Low Carbon Logistics Optimization for Multi-depot CVRP with Backhauls - Model and Solution

CVRP (Capacitated Vehicle Routing Problems) is the integrated optimization of VRP and Bin Packing Problem (BPP), which has far-reaching practical significance, because only by taking both loading and routing into consideration can we make sure the delivery route is the most economic and the items are completely and reasonably loaded into the vehicles. In this paper, the CVRP with backhauls from multiple depots is addressed from the low carbon perspective. The problem calls for the minimization of the carbon emissions of a fleet of vehicles needed for the delivery of the items demanded by the clients. The overall problem, denoted as 2L-MDCVRPB, is NP-hard and it is very difficult to get a good performance solution in practice. We propose a quantum-behaved particle swarm optimization (QPSO) and exploration heuristic local search algorithm (EHLSA) in order to solve this model. In addition, three groups of computational experiments based on well-known benchmark instances are carried out to test the efficiency and effectiveness of the proposed model and algorithm, thereby demonstrating that the proposed method takes a short computing time to generate high quality solutions. For some instances, our algorithm can obtain new better solutions

Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world

Profilin-1 regulates DNA replication forks in a context-dependent fashion by interacting with SNF2H and BOD1L

DNA replication forks are tightly controlled by a large protein network consisting of well-known core regulators and many accessory factors which remain functionally undefined. In this study, we report previously unknown nuclear functions of the actin-binding factor profilin-1 (PFN1) in DNA replication, which occur in a context-dependent fashion and require its binding to poly-L-proline (PLP)-containing proteins instead of actin. In unperturbed cells, PFN1 increases DNA replication initiation and accelerates fork progression by binding and stimulating the PLP-containing nucleosome remodeler SNF2H. Under replication stress, PFN1/SNF2H increases fork stalling and functionally collaborates with fork reversal enzymes to enable the over-resection of unprotected forks. In addition, PFN1 binds and functionally attenuates the PLP-containing fork protector BODL1 to increase the resection of a subset of stressed forks. Accordingly, raising nuclear PFN1 level decreases genome stability and cell survival during replication stress. Thus, PFN1 is a multi-functional regulator of DNA replication with exploitable anticancer potential

Microstructure and mechanical properties of wire and arc additive manufactured thin wall with low-temperature transformation

Low-temperature transformation (LTT) welding wire was initially developed to mitigate residual stress in the weld. It could also be used for internal stress optimization in Wire and Arc Additive Manufacturing (WAAM) process. In this study, a 26 layers LTT wall sample fabricated by using the WAAM technique was investigated. The microstructure of the LTT deposited wall includes elongated cellular martensite and reticular residual austenite. With the accumulation of deposition height, the prior austenite grain size increases, and the volume fraction of residual austenite and the density of dislocations in martensite decreases. According to the model of martensite transformation kinetics, the original austenite grain size is the main reason that affects the austenite fraction. In addition, the presence of a thermal cycle leads to the refinement of the martensitic microstructure and the increase in the boundary density, as well as the elimination of the sub-stable austenitic phase resulting in higher tensile properties in the middle samples than in the top ones. From the current work, it is clear that the unique thermal cycle treatment of WAAM is beneficial in improving the performance of LTT materials.</p

Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features

Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To address this problem, we propose Expressive-VC, a novel end-to-end voice conversion framework that leverages advantages from both neural bottleneck feature (BNF) approach and information perturbation approach. Specifically, we use a BNF encoder and a Perturbed-Wav encoder to form a content extractor to learn linguistic and para-linguistic features respectively, where BNFs come from a robust pre-trained ASR model and the perturbed wave becomes speaker-irrelevant after signal perturbation. We further fuse the linguistic and para-linguistic features through an attention mechanism, where speaker-dependent prosody features are adopted as the attention query, which result from a prosody encoder with target speaker embedding and normalized pitch and energy of source speech as input. Finally the decoder consumes the integrated features and the speaker-dependent prosody feature to generate the converted speech. Experiments demonstrate that Expressive-VC is superior to several state-of-the-art systems, achieving both high expressiveness captured from the source speech and high speaker similarity with the target speaker; meanwhile intelligibility is well maintained

A novel image fusion algorithm based on bandelet transform

A novel image fusion algorithm based on bandelet transform is proposed. Bandelet transform can take advantage of the geometrical regularity of image structure and represent sharp image transitions such as edges efficiently in image fusion. For reconstructing the fused image, the maximum rule is used to select source images’ geometric flow and bandelet coefficients. Experimental results indicate that the bandelet-based fusion algorithm represents the edge and detailed information well and outperforms the wavelet-based and Laplacian pyramid-based fusion algorithms, especially when the abundant texture and edges are contained in the source images.Navigation Science Foundation (No. 05F07001) and the National Natural Science Foundation of China (No. 60472081)

Cancer-associated exportin-6 upregulation inhibits the transcriptionally repressive and anticancer effects of nuclear profilin-1

