Using mHealth to promote parents’ brushing of preschool children’s teeth: a protocol for a randomized factorial trial using the Multi-phase Optimization Strategy (MOST)

Background Early childhood caries is a highly prevalent disease affecting young children. Parental brushing of children’s teeth is recommended during preschool years. Interventions to promote parental brushing of children’s teeth are assessed as a package in randomized clinical trials and the efficacy of separate components is not known. Methods and analysis The aim of this study is to develop an optimized behavior modification intervention to increase parents’ brushing of their pre-school children’s teeth using the multi-phase optimization strategy (MOST) guided by the Theory of Planned Behavior. Behavior change will be assessed by the percent reduction in children’s dental plaque index after 6 months and parents reporting of toothbrushing frequency. Two phases of MOST will be carried out. First, the preparation phase comprises the development of a conceptual framework, identifying candidate components, conducting a feasibility pilot study to assess the acceptability and the design features of three intervention components (motivational interviewing (MI), and two mobile health (mHealth) components: oral health promotion messages and storytelling videos delivered using WhatsApp messenger) in addition to setting an optimization objective. Second, the optimization phase constitutes a factorial trial assessing the three intervention components and developing the intervention by selecting the most effective components within the optimization constraint. Each component will be set at two levels: yes (the intervention is applied) and no (the intervention is not applied). A linear regression model will be used to assess the effect of the intervention components on the percent reduction in dental plaque index (primary outcome measure). The secondary outcome measure is the change in the frequency of parents’ brushing of the child’s teeth. The combination of components making up the new optimized intervention will be selected. Discussion This will be the first study to apply the MOST framework in the field of dentistry. The results of this study can guide the development of an optimized behavior modification interventions using mHealth and MI. Trial registration ClinicalTrials.gov, NCT04923581, Registered 11 June 2021.

Phase Optimization for Multipoint Haptic Feedback Based on Ultrasound Array

Ultrasound based haptic feedback is a potential technology for human-computer interaction (HCI) with the advantages of low cost, low power consumption and controlled force. In this paper, the phase optimization for multipoint haptic feedback based on ultrasound array is investigated and the corresponding experimental verification is provided. A mathematical model of acoustic pressure is established for the ultrasound array and then a phase optimization model for an ultrasound transducer is constructed. We propose a pseudo-inverse (PINV) algorithm to accurately determine the phase contribution of each transducer in the ultrasound array. By controlling the phase difference of the ultrasound array, the multipoint focusing forces are formed leading to various shapes such as geometries and letters that can be visualized. Because the unconstrained PINV solution results in unequal amplitudes for each transducer, a weighted amplitude iterative optimization is deployed to further optimize the phase solution, by which the uniform amplitude distributions of each transducer are obtained. For the purpose of experimental verifications, a platform of ultrasound haptic feedback consisting of a Field Programmable Gate Array (FPGA), an electrical circuit and an ultrasound transducer array is prototyped. The haptic performances of single point, multiple points and dynamic trajectory were verified by controlling the ultrasound force exerted on the liquid surface. The experimental results demonstrate that the proposed phase optimization model and theoretical results are effective and feasible, and the acoustic pressure distribution is consistent with the simulation results.

Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection

The Coherent Pixels Technique Interferometry Synthetic Aperture Radar (CPT-InSAR) method of inverting surface deformation parameters by using high-quality measuring points possesses the flaw inducing sparse measuring points in non-urban areas. In this paper, we propose the Adaptive Coherent Distributed Pixel InSAR (ACDP-InSAR) method, which is an adaptive method used to extract Distributed Scattering Pixel (DSP) based on statistically homogeneous pixel (SHP) cluster tests and improves the phase quality of DSP through phase optimization, which cooperates with Coherent Pixel (CP) for the retrieval of ground surface deformation parameters. For a region with sparse CPs, DSPs and its SHPs are detected by double-layer windows in two steps, i.e., multilook windows and spatial filtering windows, respectively. After counting the pixel number of maximum SHP cluster (MSHPC) in the multilook window based on the Anderson–Darling (AD) test and filtering out unsuitable pixels, the candidate DSPs are selected. For the filtering window, the SHPs of MSHPC’ pixels within the new window, which is different compared with multilook windows, were detected, and the SHPs of DSPs were obtained, which were used for coherent estimation. In phase-linking, the results of Eigen decomposition-based Maximum likelihood estimator of Interferometric phase (EMI) results are used as the initial values of the phase triangle algorithm (PTA) for the purpose of phase estimation (hereafter called as PTA-EMI). The DSPs and estimated phase are then combined with CPs in order to retrievesurface deformation parameters. The method was validated by two cases. The results show that the density of measuring points increased approximately 6–10 times compared with CPT-InSAR, and the quality of the interferometric phase significantly improved after phase optimization. It was demonstrated that the method is effective in increasing measuring point density and improving phase quality, which increases significantly the detectability of the low coherence region. Compared with the Distributed Scatterer InSAR (DS-InSAR) technique, ACDP-InSAR possesses faster processing speed at the cost of resolution loss, which is crucial for Earth surface movement monitoring at large spatial scales.

Using mHealth to Promote Mothers’ Brushing of Preschool Children’s Teeth: A Study Protocol using the Multi-phase Optimization Strategy (MOST)

BackgroundEarly childhood caries is a highly prevalent disease affecting young children. Parental brushing of children’s teeth is recommended during preschool years. Interventions to promote parental brushing of children’s teeth are assessed as a package in randomized clinical trials and the efficacy of separate components is not known. Methods and AnalysisThe aim of this study is to develop an optimized behavior modification intervention to increase mothers’ brushing of their pre-school children’s teeth using the Multi-phase Optimization Strategy (MOST) guided by the Theory of Planned Behavior. Behavior change will be assessed by the percent reduction in children’s dental plaque index after 6 months and mothers’ reporting of toothbrushing frequency. Two phases of MOST will be carried out. First: the preparation phase comprises the development of a conceptual framework, identifying candidate components, conducting a feasibility pilot study to assess the acceptability and the design features of three intervention components (motivational interviewing (MI), and two mobile health (mHealth) components: oral health promotion messages, and storytelling videos delivered using WhatsApp messenger) in addition to setting an optimization objective. Second: the optimization phase constitutes a factorial trial assessing the three intervention components and developing the intervention by selecting the most effective components within the optimization constraint. Each component will be set at two levels: yes (the intervention is applied) and no (the intervention is not applied). A linear regression model will be used to assess the effect of the intervention components on the percent reduction in dental plaque index (primary outcome measure). The secondary outcome measure is the change in the frequency of mother’s brushing of the child’s teeth. The combination of components making up the new optimized intervention will be selected.DiscussionThis will be the first study to apply the MOST framework in the field of dentistry. The results of this study can guide to the development of an optimized behaviour modification interventions using mHealth and MI. Trial Registration This trial was registered on June 11th, 2021, in Clinical trials.gov. Registration number: NCT04923581.

A Co-optimization PSO for Fuzzy Rule-Based Classifier Design Problem Based on Enlarged Hedge Algebras

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers.