New mathematical model for analysing three-phase controlled rectifier using switching functions

This paper is a postprint of a paper submitted to and accepted for publication in IET Power Electronics and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.The aim of this study is to present a set of closed-form analytical equations in order to enable the computation of the three-phase bridge rectifier steady-state performance estimation. The proposed method presented in this study is a fast, accurate and effective mathematical model for analysing three-phase full-wave controlled rectifiers. The steady-state mathematical model is based on the derivation of an appropriate set of switching functions using the general switching matrix circuit (GSMC) techniques. Once the switching functions are derived, the output current, input current and output dc voltage can all be easily derived and generated from the application of this technique. The effect of overlap is accurately modelled and the distortion (notches), frequency content on the input (voltage and current) and output voltage distortion are derived. The proposed mathematical model, unlike conventional analytical methods, can be integrated in the design of active filters. Furthermore, the output voltage reduction, the rms, average and peak values of voltages and currents for the thyristors and any other semiconductor devices used are readily available for the designer by direct substitution into closed-form equations without any need for the waste of time for worst-case scenario simulations. This method can also be applied to other types of converters, specifically to all voltage fed power converters

In vivo precision of the GE Lunar iDXA for the measurement of visceral adipose tissue in adults: the influence of body mass index.

CoreScan is a new software for the GE Lunar iDXA, which provides a quantification of visceral adipose tissue (VAT). The objective of this study was to determine the in vivo precision of CoreScan for the measurement of VAT mass in a heterogeneous group of adults. Forty-five adults (aged 34.6 (8.6) years), ranging widely in body mass index (BMI 26.0 (5.2)  kg/m(2); 16.7-42.4 kg/m(2)), received two consecutive total body scans with repositioning. The sample was divided into two subgroups based on BMI, normal-weight and overweight/obese, for precision analyses. Subgroup analyses revealed that precision errors (RMSSD:%CV; root mean square standard deviation:% coefficient of variation) for VAT mass were 20.9 g:17.0% in the normal-weight group and 43.7 g:5.4% in overweight/obese groups. Our findings indicate that precision for DXA-VAT mass measurements increases with BMI, but caution should be used with %CV-derived precision error in normal BMI subjects.European Journal of Clinical Nutrition advance online publication, 15 October 2014; doi:10.1038/ejcn.2014.213

Edmonton Obesity Staging System Prevalence and Association with Weight Loss in a Publicly Funded Referral-Based Obesity Clinic

Objectives. To determine the distribution of EOSS stages and differences in weight loss achieved according to EOSS stage, in patients attending a referral-based publically funded multisite weight management clinic. Subjects/Methods. 5,787 obese patients were categorized using EOSS staging using metabolic risk factors, medication use, and severity of doctor diagnosis of obesity-related physiological, functional, and psychological comorbidities from electronic patient files. Results. The prevalence of EOSS stages 0 (no risk factors or comorbidities), 1 (mild conditions), 2 (moderate conditions), and 3 (severe conditions) was 1.7%, 10.4%, 84.0%, and 3.9%, respectively. Prehypertension (63%), hypertension (76%), and knee replacement (33%) were the most common obesity-related comorbidities for stages 1, 2, and 3, respectively. In the models including age, sex, initial BMI, EOSS stage, and treatment time, lower EOSS stage and longer treatment times were independently associated with greater absolute (kg) and percentage of weight loss relative to initial body weight P<0.05. Conclusions. Patients attending this publicly funded, referral-based weight management clinic were more likely to be classified in the higher stages of EOSS. Patients in higher EOSS stages required longer treatment times to achieve similar weight outcomes as those in lower EOSS stages

Generalized priority-queue network dynamics: Impact of team and hierarchy

We study the effect of team and hierarchy on the waiting-time dynamics of priority-queue networks. To this end, we introduce generalized priority-queue network models incorporating interaction rules based on team-execution and hierarchy in decision making, respectively. It is numerically found that the waiting time distribution exhibits a power law for long waiting times in both cases, yet with different exponents depending on the team size and the position of queue nodes in the hierarchy, respectively. The observed power-law behaviors have in many cases a corresponding single or pairwise-interacting queue dynamics, suggesting that the pairwise interaction may constitute a major dynamics consequence in the priority-queue networks. It is also found that the reciprocity of influence is a relevant factor for the priority-queue network dynamic

