Overdetermined Steady-State Initialization Problems in Object-Oriented Fluid System Models

The formulation of steady-state initialization problems for fluid systems is a non-trivial task. If steady-state equations are specified at the component level, the corresponding system of initial equations at the system level might be overdetermined, if index reduction eliminates some states. On the other hand, steady-state equations are not sufficient to uniquely identify one equilibrium state in the case of closed systems, so additional equations are required. The paper shows how these problems might be solved in an elegant way by formulating overdetermined initialization problems, which have more equations than unknowns and a unique solution, then solving them using a least-squares minimization algorithm. The concept is tested on a representative test case using the OpenModelica compiler

Tutoramento de plantas de ervilha visando à produção de grãos secos.

bitstream/CNPT-2010/40286/1/p-bp13.pd

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model\u2019s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets

correlation between clinic pathological features msi pd l1 and survival in resectable gastric cancer looking for prognostic biomarkers

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Molecular properties and bioactivity of phosphopeptides obtained from controlled proteolysis of milk proteins in an enzymatic bioreactor

A family of casein-derived peptides, produced in vivo or in vitro from proteolysis of alpha and beta casein, enriched in phosphoseryl groups, and named casein phosphopeptides (CPP), is able to bind and solubilize calcium. A commercial CPP mixture (CPP DMV) and a single synthetic CPP, corresponding to fragment 1-25 of bovine beta casein, were found to elicit a marked and transient rise of intracellular free calcium concentration in human intestinal tumor HT-29 cells, differentiated in culture [1]. Here we report on the properties of phosphopeptides produced by controlled milk hydrolysis with food-grade proteases (TPC-CPP), and on a comparison of their bioactivity with that of CPP DMV. Large-sized peptides were obtained by controlled and limited proteolysis of milk in a custom-designed ultrafiltration pilot-scale plant [2-4]. Several heat-resistant, food-grade proteases were tested. Phosphopeptides were isolated by selective precipitation [5], followed by diafiltration. The effects of CPPs on cellular calcium uptake were assessed by video-imaging microscopy on single cells, by using fura2 as fluorescent free-calcium indicator. The molecular properties of TPC-CPPs were studied by chromatographic and mass spectroscopy techniques, and compared to those of CPP DMV. MS spectroscopy [5] indicated that CPP DMV mainly derived from the N-terminus region of beta-casein, whereas TPC-CPPs included significant amounts of phosphopeptides deriving from other portions of beta-casein, and from alpha-S1-casein. Both CPP mixtures promote calcium uptake in HT-29 cells in a dose- and extracellular calcium concentration- dependent fashion, but TPC-CPPs appear more bioactive than CPP DMV, inducing higher cellular calcium increments. The CPP bioactivity is related to their molecular structure as well as to their ability to bind and solubilize calcium. Studies are in progress to identify specific CPPs as possible nutraceuticals and/or functional food ingredients

MFFA-SARNET : Deep Transferred Multi-level Feature Fusion Attention Network for Small Samples SAR ATR with Dual Optimized Loss

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), most algorithms of which have employed and relied on sufficient training samples to receive a strong discriminative classification model, has remained a challenging task in recent years, among which the challenge of SAR data acquisition and further insight into the intuitive features of SAR images are the main concerns. In this paper, a deep transferred multi-level feature fusion attention network with dual optimized loss, called a multi-level feature attention Synthetic Aperture Radar network (MFFA-SARNET), is proposed to settle the problem of small samples in SAR ATR tasks. Firstly, a multi-level feature attention (MFFA) network is established to learn more discriminative features from SAR images with a fusion method, followed by alleviating the impact of background features on images with the following attention module that focuses more on the target features. Secondly, a novel dual optimized loss is incorporated to further optimize the classification network, which enhances the robust and discriminative learning power of features. Thirdly, transfer learning is utilized to validate the variances and small-sample classification tasks. Extensive experiments conducted on a public database with three different configurations consistently demonstrate the effectiveness of our proposed network, and the significant improvements yielded to surpass those of the state-of-the-art methods under small-sample conditions