Measurement And Prediction Of Densities And Viscosities Of Aqueous Binary And Ternary Solutions At Temparatures From 20 To 60 C [QC189.A6 P191 2007 f rb].

Ketumpatan dan kelikatan bagi sistem larutan akueus binari dan ternari 1-propanol + H2O, 2-propanol + H2O, urea + H2O, 1-propanol + natrium klorida + H2O, 1-propanol + urea + H2O, 2-propanol + natrium klorida + H2O dan 2-propanol + urea + H2O telah ditentukan dalam seluruh julat komposisi dan pada julat suhu 20 hingga 60 °C. Densities and viscosities of the binary and ternary aqueous solution of 1-propanol + H2O, 2-propanol + H2O, urea + H2O, 1-propanol + sodium chloride + H2O, 1-propanol + urea + H2O, 2-propanol + sodium chloride + H2O and 2-propanol + urea + H2O systems were measured over the whole composition range at temperatures between 20 and 60 °C

Molecular Genetics of Keratoconus: Clinical Implications

Occurrence of keratoconus is pan-ethnic with reported prevalence ranging widely from 1:400 to about 1:8000, higher in Asian than Western populations. Its genetics is complex with undefined pattern of inheritance. Familial traits are also known. More than 50 gene loci and 200 variants are associated with keratoconus, some through association studies with quantitative traits of cornea features including curvature and central thickness. Environmental, behavioral, and epigenetic factors are also involved in the etiology, likely interactively with genetic susceptibility. Regardless of sex and age of disease onset, clinical courses and responses to treatment vary. Keratoconus is a major cause of cornea transplantation and is potentially blinding. Currently collagen cross-linking provides effective treatment although responses from some patients can be unpredictable with complications. Early diagnosis is vital to obtain good treatment outcome, but in many patients early signs and symptoms are not obvious. While there are potential biomarkers, reliable pre-symptomatic detection and prediction of treatment response may require multitude of gene variants, cornea properties, and external risk factors

In situ diffraction study of self-recovery in vacuum decomposed Al 2TiO5

The ability of decomposed Al2TiO5 to undergo self-recovery or reformation during vacuum annealing was characterised by in-situ neutron diffraction. It is shown that the process of phase decomposition in Al2TiO5 was reversible and that reformation occurred readily when decomposed Al2TiO5 was re-heated above 1300°C. The kinetics of isothermal and temperature-dependent self-recovery was modelled using the Avrami equation. The influence of grain-size on the Avrami kinetics of self-recovery was also evident

Generalized Variational Oscillation Principles for Second-Order Differential Equations with Mixed-Nonlinearities

Using generalized variational principle and Riccati technique, new oscillation criteria are established for forced second-order differential equation with mixed nonlinearities, which improve and generalize some recent papers in the literature

On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-action automatically extracted from expert's trajectories, which reduces the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture which is modular and easy to scale, enabling a curriculum transferring from simpler tasks to more complex tasks. The reinforcement training algorithm for this architecture is also investigated. On a 64x64 map and using restrictive units, we achieve a winning rate of more than 99\% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93\% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong generalization performance, when tested against never seen opponents including cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We hope this study could shed some light on the future research of large-scale reinforcement learning.Comment: Appeared in AAAI 201