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The most economical, environmental, and friendly method for recycling gangue is filling mining with cemented waste rock backfill (CWRB), which solves the environmental problems caused by gangue discharge and reduces the mining damages. Evaluating the mesoscopic structure of CWRB is of great significance for maximizing the utilization of gangue recycling and improving the economic benefits of filling mining. This paper constructed the particle flow model of cemented waste rock backfill (CWRB) considering particle size distribution (PSD) of aggregates and hydration of cementing material to investigate the effect of the PSD of aggregates on its mesoscopic structural evolution. The strain energy, crack, force chain, and particle fragment of CWRB during the whole loading were discussed. The binary processing and calculation on the crack image were performed to analyze the fractal dimension of crack distribution by compiling program. The influencing mechanism of the PSD of aggregates on the strength of CWRB is revealed from the mesoscopic levels of crack evolution, force chain structure, and particle fragment. The results show that the strain energy increases firstly and then decreases with the PSD fractal dimension, while the crack number decreases firstly and then increases with that. The cracks with less number and more uniform distribution present the smaller fractal dimension, CWRB with a low fractal dimension of crack distribution has higher strength, the fractal dimension of crack distribution exhibits a correlation with the PSD of aggregates. CWRBs with the PSD fractal dimensions of 2.4–2.6 have the largest strain energy and the smallest crack number, performing the superior structural evolution during loading. This study presents the huge potential of optimizing PSD in CWRB application from a new perspective, it is of great significance for strengthening the internal structure of CWRB and reducing engineering cost. [ABSTRACT FROM AUTHOR]

In order to improve delivery network efficiency and to solve consumer satisfaction problems, parcel locker location optimisation scheme is proposed based on the delivery demand under the e-commerce environment. In this paper, a bilevel programming (BLP) model is established to identify the optimal location for parcel lockers by considering benefits of consumers and logistics planning departments. The upper-level model is to determine the optimal location by minimising the planners' cost, and the lower one gives an equilibrium demand distribution by minimising the consumers' pick-up cost. On the special form of constraints, a bilevel genetic algorithm is proposed based on GIS data and a genetic algorithm. Finally, a numerical example is employed to demonstrate the application of the method, which indicates that the model can solve the problem of parcel locker location. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new similarity measure between generalized trapezoidal fuzzy numbers and several synthesized similarity measures to solve fault diagnosis problem by merging our proposed measures with Dempster-Shafer evidence theory. Firstly, combining the exponential distance with numerical indexes of generalized trapezoidal fuzzy number, such as the span, the center width and the height, etc, we propose a new similarity measure between generalized trapezoidal fuzzy numbers. Secondly, we introduce an evaluation index, distinguish ability, to evaluate the performance of different similarity measures. The experimental results show that our proposed similarity measure can overcome the drawbacks of the existing similarity measures. Thirdly, to solve fault diagnosis problems, we propose three formulas to integrate several single similarity measures to a synthesized one. Finally, based on Dempster-Shafer evidence theory, we transform each similarity measure between fault model and test model, the synthesized similarity measures to their corresponding basic probability assignments to deal with fault diagnosis problem, the results show that our proposed similarity measure is more effective than some other existing similarity measures. [ABSTRACT FROM AUTHOR]

As modern engineering design usually involves dependence of one discipline on another, multidisciplinary system analysis (MDSA) plays an important role in the multidisciplinary simulation and design optimization on coupled systems. The paper proposes an MDSA method based on minimal feedback variables (MDSA_MF) to enhance the solving efficiency. There are two phases in the method. In phase 1, design structural matrix (DSM) is introduced to represent a coupled system, and each off-diagonal element is denoted by a coupling variable set; then an optimal sequence model is built to obtain a reordered DSM with minimal number of feedback variables. In phase 2, the feedback in the reordered DSM is broken, so that the coupled system is transformed into one directed acyclic graph; then, regarding the inputs depending on the broken feedback as independent variables, a least-squares problem is constructed to minimize the residuals of these independents and corresponding outputs to zero, which means the multidisciplinary consistence is achieved. Besides, the MDSA_MF method is implemented in a multidisciplinary platform called FlowComputer. Several examples of coupled systems are modeled and solved in the platform using several MDSA methods. The results demonstrate that the proposed method could enhance the solving efficiency of coupled systems. [ABSTRACT FROM AUTHOR]

