Partitioning surface wave propagation on reconfigurable porous plane.

This paper introduces a novel reconfigurable technique for partitioning the propagation of surface waves by utilizing a T-shaped structure and pathways established through the introduction of fluid metal or metal pins into evenly spaced cylindrical cavities within a porous surface wave platform. Notably, the co-printing of metal and dielectric materials via 3D printing is employed, resulting in an expedited fabrication process. Extensive 3D electromagnetic simulations and experimental investigations validate the proposed approach's efficacy in achieving surface wave division while minimizing interference. The study encompasses an exploration of diverse power distribution ratios achievable within the distributed surface waves. Critical physical parameters of the T-junction are comprehensively examined, including partition depth, junction geometry, output port symmetry, and asymmetry. Additionally, the research delves into the frequency-dependent behaviours of asymmetric T-junctions and pathways. These findings establish the groundwork for adaptable architectures, facilitating concurrent communication among multiple devices within a unified surface wave communication network. This innovation holds potential to enhance various applications through improved communication capabilities.

A Single-Fed Wideband Circularly Polarized Cross-Fed Cavity-Less Magneto-Electric Dipole Antenna.

In this paper, we proposed a new wideband circularly polarized cross-fed magneto-electric dipole antenna. Different from conventional cross-dipole or magneto-electric dipole antennas, the proposed simple geometry realizes a pair of complementary magnetic dipole modes by utilizing the two open slots formed between the four cross-fed microstrip patches for achieving circular polarization and high stable gain across a wide frequency band. No parasitic elements are required for extending the bandwidths; therefore, both the radiation patterns and in-band gain are stable. The simulated field distributions demonstrated the phase complementarity of the two pairs of magnetic and electric dipole modes. A parametric study was also performed to demonstrate the radiation mechanism between the electric and magnetic dipole modes. The radiating elements are realized on a piece of double-sided dielectric substrate fed and mechanically supported by a low-cost commercial semirigid cable. The overall thickness of the antenna is about 0.22λo at the center frequency of axial ratio bandwidth. The measured results show a wide impedance bandwidth (|S11| < -10 dB) of 70.2% from 2.45 to 5.10 GHz. The in-band 3-dB axial ratio bandwidth is 51.5% from 3.0 to 5.08 GHz. More importantly, the gain of the antenna is 9.25 ± 0.56 dBic across the 3-dB axial ratio bandwidth.

The Perceived Benefits of an Artificial Intelligence-Embedded Mobile App Implementing Evidence-Based Guidelines for the Self-Management of Chronic Neck and Back Pain: Observational Study.

BACKGROUND: Chronic musculoskeletal neck and back pain are disabling conditions among adults. Use of technology has been suggested as an alternative way to increase adherence to exercise therapy, which may improve clinical outcomes. OBJECTIVE: The aim was to investigate the self-perceived benefits of an artificial intelligence (AI)-embedded mobile app to self-manage chronic neck and back pain. METHODS: A total of 161 participants responded to the invitation. The evaluation questionnaire included 14 questions that were intended to explore if using the AI rehabilitation system may (1) increase time spent on therapeutic exercise, (2) affect pain level (assessed by the 0-10 Numerical Pain Rating Scale), and (3) reduce the need for other interventions. RESULTS: An increase in time spent on therapeutic exercise per day was observed. The median Numerical Pain Rating Scale scores were 6 (interquartile range [IQR] 5-8) before and 4 (IQR 3-6) after using the AI-embedded mobile app (95% CI 1.18-1.81). A 3-point reduction was reported by the participants who used the AI-embedded mobile app for more than 6 months. Reduction in the usage of other interventions while using the AI-embedded mobile app was also reported. CONCLUSIONS: This study demonstrated the positive self-perceived beneficiary effect of using the AI-embedded mobile app to provide a personalized therapeutic exercise program. The positive results suggest that it at least warrants further study to investigate the physiological effect of the AI-embedded mobile app and how it compares with routine clinical care.

Between-days intra-rater reliability with a hand held myotonometer to quantify muscle tone in the acute stroke population.

A myotonometer can objectively quantify changes in muscle tone. The between-days intra-rater reliability in a ward setting for the acute stroke population remains unknown. This study aimed to investigate the device's between-days intra-rater reliability when used in a ward setting for acute stroke participants. Muscle tone of biceps brachii, brachioradialis, rectus femoris, and tibialis anterior was recorded in the ward at bedside by one physiotherapist on two consecutive days. This study included participants who were within 1 month of their first stroke occurrence. Participants who were medically unstable or who suffered from brain stem injury were excluded. Reliability was assessed by the intraclass correlation coefficient (ICC), standard error of measurement (SEM), smallest real difference (SRD), and the Bland-Altman limits of agreement. The results indicated excellent between-days intra-rater reliability (ICC > 0.75). SEM and SRD show small differences between measurements. The Bland-Altman analysis indicated a tendency of overestimation of the rectus femoris. MyotonPRO demonstrated acceptable reliability when used in a ward setting in those patients with acute stroke. However, results should be interpreted with caution, due to the limitations of the study and the varying level of consistency observed between different muscles.

Feasibility study for future implantable neural-silicon interface devices.

The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures.