Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were −1.36 ± 7.24 and −2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

In this study, a three-dimensional (3D) interconnected porous Ni/SiC skeleton (3D Ni/SiC) was synthesized by binder-free hydrogen bubble template-assisted electrodeposition in an electrolyte containing Ni²⁺ ions and SiC nanopowders. This 3D Ni/SiC skeleton served as a substrate for directly synthesizing nickel–cobalt layered double hydroxide (LDH) nanosheets via electrodeposition, allowing the formation of a nickel–cobalt LDH nanosheet-decorated 3D Ni/SiC skeleton (NiCo@3D Ni/SiC). The multiscale hierarchical structure of NiCo@3D Ni/SiC was attributed to the synergistic interaction between the pseudocapacitor (3D Ni skeleton and Ni–Co LDH) and electrochemical double-layer capacitor (SiC nanopowders). It provided a large specific surface area to expose numerous active Ni and Co sites for Faradaic redox reactions, resulting in an enhanced pseudocapacitance. The as-fabricated NiCo@3D Ni/SiC structure demonstrated excellent rate capability with a high areal capacitance of 1565 mF cm⁻² at a current density of 1 mA cm⁻². Additionally, symmetrical supercapacitor devices based on this structure successfully powered commercial light-emitting diodes, indicating the potential of as-fabricated NiCo@3D Ni/SiC in practical energy storage applications.

Low-temperature co-fired ceramics (LTCCs) have been attracting attention due to rapid advances in wireless telecommunications. Low-dielectric-constant (Dk) and low-dissipation-factor (Df) LTCCs enable a low propagation delay and high signal quality. However, the wide ranges of glass, ceramic filler compositions, and processing features in fabricating LTCC make property modulating difficult via experimental trial-and-error approaches. In this study, we explored Dk and Df values of LTCCs using a machine learning method with a Gaussian kernel ridge regression model. A principal component analysis and k-means methods were initially performed to visually analyze data clustering and to reduce the dimension complexity. Model assessments, by using a five-fold cross-validation, residual analysis, and randomized test, suggest that the proposed Dk and Df models had some predictive ability, that the model selection was appropriate, and that the fittings were not just numerical due to a rather small data set. A cross-plot analysis and property contour plot were performed for the purpose of exploring potential LTCCs for real applications with Dk and Df values less than 10 and 2 × 10⁻³, respectively, at an operating frequency of 1 GHz. The proposed machine learning models can potentially be utilized to accelerate the design of technology-related LTCC systems.

This study explores the integration of machine learning (ML) techniques to predict and optimize the compressive strength of alkali-activated materials (AAMs) sourced from four industrial waste streams: blast furnace slag, fly ash, reducing slag, and waste glass. Aimed at mitigating the labor-intensive trial-and-error method in AAM formulation, ML models can predict the compressive strength and then streamline the mixture compositions. By leveraging a dataset of only 42 samples, the Random Forest (RF) model underwent fivefold cross-validation to ensure reliability. Despite challenges posed by the limited datasets, meticulous data processing steps facilitated the identification of pivotal features that influence compressive strength. Substantial enhancement in predicting compressive strength was achieved with the RF model, improving the model accuracy from 0.05 to 0.62. Experimental validation further confirmed the ML model’s efficacy, as the formulations ultimately achieved the desired strength threshold, with a significant 59.65% improvement over the initial experiments. Additionally, the fact that the recommended formulations using ML methods only required about 5 min underscores the transformative potential of ML in reshaping AAM design paradigms and expediting the development process.

The well-interconnected ternary Ni–Co–O nanosheets were grown on silicon carbide microspheres/graphite composite (gra@SiC/Ni–Co–O) by optimizing the electrodeposition method. Silicon carbide microspheres/graphite composite (gra@SiC) serves as a conductive template for the growth of Ni–Co–O nanosheets to form a binder-free 3D well-designed hierarchical interconnected network between the Ni–Co–O nanosheets and SiC microspheres. The obtained gra@SiC/Ni–Co–O is proposed as a great capacitance performance for supercapacitors. Field emission scanning electron microscopy (FESEM), Raman spectroscopy, high-resolution transmission electron microscopy (HRTEM) with selected area electron diffraction (SAED) and energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy, and electrochemical analysis were employed to investigate the morphology and structural and electrochemical characteristics. The synergistic effects of EDLC (SiC microspheres) and pseudo-capacitance (Ni–Co–O nanosheets) can effectively improve the supercapacitive performance. It is also worth mentioning that after electrochemical testing, the redox reaction of Ni–Co–O nanosheets greatly promoted the faradic pseudo-capacitance contribution, and silicon carbide microspheres/graphite composite contributed to the formation of a 3D interconnected network, improving the cycling stability during the charging/discharging processes.