• We make the first attempt to incorporate the deep denoiser prior for infrared small target detection. Through the plug-and-play framework, the proposed method can better preserve fine image details and remove noise. • Considering that different singular values have different importance, we use the weighted sum of weighted tensor nuclear norm for more accurate background estimation. • An efficient algorithm is developed for solving the proposed problem based on alternating direction multiplier method. Many existing low-rank methods have achieved good detection performance in uniform scenes, but they suffer from a high false alarm rate in complex noisy scenes. Therefore, it is important to improve the detection performance of low-rank models in noisy scenes. In this paper, we first formulate an implicit regularizer by plugging a denoising neural network (termed as deep denoiser), which can learn deep image priors from a large number of natural images. Then, we use the weighted sum of weighted tensor nuclear norm for more accurate background estimation. Finally, alternating direction multiplier method is used to solve the model under the plug-and-play framework. By integrating low-rank prior with deep denoiser prior, our model achieves higher accuracy. Experiments on different scenes demonstrate that our method achieves an improved performance in terms of visual effects and quantitative metrics. Specially, the overall accuracy of AUC value (AU C OA) achieved by the proposed method on Sequences 1-6 are 1.24 % , 1.16 % , 0.63 % , 1.9 % , 0.82 % , 2.06 % higher than those achieved by the second top performing methods, respectively. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal trends of temperature in the context of global warming is significant for public health. Although many studies have examined changes in temperature and the impacts on human health over the past few decades in many regions, they have often been carried out in data-rich regions and have rarely considered acclimatization explicitly. The most frequent temperature (MFT) indicator provides us with the ability to solve this problem. MFT is defined as the longest period of temperature throughout the year to which a human is exposed and therefore acclimates. In this study, we propose a new method to estimate the number of heat exposure days from the perspective of temperature distribution and MFT, based on the daily mean temperature readings of 2142 weather stations in eight major climate zones in China over the past 20 years. This method can be used to calculate the number of heat exposure days in terms of heat-related mortality risk without the need for mortality data. We estimated the distribution and changes of annual mean temperature (AMT), minimum mortality temperature (MMT), and the number of heat exposure days in different climate zones in China. The AMT, MMT, and number of heat exposure days vary considerably across China. They all tend to decrease gradually from low to high latitudes. Heat exposure days are closely related to the risk of heat-related mortality. In addition, we utilized multiple linear regression (MLR) to analyze the association between the risk of heat-related mortality and the city and its climatic characteristics. Results showed that the number of heat exposure days, GDP per capita, urban population ratio, proportion of elderly population, and climate zone were found to modify the estimate on heat effect, with an R2 of 0.71. These findings will be helpful for the creation of public policies protecting against high-temperature-induced mortalities. [ABSTRACT FROM AUTHOR]

The convolutional neural network is the most widely used deep neural network. However, it still has some disadvantages. First, the back-propagation method is usually used in the training of convolutional neural networks, but it has several inherent defects, such as the vanishing gradient problem and exploding gradient problem. Moreover, the training of a convolutional neural network often needs substantial computational resources and time. To solve the above problems, based on pseudoinverse learning (PIL), kernel pseudoinverse learning (KPIL) is proposed, showing improved performance. The number of hidden layer neurons of KPIL is equal to the number of input data and does not need to be set. KPIL uses the kernel method to calculate the output of hidden layer, avoiding the uncertainty of random input weights. Based on KPIL, a deep convolutional kernel pseudoinverse learner with a multi-filter design is proposed, named kernel pseudoinverse learning convolutional neural network (KPIL-CNN), which uses multiple fixed convolutional kernels: kernel pseudoinverse learning filters (KPIL filter), Gabor filters, and random filters. The features obtained by this multi-filter approach are combined into feature maps by learning weights. The experimental results show that the proposed KPIL-CNN performs better than other network models with the same scale in the task of natural image classification. The training of KPIL-CNN is efficient and effective. Furthermore, KPIL-CNN outperforms the existing methods in the classification of the ISIC 2017 skin lesion dataset. [ABSTRACT FROM AUTHOR]