Aberrant expression of nuclear transporters and deregulated subcellular localization of their cargo proteins are emerging as drivers and therapeutic targets of cancer. Here, we present evidence that the nuclear exporter exportin-6 and its cargo profilin-1 constitute a functionally important and frequently deregulated axis in cancer. Exportin-6 upregulation occurs in numerous cancer types and is associated with poor patient survival. Reducing exportin-6 level in breast cancer cells triggers antitumor effects by accumulating nuclear profilin-1. Mechanistically, nuclear profilin-1 interacts with eleven-nineteen-leukemia protein (ENL) within the super elongation complex (SEC) and inhibits the ability of the SEC to drive transcription of numerous pro-cancer genes including MYC. XPO6 and MYC are positively correlated across diverse cancer types including breast cancer. Therapeutically, exportin-6 loss sensitizes breast cancer cells to the bromodomain and extra-terminal (BET) inhibitor JQ1. Thus, exportin-6 upregulation is a previously unrecognized cancer driver event by spatially inhibiting nuclear profilin-1 as a tumor suppressor

RAGE signalling in obesity and diabetes: focus on the adipose tissue macrophage

The advanced glycosylation end product receptor (RAGE) acts as a recognition receptor and interacts with different types of ligands that form and accumulate in the tissues and circulation, such as diabetes, inflammation, insulin resistance, and obesity. In these environments, RAGE is expressed on the surface of various cells associated with tissue disturbance. This review mainly summarizes the characteristics of RAGE-related signalling, with a particular emphasis on the role of RAGE in the development of obesity. We also briefly describe the phenotypes and characteristics of macrophages and focus on the role of adipose tissue macrophages (ATMs) and the regulatory mechanisms in obesity, diabetes, and other related metabolic diseases. Besides, we will also elaborate on the prospect of new strategies for treating diabetes and obesity-related metabolic diseases by inhibiting RAGE signalling and regulating ATMs recruitment and polarization

NSCT-SF-PCNN-ImageFusion-Toolbox

Matlab toolbox,论文的代码， 《非降采样Contourlet域内空间频率激励的PCNN图像融合算法》屈小波, 闫敬文, 肖弘智, 朱自谦Matlab Image Fusion Toolbox for Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain. You can download this toolbox at http://quxiaobo.8866.org/project/NSCT_SF_PCNN/NSCT-SF-PCNN-ImageFusion-Toolbox.zip This toolbox contains Matlab files that implement the image fusion algorithms described in paper Qu Xiao-Bo, YAN Jing-Wen, XIAO Hong-Zhi, ZHU Zi-Qian. Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain. Acta Automatica Sinica, Vol.34, No.12, pp: 1508-1514.Dec.2008. You can download the paper at the following links: http://www.aas.net.cn/qikan/manage/wenzhang/081208.pdf Or http://dspace.xmu.edu.cn/dspace/bitstream/2288/15459/1/2007-1182.pdf %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% If you use the code, please cite the paper as follows: % References: [1] Qu Xiao-Bo, YAN Jing-Wen, XIAO Hong-Zhi, ZHU Zi-Qian. Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain. Acta Automatica Sinica, Vol.34, No.12, pp: 1508-1514.Dec.2008. (Check http://quxiaobo.8866.org or http://quxiaobo.go.8866.org, http://quxiaobo.blog.edu.cn or http://naec.stu.edu.cn for these and other related papers.) %============================================================= QU Xiao-BO, Xiamen University in P.R.China, April 2009. % --------- % Author: Qu Xiao-Bo Aug.28,2008 % Postal address: % Ro0m 509, Scientific Research Building # 2,Haiyun Campus, Xiamen University,Xiamen,Fujian, P. R. China, 361005 % Website: http://quxiaobo.8866.org or http://quxiaobo.go.8866.org %============================================================= %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% You have to other toolbox required: 1.PCNN toolbox at http://quxiaobo.8866.org/project/PCNN/PCNN_toolbox.rar or http://dspace.xmu.edu.cn/dspace/bitstream/2288/15579/1/PCNN_toolbox.rar 2. FusionEvalution toolbox at http://quxiaobo.8866.org/project/FusionEvaluation/FusionEvaluation.rar or http://dspace.xmu.edu.cn/dspace/bitstream/2288/16979/1/FusionEvaluation.rar 3.NSCT toolbox at http://www.mathworks.com/matlabcentral/fileexchange/10049 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Acknowledgement 1. This code is copyright and is supplied free of charge for research purposes only. In return for supplying the code, all I ask is that, if you use the algorithms, you give due reference to this work in any papers that you write. If the applications are good, I would be very interested in collaboration. I accept no liability arising from use of these algorithms. 2. Thanks Arthur Cunha for sharing his nsct toolbox for our image fusion algorithmSupported by Navigation Science Foundation of P. R. China (05F07001) and National Natural Science Foundation of P. R. China (60472081