Forecasting of Two-Phase Flow Patterns in Upward Inclined Pipes via Deep Learning

Conventionally, the boundaries of gas-liquid flow regime transition are extremely sensitive to the inclination of flow channels. However, traditional two-dimensional flow regime maps have difficulties to reflect this fact as it can only accommodate two independent variables, which are often the gas and liquid superficial velocities. Few investigators have been able to propose a single model with accessible inputs under the considerations of the whole range of upward inclined angels. In this paper, we developed a novel approach by applying a typical machine learning (ML) method, artificial neural network (ANN), to predict flow pattern along upward inclined pipe (0 ~ 90°) using easily accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. TensorFlow, a new generation and popular open-source foundation for ML programming, was used for building the ANN model, which was trained and tested by experimental data (1952 data points) that were reported in the literature. The predicting results show that ANN identifications have a satisfying agreement with experimental observations. The predicting accuracies of stratified smooth, stratified wavy, annular, intermittent, bubble flow are all above 90%, with the only exception of dispersed bubble flow (73%). In addition, the validation of the model was extended by comparing the ANN’s performance with well-established two-phase transition boundary models among different flow regimes. Comparing against conventional methods based on either correlation or flow regime map, the developed ANN model is expected to be a more efficient tool in flow pattern prediction. Furthermore, the impact of inclination angles on final ANN outputs was evaluated quantitatively. Results showed, given flow conditions fixed, variations of inclination angles have a significant influence on gas- liquid flow patterns in channels of conventional sizes

Predicting Growth of City\u27s Built-up Land Based on Scenario Planning

In this paper, method of scenario planning is applied to the study of land use planning, putting forward a new approach to analyze future growth of city\u27s built-up land in the context of future uncertainty. By introducing economic and policy factors into land use system, a calculation model of urban built-up land is built based on the correlation between industries and land use. And using Chongqing Municipality from China as an example, we establish 6 different scenarios and simulate future development of city\u27s land use from 2015 to 2020 under each scenario. The results indicate that Chongqing will meet fast urban expansion according to current trend and is in urgent need to improve its land use efficiency which shows strongest effect in controlling city size

Prediction of two-phase flow patterns in upward inclined pipes via deep learning

The industrial process involving gas liquid flows is one of the most frequently encountered phenomena in the energy sectors. However, traditional methods are practically unable to reliably identify flow patterns if additional independent variables/parameters are to be considered rather than gas and liquid superficial velocities. In this paper, we reported an approach to predict flow pattern along upward inclined pipes (0–90°) via deep learning neural networks, using accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. The developed approach is equipped with deep learning neural network for flow pattern identification by experimental datasets that were reported in the literature. The predictive model was further validated by comparing its performance with well-established flow regime forecasting methods based on conventional flow regime maps. Besides, the intensity of key features in flow pattern prediction was identified by the deep learning algorithm, which is difficult to be captured by commonly used correlation approache

Minute-cadence Observations of the LAMOST Fields with the TMTS: III. Statistic Study of the Flare Stars from the First Two Years

Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) aims to detect fast-evolving transients in the Universe, which has led to the discovery of thousands of short-period variables and eclipsing binaries since 2020. In this paper, we present the observed properties of 125 flare stars identified by the TMTS within the first two years, with an attempt to constrain their eruption physics. As expected, most of these flares were recorded in late-type red stars with $G_{\rm BP}-G_{\rm RP}$ > 2.0 mag, however, the flares associated with bluer stars tend to be on average more energetic and have broader profiles. The peak flux (F_peak) of the flare is found to depend strongly on the equivalent duration (ED) of the energy release, i.e., $F_{{\rm peak}} \propto {\rm ED}^{0.72\pm0.04}$, which is consistent with results derived from the Kepler and Evryscope samples. This relation is likely related to the magnetic loop emission, while -- for the more popular non-thermal electron heating model -- a specific time evolution may be required to generate this relation. We notice that flares produced by hotter stars have a flatter $F_{{\rm peak}} \propto {\rm ED}$ relation compared to that from cooler stars. This is related to the statistical discrepancy in light-curve shape of flare events with different colors. In spectra from LAMOST, we find that flare stars have apparently stronger H alpha emission than inactive stars, especially at the low temperature end, suggesting that chromospheric activity plays an important role in producing flares. On the other hand, the subclass having frequent flares are found to show H alpha emission of similar strength in their spectra to that recorded with only a single flare but similar effective temperature, implying that the chromospheric activity may not be the only trigger for eruptions.Comment: 17 pages, 15 figures, 2 tables, refereed version. For associated data files, see https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/523/219

Transcriptomic and Proteomic Analysis of Shaan2A Cytoplasmic Male Sterility and Its Maintainer Line in Brassica napus

