TTDM: A Travel Time Difference Model for Next Location Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data

DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods

MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods

Comparative Study on the Effects and Mechanisms of Salt and Alkali on the Quality Formation of Noodles

In order to systematically investigate the quality differences between salted and alkaline noodles and the underlying mechanism, the effect of NaCl and K2CO3 on the quality formation of noodles was studied by evaluating the farinograph and extensograph properties and viscosity properties of wheat flour, as well as the cooking properties, texture properties, and storage stability of noodles. Additionally, the microstructure, protein aggregation and volatile components were investigated to explore the underlying mechanisms of the quality differences. The results showed that salt improved the extensibility of dough and noodles, endowed noodles with higher smoothness and elasticity, and showed little effect on starch viscosity, while alkali enhanced the tensile resistance of dough and the hardness and breaking force of cooked noodles, and increased the gelatinization viscosity of starch. The cooking loss of noodles increased with the addition of salt and alkali. Both salt and alkali significantly inhibited the increase of total plate count (TPC) in fresh noodles and consequently enhanced the storage stability, but 0.5% K2CO3 accelerated the browning rate. The scanning electron microscopy (SEM) results showed that NaCl induced a smooth surface microstructure, while K2CO3 resulted in a rough surface of noodles; K2CO3 significantly increased the rate and extent of thermal polymerization of proteins in noodles. NaCl increased the type and concentration of volatile components in noodles, thereby making the aroma of noodles more intense, but the flavor was similar to that of the control. K2CO3 significantly changed the flavor of noodles and resulted in the generation of unique aldehyde compounds

Catalytic oxidation of chlorinated organics over lanthanide perovskites: effects of phosphoric acid etching and water vapor on chlorine desorption behavior

In this article, the underlying effect of phosphoric acid etching and additional water vapor on chlorine desorption behavior over a model catalyst La3Mn2O7 was explored. Acid treatment led to the formation of LaPO4 and enhanced the mobility of lattice oxygen of La3Mn2O7 evidenced by a range of characterization (i.e., X-ray diffraction, temperature-programmed analyses, NH3–IR, etc.). The former introduced thermally stable Brönsted acidic sites that enhanced dichloromethane (DCM) hydrolysis while the latter facilitated desorption of accumulated chlorine at elevated temperatures. The acid-modified catalyst displayed a superior catalytic activity in DCM oxidation compared to the untreated sample, which was ascribed to the abundance of proton donors and Mn­(IV) species. The addition of water vapor to the reaction favored the formation and desorption of HCl and avoided surface chlorination at low temperatures. This resulted in a further reduction in reaction temperature under humid conditions (T90 of 380 °C for the modified catalyst). These results provide an in-depth interpretation of chlorine desorption behavior for DCM oxidation, which should aid the future design of industrial catalysts for the durable catalytic combustion of chlorinated organics