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Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications.

Non-spherical distributions of ligand atoms in coordination complexes are generally unfavorable due to higher repulsion than for spherical distributions. To the best of our knowledge, non-spherical heptagonal bipyramidal nonacoordination is hitherto unreported, because of extremely high repulsion among seven equatorial ligand atoms. Herein, we report the computational prediction of such nonacoordination, which is constructed by the synergetic coordination of an equatorial hepta-dentate centripetal ligand (B 7 O 7 ) and two axial mono-dentate ligands (-BO) in the gear-like mono-anionic complexes [OB-M©B 7 O 7 -BO] – (M = Fe, Ru, Os). The high repulsion among seven equatorial ligand B atoms has been compensated by the strong B–O bonding. These complexes are the dynamically stable (up to 1500 K) global energy minima with the HOMO-LUMO gaps of 7.15 to 7.42 eV and first vertical detachment energies of 6.14 to 6.66 eV (being very high for anions), suggesting their high probability for experimental realization in both gas-phase and condensed phases. The high stability stems geometrically from the surrounded outer-shell oxygen atoms and electronically from meeting the 18e rule as well as possessing the σ + π + δ triple aromaticity. Remarkably, the ligand-metal interactions are governed not by the familiar donation and backdonation interactions, but by the electrostatic interactions and electron-sharing bonding. Complexes possessing coordination spheres that can accommodate nine ligand atoms typically display spherical distributions of these atoms. Here, the authors predict that M-centered [OB-B 7 O 7 -BO] adopts unusual heptagonal bipyramidal nonacoordination.

In this paper, the monodisperse silica nanoparticles were prepared by ultrasonic-assisted Stober method, and it explained that the ultrasonic cavitation effect shortened the reaction time from the original hours to f5 min. The effects of ultrasonic time, ultrasonic power, and stirring speed on the morphology, composition, and specific surface area of silica nanoparticles were investigated by field emission electron microscopy (FE-SEM). The results showed that nanoparticles with the best dispersity and the most uniform morphology were obtained under the optimized conditions (ultrasonic time is 5 min, ultrasonic power is 160 W, and the magnetic stirring speed is 999 rpm). The phase composition of SiO2 was characterized by high-resolution transmission electron microscopy (HR-TEM), X-ray diffraction (XRD), nano-size/zeta potential analyzer, and Fourier transform infrared spectroscopy (FT-IR). It showed that all typical peaks of samples are in line with the SiO2 spectrum, the particle size distribution and zeta potential value of the silica is 615 ± 35.6 nm and 59.87 ± 0.91 mv, respectively, which further verified that the spherical silica nanoparticles with good dispersity can be synthesized in a very short time. Hemolysis test showed that nano-SiO2 had high blood compatibility and biocompatibility when its concentration was less than 1 mg/mL. Doxorubicin (DOX·HCl) was regarded as a drug model to investigate the drug loading capacity of synthesized SiO2; the results showed that the drug loading capacity and encapsulation efficiency reached 42.6 ± 1.2 and 85.2 ± 2.5%, respectively. Furthermore, the drug release experiments fitted well with the Higuichi equation with correlation coefficient (R2) of 0.9984, which further confirmed that the SiO2/DOX drug delivery system has the controlled release property, and it also displayed pH-responsive behavior (at 96 h, the cumulative release of SiO2/DOX in PBS solution with pH 7.4, 6.5, and 5.0 was 48.33, 62.31, and 94.86%, respectively). Therefore, this paper provides the possibility for developing more effective, safer, and more targeted controlled drug carriers.

The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring surface electromyograms (sEMGs) to analyze muscle activities. The primary goal of this study is to estimate gait parameters under different power capacity of muscle by sEMGs measured from lower limbs. A self-made wireless device recorded sEMGs from two muscles of each foot, and GaitUp Physilog ® 5 sensors captured gait parameters from 18 participants under running as references. Four features including median frequency (MDF), waveform length (WL), standard deviation (SD), and sample entropy (SampEn), were extracted from the sEMG data. The analysis utilized three machine learning models (Random Forest, CatBoost, XGBoost), evaluated through various evaluation metrics. Additionally, 5-fold cross-validation was conducted to assess the influence of muscle fatigue on the estimation of gait parameters. The results show that all models successfully estimated 20 gait parameters, all showing a Pearson correlation coefficient (PCC) above 0.800. However, the performance of models significantly depends on the condition of muscle fatigue. This study represents a significant advancement in gait analysis, providing a comprehensive method for estimating gait parameters from sEMG signals, with important implications for mobile health applications.

