Using foreign currencies to explain the nominal exchange rate of rand

Includes bibliographical references (pages 34-36).The Rand-US Dollar exchange rate has been very volatile since the unification of the duo-exchange rate in 1995. Many researchers have successfully found some economic variables as the long-run determinants of Rand exchange rate. This paper tries to substitute those economic variables with some foreign currencies' exchange rates. In fact, it found that the Brazilian Real could well represent the investors' perception towards South Africa; the Australian Dollar could reflect the Terms of Trade's impact on Rand. After taking into account the structural break in the Rand exchange rate in 2002, the paper found the three currencies' exchange rates were actually cointegrated. In the final section, whether this cointegration relationship would sustain in the future is discussed

The B−LB-L Scotogenic Models for Dirac Neutrino Masses

We construct the one-loop and two-loop scotogenic models for Dirac neutrino mass generation in the context of $U(1)_{B-L}$ extensions of standard model. It is indicated that the total number of intermediate fermion singlets is uniquely fixed by anomaly free condition and the new particles may have exotic $B-L$ charges so that the direct SM Yukawa mass term $\bar{\nu}_L\nu_R\overline{\phi^0}$ and the Majorana mass term $(m_N/2)\overline{\nu_R^C}\nu_R$ are naturally forbidden. After the spontaneous breaking of $U(1)_{B-L}$ symmetry, the discrete $Z_{2}$ or $Z_{3}$ symmetry appears as the residual symmetry and give rise to the stability of intermediated fields as DM candidate. Phenomenological aspects of lepton flavor violation, DM, leptogenesis and LHC signatures are discussed.Comment: 18 pages, 16 figure

Biosensors for Rapid Detection of Avian Influenza

The scope of this chapter was to review the advancements made in the area of biosensors for rapid detection of avian influenza viruses (AIVs). It is intended to provide general background about biosensor technology and to discuss important aspects for developing biosensors, such as selection of the suitable biological recognition elements (anti-AIV bioreceptors) as well as their immobilization strategies. A major concern of this chapter is also to critically review the biosensors’ working principles and their applications in AIV detection. A table containing the types of biosensor, bioreceptors, target AIVs, methods, etc. is given in this chapter. A number of papers for the different types of biosensors give hints on the current trends in the field of biosensor research for its application on AIV detection. By discussing recent research and future trends based on many excellent publications and reviews, it is hoped to give the readers a comprehensive view on this fast-growing field

Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event, conventional methods adjusting for left truncation tend to rely on the (quasi-)independence assumption that the truncation time and the event time are "independent" on the observed region. This assumption is violated when there is dependence between the truncation time and the event time possibly induced by measured covariates. Inverse probability of truncation weighting leveraging covariate information can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply the semiparametric theory to find the efficient influence curve of an expected (arbitrarily transformed) survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches are developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two data sets from practice, with different right-censoring patterns

Generalized synchronization and control for incommensurate fractional unified chaotic system and applications in secure communication

summary:A fractional differential controller for incommensurate fractional unified chaotic system is described and proved by using the Gershgorin circle theorem in this paper. Also, based on the idea of a nonlinear observer, a new method for generalized synchronization (GS) of this system is proposed. Furthermore, the GS technique is applied in secure communication (SC), and a chaotic masking system is designed. Finally, the proposed fractional differential controller, GS and chaotic masking scheme are showed by using numerical and experimental simulations

HT2008-56313 COMPUTATIONAL STUDY OF REACTIVE FLOW IN HALIDE CHEMICAL VAPOR DEPOSITION OF SILICON CARBIDE EPITAXIAL FILM

ABSTRACT In this study, a comprehensive transport model is developed for Halide Chemical Vapor Deposition (HCVD) system which includes gas dynamics, heat and mass transfer, gas-phase and surface chemistry, and radio-frequency induction heating. This model addresses transport of multiple chemical species in high temperature environment with large temperature difference and complex chemical reactions in gas-phase and on the deposition surface. Numerical modeling of the deposition process in a horizontal hot-wall reactor using SiCl 4 /C 3 H 8 /H 2 as precursors has been performed over a wide range of operational parameters to quantify the effects of processing parameters on the film growth. The simulations of the deposition process provide detailed information on the gas-phase composition as well as the distributions of gas velocity and temperature in the reactor. The deposition rate on the substrate surface is also predicted. The results illustrate that deposition temperature and the flow rate of carrier gas play an important role in determining the processing conditions and deposition rate. A high concentration of HCl exists in the growth chamber and the etching of the SiC films by HCl has significant effect on the deposition rate. The modeling approach can be further used to improve reactor design and optimization of processing conditions

Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks

Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the UAV's limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered UAV. Each SN generates status updates at stochastic intervals, while the UAV collects and subsequently transmits these status updates to a central data center. Furthermore, the UAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the UAV's offloading policy, as well as the continuous control of UAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms

Selection, characterization, and application of DNA aptamers for detection of Mycobacterium tuberculosis secreted protein MPT64

Abstract Rapid detection of Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis (TB), is important for global control of this disease. Aptamers have emerged as a potential rival for antibodies in therapeutics, diagnostics and biosensing due to their inherent characteristics. The aim of the current study was to select and characterize single-stranded DNA aptamers against MPT64 protein, one of the predominant secreted proteins of Mtb pathogen. Aptamers specific to MPT64 protein were selected in vitro using systematic evolution of ligands through exponential enrichment (SELEX) method. The selection was started with a pool of ssDNA library with randomized 40-nucleotide region. A total of 10 cycles were performed and seventeen aptamers with unique sequences were identified by sequencing. Dot Blot analysis was performed to monitor the SELEX process and to conduct the preliminary tests on the affinity and specificity of aptamers. Enzyme linked oligonucleotide assay (ELONA) showed that most of the aptamers were specific to the MPT64 protein with a linear correlation of R2 = 0.94 for the most selective. Using Surface Plasmon Resonance (SPR), dissociation equilibrium constant KD of 8.92 nM was obtained. Bioinformatics analysis of the most specific aptamers revealed the existence of a conserved as well as distinct sequences and possible binding site on MPT64. The specificity was determined by testing non-target ESAT-6 and CFP-10. Negligible cross-reactivity confirmed the high specificity of the selected aptamer. The selected aptamer was further tested on clinical sputum samples using ELONA and had sensitivity and specificity of 91.3% and 90%, respectively. Microscopy, culture positivity and nucleotide amplification methods were used as reference standards. The aptamers studied could be further used for the development of medical diagnostic tools and detection assays for Mtb

Comparative analysis & modelling for riders’ conflict avoidance behavior of E-bikes and bicycles at un-signalized intersections

With the increasing popularity of electric-assist bikes (E-bikes) in China, U.S. and Europe, the corresponding safety issues at intersections have attracted the attention of researchers. Understanding the microscopic behavior of E-bike riders during conflicts with other road users is fundamental for safety improvement and simulation modeling of E-bikes at intersections. This study compared the conflict avoidance behaviors of E-bike and conventional bicycle riders using field data extracted from video recordings of different intersections. The impact of conflicting road user type and gender on E-bikes and bicycles were analyzed. Compared with bicycles, E-bikes appeared to enable more flexibility in conflict avoidance behavior. For example, E-bikes would behave like bicycles when conflicting with motor vehicles/Ebikes, and behave more like motor vehicles when conflicting with bicycles/pedestrians. Based on this, we built an Extended Cyclist Conflict Avoidance Movement (ECCAM) model, which can represent the conflict avoidance behavior of E-bikes/bicycles at mixed traffic flow un-signalized intersections. Field data were applied to validate the proposed model, and the results are promising