Optimization of urban distribution centres: a multi-stage dynamic location approach

Customer demand is dynamic and changeable; thus, optimality of the enterprise’s initial location cannot be guaranteed throughout the planning period in order to minimize site selection cost and maximize service reliability in the whole operation cycle. The enterprise planning period is divided into different stages, and a static location model is established at the fixed stage. In addition, a multi-stage dynamic location model is established by introducing the transfer cost between adjacent stages. To reduce the difficulty of solving the dynamic location model, first, we determined the optimal site selection and allocation strategy for each stage. Second, we designed a novel method that transforms the multi-stage dynamic location problem into the shortest path problem in graph theory. Finally, the Dijkstra algorithm was used to find the optimal dynamic location sequence so that its cumulative cost was the lowest in the whole planning period. Through a case study in China, we compare the costs of static and dynamic locations and the location cost under different objectives. The results show that this dynamic location generates more income (as it reduces cost) in comparison to the previous static location, and different location objectives have a substantial influence on location results. At the same time, the findings indicate that exploring the problem of enterprise location from a dynamic perspective could help reduce the operating cost and resources from a sustainable development perspective.Postprint (published version

Serum microRNA characterization identifies miR-885-5p as a potential marker for detecting liver pathologies

Circulating miRNAs (microRNAs) are emerging as promising biomarkers for several pathological conditions, and the aim of this study was to investigate the feasibility of using serum miRNAs as biomarkers for liver pathologies. Real-time qPCR (quantitative PCR)-based TaqMan MicroRNA arrays were first employed to profile miRNAs in serum pools from patients with HCC (hepatocellular carcinoma) or LC (liver cirrhosis) and from healthy controls. Five miRNAs (i.e. miR-885-5p, miR-574-3p, miR-224, miR-215 and miR-146a) that were up-regulated in the HCC and LC serum pools were selected and further quantified using real-time qPCR in patients with HCC, LC, CHB (chronic hepatitis B) or GC (gastric cancer) and in normal controls. The present study revealed that more than 110 miRNA species in the serum samples and wide distribution ranges of serum miRNAs were observed. The levels of miR-885-5p were significantly higher in sera from patients with HCC, LC and CHB than in healthy controls or GC patients. miR-885-5p yielded an AUC [the area under the ROC (receiver operating characteristic) curve] of 0.904 [95% CI (confidence interval), 0.837–0.951, P<0.0001) with 90.53% sensitivity and 79.17% specificity in discriminating liver pathologies from healthy controls, using a cut off value of 1.06 (normalized). No correlations between increased miR-885-5p and liver function parameters [AFP (α-fetoprotein), ALT (alanine aminotransferase), AST (aspartate aminotransferase) and GGT (γ-glutamyl transpeptidase)] were observed in patients with liver pathologies. In summary, miR-885-5p is significantly elevated in the sera of patients with liver pathologies, and our data suggest that serum miRNAs could serve as novel complementary biomarkers for the detection and assessment of liver pathologies

Structural Integrity Assessment of an NEPE Propellant Grain Considering the Tension–Compression Asymmetry in Its Mechanical Property

In order to investigate the effect of tension–compression asymmetry of propellant mechanical properties on the structural integrity of a Nitrate Ester Plasticized Polyether (NEPE) propellant grain, the unified constitutive equations under tension and compression were established, a new method for grain structural integrity assessment was proposed and the structural integrity of the NEPE propellant grain under the combined axial and transverse overloads was evaluated. The results indicate that the mechanical state of the NEPE propellant grain is in the coexistence of tension and compression under the combined axial and transverse overloads, and the tension and compression regions in the propellant grain is independent of the propellant constitutive behavior. The tension–compression asymmetry of the propellant mechanical properties has a certain impact on its mechanical response. The maximum equivalent stress and strain considering the tension–compression asymmetry falls between that obtained through the tension and compression constitutive model, and is the same as damage coefficient. The safety factor of the NEPE propellant grain considering the tension–compression asymmetry of its mechanical properties is larger than that non-considering, and the traditional method of structural integrity assessment is conservative

Kimura disease

Kimura disease is a rare, benign, chronic, immune-mediated inflammatory disorder. We report a 46-year-old man who presented with a cutaneous nodule behind his left ear. Surgical removal of the growth confirmed the histological diagnosis of KD. There was no recurrence found after 3 years follow-up

Optimal Transshipment Route Planning Method Based on Deep Learning for Multimodal Transport Scenarios

The optimal path problem is an important topic in the current geographic information system (GIS) and computer science fields. The Dijkstra algorithm is a commonly used method to find the shortest path, which is usually used to find the least cost path from a single source. Based on the analysis and research of the traditional Dijkstra algorithm, this paper points out the problems of the Dijkstra algorithm and optimizes it to improve its storage capacity and operation efficiency. Then, combined with the traffic elements, a new network-based optimal path planning method is established. However, the existing network is far from actual operation in terms of the expansion of the transportation network, the uncertainty of the transportation environment, and the differences in the transportation area. Therefore, this paper proposes an optimal transshipment path planning method based on deep learning, which is oriented to multimodal transportation scenarios. This paper mainly introduces the intelligent transportation system and intelligent navigation system, and then conducts in-depth research on optimal path planning. This paper also uses the deep neural network algorithm to optimize the calculation, and finally analyzes its use and application. Simulation experiments were also performed to analyze the relationship between energy consumption, emissions, speed, load cost, and other factors under the optimal path. The final experimental results show that within the range of the emission limit of [100,200], the emission is 50%, the emission is less than 100%, but the emission is higher than 75%. In [100,200], 75% of the loading rate emits no less than 100%. In [200,300], the 50% and 100% emissions are the same. This also means that the emissions are the same but the paths are not necessarily the same

Device Status Evaluation Method Based on Deep Learning for PHM Scenarios

The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and control system of electronic equipment are few at present, and there are many problems that need to be solved urgently in PHM technology itself. In order to solve such problems, this paper studies the application of the equipment-status-assessment method based on deep learning in PHM scenarios, in order to conduct in-depth research on the intelligent control system of electronic equipment. The experimental results in this paper show that the change in unimproved deep learning is very subtle before the performance change point, while improvements in deep learning increase the health value by about 10 times. Thus, improved deep learning amplifies subtle changes in health early in degradation and slows down mutations in health late at performance failure points. At the same time, comparing health-index-evaluation indicators, it can be concluded that although the monotonicity of the health index is low, its robustness and correlation are significantly improved. Additionally, it is very close to 1, making the health index curve more in line with traditional cognition and convenient for application. Therefore, an in-depth study of methods for health assessment by improving deep learning is of practical significance

Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting

Self-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in low-resource scenarios. Token substitution and mixup are two feasible heterogeneous self-augmentation techniques for NER that can achieve effective performance with certain specialized efforts. Noticeably, self-augmentation may introduce potentially noisy augmented data. Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually. In this paper, we revisit these two typical self-augmentation methods for NER, and propose a unified meta-reweighting strategy for them to achieve a natural integration. Our method is easily extensible, imposing little effort on a specific self-augmentation method. Experiments on different Chinese and English NER benchmarks show that our token substitution and mixup method, as well as their integration, can achieve effective performance improvement. Based on the meta-reweighting mechanism, we can enhance the advantages of the self-augmentation techniques without much extra effort.Comment: Accepted by NAACL 202