Proactive Assessment of Obesity Risk during Infancy (ProAsk): A qualitative study of parents' and professionals' perspectives on an mHealth intervention

Background: Prevention of childhood obesity is a public health priority. Interventions that establish healthy growth trajectories early in life promise lifelong benefits to health and wellbeing. Proactive Assessment of Obesity Risk during Infancy (ProAsk) is a novel mHealth intervention designed to enable health professionals to assess an infant’s risk of future overweight and motivate parental behaviour change to prevent childhood overweight and obesity. The aim of this study was to explore parents’ and health professionals’ experiences of the overweight risk communication and behaviour change aspects of this mHealth intervention. Methods: The study was conducted in four economically deprived localities in the UK. Parents (N=66) were recruited to the ProAsk feasibility study when their infant was 6-8 weeks old. Twenty two health visitors (HVs) used a hand-held tablet device to deliver ProAsk to parents when their infants were 3 months old. Parents (N=12) and HVs (N=15) were interviewed when infants in the study were 6 months old. Interview data were transcribed and analysed thematically using an inductive, interpretative approach. Results: Four key themes were identified across both parent and health visitor data: engaging and empowering with digital technology; unfamiliar technology presents challenges and opportunity; trust in the risk score; resistance to targeting. Most participants found the interactivity and visual presentation of information on ProAsk engaging. Health visitors who were unfamiliar with mobile technology drew support from parents who were more confident using tablet devices. There was evidence of resistance to targeting infants at greatest risk of future overweight and obesity, and both parents and health visitors drew on a number of reasons why a higher than average overweight risk score might not apply to a particular infant. Conclusions: An mHealth intervention actively engaged parents, enabling them to take ownership of the process of seeking strategies to reduce infant risk of overweight. However, cognitive and motivational biases that prevent effective overweight risk communication are barriers to targeting an intervention at those infants most at risk

Electrostatic-Fluid-Structure 3D Numerical Simulation of a MEMS Electrostatic Comb Resonator

The reliability and stability of MEMS electrostatic comb resonators have become bottlenecks in practical applications. However, there are few studies that comprehensively consider the nonlinear dynamic behavior characteristics of MEMS systems and devices in a coupled field so that the related simulation accuracy is low and cannot meet the needs of design applications. In this paper, to avoid the computational complexity and the uncertainty of the results of three-field direct coupling and take into the damping nonlinearity caused by coupled fields, a novel electrostatic-fluid-structure three-field indirect coupling method is proposed. Taking an actual microcomb resonant electric field sensor as an example, an electrostatic-fluid-structure multiphysics coupling 3D finite element simulation model is established. After considering the influence of nonlinear damping concerning the large displacement of the structure and the microscale effect, multifield coupling dynamics research is carried out using COMSOL software. The multiorder eigenmodes, resonant frequency, vibration amplitude, and the distribution of fluid load of the microresonator are calculated and analyzed. The simulated data of resonance frequency and displacement amplitude are compared with the measured data. The results show that the fluid load distribution of the microelectrostatic comb resonator along the thickness direction is high in the middle and low on both sides. The viscous damping of the sensor under atmospheric pressure is mainly composed of the incompressible flow damping of the comb teeth, which is an order of magnitude larger than those of other parts. Compared with the measured data, it can be concluded that the amplitude and resonance frequency of the microresonator considering the nonlinear damping force and residual thermal stress are close to the experimental values (amplitude error: 15.47%, resonance frequency error: 12.48%). This article provides a reference for studies on the dynamic characteristics of electrostatically driven MEMS devices

An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

An improved method for retrieving Above-ground Biomass (AGB) and Canopy Height (CH) based on an observable from Cyclone Global Navigation Satellite System (CYGNSS), soil moisture from Soil Moisture Active Passive (SMAP) and location is proposed. The observable derived from CYGNSS is more sensitive to vegetation. The CYGNSS observable, soil moisture and the location are used as the input features of an Artificial Neural Network (ANN) to retrieve AGB and CH. The sensitivity analysis of the CYGNSS observable to target parameters shows that the proposed observable is more sensitive to AGB/CH than the conventional observable. The AGB/CH retrievals of the improved method show that it has better performance than that of the traditional method, especially in the areas with AGB in the range of 0 to100 Mg/ha and CH in the range of 0 to10 m. For AGB retrievals, the root mean square error (RMSE) and correlation coefficient are 64.84 Mg/ha and 0.80 in the range of 0 to 550 Mg/ha. Compared with the traditional method, the RMSE is decreased by 11.63%, while the correlation coefficient is increased by 5.26%. For CH retrievals, the RMSE and correlation coefficient are 5.97 m and 0.83 in the range of 0 to 45 m. The RMSE is decreased by 12.59%, while the correlation coefficient is increased by 5.06%. The analysis of the improved method in different areas shows that the performance of the improved method over the area with high vegetation is better than the area with low vegetation. The results obtained here further strengthens the capability of GNSS-R for global AGB/CH retrievals as well as different land cover areas

