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Following the Belt and Road, the Air Silk Road has also been proposed. The coordinated development of multiple transportation modes, including air, land, and water, will create a strong transportation force in node cities. However, the current insufficient supply of cargo in various regions and the lack of integration among different transportation modes result in low transportation efficiency, which in turn affects the further advancement of the Belt and Road. To investigate these issues and attempt to find a solution, we selected 44 candidate cities from the prefecture-level cities in China as nodes based on relevant government policies, and constructed an integrated transportation network. For each node city, we first calculated the values of six classical indicators and then used the CRITIC to assign weights to each indicator. Subsequently, we employed the TOPSIS method combined with Grey Relational Analysis (GRA) to compute the comprehensive score for each node city. Based on the spatial layout and government policies under the BRI, eight cities, including Wuhan, Chongqing, Tianjin, Shanghai, Guangzhou, Lianyungang, Hefei, and Dalian, were finally recommended as the consolidation centers of the integrated transportation network. It is hoped that the results of this analysis can provide some insights for the government to outline and build the consolidation centers of the integrated transportation network composed of railway, air, highway, and water routes, which in turn can offer insights for elevating the Belt and Road Initiative (BRI) to a new level.

Energy accumulation and release are the essence of rock deformation and damage. Thus, investigating the damage mode and energy evolution law of coal specimens containing holes to elucidate the mechanism of borehole pressure relief is important. To examine the effect of the number of drilled holes on the energy and acoustic emission (AE) evolution characteristics of brittle coal, uniaxial compression AE tests were performed using four types of drilled coal specimen. The results revealed that the peak strengths and elastic moduli of the specimens exhibited nonlinear and linear decreasing trends, respectively, with the increase in hole number; however, the peak strain initially decreased and subsequently increased. The failure mode changed from shear splitting to penetration crack damage between rock bridges, accompanied by a hole collapse phenomenon. As the hole number increased, the total and elastic energy exhibited a decreasing trend, whereas the ratios of the elastic and dissipative energy to total energy exhibited a decreasing and an increasing trend, respectively. The AEs of different types of drilled coal specimens had similar evolutionary processes, but their activity exhibited an overall increasing trend with an increase in hole number. The AE fractal characteristics of a drilled coal specimen were calculated based on the G-P algorithm. The AE signal of the specimen exhibited fractal characteristics, and the sudden drop in fractal dimension was considered the precursor of specimen failure. The findings provide a reference for understanding the energy evolution mechanism and advance warning for coal pressure relief.

To mitigate the ever worsening “Power wall” and “Memory wall” problems, multi-core architectures with multi-level cache hierarchies have been widely accepted in modern processors. However, the complexity of the architectures makes modeling of shared caches extremely complex. In this article, we propose a data-sharing aware analytical model for estimating the miss rates of the downstream shared cache under multi-core scenarios. To avoid time-consuming full simulations of the cache architecture required by conventional approaches, the proposed model can also be integrated with our refined upstream cache analytical model, which also evaluates coherence misses with similar accuracies of state-of-the-art approach with only one tenth time overhead. We validate our analytical model against gem5 simulation results under 13 applications from PARSEC 2.1 benchmark suites. Compared to the results from gem5 simulations under 8 hardware configurations including dual-core and quad-core architectures, the average absolute error of the predicted shared L2 cache miss rates is less than 2% for all configurations. After integrated with the refined upstream model with coherence misses, the overall average absolute error in 4 hardware configurations is degraded to 4.82% due to the error accumulations. As an application case of the integrated model, we also evaluate the miss rates of 57 different multi-core and multi-level cache configurations.

The contradiction between the dramatic increase in the aggregate number of automobiles and the short supply of parking spaces leads to parking difficulties. Sharing mode helps improve the efficiency of existing parking spaces, increase resource utilization, and alleviate the difficulty of parking. This paper focuses on the matching mechanism in the shared parking slots problem, which involves three agents: shared parking suppliers, shared parking demanders, and shared parking platform. We propose a prospect theory-based two-sided satisfied and stable matching model (PT-TSSM) with two objectives to maximize the satisfaction degree of both shared parking demanders and shared parking suppliers. Numerical experiments are illustrated to demonstrate the efficiency of the proposed model. Moreover, the PT-TSSM model is compared with the other two shared parking mechanisms. The proposed model considers the satisfaction degrees of both shared parking demanders and suppliers, while first book first serve (FBFS) cares only one side of the participants. And compared with deferred acceptance (DA), our model not only takes two-sided stable matching into account but also considers the satisfaction degree of all the demand and supply participants, which obtain a two-sided satisfied and stable matching scheme.

This paper proposes an estimation of particle swarm distribution algorithm (EPSDA) to solve the nonlinear bilevel programming problem (NBLP) by embedding the estimation distribution algorithm (EDA) into the particle swarm optimization (PSO). One Gaussian function is selected to construct the probability distribution for the superior candidate from the present population before executing the velocity and position update rule at each iteration in PSO. Thus, some new individuals viewed as an offspring population are generated from the probability distribution to replace some inferior particles in the current population for making up a new population. Therefore, this proposed algorithm combines PSO (the local search method) and EDA (the global search method) through updating the population to enhance the efficiency of solving NBLP. In experiments, we select four representative examples for linear, quadratic, nonlinear, high-dimensional nonlinear cases to carry out sensitivity analysis on parameters of the proposed algorithm. The results reveal that linearly decreasing inertia weight and adaptive acceleration coefficients are better than constant parameters. Furthermore, the multivariate Gaussian distribution can achieve better performance compared with the normal distribution. These four examples are also used to compare the performance of EPSDA with ones of separate PSO and EDA by setting constant parameters. The experiments show that EPSDA is better than separate PSO and EDA from both the quality of solution and the computational efficiency. The results on four high-dimensional non-convex nonlinear examples demonstrate feasibility and efficiency of EPSDA to solve the high-dimensional nonlinear bilevel programming from the iteration number and computational time.

Because of the superiority in distinguishing the target from the background by learning mechanism, discriminative classifier attracts a great deal of attention in target detection and tracking systems. Generally, a discriminative classifier is learned from the initial image patch. However, the other algorithms, using only one image frame inhibit the performance of the classifier. A novel training strategy of cascade iteration, which is based on the multiple image frames, is used to initialize the discriminative classifier for the purpose of weakening the initialization limitation and improving the algorithms' adaptability and robustness. Furthermore, to cope with the changes in natural images, online learning is usually adopted to enhance the performance of classifiers, but it easily leads to tracking failures, especially in the case of full occlusion. In order to attenuate the influence of full occlusion, a multi-classifiers parallel tracking algorithm is proposed by designing the multi-classifiers and inducing extra constraints. The experiments are performed on the visual tracking benchmark, and the proposed algorithm is more robust while the target is fully occluded. The simulation results show that the proposed algorithm exhibits better performance compared to the state-of-the-art trackers, especially with KCF, TLD, and STRUCK.

Existing extended target probability hypothesis density (ET-PHD) filters are insufficient in tracking weak extended targets. Hough transform-based track-before-detect methods are designed to detect the weak targets in a straight-line constant-velocity model. Therefore, this paper presents a novel method for detecting and tracking multiple maneuvering weak extended targets by a 3-dimensional Hough transform (3DHT) and multiple hypothesis tracking (MHT). The proposed method consists of two stages. In stage 1, the measurements in multiple scans are partitioned into overlapped time windows. The tracklets in each window can be detected by the 3DHT. In stage 2, the tracklets are associated to get the entire trajectories by the MHT. The tracklets of weak targets can be detected by the 3DHT in stage 1. Association in stage 2 is designed to detect maneuvering targets. Some false alarm tracklets could be built in stage 1. However, the false alarm tracklets are independent and unlikely to form a sequential trajectory in stage 2. Merely, the trajectories whose target likelihood ratio larger than a detection threshold can be confirmed as a target. Both the real data and the synthetic data are performed with the proposed approach and several existing algorithms. The result infers that the proposed approach is superior to the others with much less prior information that is necessary.

