We present a first-of-its-kind fabric hydrogel electrode that is reusable and washable, does not cause skin irritation, and can be continuously operated for upward of 8 h without losing signal integrity. Second, we report an ion-based fabric pressure sensor that measures pulse waveforms when placed on the face. We decorate a lightweight eye mask with these novel fabric electrodes to create a portable detection platform named ''Chesma,'' which can wirelessly track eye motion and pulse in natural environments over long periods of time.

Oblique lumbar interbody fusion (OLIF) has been driven to the maturity stage in recent years. However, postoperative symptoms such as thigh paresthesia resulting from intraoperative retraction of the psoas major (PM) have sometimes occurred. The aim of this study was to assess the different positions and morphology of PM muscles and their relationship with clinical outcomes after OLIF by introducing the Moro zones. Patients who underwent L4-5 OLIF at our institution between April 2019 and June 2021 were reviewed and all data were recorded. All patients were grouped by Moro zones into a Moro A cohort and a Moro I and II cohort based on the front edges of their left PM muscles. A total of 94 patients were recruited, including 57 in the Moro A group and 37 in the Moro I and II group. Postoperative thigh pain or numbness occurred in 12 (21.1%) and 2 (5.4%) patients in the Moro A group and the Moro I and II group, respectively. There was no difference in the psoas major transverse diameter (PMTD) between groups preoperatively, while longer PMTD was revealed postoperatively in the Moro A group. The operating window (OW) and psoas major sagittal diameter (PMSD) showed significant differences within and between groups. Thirteen patients had teardrop-shaped PM muscles, with 92.3% in the Moro A group showing significantly worse clinical scores at 1-week follow-up. The Moro zones of the PM affected the short-term outcomes after OLIF. Preoperative measurements and analysis of OW, PMSD and PM morphology should be performed as necessary to predict short-term outcomes.

Ground surface monitoring (GSM) points collect information for mining surface subsidence monitoring and environmental governance. However, GSM points submerge in high groundwater mining areas, preventing the collection of monitoring data. The application of machine learning (ML) algorithms to subsidence prediction ignores the uncertainty and irregularity in subsidence changes. Thus, an innovative GSM point information prediction model, which improves the multikernel support vector machine (GA-MK-SVM) using chaos residual theory commonly used for capturing GSM point information, is proposed. The mean relative errors (MREs) between the predicted and observed results of GA-SVM and GA-MK-SVM were 8.2% and 6.1% during active periods, respectively. The GA-MK-SVM also performed better than the GA-SVM during stable periods. The residual error accumulates as the ML algorithms progress, resulting in imprecise predictions of the GSM points. Thus, the GA-MK-SVM model was improved using chaotic theory (Chaos-GA-MK-SVM), with MREs of 5.0% and 0.9% during the active and stable periods, respectively. The accuracy of the proposed model was improved by 1.1% and 3.2% compared with the unimproved GA-MK-SVM, respectively. The proposed approach provides practical GSM point information for mining subsidence studies and governance in high groundwater mines.

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