Correlated multiplexity and connectivity of multiplex random networks

Nodes in a complex networked system often engage in more than one type of interactions among them; they form a multiplex network with multiple types of links. In real-world complex systems, a node's degree for one type of links and that for the other are not randomly distributed but correlated, which we term correlated multiplexity. In this paper we study a simple model of multiplex random networks and demonstrate that the correlated multiplexity can drastically affect the properties of giant component in the network. Specifically, when the degrees of a node for different interactions in a duplex Erdos-Renyi network are maximally correlated, the network contains the giant component for any nonzero link densities. In contrast, when the degrees of a node are maximally anti-correlated, the emergence of giant component is significantly delayed, yet the entire network becomes connected into a single component at a finite link density. We also discuss the mixing patterns and the cases with imperfect correlated multiplexity.Comment: Revised version, 12 pages, 6 figure

Will algorithms blind people? The effect of explainable AI and decision-makers' experience on AI-supported decision- making in government

Computational artificial intelligence (AI) algorithms are increasingly used to support decision-making by governments. Yet algorithms often remain opaque to the decision-makers and devoid of clear explanations for the decisions made. In this study, we used an experimental approach to compare decision-making in three situations: humans making decisions 1) without any support of algorithms, 2) supported by business rules (BR) and 3) supported by machine learning (ML). Participants were asked to make the correct decisions given various scenarios, whilst BR and ML algorithms could provide correct or incorrect suggestions to the decision-maker. This enabled us to evaluate whether the participants were able to understand the limitations of BR and ML. The experiment shows that algorithms help decision-makers to make more correct decisions. The findings suggest that explainable AI combined with experience, helps them detect incorrect suggestions made by algorithms. However, even experienced persons were not able to identify all mistakes. Ensuring the ability to understand and traceback decisions are not sufficient for avoiding making incorrect decisions. The findings imply that algorithms should be adopted with care and that selecting the appropriate algorithms for supporting decisions and training of decision-makers are key factors in increasing accountability and transparency. Abstract Computational artificial intelligence (AI) algorithms are increasingly used to support decision-making by governments. Yet algorithms often remain opaque to the decision-makers and devoid of clear explanations for the decisions made. In this study, we used an experimental approach to compare decision-making in three situations: humans making decisions 1) without any support of algorithms, 2) supported by business rules (BR) and 3) supported by machine learning (ML). Participants were asked to make the correct decisions given various scenarios, whilst BR and ML algorithms could provide correct or incorrect suggestions to the decision-maker. This enabled us to evaluate whether the participants were able to understand the limitations of BR and ML. The experiment shows that algorithms help decision-makers to make more correct decisions. The findings suggest that explainable AI combined with experience, helps them detect incorrect suggestions made by algorithms. However, even experienced persons were not able to identify all mistakes. Ensuring the ability to understand and traceback decisions are not sufficient for avoiding making incorrect decisions. The findings imply that algorithms should be adopted with care and that selecting the appropriate algorithms for supporting decisions and training of decision-makers are key factors in increasing accountability and transparency

Editorial: Technology assessment for addressing grand societal challenges

Emerging technologies are both a cause of many grand societal challenges (GSCs) facing twenty-first-century societies and an integral part of some of their most promising solutions. As an element of the GSCs, technology becomes intertwined with several interrelated issues that constitute the GSCs. This calls for approaches to Technology Assessment (TA) that account for the paradoxical role of technology in the GSCs, and the imperative and complexity of pointing technological innovation toward addressing the GSCs. In this introduction to the special issue, we identify three major streams in TA research and practice, namely TA as a policy instrument, a deliberation process, and an issue field. These streams highlight tensions between relying on experts and on the inclusion of various stakeholders in TA processes, and between a TA framing around the technology and framing around critical issues, such as those constituting the GSCs. We discuss the advantages and challenges of each stream. We also outline and discuss key principles for conducting TA in the context of GSCs. We end by introducing the four papers that constitute this special issue

Real Space Imaging of One-Dimensional Standing Waves: Direct Evidence for a Luttinger Liquid

Electronic standing waves with two different wavelengths were directly mapped near one end of a single-wall carbon nanotube as a function of the tip position and the sample bias voltage with highresolution position-resolved scanning tunneling spectroscopy. The observed two standing waves caused by separate spin and charge bosonic excitations are found to constitute direct evidence for a Luttinger liquid. The increased group velocity of the charge excitation, the power-law decay of their amplitudes away from the scattering boundary, and the suppression of the density of states near the Fermi level were also directly observed or calculated from the two different standing waves.Comment: 14 pages, 4 figures. The latest version in PDF format is available from http://fy.chalmers.se/~eggert/papers/nanoLL.pd