Miracle-dz/UniFAR-FTUs huggingface dataset:
Predisposing factors for the deformation of parent artery of anterior circulation saccular aneurysm after stent-assisted embolization: A retrospective cohort study

Background It has been previously reported in several studies that deformation of parent artery (DPA) occurs after stent-assisted coil embolization (SACE) for intracranial aneurysms (IAs). Objective To investigate the predisposing factors of stent-induced DPA, as well as its effect on the follow-up of aneurysm embolization. Methods Clinical and imaging data were collected from 198 patients (201 aneurysms). Angles of the stent-covered parental artery were measured before treatment and during follow-up. Cases in which the angle had changed more than 5 degrees, were defined as DPA. The related factors of DPA were analyzed. The relation between DPA and follow-up results was also studied. Results Univariate analysis revealed that sex (p = 0.014), age (p = 0.017), aneurysm location (p < 0.001), stent type (p < 0.001), aneurysm size (p = 0.019), and pretreatment angle (p = 0.002) correlated with DPA. On the other hand, multivariate analysis revealed that aneurysms located in the anterior communicating artery (ACOA) (OR = 4.559, p = 0.013) and middle cerebral artery (MCA) (OR = 9.474, p < 0.001) were independent predisposing factors for DPA after stent implantation, whereas a braided stent (OR = 0.221, p = 0.030), flow diverter (FD) device (OR = 0.100, p = 0.028) were negative factors to develop DPA. The complete occlusion rate in the DPA group was higher (p = 0.035) than in the non-DPA group. Conclusions Aneurysms located in ACOA and MCA are more prone to DPA after SACE than aneurysms at other locations in the anterior circulation, braided stents and FD devices do not predispose to induce vascular deformation. DPA may be beneficial for the long-term cure of IAs after SACE.
Analysis of airport telematic data using data mining and machine learning

The rapid development of technology in all the scientific fields has caused the convergence of them into the same science’s field. An example of this is Telematics; a synergetic combination of informatics and telecommunications. Nowadays telematics is not very common in the context of airport technologies. However, many of the paperwork, data and services are computer-based and then retransmitted to telecommunications systems. In this thesis the characteristics that surround telematics technology are studied, and the benefits of its use are analyzed through case studies. To develop models, Machine Learning and Data Science techniques have been used. These techniques allow the extraction of proper information of the data obtained by telematics. The models include different procedures, including prediction and interpretation data techniques. As a result, the application of this model could be useful in different fields or environments. In this thesis, the models are adapted to the extraction of the information about the characteristics of Telematics in Vehicles; therefore if this model is going to be used in other ambits, it should be adapted to the new context (keeping the main base). During the thesis different application uses for the model are suggested. These applications, joining data and telematics services, might provide many advantages in the ground operations management at the airport such as the fuel consumption optimization across an accurate use of the concerning features affecting the fuel waste, reducing therefore emissions and avoiding monetary penalties for the accomplishment of certain environment protocols.
A Smartphone Serious Game for Adolescents (Grow It! App): Development, Feasibility, and Acceptance Study

