EXPRESSION OF ACTIVE FUNGAL XYLANASES IN N. BENTHAMIANA FOR HEMICELLULOSE DEGRADATION

Lack of effective biomass pretreatments has been one of the main limiting factors for cellulosic ethanol production. The addition of plant-made hemicellulases to the pretreatment procedure is an alternative method of obtaining fermentable sugars. Four putative or characterized xylanases out of 73 hydrolases from A. niger were found by bioinformatics analysis. Proteins of interest were targeted to the ER, and transiently expressed in N. benthamiana plants accumulating up to 38% of TSP. A simple surfactant- based aqueous two-phase system was used to purify xylanase-HFBI fusions with a recovery up to 83%. An endoxylanase and a P-xylosidase were characterized, and their combined action enhanced xylose production. This study presents a very promising expression and purification system for fungal hemicellulases to further improve cellulosic ethanol production

Additive Manufacturing Of (MgCoNiCuZn)O High-entropy Oxide Using A 3D Extrusion Technique And Oxide Precursors

This report presents an additive manufacturing approach, for the first time, to producing high-entropy oxides (HEOs) using a 3D extrusion-based technique with oxide precursors. The precursors were prepared by a wet chemical method from sulfates. Additives were utilized to optimize the rheological properties of the printing inks with these precursors, and the properties of the printed HEOs were improved by increasing the solid content of the inks. When ink with a solid content of 78 wt% was used for printing, the resulting HEO exhibited a relative density of 92% and a high dielectric constant after undergoing pressure less sintering at 800 °C. Compared to traditional methods of manufacturing HEOs, the 3D extrusion technique is a very promising method for producing HEOs with complex geometries

Carbon Nanostructures Production by AC Arc Discharge Plasma Process at Atmospheric Pressure

Carbon nanostructures have received much attention for a wide range of applications. In this paper, we produced carbon nanostructures by decomposition of benzene using AC arc discharge plasma process at atmospheric pressure. Discharge was carried out at a voltage of 380 V, with a current of 6 A–20 A. The products were characterized by scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), powder X-ray diffraction (XRD), and Raman spectra. The results show that the products on the inner wall of the reactor and the sand core are nanoparticles with 20–60 nm diameter, and the products on the electrode ends are nanoparticles, agglomerate carbon particles, and multiwalled carbon nanotubes (MWCNTs). The maximum yield content of carbon nanotubes occurs when the arc discharge current is 8 A. Finally, the reaction mechanism was discussed

Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of the proposed model and its better performance against the state-of-the-art pipelines.Comment: 10 pages. Accepted by the ACM BCB 201

Multivariate regression models in estimating the behavior of FRP tube encased recycled aggregate concrete

This study applied newly developed multivariate statistical models to estimating the mechanical properties of recycled aggregate concrete cylinder encased by fiber reinforced polymer (FRP). Two different types of RFPs were applied, namely flax FRP and polyester FRP. Ten independent variables were predefined including the FRP type and cylinder size. It was found that several mixed models outperformed the traditional linear regression approach, based on the accuracy and residual value distribution. Individual factor analysis indicated that the fiber thickness and layer number had more significant impacts on the strength and strain of FRP-encased concrete’s transitional point, compared to their impacts at the ultimate state

Mechanical Properties of Recycled Aggregate Concrete Modified by Nano-particles

In this study, different nano-particles were used to modify recycled aggregates concrete (RAC) containing recycled clay brick aggregates (RCBAs) to improve the RAC properties. Two stages of experimental works were performed. In the first stage, various nano-particle mixtures produced by different mixing methods, i.e. the use of surfactant and ultrasonication, were examined by optical microscope to evaluate the dispersion of the nano-particles in water liquid. The nano-particles modified cement mortar specimens were further evaluated by flexural tensile test to check how these mixing methods affect the properties of the nano-particle modified cement mortar. In the second experimental stage, the effects of four replacement ratios of recycled aggregates, three type of nano-particles, two mixing methods of RAC, additional surfactant and ultrasonication process used in the mix of nano-particle liquid, and the dosages of the nano-particles on the workability, compressive and split tensile properties of the nano-particle modified RAC were investigated

Does Momentum Change the Implicit Regularization on Separable Data?

The momentum acceleration technique is widely adopted in many optimization algorithms. However, there is no theoretical answer on how the momentum affects the generalization performance of the optimization algorithms. This paper studies this problem by analyzing the implicit regularization of momentum-based optimization. We prove that on the linear classification problem with separable data and exponential-tailed loss, gradient descent with momentum (GDM) converges to the L2 max-margin solution, which is the same as vanilla gradient descent. That means gradient descent with momentum acceleration still converges to a low-complexity model, which guarantees their generalization. We then analyze the stochastic and adaptive variants of GDM (i.e., SGDM and deterministic Adam) and show they also converge to the L2 max-margin solution. Technically, to overcome the difficulty of the error accumulation in analyzing the momentum, we construct new potential functions to analyze the gap between the model parameter and the max-margin solution. Numerical experiments are conducted and support our theoretical results