Stability analysis for discrete linear systems with state saturation by a saturation-dependent Lyapunov functional

This paper concerns the stability analysis problem of discrete linear systems with state saturation using a saturation-dependent Lyapunov functional.We introduce a free matrix characterized by the sum of the absolute value of each elements for each row less than 1,which makes the state with saturation constraint reside in a convex polyhedron.A saturation-dependent Lyapunov functional is then designed to obtain a sufficient condition for such systems to be globally asymptotically stable.Based on this stability criterion,the state feedback control law synthesis problem is also studied.The obtained results are formulated in terms of bilinear matrix inequalities that can be solved by the presented iterative linear matrix inequality algorithm.Two numerical examples are used to demonstrate the effectiveness of the proposed method.     

An acoustic micrometer and its application to layer thickness measurements

The principle and system of the acoustic micrometer are described. Its basic performance, with respect to the stability, accuracy, spatial resolution, and temperature dependence, is also discussed. Gold layers electroplated on substrates of 42% Ni-Fe alloy were taken as test specimens in the present study. The measurable range of the thickness for gold layers covered from 1 to 20 mum when a frequency range of 10-200 MHz was used. Stability and accuracy were achieved to within +/-0.2% and +/-1%, respectively.     

Excitation of plate waves in thickness measurements of layers deposited on thin plates

Layer thickness measurements with an acoustic micrometer using pseudo-Sezawa waves in which ultrasonic waves are obliquely applied to a layered surface of a specimen have been proposed. A case in which the plate thickness of the specimen is so thin that it cannot be regarded as a half space is studied. A number of modes of plate waves are then excited in addition to pseudo-Sezawa waves. The plate waves, giving rise to the appearance of extra dips in the power spectrum of reflected waves, cause difficulties in the measurements. To prevent the excitation of plate waves, it is proposed that a mask of a sound-insulating material with a slit aperture should be placed on the layered surface of the specimen. Experiments and theoretical calculations, using lead frames of LSI chips as typical test specimens with thin substrates, were performed to demonstrate the effectiveness of the present method in preventing the excitation of plate waves.     

THERMAL DEMAGNETIZATION AND RANDOMNESS OF DOMAIN NUCLEATION OF SINGLE PHASE Nd_2 Fe_(14) B

Based on the observation of temperature variation of both domain structure and magnetic con-trast.the thermal demagnetization and randomness of domain nucleation was discussed.     

Ligand-binding domain of farnesoid X receptor (FXR) had the highest sensitivity and activity among FXR variants in a fluorescence-based assay

The farnesoid X receptor (FXR, NR1H4) has been recognized as an attractive therapeutic target because it is a nuclear hormone receptor that controls the expression level of cholesterol-7α-hydroxylase, which in turn regulates bile acid production and cholesterol excretion. To compare receptor activity between each domain and the full-length protein, human FXR cDNA was cloned from a human liver cDNA library. Three human FXR cDNA, designated FXR₂₀, FXR₃₃, and FXR₅₃ cDNA, were subcloned and ligated into a pET28a expression vector. Each protein was expressed in Escherichia coli (BL21) and purified by nickel-nitrilotriacetic acid column chromatography. Approximately 5 mg of FXR₃₃ (1–182 amino acids deleted from FXR, 37 kDa) and 2 mg of FXR₅₃ (the full-length protein of FXR, 59 kDa) was purified from 1 L of Luria-Bertani culture, achieving at least 90% purity. The coactivator recruitment assay for FXR activation was carried out with the three variants of the FXR protein by using dissociation-enhanced lanthanide fluoroimmunoassay-europium-N¹-labeled anti-His antibody. From an optimized assay, a saturated hyperbolic fluorescence signal curve was produced when 250 nM of FXR₃₃ and 100 nM of steroid receptor coactivator-1 peptide, a coactivator of FXR consisting of 26 amino acids, were used with a concentration dependence on chenodeoxycholic acid (from 0 to 200 μM). The ligand-binding domain of FXR (FXR₃₃) was the most suitable protein for studying the activation of FXR with a fluorescence-based assay, because it showed better structural stability than either the full length of FXR (FXR₅₃) or the DNA-binding domain of FXR (FXR₂₀).     

Teachers cooperation: team-knowledge distillation for multiple cross-domain few-shot learning

Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target set are from different domains, which is also known as cross-domain few-shot learning (CD-FSL). Utilizing more source domain data is an effective way to improve the performance of CD-FSL. However, knowledge from different source domains may entangle and confuse with each other, which hurts the performance on the target domain. Therefore, we propose team-knowledge distillation networks (TKD-Net) to tackle this problem, which explores a strategy to help the cooperation of multiple teachers. Specifically, we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning framework. It incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen tasks. Moreover, our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective knowledge. Extensive experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.     

Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: chymotrypsin inhibitor 2 mutants

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.     

A connection rule for alpha-carbon coarse-grained elastic network models using chemical bond information

A sparser but more efficient connection rule (called a bond-cutoff method) for a simplified alpha-carbon coarse-grained elastic network model is presented. One of conventional connection rules for elastic network models is the distance-cutoff method, where virtual springs connect an alpha-carbon with all neighbor alpha-carbons within predefined distance-cutoff value. However, though the maximum interaction distance between alpha-carbons is reported as 7 angstroms, this cutoff value can make the elastic network unstable in many cases of protein structures. Thus, a larger cutoff value (>11 angstroms) is often used to establish a stable elastic network model in previous researches. To overcome this problem, a connection rule for backbone model is proposed, which satisfies the minimum condition to stabilize an elastic network. Based on the backbone connections, each type of chemical interactions is considered and added to the elastic network model: disulfide bonds, hydrogen bonds, and salt-bridges. In addition, the van der Waals forces between alpha-carbons are modeled by using the distance-cutoff method. With the proposed connection rule, one can make an elastic network model with less than 7 angstroms distance cutoff, which can reveal protein flexibility more sharply. Moreover, the normal modes from the new elastic network model can reflect conformational changes of a given protein better than ones by the distance-cutoff method. This method can save the computational cost when calculating normal modes of a given protein structure, because it can reduce the total number of connections. As a validation, six example proteins are tested. Computational times and the overlap values between the conformational change and infinitesimal motion calculated by normal mode analysis are presented. Those animations are also available at UMass Morph Server (http://biomechanics.ecs.umass.edu/umms.html).     

Evolving combinatorial problem instances that are difficult to solve

This paper demonstrates how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances. As a result of this technique, the corresponding algorithms used to solve these instances are stress-tested. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. The problem instances acquired through this technique are more difficult than the ones found in popular benchmarks. In this paper, these evolved instances are analysed with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.