Repositorio Universidad Europea del Atlántico
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Novel transfer learning approach for hand drawn mathematical geometric shapes classification
No full textHand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students
Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks
No full textEfficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into predefined categories. This research presents a unique feature identification technique based on suppression to locate interest points by computing productive sum of pixel derivatives by computing the differentials for corner scores. Scale space interpolation is applied to define interest points by combining color features from spatially ordered L2 normalized coefficients with shape and object information. Object based feature vectors are formed using high variance coefficients to reduce the complexity and are converted into bag-of-visual-words (BoVW) for effective retrieval and ranking. The presented method encompass feature vectors for information synthesis and improves the discriminating strength of the retrieval system by extracting deep image features including primitive, spatial, and overlayed using multilayer fusion of Convolutional Neural Networks(CNNs). Extensive experimentation is performed on standard image datasets benchmarks, including ALOT, Cifar-10, Corel-10k, Tropical Fruits, and Zubud. These datasets cover wide range of categories including shape, color, texture, spatial, and complicated objects. Experimental results demonstrate considerable improvements in precision and recall rates, average retrieval precision and recall, and mean average precision and recall rates across various image semantic groups within versatile datasets. The integration of traditional feature extraction methods fusion with multilevel CNN advances image sensing and retrieval systems, promising more accurate and efficient image retrieval solutions
Avelumab maintenance in advanced urothelial carcinoma: real-world data from Northern Spain (AVEBLADDER study)
Full text linkBackground
Before the incorporation of enfortumab vedotin with pembrolizumab, the standard of care for patients with locally advanced or metastatic urothelial carcinoma who do not progress after platinum-based chemotherapy was avelumab maintenance therapy, as demonstrated by the JAVELIN 100 trial. However, real-world European data remain scarce.
Patients and Methods
AVEBLADDER is a retrospective study conducted at 14 hospitals in Northern Spain, including patients with locally advanced or metastatic urothelial carcinoma diagnosed between January 2021 and June 2023. Outcomes of overall survival (OS) and progression-free survival (PFS) were analyzed for patients treated with platinum-based chemotherapy, with and without subsequent avelumab maintenance therapy. non-avelumab patients. Median PFS was 11.33 months (95% CI: 10–13.6) with avelumab and 6.43 months (95% CI: 6–7.6) without. One-year OS probabilities were 81.6% vs. 45.6% (p < 0.001) in the avelumab and non-avelumab groups, respectively. No unexpected toxicities were reported.
Conclusions
Despite proven survival benefits, avelumab uptake in real-world practice is limited by barriers like access, reimbursement, and awareness. These findings align with JAVELIN 100 and underscore the need for further real-world studies to address treatment disparities
Effects of strength training with free weights and elastic resistance in older adults: A randomised clinical study
No full textBackground
The aging process leads to negative changes in various bodily systems, including the neuromuscular system. Strength training, is considered the best strategy to counteract these neuromuscular changes, preventing sarcopenia and frailty in older adults.
Objective
To compare the effects of strength training with elastic resistance and free weights on the muscle strength of knee extensors and flexors and functional performance in the older adults.
Methods
This was a randomised clinical study. Thirty-one participants of both sexes were allocated randomly into two groups: Training Group Free Weight (TGFW, n = 15) and Training Group with Elastic Resistance (TGER, n = 16). Two individuals were excluded and so, twenty-nine individuals were evaluated before and after eight weeks training protocol, which was performed three times a week. The determination of the training load was obtained using a protocol of 10 repetitions maximum.
Results
No significant differences were found in either the intra- or the inter-group comparisons, on functional performance and peak muscle strength. In the intra-groups (pre- and post-strength training), it was observed that both groups significantly increased the training load (10 RM) for the extensors (TGFW p = 0.0002; TGER p = 0.0001) and the knee flexors (TGFW p = 0.006; TGER p = 0.0001).
