Experimental study of the local structure of a bubble flow in a flat channel with sudden expansion

The hydrodynamic structure of the flow in a flat channel with sudden expansion was studied at constant flow rates of liquid and gas in the vertical flow at Re = 6600 and gas content β = 0.03. The measurements were carried out using the PLIF method; and with this view, fluorescent particles for PIV studies and the dye Rhodamine G were added to distilled water. An optical threshold filter was installed on the lens of the video camera. When processing images to obtain data on the local gas content, only bubbles falling into the plane of the laser beam were considered (the boundary glows, casting a shadow).

Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.

Scoping review: Development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers

Objective The study sought to review the different assessment items that have been used within existing health app evaluation frameworks aimed at individual, clinician, or organizational users, and to analyze the scoring and evaluation methods used in these frameworks. Materials and Methods We searched multiple bibliographic databases and conducted backward searches of reference lists, using search terms that were synonyms of “health apps,” “evaluation,” and “frameworks.” The review covered publications from 2011 to April 2020. Studies on health app evaluation frameworks and studies that elaborated on the scaling and scoring mechanisms applied in such frameworks were included. Results Ten common domains were identified across general health app evaluation frameworks. A list of 430 assessment criteria was compiled across 97 identified studies. The most frequently used scaling mechanism was a 5-point Likert scale. Most studies have adopted summary statistics to generate the total scoring of each app, and the most popular approach taken was the calculation of mean or average scores. Other frameworks did not use any scaling or scoring mechanism and adopted criteria-based, pictorial, or descriptive approaches, or “threshold” filter. Discussion There is wide variance in the approaches to evaluating health apps within published frameworks, and this variance leads to ongoing uncertainty in how to evaluate health apps. Conclusions A new evaluation framework is needed that can integrate the full range of evaluative criteria within one structure, and provide summative guidance on health app rating, to support individual app users, clinicians, and health organizations in choosing or recommending the best health app.

Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver’s motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.

Improved wavelet hierarchical threshold filter method for optical coherence tomography image de-noising

According to the speckle feature in Optical coherence tomography (OCT), images with speckle indicate not only noise but also signals, an improved wavelet hierarchical threshold filter (IWHTF) method is proposed. At first, a modified hierarchical threshold-selected algorithm is used to prevent signals from being removed by assessing suitable thresholds for different noise levels. Then, an improved wavelet threshold function based on two traditional threshold functions is proposed to trade-off between speckle removing and sharpness degradation. The de-noising results of an OCT finger skin image shows that the IWHTF method obtains better objective evaluation metrics and visual image quality improvement. When [Formula: see text], [Formula: see text] and [Formula: see text], the improved method can achieve 9.58[Formula: see text]dB improvement in signal-to-noise ratio, with sharpness degraded by 3.81%.