To address the problem of steel strip surface defect detection, a feature fusion-based preprocessing strategy is proposed based on machine vision technology. This strategy can increase the feature dimension of the image, highlight the pixel features of the image, and improve the recognition accuracy of the convolutional neural network. This method is based on commonly used image feature extraction operators (e.g., Sobel, Laplace, Prewitt, Robert, and local binary pattern) to process the defect image data, extract the edges and texture features of the defect image, and fuse the grayscale image processed by the feature operator with the original grayscale image by using three channels. To consider also computational efficiency and reduce the number of calculation parameters, the three channels are converted into a single channel according to a certain weight ratio. With this strategy, the steel plate surface defect database of NEU is processed, and fusion schemes with different operator combinations and different weight ratios for conversion to the single channel are explored. The test results show that, under the same network framework and with the same computational cost, the fusion scheme of SobelimageLaplace and the single-channel conversion weight ratio of 0.20.60.2 can improve the recognition rate of a previously unprocessed image by 3% and can achieve a final accuracy rate of 99.77%, thereby demonstrating the effectiveness of the proposed strategy.We study a variant of the Cucker-Smale system with distributed reaction delays. Using backward-forward and stability estimates on the quadratic velocity fluctuations we derive sufficient conditions for asymptotic flocking of the solutions. The conditions are formulated in terms of moments of the delay distribution and they guarantee exponential decay of velocity fluctuations towards zero for large times. We demonstrate the applicability of our theory to particular delay distributions - exponential, uniform and linear. For the exponential distribution, the flocking condition can be resolved analytically, leading to an explicit formula. For the other two distributions, the satisfiability of the assumptions is investigated numerically.The Industrial Internet of Things (IIoT) plays an important role in the development of smart factories. However, the existing IIoT systems are prone to suffering from single points of failure and unable to provide stable service. Meanwhile, with the increase of node scale and network quantity, the maintenance cost presents to be higher. Such a disadvantage can be effectively compensated by the features such as security, privacy, non-tamperability and distributed deployment supported by the blockchain. In this paper, first, an intelligent manufacturing security model based on blockchain was proposed. Due to the high power consumption and low throughput of the traditional blockchain, IoT devices with limited power consumption can not work independently. Therefore, in this paper, a new Merkle Patricia tree (MPT) was adopted to extend the blockchain structure and provide fast query of node status. Second, since the MPT does not support concurrent operation and the data operation performance deteriorates with high data volume, a lock-free concurrent and cache-based Merkle Patricia tree was proposed (CMPT) to support lock-free concurrent data operation, which can improve the data operation efficiency in multi-core system. The experimental results indicate that, compared with the original MPT, the CMPT proposed in this paper effectively reduced the time complexity of data insertion and data query and improved the speed of block construction and data query.We explore the spread of the Coronavirus disease 2019 (COVID-19) in Lebanon by adopting two different approaches the STEIR model, which is a modified SEIR model accounting for the effect of travel, and a repeated iterations model. We fit available daily data since the first diagnosed case until the end of June 2020 and we forecast possible scenarios of contagion associated with different levels of social distancing measures and travel inflows. We determine the initial reproductive transmission rate in Lebanon and all subsequent dynamics. In the repeated iterations (RI) model we iterate the available data of currently infected people to forecast future infections under several possible scenarios of contagion. In both models, our results suggest that tougher mitigation measures would slow down the spread of the disease. On the other hand, the current relaxation of measures and partial resumption of international flights, as the STEIR reveals, would trigger a second outbreak of infections, with severity depending on the extent of relaxation. We recommend strong institutional and public commitment to mitigation measures to avoid uncontrolled spread.As an extension of intuitionistic fuzzy numbers, intuitionistic trapezoidal fuzzy numbers (ITrFNs) are useful in expressing complex fuzzy information with an 'interval value'. This study focuses on multi-attribute decision-making (MADM) problems with unknown attribute weights under an ITrFN environment. We initially present an entropy measure for ITrFNs by using the relative closeness of technique for order preference by similarity to an ideal solution. From the view of the reliability and certainty of decision data, we present an approach to determine the attribute weights.  https://www.selleckchem.com/products/OSI027.html  Subsequently, a new method to solve intuitionistic trapezoidal fuzzy MADM problems with unknown attribute weight information is proposed. A numerical example is provided to verify the practicality and effectiveness of the proposed method.In this paper, we propose an interval-valued intuitionistic fuzzy Multi-Attribute Decision Making (MADM) method based on improved TOPSIS and Grey Correlation Analysis (GCA), in which the attribute values are interval-valued intuitionistic fuzzy numbers. So that we can deal with imprecise information in fuzzy and rough form in MADM problems by using interval-valued intuitionistic fuzzy numbers Firstly, the concept of interval intuitionistic fuzzy entropy is introduced to calculate the entropy weight of attributes. And the combined weight is calculated by combining the entropy weight with the subjective weight. Secondly, the reverse order phenomenon in the traditional TOPSIS method is eliminated by constructing absolute Positive Ideal Solution (PIS) and absolute Negative Ideal Solution (NIS) in the form of interval-valued intuitionistic fuzzy numbers. Furthermore, the improved TOPSIS method and grey correlation analysis method are combined to describe the degree of closeness for each alternative from the ideal solution, and then the ranking and selection of each alternative are made accordingly to this degree.