Force production under sense of effort conditions is essential for motor control and clinical assessment; however, its reliability across different force levels remains unclear. In addition, the lack of standardized measurement methods has limited the accumulation of consistent findings. Therefore, establishing reliable measurement conditions is necessary for both research and practical applications. This study examined the test-retest reliability of grip-force production based on the sense of effort at three force levels: 25%, 50%, and 75% of maximum voluntary contraction (MVC). Thirty-three healthy young men performed grip-force tasks across two sessions separated by 1 week without external feedback. Relative reliability was assessed using intraclass correlation coefficients (ICCs), and absolute reliability was evaluated using the standard error of measurement (SEM) and the 95% minimal detectable change (MDC). The highest reliability was observed at 25% MVC across both outcome measures. At higher force levels, absolute reliability varied depending on the error index, with lower reliability at 50% MVC for absolute error and at 75% MVC for signed error. These findings indicate that reliability depends on the aspect of error being measured and that increasing force levels alter the structure of error rather than simply increasing its magnitude. Furthermore, systematic bias at low force levels suggests that high reliability does not necessarily reflect close correspondence to the prescribed force level. Overall, low-force conditions provide more reliable assessments, highlighting the importance of considering both the magnitude and direction of error.
Despite the fact that housing represents a fundamental unit of urban systems where people spend much of their daily lives, little attention has been paid to the structural relationship between residential environments and subjective well-being. In this study, structural equation modeling was used to analyze the structural relationship between residential environment and subjective well-being, controlling for the effects of demographic factors and personality traits. An online survey of 1,001 adult residents in Japan measured subjective well-being and assessed residential environments, consisting of the thermal, acoustic, light, hygiene, safety, and security. The model revealed an important pathway from the residential environment to subjective well-being mediated by life-domain satisfaction. The six environmental components described above all contributed substantially to the latent residential environment construct, with safety and hygiene showing the highest loadings. This study contributes to a more fundamental understanding of the structural relationship between residential environments and subjective well-being. It also provides insights into designing better residential environments that enable everyone to live in healthier and more supportive spaces—an endeavor that is crucial for promoting sustainable and inclusive urban development, particularly in terms of enhancing overall quality of life.
This study presents the first demonstration of Pulsed-Discharged Spouted Bed (PDSB) system, developed as a novel low-carbon smelting approach by integrating pulsed electrical discharging with spouted bed technology. The system was applied to the reduction of SnO2 powder at a repetition rate of 250 Hz with 22 kV of applied voltage. Combustion gas was introduced from bottom of the spouted bed as both reducing and fluidizing agents. The reduction times varied from 5 to 30 min. Phase and microstructure analyses confirmed the formation of metallic tin with porous morphologies, formed through melting and rapid solidification. Quantitative analysis via Rietveld refinement method indicated that the extent of reduction increased with longer reduction time. The reduction mechanism of SnO2 was presumed to follow the nucleation and growth of reduced phases. [doi:10.2320/matertrans.MT-M2025162]
The elastic moduli of recycled carbon fiber nonwoven reinforced polymer (rCFRP) plates are studied using statistical methods to account for the curved, aligned, and entangled fibers. The effect of these fibers on elastic properties is quantified by introducing a joint expected fiber length cumulative distribution function (JELCDF) per unit area with respect to an orientation angle and curvature. The curvatures and orientation angles of the fibers are measured at the crossing point with the reference line. The explicit polynomial functions of the JELCDF are determined from experimental results using the least-squares method. The joint expected fiber length density function is used to estimate the orthotropic elastic moduli of the rCFRP plate, based on a self-consistent scheme where the curvature effect is neglected. The estimated results show good agreement with the experimental results and the present probabilistic method can be an effective method to estimate the performance of the rCFRP laminates.
Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.
