Identifying aspirin polymorphs from combined DFT-based crystal structure prediction and solid-state NMR

A combined experimental and computational approach was used to distinguish between different polymorphs of the pharmaceutical drug aspirin. This method involves the use of ab initio random structure searching (AIRSS), a density functional theory (DFT)-based crystal structure prediction method for the high-accuracy prediction of polymorphic structures, with DFT calculations of nuclear magnetic resonance (NMR) parameters and solid-state NMR experiments at natural abundance. AIRSS was used to predict the crystal structures of form-I and form-II of aspirin. The root-mean-square deviation between experimental and calculated ¹H chemical shifts was used to identify form-I as the polymorph present in the experimental sample, the selection being successful despite the large similarities between the molecular environments in the crystals of the two polymorphs.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

Comparison study of exchange-correlation functionals on prediction of ground states and structural properties

Despite significant advances in first-principles calculation methods, there is no single exchange-correlation functional which predicts the ground state of materials without an error yet. We investigated how accurately ground states of binary semiconductors are described using 16 exchange-correlation functionals (with or without van der Waals corrections). LDA, PBEsol, SCAN (with or without rVV10 correction), and PBE with D3 van der Waals correction (zero or Becke-Johnson damping) show good predicting power. The lattice constants of stable phases were slightly better described by SCAN, PBEsol, PBE+D3 (Becke-Johnson damping), and MS2. We also propose a set of functionals to double-check the stability of new materials based on the majority vote.     

Effect of wall slip on the viscoelastic particle ordering in a microfluidic channel

The formation of a line of equally spaced particles at the centerline of a microchannel, referred as “particle ordering,” is desired in several microfluidic applications. Recent experiments and simulations highlighted the capability of viscoelastic fluids to form a row of particles characterized by a preferential spacing. When dealing with non-Newtonian fluids in microfluidics, the adherence condition of the liquid at the channel wall may be violated and the liquid can slip over the surface, possibly affecting the ordering efficiency. In this work, we investigate the effect of wall slip on the ordering of particles suspended in a viscoelastic liquid by numerical simulations. The dynamics of a triplet of particles in an infinite cylindrical channel is first addressed by solving the fluid and particle governing equations. The relative velocities computed for the three-particle system are used to predict the dynamics of a train of particles flowing in a long microchannel. The distributions of the interparticle spacing evaluated at different slip coefficients, linear particle concentrations, and distances from the channel inlet show that wall slip slows down the self-assembly mechanism. For strong slipping surfaces, no significant change of the initial microstructure is observed at low particle concentrations, whereas strings of particles in contact form at higher concentrations. The detrimental effect of wall slip on viscoelastic ordering suggests care when designing microdevices, especially in case of hydrophobic surfaces that may enhance the slipping phenomenon.     

Insights into stretching ratio and velocity slip on MHD rotating flow of Maxwell nanofluid over a stretching sheet: Semi-analytical technique OHAM

Main core part of the research is to develop a novel mathematical model of MHD-Maxwell nanofluid over a stretching and shrinking surface. The stretching ratio, velocity slip and convective boundary conditions are also incorporated. The PDE's with associative boundary conditions are deduced into coupled highly non-linear ODE's by utilizing suitable transformations. The deduced dimensionless sets of Ordinary differential equations are solved by Optimal-Homotopy Analysis Method (OHAM). Behavior of pertinent parameters on the velocity, temperature and concentration fields as well as important aspects skin friction, Nusselt number and Sherwood number are recorded in Table 2. Outcomes declared that role of stretching ratio plays a prominent role in stretching surfaces its clearly recorded in Table 1(a & b).     

具有连续参数齐次随机场的线性预测(Ⅲ)

提出和研究了一般的斜半平面及角状区域上的具有连续参数齐次随机场的线性预测问题,且推广了文[1]的结果.     

运用系统动力学模型预测稀土战略储备规模

基于全球稀土资源的供需形势和中国稀土资源减少的趋势,运用系统动力学的模型来预测未来17年我国稀土资源储备量。选取了2000~2013的生产和消费数据,通过定量模拟计算中国稀土供需差波动系数,设计了稀土资源供需差波动范围分别为0%,20%的两个方案,模拟计算了两个方案的年储备量、累计储备量和储备量绝对值。方案一范围是(-0%,+0%),仿真预测出稀土未来17年累积储备量为720551 t,即战略储备规模达到720551 t,该方案下不论供需差发生如何波动,国家都会进行储备或释放调节;方案二范围是(-20%,+20%),预测出未来17年战略储备规模为461331 t,国家只需在这个范围外进行收储释放调节,从而调节市场供求,降低价格波动幅度。本文从不同供需差的波动系数来模拟计算储备量,目的是为国家相关部门在进行稀土储备决策时提供参考,可以根据我国经济景气状况和供需形势,以及稀土产业发展状况,进行不同规模的稀土储备。     

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.