A Technology Acceptance Model for Deploying Masks to Combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-Based Cross-sectional Survey Study.

BACKGROUND: The successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread. OBJECTIVE: This study aims to understand people's intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform. METHODS: From March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from "strongly disagree" (1 point) to "strongly agree" (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software. RESULTS: A total of 350 valid questionnaire responses were collected (female: 219/350, 62.6%; age: 21-30 years: 238/350, 68.0%; university-level education: 228/350, 65.1%; occupation as student: 195/350, 56.6%; average monthly income

Very large photoresponsiviy and high photocurrent linearity for Ge-dot/SiO2/SiGe photoMOSFETs under gate modulation.

We report a novel visible-near infrared photoMOSFET containing a self-organized, gate-stacking heterostructure of SiO2/Ge-dot/SiO2/SiGe-channel on Si substrate that is simultaneously fabricated in a single oxidation step. Our typical photoMOSFETs exhibit very large photoresponsivity of 1000-3000A/W at low optical power (< 0.1µW) or large photocurrent gain of 103-108A/A with a wide dynamic power range of at least 6 orders of magnitude (nW-mW) linearity at 400-1250 nm illumination, depending on whether the photoMOSFET operates at VG = + 3- + 4.5V or -1- + 1V. Numerical simulations reveal that photocarrier confinement within the Ge dots and the SiGe channel modifies the oxide field and the surface potential of SiGe, significantly increasing photocurrent and improving linearity.

Design of multifold Ge/Si/Ge composite quantum-dot heterostructures for visible to near-infrared photodetection.

We demonstrate an effective approach to grow high-quality thin film (>1 µm) of multifold Ge/Si/Ge composite quantum dots (CQDs) stacked heterostructures for near infrared photodetection and optical interconnect applications. An otherwise random, self-assembly of variable-fold Ge/Si CQDs has been grown on Si through the insertion of Si spacer layers to produce micron-scale-thick, stacked Ge/Si CQD layers with desired QD morphology and composition distribution. The high crystalline quality of these multifold Ge CQD heterostructures is evidenced by low dark current density of 3.68 pA/µm2, superior photoresponsivity of 267 and 220 mA/W under 850 and 980 nm illumination, respectively, and very fast temporal response time of 0.24 ns measured on the Ge/Si CQD photodetectors.

G Tolerance Prediction Model Using Mobile Device-Measured Cardiac Force Index for Military Aircrew: Observational Study.

BACKGROUND: During flight, G force compels blood to stay in leg muscles and reduces blood flow to the heart. Cardiovascular responses activated by the autonomic nerve system and strengthened by anti-G straining maneuvers can alleviate the challenges faced during G loading. To our knowledge, no definite cardiac information measured using a mobile health device exists for analyzing G tolerance. However, our previous study developed the cardiac force index (CFI) for analyzing the G tolerance of military aircrew. OBJECTIVE: This study used the CFI to verify participants' cardiac performance when walking and obtained a formula for predicting an individual's G tolerance during centrifuge training. METHODS: Participants from an air force aircrew undertook high-G training from January 2020 to December 2022. Their heart rate (HR) in beats per minute and activity level per second were recorded using the wearable BioHarness 3.0 device. The CFI was computed using the following formula: weight × activity / HR during resting or walking. Relaxed G tolerance (RGT) and straining G tolerance (SGT) were assessed at a slowly increasing rate of G loading (0.1 G/s) during training. Other demographic factors were included in the multivariate regression to generate a model for predicting G tolerance from the CFI. RESULTS: A total of 213 eligible trainees from a military aircrew were recruited. The average age was 25.61 (SD 3.66) years, and 13.1% (28/213) of the participants were women. The mean resting CFI and walking CFI (WCFI) were 0.016 (SD 0.001) and 0.141 (SD 0.037) kg × G/beats per minute, respectively. The models for predicting RGT and SGT were as follows: RGT = 0.066 × age + 0.043 × (WCFI × 100) - 0.037 × height + 0.015 × systolic blood pressure - 0.010 × HR + 7.724 and SGT = 0.103 × (WCFI × 100) - 0.069 × height + 0.018 × systolic blood pressure + 15.899. Thus, the WCFI is a positive factor for predicting the RGT and SGT before centrifuge training. CONCLUSIONS: The WCFI is a vital component of the formula for estimating G tolerance prior to training. The WCFI can be used to monitor physiological conditions against G stress.

Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer.

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0-81.4% and 74.6-78% respectively (rfm, ACC 63.2-65.5%, AUC 61.9-74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10-8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

Usability of Wearable Devices With a Novel Cardiac Force Index for Estimating the Dynamic Cardiac Function: Observational Study.

BACKGROUND: Long-distance running can be a form of stress to the heart. Technological improvements combined with the public's gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. OBJECTIVE: We investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. METHODS: This study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. RESULTS: The results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = -2.702 - 0.096 × [waist circumference] - 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner's CFR varies with changes in activity. Since the runner's acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner's explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. CONCLUSIONS: In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.

Computer-Aided tumor diagnosis in 3-D breast elastography.

BACKGROUND AND OBJECTIVE: Breast cancer is the major cause of cancer-related mortality in women. However, the death rate can be effectively decreased if the breast cancer can be detected early and treated appropriately. In recent years, many studies have indicated that the elastography has the better diagnosis performance than conventional ultrasound (US). METHOD: In this study, the 3-D tumor contour is obtained by using the proposed segmentation methods and then the features containing texture information, shape information, ellipsoid fitting information are extracted respectively by using the segmented 3-D tumor contour and B-mode images, and the features containing elasticity information are calculated using the same contour and elastographic images. RESULTS: In this experiment, totally 40 biopsy-proved lesions containing 20 benign tumors and 20 malignant tumors are used to evaluate the proposed computer-aided diagnosis (CAD) system. From the experimental results, the combination of shape, ellipsoid fitting and elastographic features has the best performance with accuracy 90.50% (36/40), sensitivity 85.00% (17/20), specificity 95.00% (19/20), and the area under the ROC curve Az 0.987. CONCLUSION: The result shows that tumors can be diagnosed more precisely by using the elastography images.

High Photoresponsivity Ge-dot PhotoMOSFETs for Low-power Monolithically-Integrated Si Optical Interconnects.

We report the demonstration of high-photoresponsivity Ge-dot photoMOSFETs in a standard MOS configuration for the detection of 850-1550 nm illumination. Each device has a self-organized, gate-stacking heterostructure of SiO2/Ge-dot/SiO2/SiGe-channel which is simultaneously fabricated in a single oxidation step. Superior control of the geometrical size and chemical composition for our Ge nanodots/SiO2/Si1-xGex-shell MOS structure enables the practically-achievable, gate-stacking design for our Ge-dot photoMOSFETs. Both the gate oxide thickness and the diameter of the Ge dots are controllable. Large photocurrent enhancement was achieved for our Ge-dot photoMOSFETs when electrically-biased at ON- and OFF-states based on the Ge dot mediating photovoltaic and photoconductive effects, respectively. Both photoelectric conversion efficiency and response speed are significantly improved by reducing the gate-oxide thickness from 38.5 nm to 3.5 nm, and by decreasing Ge-dot size from 90 nm to 50 nm for a given areal density of Ge dots. Photoresponsivity () values as high as 1.2 × 104 A/W and 300 A/W are measured for 10 nW illumination at 850 nm and 1550 nm, respectively. A response time of 0.48 ns and a 3 dB-frequency of 2 GHz were achieved for 50 nm-Ge-dot photoMOSFETs with channel lengths of 3 µm under pulsed 850 nm illumination.

The curious case of exploding quantum dots: anomalous migration and growth behaviors of Ge under Si oxidation.

We have previously demonstrated the unique migration behavior of Ge quantum dots (QDs) through Si3N4 layers during high-temperature oxidation. Penetration of these QDs into the underlying Si substrate however, leads to a completely different behavior: the Ge QDs 'explode,' regressing back almost to their origins as individual Ge nuclei as formed during the oxidation of the original nanopatterned SiGe structures used for their generation. A kinetics-based model is proposed to explain the anomalous migration behavior and morphology changes of the Ge QDs based on the Si flux generated during the oxidation of Si-containing layers.