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Effect of thigh muscle strength training through telerehabilitation in volunteers with low back pain. A controlled and randomized clinical trial

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Journal of Bodywork and Movement Therapies 2025年 43卷 28-34页

作者： Abdala, Dennis William Castro, Thais Costa, Willian dos Santos da Alves Silveira, Bruno Silva, Gabriel João de Almeida Fonseca, Natália da Silva Martins Simão, Adriano Prado Carvalho, Leonardo César Graduate Program Program in Biosciences Applied to Health Federal University of Alfenas Alfenas Brazil Motor Science Institute Federal University of Alfenas Alfenas Brazil Rehabilitation Science Graduate Program Federal University of Alfenas Alfenas Brazil Graduate Program in Statistics Applied to Biometrics Federal University of Alfenas Alfenas Brazil

Background: Low back pain (LBP) is a clinical condition with a high global incidence that significantly impacts the quality of life, including psychological, physical, and social aspects. It commonly leads to an impairment in muscle strength and activation. Research question: The aim of the study was to assess the short-term effects of thigh muscle strength training conducted via telerehabilitation and in-person in patients with self-reported LBP. Methods: The telerehabilitation and control groups completed 10 strength training sessions, five times a week. Both groups were evaluated for electromyographic parameters, maximal isometric voluntary contraction (MIVC), and the number of maximum repetitions of knee flexion-extension movements. Results: Telerehabilitation increased the recruitment of the right rectus femoris (p < 0.05). It improved the maximum repetition capacity for the left knee extension (p < 0.01) movement and for the right and left knee flexion(p < 0.05);however, it was able to reduce the MIVC right and left knee flexion (p < 0.05). Conclusion: Telerehabilitation was able to increase the recruitment of the right biceps femoris muscles and maintain the recruitment of the right and left rectus femoris;improve the maximum repetition capacity for left knee extension and of both the right and left knees flexion movement. However, it could not increase the MVIC after the training period. © 2025 Elsevier Ltd

关键词： Exercise Therapy Low back pain Telerehabilitation

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Shearless effective barriers to chaotic transport induced by even twin islands in nontwist systems

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Physical Review E 2024年第4期110卷 044201页

作者： M. Mugnaine I. L. Caldas J. D. Szezech, Jr. R. L. Viana P. J. Morrison Graduate in Science Program—Physics Department of Mathematics and Statistics Department of Physics Department of Physics and Institute for Fusion Studies

For several decades now it has been known that systems with shearless invariant tori, nontwist Hamiltonian systems, possess barriers to chaotic transport. These barriers are resilient to breakage under perturbation and therefore regions where they occur are natural places to look for barriers to transport. Here we describe a kind of effective barrier that persists after the shearless torus is broken. Because phenomena are generic, for convenience we study the standard nontwist map (SNM), an area-preserving map that violates the twist condition locally in the phase space. The barrier occurs in nontwist systems when twin even period islands are present, which happens for a broad range of parameter values in the SNM. With a phase space composed of regular and irregular orbits, the movement of chaotic trajectories is hampered by the existence of shearless curves, total barriers, and a network of partial barriers formed by the stable and unstable manifolds of the hyperbolic points. Being a degenerate system, the SNM has twin islands and, consequently, twin hyperbolic points. We show that the structures formed by the manifolds intrinsically depend on period parity of the twin islands. For this even scenario the structure that we call a torus free barrier occurs because the manifolds of different hyperbolic points form an intricate chain atop a dipole configuration and the transport of chaotic trajectories through the chain becomes a rare event. This structure impacts the emergence of transport, the escape basin for chaotic trajectories, the transport mechanism, and the chaotic saddle. The case of odd periodic orbits is different: we find for this case the emergence of transport immediately after the breakup of the last invariant curve, and this leads to a scenario of higher transport, with intricate escape basin boundary and a chaotic saddle with nonuniformly distributed points.

