Model free real-time optimization for vapor compression systems

A vapor compression system's optimal input settings vary according to changes in environmental conditions. Tracking these optimal input trajectories can be challenging when insufficient information for a reliable system model is available. An alternative set of optimization approaches use system measurements. This thesis focuses on one such approach, extremum seeking control, which uses performance index measurements to determine optimal system settings. Forgoing system model knowledge and relying exclusively on data allows an optimization approach to function well on many different plants. However, this added adaptivity comes at a performance cost. Using prior system model knowledge can be helpful for ensuring that a controller design works from the start of operation and inputs can be changed as soon as information about environmental conditions is updated. By contrast, data based methods may require the control designer to spend a time generating data in order to obtain enough information about the system to make good decisions online. A central theme of this work is addressing the trade off between using prior system model knowledge and ensuring sufficient adaptability of the extremum seeking optimization approach. Two main factors in the extremum seeking design are considered: the choice of extremum seeking control law and the choice of extremum seeking control input. Extremum seeking control laws come from the field of mathematical optimization; this thesis considers the pros and cons of choosing between gradient descent and Newton descent. Both simulations and experimental results show that while Newton descent extremum seeking is less reliant on model knowledge, but slower to find optimal inputs than gradient descent extremum seeking. Because of extremum seeking's adaptability to different plants, many different inputs can be chosen for implementation. However, using an approach known as self-optimizing control, knowledge about the plant's behavior can help choose set points with optimal values that are insensitive to changes in environmental conditions. Finding these special inputs turns the input tracking problem into a regulation problem. Both simulation and experimental results confirm that combining self-optimizing control and extremum seeking control can help improve tracking even as environmental conditions change

Data Driven Chiller Plant Energy Optimization with Domain Knowledge

Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments. Conventional optimization techniques using physical models make decisions of online parameter tuning, based on very limited information of hardware specifications and external conditions, e.g., outdoor weather. In recent years, new generation of sensors is becoming essential part of new chiller plants, for the first time allowing the system administrators to continuously monitor the running status of all equipments in a timely and accurate way. The explosive growth of data flowing to databases, driven by the increasing analytical power by machine learning and data mining, unveils new possibilities of data-driven approaches for real-time chiller plant optimization. This paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants. Instead of employing complex machine learning models, we emphasize the incorporation of appropriate domain knowledge into data analysis tools, which turns out to be the key performance improver over state-of-the-art deep learning techniques by a significant margin. Our empirical evaluation on a real world chiller plant achieves savings by more than 7% on daily power consumption.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

The Epstein-Barr Virus G-Protein-Coupled Receptor Contributes to Immune Evasion by Targeting MHC Class I Molecules for Degradation

Epstein-Barr virus (EBV) is a human herpesvirus that persists as a largely subclinical infection in the vast majority of adults worldwide. Recent evidence indicates that an important component of the persistence strategy involves active interference with the MHC class I antigen processing pathway during the lytic replication cycle. We have now identified a novel role for the lytic cycle gene, BILF1, which encodes a glycoprotein with the properties of a constitutive signaling G-protein-coupled receptor (GPCR). BILF1 reduced the levels of MHC class I at the cell surface and inhibited CD8+ T cell recognition of endogenous target antigens. The underlying mechanism involves physical association of BILF1 with MHC class I molecules, an increased turnover from the cell surface, and enhanced degradation via lysosomal proteases. The BILF1 protein of the closely related CeHV15 c1-herpesvirus of the Rhesus Old World primate (80% amino acid sequence identity) downregulated surface MHC class I similarly to EBV BILF1. Amongst the human herpesviruses, the GPCR encoded by the ORF74 of the KSHV c2-herpesvirus is most closely related to EBV BILF1 (15% amino acid sequence identity) but did not affect levels of surface MHC class I. An engineered mutant of BILF1 that was unable to activate G protein signaling pathways retained the ability to downregulate MHC class I, indicating that the immune-modulating and GPCR-signaling properties are two distinct functions of BILF1. These findings extend our understanding of the normal biology of an important human pathogen. The discovery of a third EBV lytic cycle gene that cooperates to interfere with MHC class I antigen processing underscores the importance of the need for EBV to be able to evade CD8+ T cell responses during the lytic replication cycle, at a time when such a large number of potential viral targets are expressed

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. Results: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. Conclusions: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks

Pedestrian, Crowd, and Evacuation Dynamics

This contribution describes efforts to model the behavior of individual pedestrians and their interactions in crowds, which generate certain kinds of self-organized patterns of motion. Moreover, this article focusses on the dynamics of crowds in panic or evacuation situations, methods to optimize building designs for egress, and factors potentially causing the breakdown of orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c

Universal logic with encoded spin qubits in silicon

Qubits encoded in a decoherence-free subsystem and realized in exchange-coupled silicon quantum dots are promising candidates for fault-tolerant quantum computing. Benefits of this approach include excellent coherence, low control crosstalk, and configurable insensitivity to certain error sources. Key difficulties are that encoded entangling gates require a large number of control pulses and high-yielding quantum dot arrays. Here we show a device made using the single-layer etch-defined gate electrode architecture that achieves both the required functional yield needed for full control and the coherence necessary for thousands of calibrated exchange pulses to be applied. We measure an average two-qubit Clifford fidelity of $97.1 \pm 0.2\%$ with randomized benchmarking. We also use interleaved randomized benchmarking to demonstrate the controlled-NOT gate with $96.3 \pm 0.7\%$ fidelity, SWAP with $99.3 \pm 0.5\%$ fidelity, and a specialized entangling gate that limits spreading of leakage with $93.8 \pm 0.7\%$ fidelity

Improvements in clinical outcomes in children with cystic fibrosis aged six and 16 years

Aims: Our aim was to assess if outcomes for cystic fibrosis (CF) patients at six & sixteen years of age have improved in the last 17 years looking at FEV1, BMI and death. Methods: A retrospective observational study using a prospectively maintained database of CF patients at Cork University Hospital. Results: 84 patients were included in the 16-year-old data and 89 patients were included in the six-year-old data. The mean FEV1 and BMI (16 years) for the 2002-2007 group was 72.9±21.0% and 18.9±2.53 respectively, 2008-2013 group was 75.4±27.2% and 19.8±2.7 and for the 2014-2018 group was 95.2±16.0% and 22.9±4.1. The percentage of patients (16 years) with chronic pseudomonas status was 37.9% (11/30) in the 2002-2007 group, 51.6 % (16/31) in the 2008-2013 group and 4.2% (1/24) in the 2014-2018 group. The relationship between FEV1 and FVC with BMI remained significant in multivariate analysis (P <0.001). The mean FEV1 (six years) for the 2002-2007 group was 90.7±16.1%, 2008-2013 group was 99.3±17.9% and for the 2014-2018 group was 100.9±15.8%. Conclusions: Improvements in FEV1 and BMI aged six and 16 years are notable as well as a significant decline in the number of patients with chronic pseudomonas

RfaH Suppresses Small RNA MicA Inhibition of fimB Expression in Escherichia coli K-12

The phase variation (reversible on-off switching) of the type 1 fimbrial adhesin of Escherichia coli involves a DNA inversion catalyzed by FimB (switching in either direction) or FimE (on-to-off switching). Here, we demonstrate that RfaH activates expression of a FimB-LacZ protein fusion while having a modest inhibitory effect on a comparable fimB-lacZ operon construct and on a FimE-LacZ protein fusion, indicating that RfaH selectively controls fimB expression at the posttranscriptional level. Further work demonstrates that loss of RfaH enables small RNA (sRNA) MicA inhibition of fimB expression even in the absence of exogenous inducing stress. This effect is explained by induction of σE , and hence MicA, in the absence of RfaH. Additional work con- firms that the procaine-dependent induction of micA requires OmpR, as reported previously (A. Coornaert et al., Mol. Microbiol. 76:467–479, 2010, doi:10.1111/j.1365-2958.2010.07115.x), but also demonstrates that RfaH inhibition of fimB transcription is enhanced by procaine independently of OmpR. While the effect of procaine on fimB transcription is shown to be independent of RcsB, it was found to require SlyA, another known regulator of fimB transcription. These results demonstrate a complex role for RfaH as a regulator of fimB expression