An Empirical Study of Factors Influencing the Intention to Use SNS App─The Case of Facebook

Mobile Internet is coming. The social networking site application (SNS app) has become an important portal for users accessing social networking services. Based on the point of view of existing social network users, this study integrates the technology value-based adoption model and social influence to propose a framework to investigate factors influencing the use intention of the SNS app. A sample of 223 subjects surveyed from Facebook, it was found that user’s perceived value regarding the SNS app positively affects the use intention of the social networking app. Usefulness and Technicality provided by SNS app positively affects perceived value of the app. In addition, user perceived social influence also positively affects the use intention of the social networking app. Results not only advance knowledge related to social network research, as well as provide practical advice to social networking companies. They also suggest how to attract users to continually participate in social networks. Increase activeness and stickiness is critical for social network companies to facilitate long-term development

BETTER POSTURAL CONTROL DURING ACCURATE SHOOTING IN ELITE FEMALE BASKETBALL PLAYERS

The purpose of this study was to evaluate the differences of postural control (PC) during accurate and inaccurate shooting in elite female basketball players. 21 female professional basketball players recruited as subjects. The PC was evaluated by the Accusway as sway radius, velocity, radial and 95% area of the center of pressure (COP) during standard penalty line shooting. The results showed that the COP sway area during accurate shooting was significantly smaller than during inaccurate shooting (74.0 ± 37.9 vs. 110.6 ± 49.1, p < .05). Moreover, no significant differences were found between situations in the COP radius and velocity. This study found that during the accurate shooting, elite female basketball player had better PC which demonstrated that significant smaller COP sway area than inaccurate shooting

High-Frequency Sea Level Variations Observed by GPS Buoys Using Precise Point Positioning Technique

In this study, sea level variation observed by a 1-Hz Global Positioning System (GPS) buoy system is verified by comparing with tide gauge records and is decomposed to reveal high-frequency signals that cannot be detected from 6-minute tide gauge records. Compared to tide gauges traditionally used to monitor sea level changes and affected by land motion, GPS buoys provide high-frequency geocentric measurements of sea level variations. Data from five GPS buoy campaigns near a tide gauge at Anping, Tainan, Taiwan, were processed using the Precise Point Positioning (PPP) technique with four different satellite orbit products from the International GNSS Service (IGS). The GPS buoy data were also processed by a differential GPS (DGPS) method that needs an additional GPS receiver as a reference station and the accuracy of the solution depends on the baseline length. The computation shows the average Root Mean Square Error (RMSE) difference of the GPS buoy using DGPS and tide gauge records is around 3 - 5 cm. When using the aforementioned IGS orbit products for the buoy derived by PPP, its average RMSE differences are 5 - 8 cm, 8 - 13 cm, decimeter level, and decimeter-meter level, respectively, so the accuracy of the solution derived by PPP highly depends on the accuracy of IGS orbit products. Therefore, the result indicates that the accuracy of a GPS buoy using PPP has the potential to measure the sea surface variations to several cm. Finally, high-frequency sea level signals with periods of a few seconds to a day can be successfully detected in GPS buoy observations using the Ensemble Empirical Mode Decomposition (EMD) method and are identified as waves, meteotsunamis, and tides

ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning

Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges between encoders, it suffers from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of uni-modal semantic knowledge. In this work, we propose ManagerTower, a novel VL model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels. The managers introduced in each cross-modal layer can adaptively aggregate uni-modal semantic knowledge to facilitate more comprehensive cross-modal alignment and fusion. ManagerTower outperforms previous strong baselines both with and without Vision-Language Pre-training (VLP). With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79.15% accuracy on VQAv2 Test-Std, 86.56% IR@1 and 95.64% TR@1 on Flickr30K. Code and checkpoints are available at https://github.com/LooperXX/ManagerTower.Comment: Accepted by ACL 2023 Main Conference, Ora

New Plasma Separation Glucose Oxidase-based Glucometer in Monitoring of Blood With Different PO2 Levels

BackgroundThe PalmLab glucometer is a newly designed plasma separation glucose oxidase (GO)-based glucometer. Past studies have shown that the accuracy of GO-based glucometers is compromised when measurements are taken in patients with high PO2 levels. We performed a two-arm study comparing the fitness of the PalmLab blood glucometer with that of a standard glucose analyzer in monitoring blood glucose levels in pediatric patients, especially when arterial partial pressure of oxygen (PO2) was high.MethodsIn the first arm of the study, arterial blood samples from pediatric patients were measured by the PalmLab blood glucometer and the YSI 2302 Plus Glucose/Lactate analyzer. In the second arm of the study, venous blood samples from adult volunteers were spiked with glucose water to prepare three different levels of glucose (65, 150, and 300mg/dL) and then oxygenated to six levels of PO2 (range, 40–400mmHg). The biases of the PalmLab glucometer were calculated.ResultsA total of 162 samples were collected in the first arm of the study. Results of linear regression showed that the coefficient of determination (R2) between PalmLab glucometer and standard glucose analyzer was 0.9864. Error grid analysis revealed that all the results were within Zone A (clinically accurate estimate zone). The biases between the two systems were low at different PO2 levels. In the second arm of the study, the results were also unaffected by changes in PO2.ConclusionThe PalmLab glucometer provides accurate results in samples with high PO2 and is suitable for measuring arterial glucose levels in pediatric patients

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

Removal of Mercury by Foam Fractionation Using Surfactin, a Biosurfactant

The separation of mercury ions from artificially contaminated water by the foam fractionation process using a biosurfactant (surfactin) and chemical surfactants (SDS and Tween-80) was investigated in this study. Parameters such as surfactant and mercury concentration, pH, foam volume, and digestion time were varied and their effects on the efficiency of mercury removal were investigated. The recovery efficiency of mercury ions was highly sensitive to the concentration of the surfactant. The highest mercury ion recovery by surfactin was obtained using a surfactin concentration of 10 × CMC, while recovery using SDS required < 10 × CMC and Tween-80 >10 × CMC. However, the enrichment of mercury ions in the foam was superior with surfactin, the mercury enrichment value corresponding to the highest metal recovery (10.4%) by surfactin being 1.53. Dilute solutions (2-mg L−1 Hg2+) resulted in better separation (36.4%), while concentrated solutions (100 mg L−1) enabled only a 2.3% recovery using surfactin. An increase in the digestion time of the metal solution with surfactin yielded better separation as compared with a freshly-prepared solution, and an increase in the airflow rate increased bubble production, resulting in higher metal recovery but low enrichment. Basic solutions yielded higher mercury separation as compared with acidic solutions due to the precipitation of surfactin under acidic conditions