1,721,023 research outputs found
Signal Processing Techniques for Data Analysis in Telerehabilitation: Intelligent Remote Rehabilitation Monitoring Enhancement
No full textIn recent years, ICT and IOT devices have been employed to monitor and assist
with patients’ rehabilitation, as well as to analyze their conditions and create and
update individualized care plans. Additionally, they promote continuity of care by
allowing a patient to continue receiving supervision from a multidisciplinary team
even after being released from the hospital. Virtual reality and exergames are further
ICT-enabled technologies that have shown strong potential in the treatment
of cognitive and motor impairments.
The implementation of the ReMoVES telerehabilitation platform in different situations
is the focus of the current thesis. In order to extract the key features and
examine the statistical significance between the patient and healthy groups, the
main contribution of the research activity is to offer a method for evaluating a
subject’s rehabilitation efforts while giving special attention to the pre-processing
of the multidimensional signals obtained during rehabilitation sessions.
In addition, there will be a proposal, description, and application of a systematic
protocol for signal processing and data analysis for specific clinical scenarios
The role of chemistry in the retardant effect of dimethyl methylphosphonate in flame–wall interaction
Full text linkOrganophosphorous compounds can act as chemical inhibitors for fire suppression and have recently received significant attention in the combustion community due to their potential to induce flame extinction through a radical recombination process. To optimize their use, a comprehensive understanding of the extinction dynamics is essential. In this study, mixtures of methane with dimethyl methylphosphonate (DMMP) are investigated in laminar conditions, with a specific focus on flame–wall interaction. The ultimate objective is obtaining a deeper insight into the flame quenching process, which is synergistically enhanced by heat transfer to the wall and the chemical retardant effect of DMMP. Detailed chemistry information is implemented by setting up an ad-hoc skeletal kinetic mechanism. This is developed and validated for flame configurations with increasing complexity, including freely propagating premixed flames, head-on-quenching flames, and side-wall quenching flames. The results indicate that the skeletal mechanism is able to reproduce available experimental data for flame speed and ignition delay time, as well as the main flame features and quenching characteristics in flame–wall interactions for an increasing level of DMMP. Simulations of a side-wall quenching burner, incorporating a secondary fuel injection through a porous insert at the wall, are carried out. Chemical analyses provide detailed insight into the role of flame retardant during the flame quenching process. The results show that the HOPO/PO2 catalytic cycle is of major importance for the suppressant effect, further supported by the formation of CH3PO2 as an intermediate, increasing the formation of HOPO itself. Moreover, the CO mass fraction is observed to increase also because of the radical scavenging effect, inhibiting the conversion to CO2 via CO + OH → CO2 + H. Overall, this study advances the understanding of the chemical features of flame quenching in the presence of flame retardants containing implications for fire safety applications
Evaluation of Machine Learning Models for Movement Classification in Exergame-Based Rehabilitation
No full textThis paper explores the innovative integration of exergames and machine learning techniques to enhance rehabilitation outcomes for patients with motor impairments, particularly those resulting from stroke or other neurological conditions. Utilizing the Remote Monitoring Validation Engineering System (ReMoVES) and the Microsoft Kinect sensor, the study captures and analyzes patient movements during Sit-to-Stand exercises—key activities for improving strength, endurance, and functional mobility. The primary aim is to classify these movements as either normal or abnormal, providing insights into recovery progress. Comprehensive motion data, including joint angles, movement trajectories, and timing metrics, are collected and rigorously processed to extract features that differentiate between normal and impaired patterns. Various machine learning algorithms, such as Support Vector Machines (SVM), Extreme Gradient Boosting (XGBClassifier), and K-Nearest Neighbors (K-NN), are employed to assess and classify patient performance in real-time. The system offers immediate feedback, including performance scores, visual cues, and tailored suggestions, which are crucial for maintaining engagement and motivation. Additionally, ReMoVES allows healthcare professionals to remotely monitor patient progress, enabling timely adjustments to rehabilitation plans. Findings show that combining exergames with advanced machine learning significantly improves movement classification accuracy and enhances patient engagement and motivation, providing a promising solution for optimizing rehabilitation processes and improving patient outcomes in both clinical and home settings
Experimental and numerical study on the effect of oxymethylene ether-3 (OME3) on soot particle formation
Full text linkThe reduction and control of particulate matter generated by fossil fuel combustion are among the main issues for actual and future combustion devices due to the increasingly stringent emission regulations. Recently, various fuels have been investigated as a potential substitute or additive for diesel and gasoline. This work focuses on how oxymethylene ether-3 (OME3), the smallest promising OME compound, affects carbon particulate formation when blended with ethylene in burner-stabilized premixed flames at different equivalence ratios. Particle size distribution (PSD) and Laser-Induced Fluorescence (LIF) and Incandescence (LII) along with numerical (Conditional Quadrature Method of Moments – CQMOM, based on D'Anna physico-chemical soot model) investigations were conducted to study particle formation and growth in pure ethylene and ethylene/OME3 flames. The soot volume fraction and PSD indicate a reduction in the total number and the size of the soot particles at all equivalence ratios, while the number of small nanoparticles remains almost unchanged. The CQMOM model is able to predict similar trends for the soot volume fraction and, using the entropy maximization concept, the general shape of the PSD for both pure ethylene and OME3-blended flames, compared to the experimental measurements. Further, carbon particulate matter was thermophoretically sampled in the highest equivalence ratio conditions and spectroscopically analyzed. The soot structure was investigated using UV–Visible and Raman spectroscopy, finding a slightly higher aromaticity for the pure ethylene soot. FTIR analysis showed that carbon particulate matter produced from an OME3-doped flame contained larger amounts of oxygen, mainly in the form of C[dbnd]O
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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