H∞ filtering for nonlinear discrete-time stochastic systems with randomly varying sensor delays

This is the post print version of the article. The official published version can be obained from the link - Copyright 2009 Elsevier LtdThis paper is concerned with the H∞ filtering problem for a general class of nonlinear discrete-time stochastic systems with randomly varying sensor delays, where the delayed sensor measurement is governed by a stochastic variable satisfying the Bernoulli random binary distribution law. In terms of the Hamilton–Jacobi–Isaacs inequalities, preliminary results are first obtained that ensure the addressed system to possess an l2-gain less than a given positive scalar γ. Next, a sufficient condition is established under which the filtering process is asymptotically stable in the mean square and the filtering error satisfies the H∞ performance constraint for all nonzero exogenous disturbances under the zero-initial condition. Such a sufficient condition is then decoupled into four inequalities for the purpose of easy implementation. Furthermore, it is shown that our main results can be readily specialized to the case of linear stochastic systems. Finally, a numerical simulation example is used to demonstrate the effectiveness of the results derived.This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor James Lam under the direction of Editor Ian R. Petersen. This work was supported by the Shanghai Natural Science Foundation under Grant 07ZR14002, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK and the Alexander von Humboldt Foundation of Germany

Almost surely asymptotic stability of neutral stochastic differential delay equations with Markovian switching

The main aim of this paper is to discuss the almost surely asymptotic stability of the neutral stochastic differential delay equations (NSDDEs) with Markovian switching. Linear NSDDEs with Markovian switching and nonlinear examples will be discussed to illustrate the theory

Mechanical Strength of 17 134 Model Proteins and Cysteine Slipknots

A new theoretical survey of proteins' resistance to constant speed stretching is performed for a set of 17 134 proteins as described by a structure-based model. The proteins selected have no gaps in their structure determination and consist of no more than 250 amino acids. Our previous studies have dealt with 7510 proteins of no more than 150 amino acids. The proteins are ranked according to the strength of the resistance. Most of the predicted top-strength proteins have not yet been studied experimentally. Architectures and folds which are likely to yield large forces are identified. New types of potent force clamps are discovered. They involve disulphide bridges and, in particular, cysteine slipknots. An effective energy parameter of the model is estimated by comparing the theoretical data on characteristic forces to the corresponding experimental values combined with an extrapolation of the theoretical data to the experimental pulling speeds. These studies provide guidance for future experiments on single molecule manipulation and should lead to selection of proteins for applications. A new class of proteins, involving cystein slipknots, is identified as one that is expected to lead to the strongest force clamps known. This class is characterized through molecular dynamics simulations.Comment: 40 pages, 13 PostScript figure

Substance abusers' personality disorders and staff members' emotional reactions

<p>Abstract</p> <p>Background</p> <p>Previous research has indicated that aggressive behaviour and DSM-IV cluster B personality disorders (PD) may be associated with professionals' emotional reactions to clients, and that cluster C PD may be associated with positive emotional reactions.</p> <p>Methods</p> <p>Staff members recruited from workshops completed a self-report inventory of emotional reactions to patients, the Feeling Word Checklist-58, and substance abusers completed a self-report of DSM-IV personality disorder, the DSM-IV and ICD-10 Personality Disorder Questionnaire. Correlational analysis and multiple regression analysis was used to assess the associations between personality disorders and emotional reations.</p> <p>Results</p> <p>Cluster B disorder features were associated with feeling distance to patients, and cluster C disorder features were associated with feeling helpful towards patients. Cluster A disorders had no significant impact on emotional reactions.</p> <p>Conclusion</p> <p>The findings confirm clinical experiences that personality disorder features in patients with substance abuse have an impact on staff members reactions to them. These reactions should be considered in supervision of staff, and in treatment models for patients with co-morbid personality disorders and substance abuse.</p

Determination of trace elements in natural water samples by air-segmented flow-injection/ICP-MS after preconcentration with a chitosan-based chelating resin

本法では,各種天然水中の極微量金属成分を同時定量する目的で空気分節試料導入/ICP-MSシステムを用いて,微少量試料(数十μl)を前処理せずにネブライザーに送り込み,多数の金属成分の定量が可能であった。共存主成分による質量干渉を受ける一部遷移金属や直接試料導入では感度の足りない元素については,イミノ二酢酸型キトサンキレート樹脂充填カラムによる分離·濃縮操作の併用によって更に信頼性の高いデータが得られることが分かった。前処理においては,体積1mlのミニカラムを用いて50mlの溶液試料から50倍濃縮を行い,試料·試薬·廃液すべてを少量化することができた。本ICP-MSシステムでは試料導入量は80μlで十分であり,1mlでも数回繰り返し測定が可能で,しかも多元素同時分析ができた。確立した分析法を用いて河川水や市販のミネラルウォーターに応用し,希土類を含め45種の微量元素の定量が可能となった。Ultratrace elements in natural water samples were determined simultaneously by air-segmented flow-injection/inductively coupled plasma-mass spectrometry(SFI/ICP-MS).A small volume of the sample solutions(80μl) was introduced into a nebulizer by an air-segmented flow-injection(SFI) system, and a maximum of fifteen elements were measured during each run.A chitosan-based chelating resin containing functional groups of iminodiacetate was used to separate and enrich analyte metal ions.A 50-fold preconcentration using 50ml of sample solutions was achieved by the proposed method, where 1ml of 0.1M nitric acid was added to residues after drying the chelating column effluent.At pH6, several heavy metals(Fe, Ni, Co, Cu, Zn, Ag, Cd, Pb and U) and rare earth elements(REEs) were quantitatively retained on the chelating resin column, whereas alkali and alkaline earth metals were eluted from the column by rinsing with 5ml of a 0.2M ammonium acetate solution.Metals adsorbed on the chelating resin column were recovered by elution with 10ml of 1M nitric acid.The proposed method was applied to the determination of trace elements in several natural water samples, such as river water and mineral drinking water

RNA FRABASE version 1.0: an engine with a database to search for the three-dimensional fragments within RNA structures

The RNA FRABASE is a web-accessible engine with a relational database, which allows for the automatic search of user-defined, 3D RNA fragments within a set of RNA structures. This is a new tool to search and analyse RNA structures, directed at the 3D structure modelling. The user needs to input either RNA sequence(s) and/or secondary structure(s) given in a ‘dot-bracket’ notation. The algorithm searching for the requested 3D RNA fragments is very efficient. As of August 2007, the database contains: (i) RNA sequences and secondary structures, in the ‘dot-bracket’ notation, derived from 1065 protein data bank (PDB)-deposited RNA structures and their complexes, (ii) a collection of atom coordinates of unmodified and modified nucleotide residues occurring in RNA structures, (iii) calculated RNA torsion angles and sugar pucker parameters and (iv) information about base pairs. Advanced query involves filters sensitive to: modified residue contents, experimental method used and limits of conformational parameters. The output list of query-matching RNA fragments gives access to their coordinates in the PDB-format files, ready for direct download and visualization, conformational parameters and information about base pairs. The RNA FRABASE is automatically, monthly updated and is freely accessible at http://rnafrabase.ibch.poznan.pl (mirror at http://cerber.cs.put.poznan.pl/rnadb)

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Compton Scattering by Nuclei

The concept of Compton scattering by even-even nuclei from giant-resonance to nucleon-resonance energies and the status of experimental and theoretical researches in this field are outlined. Nuclear Compton scattering in the giant-resonance energy-region provides information on the dynamical properties of the in-medium mass of the nucleon. The electromagnetic polarizabilities of the nucleon in the nuclear medium can be extracted from nuclear Compton scattering data obtained in the quasi-deuteron energy-region. Recent results are presented for two-body effects due to the mesonic seagull amplitude and due to the excitation of nucleon internal degrees of freedom accompanied by meson exchanges. Due to these studies the in-medium electromagnetic polarizabilities are by now well understood, whereas the understanding of nuclear Compton scattering in the Delta-resonance range is only at the beginning. Phenomenological methods how to include retardation effects in the scattering amplitude are discussed and compared with model predictions.Comment: 146 pages, 37 figures, submitted to Phys. Report