MODELING OF HOPPER DISCHARGE

Hoppers are widely used in many engineering processes. The discharging of granular mate- rials from a hopper is a critical topic of industrial importance, and the discharge flow rate from hoppers is the focus of the current work. Many parameters influence the discharge rate including: the hopper outlet width, the angle of the hopper wall, the particle size, and particle friction, and so on. Due to the expensive of examining a large variety of particle types and hopper conditions, computational simulation has been widely studied in an effort to establish an alternative method of determining critical factors impacting hopper flow. In this thesis, the process of hopper discharge has been simulated by the Discrete Element Method (DEM), which is one of the most popular methods for granular flow simulation. To validate against existing experiments, all conditions were matched as closely as possible to those in the experiment. The particles used in our simulation are spheroids with diameters of 0.77 cm. The angles of the hoppers examined range from 0◦ to 90◦, while the opening sizes vary from 2.9 cm to 4.3 cm. Computationally, the friction coefficient has been adjusted several times and finally is set to 0.5 in the simulation in order to fit the experimental resultsas closely as possible. As a quantitative test of the simulation fidelity we compare the hopper empty time t – which is related to the hopper discharge rate – for these different hopper angles and hopper opening size. As a secondary test of the fit, the survival time τ, the normal force profile, the velocity profile, and the probability of jamming Ps are also computed and compared to existing experimental data from collaborators at Duke University. Ultimately, the goal of the work is to establish the degree of model fidelity necessary in order to closely mimic the experimental results obtained

Validation of the plasma-wall self-organization model for density limit in ECRH-assisted start-up of Ohmic discharges on J-TEXT

A recently developed plasma-wall self-organization (PWSO) model predicts a significantly enhanced density limit, which may be attainable in tokamaks with ECRH-assisted ohmic startup and sufficiently high initial neutral density. Experiments have been conducted on J-TEXT to validate such a density limit scenario based on this model. Experimental results demonstrate that increasing the pre-filled gas pressure or ECRH power during the startup phase can effectively enhance plasma purity and raise the density limit at the flat-top. Despite the dominant carbon fraction in the wall material, some discharges approach the edge of the density-free regime of the 1D model of PWSO.Comment: 17 pages, 8 figure

Overexpressed transient receptor potential vanilloid 1 (TRPV1) in lung adenocarcinoma harbours a new opportunity for therapeutic targeting

The specific biological function of transient receptor potential vanilloid 1 (TRPV1) in pathogenesis of lung adenocarcinoma (LUAD) remains unclear. In this study, TRPV1 expression in tumor tissues, primary cells and cell lines of LUAD, as well as the mechanism mediating its hyperexpression were systematically studied. Multiple models and techniques were adopted to elucidate the relationship between TRPV1 hyperexpression and tumor recurrence and metastasis. Results showed that TRPV1 expression was increased in tumor tissues and primary tumor cells of LUAD patients. The increased expression was associated with worse overall survival outcome and raised HIF1α levels. TRPV1 expression in A549 and NCI-H292 cells was increased after pretreatment with cigarette smoke extract or spermine NONOate. Moreover, A549 cells with TRPV1 overexpression has enhanced tumor growth rates in subcutaneous grafted tumor models, and increased intrapulmonary metastasis after tail vein infusion in nude BALB/c nude mice. Mechanistically, TRPV1 overexpression in A549 cells promoted HIF1α expression and nuclear translocation by promoting CREB phosphorylation and activation of NOS1-NO pathway, ultimately leading to accelerated cell proliferation and stronger invasiveness. In addition, based on photothermal effects, CuS-TRPV1 mAb effectively targeted and induced apoptosis of TRPV1-A549 cells both in vivo and in vitro, thereby mitigating tumor growth and metastasis induced by xenotransplantation of TRPV1-A549 cells. In conclusion, TRPV1 hyperexpression in LUAD is a risk factor for tumor progression and is involved in proliferation and migration of tumor cells through activation of HIF1α. Our study also attempted a new strategy inhibiting the recurrence and metastasis of LUAD: by CuS-TRPV1 mAb precisely kill TRPV1 hyperexpression cells through photothermal effects

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages