Spectral Radius of Non-negative Matrices and Digraphs

We present,an upper and a lower bound for the spectral radius of non-negative matrices. Then we give the bounds for the spectral radius of digraphs.

Highly Aligned Ternary Nanofiber Matrices Loaded with MXene Expedite Regeneration of Volumetric Muscle Loss

Current therapeutic approaches for volumetric muscle loss(VML)face challenges due to limited graft availability and insufficient bioactivities.To overcome these limitations,tissue-engineered scaffolds have emerged as a promising alternative.In this study,we developed aligned ternary nanofibrous matrices comprised of poly(lactide-co-ε-caprolactone)integrated with collagen and Ti_(3)C_(2)T_(x)MXene nanoparticles(NPs)(PCM matrices),and explored their myogenic potential for skeletal muscle tissue regeneration.The PCM matrices demonstrated favorable physicochemical properties,including structural uniformity,alignment,microporosity,and hydrophilicity.In vitro assays revealed that the PCM matrices promoted cellular behaviors and myogenic differentiation of C2C12 myoblasts.Moreover,in vivo experiments demonstrated enhanced muscle remodeling and recovery in mice treated with PCM matrices following VML injury.Mechanistic insights from next-generation sequencing revealed that MXene NPs facilitated protein and ion availability within PCM matrices,leading to elevated intracellular Ca^(2+)levels in myoblasts through the activation of inducible nitric oxide synthase(i NOS)and serum/glucocorticoid regulated kinase 1(SGK1),ultimately promoting myogenic differentiation via the m TOR-AKT pathway.Additionally,upregulated i NOS and increased NO–contributed to myoblast proliferation and fiber fusion,thereby facilitating overall myoblast maturation.These findings underscore the potential of MXene NPs loaded within highly aligned matrices as therapeutic agents to promote skeletal muscle tissue recovery.

The“depict”strategy for discovering new compounds in complex matrices:Lycibarbarspermidines as a case

The comprehensive detection and identification of active ingredients in complex matrices is a crucial challenge.Liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS)is the most prominent analytical platform for the exploration of novel active compounds from complex matrices.However,the LC-HRMS-based analysis workflow suffers from several bottleneck issues,such as trace content of target compounds,limited acquisition for fragment information,and uncertainty in interpreting relevant MS2 spectra.Lycibarbarspermidines are vital antioxidant active ingredients in Lycii Fructus,while the reported structures are merely focused on dicaffeoylspermidines due to their low content.To comprehensively detect the new structures of lycibarbarspermidine derivatives,a“depict”strategy was developed in this study.First,potential new lycibarbarspermidine derivatives were designed according to the biosynthetic pathway,and a comprehensive database was established,which enlarged the coverage of lycibarbarspermidine derivatives.Second,the polarity-oriented sample preparation of potential new compounds increased the concentration of the target compounds.Third,the construction of the molecular network based on the fragmentation pathway of lycibarbarspermidine derivatives broadened the comprehensiveness of identification.Finally,the weak response signals were captured by data-dependent scanning(DDA)followed by parallel reaction monitoring(PRM),and the efficiency of acquiring MS2 fragment ions of target compounds was significantly improved.Based on the integrated strategy above,210 lycibarbarspermidine derivatives were detected and identified from Lycii Fructus,and in particular,170 potential new compounds were structurally characterized.The integrated strategy improved the sensitivity of detection and the coverage of low-response components,and it is expected to be a promising pipeline for discovering new compounds.

MAXIMAL FUNCTION CHARACTERIZATIONS OF HARDY SPACES ASSOCIATED WITH BOTH NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES AND BALL QUASI-BANACH FUNCTION SPACES

Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.

Acellular dermal matrices in reconstructive surgery;history,current implications and future perspectives for surgeons

Large-scale defects of body in the reconstructive surgical practice,and the help-lessness of their repair with autologous tissues,have been an important factor in the development of artificial biological products for the temporary,definitive,or staged repair of these defects.A major advance in the field of plastic and other reconstructive surgery in this regard has been the introduction and successful use of acellular dermal matrices(ADMs).In recent years,not only the type of tissue from which ADMs are produced,product range,diversity and areas of use have increased,but their use in reconstructive fields,especially in post oncologic breast surgery,has become highly regarded and this has favored ADMs to be a potential cornerstone in specific and well-defined surgical fields in future.It is essential that reconstructive surgeons become familiar with some of the ADM’s as well as the advantages and limitations to their use.This review not only provides basic science and clinical evidence of the current use of ADMs in wide range of surgical fields but also targets to keep them as an important backdrop in the arma-mentarium of reconstructive surgeons.Brief considerations of possible future directions for ADMs are also conducted in the end.

