El entrenamiento físico-militar y el desarrollo de las capacidades físicas en el periodo de reclutamiento de los Aspirantes a Cadetes de la Escuela Superior Militar de Aviación “Cosme Rennella B.”

Thesis Subjects > Physical Education and Sport Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Español El presente trabajo se desarrolló con la finalidad de relacionar el entrenamiento físico-militar y el desarrollo de las capacidades físicas en el periodo de reclutamiento de los Aspirantes a Cadetes. Para el cumplimiento de este objetivo se consideró a 48 Aspirantes a Cadetes de la Escuela Superior Militar de Aviación “Cosme Renella B.” (ESMA), quienes participaron en el entrenamiento físico-militar y fueron evaluados en tres ocasiones mediante la aplicación de las pruebas físicas de flexión y extensión de cadera, flexión y extensión de codo, test de carrera de 3200m, test de natación de 200m y test de trepar el cabo. El procedimiento estadístico empleado para el análisis de los datos fue la Prueba de Friedman para muestras relacionadas de más de dos observaciones. Se logró comprobar la relación existente entre las variables de estudio, pues se obtuvo el p-valor menor a α (0,05), lo que permite afirmar que el entrenamiento físico-militar tiene relación y el desarrollo de las capacidades físicas, evidenciando que esto en torno al contexto socio-histórico de la pandemia por el COVID-19, en el que se ejecutó el entrenamiento físico y la toma de datos, por lo que, este trabajo puede considerarse como un aporte inicial a la ESMA para indagar sobre el desarrollo físico-militar en torno a la nueva normalidad pos-COVID-19. metadata Acosta Duque, Oscar Andres mail jandresacosta@hotmail.com (2022) El entrenamiento físico-militar y el desarrollo de las capacidades físicas en el periodo de reclutamiento de los Aspirantes a Cadetes de la Escuela Superior Militar de Aviación “Cosme Rennella B.”. Masters thesis, UNSPECIFIED.

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Abstract

El presente trabajo se desarrolló con la finalidad de relacionar el entrenamiento físico-militar y el desarrollo de las capacidades físicas en el periodo de reclutamiento de los Aspirantes a Cadetes. Para el cumplimiento de este objetivo se consideró a 48 Aspirantes a Cadetes de la Escuela Superior Militar de Aviación “Cosme Renella B.” (ESMA), quienes participaron en el entrenamiento físico-militar y fueron evaluados en tres ocasiones mediante la aplicación de las pruebas físicas de flexión y extensión de cadera, flexión y extensión de codo, test de carrera de 3200m, test de natación de 200m y test de trepar el cabo. El procedimiento estadístico empleado para el análisis de los datos fue la Prueba de Friedman para muestras relacionadas de más de dos observaciones. Se logró comprobar la relación existente entre las variables de estudio, pues se obtuvo el p-valor menor a α (0,05), lo que permite afirmar que el entrenamiento físico-militar tiene relación y el desarrollo de las capacidades físicas, evidenciando que esto en torno al contexto socio-histórico de la pandemia por el COVID-19, en el que se ejecutó el entrenamiento físico y la toma de datos, por lo que, este trabajo puede considerarse como un aporte inicial a la ESMA para indagar sobre el desarrollo físico-militar en torno a la nueva normalidad pos-COVID-19.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Entrenamiento físico, capacidades físicas, formación militar, reclutamiento, escuela de aviación.
Subjects: Subjects > Physical Education and Sport
Divisions: Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Date Deposited: 24 Oct 2023 23:30
Last Modified: 24 Oct 2023 23:30
URI: https://repositorio.unini.edu.mx/id/eprint/1070

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Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.

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Muhammad Usama Tanveer mail , Kashif Munir mail , Ali Raza mail , Laith Abualigah mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,

Tanveer

<a class="ep_document_link" href="/16270/1/s12880-024-01546-4.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Novel transfer learning based bone fracture detection using radiographic images

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients. The detection of bone fractures is crucial, and radiographic images are often relied on for accurate assessment. An efficient neural network method is essential for the early detection and timely treatment of fractures. In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. Several machine learning (ML) techniques are applied to the subsets of newly generated transfer features to compare the results. K-nearest neighbor (KNN), LGBM, logistic regression (LR), and random forest (RF) are implemented using the novel features with optimized hyperparameters. The LGBM and LR models trained on proposed MobLG-Net (MobileNet-LGBM) based features outperformed others, achieving an accuracy of 99% in predicting bone fractures. A cross-validation mechanism is used to evaluate the performance of each model. The proposed study can improve the detection of bone fractures using X-ray images.

