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Localized corrosion often occurs on steel members due to paint peeling led from insufficient durability of the coating material or human collision, which may significantly reduce the load carrying capacity and safety performance of the structures. The effects of the parameters such as localized corrosion penetration, circumferential corrosion ratio, longitudinal corrosion ratio, slenderness ratio and eccentricity of mechanical behaviors of locally corroded circular steel tubes under compression were studied experimentally and numerically. Based on the test results and the finite element analysis, a calculation method was proposed for predicting load carrying capacity of the corroded specimen under compression. It is found that localized corrosion did not change the failure modes of test specimens, and all of them failed in the limit point buckling mode. The factors affecting the degradation of load carrying capacity and stiffness of localized corrosion specimens from high to low were corrosion time, circumferential corrosion ratio and longitudinal corrosion ratio. The most unfavorable localized corrosion modes of load carrying capacity and stiffness were H3L3-270 and H3L1-270, respectively. The load carrying capacity degradation of members increases with the increase of the localized corrosion ratio, circumferential corrosion ratio and longitudinal corrosion ratio, while decreases with the increase of the eccentricity. The formula proposed can be used to predict the load carrying capacity of locally corroded circular steel tube members under compression.

In order to achieve a comprehensive and accurate evaluation of the safety of compression members made of circular steel tubes, image processing technology is commonly utilized to extract the morphology of the steel tubes before and after rust removal. The obtained results have validated the feasibility and applicability of employing digital cameras and image processing technology to analyze the images of the steel tubes before and after rust removal and to extract useful structural mechanics features. The feature values of the apparent morphology before rust removal grow with the increase of the corrosion depth, while after rust removal, the feature values first increase and then decrease with the growth of the corrosion depth. Based on this fact, a simplified equation is proposed to quantify the relationship between the feature values of the apparent morphology before and after rust removal and the corrosion depth. In continuing, a simple, fairly accurate, and comprehensive safety evaluation methodology for corroded circular steel tubes under compression is established. Finally, an example is illustrated to check the applicability and effectiveness of the proposed approach.

We introduce ($α,β,γ$) weighted Wigner-Yanase-Dyson (($α,β,γ$) WWYD) skew information and ($α,β,γ$) modified weighted Wigner-Yanase-Dyson (($α,β,γ$) MWWYD) skew information. We explore the sum uncertainty relations for arbitrary $N$ mutually noncommutative observables based on ($α,β,γ$) WWYD skew information. A series of uncertainty inequalities are derived. We show by detailed example that our results cover and improve the previous ones based on the original Wigner-Yanase (WY) skew information. Finally, we establish new sum uncertainty relations in terms of the ($α,β,γ$) MWWYD skew information for arbitrary $N$ quantum channels., 19 pages, 1 figure

The Gram matrix of a set of quantum pure states plays key roles in quantum information theory. It has been highlighted that the Gram matrix of a pure-state ensemble can be viewed as a quantum state, and the quantumness of a pure-state ensemble can thus be quantified by the coherence of the Gram matrix [Europhys. Lett. \textbf{134} 30003]. Instead of the $l_1$-norm of coherence and the relative entropy of coherence, we utilize the generalized $\alpha$-$z$-relative R\'enyi entropy of coherence of the Gram matrix to quantify the quantumness of a pure-state ensemble and explore its properties. We show the usefulness of this quantifier by calculating the quantumness of six important pure-state ensembles. Furthermore, we compare our quantumness with other existing ones and show their features as well as orderings., Comment: 18pages,6figures