Sparseness-controlled non-negative tensor factorization and its application in machinery fault diagnosis

Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.

Local hierarchical non-negative tensor factorization and its application in machinery fault diagnosis

Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization（NMF） model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor＇s vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods.

Criteria Condition for Generalized Diagonally Dominant Matrices

In this paper, we provide some new necessary and sufficient conditions for generalized diagonally dominant matrices and also obtain some criteria for nongeneralized dominant matrices.

The Monotonicity Problems for Generalized Inverses of Matrices in H (n, ≥)

On the basis of the paoers[3—7],this paper study the monotonicity problems for the positive semidefinite generalized inverses of the positive semidefinite self-conjugate matrices of quaternions in the Lowner partial order,give the explicit formulations of the monotonicity solution sets A{1;≥,T_1;≤B^(1)}and B{1;≥,T_2≥A^(1)}for the(1)-inverse,and two results of the monotonicity charac teriaztion for the(1,2)-inverse.

BEST APPROXIMATION BY NORMAL MATRICES WITH SPECTRAL CONSTRAINTS

The problem of best approximating, a given square complex matrix in the Frobenius norm by normal matrices under a given spectral restriction is considered. The ne cessary and sufficient condition for the solvability of the problem is given. A numerical algorithm for solving the problem is provided and a numerical example is presented.

Formulation and evaluation the bioadhesive properties of drug delivery system based on PEGylated mucin matrices

Objective:To formulate and evaluate the application of PEGylated mucin in oral bioadhesive drug(OBD) delivery system.Methods:The bioadhesive strength of different formulation ratios of polyethylene glycol(PEG) and mucin was evaluated by tensiometry.Co polymer matrices containing mucin and PEG(PEGylated mucin) in the following ratios of 1:1(A),2:1(B),1:2(C), 0:1(D) and 1:0(E) were prepared by co-precipitation.Microparticles were prepared from the matrices using a size reduction technique.Five different samples were prepared using direct mixtures in the appropriate solvenfs having mucin and PEG ratios of 1:1,2:1,1:2,0:1, 1:0 and labeled as(A-E).The matrices were evaluated for their flow properties and the in vitro bioadhesion characteristics of the samples were examined.Results:The range of flow rates of the matrices was 1.35-2.23 g/sec.The angle of repose was in the range of 37.3-39.9 degree.The bulk and tapped densities were within the ranges of 0.41- 0.49.The Hausner’s quotient(HQ) were 1.12, 1.24,1.14,1.25 and 1.2 for matrix batches A-E respectively,The bioadhesive strength of polymer matrices appeared to be directly related to amount of mucin.The order of bioadhesive strength is 2:1】1:1】1:0】1:2】0:1 of PEG:mucin in both simulated intestine fluid(SEP) and simulated gastrointestinal fluid(SGF).The physical properties of the micrometrics properties of the matrices were within the accepted values.Conclusions:OBD preparation containing PEGylated mucin can be prepared by direct compression and be used in drug delivery to the oral cavity.

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

Generalized Drazin spectrum of operator matrices

Let A ∈ B（X） and B ∈ B（Y）, Me be an operator on Banach space X ＋ Y given by Mc =（A C 0 B）A generalized Drazin spectrum defined by σgD（T） = {λ∈C ： T-λI is not generalized Drazin invertible} is considered in this paper. It is shown that

Real-time origin-destination matrices estimation for urban rail transit network based on structural state-space model

The major objective of this work was to establish a structural state-space model to estimate the dynamic origin-destination(O-D) matrices for urban rail transit network, using in- and out-flows at each station from automatic fare collection(AFC) system as the real time observed passenger flow counts. For lacking of measurable passenger flow information, the proposed model employs priori O-D matrices and travel time distribution from historical travel records in AFC system to establish the dynamic system equations. An arriving rate based on travel time distribution is defined to identify the dynamic interrelations between time-varying O-D flows and observed flows, which greatly decreases the computational complexity and improve the model's applicability for large-scale network. This methodology is tested in a real transit network from Beijing subway network in China through comparing the predicted matrices with the true matrices. Case study results indicate that the proposed model is effective and applicative for estimating dynamic O-D matrices for large-scale rail transit network.

