Repositorio Universidad Europea del Atlántico
Not a member yet
2719 research outputs found
Sort by
Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems
Get PDFPower Quality (PQ) has become a significant issue in power networks. Power quality disturbances must be precisely and appropriately identified. This activity involves identifying, classifying, and mitigating power quality problems. A case study of the Awada industrial zone in Ethiopia is taken into consideration to show the practical applicability of the proposed work. It is found that the current harmonic distortion levels exceed the restrictions with a maximum percentage Total Harmonic Distortion of Current (THDI) value of up to 23.09%. The signal processing technique, i.e., Stockwell Transform (ST) is utilized for the identification of power quality issues, and it covers the most important and common power quality issues. The Support Vector Machine (SVM) method is used to categorize power quality issues, which enhances the classification procedure. The ST scored better in terms of accuracy than the Wavelet Transform (WT), Fourier Transform (FT), and Hilbert Transform (HT), obtaining 97.1%, as compared to 91.08%, 88.91%, and 86.8%, respectively. The maximum classification accuracy of SVM was 98.3%. To lower the current level of harmonic distortion in the industrial sector, a Distribution Static Compensator (D-STATCOM) is developed in the current control mode. To evaluate the performance of the D-STATCOM, the performance of the distribution network with and without D-STATCOM is simulated. The simulation results show that THDI is reduced to 4.36% when the suggested D-STATCOM is applied in the system
Proyecto de conservación y recuperación de cepas ancestrales en el Municipio de Suances
No full textEl proyecto de conservación y recuperación de cepas ancestrales desarrollado en los últimos años por José M. Gómez Eguren, Pablo Oria Chaveli y Diego González Rodríguez ha permitido recuperar cepas presumiblemente antiguas de las variedades tintas petit verdot y graciano. La hipótesis que manejan estos estudiosos y técnicos es que pueden tratarse de cepas prefiloxéricas y que pueden responder a los restos de una tradición que está documentada en la zona, cuyos orígenes se remontan a la época romana, a raíz de la conquista del norte de la península ibérica por parte de las tropas de Augusto
Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data
No full textRailway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings
Bolsa para ensayos de microbiología
No full textRealización de ensayos de microbiología según solicitu
A Multimodal Research Approach to Assessing the Karst Structural Conditions of the Ceiling of a Cave with Palaeolithic Cave Art Paintings: Polychrome Hall at Altamira Cave (Spain)
Get PDFIntegrating geomatics remote sensing technologies, including 3D terrestrial laser scanning, unmanned aerial vehicles, and ground penetrating radar enables the generation of comprehensive 2D, 2.5D, and 3D documentation for caves and their surroundings. This study focuses on the Altamira Cave’s karst system in Spain, resulting in a thorough 3D mapping encompassing both cave interior and exterior topography along with significant discontinuities and karst features in the vicinity. Crucially, GPR mapping confirms that primary vertical discontinuities extend from the near-surface (Upper Layer) to the base of the Polychrome layer housing prehistoric paintings. This discovery signifies direct interconnections helping with fluid exchange between the cave’s interior and exterior, a groundbreaking revelation. Such fluid movement has profound implications for site conservation. The utilization of various GPR antennas corroborates the initial hypothesis regarding fluid exchanges and provides concrete proof of their occurrence. This study underscores the indispensability of integrated 3D mapping and GPR techniques for monitoring fluid dynamics within the cave. These tools are vital for safeguarding Altamira, a site of exceptional significance due to its invaluable prehistoric cave paintings
Cytotoxicity and Antimicrobial Resistance of Aeromonas Strains Isolated from Fresh Produce and Irrigation Water
Get PDFThe genus Aeromonas has received constant attention in different areas, from aquaculture and veterinary medicine to food safety, where more and more frequent isolates are occurring with increased resistance to antibiotics. The present paper studied the interaction of Aeromonas strains isolated from fresh produce and water with different eukaryotic cell types with the aim of better understanding the cytotoxic capacity of these strains. To study host-cell pathogen interactions in Aeromonas, we used HT-29, Vero, J774A.1, and primary mouse embryonic fibroblasts. These interactions were analyzed by confocal microscopy to determine the cytotoxicity of the strains. We also used Galleria mellonella larvae to test their pathogenicity in this experimental model. Our results demonstrated that two strains showed high cytotoxicity in epithelial cells, fibroblasts, and macrophages. Furthermore, these strains showed high virulence using the G. mellonella model. All strains used in this paper generally showed low levels of resistance to the different families of the antibiotics being tested. These results indicated that some strains of Aeromonas present in vegetables and water pose a potential health hazard, displaying very high in vitro and in vivo virulence. This pathogenic potential, and some recent concerning findings on antimicrobial resistance in Aeromonas, encourage further efforts in examining the precise significance of Aeromonas strains isolated from foods for human consumption
Prevalence of undiagnosed diabetes and prediabetes related to periodontitis and its risk factors in elderly individuals
Get PDFObjective
The prevalence of undiagnosed diabetes was estimated to increase with age and can reach 3.5%. The purpose of the study was to evaluate the prevalence of undiagnosed diabetes and prediabetes in the elderly patients who attended a dental clinic and to find common risk factors.
Methods
Male patients, older than 50 years, attended their first dental visit to the School of Dentistry for a period of two years, and it was proposed to evaluate undiagnosed type 2 diabetes mellitus. Periodontal, biochemical, microbiological examinations, nutritional profile, and physical activity were performed.
Results
A total of 106 patients were examined, 6 (5.6%) had diabetes, and 37 (34.9%) had prediabetes without prior diagnosis. The severity of periodontitis was greater in patients with diabetes. Most of the patients were overweight and had increased systolic blood pressure. Patients with prediabetes and periodontitis had a low adherence to the Mediterranean diet. Tannerella forsythia was present in more patients with periodontitis, and the prevalence of Aggregatibacter actinomycetemcomitans is practically absent in groups with periodontitis, except for the group with diabetes.
Conclusions
In the population studied, the prevalence of patients without a diagnosis of diabetes and prediabetes was very high and underestimated. The increased severity of periodontitis in patients with diabetes and in conjunction with the high level of cortisol seen in patients with periodontitis, especially those with diabetes, emphasize the dysregulation of the immunoinflammatory system
Malvidin: Advances in the Resources, Biosynthesis Pathway, Bioavailability, Bioactivity, and Pharmacology
No full textMalvidin is an O-methylated anthocyanidin, the 3′,5′-methoxy derivative of delphinidin responsible for the blue-red color found in flowers and fruits. Its distribution covers a wide group of sources, such as flowers (edible and nonedible), medicinal plants, and fruits. It is the main substance responsible for the color of red grapes and red wine, being Vitis vinifera one of its main sources. Its consumption is important as it has been associated with important biological effects, such as anti-inflammatory activity, powerful antioxidant activity, and anticancer activity. Against this background, this chapter presents a general overview of malvidin’s main sources, biosynthesis pathway and biotransformation properties, physicochemical properties and stability, and aspects of its absorption, metabolism, and excretion. Additionally, we summarize recent studies using in vitro and in vivo models related to its various biological properties. In conclusion, this chapter aims to provide as detailed a picture as possible of the potential of malvidin in human health, as well as its main sources, chemical characteristics, and biotransformation
A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling
Get PDFCOVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model
Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification
No full textCorrect identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy-preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open-source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201-based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor