Quantitative Prediction of Air Entrainment Defects in Casting Filling Process

Air entrainment defect is a common type of defect in the casting process, which will seriously affect the quality of the casting. Numerical simulation technology can predict the occurrence of casting defects according to the evolution law of liquid metal in the process of fill ing and solidification. The simulation of air entrainment process is a hot and difficult issue in the field of numerical simulation. The evolution law of air entrainment and the tracking of induced bubbles in the process of metal filling are still lacking. So is the quantitative prediction of trained gas. In this paper, based on the numerical simulation software of Inte CAST, this paper proposes an algorithm for air entrainment search and tracking, which is used to develop a quantitative prediction system for air entrainment. The feasibility of the system is verified through the simulation calculation of the typical test pieces of the air entrainment and the prediction of air entrainment defects of the casting in the process of filling is obtained through the simulation calculation of the actual casting, which can provide a certain guiding role for the optimization of the process in the production practice

Burst phase distribution of SGR J1935+2154 based on Insight-HXMT

On April 27, 2020, the soft gamma ray repeater SGR J1935+2154 entered its intense outburst episode again. Insight-HXMT carried out about one month observation of the source. A total number of 75 bursts were detected during this activity episode by Insight-HXMT, and persistent emission data were also accumulated. We report on the spin period search result and the phase distribution of burst start times and burst photon arrival times of the Insight-HXMT high energy detectors and Fermi Gamma-ray Burst Monitor (GBM). We find that the distribution of burst start times is uniform within its spin phase for both Insight-HXMT and Fermi-GBM observations, whereas the phase distribution of burst photons is related to the type of a burst's energy spectrum. The bursts with the same spectrum have different distribution characteristics in the initial and decay episodes for the activity of magnetar SGR J1935+2154.Comment: 12 pages, 9 figure

Effects of N addition on soil enzyme activities in marshland ecosystem of Northeast China: an incubation experiment

In the Sanjiang Plain of Northeast China, the marshland ecosystem have received increased nitrogen (N) input due to the N fertilizer application during agricultural activities. However, there is litter information on the effects of increased soil N availability on soil enzyme activities in this ecosystem. The objective of present study was to assess the effects of increased N input on marshland soil enzyme activities in the Sanjiang Plain, China. N was added as ammonium nitrate (NH4NO3) solution at four levels (0 mg N g(-1), N0; 0.1 mg N g(-1), N1; 0.2 mg N g(-1), N2; 0.5 mg N g(-1), N3). N addition suppressed soil urease activity throughout the incubation, stimulated invertase activity after 15 days of incubation, and had no effect on acid phosphatase activity except for N3 treatment at 27 days. Negative correlations were observed between the urease activity and inorganic N contents, both for NH4+ and NO3-. These findings suggested that N addition can potentially alter soil enzyme activities and might significantly change the nutrient cycles in the marsh ecosystem

Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency

Wenyangbushen-induced-expression of VEGF, OPG, RANK, and RANKL in rabbits with steroid-induced femoral head avascular necrosis

This study was aimed to investigate the effects of Wenyangbushen formula on the mRNA and protein expression of VEGF, OPG, RANK, and RANKL in the steroid- induced avascular necrosis of the rabbit femoral head (SANFH) and to further explore the potential mechanism of this formula on the treatment of SANFH. One hundred and thirty six New Zealand rabbits were randomly divided into 5 groups (n=24 rabbits in each group): normal group, model group, Traditional Chinese Medicine (TCM) Wenyangbushen decoction at low, moderate and high dose group. The normal group and positive control group were intragastrically (i.g.) administered with saline. The TCM group was treated with Wenyangbushen decoction at the indicated dosage. After treated for eight weeks, the mRNA and protein expression of VEGF, OPG, RANK, and RANKL in the femoral head tissues was determined by RT-PCR and Western blotting, respectively. Wenyangbushen decoction treatment can effectively promote bone cells, osteoblasts and chondrocytes growth, as well as prevent the cell apoptosis in SANFH. The mRNA and protein expression of OPG and VEGF was increased while the levels of RANK and RANKL were reduced in the necrotic tissue of model group compared with those in the normal rabbits. And Wenyangbushen treatment prevented those changes, as manifested by up-regulation of VEGF and OPG, while down-regulation of RANK and RANKL levels in a dose-dependent manner. Wenyangbushen Formula treatment can alleviate necrosis of femoral head induced by steroid. It can promote bone cells, osteoblasts and chondrocytes growth, as well as prevent the cell apoptosis. Meanwhile, it up-regulates OPG and VEGF while inhibits RANK and RANKL expression. This may be one of the mechanisms for the effective treatment of SANFH

Three decades of changes in water environment of a large freshwater Lake and its relationship with socio-economic indicators

Tai Lake (Ch: Taihu) has attracted international attention forcyanobacteria blooms. However, the drivers of cultural eutrophication, especially long-term socio-economic indicators have been little researched. The results of research demonstrate how socioeconomic development affected quality of water and how it has been improved by anthropogenic activities. This study described variability in indicators of water quality in Tai Lakeand investigated thedrivers. Significant relationships existed between concentrations of annual mean total nitrogen (TN), total phosphorous (TP), chemical oxygen demand (COD) and biological oxygen demand (BOD), and population, per capital gross domestic production (CDP) and sewage discharge (p < 0.05). However, mechanisms causing change varied among TN, TP, COD and BOD. Before 2000, the main contributors to increases in concentrations of TN were human population, GDP and volumes of domestic sewage discharges. After 2000, discharges of industrial sewage become the primary contributor. After 1998, the regressions of annual mean TN, TP and COD on per capital GDP, population and domestic sewage discharge were reversed compared to the former period. Since 1999, an apparent inverted U-shaped relationship between environmental pollution and economic development has developed, which indicated that actions taken by governments have markedly improved quality of water in Tai Lake. The statistical relationship between BOD and per capital GDP didn't conform to the Kuznet curve. The Ushaped Kuznet curve may offer hope for the future that with significant environmental investments a high GDP can be reached and maintained without degradation of the environment, especially through appropriate management of industrial sewage discharge. (C) 2018 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V

Novel Insights into the Kinetics, Evolved Gases, and Mechanisms for Biomass (Sugar Cane Residue) Pyrolysis

Biomass, a renewable energy source, via available thermo-chemical processes has both engineering and environmental advantages. However, the understanding of the kinetics, evolved gases, and mechanisms for biomass pyrolysis is limited. We first propose a novel temperature response mechanism for the pyrolysis of sugar cane residue using thermogravimetric analysis-Fourier transform infrared spectrometry-mass spectrometry (TG-FTIR-MS) combined with Gaussian model and two-dimensional correlation spectroscopy (2D COS). The existence and contribution of distinct peaks in TG-FTIR spectra were innovatively distinguished and quantified, and the temperature-dependent dynamics of gas amounts were determined using Gaussian deconvolution. The 2D-TG-FTIR/MS-COS results revealed for the first time that the primary sequential temperature responses of gases occurred in the order: H2O/CH4 > phenols/alkanes/aromatics/alcohols > carboxylic acids/ketones > CO2/ethers > aldehyde groups/acetaldehyde. Subtle sequential changes even occurred within the same gases during pyrolysis. The quantity dynamics and sequential responses of gases were fitted to the combined effects of the order-based, diffusion, and chemical reaction mechanisms for the component degradation. The combination of TG-FTIR-MS, Gaussian model, and 2D COS is a promising approach for the online monitoring and real-time management of biomass pyrolysis, providing favorable strategies for pyrolysis optimization, byproduct recovery, energy generation, and gas emission control in engineering and environmental applications