Leveraging Joint Planning in Early Intervention: Documenting with Intentionality and Specificity

A family-capacity building approach to coaching, where providers support caregivers to embed identified strategies into daily routines and activities, is commonly embraced in Part C Early Intervention (EI). EI providers use several coaching strategies within this approach, yet few studies have reported process features, and coaching strategies are not well defined in the literature. We partnered in this Participatory Action Research (PAR) with current EI providers engaged in a year-long self-study process to provide an empirical account of one coaching strategy, joint planning, and related documentation. Our results indicate both providers and caregivers view documentation of joint planning as beneficial, highlight supports and challenges, and suggest that joint planning documentation holds significant promise for improving practice, data-based decision making, and progress monitoring of child and family outcomes including changes in caregiver capacity.

UAV Obstacle Avoidance Algorithm to Navigate in Dynamic Building Environments

In this work, a real-time collision avoidance algorithm was presented for autonomous navigation in the presence of fixed and moving obstacles in building environments. The current implementation is designed for autonomous navigation between waypoints of a predefined flight trajectory that would be performed by an UAV during tasks such as inspections or construction progress monitoring. It uses a simplified geometry generated from a point cloud of the scenario. In addition, it also employs information from 3D sensors to detect and position obstacles such as people or other UAVs, which are not registered in the original cloud. If an obstacle is detected, the algorithm estimates its motion and computes an evasion path considering the geometry of the environment. The method has been successfully tested in different scenarios, offering robust results in all avoidance maneuvers. Execution times were measured, demonstrating that the algorithm is computationally feasible to be implemented onboard an UAV.

Execution Time Prediction for Cypher Queries in the Neo4j Database Using a Learning Approach

With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets.

Optimize Performance

The O-Optimize performance phase of the VECTOR process is where implementation occurs. In this chapter, the authors tell the story of a child learning how to do a basketball layup as a concrete example for everything that is required in this phase. Additionally, the authors unpack the psychology of working toward a goal, the power of data, and why feedback and progress monitoring are essential during this phase. Finally, there are practical suggestions for how to do the work as a virtual coach during this phase, including questions to ask and how video-based observations play a key role for the coach and coachee to stay focused on the goal.

Telegram Bot as a Data Collection Tool for Progress Reports in Area Mapping Progress Monitoring System

In the Area Mapping Project (Preparation for the 2020 Population Census), there is monitoring and collecting data process on the progress of activities in the field. There are weaknesses in data collection on the progress of activities in the field; The manual recapitulation of progress reporting makes the progress data not displayed in real-time, the SMS Gateway is often interrupted, and progress data collection through the monitoring website is less effective if reported directly by field officers. Telegram Bot is used as a data collection tool for field progress reports on Area Mapping activities to overcome these weaknesses. This study aims to prove that Telegram Bot can be used as a real-time data collection tool, has good performance, and is acceptable to users. Telegram Bot is integrated with Monitoring Website into one system and database in this research. This system uses PHP, Yii2, and MySQL, and communication between the web server and Telegram Server uses the webhook method. Based on the Black Box test results, all functions in this system run as expected. The average bot response time was 7.72 seconds for images and 2.25 seconds for text data in the performance test. In the usability test, Telegram Bot obtained a SUS Score of 66 and an NPS of 12.195. These results show that Telegram Bot can be used as a real-time data collection tool, has good performance, and is well accepted by users.

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

Mapping Unmanned Aerial System Data onto Building Information Modeling Parameters for Highway Construction Progress Monitoring

The transportation infrastructure management sector lacks automated procedures that can help it find and resolve the performance deviations. The objective of this research is to illustrate the mapping of Unmanned Aerial System (UAS) collected photogrammetric data to building information modeling (BIM) parameters, and their application for automated construction progress monitoring and the generation of as-built models. The goal is to support project managers to estimate project progress during highway construction. As a part of ongoing work, this paper takes into account 4D (3D + time) data that is acquired from 3D surface digital elevation models, point clouds, LiDAR data, and orthographic photos. It maps these 4D data onto BIM parameters to create as-built models of the project at different intervals. A comparison between as-planned and as-built models using the earned value management method is employed to develop metrics that can be used for indicating cost and schedule deviations during construction. The mapping methodology introduced in this paper is illustrated using an ongoing highway construction project case study. The main contribution of this paper is the organization, processing, and integration of UAS data with BIM data structures and project management workflows. The research outcomes will assist project managers in an easy and quick identification of potential performance problems and support the project management decision-making process.

