As a popular defect of steel, corrosion had been a big challenge to industry safe and structural health. For atmosphere corrosion characterization and evaluation, a clustering by fast search and find of density peaks (CFSFDP) algorithm, combined with gap statistic (GS) method is utilized to corroded Q235 carbon steel tubes. With the proposed method, three natural atmosphere corroded samples are investigated and classified. The proposed method successfully identifies the samples with different service periods. The temperature gradient, which indicates the heat generation and conductivity, is used to analyze cluster center selection. The matching rate is presented as a feature to reflect the corrosion state difference.
In a previous paper, we have shown that forward use of the steady-state difference equations arising from homogeneous discrete-state space Markov chains may be subject to inherent numerical instability. More precisely, we have proven that, under some appropriate assumptions on the transition probability matrix P, the solution space S of the difference equation may be partitioned into two subspaces S=S1⊕S2, where the stationary measure of P is an element of S1, and all solutions in S1 are asymptotically dominated by the solutions corresponding to S2. In this paper, we discuss the analogous problem of computing hitting probabilities of Markov chains, which is affected by the same numerical phenomenon. In addition, we have to fulfill a somewhat complicated side condition which essentially differs from those conditions one is usually confronted with when solving initial and boundary value problems. To extract the desired solution, an efficient and numerically stable generalized-continued-fraction-based algorithm is developed.
As game theory thrives in networked interactions, we usually neglect the cost of information exchange between involved individuals. Individuals may decide (or refuse) to follow the state of their neighbors, which depends on the cost of the interactions. The payoff of a node’s behavior is associated with the state difference between the node and its neighbors. Here, based on Kuramoto model, we investigate the collective behavior of different individuals in the game theory and the synchronization byproduct that is induced by the cooperation of connected nodes. Specially, we investigate the influence of network structure on the coevolutionary progress of cooperation and synchronization. We find that the networks with the higher average degree are more likely to reach synchronization in real networks. Strong synchronization is a sufficient, but not necessary condition to guarantee the cooperation. Besides, we show that synchronization is largely influenced by the average degree in both Erdös–Rényi (ER) and Barabási–Albert (BA) networks, which is also illustrated by theoretical analysis.
Based on the deterministic description of batch culture expressed in form of switched ordinary differential equations, we introduce a switched stochastic counterpart system with initial state difference together with uncertain switching instants and system parameters to model the process of glycerol biodissimilation to 1,3-propanediol (1,3-PD) induced byKlebsiella pneumoniae(K. pneumoniae). Important properties of the stochastic system are discussed. Our aim is to obtain the unified switched instants and system parameters under the condition of different initial states. To do this, we will formulate a system identification problem in which these uncertain switched instants and system parameters are regarded as decision variables to be chosen such that the relative error between experimental data and computational results is minimized. Such problem governed by the stochastic system is subject to continuous state inequality constraints and box constraints. By performing a time-scaling transformation as well as introducing the constraint transcription and local smoothing approximation techniques, we convert such problem into a sequence of approximation subproblems. Considering both the difficulty of finding analytical solutions and the complex nature of these subproblems, we develop a parallelized differential evolution (DE) algorithm to solve these approximation subproblems. From an extensive simulation, we show that the obtained optimal switched instants and system parameters are satisfactory with initial state difference.
The concepts called STED/RESOLFT superresolve features by a light-driven transfer of closely packed molecules between two different states, typically a nonfluorescent “off” state and a fluorescent “on” state at well-defined coordinates on subdiffraction scales. For this, the applied light intensity must be sufficient to guarantee the state difference for molecules spaced at the resolution sought. Relatively high intensities have therefore been applied throughout the imaging to obtain the highest resolutions. At regions where features are far enough apart that molecules could be separated with lower intensity, the excess intensity just adds to photobleaching. Here, we introduce DyMIN (standing for Dynamic Intensity Minimum) scanning, generalizing and expanding on earlier concepts of RESCue and MINFIELD to reduce sample exposure. The principle of DyMIN is that it only uses as much on/off-switching light as needed to image at the desired resolution. Fluorescence can be recorded at those positions where fluorophores are found within a subresolution neighborhood. By tuning the intensity (and thus resolution) during the acquisition of each pixel/voxel, we match the size of this neighborhood to the structures being imaged. DyMIN is shown to lower the dose of STED light on the scanned region up to ∼20-fold under common biological imaging conditions, and >100-fold for sparser 2D and 3D samples. The bleaching reduction can be converted into accordingly brighter images at <30-nm resolution.