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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.

We study the ability of a quantum channel to generate quantum coherence when it applies to incoherent states. We define the measure of coherence generating power (CGP) for a generic quantum channel to be the average coherence generated by the quantum channel acting on a uniform ensemble of incoherent states based on the skew information-based coherence measure. We present explicitly the analytical formulae of the CGP for any arbitrary finite dimensional unitary channels. We derive the mean value of the CGP over the unitary groups and investigate the typicality of the normalized CGP. Furthermore, we give an upper bound of the CGP for the convex combinations of unitary channels. Detailed examples are provided to calculate exactly the values of the CGP for the unitary channels related to specific quantum gates and for some qubit channels., Comment: 21pages. arXiv admin note: text overlap with arXiv:2101.07427

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.

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.

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.

We introduce modified generalized Wigner-Yanase-Dyson (MGWYD) skew information and modified weighted generalized Wigner-Yanase-Dyson (MWGWYD) skew information. By revisiting state-channel interaction based on MGWYD skew information, a family of coherence measures with respect to quantum channels is proposed. Furthermore, explicit analytical expressions of these coherence measures of qubit states are derived with respect to different quantum channels. Moreover, complementarity relations based on MGWYD skew information and MWGWYD skew information are also presented. Specifically, the conservation relations are investigated, while two interpretations of them including symmetry-asymmetry complementarity and wave-particle duality have been proposed., 20pages

Canonical correlation analysis (CCA) is a popular and powerful technique for two-view dimension reduction and feature extraction. But, CCA is not able to directly handle more than two view data and has a rigorous assumption that all the samples from two different views are paired. However, practical multiple view data are often semi-paired. To address this problem, we in this paper propose a novel semi-paired multiview dimension reduction approach, which takes cross-view neighborhood relationship among semi-paired data and within-view global structure information into consideration. The proposed approach can not only deal with multiview (more than two) data, but also take sufficient advantage of unpaired multiview data and then mitigate overfitting effectively caused by the limited paired data. Experimental results on two benchmark data sets demonstrate the effectiveness of our proposed method.

Depression has been a major concern for the employers due to its high prevalence and cost. In United States, 6.8% of the work population had depression and it is accountable for over $100 billion workplace loss every year. Early detection and timely intervention is the key to address this challenge. In this paper, we proposed to leverage a large claim dataset to build depression risk prediction model. This approach is easy to implement and scale because the claim data is accessible for employers at no additional cost. We derived a subset of over 7.2 million patients in a 3-year period, with 2,099 variables including the diagnosis, procedure, medication, and health service provider information. We trained two models (Least Absolute Shrinkage and Selection Operator (LASSO) and random forest *RF)) with first two years data to predict the onset of depression in the third year. Both models achieved the best comparable performance so far according to our knowledge. The important variables identified by our algorithms are mental health related medications and diagnoses (other than depression), gender (female), and other chronic conditions such as back pain. We further validated these variables by confirming in clinical literature. We concluded that the claim data based depression prediction is a viable and low cost approach, especially for self-insured employers.

In this paper, we introduce the concepts of cyclic $$\varphi $$ -contractions in Menger PM-spaces and cyclic weak $$\phi $$ -contractions in generalized Menger PM-spaces. Based on these concepts, some fixed point results for cyclic $$\varphi $$ -contractions in Menger PM-spaces are obtained. Furthermore, some fixed point theorems for cyclic weak $$\phi $$ -contractions and cyclic $$\varphi $$ -contractions in generalized Menger PM-spaces are obtained by assuming that the number of cyclic sets is odd. An example is also given to show the validity of our main results.

In this paper, we obtain some new fixed point theorems for (?,?,?,?) -contractive multi-valued mappings in ?-?-T-complete Menger PM-spaces, which turn out to generalize many results in existing literatures. Some examples are also given to illustrate the definition and support our new results. Moreover, some interesting fixed point results endowed with binary relations or graphs are derived as applications of our main results.

