https://repositorio.ufu.br/handle/123456789/5145 2026-05-26T16:59:43Z https://repositorio.ufu.br/handle/123456789/48707 Title: Avaliação da atividade eletromiográfica de músculos do core no exercício prancha ventral até a exaustão com e sem exergame plankpad Abstract: Core training plays an important role in quality of life, especially in reducing the prevalence of low back pain. Recently, exergames such as Plankpad have been developed to improve adherence to physical training and rehabilitation programs focused on core muscle strengthening. However, no studies have analyzed electromyographic (EMG) activity during the ventral plank (VP) exercise with and without exergame, including time-domain and frequency-domain analysis as well as muscle co-contraction. This study aimed to investigate the effects of incorporating the Plankpad exergame on core muscle EMG activity during the VP exercise until exhaustion. Fifteen young, healthy, physically active men with no history of low back pain participated. Participants performed the VP exercise until exhaustion under five conditions: stable ground (VP-GR), Plankpad platform without game (VP-PP), and Plankpad platform with the Fruit Splicer game at easy (VP-EA), medium (VP-ME), and hard (VP-HA) levels. EMG signals were collected from the rectus abdominis (RA), external oblique (EO), internal oblique (IO), erector spinae (ES), and multifidus (MU) muscles, along with time to exhaustion and rating of perceived exertion (RPE). Results showed progressive increases in RMS and decreases in median frequency (MF) over time in all exercises, indicating peripheral neuromuscular fatigue, with no significant differences among the five conditions for most parameters. Local co-contraction (IO/MU) was significantly higher in VP-PP compared to VP EA and decreased with fatigue across all exercises. Time to exhaustion and RPE were similar among conditions. Correlations between RPE and EMG parameters were weak to moderate. We conclude that the Plankpad exergame, at easy, medium, and hard levels, does not increase electromyographic activation, spectral fatigue, time to exhaustion, or RPE compared to traditional plank exercise in young healthy trained men. 2026-05-04T00:00:00Z https://repositorio.ufu.br/handle/123456789/48700 Title: Deep learning-based detection of carotid artery atheromas in panoramic radiographs Abstract: Stroke is among the leading global causes of mortality and long-term disability resulting from neurological sequelae, with carotid atherosclerosis being one of its main etiological mechanisms. This condition often progresses silently, which limits the use of standard diagnostic examinations such as Doppler ultrasound and computed tomography angiography (CTA) for population screening. However, calcifications located at the carotid bifurcation may be incidentally visualized on panoramic dental radiographs, making this exam a potential opportunistic screening tool due to its wide availability, low cost, and coverage of the C3–C4 region, where atheromas usually manifest. In this context, the present study aimed to develop and evaluate a deep-learning-based method using the MobileNetV2 architecture for the automatic detection of carotid atheromas in panoramic radiographs, aiming to optimize the clinical value of this routine exam as a screening tool. A total of 378 publicly available and fully anonymized Regions of Interest (ROIs) were used, cropped in the carotid region (640×320 px) and divided into training, validation, and test sets (264/57/57). The images underwent normalization, grayscale channel replication, and real-time data augmentation, and class imbalance was addressed using weighted loss. The model was implemented using MobileNetV2 with pretrained weights and a two-stage training scheme, consisting of initial backbone freezing followed by partial fine-tuning (~70%), with batch normalization and dropout (0.3). On the independent test set, the model achieved robust and well-balanced performance, with 94.7% accuracy, 95.7% sensitivity, 94.1% specificity, and AUC and AUPRC values of 0.963 and 0.968, respectively, using a threshold optimized by Youden’s J index. These results demonstrate strong discriminative capability and reinforce the potential of panoramic radiography combined with deep learning as an opportunistic screening tool for carotid atheromas. The proposed method shows promising performance and supports the use of routine panoramic radiographs for opportunistic screening, enabling the early referral of patients for specialized vascular evaluation and strengthening the interface between oral and systemic health. 2026-02-27T00:00:00Z https://repositorio.ufu.br/handle/123456789/48694 Title: Análise da eficiência logística no transporte de madeira com pentatrens em uma fábrica de celulose com Simulação de Eventos Discretos Abstract: This course conclusion work aims to study, analyze, and implement improvements in the logistics process of a dry wood pulp factory, using the concepts of Discrete Event Simulation. The purpose of this study is to understand each stage of the process and analyze it both individually and as a whole, in order to identify possible bottlenecks and optimization points that may be preventing the process from reaching its full performance capacity. To ensure better understanding, some logistics concepts, Discrete Event Modeling, and the tools that can be used to simulate systems will be explained. In order to measure essential parameters for the analysis, factors such as travel time, cargo capacity, average distance traveled, number of daily trips, and others will be monitored and measured. In this context, the study aims to demonstrate the applicability of Discrete Event Simulation in everyday scenarios, through the resolution of a real case from the job market, highlighting the importance of this field of study and its correlation with Engineering. 2026-03-11T00:00:00Z https://repositorio.ufu.br/handle/123456789/48680 Title: Análise de Sistemas de Distribuição com REDs Utilizando Python e DSS: Comparação entre Armazenamento Centralizado e Distribuído Abstract: This work analyzes the impact of battery energy storage allocation in distribution networks with distributed generation, comparing centralized and distributed strategies in terms of voltage violation mitigation. The study models a low-voltage distribution feeder with distributed photovoltaic generation, single-phase loads, and representative time-varying demand and generation profiles. To do so, it integrates quasi-static time-series simulations developed in Python with OpenDSS and formulates an optimization problem with discrete and continuous decision variables, solved through a genetic algorithm. The objective function seeks to minimize steady-state voltage violation indicators while considering technical constraints related to the size and placement of the storage systems. The results show that the insertion of distributed generation without storage worsens the electrical performance of the network, increasing the occurrence of overvoltages and expanding the violation indexes. The centralized strategy significantly reduces these problems, eliminating critical violations and substantially decreasing precarious violations, although residual violations remain. In contrast, the distributed strategy presents superior performance, fully eliminating the violations in the evaluated arrangements and providing more homogeneous voltage support along the feeder. However, the results also indicate that progressively increasing the number of batteries does not lead to proportional gains, which suggests diminishing marginal returns. It is concluded that distributed storage is more effective in ensuring voltage compliance, whereas the centralized solution provides relevant improvement with lower installed capacity. 2026-03-20T00:00:00Z