Simulation study of particle–fluid two-phase coupling flow field and its influencing factors of crystallization process

Obtaining the morphology of two-phase flow field accurately through experiments is very challenging, due to the complexity and the drainage area diversity of particle–fluid two-phase flow. Depending on the particle concentration, size, flow velocity, and so on, the two-phase flow tends to be in a more complex form, known as coupled flow status. Crystallisation process within a crystalliser is a typical engineering application of particle–fluid two-phase flow, and hence, the flow field within a potassium salt crystallizer is implemented to simulate the crystal suspension and to mix flow state during a continuous crystallisation process. Because the two-fluid model treats the particle phase and fluid phase as two distinct continuous media, this simulation model takes the effect of virtual mass force into considerations. The enhanced two-fluid model is then applied to investigate the influencing factors of the coupled flow field between the potassium salt particles and the fluid in the crystalliser under various operating conditions. The results indicated that the stirring speed, the concentration of the feed particles, and the particle size affected the distribution of coupled flow field at different levels and, thus, affected the crystallisation phenomena of a potassium salt. Among those factors, the stirring speed appears to have the most obvious effect on the flow field, as it affects the velocity of the two-phase flow. In the conditions listed in this paper, the minimum stirring speed is roughly 50 rpm to form a stable and circular flow field in the crystallizer, and the maximum particle size is controlled at around 12 mm and the feed particle concentration of roughly 32% to ensure cyclic crystallization. The research method used in this article provides a baseline for the study of the coupled flow field of particle–fluid two-phase flow and its influencing factors. This research also states theoretical guidance for the optimisation of operating conditions in the production and application of potassium salt crystallizer

Plant-related subsistence in the Pearl River Delta, Southern China, from 6,000 BP to 3,000 BP

In South China, there is limited evidence for prehistoric plant-related subsistence practices, due to poor macrofossils preservation in the acid soils and humid climate and the limitation of phytoliths analysis. The Pearl River Delta has an important sea-land transition position in South China, where the native Neolithic cultures had weak cultural continuity but easily adopted external features from other regions. To understand prehistoric plant-related subsistence, especially starch plant use, in the Pearl River Delta, the analysis of archaeological starch grains recovered from plant processing tools is selected as the main research approach of this thesis.A total of 61 grinding and pounding tools were selected as archaeological samples from two sites from around 6,000 to 5,000 BP (Guye site and Haogang site (the second phase)) and five settlements during 4,500 to 3,000 BP (Haogang site (the third phase), Yuanzhou site, Cuntou site, Yinzhou site, and Hengling site) in the Pearl River Delta.The results show that from 6,000 to 3,000 BP, acorns and geophytes were important food sources in the Pearl River Delta. In the early phase (from 6,000 BP to 5,000 BP), the proportion of acorn/oak-chestnut starch was higher than that of geophytes (in this thesis, it refers to the plants with starch-rich underground storage organs), but that after 4,500 BP the role of geophytes in the diet appears to have increased. The starch granule data from grinding and pounding tools combined with other archaeological site data strongly suggest that during the mid-Holocene small, sedentary, village settlements were heavily dependent on hunting and gathering, with only limited impact from cereal cultivation (e.g. rice and millets).</div

Identification of 14-3-3 Proteins Phosphopeptide-Binding Specificity Using an Affinity-Based Computational Approach

<div><p>The 14-3-3 proteins are a highly conserved family of homodimeric and heterodimeric molecules, expressed in all eukaryotic cells. In human cells, this family consists of seven distinct but highly homologous 14-3-3 isoforms. 14-3-3<i>σ</i> is the only isoform directly linked to cancer in epithelial cells, which is regulated by major tumor suppressor genes. For each 14-3-3 isoform, we have 1,000 peptide motifs with experimental binding affinity values. In this paper, we present a novel method for identifying peptide motifs binding to 14-3-3<i>σ</i> isoform. First, we propose a sampling criteria to build a predictor for each new peptide sequence. Then, we select nine physicochemical properties of amino acids to describe each peptide motif. We also use auto-cross covariance to extract correlative properties of amino acids in any two positions. Finally, we consider elastic net to predict affinity values of peptide motifs, based on ridge regression and least absolute shrinkage and selection operator (LASSO). Our method tests on the 1,000 known peptide motifs binding to seven 14-3-3 isoforms. On the 14-3-3<i>σ</i> isoform, our method has overall pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) values of 0.84 and 252.31 for <i>N</i>–terminal sublibrary, and 0.77 and 269.13 for <i>C</i>–terminal sublibrary. We predict affinity values of 16,000 peptide sequences and relative binding ability across six permutated positions similar with experimental values. We identify phosphopeptides that preferentially bind to 14-3-3<i>σ</i> over other isoforms. Several positions on peptide motifs are in the same amino acid category with experimental substrate specificity of phosphopeptides binding to 14-3-3<i>σ</i>. Our method is fast and reliable and is a general computational method that can be used in peptide-protein binding identification in proteomics research.</p></div

Rozložení tepelných toků na stěnu tokamaku způsobených okrajovými nestabilitami

Edge localized modes (ELMs) are a concern for future magnetic fusion devices, such as ITER, due to the large transient heat loads they generate on the plasma facing components. A very promising method of ELM suppression is an application of resonant magnetic perturbations (RMP); however, such application leads to localized places of higher heat fluxes called footprints. Both ELMs and RMP could limit the operational lifetime of the device. In this thesis, we analyze the temporal and spatial distribution of footprints using the tangle distance method in the aim to prevent a transient overheating. We also analyze quasi-double-null configuration of the ITER plasma which can be expected to be the most susceptible to overheating of the upper wall. Based on the modelling, the potentially dangerous configurations of the RMP have been shown. Using the ELM filament model included in the LOCUST GPU code, we study temporal and spatial distribution of the heat fluxes caused by ELMs in the axially symmetric and the asymmetric magnetic field. The results are compared with published experimental observations. Powered by TCPDF (www.tcpdf.org

Five categories of 20 amino acids.

<p>Five categories of 20 amino acids.</p

List of four preferable binders of 14-3-3<i>σ</i> from 1,000 peptide sequences.

<p>List of four preferable binders of 14-3-3<i>σ</i> from 1,000 peptide sequences.</p

Position-specific scoring matrix on top 500 motifs identified from 16,000 peptide sequences against individual 14-3-3 isoforms.

<p>Position-specific scoring matrix on top 500 motifs identified from 16,000 peptide sequences against individual 14-3-3 isoforms.</p

Details on predicting peptide motifs binding to 14-3-3 isoforms.

<p>Details on predicting peptide motifs binding to 14-3-3 isoforms.</p

14-3-3 preferences determined with different methods on 1,000 peptide motifs.

<p>14-3-3 preferences determined with different methods on 1,000 peptide motifs.</p

14-3-3 preferences determined with different methods on 16,000 peptide sequences.

<p>14-3-3 preferences determined with different methods on 16,000 peptide sequences.</p