Production rates for hadrons, pentaquarks Θ+\Theta ^+ and Θ∗++\Theta ^{*++}, and di-baryon (ΩΩ)0+(\Omega\Omega)_{0^{+}} in relativistic heavy ion collisions by a quark combination model

The hadron production in relativistic heavy ion collisions is well described by the quark combination model. The mixed ratios for various hadrons and the transverse momentum spectra for long-life hadrons are predicted and agree with recent RHIC data. The production rates for the pentaquarks $\Theta ^+$, $\Theta ^{*++}$ and the di-baryon $(\Omega\Omega)_{0^{+}}$ are estimated, neglecting the effect from the transition amplitude for constituent quarks to form an exotic state.Comment: The difference between our model and other combination models is clarified. The scaled transverse momentum spectra for pions, kaons and protoms at both 130 AGeV and 200 AGeV are given, replacing the previous results in transverse momentum spectr

Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation

Robot-assisted rehabilitation and therapy has become more and more frequently used to help the elderly, disabled patients or movement disorders to perform exercise and training. The field of robot-assisted lower limb rehabilitation has rapidly evolved in the last decade. This article presents a review on the most recent progress (from year 2001 to 2014) of mechanisms, training modes and control strategies for lower limb rehabilitation robots. Special attention is paid to the adaptive robot control methods considering hybrid data fusion and patient evaluation in robot-assisted passive and active lower limb rehabilitation. The characteristics and clinical outcomes of different training modes and control algorithms in recent studies are analysed and summarized. Research gaps and future directions are also highlighted in this paper to improve the outcome of robot-assisted rehabilitation

Numerical simulation of focusing wave interaction with FPSO-like structure using FNPT-NS Solver

The paper presents the numerical results for the focusing wave interacting with a FPSO-like structure using the FNPT-NS solver. The case configuration is defined by CCP-WSI blind test in ISOPE 2018. The incident waves are uni-directional focusing waves with different spectrum bandwidths and wave heights. The structure is fixed and subjected to different heading. The numerical model used in present work is a hybrid model combining a fully nonlinear potential theory (FNPT) and a Navier-Stokes （ NS ） theory using a domain-decomposition approach. The Quasi Arbitrary Lagrangian Eulerian method is used to solve the FNPT in a large domain covering the entire wave basin with the same size as the experiments. For the later, the OpenFOAM (interDyMFoam) is utilized to achieve the solution near the structures, where the viscous effect may be important. In addition, the wave generation and the convergence of the hybrid model are discussed in detail

A MUSIC-based method for SSVEP signal processing

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100 %. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications

Co-training an improved recurrent neural network with probability statistic models for named entity recognition

© Springer International Publishing AG 2017. Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further