Blood pressure (BP) serves as a crucial parameter in the management of three prevalent chronic diseases, hypertension, cardiovascular diseases, and cerebrovascular diseases. However, the conventional sphygmomanometer, utilizing a cuff, is unsuitable for the approach of mobile health (mHealth). Cuffless blood pressure measurement, which eliminates the need for a cuff, is considered a promising avenue. This method is based on the relationship between pulse arrival time (PAT) parameters and BP. In this study, pulse transit time (PTT) was derived from ballistocardiograms (BCG) and impedance plethysmograms (IPG) obtained from a weight-fat scale. This study aims to address two challenges using deep learning and machine learning technologies: first, identifying BCG and IPG signals with good quality, and then extracting PTT parameters from them to estimate BP. A stacked model comprising a one-dimensional convolutional neural network (1D CNN) and gated recurrent unit (GRU) was proposed to classify the quality of BCG and IPG signals. Seven parameters, including calibration-based and calibration-free PTT parameters and heart rate (HR), were examined to estimate BP using random forest (RF) and XGBoost models. Seventeen healthy subjects participated in the study, with their BP elevated through exercise. A digital sphygmomanometer was employed to measure BP as reference values. Our methodology was validated using data collected from our custom-made device. The results demonstrated a signal quality classification accuracy of 0.989. Furthermore, in the five-fold cross-validation, Pearson correlation coefficients of 0.953 ± 0.007 and 0.935 ± 0.007 were achieved for systolic BP (SBP) and diastolic BP (DBP) estimations, respectively. The mean absolute differences (MADs) of XGBoost model were calculated as 3.54 ± 0.34 and 2.57 ± 0.17 mmHg for SBP and DBP, respectively. The proposed method significantly improved the accuracy of cuffless BP measurement, indicating its potential integration into weight-fat scales as an unconstrained device for effective utilization in mHealth applications.

Bullvalene C 10 H 10 and its analogs semibullvalene C 8 H 8 , barbaralane C 9 H 10 , and 9-Borabarbaralane C 8 BH 9 are prototypical fluxional molecules with rapid Cope rearrangements at finite temperatures. Detailed bonding analyses performed in this work reveal the existence of two fluxional π-bonds (2 2c-2e π → 2 3c-2e π → 2 2c-2e π) and one fluxional σ-bond (1 2c-2e σ → 1 4c-2e σ → 1 2c-2e σ) in their ground states and transition states, unveiling the universal π + σ double fluxional bonding nature of these fluctuating cage-like species. The highest occupied natural bond orbitals (HONBOs) turn out to be typical fluxional bonds dominating the dynamics of the systems. The 13 C-NMR and 1 H-NMR shielding tensors and chemical shifts of the model compound C 8 BH 9 are computationally predicted to facilitate future experiments.

After the discovery of fullerene-C 60 , it took almost two decades for the possibility of boron-based fullerene structures to be considered. So far, there has been no experimental evidence for these nanostructures, in spite of the progress made in theoretical investigations of their structure and bonding. Here we report the observation, by photoelectron spectroscopy, of an all-boron fullerene-like cage cluster at B 40 − with an extremely low electron-binding energy. Theoretical calculations show that this arises from a cage structure with a large energy gap, but that a quasi-planar isomer of B 40 − with two adjacent hexagonal holes is slightly more stable than the fullerene structure. In contrast, for neutral B 40 the fullerene-like cage is calculated to be the most stable structure. The surface of the all-boron fullerene, bonded uniformly via delocalized σ and π bonds, is not perfectly smooth and exhibits unusual heptagonal faces, in contrast to C 60 fullerene. Main-group analogues to fullerene-C 60 have been predicted theoretically many times. Now, B 40 − has been observed using photoelectron spectroscopy and, with its neutral analogue, B40, confirmed computationally. In contrast to fullerene-C 60 , the all-boron fullerene (or borospherene) features triangles, hexagons and heptagons, bonded uniformly by delocalized σ and π bonds over the cage surface.