A key aspect of the mimetic defense system is the scheduling of heterogeneous executions to ensure that the system is dynamic. The existing scheduling strategy only performs random scheduling to satisfy heterogeneity and service quality. Combined with the historical information of the arbiter in the mimicry defense system, this paper proposed a random seed scheduling algorithm based on historical confidence for maximum heterogeneity and quality of service. First, the seed performer is randomly selected in the historical confidence executor that satisfies the threshold, and then the scheduling scheme is determined according to the maximum heterogeneity type and the quality of service indicator. The simulation results showed that the scheduling period of the algorithm is lower than the random scheduling algorithm. However, the scheduling effect is higher than the random scheduling algorithm, which achieved a good dynamic balance between dynamics, security, and quality of service.

In this paper, we propose some new coupled coincidence point and fixed point theorems for the mappings F and g. Our results are obtained by exploring the corresponding initial value problems for the mapping F and weakening the involved contractive conditions. As an application, we study the existence and uniqueness of solution to integro-differential equations.

We investigate facial expression recognition using state-of-the-art classification models. Recently, CNNs have been extensively used for face recognition. However, CNNs have not been thoroughly evaluated for facial expression recognition. In this paper, we train and test a CNN model for facial expression recognition. The performance of the CNN model is used as benchmark for evaluating other pre-trained deep CNN models. We evaluate the performance of Inception and VGG which are pre-trained for object recognition, and compare these with VGG-Face which is pre-trained for face recognition. All experiments are performed on publicly available face databases, namely, CK+, JAFFE and FACES.

The initial rotational stiffness of connections is important for the design of semirigid frames. However, most current studies on steel beam to concrete-filled steel tube column connections...

The conflict between the dynamics postulate (unitary evolution) and the measurement postulate (wave-packet collapse) of quantum mechanics has been reconciled by Zurek from an information transfer perspective [Phys. Rev. A 76 (2007) 052110], and has further been extended to a more general scenario [Phys. Rev. A 87 (2013) 052111]. In this paper, we reconsider Zurek's new derivation by using weak repeatability postulate or covariant condition instead of repeatability postulate.

In this paper, the new concepts of \(\alpha \)-admissible mappings with respect to \(\eta \) in single-valued case and \(\alpha _{*}\)-admissible mappings with respect to \(\eta _{*}\) in set-valued case in Menger PM-spaces are introduced, and some new fixed point results for both single-valued and set-valued mappings under certain contractive conditions are studied, which extend and generalize some results in corresponding literatures. Some examples are provided to illustrate the validity of our main results, and an application is also given to discuss the existence of solution for Volterra type integral equations.

The tension between unitarity and wave-packet collapse is an annoying problem in quantum mechanics, while a breakthrough was made by Zurek recently from the point of view of information transfer. In this paper, we reconsider Zurek's derivation in the setting of generalized probabilistic theories (GPT), and establish that actionable information about a system can be repeatedly passed on to other systems only when the chosen states of the system have mutual zero fidelity. This may be interpreted as an extension of Zurek's result to GPT.

The bolted T-stub connection joining beam with column is being widely applied. To enhance the energy dissipation capacity of conventional T-stub connections, two rectangular plates are proposed to be inserted between the T-stub and column, so that the T-stub flange can yield both under tensile and compressive loads. This study put forward a mechanical model of a new T-stub connection with inserted plates and investigated important factors that could affect its mechanical behavior through experimental tests. Thirty specimens were designed with different configurations that differed according to the existence or absence of inserted plates, the fabrication method and the width of inserted plates. These configurations were tested under axial and cyclic loading conditions, and results showed that the proposal aiming to improve the energy dissipation capacity was feasible. The mechanical model presented coincided with the test observation and data. The advent of two inserted plates elevated the load bearing capacity, stiffness and ductility of connections under compression, whereas in tension the properties were not substantially enhanced. The welded T-stub connections outperformed those cut from standard section steel. The energy dissipated by connections with inserted plates was about 150% of that by traditional connections without inserted plates. Only within a reasonable range can the increment of plate width promote the energy dissipation capacity of T-stub connections. The optimum width of plates in terms of energy consumption accounted for around 31% of the overall width of connections.

Circular steel tube members with the absence of anticorrosive protection or coating failure are prone to uniform corrosion, which threatens the reliability and safety of members in the atmospheric environment. To fully study the mechanical behavior of uniformly corroded circular steel tubes, compression test and theoretical analysis were conducted, and two methods considering section reduction and material degradation, respectively, were adopted for the calculation of ultimate load carrying capacities of specimens. The results indicate that uniform corrosion did not change the failure modes of specimens, and all of them belonged to global buckling failure. The load carrying capacities and stiffness of specimens decreased with the increase of corrosion ratio, and the degree of reduction was greater than that of material degradation, showing a linear relationship with the corrosion rate. Under the same corrosion ratio, the specimens with larger eccentricity represented more obvious load carrying capacity and stiffness degradation. The load carrying capacities predicted by both methods were in good agreement with the test results and had a certain safety margin. The conservative degree of calculation results from three specifications followed a descending order of ANSI/AISC 360-16, GB 50017-2017, and EN 1993-1-1. Under the same corrosion ratio, the load carrying capacity variation of specimens between one-sided corrosion and two-sided corrosion was less than 3%.

Five square reinforced concrete (RC) stub columns were fabricated and experimental tested under an axial compression load in order to study the effectiveness of strengthening by using prestressed carbon fiber reinforced polymer (CFRP) strips. The specimens tested included one un-strengthened and four strengthened. Three specimens were strengthened by using prestressed strips, and the other one strengthened by using non-prestressed was used to compare. The prestress of the strip was achieved by tensed using a special device before it was pasted to the column. The experimental data such as the failure modes, load-displacement curves and strain distribution of CFRP strips were obtained. The effects of prestress magnitude of CFRP strips on the ultimate bearing capacity and deformation capacity of the specimens were analyzed. The results show that the prestress of CFRP strips can effectively increase the ultimate capacity and delay the crack occurrence of the specimens. And ultimate strain, plastic deformation of the specimens were also improved with increase of prestress magnitude of strip.

Local specialties or souvenirs are called ‘shouxin’ in the Lingnan area. Through market research on a number of shouxin enterprises in Zhuhai, this thesis contrasts different products of the same type investigated with multi-dimensional analysis, and summarizes the features and benefits of the various products. Referring to theories and experience of cultural innovation in Taiwan especially the design context “building a brand on the basis of culture and using it in daily life in the form of a specific product”, it proposes that souvenir enterprises in Zhuhai could take humanity as the core of its creative design and culture as the basis. It constructs a development strategy and framework of creative design for local shouxin enterprises in Zhuhai.

Based on the aesthetic conclusions from works on Ding white porcelain of Song dynasty, this paper is intended to illustrate the relationship between design and culture symbols by studying a case on the design of a culturally creative product combining cultural icons derived from both the aesthetic perfection of white porcelain and modern products. Ding white porcelain of Song dynasty is one of the most prominent products in the history of Chinese porcelain craft. The purpose of this study is to expound that the design styles of modern culturally creative products in China should be based on the heritage of China porcelain aesthetics, and uniquely integrate both fascination of ancient culture and the charisma of modern times, and that the design of porcelain products should be stemmed from culture. Therefore, the purpose of this paper is to build up a theoretical frame for designers to follow in their design of culturally creative porcelain products, and to explicitly discuss on cross-cultural communication and propagation and the Chinese porcelain culture as the foundation of modern porcelain products design.

We investigate facial expression recognition based on geometric features, and image appearance, using a range of classifier models. First, we evaluate expression recognition of face images based on geometric features, namely, facial landmarks based on the Active Appearance Model, and Action Units based on the Facial Action Coding System. A generalized linear model and a neural network are used to classify face images based on these geometric features. Second, the classification achieved by facial landmarks and Action Units is compared with the classification achieved by convolutional neural network (CNN) which extracts image features from raw pixels. Finally, we use transfer learning technique to evaluate classification using a pre-trained model. All experiments are performed on the state-of-the-art CK+ face database.