimpanzees. The presence of IgG-a 1PI immune complexes correlated with decreased CD4 + lymphocytes in HIV-1 subjects

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales

Towards an understanding of decision complexity in IT configuration

There exist many opportunities for deploying autonomic computing in an IT environment. The highest-value opportunities are going to be where we can reduce human decision-making complexity for systems administrators. To identify these opportunities, we need a model of decision complexity for configuring and operating computing systems. This paper extends previous work on models and metrics for IT configuration complexity by adding the concept of decision complexity. As the first step towards a complete model of decision complexity, we describe an extensive user study of decision making in a carefully-mapped analogous domain (route planning), and illustrate how the results of that study suggest an initial model of decision complexity applicable to IT configuration. The model identifies the key factors affecting decision complexity and highlights several interesting results, including the fact that decision complexity has significantly different impacts on user-perceived difficulty than on objective measures like time and error rate. We also describe some of the implications of our decision complexity model for system designers seeking to automate the decision-making and reduce the configuration complexity of their systems. © 2006 IEEE.link_to_subscribed_fulltex

Recognizing End-User Transactions in Performance Management

Providing good quality of service (e.g., low response times) in distributed computer systems requires measuring end-user perceptions of performance. Unfortunately, in practice such measures are often expensive or impossible to obtain. Herein, we propose a machine learning approach to recognizing end-user transactions consisting of sequences of remote procedure calls (RPCs) received at a server. Two problems are addressed. The first is labeling previously segmented transaction instances with the correct transaction type. This is akin to work done in document classification. The second problem is segmenting RPC sequences into transaction instances. This is a more difficult problem, but it is similar to segmenting sounds into words as in speech understanding. Using Naive Bayes, we tackle the labeling problem with four combinations of feature vectors and probability distributions: RPC occurrences with the Bernoulli distribution and RPC counts with the multinomial, geometric, and shifted ge..