Design and Development of a Mobile Health (mHealth) Platform for Dementia Prevention in the Prevention of Dementia by Mobile Phone Applications (PRODEMOS) Project

Background: Mobile health (mHealth) has the potential to bring preventive healthcare within reach of populations with limited access to preventive services, by delivering personalized support at low cost. Although numerous mHealth interventions are available, very few have been developed following an evidence-based rationale or have been tested for efficacy. This article describes the systematic development of a coach-supported mHealth application to improve healthy lifestyles for the prevention of dementia and cardiovascular disease in the United Kingdom (UK) and China.Methods: Development of the Prevention of Dementia by Mobile Phone applications (PRODEMOS) platform built upon the experiences with the Healthy Aging Through Internet Counseling in the Elderly (HATICE) eHealth platform. In the conceptualization phase, experiences from the HATICE trial and needs and wishes of the PRODEMOS target population were assessed through semi-structured interviews and focus group sessions. Initial technical development of the platform was based on these findings and took place in consecutive sprint sessions. Finally, during the evaluation and adaptation phase, functionality and usability of the platform were evaluated during pilot studies in UK and China.Results: The PRODEMOS mHealth platform facilitates self-management of a healthy lifestyle by goal setting, progress monitoring, and educational materials on healthy lifestyles. Participants receive remote coaching through a chat functionality. Based on lessons learned from the HATICE study and end-users, we made the intervention easy-to-use and included features to personalize the intervention. Following the pilot studies, in which in total 77 people used the mobile application for 6 weeks, the application was made more intuitive, and we improved its functionalities.Conclusion: Early involvement of end-users in the development process and during evaluation phases improved acceptability of the mHealth intervention. The actual use and usability of the PRODEMOS intervention will be assessed during the ongoing PRODEMOS randomized controlled trial, taking a dual focus on effectiveness and implementation outcomes.

Diversity of Immunoglobulin Heavy Chain Repertoire in Patients With Rheumatoid Arthritis

Objectives Rheumatoid Arthritis (RA) is associated with polymorphism in major histocompatibility complex class II genes and dysregulations of CD4+ T cells which cause abnormalities in immune repertoire (iR) expression and intracellular signaling. We monitored nucleotide sequence changes in iR of immunoglobulin heavy chain (IGH), particularly complementarity determining region 3 (CDR3) during the course of treatments in RA patients using massively parallel sequencing technology.Methods CDR3 sequencing was carried out on clinical blood samples from RA patients for disease progress monitoring. The iR of each sample was measured using next generation sequencing (NGS) pipeline. Data analysis was done with a web-based iRweb server. Principal components analysis (PCA) was completed with commercial statistical pipeline. Results Datasets from 14 patients covered VDJ regions of IGH gene. D50 stayed low for all cases (mean D50 = 6.5). A pattern of shared CDR3 sequences was confirmed by a clustering pattern using PCA. Shared profile of 608 CDR3 sequences unique to the disease baseline was identified. D50 analyses revealed clonal diversity would remain low throughout the disease course even after treatment (mean D50 = 11.7 & 8.2 for csDMARD & bDMARD groups respectively) regardless of fluctuated disease activity. PCA has provided a correlation of change in immune diversity along the whole course of RA. Conclusion We have successfully constructed the experimental design, data acquisition, processing, and analysis pipeline of a high throughput massively parallel CDR3 sequences detection to be used to correlate RA disease activity and IGH CDR3 iR during disease progression with or without treatments.