Controlled Cardiac Computed Tomography

Cardiac computed tomography (CT) has been a hot topic for years because of the clinical importance of cardiac diseases and the rapid evolution of CT systems. In this paper, we propose a novel strategy for controlled cardiac CT that may effectively reduce image artifacts due to cardiac and respiratory motions. Our approach is radically different from existing ones and is based on controlling the X-ray source rotation velocity and powering status in reference to the cardiac motion. We theoretically show that by such a control-based intervention the data acquisition process can be optimized for cardiac CT in the cases of periodic and quasiperiodic cardiac motions. Specifically, we formulate the corresponding coordination/control schemes for either exact or approximate matches between the ideal and actual source positions, and report representative simulation results that support our analytic findings

The Effects of Weather on Passenger Flow of Urban Rail Transit

Predicting passenger flow on urban rail transit is important for the planning, design and decision-making of rail transit. Weather is an important factor that affects the passenger flow of rail transit by changing the travel mode choice of urban residents. This study aims to explore the influence of weather on urban rail transit ridership, taking four cities in China as examples, Beijing, Shanghai, Guangzhou and Chengdu. To determine the weather effect on daily ridership rate, the three models were proposed with different combinations of the factors of temperature and weather type, using linear regression method.   The large quantities of data were applied to validate the developed models.  The results show that in Guangzhou, the daily ridership rate of rail transit increases with increasing temperature. In Chengdu, the ridership rate increases in rainy days compared to sunny days. While, in Beijing and Shanghai, the ridership rate increases in light rainfall and heavy rainfall (except moderate rainfall) compared to sunny days. The research findings are important to understand the impact of weather on passenger flow of urban rail transit. The findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow

Early atrial remodeling predicts the risk of cardiovascular events in patients with metabolic syndrome: a retrospective cohort study

BackgroundThis study aims to assess the prevalence of atrial cardiomyopathy (ACM) in patients with new-onset metabolic syndrome (MetS) and investigate whether ACM could be a predictor of hospital admission for cardiovascular (CV) events.MethodsPatients with MetS who were free of clinically proven atrial fibrillation and other CV diseases (CVDs) at baseline were included in the present study. The prevalence of ACM was compared between MetS patients with and without left ventricular hypertrophy (LVH). The time to first hospital admission for a CV event between subgroups was assessed using the Cox proportional hazard model.ResultsA total of 15,528 MetS patients were included in the final analysis. Overall, LVH patients accounted for 25.6% of all newly diagnosed MetS patients. ACM occurred in 52.9% of the cohort and involved 74.8% of LVH patients. Interestingly, a significant percentage of ACM patients (45.4%) experienced MetS without LVH. After 33.2 ± 20.6 months of follow-up, 7,468 (48.1%) patients had a history of readmission due to CV events. Multivariable Cox regression analysis revealed that ACM was associated with an increased risk of admission for CVDs in the MetS patients with LVH [hazard ratio (HR), 1.29; 95% confidence interval (CI), 1.142–1.458; P < 0.001]. Likewise, ACM was found to be independently associated with hospital readmission due to CVD-related events in MetS patients without LVH (HR, 1.175; 95% CI, 1.105–1.250; P < 0.001).ConclusionACM is a marker of early myocardial remodeling and predicts hospitalization for CV events in patients with MetS

The Work Softening Behavior of Pure Mg Wire during Cold Drawing

We performed multiple-pass cold drawing for pure Mg wire which showed excellent formability (~138% accumulative true strain) at room temperature. Different from the continuous work hardening occurring during cold drawing of Mg alloy wires, for pure Mg, an initially rapid increase in hardness and strength was followed by significant work softening and finally reached a steady-state level, approximately 40~45 HV. The work softening can be attributed to the dynamic recovery and recrystallization of pure Mg at room temperature. Meanwhile, an abrupt change in texture component also was detected with the transition from work hardening to softening in the strain range of 28~34%. During the whole drawing, the strongest texture component gradually transformed from as-extruded basal to <10 1 ¯ 0> fiber (~28% accumulative true strain), and then rapidly returned to the weak basal texture

On Enhancing TCP to Deal with High Latency and Transmission Errors in Geostationary Satellite Network for 5G-IoT

The geostationary (GEO) satellite networks have two important influencing factors: high latency and transmission errors. Similarly, they will happen in the large-scale multihop network of the Internet of things (IoT), which will affect the application of 5G- (5th-generation mobile networks-) IoT. In this paper, we propose an enhanced TCP mechanism that increases the amount of data transferred in the slow start phase of TCP Hybla to mitigate the effect of long RTT and incorporates a refined mechanism of TCP Veno, which can distinguish packet loss between random and congestion. This scheme is evaluated and compared with NewReno, Hybla, and Veno by simulation, and the performance improvement of the proposed TCP scheme for GEO satellite network in the presence of random packet losses is demonstrated. At the same time, the enhanced TCP scheme can improve the transmission performance in the future 5G-IoT heterogeneous network with high delay and transmission

Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps

Jigsaw puzzle solving has recently become an emerging research area. The developed techniques have been widely used in applications beyond puzzle solving. This paper focuses on solving Jigsaw Puzzles with Large Eroded Gaps (JPwLEG). We formulate the puzzle reassembly as a combinatorial optimization problem and propose a Siamese-Discriminant Deep Reinforcement Learning (SD2RL) to solve it. A Deep Q-network (DQN) is designed to visually understand the puzzles, which consists of two sets of Siamese Discriminant Networks, one set to perceive the pairwise relations between vertical neighbors and another set for horizontal neighbors. The proposed DQN considers not only the evidence from the incumbent fragment but also the support from its four neighbors. The DQN is trained using replay experience with carefully designed rewards to guide the search for a sequence of fragment swaps to reach the correct puzzle solution. Two JPwLEG datasets are constructed to evaluate the proposed method, and the experimental results show that the proposed SD2RL significantly outperforms state-of-the-art methods

Modeling Crack Propagation with the Extended Scaled Boundary Finite Element Method based on the Level Set Method

The extended scaled boundary finite element method (X-SBFEM) based on the level set method (LSM) is proposed in this paper to combine the advantages of the scaled boundary finite element method (SBFEM) and the extended finite element method (XFEM). The level set method (LSM) algorithm is applied to further develop the X-SBFEM, especially for the crack propagation problem. The Heaviside enrichment function is used to represent a jump across a discontinuity surface in a split element, and the non-smooth behavior around the crack tip is described using the semi-analytical SBFEM. The stiffness of the region containing the crack tip is computed directly, and the generalized stress intensity factors of many types of singularities are obtained directly from their definitions using consistent formulas. In the numerical simulations, a square plate with an edge crack under tension, a three-point bending beam, a four-point shear beam and a dam (the Koyna dam) with a single propagating crack are modeled. The results show that the proposed X-SBFEM is capable of calculating the stress intensity factors of cracks and predicting crack trajectories and load-displacement relations accurately. An analysis of the sensitivity of the parameters is employed to demonstrate that various mesh densities and crack propagation step lengths led to consistent results