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring

Performance Evaluation of Real-Time Precise Point Positioning with Both BDS-3 and BDS-2 Observations

For time-critical precise applications, one popular technology is the real-time precise point positioning (PPP). In recent years, there has been a rapid development in the BeiDou Navigation Satellite System (BDS), and the constellation of global BDS (BDS-3) has been fully deployed. In addition to the regional BDS (BDS-2) constellation, the real-time stream CLK93 has started to support the BDS-3 constellation, indicating that the real-time PPP processing involving BDS-3 observations is feasible. In this study, the global positioning performance of real-time PPP with BDS-3/BDS-2 observations is initially evaluated using the datasets from 147 stations. In the east, north and upward directions, positioning accuracy of 1.8, 1.2 and 2.5 cm in the static mode, and of 6.7, 5.1 and 10.4 cm in the kinematic mode can be achieved for the BDS-3/BDS-2 real-time PPP, respectively, while the corresponding convergence time with a threshold of 10 cm is 32.9, 23.7 and 32.8 min, and 66.9, 42.9 and 69.1 min in the two modes in the three directions, respectively. To complete this, the availability of BDS-3/BDS-2 constellations, the quality of BDS-3/BDS-2 real-time precise satellite products, and the BDS-3/BDS-2 post-processed PPP solutions are also analyzed. For comparison, the results for the GPS are also presented

Optimised weighted mean temperature model based on generalised regression neural network

ABSTRACTThe weighted mean temperature (Tm) is a key parameter to calculate the Global Navigation Satellite System (GNSS)-based precipitable water vapour (PWV). Data fusion provides a solution to depict the characteristics of Tm in detail. However, multi-source heterogeneity, unequal accuracies and even serious system deviation may lead to unreliable and inconsistent accuracies in the fusion results. We utilise generalised regression neural network (GRNN) to establish an optimised model for the Tm from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China and the Tm from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data around China from 2016 to 2017. Then, an example fusion using the radiosonde (RS) Tm and the optimised Tm is carried out. The results confirm the systematic deviations between GRAPES/ERA5 Tm and RS Tm. After optimisation, the bias of GRAPES and ERA5 Tm is almost eliminated, and the root mean squared error (RMSE) decreased by 21.1% and 18.7%, respectively. Compared to RS Tm, the fusion results based on the optimised Tm have good consistencies and unbiased accuracies, and can merge more detailed spatial features than that of a single data source

A Novel Global Grid Model for Atmospheric Weighted Mean Temperature in Real-Time GNSS Precipitable Water Vapor Sounding

The atmospheric weighted mean temperature (Tm) is an important parameter in calculating the precipitable water vapor from Global Navigation Satellite System (GNSS) signals. As both GNSS positioning and GNSS precipitable water vapor detection require high spatial and temporal resolutions for calculating Tm, high-precision modeling of Tm has gained widespread attention in recent years. The previous models for calculating Tm have the limitation of too many model parameters or single-grid data. Therefore, this study presents a global high-precision Tm model (GGTm-H model) developed from the latest Modern-Era Retrospective Analysis for Research and Applications, version-2 (MERRA-2) atmospheric reanalysis data provided by the United States National Aeronautics and Space Administration. The accuracy of the GGTm-H model was verified by combining the MERRA-2 surface Tm data and 319 radiosonde data. The results highlighted that 1) When the MERRA-2 Tm data were used as a reference value, the mean annual RMSE of the GGTm-H model was observed to be 2.72 K. When compared with the Bevis model, GPT2w-5 model, and GPT2w-1 model, the GGTm-H model showed an improvement of 1.5, 0.33, and 0.21 K, respectively. 2) When the radiosonde data were used as a reference value, the mean bias and RMSE of the GGTm-H model were −0.41 K and 3.82 K, respectively. Compared with the other models, the GGTm-H model had the lowest mean annual bias and RMSE. The developed model does not consider any meteorological parameters while calculating Tm. Therefore, it has important applications in the real-time and high-precision monitoring of precipitable water vapor from GNSS signals