Sparse recovery in probability via l_q-minimization with Weibull random matrices for 0 < q ≤ 1

Although Gaussian random matrices play an important role of measurement matrices in compressed sensing, one hopes that there exist other random matrices which can also be used to serve as the measurement matrices. Hence, Weibull random matrices induce extensive interest. In this paper, we first propose the lrobust null space property that can weaken the D-RIP, and show that Weibull random matrices satisfy the lrobust null space property with high probability. Besides, we prove that Weibull random matrices also possess the lquotient property with high probability. Finally, with the combination of the above mentioned properties,we give two important approximation characteristics of the solutions to the l-minimization with Weibull random matrices, one is on the stability estimate when the measurement noise e ∈ R~n needs a priori ‖e‖≤ε, the other is on the robustness estimate without needing to estimate the bound of ‖e‖. The results indicate that the performance of Weibull random matrices is similar to that of Gaussian random matrices in sparse recovery.

Synchronization of complex switched delay dynamical networks with simultaneously diagonalizable coupling matrices

This paper studies local exponential synchronization of complex delayed networks with switching topology via switched system stability theory. First, by a common unitary matrix, the problem of synchronization is transformed into the stability analysis of some linear switched delay systems. Then, when all subnetworks are synchronizable, a delay-dependent sufficient condition is given in terms of linear matrix inequalities （LMIs） which guarantees the solvability of the synchronization problem under an average dwell time scheme. We extend this result to the case that not all subnetworks are synchronizable. It is shown that in addition to average dwell time, if the ratio of the total activation time of synchronizable and non-synchronizable subnetworks satisfy an extra condition, then the problem is also solvable. Two numerical examples of delayed dynamical networks with switching topology are given, which demonstrate the effectiveness of obtained results.

Inverse Eigenvalue Problem for Generalized Arrow-Like Matrices

This paper researches the following inverse eigenvalue problem for arrow-like matrices. Give two characteristic pairs, get a generalized arrow-like matrix, let the two characteristic pairs are the characteristic pairs of this generalized arrow-like matrix. The expression and an algorithm of the solution of the problem is given, and a numerical example is provided.

Development of damage probability matrices based on Greek earthquake damage data

A comprehensive study is presented for empirical seismic vulnerability assessment of typical structural types, representative of the building stock of Southern Europe, based on a large set of damage statistics. The observational database was obtained from post-earthquake surveys carried out in the area struck by the September 7, 1999 Athens earthquake. After analysis of the collected observational data, a unified damage database has been created which comprises 180,945 damaged buildings from/after the near-field area of the earthquake. The damaged buildings are classified in specific structural types, according to the materials, seismic codes and construction techniques in Southern Europe. The seismic demand is described in terms of both the regional macroseismic intensity and the ratio αg/ao, where αg is the maximum peak ground acceleration （PGA） of the earthquake event and ao is the unique value PGA that characterizes each municipality shown on the Greek hazard map. The relative and cumulative frequencies of the different damage states for each structural type and each intensity level are computed in terms of damage ratio. Damage probability matrices （DPMs） and vulnerability curves are obtained for specific structural types. A comparison analysis is fulfilled between the produced and the existing vulnerability models.

Construction of protograph LDPC codes with circular generator matrices

The application of protograph low density parity check （LDPC） codes involves the encoding complexity problem. Since the generator matrices are dense, and if the positions of ＂1＂ s are irregularity, the encoder needs to store every ＂1＂ of the generator matrices by using huge chip area. In order to solve this problem, we need to design the protograph LDPC codes with circular generator matrices. A theorem concerning the circulating property of generator matrices of nonsingular protograph LDPC codes is proposed. The circulating property of generator matrix of nonsingular protograph LDPC codes can be obtained from the corresponding quasi-cyclic parity check matrix. This paper gives a scheme of constructing protograph LDPC codes with circulating generator matrices, and it reveals that the fast encoding algorithm of protograph LDPC codes has lower encoding complexity under the condition of the proposed theorem. Simulation results in ad- ditive white Gaussian noise （AWGN） channels show that the bit error rate （BER） performance of the designed codes based on the proposed theorem is much better than that of GB20600 LDPC codes and Tanner LDPC codes.

Compressed data separation via dual frames based split-analysis with Weibull matrices

In this paper, we consider data separation problem, where the original signal is composed of two distinct subcomponents, via dual frames based Split-analysis approach. We show that the two distinct subcomponents, which are sparse in two diff erent general frames respectively, can be exactly recovered with high probability, when the measurement matrix is a Weibull random matrix （not Gaussian） and the two frames satisfy a mutual coherence property. Our result may be significant for analysing Split-analysis model for data separation.