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Estimated Time of Arrival (ETA) plays a vital role in many application scenarios. For example, in various scenarios such as online car-hailing order distribution, price estimation, mid-trip estimation, and route decision-making. Accurate arrival time estimation can help the platform improve efficiency. However, accurate arrival time estimation is affected by static information and dynamic information, and the estimated arrival time has high technical difficulties and challenges. The organizers of this competition provided departure time and date, itinerary, road conditions, as well as topological structure data and weather information of the city's road network. At the same time, according to the characteristics of the given data, rich feature processing methods such as statistical features, category features, graph features, embedding features, and sequence features are used to provide massive feature information for model learning. One of the most important points is the application of "future data". Of course, in addition to the features, a lot of work has been done on the model structure and model fusion through the combination of machine learning and deep learning, ensuring the accuracy and stability of the model. The ETA is a typical time series problem. Therefore, In the deep learning section, we choose DCN [3] model and WDR [4] model as the basis, and the model distillation is combined on this as the deep learning part of the integrated model. At the same time, traditional machine learning is also used as a part of the integrated model, through a large number of different dimensions of feature engineering, to make up for the machine learning model's inability to better express the time series problem, and build a machine learning model with higher accuracy. Finally, through the fusion of the deep learning model and the machine learning model, extremely high accuracy is achieved in the ETA problem.

The ship hull form optimization using the Computational Fluid Dynamics (CFD) method is increasingly employed in the early design of a ship, as an optimal ship hull form can obtain good hydrodynamics. However, it is time-consuming due to its many CFD simulations for the optimization. This paper presents a ship hull form optimization loop using the surrogate model, deep belief network (DBN), to reduce the wave-making resistance of the Wigley ship. The prediction performance of the wave-making resistance of the Wigley ship using the DBN method is discussed and compared with the traditional surrogate models found in this study. The results show that the resistance obtained using the deep belief network algorithm is superior to that obtained using the typical surrogate models. Then, a ship hull form optimization framework is built by integrating the Free From Deformation, non-linear programming by quadratic Lagrangian and deep belief network algorithms. The optimization results show that the deep belief network-based ship hull form optimization loop can be used to optimize the Wigley ship. The study presented in this paper could provide a deep learning algorithm for the ship design optimization.

Objective: The aim of this study was to compare the clinical outcomes of ex vivo liver resection and autotransplantation (ERAT) with allotransplantation in patients with end-stage hepatic alveolar echinococcosis (HAE). Methods: From January 2008 to October 2017, 41 of 254 patients with end-stage HAE fulfilled the inclusion criteria (ERAT group, n = 35; allotransplantation group, n = 6). Each group was assessed for indications and short- and long-term outcomes. Results: The intraoperative mortality was zero in both groups. Two patients died after ERAT due to intra-abdominal bleeding and acute cerebral hemorrhage. The most frequent postoperative complications were biliary complications, which occurred in six patients (14.6%). Parasite recurrence was recorded in two patients, at 16 months and 52 months after allotransplantation. The survival curve showed a longer survival time in the ERAT group than in the allotransplantation group (P = 0.028). In the ERAT group, the 1-, 3-, and 5-year overall survival rates were all 100%. In the allotransplantation group, the 1-, 3-, and 5-year overall survival rates were 100%, 83.3%, and 66.7%, respectively. Conclusions: This appears to be the first series comparing ERAT with allotransplantation for end-stage HAE. ERAT may be a superior alternative to allotransplantation in some cases, as it requires neither an organ donor nor immunosuppressive therapy. Keywords: Hepatic alveolar echinococcosis, Ex vivo, Resection, Autotransplantation, Allotransplantation, Immunosuppression

Background Associations between polymorphisms in cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) / interleukin-18 (IL-18) and susceptibility to liver diseases were already reported by many publications. The aim of this meta-analysis was to clarify associations between polymorphisms in CTLA-4/IL-18 and liver diseases by combing the results of all relevant publications. Methods Eligible publications were searched from Pubmed, Embase, WOS and CNKI. The latest literature searching update was performed on 2nd October, 2019. We used Review Manager to combine the results of individual studies. Results Sixty-seven studies were included in this study. Combined results revealed that CTLA-4 rs231775 (dominant comparison: OR 0.83, 95 % CI 0.79-0.88; recessive comparison: OR 1.33, 95 % CI 1.23-1.43; allele comparison: OR 0.84, 95 % CI 0.78-0.90), IL-18 rs1946518 (dominant comparison: OR 0.85, 95 % CI 0.78-0.92; recessive comparison: OR 1.29, 95 % CI 1.13-1.48; allele comparison: OR 0.79, 95 % CI 0.71-0.88) and IL-18 rs187238 (dominant comparison: OR 1.28, 95 % CI 1.07-1.53; over-dominant comparison: OR 0.81, 95 % CI 0.68-0.97; allele comparison: OR 1.22, 95 % CI 1.07-1.39) polymorphisms were all significantly associated with liver diseases in the general population. We also obtained similar significant associations for CTLA-4 rs231775, CTLA-4 rs5742909, CTLA-4 rs3087243, IL-18 rs1946518 and IL-18 rs187238 polymorphisms in subgroup analyses. Conclusions Collectively, this meta-analysis proved that CTLA-4 rs231775, CTLA-4 rs5742909, CTLA-4 rs3087243, IL-18 rs1946518 and IL-18 rs187238 polymorphisms may confer susceptibility to various types of liver diseases.

In order to reduce the resistance and improve the hydrodynamic performance of a ship, two hull form design methods are proposed based on the potential flow theory and viscous flow theory. The flow fields are meshed using body-fitted mesh and structured grids. The parameters of the hull modification function are the design variables. A three-dimensional modeling method is used to alter the geometry. The Non-Linear Programming (NLP) method is utilized to optimize a David Taylor Model Basin (DTMB) model 5415 ship under the constraints, including the displacement constraint. The optimization results show an effective reduction of the resistance. The two hull form design methods developed in this study can provide technical support and theoretical basis for designing green ships.

The seakeeping behavior of a ship in waves is different from its behavior in calm water. The resistance and seakeeping performance of a ship are of great importance and must be considered in the early-stage design of a ship’s hull form design. Therefore, this paper proposes a hull form optimization framework aiming to achieve the minimum total resistance in waves using a CFD technique. A sinusoidal wave is adopted to establish the numerical wave tank and the overset mesh technique is used to facilitate the motions of the ships in question. Considering the motions of pitching and heaving, the total resistance of the hull in waves is regarded as the objective function which is calculated using the Reynolds averaged Navier–Stokes (RANS) method. The arbitrary shape deformation (ASD) technique is used to change the geometry. Under displacement and design variables, a hybrid algorithm is developed to evaluate the objective function combining the optimal Latin hypercube design (Opt LHD) and the non-linear programming by quadratic Lagrangian (NLPQL) algorithm. Finally, two examples of hull form optimization are presented and discussed for David Taylor Model Basin (DTMB) model 5512 and WIGLEY III cases. The results show the effectiveness of the optimization framework developed in the present study, which can lay the foundation for further optimization of full-scale ships.

In order to reduce the total resistance of a hull, an optimization framework for the bulbous bow optimization was presented. The total resistance in calm water was selected as the objective function, and the overset mesh technique was used for mesh generation. RANS method was used to calculate the total resistance of the hull. In order to improve the efficiency and smoothness of the geometric reconstruction, the arbitrary shape deformation (ASD) technique was introduced to change the shape of the bulbous bow. To improve the global search ability of the particle swarm optimization (PSO) algorithm, an improved particle swarm optimization (IPSO) algorithm was proposed to set up the optimization model. After a series of optimization analyses, the optimal hull form was found. It can be concluded that the simulation based design framework built in this paper is a promising method for bulbous bow optimization.

With the rapid development of the computational technology, computational fluid dynamics (CFD) tools have been widely used to evaluate the ship hydrodynamic performances in the hull forms optimization. However, it is very time consuming since a great number of the CFD simulations need to be performed for one single optimization. It is of great importance to find a high-effective method to replace the calculation of the CFD tools. In this study, a CFD-based hull form optimization loop has been developed by integrating an approximate method to optimize hull form for reducing the total resistance in calm water. In order to improve the optimization accuracy of particle swarm optimization (PSO) algorithm, an improved PSO (IPSO) algorithm is presented where the inertia weight coefficient and search method are designed based on random inertia weight and convergence evaluation, respectively. To improve the prediction accuracy of total resistance, a data prediction method based on IPSO-Elman neural network (NN) is proposed. Herein, IPSO algorithm is used to train the weight coefficients and self-feedback gain coefficient of ElmanNN. In order to build IPSO-ElmanNN model, optimal Latin hypercube design (Opt LHD) is used to design the sampling hull forms, and the total resistance (objective function) of these hull forms are calculated by Reynolds averaged Navier–Stokes (RANS) method. For the purpose of this paper, this optimization framework has been employed to optimize two ships, namely, the DTMB5512 and WIGLEY III ships, and these hull forms are changed by arbitrary shape deformation (ASD) technique. The results show that the optimization framework developed in this study can be used to optimize hull forms with significantly reduced computational effort.

This paper present a novel algorithm for cartoon image segmentation based on the simple linear iterative clustering (SLIC) superpixels and adaptive region propagation merging. To break the limitation of the original SLIC algorithm in confirming to image boundaries, this paper proposed to improve the quality of the superpixels generation based on the connectivity constraint. To achieve efficient segmentation from the superpixels, this paper employed an adaptive region propagation merging algorithm to obtain independent segmented object. Compared with the pixel-based segmentation algorithms and other superpixel-based segmentation methods, the method proposed in this paper is more effective and more efficient by determining the propagation center adaptively. Experiments on abundant cartoon images showed that our algorithm outperforms classical segmentation algorithms with the boundary-based and region-based criteria. Furthermore, the final cartoon image segmentation results are also well consistent with the human visual perception.