Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Task-driven data fusion for additive manufacturing

Additive manufacturing (AM) is a critical technology for the next industrial revolution, offering the prospect of mass customization, flexible production, and on-demand manufacturing. However, difficulties in understanding underlying mechanisms and identifying latent factors that influence AM processes build up barriers to in-depth research and hinder its widespread adoption in industries. Recent advancements in data sensing and collection technologies have enabled capturing extensive data from AM production for analytics to improve process reliability and part quality. However, modelling the complex relationships between the manufacturing process and its outcomes is challenging due to the multi-physics nature of AM processes. The critical information of AM production is embedded within multi-source, multi-dimensional, and multi-modal heterogeneous data, leading to difficulties when jointly analysing. Therefore, how to bridge the gap between the multi-physics interactions and their outcomes through heterogeneous data analytics becomes a crucial research challenge. Data fusion strategies and techniques can effectively leverage multi-faceted information. Since AM tasks can have various requirements, the corresponding fusion techniques should be task-specific. Hence, this thesis will focus on how to deal with task-driven data fusion for AM. To address the challenges stated above, a comprehensive task-driven data fusion framework and methodology are proposed to provide systematic guidelines to identify, collect, characterise, and fuse AM data for supporting decision-making activities. In this framework, AM data is classified into three major categories, process-input data, process-generated data, and validation data. The proposed methodology consists of three steps, including the identification of data analytics types, data required for tasks, acquisition, and characterization, and task-driven data fusion techniques. To implement the framework and methodology, critical strategies for multi-source and multi-hierarchy data fusion, and Cloud-edge fusion, are introduced and the detailed approaches are described in the following chapters. One of the major challenges in AM data fusion is that the multi-source data normally has various dimensions, involving nested hierarchies. To fuse this data for analytics, a hybrid deep learning (DL) model called M-CNN-LSTM is developed. In general, two levels of data and information are focused on, layer level and build level. In the proposed hybrid model, the CNN part is used to extract features from layer-wise images of sliced 3D models, and the LSTM is used to process the layer-level data concatenated with convolutional features for time-series modelling. The build-level information is used as input into a separate neural network and merged with the CNN-LSTM for final predictions. An experimental study on an energy consumption prediction task was conducted where the results demonstrated the merits of the proposed approach. In many AM tasks at the initial stage, it is usually time-consuming and costly to acquire sufficient data for training DL-based models. Additionally, these models are hard to make fast inferences during production. Hence, a Cloud-edge fusion paradigm based on transfer learning and knowledge distillation (KD)-enabled incremental learning is proposed to tackle the challenges. The proposed methodology consists of three main steps, including (1) transfer learning for feature extraction, (2) base model building via deep mutual learning (DML) and model ensemble, and (3) multi-stage KD-enabled incremental learning. The 3-step method is developed to transfer knowledge from the ensemble model to the compressed model and learn new knowledge incrementally from newly collected data. After each incremental learning in the Cloud platform, the compressed model will be updated to the edge devices for making inferences on the incoming new data. An experimental study on the AM energy consumption prediction task was carried out for demonstration. Under the proposed task-driven data fusion framework and methodology, case studies focusing on three different AM tasks, (1) mechanical property prediction of additively manufactured lattice structures (LS), (2) porosity defects classification of parts, and (3) investigating the effect of the remelting process on part density, were carried out for demonstration. Experimental results were presented and discussed, revealing the feasibility and effectiveness of the proposed framework and approaches. This research aims to pave the way for leveraging AM data with various sources and modalities to support decision-making for AM tasks using data fusion and advanced data analytics techniques. The feasibility and effectiveness of the developed fusion strategies and methods demonstrate their potential to facilitate the AM industry, making it more adaptable and responsive to the dynamic demands of modern manufacturing

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen