Production of Reducing Sugars from Laminaria japonica by Subcritical Water Hydrolysis

AbstractThis study was to investigate the production of reducing sugars in hydrolysates from raw and deoiled Laminaria japonica produced by subcritical water hydrolysis. Deoiled Laminaria japonica was collected by supercritical carbon dioxide (SCO2) extraction process. Experiments were performed in a batch-type reactor with stirring. It investigated that the effects of reaction temperature and acetic acid as catalyst on content of reducing sugar production. The addition of acetic acid led to an increase in content of reducing sugar. But Removal of oil in Laminaria japonica by SCO2 and increasing of temperature led to decrease in content of reducing sugar production. The highest content of reducing sugar was 814.10mg/100g raw dried sample at 200°C, adding of 1% acetic acid as catalyst

On Investigating the Conservative Property of Score-Based Generative Models

Existing Score-based Generative Models (SGMs) can be categorized into constrained SGMs (CSGMs) or unconstrained SGMs (USGMs) according to their parameterization approaches. CSGMs model probability density functions as Boltzmann distributions, and assign their predictions as the negative gradients of some scalar-valued energy functions. On the other hand, USGMs employ flexible architectures capable of directly estimating scores without the need to explicitly model energy functions. In this paper, we demonstrate that the architectural constraints of CSGMs may limit their modeling ability. In addition, we show that USGMs' inability to preserve the property of conservativeness may lead to degraded sampling performance in practice. To address the above issues, we propose Quasi-Conservative Score-based Generative Models (QCSGMs) for keeping the advantages of both CSGMs and USGMs. Our theoretical derivations demonstrate that the training objective of QCSGMs can be efficiently integrated into the training processes by leveraging the Hutchinson trace estimator. In addition, our experimental results on the CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets validate the effectiveness of QCSGMs. Finally, we justify the advantage of QCSGMs using an example of a one-layered autoencoder

Titanium-capped carbon chains as promising new hydrogen storage media

The capacity of Ti-capped sp carbon atomic chains for use as hydrogen storage media is studied using first-principles density functional theory. The Ti atom is strongly attached at one end of the carbon chains via d-p hybridization, forming stable TiCn complexes. We demonstrate that the number of adsorbed H2 on Ti through Kubas interaction depends upon the chain types. For polyyne (n even) or cumulene (n odd) structures, each Ti atom can hold up to five or six H2 molecules, respectively. Furthermore, the TiC5 chain effectively terminated on a C20 fullerene can store hydrogen with optimal binding of 0.52 eV/H2. Our results reveal a possible way to explore high-capacity hydrogen storage materials in truly one-dimensional carbon structures.Comment: accepted for publication in Physical Chemistry Chemical Physic

Effect of Total Leaf Numbers on the Growth and Fruit Quality in Muskmelon Plants Showing Leaf Yellowing Symptoms

This study was conducted to evaluate the influence of total leaf numbers on the growth, net formation of fruits, and occurrence of leaf yellowing symptoms (LYS) in muskmelon plants. The growth and development of LYS on muskmelon plants having 25, 30, and 35 fully expanded leaves on the vine were compared to those of the control plant having 20 leaves. Plant height, leaf area, root fresh weight, and root dry weight increased as the number of leaves increased. Plants with 35 leaves showed the greatest plant growth. Net photosynthetic rate was positively related to increasing leaf numbers with plants having over 25 leaves showing the greatest photosynthetic rates. On the other hand, there were no significant differences in chlorophyll content and root activity among treatments with different leaf numbers. The ratio of LYS infection was also greater in plants having 25-30 leaves, than in those having leaf numbers. Plants with different leaf numbers and LYS infection showed a variation in fruit quality, although LYS did not significantly affect fruit quality except net index. The plants having 20 leaves that showed LYS developed fruits that had significantly smaller flesh (mesocarp) thickness than, the plants having greater numbers of leaves. The higher sugar contents of fruits were found in the plants having 35 leaves whether they showed LYS (12.1°Bx) or not (12.5°Bx). Therefore, leaving more than 25 healthy leaves per plant was recommended for minimizing damage from LYS.OAIID:oai:osos.snu.ac.kr:snu2015-01/104/0000027607/11ADJUST_YN:NEMP_ID:A075898DEPT_CD:517CITE_RATE:0FILENAME:(이희주)effect_of_total_leaf_numbers_on_the_growth_and_fruit_quality_in_muskmelon_plants_showing_leaf_yell··.pdfDEPT_NM:식물생산과학부CONFIRM:

Training Energy-Based Normalizing Flow with Score-Matching Objectives

In this paper, we establish a connection between the parameterization of flow-based and energy-based generative models, and present a new flow-based modeling approach called energy-based normalizing flow (EBFlow). We demonstrate that by optimizing EBFlow with score-matching objectives, the computation of Jacobian determinants for linear transformations can be entirely bypassed. This feature enables the use of arbitrary linear layers in the construction of flow-based models without increasing the computational time complexity of each training iteration from $\mathcal{O}(D^2L)$ to $\mathcal{O}(D^3L)$ for an $L$-layered model that accepts $D$-dimensional inputs. This makes the training of EBFlow more efficient than the commonly-adopted maximum likelihood training method. In addition to the reduction in runtime, we enhance the training stability and empirical performance of EBFlow through a number of techniques developed based on our analysis on the score-matching methods. The experimental results demonstrate that our approach achieves a significant speedup compared to maximum likelihood estimation, while outperforming prior efficient training techniques with a noticeable margin in terms of negative log-likelihood (NLL)