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We have successfully produced porous CDC nanospheres from SiC nanospheres by using a simple molten salt electrochemical etching method which is much safer, less expensive and more environmentally friendly than the chlorination process.

This study proposes a method for combining capsizing probability and flooding process to investigate the time-domain dead ship stability of a damaged ship. It focuses on a nonlinear righting lever GZ curve solution in the following aspects: one aspect subjects the influence of damaged tanks on a hull shape to the wind and wave, and the other aspect is based on a real-time calculation of the GZ curve. According to 1‐degree-of-freedom rolling equation, the damaged capsizing probability model is established through fourth-order Runge-Kutta algorithm and Monte Carlo simulation. Also, the model solution is applied on the basis of Visual Basic 6.0 language, and the results are compared with the NAPA platform. The most significant development in this study is combining time-domain flooding process and capsizing probability calculation. To verify the proposed methods, two damaged fishery bureau vessels are used as the sample ships. Results of time-domain capsizing probability under different loading conditions are compared, and the difference and its possible reasons are analyzed.

Since multispectral images (MSIs) and RGB images (RGBs) have significantly different definitions and severely imbalanced information entropies, the spectrum transformation between them, especially reconstructing MSIs from RGBs, is a big challenge. We propose a new approach, the Taiji Generative Neural Network (TaijiGNN), to address the above-mentioned problems. TaijiGNN consists of two generators, G_MSI, and G_RGB. These two generators establish two cycles by connecting one generator’s output with the other’s input. One cycle translates the RGBs into the MSIs and converts the MSIs back to the RGBs. The other cycle does the reverse. The cycles can turn the problem of comparing two different domain images into comparing the same domain images. In the same domain, there are neither different domain definition problems nor severely underconstrained challenges, such as reconstructing MSIs from RGBs. Moreover, according to several investigations and validations, we effectively designed a multilayer perceptron neural network (MLP) to substitute the convolutional neural network (CNN) when implementing the generators to make them simple and high performance. Furthermore, we cut off the two traditional CycleGAN’s identity losses to fit the spectral image translation. We also added two consistent losses of comparing paired images to improve the two generators’ training effectiveness. In addition, during the training process, similar to the ancient Chinese philosophy Taiji’s polarity Yang and polarity Yin, the two generators update their neural network parameters by interacting with and complementing each other until they all converge and the system reaches a dynamic balance. Furthermore, several qualitative and quantitative experiments were conducted on the two classical datasets, CAVE and ICVL, to evaluate the performance of our proposed approach. Promising results were obtained with a well-designed simplistic MLP requiring a minimal amount of training data. Specifically, in the CAVE dataset, to achieve comparable state-of-the-art results, we only need half of the dataset for training, for the ICVL dataset, we used only one-fifth of the dataset to train the model, but obtained state-of-the-art results.

Background: Phase-Contrast Angiography (PCA) is an acceptable magnetic resonance imaging method for cerebrovascular diseases diagnosis. However, it is an important and great challenge to accurately extract cerebrovascular structures from PCA images because of the complex vascular structures and large amount of noise. To accomplish this task, this work proposes a cerebrovascular segmentation algorithm based on Local Binary Fitting (LBF) and Hidden Markov Model (HMM), which can accurately extraction features from PCA data. Results: Dice Similarity Coefficient (DSC), False Positive Score (FPN), and False Negative Score (FTN) are defined as metrics to assess this algorithm. Results show this method obtain higher accuracy (74.58%, 4.93%, 24.48%) than compared methods. Conclusion: Based on quantitative results, it appears that the proposed method has a higher accuracy rate compared to other methods. Due to no human correction and has no training process, it performs well on small datasets. Thus, this algorithm can accord with clinical requirements.