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

Continuous network design problem (CNDP) is searching for a transportation network configuration to minimize the sum of the total system travel time and the investment cost of link capacity expansions by considering that the travellers follow a traditional Wardrop user equilibrium (UE) to choose their routes. In this paper, the CNDP model can be formulated as mathematical programs with complementarity constraints (MPCC) by describing UE as a non-linear complementarity problem (NCP). To address the difficulty resulting from complementarity constraints in MPCC, they are substituted by the Fischer-Burmeister (FB) function, which can be smoothed by the introduction of the smoothing parameter. Therefore, the MPCC can be transformed into a well-behaved non-linear program (NLP) by replacing the complementarity constraints with a smooth equation. Consequently, the solver such as LINDOGLOBAL in GAMS can be used to solve the smooth approximate NLP to obtain the solution to MPCC for modelling CNDP. The numerical experiments on the example from the literature demonstrate that the proposed algorithm is feasible.

In this paper, we provide cooperative and non-cooperative interpretations of the Shapley–Solidarity value for cooperative games with coalition structure. Firstly, we present two new characterizations of this value based on intracoalitional quasi-balanced contributions property. Secondly, we study a potential function of the Shapley–Solidarity value. Finally, we propose a new bidding mechanism for the Solidarity value and then apply the result to the Shapley–Solidarity value.

An algorithm based on the human evolutionary model is proposed for solving nonlinear bilevel programing problems. In view of the hierarchical structure of this problem, the algorithm is designed through feeding back the optimal solution of the lower-level problem to the upper-level. Based on the quality of individuals at each iteration, this proposed algorithm can independently change the population size to achieve the balance between global and local searching ability during the progress of evolution, which can perform an exhaustive search in the whole landscape through creating an individual by using the tabu search method. Finally, we test four typical bilevel programing problems by using the proposed algorithm to verify its feasibility. The experimental results indicate the proposed algorithm can not only solve bilevel programing problems but also get the global optimal solution.

The minimum error entropy (MEE) criterion has been successfully used in fields such as parameter estimation, system identification and the supervised machine learning. There is in general no explicit expression for the optimal MEE estimate unless some constraints on the conditional distribution are imposed. A recent paper has proved that if the conditional density is conditionally symmetric and unimodal (CSUM), then the optimal MEE estimate (with Shannon entropy) equals the conditional median. In this study, we extend this result to the generalized MEE estimation where the optimality criterion is the Renyi entropy or equivalently, the α-order information potential (IP).

The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated.

The fall armyworm (FAW), Spodoptera frugiperda is a notorious agricultural pest with significant developmental stage dependent control challenges. Entomopathogenic fungi are promising biological control agents for FAW management, including when combined with other control measures. Here, we isolated a strain of Metarhizium rileyi (GZUMr46) and first tested its virulence and compatibility with Bacillus thuringiensis (Bt) and/or a chemical pesticide, emamectin benzoate, in laboratory bioassays. The laboratory bioassays using 108 spores/ml of M. rileyi showed ~70% mortality of FAW 2nd and 4th instars, 5 d post-topical inoculation. We then tested the efficacy of M. rileyi with Bt, sex pheromone, emamectin benzoate, and/or the parasitoid Telonomus remus against FAW in field assays. For the field applications, M. rileyi granules were produced by liquid-solid dual fermentation using different grains, with conidial production reaching 8.3 x 108 conidia/g substrate after 25 d of fermentation. In the field trials, application of the M. rileyi granules alone reduced FAW larval populations by > 60%, with a 40% reduction in plant damage compared to controls. When any of the other four control measures were used in combination with M. rileyi granules, 50-70% reductions in larval populations were observed, and undamaged plant levels improved from 16-37% to 48-79%. These results support a practical integrated pest management protocol by combining the use of M. rileyi granules with either sex pheromone, T. remus, emamectin benzoate, and/or Bt for the successful control of FAW. [ABSTRACT FROM AUTHOR]