A dinuclear platinum(II) complex, [Pt 2 (μ-L) 3 (μ-HL)]·Cl·3H 2 O·DMSO ( 1 , HL = 4-Amino-5-pyridin-4-yl-2,4-dihydro-[1,2,4]triazole-3-thione, DMSO = dimethyl sulfoxide), has been synthesized and characterized. The X-ray crystal structural analysis shows that the complex crystallizes in the triclinic, space group P 1 ¯ . Each Pt(II) atom is four-coordinated with two N atoms and two S atoms from triazole ligands. The two platinum centers of the complex formed a paddle wheel motif with four N atoms and four S atoms from four chelating triazole ligands as bridges. The complex forms a 3D network structure by intermolecular hydrogen bonds and C-H… π interactions. The inhibition of complex 1 was evaluated against protein tyrosine phosphatase 1B (PTP1B) and T-cell protein tyrosine phosphatase (TCPTP). It has been found that the complex can both inhibit PTP1B and TCPTP with IC 50 values of 11 and 17 μM, respectively. By comparing with the other platinum complexes, we found that complex 1 exhibits more effective inhibition to PTP1B and TCPTP than the reported paddle wheel dinuclear platinum(II) complexes and weaker inhibition against the two protein tyrosine phosphatases (PTPs) than the mononuclear platinum(II) complex with Schiff base ligand. It is suggested that both the modification and change of the ligand and the spatial structure of the complex will influence their inhibitory ability against PTPs.

Polyacrylonitrile (PAN), PAN/cellulose acetate (CA), and PAN/CA-Ag based activated carbon nanofiber (ACNF) were prepared using electrostatic spinning and further heat treatment. Thermogravimetry-differential scanning calorimetry (TG-DSC) analysis indicated that the addition of CA or Ag did not have a significant impact on the thermal decomposition of PAN materials but the yields of fibers could be improved. Scanning electron microscopy (SEM) analysis showed that the micromorphologies of produced fibers were greatly influenced by the viscosity and conductivity of precursor solutions. Fourier transform infrared spectroscopy (FT-IR) analysis proved that a cyclized or trapezoidal structure could form and the carbon scaffold composed of C=C bonds appeared in the PAN-based ACNFs. The characteristic diffraction peaks in X-ray diffraction (XRD) spectra were the evidence of a turbostratic structure and silver existed in the PAN/CA-Ag based ACNF. Brunner-Emmett-Teller (BET) analysis showed that the doping of CA and Ag increased surface area and micropore volume of fibers; particularly, PAN/CA-Ag based ACNF exhibited the best porosity feature. Furthermore, SO 2 adsorption experiments indicated that all the three fibers had good adsorption effects on lower concentrations of SO 2 at room temperature; especially, the PAN/CA-Ag based ACNF showed the best adsorption performance, and it may be one of the most promising adsorbents used in the fields of chemical industry and environment protection.

Carbon-coated LiFePO 4 hollow nanofibers as cathode materials for Li-ion batteries were obtained by coaxial electrospinning. X-ray diffraction, scanning electron microscopy, transmission electron microscopy, Brunauer–Emmett–Teller specific surface area analysis, galvanostatic charge–discharge, and electrochemical impedance spectroscopy (EIS) were employed to investigate the crystalline structure, morphology, and electrochemical performance of the as-prepared hollow nanofibers. The results indicate that the carbon-coated LiFePO 4 hollow nanofibers have good long-term cycling performance and good rate capability: at a current density of 0.2C (1.0C = 170 mA·g −1 ) in the voltage range of 2.5–4.2 V, the cathode materials achieve an initial discharge specific capacity of 153.16 mAh·g −1 with a first charge–discharge coulombic efficiency of more than 97%, as well as a high capacity retention of 99% after 10 cycles; moreover, the materials can retain a specific capacity of 135.68 mAh·g −1 , even at 2C.