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This study aims to leverage inertial sensors via a walk test to associate kinematic variables with functional assessment results among walkable subjects with chronic stroke.

Adults with first-ever stroke survivors were recruited for this study. First, functional assessments were obtained by using Fugl–Meyer Assessment for lower extremity and Berg balance scales. A self-assembled inertial measurement system obtained walking variables from a walk test after being deployed on subjects’ affected limbs and lower back. The average walking speeds, average range of motion in the affected limbs and a new gait symmetry index were computed and correlated with the two functional assessment scales using Spearman’s rank correlation test.

The average walking speeds were moderately correlated with both Fugl–Meyer assessment scales (γ = 0.62, p < 0.01, n = 23) and Berg balance scales (γ = 0.68, p < 0.01, n = 23). After being modified by the subjects’ height, the new gait symmetry index revealed moderate negative correlations with the Fugl–Meyer assessment scales (γ = −0.51, p < 0.05) and Berg balance scales (γ = −0.52, p < 0.05). The other kinematics failed to correlate well with the functional scales.

Neuromotor and functional assessment results from inertial sensors can facilitate their application in telemonitoring and telerehabilitation.

The average walking speeds and modified gait symmetry index are valuable parameters for inertial sensors in clinical research to deduce neuromotor and functional assessment results. In addition, the lower back is the optimal location for the inertial sensors.

In this paper, a first‐order approximation coupling (FOAC) model is investigated to analyze the dynamics of the hub‐beam system, which is based on the Hamilton theory and the finite element discretization method. The FOAC model for the hub‐beam system considers the second‐order coupling quantity of the axial displacement caused by the transverse displacement of the beam. The dynamic characteristics of the system are studied through numerical simulations under twos cases: the rotary inertia of the hub is much larger than, and is close to, that of the flexible beam. Simulation and comparison studies using both the traditional zeroth‐order approximation coupling (ZOAC) model and the FOAC model shows that when large motion of the system is unknown, possible failure exists by using the ZOAC model, whereas the FOAC model is valid. When the rotary inertia of the hub is much larger than that of the beam, the result using the ZOAC model is similar to that using the FOAC model. But when the rotary inertia of the hub is close to that of the beam, the ZOAC model may lead to a large error, while the FOAC model can still accurately describe the dynamic hub‐beam system.

This paper aims to examine the impact of interest rate liberalisation on the constancy of mean interest rates in China to test the effect of financial reforms and provide strategies for future practices.

Bai and Perron’s (1998, 2003) methodology is used to test for structural breaks in the mean of different interest rates using Chinese data, and break dates are measured against the exact dates of the interest rate liberalisation. The performance of mean interest rates across the regimes defined by liberalisation dates is also investigated.

The main results show that interest rates generally increase (decrease) after deregulations on lending (deposit) rates, but these changes are not significant to induce a negative impact on the domestic economy. Instead, the infrequent but important shifts (structural breaks) in mean interest rates are caused by factors other than liberalisation such as economic shocks, inflationary expectation and liquidity crunch in China.

To the best of the author’s knowledge, this paper provides unprecedented evidence on significant changes in interest rates attributable to the liberalisation within the Chinese context.

The purpose of the paper is to assess the systemic risk in the new energy stock markets of China.

This paper first uses the VaR method to study individual stock market risks. It then introduces the DCC model to capture the dynamic conditional correlation among the new energy stock markets.

The paper shows a generally upward trend of the stock market risk over time in the recent decade. Among all the markets considered, the solar power market demonstrates the highest risk, closely followed by the wind power market, while the hydropower market exhibits the lowest risk. Furthermore, the average dynamic conditional correlations among the new energy markets stay high during the period under investigation though daily correlations vary and significantly declined in 2020.

To the best of the authors’ knowledge, this paper is the first of its kind to study the systemic risk within the new energy stock market context. In addition, it not only investigates individual new energy stock market risks but also examines the dynamic linkages among those markets, thus providing comprehensive and unprecedented evidence of systemic risk in China new energy markets, which have useful implications for both regulators and investors.