Conclusion
Both training protocols similarly were effective in increasing the training load observed by the 10 RM test of the extension and flexion movements of the knee
Virtual nutritional clinic (E+DIETing_LAB)
No full textUna herramienta que ofrece una formación centrada en el Proceso de Atención Nutricional (PAN) y el servicio a la comunidad. Mediante videollamada las personas interesadas pueden recibir consejo dietético gratuito y unas recomendaciones de cómo mejorar su alimentación, bajo la supervisión de un profesor. Desarrollada en el marco del proyecto E+DIETing_LA
Pyroptosis: A Novel Therapeutic Target for Bioactive Compounds in Human Disease Treatment? A Narrative Review
No full textBackground/Objectives: Bioactive compounds possess the ability to maintain health and improve diseases by regulating inflammation and cell death processes. Pyroptosis is programmed cell death related to inflammation and exerts a critical role in the development and progression of different types of diseases. This narrative review aims to investigate and discuss the effects of dietary bioactive compounds on pyroptosis in different common human pathologies, such as inflammatory disease, bacterial infection, injury disease, cancer, diabetes and heart disease, etc. Method: Studies published in the major databases until December 2024 in English were considered, for a total of 50 papers. Results: The current evidence demonstrated that the bioactive compounds are able to regulate the pyroptosis process by modulating different inflammasome sensors (NLRP1, NLRP3, and AIM2), caspase family proteins (caspase-1, caspase-3, and caspase-11), and gasdermins (GSDMD and GSDME) in many pathological conditions related to inflammation, including cancer and cardiovascular diseases. Conclusions: Bioactive compounds have powerful potential to be the candidate drug for pyroptosis modulation in inflammatory diseases, even if more clinical studies are needed to confirm the effects and establish efficient doses for humans
Novel transfer learning based bone fracture detection using radiographic images
No full textA 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
Incorporating soil information with machine learning for crop recommendation to improve agricultural output
No full textThe agriculture field is the basis of a country’s change and financial system. Crops are the main source of revenue for the people. One of the farmer’s most challenging problems is choosing the right crops for their land. This critical decision has a direct impact on productivity and profit. Wrong crop selection not only reduces yields but also causes food shortages, creating more problems for farmers. The best crop depends on many parameters such as illustration humidity, N, K, P, pH, rainfall, and temperature of the soil. Getting advice from experts is not an easy task. This requires intelligent models in crop recommendations that use machine-learning models to suggest suitable crops for soil and other environmental conditions. Temperature, humidity, and pH are important data for growing crops in agriculture. In this study, we gather and preprocess relevant data. To recommend the most suitable crop, we propose a novel ensemble learning approach called RFXG based on random forest (RF) and extreme gradient boosting (XGB) to suggest the best crop out of the twenty-two major crops. To measure the capability of the proposed approach, various machine learning models are utilized including extra tree classifier, multilayer perceptron, RF, decision trees, logistic regression, and XGB classifiers. To get the best performance, optimization of hyperparameter, and K-fold cross-validation procedures are performed. Experimental outcomes show that the proposed RFXG technique achieves a recommendation accuracy is 98%. Specifically, the proposed solution provides immediate recommendations to help farmers make timely decisions
Intergenerational inheritance of quercetin-induced abnormal immunity in mice
No full textQuercetin, a dietary flavonol enriched in food, regulates immune-related models through epigenetic modifications. However, few studies have explored the transmission of regulatory effects across generations to the progeny. Here, we selected Escherichia coli, a conditional pathogen capable of causing gastrointestinal infections or various localized tissue and organ infections under specific conditions, as the pathogenic strain to infect mice. We provide evidence that quercetin can not only induce responsiveness changes against systemic E. coli infection in directly exposed organisms, but also in subsequent generations through the transgenerational inheritance of epigenetic traits. Both parental male mice and their progeny exhibited cellular and phenotypic changes associated with metabolic alterations. Surprisingly, the male and female progeny of mice treated with quercetin (200 mg/kg) for six weeks negatively enhanced the survival rate under systemic E. coli (1 × 108 CFU/mL) infection, concurrent with an increase in bacterial loads in the liver and spleen. Serum TNF-α and IL-1β levels significantly increased post-infection in the progeny. Our results provide the first evidence of the inheritance of immunity driven by quercetin in mammals and the attenuation of protection against bacterial infection
Desarrollando Competencias Docentes en AICLE: Experiencias y Desafíos en la Università degli Studi di Palermo
No full textEste artículo aborda la necesidad de capacitar a docentes en el enfoque AICLE (Aprendizaje Integrado de Contenidos y Lenguas Extranjeras) y presenta los resultados de un seminario realizado en inglés en la Università degli Studi di Palermo, Italia. A pesar de las dificultades lingüísticas enfrentadas por los estudiantes, el taller introdujo el concepto de AICLE, su historia, características y beneficios mediante el uso de vídeos y actividades prácticas. Los hallazgos indicaron que algunos estudiantes no habían cursado asignaturas con AICLE debido a que finalizaron sus estudios antes de 2013. Las materias enseñadas con AICLE incluían ciencias naturales, economía, historia y artes. Aunque algunos estudiantes no conocían previamente el enfoque AICLE, valoraron positivamente el seminario, considerándolo útil para sus futuras carreras en escuelas plurilingües. Las conclusiones resaltan la necesidad de extender la formación para abordar aspectos esenciales como el andamiaje y las estrategias de evaluación adaptadas