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The six-minute walking test (6MWT) is an essential test for evaluating exercise tolerance in many respiratory and cardiovascular diseases. Frailty and sarcopenia can cause rapid aging of the cardiovascular system in elderly people. Early detection and evaluation of frailty and sarcopenia are crucial for determining the treatment method. We aimed to develop a wearable measuring system for the 6MWT and propose a method for identifying frailty and quantifying walking muscle strength (WMS). In this study, 60 elderly participants were asked to wear accelerometers behind their left and right ankles during the 6MWT. The gait data were collected by a computer or smartphone. We proposed a method for analyzing walking performance using the stride length (SL) and step cadence (SC) instead of gait speed directly. Four regions (Range I–IV) were divided by cutoff values of SC = 2.0 [step/s] and SL = 0.6 [m/step] for a quick view of the frail state. There were 62.5% of frail individuals distributed in Range III and 72.4% of non-frail individuals in Range I. A concept of a WMS score was proposed for estimating WMS quantitatively. We found that 62.5% of frail individuals were scored as WMS1 and 41.4% of the non-frail elderly as WMS4. The average walking distances corresponding to WMS1–4 were 207 m, 370 m, 432 m, and 462 m, respectively. The WMS score may be a useful tool for quantitatively estimating sarcopenia or frailty due to reduced cardiopulmonary function. [ABSTRACT FROM AUTHOR]

van der Waals (vdW) heterogeneous integration and doping engineering have emerged as crucial factors in advancing the development of functional device systems. This work presents a fully vertical 2D/3D vdW stacking p‐MoxRe1‐xS2/GaN (x = 0.10 ± 0.02) heterojunction photodetector, integrating multiple strategies for enhanced performance, such as mixed‐dimensional stacking, p‐type doping, vertical device design, and type‐II band alignment. By integrating horizontal, vertical, and quasi‐vertical devices on a Free‐standing (FS)‐GaN substrate, the vertical p‐MoxRe1‐xS2/GaN device demonstrates superior performance, including high Ilight/Idark ratio (1.48 × 106), large Responsivity (888.69 AW−1), high specific detectivity (D*) (6.13 × 1014 Jones), and fast response speed (rise/decay time of 181 ms/259 ms). Moreover, the spectral response encompasses the ultraviolet (UV), visible, and near‐infrared (NIR) regions through energy band integration and bandgap modulation. This design surpasses previous devices, highlighting the potential of highly sensitive and micro‐integrated optoelectronic devices enabled by vertical vdW heterogeneous integration. [ABSTRACT FROM AUTHOR]

The utilization of alloyed 2D transition metal dichalcogenides (TMDs) has become a pivotal approach for addressing challenges in material applications. The judicious selection of dopant constituents offers a potent means to finely modulate the materials' bandgap, consequently broadening the potential applications of 2D materials. In the context of an investigation, Mo1−x−yRexWyS2 is successfully synthesized using chemical vapor deposition. With a bandgap of 1.33 eV, this material exhibits promising prospects for application in the realm of optoelectronics. This advancement enables the fabrication of the Mo1−x−yRexWyS2 photodetector. The rigorous testing and analysis of photoelectric performance reveal significant improvements in both responsivity and response speed compared to analogous detectors. This accomplishment not only furnishes a novel paradigm for the advancement of photodetectors but also contributes fresh insights to the domain of alloyed 2D TMDs. [ABSTRACT FROM AUTHOR]

With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk assessment. However, these methods face difficulty when identifying overlapping and interactive nursing tasks, resulting in missing and misidentified skeletons. To address this, we propose a skeleton compensation method using improved spatial temporal graph convolutional networks (ST-GCN), which integrates kinematic chain and action features to assess skeleton integrity and compensate for it. The results verified the effectiveness of our approach in optimizing skeletal loss and misidentification in nursing care tasks, leading to improved accuracy in calculating both skeleton joint angles and REBA scores. Moreover, comparative analysis against other skeleton compensation methods demonstrated the superior performance of our approach, achieving an 87.34% REBA accuracy score. Collectively, our method might hold promising potential for optimizing the skeleton loss and misidentification in nursing care tasks. [ABSTRACT FROM AUTHOR]