Stretchable electronics have demonstrated excellent potential in wearable healthcare and conformal integration. Achieving the scalable fabrication of stretchable devices with high functional density is the cornerstone to enable the practical applications of stretchable electronics. Here, a comprehensive methodology for realizing large-scale, 3D, and stretchable circuits (3D-LSC) is reported. The soft copper-clad laminate (S-CCL) based on the "cast and cure" process facilitates patterning the planar interconnects with the scale beyond 1 m. With the ability to form through, buried and blind VIAs in the multilayer stack of S-CCLs, high functional density can be achieved by further creating vertical interconnects in stacked S-CCLs. The application of temporary bonding substrate effectively minimizes the misalignments caused by residual strain and thermal strain. 3D-LSC enables the batch production of stretchable skin patches based on five-layer stretchable circuits, which can serve as a miniaturized system for physiological signals monitoring with wireless power delivery. The fabrications of conformal antenna and stretchable light-emitting diode display further illustrate the potential of 3D-LSC in realizing large-scale stretchable devices.
The approach for analyzing the propagation constant of leaky circular waveguide with periodic slots is given in this paper, which is different from that of the traditional closed waveguide. The relationships between the waveguide parameters and the normalized propagation constant of the leaky circular waveguide are analyzed. Based on the analysis, empirical formulas for propagation constant calculation of leaky circular waveguide can be derived via curve-fitting method using the data obtained from numerical method, which are very valuable for the investigation on the cut-off wavelength and the spatial harmonics of leaky circular waveguide.
Using robots in rocket launch missions has difficulties in technical verification, task scheduling, monitoring, and optimization. In this paper, we take digital twin (DT) as a systematic toolkit to manage the task of robots, making full use of its inherent advantages in simulation and scheduling complex tasks. We propose a DT-based robotic control framework (DTRCF) for rocket launch missions, which consists of three subsystems: physical entity system, task scheduling system, and virtual entity system. During the launch mission, commands are generated autonomously in the task scheduling system deployed on the cloud and sent to physical entities of robots through the connection layer. Through performing bi-directional mapping of virtual space and physical space, the DT-based robotic control framework covers three stages of launch missions, including verification, execution, and optimization. We designed a prototype of this framework and conducted related experiments to prove the feasibility of our design.
Alzheimer's disease (AD) is a progressive neurodegenerative condition and is currently the fourth leading cause of death in advanced nations. The primary cause of AD is the deterioration of neurons in areas of the brain crucial for memory, typically presenting symptoms like loss of memory and a decline in cognitive abilities. Mild Cognitive Impairment (MCI) represents a transitional phase between normal cognitive health and AD. Recent studies have shown that within five years, 32% of individuals diagnosed with MCI experience a progression to Alzheimer's disease. Hence, the early detection and treatment of MCI are vital in decreasing the likelihood of developing AD. Traditionally, MCI assessment has relied on neuropsychological tests such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Nevertheless, these methods have limitations, including inducing participant anxiety and fatigue, cultural biases, and the need for skilled administrators. This has shifted towards more innovative assessment methods, particularly Virtual Reality (VR) technology. VR's engaging and multisensory environment offers the potential for more effective MCI assessment. Various VR tasks, such as the Virtual Supermarket Task (VST) and VR adaptations of the Morris Water Maze and Trail Making Test, have shown promise in delivering insightful performance metrics. However, existing research has primarily focused on VR task performance evaluation, often overlooking the corresponding brain activation patterns these tasks stimulate. Compared with the task performance, the stimulated brain patterns could more directly reflect the cognitive function changes resulting from MCI. Whether these VR tasks can induce distinguishable changes in functional near-infrared spectroscopy (fNIRS) data between MCI and healthy individuals and which fNIRS parameters could be useful for MCI assessment is still unknown. To address this research gap, we investigated human brain activity across MIC and healthy individuals in multi-domain VR tasks. First, we selected a VR drumming task which engages multiple cognitive domains, including motor skills, rhythm, and spatial-temporal orientation. Second, we extracted some potential MCI indicators, such as functional connectivity from fNIRS data to analyse brain activity across MIC and healthy individuals in the VR task. Lastly, we examined the statistically significant parameters and discussed the underlying brain activity patterns and their potential for MCI assessment. Our findings revealed that specific brain activity and functional connectivity parameters indicated significant differences between healthy and MCI groups, suggesting the potential value of these parameters as biomarkers for VR-based MCI assessment. This study introduced the potential fNIRS parameters for MCI assessment and discussed their implications and underlying reasons. In conclusion, our study lays a promising foundation for developing and refining VR-based MCI assessments. We anticipate our findings will lead to more effective VR task designs and promote widespread MCI screening in larger populations, ultimately aiding early detection and intervention in individuals at risk of dementia. Future research should address the identified limitations and explore further enhancements in MCI and related condition assessments.
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