We study the degree to which the coherence of quantum states is affected by noise. We give the definition of the $n$-th decay rate and investigate the coherence of Bell-diagonal states under $n$ iterations of channels. We derive explicit formulas of the $n$-th decay rates based on $l_1$ norm of coherence, relative entropy of coherence and skew information-based coherence. It is found that the larger $n$ is, the faster the $n$-th decay rate decreases as the parameter $p$ of Bell-diagonal states increases. Moreover, for any fixed $n$, with the increase of $p$, Bell-diagonal states can be completely incoherent under generalized amplitude damping (GAD) channels, depolarization (DEP) channels and phase flip (PF) channels, while this is not the case for bit flip (BF) channels and bit-phase flip (BPF) channels. We also investigate the geometry of the relative entropy of coherence and skew information-based coherence of Bell-diagonal states under different channels when the $n$-th decay rate is one, i.e., the coherence is frozen. It is shown that compared with BF and BPF channels, when $n$ is large enough, the coherence of Bell-diagonal states will not be frozen under GAD, DEP and PF channels. For skew information-based coherence, similar properties of coherence freezing are found.

Quantifying the quantumness of ensembles is a vital and practical task in quantum information theory. In this paper, we quantify the quantumness of quantum ensembles based on a quantifier of the coherence of ensembles via generalized α-z-relative Renyi entropy. It is shown that this measure satisfies the intuitive and desirable properties which are required by a measure of quantumness, such as positivity, unitary invariance, monotonicity under any commutativity preserving operations and so on. The relationship between our measure and some existing measures for quantumness of quantum ensembles is discussed. Detailed examples are presented to some important ensembles to illustrate the quantumness given by our measure.

Uncertainty relation is a core issue in quantum mechanics and quantum information theory. We introduce modified generalized Wigner-Yanase-Dyson (MGWYD) skew information and modified weighted generalizedWigner-Yanase-Dyson (MWGWYD) skew information, and establish new uncertainty relations in terms of the MGWYD skew information and MWGWYD skew information., 16 pages

The Gram matrix of an ensemble of pure states can be regarded as a quantum state, and the quantumness of the ensemble can be quantified by the coherence of the Gram matrix. By using the affinity between mixed states, the concept of Gram matrix of pure-state ensembles can be extended to the one of mixed-state ensembles. By utilizing the generalized α-z-relative Rényi entropy of coherence of Gram matrices, we present a new quantifier of quantumness of mixed-sate ensembles and further reveal its peculiar properties. To illustrate our quantumness of mixed-sate ensembles, we also calculate the quantumness for some detailed mixed-sate ensembles by deriving their analytical formulae.

Uncertainty relations and complementarity relations are core issues in quantum mechanics and quantum information theory. By use of the generalized Wigner-Yanase-Dyson (GWYD) skew information, we derive several uncertainty and complementarity relations with respect to mutually unbiased measurements (MUMs), and general symmetric informationally complete positive operator valued measurements (SIC-POVMs), respectively. Our results include some existing ones as particular cases. We also exemplify our results by providing a detailed example., 7 pages

We study the skew information-based coherence of quantum states and derive explicit formulas for Werner states and isotropic states in a set of autotensor of mutually unbiased bases (AMUBs). We also give surfaces of skew information-based coherence for Bell-diagonal states and a special class of X states in both computational basis and in mutually unbiased bases. Moreover, we depict the surfaces of the skew information-based coherence for Bell-diagonal states under various types of local nondissipative quantum channels. The results show similar as well as different features compared with relative entropy of coherence and l1 norm of coherence., 18 pages

We study an approximate coincidence point and a common fixed point problem for a hybrid pair of mappings with constraints in Menger PM-spaces, and obtain some new results. We derive interesting consequences of the main results by using the properties of a Menger–Hausdorff metric, and analogous results based on graphs instead of partial orders can be similarly formulated. Moreover, we construct two examples to reveal that the main results are valid, and show that the main results can be used to explore the existence of solutions to a system of nonlinear integral equations.

In this paper, some new theorems for various cyclic contractions are established in Gb-metric spaces to discuss the existence and uniqueness of the solutions for a class of the operator equations, which generalize many known results in corresponding literatures. On the other hand, we introduce the notion of the cyclic α-ψφ-contractive mappings in Gb-metric spaces and establish a new fixed point theorem, which is applied to consider the existence of the solutions of the integral equations and ordinary differential equations. Moreover, some examples are given to support our main results.

The aim of this paper is to propose a new φ -coupled fixed point theorem for a mixed monotone mapping in a metric space, which is taken no account of the continuity of the mapping. To this end, we introduce the notions of φ -coupled fixed point and modified F -control function. Our obtained results are applied to derive some φ -coupled fixed point theorems in partial metric spaces and quasi-metric spaces, respectively. Some applications to matrix equations and integral equations are also presented so as to illustrate our results.

Joint human activity localization and recognition has broad application prospects in human-computer interaction, virtual reality, smart healthcare system, security monitoring and robotics. Ultra-wideband (UWB) is an emerging technology adopted in real-time location system (RTLS) and has shown satisfactory performance in the task of human activity localization. However, few studies have been carried out to simultaneously recognize human activities based on UWB RTLS, which limits the use of UWB RTLS in many applications. In this study, we develop a RTLS based on UWB for the joint task of activity localization and recognition. A compressed sensing-based activity recognition approach is proposed for the task of activity recognition and several machine learning methods are designed to further improve the activity localization accuracy for the task of activity localization. The experimental results show that our UWB RTLS achieves good performance in this joint task.

We study the average skew information-based coherence for both random pure and mixed states. The explicit formulae of the average skew information-based coherence are derived and shown to be the functions of the dimension N of the state space. We demonstrate that as N approaches to infinity, the average coherence is 1 for random pure states, and a positive constant less than 1/2 for random mixed states. We also explore the typicality of average skew information-based coherence of random quantum states. Furthermore, we identify a coherent subspace such that the amount of the skew information-based coherence for each pure state in this subspace can be bounded from below almost always by a fixed number that is arbitrarily close to the typical value of coherence., 24pages

High-precision indoor positioning has nowadays emerged as a critical function for many applications. However, many existing indoor positioning systems either fail to achieve a high positioning accuracy or are very easily affected by indoor environments composed of many obstacles, preventing them from satisfying many application requirements. Ultra wideband (UWB) has recently drawn extensive attention in the field of indoor positioning due to its great ability to achieve high ranging and localization accuracy while minimizing the effect of multipath interference. Meanwhile, advanced artificial intelligence and signal processing techniques have been explored to improve the precision and performance of indoor positioning system. In this paper, a high-precision UWB indoor positioning system integrating artificial intelligence and signal processing techniques is designed. And field tests are also conducted to validate the design of the system.

Background As the number of diabetics worldwide continues to increase, diabetic cardiomyopathy has become one of the main causes of cardiovascular disease risk in diabetic patients. Currently, the pathophysiological mechanism of DCM has not been fully elucidated. In the present study, relevant pathological changes of cardiomyocytes in the high glucose environment were simulated by in vitro culture of rat H9C2 cardiomyocytes, to explore the mechanism by which MCU induces apoptosis in cardiomyocytes. Method: Cultured rat myocardium H9C2 cells in vitro and divided into high glucose group (glucose concentration 33 mmol/L), normal group (glucose concentration 5.5 mmol/L), experimental group (5.5 mmol/L glucose and transfected with MCU siRNA) and control group (5.5 mmol/L glucose and transfected negative control siRNA). Comparative analysis of MCU expression, Ca2+ uptake, mitochondrial function, oxidative stress and apoptosis of two groups of cells. Results (1) Compared with normal group, in the high glucose group the MCU expression of myocardial cells in H9C2 rats decreased, The Ca2+ levels, membrane potential and mitochondrial ATP levels decreased, mitochondrial ROS levels increased, NADH+/NADPH ratio in cardiomyocytes increased, GSH/GSSG ratio decreased, the expression levels of cleaved caspase-3 and cleaved caspase-9 increased, bcl-2 expression decreased, the number of cardiomyocytes apoptotic cells increases. (2) Compared with the normal group and the control group, the experimental group MCU expression of myocardial cells in H9C2 rats decreased, The Ca2+ levels, membrane potential and mitochondrial ATP levels decreased, mitochondrial ROS levels increased, NADH+/NADPH ratio in cardiomyocytes increased, GSH/GSSG ratio decreased, the expression levels of cleaved caspase-3 and cleaved caspase-9 increased, bcl-2 expression decreased, the number of cardiomyocytes apoptotic cells increases. Discussion This study suggested that MCU expression in rat H9C2 cardiomyocytes was decreased in the high glucose environment, causing abnormal mitochondrial calcium uptake and imbalanced calcium homeostasis, which may further contribute to mitochondrial dysfunction and enhanced oxidative stress in cardiomyocytes. Mitochondrial dysfunction and enhanced oxidative stress ultimately led to apoptosis in cardiomyocytes.

