PERFORMANCE ANALYSIS FOR THE TWO-MINUTE PORTFOLIO IN BOTH CANADIAN AND U.S. STOCK MARKET

The “Two-Minute Portfolio” was first introduced by Rob Carrick in 1999 for the Globe and Mail’s Finance Section. By using his strategy with equal weighting in each market sector, Rob claims that individual conservative long-term investors would spend little time in the portfolio selection and still outperform the market (TSX). Over time, the “Two-Minute Portfolio” evolves its strategy to improve the performance. Based on the four main characteristics of the Two-Minute Portfolio: Equal-weight strategy, Large-Cap (blue-chip) companies, Dividend-paying constraint, and Annual rebalancing schedule, we construct the Two-Minute Portfolios in both TSX and S&P 500 markets. We tested the “Two-Minute Portfolio” strategy for its long-term mean return and risk-adjusted return. We found that the Two-Minute Portfolios do not provide statistically significant excess returns. However, in terms of the risk-adjusted measurement, Two-Minutes Portfolios may perform better than benchmarks. We further found that the added Dividend-Paying constraint does not provide significant improvement to the portfolio

Conceal an entrance by means of superscatterer

By using the novel property of the rectangular superscatterer, we propose a design which can conceal an entrance from electromagnetic wave detection. Such a superscatterer is realized by coating a negative index material shell on a perfect electrical conductor rectangle cylinder. The results are numerically confirmed by full-wave simulations both in the far-field and near-field.Comment: 10 pages, 4 figure

Low frequency elastic wave propagation in 2D locally resonant phononic crystal with asymmetric resonator

The resonance modes and the related effects to the transmission of elastic waves in a two dimensional phononic crystal formed by periodic arrangements of a two blocks unit cell in one direction are studied. The unit cell consists of two asymmetric elliptic cylinders coated with silicon rubber and embedded in a rigid matrix. The modes are obtained by the semi-analytic method in the least square collocation scheme and confirmed by the finite element method simulations. Two resonance modes, corresponding to the vibration of the cylinder along the long and short axes, give rise to resonance reflections of elastic waves. One mode in between the two modes, related to the opposite vibration of the two cylinders in the unit cell in the direction along the layer, results in the total transmission of elastic waves due to zero effective mass density at the frequency. The resonance frequency of this new mode changes continuously with the orientation angle of the elliptic resonator.Comment: 17 pages, 7 figure

GPU Accelerated High Intensity Ultrasound Acoustical Power Computation

International audienceThe simulation of the hepatocellular carcinoma therapy effects is often used for the intervention planning. As the physical-based model of the simulation is very time-consuming, the speed of this method becomes an obstacle during the clinical application simulation. In order to accelerate the simulation, a GPU-based (Graphic Processing Unit) acceleration method of the pressure field estimation is proposed in this paper. The results demonstrate that the proposed acceleration method can solve the time-consuming problem

A comparison of machine learning classifiers for smartphone-based gait analysis

This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected by using a smartphone. The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs. Graphical Abstract. This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected and processed by using a smartphone(see figure). The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs

Additional effects of acupuncture on early comprehensive rehabilitation in patients with mild to moderate acute ischemic stroke: a multicenter randomized controlled trial

CONSORT checklist. (PDF 46 kb

Change in household fuels dominates the decrease in PM_(2.5) exposure and premature mortality in China in 2005–2015

To tackle the severe fine particle (PM_(2.5)) pollution in China, the government has implemented stringent control policies mainly on power plants, industry, and transportation since 2005, but estimates of the effectiveness of the policy and the temporal trends in health impacts are subject to large uncertainties. By adopting an integrated approach that combines chemical transport simulation, ambient/household exposure evaluation, and health-impact assessment, we find that the integrated population-weighted exposure to PM_(2.5) (IPWE) decreased by 47% (95% confidence interval, 37–55%) from 2005 [180 (146–219) μg/m^3] to 2015 [96 (83–111) μg/m^3]. Unexpectedly, 90% (86–93%) of such reduction is attributed to reduced household solid-fuel use, primarily resulting from rapid urbanization and improved incomes rather than specific control policies. The IPWE due to household fuels for both cooking and heating decreased, but the impact of cooking is significantly larger. The reduced household-related IPWE is estimated to avoid 0.40 (0.25–0.57) million premature deaths annually, accounting for 33% of the PM_(2.5)-induced mortality in 2015. The IPWE would be further reduced by 63% (57–68%) if the remaining household solid fuels were replaced by clean fuels, which would avoid an additional 0.51 (0.40–0.64) million premature deaths. Such a transition to clean fuels, especially for heating, requires technology innovation and policy support to overcome the barriers of high cost of distribution systems, as is recently being attempted in the Beijing–Tianjin–Hebei area. We suggest that household-fuel use be more highly prioritized in national control policies, considering its effects on PM_(2.5) exposures

Virtual reality-induced motor function of the upper extremity and brain activation in stroke: study protocol for a randomized controlled trial

BackgroundThe benefits of virtual reality (VR)-based rehabilitation were reported in patients after stroke, but there is insufficient evidence about how VR promotes brain activation in the central nervous system. Hence, we designed this study to explore the effects of VR-based intervention on upper extremity motor function and associated brain activation in stroke patients.Methods/designIn this single-center, randomized, parallel-group clinical trial with a blinded assessment of outcomes, a total of 78 stroke patients will be assigned randomly to either the VR group or the control group. All stroke patients who have upper extremity motor deficits will be tested with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and clinical evaluation. Clinical assessment and fMRI will be performed three times on each subject. The primary outcome is the change in performance on the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE). Secondary outcomes are functional independence measure (FIM), Barthel Index (BI), grip strength, and changes in the blood oxygenation level-dependent (BOLD) effect in the ipsilesional and contralesional primary motor cortex (M1) on the left and right hemispheres assessed with resting-state fMRI (rs-fMRI), task-state fMRI (ts-fMRI), and changes in EEG at the baseline and weeks 4 and 8.DiscussionThis study aims to provide high-quality evidence for the relationship between upper extremity motor function and brain activation in stroke. In addition, this is the first multimodal neuroimaging study that explores the evidence for neuroplasticity and associated upper motor function recovery after VR in stroke patients.Clinical trial registrationChinese Clinical Trial Registry, identifier: ChiCTR2200063425

Gyroscope Pivot Bearing Dimension and Surface Defect Detection

Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN) method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO) algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective