Meltonbradley5094

We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research.Remote sensing image classification exploiting multiple sensors is a very challenging problem The traditional methods based on the medium- or low-resolution remote sensing images always provide low accuracy and poor automation level because the potential of multi-source remote sensing data are not fully utilized and the low-level features are not effectively organized. The recent method based on deep learning can efficiently improve the classification accuracy, but as the depth of deep neural network increases, the network is prone to be overfitting. In order to address these problems, a novel Two-channel Densely Connected Convolutional Networks (TDCC) is proposed to automatically classify the ground surfaces based on deep learning and multi-source remote sensing data. The main contributions of this paper includes the following aspects First, the multi-source remote sensing data consisting of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) are pre-processed and re-sampled, and then the hyperspectral data and LiDAR data are input into the feature extraction channel, respectively. Secondly, two-channel densely connected convolutional networks for feature extraction were proposed to automatically extract the spatial-spectral feature of HSI and LiDAR. Thirdly, a feature fusion network is designed to fuse the hyperspectral image features and LiDAR features. The fused features were classified and the output result is the category of the corresponding pixel. The experiments were conducted on popular dataset, the results demonstrate that the competitive performance of the TDCC with respect to classification performance compared with other state-of-the-art classification methods in terms of the OA, AA and Kappa, and it is more suitable for the classification of complex ground surfaces.The present work is devoted to the global stability analysis for a class of functional differential equations with distributed delay and non-monotone bistable nonlinearity. First, we characterize some subsets of attraction basins of equilibria. Next, by Lyapunov functional and fluctuation method, we obtain a series of criteria for the global stability of equilibria. Finally, we illustrate our results by applying them to a problem with Allee effect.To improve sustainable development, increasingly more attention has been paid to the evaluation of the resilience to waterlogging disasters. This paper proposed a projection pursuit model (PPM) improved by quantum particle swarm optimization (QPSO) for the evaluation of the resilience of subway station projects to waterlogging disasters. In view of the lack of research results related to the evaluation of the resilience of subway station projects to waterlogging disasters, 16 secondary indicators that affected the ability of subway station projects to recover from waterlogging disasters were identified from defense, recovery, and adaptability, for the first time. A PPM improved by QPSO was then proposed to effectively deal with the high-dimensional data about the resilience of subway station projects to waterlogging disasters. The QPSO was used to solve the best projection vector of the PPM, and interpolation algorithm was used to construct the mathematical model of evaluation. Finally, four station projects of Chengdu Metro Line 11 in China were selected for a case study analysis. The case study revealed that, among the secondary indicators, the emergency plan of construction order, the exercise frequency of emergency plans, and relief supplies had the greatest weights. The recovery was found to be the most important in the primary indicators. The values of the resilience of Lushan Avenue Station, Miaoeryan Station, Shenyang Road Station, and Tianfu CBD North Station to waterlogging disasters were found to be 2, 1.6571, 2.8318, and 3 respectively. This resilience ranking was consistent with the actual disaster situation in the flood season of 2019. In addition, the case study results showed that QPSO had the advantages of fewer parameter settings and a faster convergence speed as compared with PSO and the genetic algorithm.Discussion regarding hybrid manufacturing has dominated research in recent years. By synergistically integrating additive and subtractive manufacturing within a single workstation, the relative benefits of each manufacturing strategy are leveraged. The ability to add, remove feature flexibly enables remanufacturing end-of-life components into a "new" part with new features and functionalities. However, in the remanufacturing context, the process planning for hybrid additive-subtractive manufacturing is still an unsolved research topic. In general, a hybrid remanufacturing process is signified by an alternating sequence of additive and subtractive operations that alternatively add and remove materials on a used part, which results in a non-unique process planning. For determining an optimal sequence for hybrid remanufacturing, a quantitative evolution mechanism is demanded. Moreover, the constraints in process planning are required to be considered. For example, the collision avoidance between the workpiece and the material-dispensing nozzle is one of the most critical limitations that affect the alternating sequence. read more To fill the gap, automated feature extraction and cost-driven process planning method for hybrid remanufacturing are proposed in this paper. The feature extraction, developed under the level set framework, can extract optimal and collision-free additive-subtractive features. Then, the