The application of compressed sensing and machine learning in power quality disturbance (PQD) classification has drawn more and more attention. This paper presents an adaptive compressed sensing and machine learning (ACSML) method to classify both single PQD and combined PQDs with the consideration of the correlation and the sparsity properties in the PQD signals. This method first uses random projection to reduce the dimensionality of the PQD signals. Meanwhile, a simplified near neighbor algorithm is proposed to reduce the size of the required PQD signal training dataset. The PQD classification problem is finally solved using an adaptive compressed sensing classification algorithm. Experiment results show that the proposed ACSML method achieves higher classification accuracy and faster classification speed in classifying PQDs than the other existing compressed sensing based PQD classification methods.

Indoor positioning system plays an important role in smart grid. Although GPS is the predominant outdoor positioning technology, it is unsuitable to be used in many fields of smart grid for three main reasons: first, signals sent from GPS could easily get blocked by solid materials such as metal or brick; second, the complex electromagnetic interference induced by electrical circuits greatly affects GPS signals; third, GPS can only achieve meter-level real time positioning accuracy, which is far from sufficient for many requirements of smart grid applications. Some other indoor positioning technologies, such as Bluetooth, Wi-Fi, ultrasound, infrared and RFID, fail in either the positioning accuracy, the positioning range, or the positioning speed required in many smart grid applications. Therefore, this paper proposes a real time indoor positioning system for smart gird based on a more promising technology, ultra-wideband (UWB). UWB is suitable for real-time localization in smart grid because UWB has short radio frequency pulse duration and wide bandwidth, which can minimize the effects of multipath interference and allow for high-resolution ranging and easier material penetration. In addition, since high-accuracy position information is required in many smart grid fields, a comprehensive framework integrating several artificial intelligence techniques, including outlier detection, line-of-sight/non-line-of-sight classification, filter design, range measurement correction and maximum likelihood localization estimation, is also proposed to further improve the positioning accuracy. At last, the performance of this system is verified through a series of experiments.

Compressive sensing related methods have achieved success in power system fault classification. However, those methods, either only focus on sparsity without considering the correlation within power system fault signals, or emphasize too much on the correlation but neglect the sparsity property of power system fault signals, resulting in the decrease of classification precision and the increase of the number of training samples needed. In addition, those methods do not explore other machine learning techniques at the same time to further improve the classification accuracy and speed. To overcome the drawbacks and limitations of those methods, this paper proposes an adaptive compressive sensing and machine learning classification framework to quickly and accurately classify power system faults considering both the correlation and the sparsity properties inherited in power system fault signals. First, random projection is applied to reduce the dimensionality of the fault signal. Second, a k-nearest neighbor based algorithm is designed to reduce the number of fault signals needed in the training dataset. Finally, the classification of power system faults is formulated as solving an adaptive L1-norm and L2-norm combined objective function using an alternating direction optimization method. Simulation results and analyses show that the proposed framework has better performance than the existing compressive sensing based power system faulty classification methods in both accuracy and speed.