Sn(II)-containing phosphates as optoelectronic materials

We theoretically investigate Sn(II) phosphates as optoelectronic materials using first principles calculations. We focus on known prototype materials Sn$_n$P$_2$O$_{5+n}$ (n=2, 3, 4, 5) and a previously unreported compound, SnP$_2$O$_6$ (n=1), which we find using global optimization structure prediction. The electronic structure calculations indicate that these compounds all have large band gaps above 3.2 eV, meaning their transparency to visible light. Several of these compounds show relatively low hole effective masses ($\sim$2-3 m$_0$), comparable the electron masses. This suggests potential bipolar conductivity depending on doping. The dispersive valence band-edges underlying the low hole masses, originate from the anti-bonding hybridization between the Sn 5s orbitals and the phosphate groups. Analysis of structure-property relationships for the metastable structures generated during structure search shows considerable variation in combinations of band gap and carrier effective masses, implying chemical tunability of these properties. The unusual combinations of relatively high band gap, low carrier masses and high chemical stability suggests possible optoelectronic applications of these Sn(II) phosphates, including p-type transparent conductors. Related to this, calculations for doped material indicate low visible light absorption, combined with high plasma frequencies.Comment: 10 pages, 10 figures, Supplementary informatio

Subdivision and manifold techniques for isogeometric design and analysis of surfaces

Design of surfaces and analysis of partial differential equations defined on them are of great importance in engineering applications, e.g., structural engineering, automotive and aerospace. This thesis focuses on isogeometric design and analysis of surfaces, which aims to integrate engineering design and analysis by using the same representation for both. The unresolved challenge is to develop a desirable surface representation that simultaneously satisfies certain favourable properties on meshes of arbitrary topology around the extraordinary vertices (EVs), i.e., vertices not shared by four quadrilaterals or three triangles. These properties include high continuity (geometric or parametric), optimal convergence in finite element analysis as well as simplicity in terms of implementation. To overcome the challenge, we further develop subdivision and manifold surface modelling techniques, and explore a possible scheme to combine the distinct appealing properties of the two. The unique advantages of the developed techniques have been confirmed with numerical experiments. Subdivision surfaces generate smooth surfaces from coarse control meshes of arbitrary topology by recursive refinement. Around EVs the optimal refinement weights are application-dependent. We first review subdivision-based finite elements. We then proceed to derive the optimal subdivision weights that minimise finite element errors and can be easily incorporated into existing implementations of subdivision schemes to achieve the same accuracy with much coarser meshes in engineering computations. To this end, the eigenstructure of the subdivision matrix is extensively used and a novel local shape decomposition approach is proposed to choose the optimal weights for each EV independently. Manifold-based basis functions are derived by combining differential-geometric manifold techniques with conformal parametrisations and the partition of unity method. This thesis derives novel manifold-based basis functions with arbitrary prescribed smoothness using quasi-conformal maps, enabling us to model and analyse surfaces with sharp features, such as creases and corners. Their practical utility in finite element simulation of hinged or rigidly joined structures is demonstrated with Kirchhoff-Love thin shell examples. We also propose a particular manifold basis reproducing subdivision surfaces away from EVs, i.e., B-splines, providing a way to combine the appealing properties of subdivision (available in industrial software) for design and manifold basis (relatively new) for analysis.Cambridge International Scholarship Scheme (CISS) by Cambridge Trus

Traditional Chinese medicine combined with conventional treatment for the patients after percutaneous coronary intervention: A systematic review and meta-analysis

Purpose: To evaluate the efficacy, quality of care and safety of Traditional Chinese Medicine (TCM) after Percutaneous Coronary Intervention (PCI). using systematic review and meta-analysis of randomized controlled trials.Methods: Relevant studies published between January 1st 2010 and August 20th, 2021, on traditional Chinese medicine (TCM) and conventional treatment (CT) after PCI were sourced from different databases including CNKI, CBM, Web of Science, PubMed, Embase and Cochrane library. The TCM was composed of preparations of chinese eaglewood, peppermint, radix notoginseng, scabrous elephant foot herb, Tongxinluo, Danhong, Naoxintong capsule, Huxin Formula and liquorice root while the CT included aspirin (100 mg/day), clopidogrel (75 mg/day), and statins. PRISMA guidelines were used. Primary outcome was to evaluate the efficacy, quality of care and safety of TCM versus conventional treatment post percutaneous coronary intervention (PCI).Results: 110 randomized controlled trials (RCTs) were retrieved and analyzed. The results from metaanalysis showed an enhanced left ventricular ejection fraction (LVEF) % among patients that received TCM compared to those on CT [mean difference ± sd (MD)=5.17, 95% CI (3.29-7.06), Z = 5.38, (P < 0.001)]. Further, hypersensitive C-reactive protein (HS-CRP) level in TCM group was found to be relatively lower than that of the CT group (CG) [MD=-1.44, 95% CI (-2.87-0.00), Z=1.96, (P=0.05)]. In terms of safety, TCM group relative risk score in fixed-effect model was lower than that of the CG [RR=0.66, 95% CI (0.40, 1.10), Z=1.66,].Conclusion: It can be inferred from the results that TCM has more advantages in terms of clinical efficacy, quality of care and safety compared to conventional therapy. However, the lack of substantial research in deploying TCM for the treatment of CHD demands further exploration and strong evidence prior to clinical application of TCM

SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture

Support vector machine (SVM) and neural networks (NN) have strong complementarity. SVM focuses on the inner operation among samples while NN focuses on the operation among the features within samples. Thus, it is promising and attractive to combine SVM and NN, as it may provide a more powerful function than SVM or NN alone. However, current work on combining them lacks true integration. To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN. SVM can be viewed as a sample attention machine. It allows us to add a sample attention module to NN to implement the main function of SVM. Class prototypes are representatives of all classes, which can be viewed as alternatives to support vectors. The memory block is used for the storage and update of class prototypes. Class prototypes and memory block effectively reduce the computational cost of sample attention and make SAMN suitable for multi-classification tasks. Extensive experiments show that SAMN achieves better classification performance than single SVM or single NN with similar parameter sizes, as well as the previous best model for combining SVM and NN. The sample attention mechanism is a flexible module that can be easily deepened and incorporated into neural networks that require it