Cytoplasmic male sterility (CMS) lines are widely used for hybrid production in Brassica napus. The Shaan2A CMS system is one of the most important in China and has been used for decades; however, the male sterility mechanism underlying Shaan2A CMS remains unknown. Here, we performed transcriptomic and proteomic analysis, combined with additional morphological observation, in the Shaan2A CMS. Sporogenous cells, endothecium, middle layer, and tapetum could not be clearly distinguished in Shaan2A anthers. Furthermore, Shaan2A anther chloroplasts contained fewer starch grains than those in Shaan2B (a near-isogenic line of Shaan2A), and the lamella structure of chloroplasts in Shaan2A anther wall cells was obviously aberrant. Transcriptomic analysis revealed differentially expressed genes (DEGs) mainly related to carbon metabolism, lipid and flavonoid metabolism, and the mitochondrial electron transport/ATP synthesis pathway. Proteomic results showed that differentially expressed proteins were mainly associated with carbohydrate metabolism, energy metabolism, and genetic information processing pathways. Importantly, nine gene ontology categories associated with anther and pollen development were enriched among down-regulated DEGs at the young bud (YB) stage, including microsporogenesis, sporopollenin biosynthetic process, and tapetal layer development. Additionally, 464 down-regulated transcription factor (TF) genes were identified at the YB stage, including some related to early anther differentiation such as SPOROCYTELESS (SPL, also named NOZZLE, NZZ), DYSFUNCTIONAL TAPETUM 1 (DYT1), MYB80 (formerly named MYB103), and ABORTED MICROSPORES (AMS). These results suggested that the sterility gene in the Shaan2A mitochondrion might suppress expression of these TF genes in the nucleus, affecting early anther development. Finally, we constructed an interaction network of candidate proteins based on integrative analysis. The present study provides new insights into the molecular mechanism of Shaan2A CMS in B. napus

Efficacy of Chuanxiong Ding Tong Herbal Formula Granule in the Treatment and Prophylactic of Migraine Patients: A Randomized, Double-Blind, Multicenter, Placebo-Controlled Trial

Objective. To evaluate the efficacy of traditional Chinese herbal ChuanXiong Ding Tong herbal formula granule (CXDT-HFG) for migraine patients with “the Syndrome of Liver Wind and Blood Stasis.” Methods. 150 migraine patients were recruited and assigned randomly in a double-blind, placebo-controlled study to receive CXDT-HFG (n=99) plus necessary analgesics, or placebo (n=51) plus necessary analgesics for 16 weeks (12 weeks’ intervention and 4 weeks’ follow up). Outcome measures included migraine days, frequency of migraine attacks, analgesics consumption for acute treatment, and the proportion of responders as well as the visual analogue scale (VAS) scores and intensity for pain. Results. Compared with the placebo group, the CXDT-HFG group showed significant reduction in migraine days and attacks frequency at week 12 and follow-up period (P0.05). Conclusion. CXDT-HFG was more effective than placebo in decreasing days of migraine attacks, frequency, VAS scores, and relieving pain intensity for migraine patients

Notch-dependent repression of miR-155 in the bone marrow niche regulates hematopoiesis in an NF-κB-dependent manner

The microRNA miR-155 has been implicated in regulating inflammatory responses and tumorigenesis, but its precise role in linking inflammation and cancer has remained elusive. Here, we identify a connection between miR-155 and Notch signaling in this context. Loss of Notch signaling in the bone marrow (BM) niche alters hematopoietic homeostasis and leads to lethal myeloproliferative-like disease. Mechanistically, Notch signaling represses miR-155 expression by promoting binding of RBPJ to the miR-155 promoter. Loss of Notch/RBPJ signaling upregulates miR-155 in BM endothelial cells, leading to miR-155-mediated targeting of the nuclear factor κB (NF-κB) inhibitor κB-Ras1, NF-κB activation, and increased proinflammatory cytokine production. Deletion of miR-155 in the stroma of RBPJ(-/-) mice prevented the development of myeloproliferative-like disease and cytokine induction. Analysis of BM from patients carrying myeloproliferative neoplasia also revealed elevated expression of miR-155. Thus, the Notch/miR-155/κB-Ras1/NF-κB axis regulates the inflammatory state of the BM niche and affects the development of myeloproliferative disorders

A common variant near TGFBR3 is associated with primary open angle glaucoma

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Primary open angle glaucoma (POAG), a major cause of blindness worldwide, is a complex disease with a significant genetic contribution.We performed Exome Array (Illumina) analysis on 3504 POAG cases and 9746 controls with replication of the most significant findings in 9173 POAG cases and 26 780 controls across 18 collections of Asian, African and European descent. Apart from confirming strong evidence of association at CDKN2B-AS1 (rs2157719 [G], odds ratio [OR] = 0.71, P = 2.81 × 10−33), we observed one SNP showing significant association to POAG (CDC7–TGFBR3 rs1192415, ORG-allele = 1.13, Pmeta = 1.60 × 10−8). This particular SNP has previously been shown to be strongly associated with optic disc area and vertical cup-to-disc ratio, which are regarded as glaucoma-related quantitative traits. Our study now extends this by directly implicating it in POAG disease pathogenesis

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan