In situ photogalvanic acceleration of optofluidic kinetics: a new paradigm for advanced photocatalytic technologies

A multiscale-designed optofluidic reactor is demonstrated in this work, featuring an overall reaction rate constant of 1.32 s¯¹ for photocatalytic decolourization of methylene blue, which is an order of magnitude higher as compared to literature records. A novel performance-enhancement mechanism of microscale in situ photogalvanic acceleration was found to be the main reason for the superior optofluidic performance in the photocatalytic degradation of dyes as a model reaction

Modeling the IDV emissions of the BL Lac Objects with a Langevin type stochastic differential equation

In this paper, we introduce a simplified model for explaining the observations of the optical intraday variability (IDV) of the BL Lac Objects. We assume that the source of the IDV are the stochastic oscillations of an accretion disk around a supermassive black hole. The Stochastic Fluctuations on the vertical direction of the accretion disk are described by using a Langevin type equation with a damping term and a random, white noise type force. Furthermore, the preliminary numerical simulation results are presented, which are based on the numerical analysis of the Langevin stochastic differential equation.Comment: 4 pages, 4 figures, accepted for publication in J. Astrophys. Ast

Is the Convergence of Accounting Standards Good for Stock Markets?

This paper examines the impact of the convergence of Hong Kong Accounting Standard 40 (HKAS 40) with the International Financial Reporting Standard (IFRS) on the stock prices of firms in the property industry. Using a sample of 22111 firm-day observations, we show that the new standard has a negative impact on the stock performance of these firms.Hong Kong Accounting Standard 40, Event Window, Stock Return.

The Role of Sirtuin 6 in Maintaining Vascular Integrity

Oxidative stress is an underlying cause for vascular pathologies including inflammation, hypertension, and atherosclerosis. Sirtuins (SIRTs) are a family of NAD+ dependent deacetylases with pronounced roles in cellular metabolism and aging. SIRT6 is expressed in vascular smooth muscle cells (SMCs) and may offer protection from oxidative stress-induced damage. To study the role of SIRT6 in SMCs, we created a novel strain of SMC-specific SIRT6-deficient (SIRT6KO) mice with Cre-lox technology. Because no defects were observed in the aortas of SIRT6KO mice, they were then infused with angiotensin II (Ang II) to induce oxidative stress. Compared with vehicle controls, SIRT6KO mice developed aortitis, aortic hemorrhage, and aneurysms in response to Ang II. Therefore, we propose that SIRT6 has an anti-inflammatory role in aortic SMCs that is necessary for maintaining vessel wall integrity in the presence of oxidative stress

An experimental study on a motion sensing system for sports training

In sports science, motion data collected from athletes is used to derive key performance characteristics, such as stride length and stride frequency, that are vital coaching support information. The sensors for use must be more accurate, must capture more vigorous events, and have strict weight and size requirements, since they must not themselves affect performance. These requirements mean each wireless sensor device is necessarily resource poor and yet must be capable of communicating a considerable amount of data, contending for the bandwidth with other sensors on the body. This paper analyses the results of a set of network traffic experiments that were designed to investigate the suitability of conventional wireless motion sensing system design � which generally assumes in-network processing - as an efficient and scalable design for use in sports training

Ab initio molecular dynamics study of manganese porphine hydration and interaction with nitric oxide

The authors use ab initio molecular dynamics and the density functional theory+U (DFT+U) method to compute the hydration environment of the manganese ion in manganese (II) and manganese (III) porphines (MnP) dispersed in liquid water. These are intended as simple models for more complex water soluble porphyrins, which have important physiological and electrochemical applications. The manganese ion in Mn(II)P exhibits significant out-of-porphine plane displacement and binds strongly to a single H2O molecule in liquid water. The Mn in Mn(III)P is on average coplanar with the porphine plane and forms a stable complex with two H2O molecules. The residence times of these water molecules exceed 15 ps. The DFT+U method correctly predicts that water displaces NO from Mn(III)P-NO, but yields an ambiguous spin state for the MnP(II)-NO complex.Comment: 10 pages, 6 figure

Detecting anions of human saliva by ion chromatography

Abstract no. 3007published_or_final_versio

Adaptive Data Depth via Multi-Armed Bandits

Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is that many common measures of depth are computationally intensive, requiring on the order of $n^d$ operations to exactly compute the depth of a single point within a data set of $n$ points in $d$-dimensional space. Often however, we are not directly interested in the absolute depths of the points, but rather in their relative ordering. For example, we may want to find the most central point in a data set (a generalized median), or to identify and remove all outliers (points on the fringe of the data set with low depth). With this observation, we develop a novel and instance-adaptive algorithm for adaptive data depth computation by reducing the problem of exactly computing $n$ depths to an $n$-armed stochastic multi-armed bandit problem which we can efficiently solve. We focus our exposition on simplicial depth, developed by Liu (1990), which has emerged as a promising notion of depth due to its interpretability and asymptotic properties. We provide general instance-dependent theoretical guarantees for our proposed algorithms, which readily extend to many other common measures of data depth including majority depth, Oja depth, and likelihood depth. When specialized to the case where the gaps in the data follow a power law distribution with parameter $\alpha<2$, we show that we can reduce the complexity of identifying the deepest point in the data set (the simplicial median) from $O(n^d)$ to $\tilde{O}(n^{d-(d-1)\alpha/2})$, where $\tilde{O}$ suppresses logarithmic factors. We corroborate our theoretical results with numerical experiments on synthetic data, showing the practical utility of our proposed methods.Comment: Keywords: multi-armed bandits, data depth, adaptivity, large-scale computation, simplicial dept