Background Anxiety and mood problems in adolescents often go unnoticed and may therefore remain untreated. Identifying and preventing the development of emotional problems requires monitoring and effective tools to strengthen adolescents' resilience, for example, by enhancing coping skills. Objective This study describes the developmental process, feasibility, and acceptance of Grow It! , a multiplayer serious game app for adolescents aged 12-25 years. The app consists of the experience sampling method (ESM) to monitor thoughts, behaviors, and emotions in daily life to enhance self-insight and daily cognitive behavioral therapy–based challenges to promote adaptive coping. Methods Our approach entails an iterative game design process combined with an agile method to develop the smartphone app. The incorporated game features (ie, challenges, chat functionality, and visual representation) in the Grow It! app were co-designed with adolescent end users to increase participant engagement and adherence. Results The Grow It! app was delivered for Android and iOS in May 2020. Grow It! was offered to adolescents during the COVID-19 crisis between May and December 2020. Participants of the Grow It! COVID-19 study (sample 1: N=685; mean age 16.19, SD 3.11 years; 193/685, 28.2% boys; sample 2: N=1035; mean age 18.78, SD 3.51 years; 193/1035, 18.64% boys) completed 31.5% (13.2/42) to 49.5% (10.4/21) of challenges. Compliance of ESM was suboptimal (35.1/210, 16.7% to 32.5/105, 30.9%). Follow-up questionnaires indicated an overall score of the app of 7.1 out of 10. Moreover, 72.6% (278/383) to 75.6% (487/644) would recommend the app to friends. Conclusions To our knowledge, Grow It! is the first gamified ESM app that both measures individual differences in emotional dynamics and offers an integrated cognitive behavioral therapy–based intervention. Our findings support the feasibility and acceptance, and therefore applicability, of the Grow It! app in adolescents. Further iterations of this serious game app will focus on the increase of compliance and on providing participants feedback through their personal mood profiles.
Plataforma de Serviços Inteligentes de Learning Analytics para grandes quantidades de dados

This article presents SmartLAK an architecture for large data analysis generated by the students during a course through Smart Services. Developed from Big Data technologies, ontologies and architectural patterns, which were combined to facilitate the development of a flexible and interoperable architecture and as a way to support intelligent services oriented to Learning Analytics. Currently, this platform is being used experimentally in a Department of Education with good results already signposted from 250,000 declarative analysis on a course in blended learning mode with 60 subscribers.
Architecture for Large-Scale Autonomous Robot System

An architecture for a large-scale robot system is proposed,including group architecture and single-robot architecture.Group architecture is a hybrid structure,but it distinguished from general hybrid structure.The hierarchical control of the architecture only serves as task level and there is little control information in whole system.The coordination of motion level is implemented by robots autonomously.This group architecture can be applied to a large-scale autonomous robot system.A layered hybrid architecture for single robot is presented and it blends the advantage of reactive structure and deliberative structure.The whole control mechanism implements the combination of robots autonomous motion and efficient task coordination.
Plan quality in radiotherapy treatment planning – Review of the factors and challenges

A high‐quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ‐at‐risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement‐based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high‐quality treatment plan continues to evolve with new techniques, delivery methods and related evidence‐based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.
Electroosmotic micropump for lab‐on‐a‐chip biomedical applications

Lab‐on‐a‐chip (LOC) integrated microfluidics has been a powerful tool for new developments in analytical chemistry. These microfluidic systems enable the miniaturization, integration and automation of complex biochemical assays through the reduction of reagent use and enabling portability. Electroosmotic micropumps could be employed as powerful tools to generate required flow in point of care (POC) devices. In the present study, parallel electroosmotic micropumps are investigated to improve the efficiency of simple micropumps. According to the results, parallel micropumps generate higher flow rate in comparison with conventional electroosmotic pump. In the last decade, a large variety of non‐Newtonian fluids have been utilized in biomedical application but requirements for a POC device such as high efficient driving flow, miniaturization and simple handling of POC devices remain unmet. As a consequence, in this study, power law model as non‐Newtonian fluids that flow through the parallel micropumps are investigated in order to enhance fluid pumping and decreasing voltage requirement. It is found that as the power law index increases the mass flow rate decreases. Also, the flow rate is almost constant for the higher power law index. Obtained results, demonstrated that parallel micropump could enhance pumping of non‐Newtonian fluid (blood) up to 30%. Copyright © 2016 John Wiley & Sons, Ltd.
Uncovering Associations Between Cognitive Presence and Speech Acts: A Network-Based Approach

