A two-stage traveling-wave thermoacoustic electric generator with loudspeakers as alternators

This paper presents the design, construction and tests of a traveling-wave thermoacoustic electric generator. A two-stage travelling-wave thermoacoustic engine converts thermal energy to acoustic power. Two low-impedance linear alternators (i.e., audio loudspeakers) were installed to extract and convert the engine’s acoustic power to electricity. The coupling mechanism between the thermoacoustic engine and alternators has been systematically studied numerically and experimentally, hence the optimal locations for installing the linear alternators were identified to maximize the electric power output and/or the thermal-to-electric conversion efficiency. A ball valve was used in the loop to partly correct the acoustic field that was altered by manufacturing errors. A prototype was built based on this new concept, which used pressurized helium at 1.8 MPa as the working gas and operated at a frequency of about 171 Hz. In the experiment, a maximum electric power of 204 W when the hot end temperature of the two regenerators reaches 512℃ and 452℃, respectively. A maximum thermal-to-electric efficiency of 3.43% was achieved when the hot end temperature of the two regenerators reaches 597℃ and 511℃, respectively. The research results presented in this paper demonstrate that multi-stage travelling-wave thermoacoustic electricity generator has a great potential for developing inexpensive electric generators

An Algorithmic Framework for Efficient Large-Scale Circuit Simulation Using Exponential Integrators

We propose an efficient algorithmic framework for time domain circuit simulation using exponential integrator. This work addresses several critical issues exposed by previous matrix exponential based circuit simulation research, and makes it capable of simulating stiff nonlinear circuit system at a large scale. In this framework, the system's nonlinearity is treated with exponential Rosenbrock-Euler formulation. The matrix exponential and vector product is computed using invert Krylov subspace method. Our proposed method has several distinguished advantages over conventional formulations (e.g., the well-known backward Euler with Newton-Raphson method). The matrix factorization is performed only for the conductance/resistance matrix G, without being performed for the combinations of the capacitance/inductance matrix C and matrix G, which are used in traditional implicit formulations. Furthermore, due to the explicit nature of our formulation, we do not need to repeat LU decompositions when adjusting the length of time steps for error controls. Our algorithm is better suited to solving tightly coupled post-layout circuits in the pursuit for full-chip simulation. Our experimental results validate the advantages of our framework.Comment: 6 pages; ACM/IEEE DAC 201

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces a $\ell_\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\tau_{\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\tau_{\min}$ only increases logarithmically with the number of workers and intrinsic dimensionality, while nearly invariant to the nominal dimensionality. We test our theory by extensive simulation studies, and a variable screening task on a semi-synthetic dataset based on the US Airline On-time Performance dataset. The code to reproduce the numerical results is available at GitHub: https://github.com/skchao74/Distributed-bootstrap.Comment: arXiv admin note: text overlap with arXiv:2002.0844

Angiogenesis and Vasculogenesis at 7-Day of Reperfused Acute Myocardial Infarction

Objectives 
This study is to investigate the angiogenesis and vasculogenesis at the first week of reperfused acute myocardial infarction (AMI).
Methods 
16 of mini-swines (20 to 30 Kg) were randomly assigned to the sham-operated group and the AMI group. The acute myocardial infarction and reperfusion model was created and the pig tail catheter was performed to monitor hemodynamics before left anterior descending coronary artery (LAD) occlusion, 90 min of LAD occlusion and 120 min of LAD reperfusion. Pathologic myocardial tissue was collected at 7-day of LAD reperfusion and further assessed by immunochemistry, dual immunochemistry, in-situ hybridization, real-time quantitative polymerase chain reaction and western blot. 
Results 
The infarcted area had higher FLK1 mRNA expression than sham-operated area and the normal area (all P<0.05), and the infarcted and marginal areas showed higher CD146 protein expression than the sham-operated area (all P<0.05), but the microvessel density (CD31 positive expression of microvessels/HP) was not significantly different between the infarcted area and the sham-operated area (8.92±3.05 vs 6.43±1.54) at 7-day of reperfused acute myocardial infarction (P>0.05). 
Conclusions 
FLK1 and CD146 expression significantly increase in the infarcted and marginal areas, and the microvessel density is not significantly different between the infarcted area and the sham-operated area, suggesting that angiogenesis and vasculogenesis in the infarcted area appear to high frequency of increase in 7-day of reperfused myocardial infarction. 
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