hybrid process planning task is formulated into an integer programming model with cost estimations. A case study is conducted, and the results confirm the correctness and effectiveness of the proposed method.Different epidemic models with one or two characteristics of multi-group, age structure and spatial diffusion have been proposed, but few models take all three into consideration. In this paper, a novel multi-group SEIR epidemic model with both age structure and spatial diffusion is constructed for the first time ever to study the transmission dynamics of infectious diseases. We first analytically study the positivity, boundedness, existence and uniqueness of solution and the existence of compact global attractor of the associated solution semiflow. Based on some assumptions for parameters, we then show that the disease-free steady state is globally asymptotically stable by utilizing appropriate Lyapunov functionals and the LaSalle's invariance principle. By means of Perron-Frobenius theorem and graph-theoretical results, the existence and global stability of endemic steady state are ensured under appropriate conditions. Finally, feasibility of main theoretical results is showed with the aid of numerical examples for model with two groups which is important from the viewpoint of applications.Mathematical modeling for cancerous disease has attracted increasing attention from the researchers around the world. Being an effective tool, it helps to describe the processes that happen to the tumour as the diverse treatment scenarios. In this paper, a density-dependent reaction-diffusion equation is applied to the most aggressive type of brain cancer, Glioblastoma multiforme. The model contains the terms responsible for the growth, migration and proliferation of the malignant tumour. The traveling wave solution used is justified by stability analysis. Numerical simulation of the model is provided and the results are compared with the experimental data obtained from the reference papers.Dynamic computer forensics is a popular area in computer forensics that combines network intrusion technology with computer forensics technology. A novel dynamic computer forensics model is proposed based on an artificial immune system. Simulating the artificial immune mechanism, the definitions of self, non-self, and immunocyte in the network transactions are given. Then, detailed evolution processes for immature detectors, mature detectors, and memory detectors are given. Real-time network risk evaluation equations are constructed, which can compute the risk of each type of network attack. Finally, computer forensics is accomplished according to the real-time network risk. The immune cells dynamically capture the real-time computer system status of the invading antigen, including CPU utilization, memory utilization, network bandwidth utilization status, etc. Theoretical analysis and comparative experimental results demonstrate that the proposed model improves the real-time efficiency and performance with low technical requirements for technicians compared with existing models.A mathematical model is designed and used to study the transmission dynamics and control of COVID-19 in Nigeria. The model, which was rigorously analysed and parametrized using COVID-19 data published by the Nigeria Centre for Disease Control (NCDC), was used to assess the community-wide impact of various control and mitigation strategies in some jurisdictions within Nigeria (notably the states of Kano and Lagos, and the Federal Capital Territory, Abuja). Numerical simulations of the model showed that COVID-19 can be effectively controlled in Nigeria using moderate levels of social-distancing strategy in the jurisdictions and in the entire nation. Although the use of face masks in public can significantly reduce COVID-19 in Nigeria, its use, as a sole intervention strategy, may fail to lead to a substantial reduction in disease burden. Such substantial reduction is feasible in the jurisdictions (and the entire Nigerian nation) if the public face mask use strategy is complemented with a social-distancing strategy. The community lockdown measures implemented in Nigeria on March 30, 2020 need to be maintained for at least three to four months to lead to the effective containment of COVID-19 outbreaks in the country. Relaxing, or fully lifting, the lockdown measures sooner, in an effort to re-open the economy or the country, may trigger a deadly second wave of the pandemic.Obtaining coordinate data for geometric morphometric studies often involves the sampling of dry skeletal specimens from museum collections. But many specimens exhibit damage and/or pathologic conditions. Such specimens can be considered inadequate for the analyses of shape and are excluded from study. However, the influences that damaged specimens may have on the assessment of normal shape variation have only been explored in two-dimensional coordinate data and no studies have addressed the inclusion of pathological specimens to date. We collected three-dimensional coordinate data from the cranium and mandible of 100 crab-eating macaques (Macaca fascicularis). Tests typically employed to analyze shape variation were performed on five datasets that included specimens with varying degrees of damage/pathology. We hypothesized that the inclusion of these specimens into larger datasets would strengthen statistical support for dominant biological predictors of shape, such as sex and size. However, we also anticipated that the analysis of only the most questionable specimens may confound statistical outputs.