关键词： Classical mechanics

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Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices

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Journal of Voice 2025年第3期39卷 577-590页

作者： Leite, Danilo Rangel Arruda de Moraes, Ronei Marcos Lopes, Leonardo Wanderley Department of Statistics Graduate Program in Health Decision Models Universidade Federal da Paraíba – UFPB Paraíba João Pessoa Brazil Brazilian Hospital Services Company- Ebserh Universidade Federal da Paraíba – UFPB Paraíba João Pessoa Brazil Department of Statistics Universidade Federal da Paraíba – UFPB Paraíba João Pessoa Brazil Department of Speech-Language and Hearing Sciences Graduate Program in Linguistics Universidade Federal da Paraíba – UFPB Paraíba João Pessoa Brazil

Objective: To analyze the performance of 10 different machine learning (ML) classifiers for discrimination between dysphonic and non-dysphonic voices, using a variance threshold as a method for the selection and reduction of acoustic measurements used in the classifier. Method: We analyzed 435 samples of individuals (337 female and 98 male), with a mean age of 41.07 ± 13.73 years, of which 384 were dysphonic and 51 were non-dysphonic. From the sustained /ε/ vowel sample, 34 acoustic measurements were extracted, including traditional perturbation and noise measurements, cepstral/spectral measurements, and measurements based on nonlinear models. The variance method was used to select the best set of acoustic measurements. We tested the performance of the best-selected set with 10 ML classifiers using precision, sensitivity, specificity, accuracy, and F1-Score measurements. The kappa coefficient was used to verify the reproducibility between the two datasets (training and testing). Results: The naive Bayes (NB) and stochastic gradient descent classifier (SGDC) models performed best in terms of accuracy, AUC, sensitivity, and specificity for a reduced dataset of 15 acoustic measures compared to the full dataset of 34 acoustic measures. SGDC and NB obtained the best performance results, with an accuracy of 0.91 and 0.76, respectively. These two classifiers presented moderate agreement, with a Kappa of 0.57 (SGDC) and 0.45 (NB). Conclusion: Among the tested models, the NB and SGDC models performed better in discriminating between dysphonic and non-dysphonic voices from a set of 15 acoustic measures. © 2022 The Voice Foundation

关键词： Acoustic Artificial intelligence Machine learning Voice Voice disorders

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On the Challenges and Perspectives of Stroke Segmentation Using AI

On the Challenges and Perspectives of Stroke Segmentation Us...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Mabrok, Mohamed Zafari-Ghadim, Yalda Mabrok, Mostafa Rashed, Essam A. College of Arts and Sciences Qatar University Mathematics Program Department of Mathematics Statistics and Physics P.O. box 2713 Doha Qatar Graduate School of Information Science University of Hyogo Kobe650-0047 Japan

ISBN: (纸本)9798350391213

Stroke is one of the leading causes of disability and death worldwide, underscoring the need for early and precise diagnosis to enhance patient outcomes. Artificial intelligence (AI) has emerged as a promising tool for stroke diagnosis, offering the potential to significantly improve diagnostic accuracy and efficiency. This paper reviews recent AI-based methods for stroke diagnosis, focusing on available datasets with various modalities and the AI techniques employed. We analyze the limitations of existing approaches, including data and model biases, lack of generalizability, and transparency issues. We then propose future directions for AI in stroke diagnosis, aiming to address these identified gaps, including the development of more robust and adaptable models, and improved data integration strategies. Finally, we emphasize the importance of ethical considerations and regulatory frameworks to ensure the responsible implementation of AI in this critical field. A GitHub repository is created to include recent codes and platforms that are available for stroke diagnosis. © 2024 IEEE.

关键词： artificial intelligence stroke diagnosis

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Fear of COVID-19, physical activity practice and chronic pain symptoms in the postvaccination period

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Journal of Bodywork and Movement Therapies 2025年 43卷 324-330页

作者： Faria, Suelen Carla Souza de Andrade, Eric Francelino Lunkes, Luciana Crepaldi Lima, Renato Ribeiro de Pereira, Luciano José Health Sciences Graduate Program Federal University of Lavras (UFLA) Lavras Brazil Department of Medicine Federal University of Lavras (UFLA) Lavras Brazil Department of Physiotherapy University Center of Lavras (UNILAVRAS) Lavras Brazil Department of Statistics Federal University of Lavras (UFLA) Lavras Brazil

