SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations

<a href="/5397/1/drones-07-00031-v4.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively.

Producción Científica

Imran Shafi mail , Muhammad Fawad Mazhar mail , Anum Fatima mail , Roberto Marcelo Álvarez mail roberto.alvarez@uneatlantico.es, Yini Airet Miró Vera mail yini.miro@uneatlantico.es, Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Imran Ashraf mail ,

Shafi

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Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area

Regulatory dispersion and a utilitarian use of sustainability deepen the gap within the teaching–learning process and limit the introduction of sustainable criteria in organizations through projects. The objective of this research consisted in developing a sustainable and holistic educational proposal for an online postgraduate program belonging to the Universidad Europea del Atlántico (UNEATLANTICO) within the field of projects. The proposal was based on the instrumentalization of a model comprised of national and international bibliographic references, resulting in a sustainability guide with significant improvements in relation to the reference standard par excellence: ISO 26000:2010. This guide formed the basis of a sustainability management plan, which was key in the project methodology and during the development of sustainable objectives and descriptors for each of the subjects. Lastly, the entities, attributes, and cardinal relationships were established for the development of a physical model used to facilitate the management of all this information within a SQL database. The rigor when determining the educational program, as well as the subsequent analysis of results as supported by the literature review, presupposes the application of this methodology toward other multidisciplinary programs contributing to the adoption of good sustainability practices within the educational field

Producción Científica

Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Roberto Marcelo Álvarez mail roberto.alvarez@uneatlantico.es, Santiago Brie mail santiago.brie@uneatlantico.es, Yini Airet Miró Vera mail yini.miro@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es,

Gracia Villar

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Anthocyanins: what do we know until now?

Diets enriched in plant-based foods are associated with the maintenance of a good well-being and with the prevention of many non-communicable diseases. The health effects of fruits and vegetables consumption are mainly due to the presence of micronutrients, including vitamins and minerals, and polyphenols, plant secondary metabolites. One of the most important classes of phenolic compounds are anthocyanins, that confer the typical purple-red color to many foods, such as berries, peaches, plums, red onions, purple corn, eggplants, as well as purple carrots, sweet potatoes and red cabbages, among others. This commentary aims to briefly highlight the progress made by science in the last years, focusing on some unexpected aspects related with anthocyanins, such as their bioavailability, their health effects and their relationship with gut microbiota

Producción Científica

Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Danila Cianciosi mail , José M. Alvarez-Suarez mail , José L. Quiles mail jose.quiles@uneatlantico.es, Tamara Y. Forbes-Hernández mail , María D. Navarro-Hortal mail , Michele Machì mail , Ramón Pali-Casanova mail ramon.pali@unini.edu.mx, Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Xiumin Chen mail , Di Zhang mail , Weibin Bai mail , Tian Lingmin mail , Bruno Mezzetti mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Yasmany Armas Diaz mail ,

Giampieri

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Contextual Urdu Lemmatization Using Recurrent Neural Network Models

In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models

Producción Científica

Rabab Hafeez mail , Muhammad Waqas Anwar mail , Muhammad Hasan Jamal mail , Tayyaba Fatima mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Ernesto Bautista Thompson mail ernesto.bautista@unini.edu.mx, Imran Ashraf mail ,

Hafeez

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Image Watermarking Using Least Significant Bit and Canny Edge Detection

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks

Producción Científica

Zaid Bin Faheem mail , Abid Ishaq mail , Furqan Rustam mail , Isabel de la Torre Díez mail , Daniel Gavilanes mail daniel.gavilanes@uneatlantico.es, Manuel Masías Vergara mail manuel.masias@uneatlantico.es, Imran Ashraf mail ,

Faheem