Investigation of Ce3+ Adsorption by Sn(OH)X by the Gravimetric Method

In this work, the adsorption of Ce3+ by Sn(OH)2, SnO, and Sn(OH)4 was investigated. By comparing the mass of cerium oxalate caused by the adsorbed Ce3+, Sn(OH)2 and Sn(OH)4 have the ability to adsorb Ce3+, while Sn(OH)4 has a stronger adsorption capacity of Ce3+. However, SnO does not have the ability. The possible mechanism of Sn(OH)X adsorption Ce3+ was further discussed. And the result indicates that the hydroxide can adsorb cations by means of anionic groups on its surface in the solution so that the cations can be enriched on the hydroxide surface. The paper provides a new method for adjusting the microstructure of catalysts, which has a promising prospect in the field of catalysts preparation

Numerical Simulation on Heat Transfer Performance of Silicon Carbide/ Nitrate Composite for Solar Power Generation

KNO3 was used as the phase change material (PCM), but its thermal conductivity is too low to transfer heat between the PCM and conduction oil efficiently. In this thesis, on the basis of the previous studies (Yong Li, 2015), the solar power generation efficiency is enhanced with high temperature interval (280℃—400℃), and the new composite which are composed by the SiC honeycomb (SCH) frame and infiltrated KNO3 is simulated by using Fluent software. The results show that the new composite of the KNO3 +30%SCH suit for the requirement of the charging time and capacity in the design of the thermal energy storage units (TESU); The comparable simulation for the long and short pipe models supplies the evidences that the long pipe simulation can be substituted by the short pipe simulation relatively, which reduces the 3-D simulation time enormously; The comparable simulation of the radial dimensions supplies some theory foundations for the design of the module thermal energy storage tank (MTEST) . These simulation results have important guidance on the design of the thermal energy storage unit and the module thermal energy storage tank

Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

<p>Abstract</p> <p>Background</p> <p>HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.</p> <p>Results</p> <p>In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the <it>L</it>-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.</p> <p>Conclusions</p> <p>The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.</p

A Numerical Study on the Temperature Field of a R290 Hermetic Reciprocating Compressor with Experimental Validation

A numerical model to predict the temperature field in a R290 hermetic reciprocating compressor is presented in this work. The control volume method and the lumped parameter method are used in the simulation. The compressor is divided into 6 control volumes, including the suction muffler, the cylinder, the discharge chamber, the discharge muffler, the discharge pipe and the shell. The system of non-linear equations is formed of the energy balance equations of every control column. The temperature field is derived by solving the equations. To valid the numerical model accurately, temperature experiment has been carried out in 3 same-type hermetic reciprocating compressors using R290 as working fluid. The simulation result shows a good agreement compared with the experiment

The Annual Rhythmic Differentiation of Populus davidiana Growth–Climate Response Under a Warming Climate in The Greater Hinggan Mountains

The stability and balance of forest ecosystems have been seriously affected by climate change. Herein, we use dendrochronological methods to investigate the radial growth and climate response of pioneer tree species in the southern margin of cold temperate coniferous forest based on Populus davidiana growing on the Greater Hinggan Mountains in northeastern China. Correlations of P. davidiana growth with temperature and precipitation in a year (October–September) were rhythmically opposed: while temperatures in previous October–June (winter and spring) and in May–September (growing season) respectively inhibited and promoted radial growth on P. davidiana (p \u3c 0.01), precipitation in the same periods respectively promoted and inhibited of growth (p \u3c 0.01). High temperature or less rain/snow in winter and early spring, and low temperature or excess rainfall in summer, are inconducive to P. davidiana growth and vice versa (p \u3c 0.01). In addition, in March–April, when air temperature was above 0 °C and ground temperature below 0 °C, physiological drought caused significant growth inhibition in P. davidiana (p \u3c 0.05). In general, temperatures play a driving and controlling role in the synergistic effect of temperature and precipitation on P. davidiana growth. Under current conditions of available water supply, changes of temperature, especially warming, are beneficial to the growth of P. davidiana in the study area. The current climate conditions promote the growth of P. davidiana, the pioneer species, compared with the growth inhibition of Larix gmelinii, the dominant species. Thus, the structure and function of boreal forest might be changed under global warming by irreversible alterations in the growth and composition of coniferous and broadleaf tree species in the forest

SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images

Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework \textit{SyreaNet} for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://github.com/RockWenJJ/SyreaNet.git.Comment: 7 pages; 10 figure

Optimal Power Allocation for Integrated Visible Light Positioning and Communication System with a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver's location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.Comment: 13 pages, 14 figures, accepted by IEEE Transactions on Communication

Conformational effects of UV light on DNA origami

The responses of DNA origami conformation to UV radiation of different wavelengths and doses are investigated. Short- and medium-wavelength UV light can cause photo-lesions in DNA origami. At moderate doses, the lesions do not cause any visible defects in the origami, nor do they significantly affect the hybridization capability. Instead, they help relieve the internal stress in the origami structure and restore it to the designed conformation. At high doses, staple dissociation increases which causes structural disintegration. Long-wavelength UV does not show any effect on origami conformation by itself. We show that this UV range can be used in conjunction with photoactive molecules for photo-reconfiguration, while avoiding any damage to the DNA structures