Background and purpose: During the COVID-19 pandemic, restricted access to elective health services significantly affected individuals with chronic pain. Thus, we aimed to assess the connection between fear of COVID-19, physical activity levels, and the persistence of chronic pain post-vaccination period. Methods: Conducted from December 2021 to June 2022, this cross-sectional observational study involved 507 participants, primarily females (54.4 %), aged 30–60 years (58.6 %), with 42.8 % reporting chronic pain. Participants completed an online questionnaire, including translated versions of the Fear of COVID-19 Scale, Global Physical Activity Questionnaire (GPAQ), and the ImpactQ questionnaire designed for chronic pain assessment during the pandemic. Results: ImpactQ scores were notably lower in participants with chronic pain (p < 0.01), indicating a greater pandemic impact, with no sex-related interaction. Women exhibited significantly higher Fear of COVID-19 Scale scores (indicating more fear) than men (p < 0.01), irrespective of chronic pain. There was a significant negative correlation (R = − 0.231 ep < 0.001) between the ImpactQ score and Fear of COVID-19 Scale score;the higher the Fear of COVID-19 Scale score was, the lower the ImpactQ score (which denoted a greater impact of the pandemic on patients with chronic pain). No association was found between physical activity levels and chronic pain presence (p > 0.05) during work, commuting, or recreational activities assessed by GPAQ. Conclusion: Even with the implementation of vaccines against COVID-19, individuals suffering from chronic pain continue to experience a substantial impact of the pandemic, especially women who still experience high fear of COVID-19. © 2025 Elsevier Ltd

关键词： Chronic pain COVID-19 Fear Physical activity Post-pandemic

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Quantifying intragroup and intergroup connections in non-disjoint groups in social networks: A comprehensive analysis incorporating edges' and nodes' weights

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Information Sciences 2024年 670卷

作者： de Andrade, Ricardo Lopes Rêgo, Leandro Chaves Management Engineering Graduate Program Federal University of Pernambuco Brazil Statistics and Applied Math Department Universidade Federal do Ceará Brazil

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems. © 2024 Elsevier Inc.

关键词： Commerce

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Losses make choices nonpositional

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SN Business and Economics 2022年第11期2卷 1-11页

作者： Da Silva, Sergio Matsushita, Raul Valcanover, Vanessa Campara, Jessica Da Costa, Newton Department of Economics Federal University of Santa Catarina SC Florianopolis 88049-970 Brazil Graduate Program in Economics Federal University of Santa Catarina SC Florianopolis 88049-970 Brazil Graduate Program in Economics Federal University of Espirito Santo ES Vitoria 29075-910 Brazil Department of Statistics University of Brasilia DF Brasilia 70910-900 Brazil Graduate Program in Statistics University of Brasilia DF Brasilia 70910-900 Brazil Graduate Program in Business Administration University of Brasilia DF Brasilia 70910-900 Brazil Graduate Program in Business Administration Federal University of Santa Catarina SC Florianopolis 88049-970 Brazil Department of Business Administration and Accounting Energia College SC Florianopolis 88049-970 Brazil

Positional decisions are influenced by context. We argue here that the peculiarities of such contexts boil down to choices in the domain of gains versus choices in the domain of losses. A total of 420 people completed one gain domain questionnaire and 445 completed one loss domain questionnaire. The survey evidence suggests that positional choices in the domain of gains shift to nonpositional choices in the domain of losses. This implies that losses are personal rather than positional. The literature identifies several contexts and the contribution of this paper is to condense them all into a single framework. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.

关键词： Domain of gains Domain of losses Positional choices

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Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-contextual Analysis

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SN Computer Science 2023年第1期4卷 18页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Department of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC)-Florianópolis Santa Catarina Brazil

Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar

关键词： Autonomous vehicles Deep learning Inertial sensors Intelligent transport systems Road conditions Speed bump

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EARLY-STAGE DETECTION OF COGNITIVE IMPAIRMENT BY HYBRID QUANTUM-CLASSICAL ALGORITHM USING RESTING-STATE FUNCTIONAL MRI TIME-SERIES

arXiv

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arXiv 2024年

作者： Choi, Junggu Park, Daniel K. Lee, Seung-Koo Hur, Tak Shin, Na-Young Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive science Yonsei University Seoul Korea Republic of Department of Applied Statistics Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Radiology College of Medicine Yonsei University Seoul Korea Republic of Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Psychology Yonsei Graduate program in Cognitive science Yonsei University Seoul Korea Republic of

Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Increasing power plant efficiency with clustering methods and Variable Importance Index assessment

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Energy and AI 2021年第3期5卷 82-90页

作者： Jéssica Duarte Lara Werncke Vieira Augusto Delavald Marques Paulo Smith Schneider Guilherme Pumi Taiane Schaedler Prass Department of Mechanical Engineering Federal University of Rio Grande do SulPorto AlegreBrazil Graduate Program in Statistics Federal University of Rio Grande do SulPorto AlegreBrazil

Power plant performance can decrease along with its life span,and move away from the design and commissioning *** issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational *** paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency *** selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance *** metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more *** Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power *** adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target *** show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.

关键词： Thermal power plant performance enhancement Operating patterns identification K-means clustering Principal component analysis Unsupervised machine learning

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