A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method

iQIST : An open source continuous-time quantum Monte Carlo impurity solver toolkit

Quantum impurity solvers have a broad range of applications in theoretical studies of strongly correlated electron systems. Especially, they play a key role in dynamical mean-field theory calculations of correlated lattice models and realistic materials. Therefore, the development and implementation of efficient quantum impurity solvers is an important task. In this paper, we present an open source interacting quantum impurity solver toolkit (dubbed iQIST). This package contains several highly optimized quantum impurity solvers which are based on the hybridization expansion continuous-time quantum Monte Carlo algorithm, as well as some essential pre- and post-processing tools. We first introduce the basic principle of continuous-time quantum Monte Carlo algorithm and then discuss the implementation details and optimization strategies. The software framework, major features, and installation procedure for iQIST are also explained. Finally, several simple tutorials are presented in order to demonstrate the usage and power of iQIST

Risk factors and management of hyperuricemia after renal transplantation

Hyperuricemia (HUA) is a common complication after renal transplantation. Currently, there is no uniform consensus on factors which increase the risk for and treatment of HUA in renal transplant recipients. The purpose of this review is to summarize current and proposed risk factors and strategies to manage HUA after renal transplantation in order to assist renal function protection and prolong graft survival time

Chlorido{N 2,N 6-dibenzyl-N 2,N 6-bis­[(diphenyl­phosphan­yl)meth­yl]pyridine-2,6-diamine}­methyl­platinum(II)

In the title mononuclear complex, [Pt(CH3)Cl(C45H41N3P2)], the pyridine-2,6-diamine ligand can be viewed as a centrosymmetric motif having two pendant N-benzyl-N-[(diphenyl­phosphan­yl)meth­yl] arms, the two P atoms of which chelate to the PtII ion, forming a ten-membered metallocycle. A distorted square-planar coordination geometry around the PtII atom is completed by a methyl ligand and a chloride ion. The packing between the mononuclear units is achieved through C—H⋯π inter­actions, which link the mol­ecules into chains along the c axis