Electronic Health Literacy Scale-Web3.0 for Older Adults with Noncommunicable Diseases: Validation Study.

BACKGROUND: In the current digital era, eHealth literacy plays an indispensable role in health care and self-management among older adults with noncommunicable diseases (NCDs). Measuring eHealth literacy appropriately and accurately ensures the successful implementation and evaluation of pertinent research and interventions. However, existing eHealth literacy measures focus mainly on individuals' abilities of accessing and comprehending eHealth information (Web1.0), whereas the capabilities for web-based interaction (Web2.0) and using eHealth information (Web3.0) have not been adequately evaluated. OBJECTIVE: This study aimed to examine the reliability, validity, and measurement invariance of the eHealth Literacy Scale-Web3.0 (eHLS-Web3.0) among older adults with NCDs. METHODS: A total of 642 Chinese older adults with NCDs (mean age 65.78, SD 3.91 years; 55.8% female) were recruited in the baseline assessment, of whom 134 (mean age 65.63, SD 3.99 years; 58.2% female) completed the 1-month follow-up assessment. Baseline measures included the Chinese version of the 24-item 3D eHLS-Web3.0, the Chinese version of the 8-item unidimensional eHealth Literacy Scale (eHEALS), and demographic information. Follow-up measures included the 24-item eHLS-Web3.0 and accelerometer-measured physical activity and sedentary behavior. A series of statistical analyses, for example, Cronbach α, composite reliability coefficient (CR), confirmatory factor analysis (CFA), and multigroup CFA, were performed to examine the internal consistency and test-retest reliabilities, as well as the construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence. RESULTS: Cronbach α and CR were within acceptable ranges of 0.89-0.94 and 0.90-0.97, respectively, indicating adequate internal consistency of the eHLS-Web3.0 and its subscales. The eHLS-Web3.0 also demonstrated cross-time stability, with baseline and follow-up measures showing a significant intraclass correlation of 0.81-0.91. The construct validity of the 3D structure model of the eHLS-Web3.0 was supported by confirmatory factor analyses. The eHLS-Web3.0 exhibited convergent validity with an average variance extracted value of 0.58 and a CR value of 0.97. Discriminant validity was supported by CFA results for a proposed 4-factor model integrating the 3 eHLS-Web3.0 subscales and eHEALS. The predictive validity of the eHLS-Web3.0 for health behaviors was supported by significant associations of the eHLS-Web3.0 with light physical activity (ß=.36, P=.004), moderate to vigorous physical activity (ß=.49, P<.001), and sedentary behavior (ß=-.26, P=.002). Finally, the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence was supported by the establishment of configural, metric, strong, and strict invariances. CONCLUSIONS: The present study provides timely empirical evidence on the reliability, validity, and measurement invariance of the eHLS-Web3.0, suggesting that the 24-item 3D eHLS-Web3.0 is an appropriate and valid tool for measuring eHealth literacy among older adults with NCDs within the Web3.0 sphere.

Boosting CO2 piezo-reduction via metal-support interactions in Au/ZnO based catalysts.

Confronting the challenge of climate change necessitates innovative approaches for the reduction of CO2 emissions. Metal-support interaction has been widely demonstrated to enable greatly improved performances in thermal-catalytic, photocatalytic and electrocatalytic CO2 reduction. However, its applicability and specifically its role in the emerging piezo-electrocatalytic CO2 reduction are unknown, severely hampering the utilizations of piezo-electrocatalysis in CO2 conversion. Herein, by adopting Au particles supported on ZnO (Au/ZnO) as a paradigm, it is found that the metal-support interaction can remarkably improve the separation and transfer of piezo-carriers and enhance CO2 adsorption. As a result, Au/ZnO demonstrates a substantially boosted activity for piezo-electrocatalytic CO2 reduction and the optimal sample exhibits a 37.3% increase in CO yield compared to the pristine ZnO. The integration of metal-support interactions opens a new avenue to the design of advanced piezo-electrocatalysts for CO2 reduction.

Sigmatropic [1,5] Carbon Shift of Transient C3 Ammonium Enolates.

The stereospecific sigmatropic [1,5] carbon shift of C3 ammonium enolates is discovered. According to mechanistic, kinetic and computational experiments, this new rearrangement proceeds via the catalytic generation of a transient C3 ammonium enolate by intramolecular aza-Michael addition. This intermediate rapidly undergoes [1,5] sigmatropic carbon migration to furnish the respective tetrahydroquinoline-4-ones with excellent diastereoselectivities of d.r. >99 : 1 and in 61-98 % yield.

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

[Study on Indicator Densitometry Determination Method of Hemodynamic Parameters].

Measurement for hemodynamic parameters has always been a hot spot of clinical research. Methods for measuring hemodynamic parameters clinically have the problems of invasiveness, complex operation and being unfit for repeated measurement. To solve the problems, an indicator densitometry analysis method is presented based on near-infrared spectroscopy (NIRS) and indicator dilution theory, which realizes the hemodynamic parameters measured noninvasively. While the indocyanine green (ICG) was injected into human body, circulation carried the indicator mixing and diluting with the bloodstream. Then the near-nfrared probe was used to emit near-infrared light at 735, 805 and 940 nm wavelengths through the sufferer's fingertip and synchronously capture the transmission light containing the information of arterial pulse wave. By uploading the measured data, the computer would calculate the ICG concentration, establish continuous concentration curve and compute some intermediate variables such as the mean transmission time (MTT) and the initial blood ICG concentration (c(t0)). Accordingly Cardiac Output (CO) and Circulating Blood Volume (CBV) could be calculated. Compared with the clinical "gold standard" methods of thermodilution and I-131 isotope-labelling method to measure the two parameters by clinical controlled trials, ten sets of data were obtained. The maximum relative errors of this method were 8.88% and 4.28% respectively, and both of the average relative errors were below 5%. The result indicates that this method can meet the clinical accuracy requirement and can be used as a noninvasive, repeatable and applied solution for clinical hemodynamnic parameters measurement.

[EEMD-ICA Applied in Signal Extraction in Functional Near-Infrared Spectroscopy].

Currently, functional near-infrared spectroscopy (fNIRS) is widely used in the field of Neuroimaging. To solve the signal-noise frequency spectrum aliasing in non-linear and non-stationary fNIRS characteristic signal extraction, a new joint multi-resolution algorithm, EEMD-ICA, is proposed based on combining Independent Component Analysis with Ensemble Empirical Mode Decomposing. After functional brain imaging instrument detected the multi-channel and multi-wavelength NIR optical density signals, EEMD was performed to decompose measurement signals into multiple intrinsic mode function according to the signal frequency component. Then ICA was applied to extract the interest data from IMFs into ICs. Finally, reconstructed signals were obtained by accumulating the ICs set. EEMD-ICA was applied in de-noising Valsalva test signals which were considered as original signals and compared with Empirical Mode Decomposing and Ensemble Empirical Mode Decomposing to illustrate validity of this algorithm. It is proved that useful information loss during de-noising and invalidity of noise elimination are completely solved by EEMD-ICA. This algorithm is more optimized than other two de-noising methods in error parameters and signal-noise-ratio analysis.