Stamp stress analysis with low temperature nanoimprint lithography

High temperature nanoimprint lithography has the drawback of long process cycle, demoulding difficulty, polymer degradation, thermal stress. Low temperature nanoimprint lithography (LTNIL) can avoid these problems. LTNIL is also ideal for manufacturing biological compatibility samples since the samples do not sustain high temperature. However, LTNIL need to optimize the press parameters in order to fully transfer patterns. Finite Element Method (FEM) is an excellent approach to examine the filling process. The stamp stress was simulated from four points of view, imprint pressure, imprint temperature, stamp pattern and stamp material. It was found that the stress in the stamp corners is especially bigger than other areas, the stress increases with the stamps aspect ratio increases, and stress distribution is more uniform for dense pattern stamp

Learning Explicit Contact for Implicit Reconstruction of Hand-held Objects from Monocular Images

Reconstructing hand-held objects from monocular RGB images is an appealing yet challenging task. In this task, contacts between hands and objects provide important cues for recovering the 3D geometry of the hand-held objects. Though recent works have employed implicit functions to achieve impressive progress, they ignore formulating contacts in their frameworks, which results in producing less realistic object meshes. In this work, we explore how to model contacts in an explicit way to benefit the implicit reconstruction of hand-held objects. Our method consists of two components: explicit contact prediction and implicit shape reconstruction. In the first part, we propose a new subtask of directly estimating 3D hand-object contacts from a single image. The part-level and vertex-level graph-based transformers are cascaded and jointly learned in a coarse-to-fine manner for more accurate contact probabilities. In the second part, we introduce a novel method to diffuse estimated contact states from the hand mesh surface to nearby 3D space and leverage diffused contact probabilities to construct the implicit neural representation for the manipulated object. Benefiting from estimating the interaction patterns between the hand and the object, our method can reconstruct more realistic object meshes, especially for object parts that are in contact with hands. Extensive experiments on challenging benchmarks show that the proposed method outperforms the current state of the arts by a great margin.Comment: 17 pages, 8 figure

DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence. In this paper, an SDN intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the multicast tree construction problem is decomposed into two sub-problems: the fork node selection problem and the construction of the optimal path from the fork node to the destination node. Second, based on the information characteristics of SDN global network perception, the multicast tree state matrix, link bandwidth matrix, link delay matrix, link packet loss rate matrix, and sub-goal matrix are designed as the state space of intrinsic and meta controllers. Then, in order to mitigate the excessive action space, our approach constructs different action spaces at the upper and lower levels. The meta-controller generates an action space using network nodes to select the fork node, and the intrinsic controller uses the adjacent edges of the current node as its action space, thus implementing four different action selection strategies in the construction of the multicast tree. To facilitate the intelligent agent in constructing the optimal multicast tree with greater speed, we developed alternative reward strategies that distinguish between single-step node actions and multi-step actions towards multiple destination nodes

Effects of forage type on the rumen microbiota, growth performance, carcass traits, and meat quality in fattening goats

Forages fed to goats influence ruminal microbiota, and further contribute to affect growth performance, meat quality and its nutritional composition. Our objective for current study was to investigate the effects of different forages on growth performance, carcass traits, meat nutritional composition, rumen microflora, and the relationships between key bacteria and amino acids and fatty acids in the longissimus dorsi and semimembranosus muscles of goats. Boer crossbred goats were separately fed commercial concentrate diet supplemented with Hemarthria altissima (HA), Pennisetum sinese (PS), or forage maize (FG), and then slaughtered 90 days after the beginning of the experiment. Growth performances did not vary but carcass traits of dressing percentage, semi-eviscerated slaughter percentage, and eviscerated slaughter percentage displayed significant difference with the treatment studied. Meats from goats fed forage maize, especially semimembranosus muscles are rich in essential amino acids, as well as an increase in the amount of beneficial fatty acids. Our 16S rRNA gene sequencing results showed that the Firmicutes, Bacteroidetes, and Proteobacteria were the most dominant phyla in all groups but different in relative abundance. Further, the taxonomic analysis and linear discriminant analysis effect size (LEfSe) identified the specific taxa that were differentially represented among three forage treatments. The spearman’s correlation analysis showed that rumen microbiota was significantly associated with the goat meat nutritional composition, and more significant positive correlations were identified in semimembranosus muscles when compared with longissimus dorsi muscles. More specifically, the lipid metabolism-related bacteria Rikenellaceae_RC9_gut_group showed positively correlated with meat amino acid profile, while genera Oscillospiraceae_UCG-005 were positively correlated with fatty acid composition. These bacteria genera might have the potential to improve nutritional value and meat quality. Collectively, our results showed that different forages alter the carcass traits, meat nutritional composition, and rumen microflora in fattening goats, and forage maize induced an improvement in its nutritional value

A novel risk stratification model for STEMI after primary PCI: global longitudinal strain and deep neural network assisted myocardial contrast echocardiography quantitative analysis

BackgroundIn ST-segment elevation myocardial infarction (STEMI) with the restoration of TIMI 3 flow by percutaneous coronary intervention (PCI), visually defined microvascular obstruction (MVO) was shown to be the predictor of poor prognosis, but not an ideal risk stratification method. We intend to introduce deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis and propose a better risk stratification model.Methods194 STEMI patients with successful primary PCI with at least 6 months follow-up were included. MCE was performed within 48 h after PCI. The major adverse cardiovascular events (MACE) were defined as cardiac death, congestive heart failure, reinfarction, stroke, and recurrent angina. The perfusion parameters were derived from a DNN-based myocardial segmentation framework. Three patterns of visual microvascular perfusion (MVP) qualitative analysis: normal, delay, and MVO. Clinical markers and imaging features, including global longitudinal strain (GLS) were analyzed. A calculator for risk was constructed and validated with bootstrap resampling.ResultsThe time-cost for processing 7,403 MCE frames is 773 s. The correlation coefficients of microvascular blood flow (MBF) were 0.99 to 0.97 for intra-observer and inter-observer variability. 38 patients met MACE in 6-month follow-up. We proposed A risk prediction model based on MBF [HR: 0.93 (0.91–0.95)] in culprit lesion areas and GLS [HR: 0.80 (0.73–0.88)]. At the best risk threshold of 40%, the AUC was 0.95 (sensitivity: 0.84, specificity: 0.94), better than visual MVP method (AUC: 0.70, Sensitivity: 0.89, Specificity: 0.40, IDI: −0.49). The Kaplan-Meier curves showed that the proposed risk prediction model allowed for better risk stratification.ConclusionThe MBF + GLS model allowed more accurate risk stratification of STEMI after PCI than visual qualitative analysis. The DNN-assisted MCE quantitative analysis is an objective, efficient and reproducible method to evaluate microvascular perfusion

Prussian blue caged in spongiform adsorbents using diatomite and carbon nanotubes for elimination of cesium

We developed a spongiform adsorbent that contains Prussian blue, which showed a high capacity for eliminating cesium. An in situ synthesizing approach was used to synthesize Prussian blue inside diatomite cavities. Highly dispersed carbon nanotubes (CNTs) were used to form CNT networks that coated the diatomite to seal in the Prussian blue particles. These ternary (CNT/diatomite/Prussian-blue) composites were mixed with polyurethane (PU) prepolymers to produce a quaternary (PU/CNT/diatomite/Prussian-blue), spongiform adsorbent with an in situ foaming procedure. Prussian blue was permanently immobilized in the cell walls of the spongiform matrix and preferentially adsorbed cesium with a theoretical capacity of 168 mg/g cesium. Cesium was absorbed primarily by an ion-exchange mechanism, and the absorption was accomplished by self-uptake of radioactive water by the quaternary spongiform adsorbent

The Hausdorff dimension and exact Hausdorff measure of random recursive sets with overlapping

We weaken the open set condition and define a finite intersection property in the construction of the random recursive sets. We prove that this larger class of random sets are fractals in the sense of Taylor, and give conditions when these sets have positive and finite Hausdorff measures, which in certain extent generalize some of the known results, about random recursive fractals