An Experimental Study on the Defrosting Performance of a PCM-Based Reverse-Cycle Defrosting Method for Air Source Heat Pumps

Department of Building Services EngineeringRefereed conference pape

Leaching Behaviors of Calcium and Aluminum from an Ionic Type Rare Earth Ore Using MgSO4 as Leaching Agent

During the leaching process of ionic rare earth ore (ICREO), ion-exchangeable phase calcium (IEP-Ca) and ion-exchangeable phase aluminum (IEP-Al) are leached along with rare earth, which causes many problems in the enrichment process, such as increasing the precipitant agent consumption and rare earth loss, etc. The agitation leaching kinetics and the column leaching mass transfer process of IEP-Ca and IEP-Al were studied to understand the leaching behavior of impurity in ICREO, which provides guides for the adjustment of the leaching process and to limit the co-leaching of impurities. IEP-Ca and IEP-Al were leached by ion exchange, with the leaching agent cations and the leaching kinetics described by an internal diffusion-controlled shrinking core model with an apparent activation energy of 8.97 kJ/mol and 10.48 kJ/mol, respectively. In addition, a significant reduction in the leaching efficiency of aluminum was caused by the hydrolysis reaction reinforced by the increase in MgSO4 concentration and temperature. The leaching kinetic data of IEP-Ca and IEP-Al was verified by the column leaching mass transfer process. There was a synchronous increase in the peak concentration of the outflow curve and leaching efficiency of calcium with the concentration of MgSO4 since IEP-Ca was easily leached. Therefore, as the leaching efficiency of calcium was already very high in the 0.20 mol/L MgSO4 leaching process, the leaching rate of calcium was limited by the leaching temperature and injection rate of MgSO4. For aluminum, the hydrolysis of Al3+ was promoted by increasing the MgSO4 concentration and the leaching temperature, thereby effectively reducing the content of aluminum in the leachate

Low-Illumination Image Enhancement in the Space Environment Based on the DC-WGAN Algorithm

Owing to insufficient illumination of the space station, the image information collected by the intelligent robot will be degraded, and it will not be able to accurately identify the tools required for the robot’s on-orbit maintenance. This situation increases the difficulty of the robot’s maintenance in a low-illumination environment. We proposes a novel enhancement method for images under low-illumination, namely, a deep learning algorithm based on the combination of deep convolutional and Wasserstein generative adversarial networks (DC-WGAN) in CIELAB color space. The original low-illuminance image is converted from the RGB space to the CIELAB color space which is relatively close to human vision, to accurately estimate the illumination image, and effectively reduce the effect of uneven illumination. DC-WGAN is applied to enhance the brightness component by increasing the width of the generation network to obtain more image features. Subsequently, the LAB is converted into RGB space to obtain the final enhanced image. The feasibility of the algorithm is verified by experiments on low-illuminance image under general, special, and actual conditions and comparing the experimental results with four commonly used algorithms. This study lays a technical foundation for robot target recognition and on-orbit maintenance in a space environment

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy

Proteomic analysis of neonatal mouse hearts shows PKA functions as a cardiomyocyte replication regulator

Abstract The ability of the adult mammalian heart to regenerate can save the cardiac muscle from a loss of function caused by injury. Cardiomyocyte regeneration is a key aspect of research for the treatment of cardiovascular diseases. The mouse heart shows temporary regeneration in the first week after birth; thus, the newborn mouse heart is an ideal model to study heart muscle regeneration. In this study, proteomic analysis was used to investigate the differences in protein expression in the hearts of neonatal mice at days 1 (P1 group), 4 (P4 group), and 7 (P7 group). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed changes in several groups of proteins, including the protein kinase A (PKA) signaling pathway. Moreover, it was found that PKA inhibitors and agonists regulated cardiomyocyte replication in neonatal mouse hearts. These findings suggest that PKA may be a target for the regulation of the cardiomyocyte cell cycle

Biodegradable Carriers for Delivery of VEGF Plasmid DNA for the Treatment of Critical Limb Ischemia

The safe and efficient delivery of therapeutic nucleic acid is a prerequisite for an effective DNA therapy. In this study, we condensed the low molecular weight polyethylenimine (PEI, 1.8k Da) with 2,6-pyridinedicarboxaldehyde (PDA), both of which are degradable in vivo, to synthesize a biodegradable polycationic material (PDAPEI) to deliver vascular endothelial growth factor (VEGF) plasmid DNA (pDNA). Particle size and zeta potential of this novel degradable PEI derivatives-pDNA nanoparticle were investigated and in vitro cytotoxicity was estimated on human umbilical vein endothelial cells (HUVECs). Using pDNA-encoding VEGF-A and green fluorescence protein (GFP), we also checked transfection efficiency of the vector (PDAPEI) and found its excellent performance at 40 w/w ratio. We successfully established peripheral ischemia animal model on C57/BL6J mice to evaluate the therapeutic effect of PDAPEI/pVEGF-A polyplex system on ischemic disease and a conclusion was made that PDAPEI is a promising gene vector in the treatment of peripheral ischemic artery disease (PAD)

Additional file 1 of Proteomic analysis of neonatal mouse hearts shows PKA functions as a cardiomyocyte replication regulator

Additional file 1: Figure S1. Statistical enrichment test” results from the PANTHER website. Changes in protein enrichment between the P1 and P4 groups determined by the “statistical enrichment test