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Aneeza Alam mail , Ahmad Sami Al-Shamayleh mail , Nisrean Thalji mail , Ali Raza mail , Edgar Aníbal Morales Barajas mail , Ernesto Bautista Thompson mail ernesto.bautista@unini.edu.mx, Isabel de la Torre Diez mail , Imran Ashraf mail ,

Alam

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Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification

Faced with anomalies in medical images, Deep learning is facing major challenges in detecting, diagnosing, and classifying the various pathologies that can be treated via medical imaging. The main challenges encountered are mainly due to the imbalance and variability of the data, as well as its complexity. The detection and classification of skin diseases is one such challenge that researchers are trying to overcome, as these anomalies present great variability in terms of appearance, texture, color, and localization, which sometimes makes them difficult to identify accurately and quickly, particularly by doctors, or by the various Deep Learning techniques on offer. In this study, an innovative and robust hybrid architecture is unveiled, underscoring the symbiotic potential of wavelet decomposition in conjunction with EfficientNet models. This approach integrates wavelet transformations with an EfficientNet backbone and incorporates advanced data augmentation, loss function, and optimization strategies. The model tested on the publicly accessible HAM10000 and ISIC2017 datasets has achieved an accuracy rate of 94.7%, and 92.2% respectively.

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Amina Aboulmira mail , Hamid Hrimech mail , Mohamed Lachgar mail , Mohamed Hanine mail , Carlos Manuel Osorio García mail carlos.osorio@uneatlantico.es, Gerardo Méndez Mezquita mail , Imran Ashraf mail ,

Aboulmira

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Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults

Background: Nut consumption has been considered a potential protective factor against cognitive decline. The aim of this study was to test whether higher total and specific nut intake was associated with better cognitive status in a sample of older Italian adults. Methods: A cross-sectional analysis on 883 older adults (>50 y) was conducted. A 110-item food frequency questionnaire was used to collect information on the consumption of various types of nuts. The Short Portable Mental Status Questionnaire was used to assess cognitive status. Multivariate logistic regression analyses were performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between nut intake and cognitive status after adjusting for potential confounding factors. Results: The median intake of total nuts was 11.7 g/day and served as a cut-off to categorize low and high consumers (mean intake 4.3 g/day vs. 39.7 g/day, respectively). Higher total nut intake was significantly associated with a lower prevalence of impaired cognitive status among older individuals (OR = 0.35, CI 95%: 0.15, 0.84) after adjusting for potential confounding factors. Notably, this association remained significant after additional adjustment for adherence to the Mediterranean dietary pattern as an indicator of diet quality, (OR = 0.32, CI 95%: 0.13, 0.77). No significant associations were found between cognitive status and specific types of nuts. Conclusions: Habitual nut intake is associated with better cognitive status in older adults.

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Justyna Godos mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Evelyn Frias-Toral mail , Raynier Zambrano-Villacres mail , Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Vanessa Yélamos Torres mail vanessa.yelamos@funiber.org, Maurizio Battino mail maurizio.battino@uneatlantico.es, Fabio Galvano mail , Sabrina Castellano mail , Giuseppe Grosso mail ,

Godos

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Clinical epidemiology of dengue and COVID-19 co-infection among the residents in Dhaka, Bangladesh, 2021-2023: A cross-sectional study

Background Co-infection of dengue and COVID-19 has increased the health burden worldwide. We found a significant knowledge gap in epidemiology and risk factors of co-infection in Bangladesh. Methods This study included 2458 participants from Dhaka city from December 1, 2021, to November 30, 2023. We performed Kruskal-Walli’s test and χ2 test. Multivariable logistic regression was also performed. Results Co-infection of dengue and COVID-19 was found among 31% of the participants. Co-prevalence of dengue and COVID-19 was found in higher frequency in Jatrabari (14%), and Motijhil (11%). Severe (65%, p-value 0.001) and very severe (78%, p-value 0.005) symptoms were prevalent among the participants aged >50 years. Long-term illness was prevalent among the participants with co-infection (35%, 95% CI 33%- 36%) and COVID-19 (28%, 95% CI 26%- 30%). Co-infected participants had a higher frequency of heart damage (31.6%, p-value 0.005), brain fog (22%, p-value 0.03), and kidney damage (49.3%, p-value 0.001). Fever (100%) was the most prevalent symptom followed by weakness (89.6%), chills (82.4%), fatigue (81.4%), headache (80.6%), feeling thirsty (76.3%), myalgia (75%), pressure in the chest (69.1%), and shortness of breath (68.3%), respectively. Area of residence (OR 2.26, 95% CI 1.96-2.49, p-value 0.01), number of family members (OR 1.45, 95% CI 1.08-1.87, p-value <0.001), and population density (OR 2.43, 95% CI 2.15-3.01, p-value 0.001) were associated with higher odds of co-infection. We found that coinfected participants had a 4 times higher risk of developing severe health conditions (OR 4.22, 95% CI 4.11-4.67, p-value 0.02). Conclusions This is one of the early epidemiologic studies of co-infection of dengue and COVID-19 in Bangladesh.

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Nadim Sharif mail , Rubayet Rayhan Opu mail , Afsana Khan mail , Tama Saha mail , Abdullah Ibna Masud mail , Jannatin Naim mail , Zaily Leticia Velázquez Martínez mail zaily.velazquez@unini.edu.mx, Carlos Manuel Osorio García mail carlos.osorio@uneatlantico.es, Meshari A Alsuwat mail , Fuad M Alzahrani mail , Khalid J Alzahrani mail , Isabel De la Torre Díez mail , Shuvra Kanti Dey mail ,

Sharif