Three-dimensional superlattices built from (M4In16S33)(10-) (M = Mn, Co, Zn, Cd) supertetrahedral clusters

通讯作者地址: Feng, PY (通讯作者), Univ Calif Riverside, Dept Chem, Riverside, CA 92521 USA 地址: 1. Univ Calif Riverside, Dept Chem, Riverside, CA 92521 US

Failure mechanisms of a SiC particles 2024Al composite under dynamic loading

Dynamic mechanical response of a 20 vol% silicon carbide particles (SiCp) reinforced 2024 Al composite prepared by powder metallurgy techniques were studied with a split Hopkinson bar. The fracture mechanisms and the deformation microstructure were examined with Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The present results indicate that the composite has a strong SiC-Al interfacial bonding; failure of the material is mainly caused by fracture of SiC particles and tearing failure of the SiC-Al interface. This failure by interface tearing with adhesion of an aluminium layer on SiC particles on the fracture surfaces has not been reported in SiC particle-reinforced aluminium composites. High-resolution transmission electron microscopy studies showed that many of the SiC-Al interfaces have coincident site lattice structures, which are considered to make a significant contribution to the strong interfacial bonding

Self-healing in fractured GaAs nanowires

Molecular dynamics simulations are performed to investigate a spontaneous self-healing process in fractured GaAs nanowires with a zinc blende structure. The results show that such self-healing can indeed occur via rebonding of Ga and As atoms across the fracture surfaces, but it can be strongly influenced by several factors, including wire size, number of healing cycles, temperature, fracture morphology, oriented attachment and atomic diffusion. For example, it is found that the self-healing capacity is reduced by 46% as the lateral dimension of the wire increases from 2.3 to 9.2 nm, and by 64% after 24 repeated cycles of fracture and healing. Other factors influencing the self-healing behavior are also discussed

Predictors of glycemic control among patients with Type 2 diabetes: A longitudinal study

BACKGROUND: Diabetes is the sixth leading cause of death and results in significant morbidity. The purpose of this study is to determine what demographic, health status, treatment, access/quality of care, and behavioral factors are associated with poor glycemic control in a Type 2 diabetic, low-income, minority, San Diego population. METHODS: Longitudinal observational data was collected on patients with Type 2 diabetes from Project Dulce, a program in San Diego County designed to care for an underserved diabetic population. The study sample included 573 patients with a racial/ethnic mix of 53% Hispanic, 7% black, 18% Asian, 20% white, and 2% other. We utilized mixed effects models to determine the factors associated with poor glycemic control using hemoglobin A1C (A1C) as the outcome of interest. A multi-step model building process was used resulting in a final parsimonious model with main effects and interaction terms. RESULTS: Patients had a mean age of 55 years, 69% were female, the mean duration of diabetes was 7.1 years, 31% were treated with insulin, and 57% were obese. American Diabetes Association (ADA) recommendations for blood pressure and total cholesterol were met by 71% and 68%, respectively. Results of the mixed effects model showed that patients who were uninsured, had diabetes for a longer period of time, used insulin or multiple oral agents, or had high cholesterol had higher A1C values over time indicating poorer glycemic control. The younger subjects also had poorer control. CONCLUSION: This study provides factors that predict glycemic control in a specific low-income, multiethnic, Type 2 diabetic population. With this information, subgroups with high risk of disease morbidity were identified. Barriers that prevent these patients from meeting their goals must be explored to improve health outcomes

AN EXTENSION OF ASSOCIATION RULES USING FUZZY SETS Jee-Hyong Lee , Hyung Lee-Kwang

. Association rules are a class of regularities existing between binary data tuples. This paper proposes an extension of association rules which can be applied to real-valued tuples. It discovers and describes association rules among real-valued tuples using fuzzy sets. The proposed method needs user-defined fuzzy sets for describing association rules. It extends the given tuples using the fuzzy sets and converts the extended tuples into binary tuples. Finally, it finds association rules by applying the existing algorithms for binary tuples to the converted binary tuples. Keywords: Data mining, Association rules, Fuzzy sets 1 Introduction Data mining is the technique which extracts the previously unknown and potentially useful information from large amount of data [1], [2]. Discovering association rules is one of the data mining techniques. Association rules give simple but strong knowledge on binary data tuples. They are the description that the tuples having a certain set of attrib..

Microstructure and mechanical behaviour of a SiC particles reinforced Al-5Cu composite under dynamic loading

The mechanical behaviour of a composite of Al–5Cu matrix reinforced with 15% SiC particles was studied at different strain rates from 1×10−3 to 2.5×103 s−1 using both a conventional universal testing machine (for low strain-rate tests) and a split Hopkinson bar (for tests at dynamic strain rates). Whilst the yield stress of the composite increases as the strain rate increases, the maximum flow stresses, 440 MPa for compression and 450 MPa for tension, are independent of strain rate. The microstructures and defect structures of the deformed composite were studied with both scanning electron microscopy and transmission electron microscopy and were correlated to the observed mechanical behaviour. Fracture surface studies of samples after dynamic tensile testing indicates that failure of the composite is controlled by ductile failure of the aluminium matrix by the nucleation, growth and coalescence of voids