STABILITY AND PERFORMANCE OF NETWORKED CONTROL SYSTEMS

Network control systems (NCSs), as one of the most active research areas, are arousing comprehensive concerns along with the rapid development of network. This dissertation mainly discusses the stability and performance of NCSs into the following two parts. In the first part, a new approach is proposed to reduce the data transmitted in networked control systems (NCSs) via model reduction method. Up to our best knowledge, we are the first to propose this new approach in the scientific and engineering society. The "unimportant" information of system states vector is truncated by balanced truncation method (BTM) before sending to the networked controller via network based on the balance property of the remote controlled plant controllability and observability. Then, the exponential stability condition of the truncated NCSs is derived via linear matrix inequality (LMI) forms. This method of data truncation can usually reduce the time delay and further improve the performance of the NCSs. In addition, all the above results are extended to the switched NCSs. The second part presents a new robust sliding mode control (SMC) method for general uncertain time-varying delay stochastic systems with structural uncertainties and the Brownian noise (Wiener process). The key features of the proposed method are to apply singular value decomposition (SVD) to all structural uncertainties, to introduce adjustable parameters for control design along with the SMC method, and new Lyapunov-type functional. Then, a less-conservative condition for robust stability and a new robust controller for the general uncertain stochastic systems are derived via linear matrix inequality (LMI) forms. The system states are able to reach the SMC switching surface as guaranteed in probability 1 by the proposed control rule. Furthermore, the novel Lyapunov-type functional for the uncertain stochastic systems is used to design a new robust control for the general case where the derivative of time-varying delay can be any bounded value (e.g., greater than one). It is theoretically proved that the conservatism of the proposed method is less than the previous methods. All theoretical proofs are presented in the dissertation. The simulations validate the correctness of the theoretical results and have better performance than the existing results

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning

BACKGROUND: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. METHODS: A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm ('machine') performance was compared to three clinicians ('human') and a commercial tool (cvi42, Circle Cardiovascular Imaging). FINDINGS: Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. CONCLUSION: We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision

Suppression of EAE and Prevention of Blood-Brain Barrier Breakdown after Vaccination with Novel Bifunctional Peptide Inhibitor

The efficacy of bifunctional peptide inhibitor (BPI) in preventing blood-brain barrier (BBB) breakdown during onset of experimental autoimmune encephalomyelitis (EAE) and suppression of the disease was evaluated in mice. The mechanism that defines how BPI prevents the disease was investigated by measuring the in vitro cytokine production of splenocytes. Peptides were injected 5 to 11 days prior to induction of EAE, and the severity of the disease was monitored by a standard clinical scoring protocol and change in body weight. The BBB breakdown in diseased and treated mice was compared to that in normal control mice by determining deposition of gadolinium diethylenetriaminepentaacetate (Gd-DTPA) in the brain using magnetic resonance imaging (MRI). Mice treated with PLP-BPI showed no or low indication of EAE as well as normal increase in body weight. In contrast, mice treated with the control peptide or PBS showed a decrease in body weight and a high disease score. The diseased mice had high deposition of Gd-DTPA in the brain, indicating breakdown in the BBB. However, the deposition of Gd-DTPA in PLP-BPI-treated mice was similar to that in normal control mice. Thus, PLP-BPI can suppress EAE when administered as a peptide vaccine and maintain the integrity of the BBB

A decentralized approach to model national and global food and land use systems

The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO2 sink of 3.7 GtCO2e yr−1 over the period 2020-2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities.Fil: Mosnier, Aline. Sustainable Development Solutions Network; FranciaFil: Javalera Rincon, Valeria. International Institute For Applied Systems Analysis, Laxenburg; AustriaFil: Jones, Sarah K. Alliance of Bioversity International; FranciaFil: Andrew, Robbie. Center for International Climate Research; NoruegaFil: Bai, Zhaohai. Chinese Academy of Sciences; República de ChinaFil: Baker, Justin. North Carolina State University; Estados UnidosFil: Basnet, Shyam. Stockholm Resilience Centre; SueciaFil: Boer, Rizaldi. Bogor Agricultural University; IndonesiaFil: Chavarro, John. Geo-agro-environmental Sciences And Resources Research Center; ColombiaFil: Costa, Wanderson. Centro de Previsao de Tempo e Estudos Climáticos. Instituto Nacional de Pesquisas Espaciais; BrasilFil: Daloz, Anne Sophie. Center for International Climate Research; NoruegaFil: DeClerck, Fabrice A.. Alliance of Bioversity International; Francia. Stockholm Resilience Centre; SueciaFil: Diaz, Maria. Sustainable Development Solutions Network; FranciaFil: Douzal, Clara. Sustainable Development Solutions Network; FranciaFil: Howe Fan, Andrew Chiah. Sunway University; MalasiaFil: Fetzer, Ingo. Stockholm Resilience Centre; SueciaFil: Frank, Federico. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Gonzalez Abraham, Charlotte E.. University of California at San Diego; Estados UnidosFil: Habiburrachman, A. H. F.. Universitas Indonesia; IndonesiaFil: Immanuel, Gito. Stockholm Resilience Centre; SueciaFil: Harrison, Paula A.. Centre for Ecology & Hydrology; Reino UnidoFil: Imanirareba, Dative. Uganda Martyrs University; UgandaFil: Jha, Chandan. Indian Institute Of Management Ahmedabad; IndiaFil: Monjeau, Jorge Adrian. Fundación Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vittis, Yiorgos. International Institute For Applied Systems Analysis; AustriaFil: Wade, Chris. North Carolina State University; Estados UnidosFil: Winarni, Nurul L.. Universitas Indonesia; IndonesiaFil: Woldeyes, Firew Bekele. Ethiopian Development Research Institute; EtiopíaFil: Wu, Grace C.. University of California; Estados UnidosFil: Zerriffi, Hisham. University of British Columbia; Canad

The projection score - an evaluation criterion for variable subset selection in PCA visualization

<p>Abstract</p> <p>Background</p> <p>In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.</p> <p>Results</p> <p>We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA.</p> <p>Conclusions</p> <p>We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.</p

Use of triazole-ring formation to attach a Ru/TsDPEN complex for asymmetric transfer hydrogenation to a soluble polymer

The cycloaddition of a chiral ligand containing a terminal alkyne to a soluble polymer containing an azide provides a convenient means for the attachment of an asymmetric transfer hydrogenation catalyst to a soluble polymer support. Using these ligands in complexes with Ru(II), gave good results in terms of conversion and enantioselectivity (up to 95% ee) in ketone reduction reactions