Most cities in the world promote bike-sharing services to encourage people to decrease carbon exhausting and to enhance their health. However, it is a big challenge for a bike-sharing service supplying corporation to re-balance bikes efficiently among different bike-sharing dockers without a forecasting ability. For solving this problem, we contribute two new approaches based on standard Long short-term memory (LSTM), which can not only take advantages of multi features inputs and multi-time steps outputs to improve the accuracy of predicting available bikes in one-time step, but also can forecast the number of bikes in multi-time steps. These approaches will help the bike-sharing agencies to make a better decision to distribute their bikes to each docker efficiently. The experimental results confirmed that our multi-feature and multi-time steps models outperform the standard LSTM model.

The cold‐chain information has characterized by the loss and dispersion according to the different collecting methods. The description for the quality decay factors of aquatic products can be defined as the multidimensional information. A series of nodes with multidimensional information are assembled to be hierarchies aiming at describing the environment conditions and locations in the supply chain. Each of the single hierarchy levels constitutes a sequence of node information in a network, which is applied as internal information analysis. The cross‐layer information structure is defined as “bridge” information which is able to record the information transmissions among every hierarchy from the point of view of the whole chain. The study has established a novel structured modeling to describe the cold chain of aquatic products based on a network‐hierarchy framework. An organized and sustainable transmission process can be built and recorded by the multidimensional attributes for the whole course of cold chain of aquatic products. In addition, seamless connections among every hierarchy are attainable by the environmental information records continuously to monitor the quality of aquatic products. The quality assessments and shelf life predictions are estimated properly as the risk control in order to monitor and trace the safety of aquatic products under the supply chain perspective.

The seakeeping performance of a damaged ship is highly correlated to the wave incident direction. Therefore, predicting the motion responses of a damaged ship under various incoming waves is important. In this study, the seakeeping performance of a DTMB 5415 model is investigated by focusing on its roll motion under beam wave condition. The model performance under an incident wave angle (α) of 180° is widely studied experimentally and numerically. But the behaviours of a damaged ship under α = 0° remain unknown. This study aims to analyse the damaged ship motion performance under this rarely studied condition. The volume of fluid (VOF) free-surface technique is used to solve the Reynolds-averaged Navier–Stokes equations, as well as with the dynamic overset technology that handles the mesh update, the wave generation, time step and fast Fourier transform are utilised to validate the effectiveness of the adopted method. The numerical results are validated against the experimental results obtained when α = 180°. The same methodology is extended to investigate the three-degrees-of-freedom motion (roll, pitch and heave) at α = 0°. Results show that the roll motions under the two wave conditions greatly differ from each other. The discussions highlight the analysis of the possible reasons for this difference.

The paper summarizes general information, the latest results and development plans of the BES-III distributed computing system. The BES-III experiment at the Institute of High Energy Physics (Beijing, China) is aimed at the precision measurements in e+e- annihilation at the energy range from 2.0 to 4.6 GeV. The world's largest samples of J/psi and psi’ events and unique samples of XYZ data have been already collected. To process the BES-III experiment's data the distributed computing infrastructure based on DIRAC interware has been setup and became operational since 2013. The work was done in order to tune and enhance DIRAC itself for BES-III purposes: monitoring system and data transfer system were developed. Experience of the BES-III development team may be very useful for medium scale HEP experiments aiming to apply distributed data processing in their computing model.

We present the fundamental challenges for dynamic provisioning of big data applications. The findings are based on our previous experience in this domain, as well as a comprehensive study on a selected set of state-of-the-art tools in the big data ecosystem. We then incorporate these findings in a framework aiming at dynamically provisioning big data applications as services on containerised cloud. The innovations behind the framework are to optimise the whole lifecycle of big data applications in a holistic manner by the adoption of microservices(μServices) methodologies. The feasibility of our approach is verified through a case study of provisioning a large-scale user traffic data processing application in a private cloud environment backed by Kubernetes. We also show that while hosting big data applications in containerised cloud can significantly eliminate the presumed complexity of deployment and operation, it in the meantime also comes with a certain amount of cost in terms of learning curve and traceability. Our research helps technical decision makers to assess the adoption of microservices for big data applications more objectively.