This research aimed to explore the relationship between different indicators of the depth and quality of participation in computer-mediated learning environments. By using network analyses and statistical tests, we discovered significant associations between the cognitive presence phases of the Community of Inquiry framework and speech acts, and examined the impact of two different instructional interventions on these associations. We found that there are strong associations between some speech acts and cognitive presence phases. In addition, the study revealed that the association between speech acts and cognitive presence is moderated by external facilitation, but not affected by user role assignment. The results suggest that speech acts can plausibly be used to provide feedback in relation to cognitive presence and can potentially be used to increase the generalizability of cognitive presence classification.
Effects of Technological Interventions for Self-regulation: A Control Experiment in Learnersourcing

The benefits of incorporating scaffolds that promote strategies of self-regulated learning (SRL) to help student learning are widely studied and recognised in the literature. However, the best methods for incorporating them in educational technologies and empirical evidence about which scaffolds are most beneficial to students are still emerging. In this paper, we report our findings from conducting an in-the-field controlled experiment with 797 post-secondary students to evaluate the impact of incorporating scaffolds for promoting SRL strategies in the context of assisting students in creating novel content, also known as learnersourcing. The experiment had five conditions, including a control group that had access to none of the scaffolding strategies for creating content, three groups each having access to one of the scaffolding strategies (planning, externally-facilitated monitoring and self-assessing) and a group with access to all of the aforementioned scaffolds. The results revealed that the addition of the scaffolds for SRL strategies increased the complexity and effort required for creating content, were not positively assessed by learners and led to slight improvements in the quality of the generated content. We discuss the implications of our findings for incorporating SRL strategies in educational technologies.
Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.
Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics

Multimodal Learning Analytics (MMLA) innovations are commonly aimed at supporting learners in physical learning spaces through state-of-the-art sensing technologies and analysis techniques. Although a growing body of MMLA research has demonstrated the potential benefits of sensor-based technologies in education, whether their use can be scalable, sustainable, and ethical remains questionable. Such uncertainty can limit future research and the potential adoption of MMLA by educational stakeholders in authentic learning situations. To address this, we systematically reviewed the methodological, operational, and ethical challenges faced by current MMLA works that can affect the scalability and sustainability of future MMLA innovations. A total of 96 peer-reviewed articles published after 2010 were included. The findings were summarised into three recommendations, including i) improving reporting standards by including sufficient details about sensors, analysis techniques, and the full disclosure of evaluation metrics, ii) fostering interdisciplinary collaborations among experts in learning analytics, software, and hardware engineering to develop affordable sensors and upgrade MMLA innovations that used discontinued technologies, and iii) developing ethical guidelines to address the potential risks of bias, privacy, and equality concerns with using MMLA innovations. Through these future research directions, MMLA can remain relevant and eventually have actual impacts on educational practices.
Pilot Recommender System Enabling Students to Indirectly Help Each Other and Foster Belonging Through Reflections

Without a sense of belonging, students may become disheartened and give up when faced with new challenges. Moreover, with the sudden growth of remote learning due to COVID-19, it may be even more difficult for students to feel connected to the course and peers in isolation. Therefore, we propose a recommendation system to build connections between students while recommending solutions to challenges. This pilot system utilizes students’ reflections from previous semesters, asking about learning challenges and potential solutions. It then generates sentence embeddings and calculates cosine similarities between the challenges of current and prior students. The possible solutions given by previous students are then recommended to present students with similar challenges. Self-reflection encourages students to think deeply about their learning experiences and benefit both learners and instructors. This system has the potential to allow reflections also to help future learners. By demonstrating that previous students encountered and overcame similar challenges, we could help improve students’ sense of belonging. We then perform user studies to evaluate this system’s potential and find that participants rated 70% of the recommended solutions as useful. Our findings suggest an increase in students’ sense of membership and acceptance, and a decrease in the desire to withdraw.
Time-on-Task Estimation by data-driven Outlier Detection based on Learning Activities

Temporal analysis has been demonstrated to be relevant in Learning Analytics research, and capturing time-on-task, i.e., the amount of time spent by students in quality learning, as a proxy to model learning behaviour, predict performance, and avoid drop-out has been the focus of a number of investigations. Nonetheless, most studies do not provide enough information on how their data were prepared for their findings to be easily replicated, even though data pre-processing decisions have an impact on the analysis’ outcomes and can lead to inaccurate predictions. One of the key aspects in the preparation of learning data for temporal analysis is the detection of anomalous values of temporal duration of students’ activities. Most of the works in the literature address this problem without taking into account the fact that different activities can have very different typical execution times. In this paper, we propose a methodology for estimating time-on-task that starts with a well-defined data consolidation and then applies an outlier detection strategy to the data based on a distinct study of each learning activity and its peculiarities. Our real-world data experiments show that the proposed methodology outperforms the current state of the art, providing more accurate time estimations for students’ learning tasks.
Conceptualizing consumption behaviours of rugby fans in New Zealand : understanding consumption behaviours of rugby fans and the impact on sport consumption

While there is an abundance of research available on consumption behaviours and fan behaviours there is a limited amount specifically about rugby fans in New Zealand. This thesis aims to contribute a study on this topic, to add knowledge and insight on these types of sports fans. This study seeks to understand the prominent factors that influence consumption behaviours of rugby fans. Drawing on the research results the creation of a conceptual model is proposed for sports organisations to use in their relative areas of the industry. The research method used is qualitative within an interpretive paradigm and uses 22 one to one interviews for data – collection. To analyse the data a theoretical framework was applied and the decision to use this method was to accommodate the exploratory nature of the research question. Because consumption behaviours involve social behaviours there is a need to explore self-concept and self-identity in relation to group affiliation as a sports fan. The structure of this thesis has adopted the seminal work of Holt’s (1995) on consumer behaviours. The Holt (1995) work has four key components that transfer into the four major themes of this study these are; consuming as experience, consuming as integration, consuming as classification and consuming as play. This framework (Holt, 1995) has been used to provide a methodical structure through-out all the chapters of this work. The emerging sub-themes of this work are rugby consumption motives of family and group affiliation; Self-identity and self-concept that is created through family identity and the strong presence of rugby that shapes New Zealand cultural identity, three fan types are identified and their consumption behaviours explored, and finally the physical attributes of being a sports fan of affiliation, expressions and child’s play that connect fans and sports brands together. This work developed two conceptual models. The overarching model is called the Sport Circle Consumption Model, which incorporates the major themes and sub-themes from the research results. The second model is called the Inner Fan Model which focusses more specifically as the internal motives and influences towards being a sports fan.
Digital and analogue memory: a theoretical and historical framework for e-learning

The digital revolution has exposed cognitive and mnemonic functions: in this work we discuss the concept of collective memory not so much in terms of sharing opinions and events, but in terms of memory as container, support, custodian and origin of one's own individuality. We attempt to hypothesize the potentialities of connection and sharing of the self on the Net, focusing specifically on the issues and impacts that these findings have on e-learning and digital learning.
Neurofeedback video games in the rehabilitative treatment of hyperactivity and attention deficit disorder for attention enhancement and hyperactivity reduction

In the field of special education, and in particular, in the rehabilitation treatment of patients with Attention Deficit Hyperactivity Disorder, a multidimensional neurodevelopmental disorder characterized by a mixed pattern of inattention and hyperactivity and/or impulsivity, virtual reality integrated with neurofeedback produces an intensive neurocognitive training capable of enhancing long-term attentional capacity and, at the same time, reduce hyperactivity and/or impulsivity. Research has shown that learning enhanced by technology and, in particular, by neurofeedback combined with cognitive training based on video games produces better therapeutic effects than other common therapeutic approaches, such as conventional therapies and pharmacological proposals, as the playful component greatly increases the motivation to learn by the patient / player, providing the opportunity to live not only an immersive and fun experience, but also therapeutic in terms of symptomatic reduction.