Repositorio Universidad Europea del Atlántico
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An In-Depth Study on the Inhibition of Quorum Sensing by Bacillus velezensis D-18: Its Significant Impact on Vibrio Biofilm Formation in Aquaculture
No full textAmid growing concerns about antibiotic resistance, innovative strategies are imperative in addressing bacterial infections in aquaculture. Quorum quenching (QQ), the enzymatic inhibition of quorum sensing (QS), has emerged as a promising solution. This study delves into the QQ capabilities of the probiotic strain Bacillus velezensis D-18 and its products, particularly in Vibrio anguillarum 507 communication and biofilm formation. Chromobacterium violaceum MK was used as a biomarker in this study, and the results confirmed that B. velezensis D-18 effectively inhibits QS. Further exploration into the QQ mechanism revealed the presence of lactonase activity by B. velezensis D-18 that degraded both long- and short-chain acyl homoserine lactones (AHLs). PCR analysis demonstrated the presence of a homologous lactonase-producing gene, ytnP, in the genome of B. velezensis D-18. The study evaluated the impact of B. velezensis D-18 on V. anguillarum 507 growth and biofilm formation. The probiotic not only controls the biofilm formation of V. anguillarum but also significantly restrains pathogen growth. Therefore, B. velezensis D-18 demonstrates substantial potential for preventing V. anguillarum diseases in aquaculture through its QQ capacity. The ability to disrupt bacterial communication and control biofilm formation positions B. velezensis D-18 as a promising eco-friendly alternative to conventional antibiotics in managing bacterial diseases in aquaculture
A Hybrid Model for Improving Software Cost Estimation in Global Software Development
No full textAccurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks (ANN) to address these challenges. The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts. This article compares the effectiveness of the proposed model with state-of-the-art machine learning-based models for software cost estimation. Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy, outperforming existing state-of-the-art models. The findings indicate the potential of combining COCOMO II, ANN, and additional GSD-based cost drivers to transform cost estimation in GSD
Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model
No full textWafer mappings (WM) help diagnose low-yield issues in semiconductor production by offering vital information about process anomalies. As integrated circuits continue to grow in complexity, doing efficient yield analyses is becoming more essential but also more difficult. Semiconductor manufacturers require constant attention to reliability and efficiency. Using the capabilities of convolutional neural network (CNN) models improved by hierarchical attention module (HAM), wafer hotspot detection is achieved throughout the fabrication process. In an effort to achieve accurate hotspot detection, this study examines a variety of model combinations, including CNN, CNN+long short-term memory (LSTM) LSTM, CNN+Autoencoder, CNN+artificial neural network (ANN), LSTM+HAM, Autoencoder+HAM, ANN+HAM, and CNN+HAM. Data augmentation strategies are utilized to enhance the model’s resilience by optimizing its performance on a variety of datasets. Experimental results indicate a superior performance of 94.58% accuracy using the CNN+HAM model. K-fold cross-validation results using 3, 5, 7, and 10 folds indicate mean accuracy of 94.66%, 94.67%, 94.66%, and 94.66%, for the proposed approach, respectively. The proposed model performs better than recent existing works on wafer hotspot detection. Performance comparison with existing models further validates its robustness and performance
Diseño y desarrollo de un sistema prototipo de automatización de procesos de configuración de entornos virtuales de aprendizaje para la mejora operativa y de calidad del contenido educativo
No full textLa línea de actividad de I+D que se propone se orienta al diseño y desarrollo de un prototipo digital de automatización de procesos de configuración de entornos virtuales de aprendizaje que permita mejorar la operatividad de este tipo de plataformas y así también mejorar la calidad del contenido educativo.
Las actividades a llevar a cabo por parte de la persona joven investigadora corresponderán a las propias de una iniciativa de innovación en el campo de las tecnologías educativas. En este caso se tratará de llevar a cabo algunas actividades de prospección de mercado por un lado en referencia a productos preexistentes, y de otro lado, de prospección tecnológica en el estado del arte tecnológico.
Seguidamente se llevarían a cabo las tareas habituales de desarrollo de software en modo prototipo y la evaluación de los resultados alcanzados.
La persona joven deberá también contar con colaboraciones de los expertos de CITICAN en plataformas de formación online y potenciales usuarios (docentes, gestores).
El técnico tendrá acceso al equipamiento informático necesario para llevar a cabo la presente actividad de I+D+i según las capacidades de CITICAN
Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects
No full textWith the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA’s utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems
Latest advancements and prospects in the next-generation of Internet of Things technologies
No full textThe Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba
No full textTo improve organisational performance, it is crucial to cultivate an environment and culture that, through shared values, attitudes, behaviours, and sentiments, enables all employees to feel comfortable in performing their work. This represents a recognised gap within the current Cuban business context. Drawing from identified challenges and the introduction of a values-based coaching programme at the state-owned company GEDEME to address this gap, the aim of this study is to evaluate the impact of the values-based coaching programme (CpV) on organisational culture among both tactical and strategic employees within GEDEME. The research adopts a mixed-methods design. On one hand, the non-parametric McNemar test was utilised to assess before-and-after differences, while a case-study approach facilitated the exploration of specific questions, such as identifying the values actually practised beyond those outlined in the formal business plan and understanding the extent and nature of value shifts following the implementation of the coaching programme. The results confirmed the primary hypothesis: the values-based coaching programme at GEDEME had a positive effect on employees' perceptions of organisational culture, resulting in a substantial increase in the number of values both practised and perceived by its members
Effectiveness of a Kegel protocol on urinary incontinence in female weightlifters
Get PDFObjectives: The aim of this study was to evaluate the incidence and associated factors of urinary incontinence in women
who participate in weightlifting sports.
Material and method: The final sample included 22 female athletes aged 18–27. Participants were divided into a control
group (n = 6) and two intervention groups based on training frequency: IG1 (≥4 days/week, n = 8) and IG2 (<4 days/week,
n = 8). The intervention, based on Kegel exercises performed five times weekly for six weeks. UI impact was assessed pre and
post intervention using the ICIQ-LUTSqol questionnaire.
Results: A high prevalence of urinary incontinence (UI) was reported (63.6%), especially during sports activity. In the intervention groups, no statistically significant differences were observed in ICIQ-LUTSqol scores between groups (P = 0.65) or
over time (P = 0.47), nor in the group-by-time interaction (P = 0.15). However, a non-significant trend suggested a reduction in
ICIQ-LUTSqol scores in IG2 post-intervention. However, a non significant trend suggested a reduction in ICIQ-LUTSqol scores
in IG2 post-intervention, although eta 2 was low.
Conclusions: The six-week Kegel intervention had limited impact on UI symptoms among these young female athletes,
suggesting that longer interventions and consideration of additional factors may be necessary to achieve significant UI
reduction in this population
Patterns of cognitive-emotional change after cognitive-behavioural treatment in emotional disorders: A 12-month longitudinal cluster analysis
No full textIntroduction
The aim of this study was to use cluster analysis based on the trajectory of five cognitive-emotional processes (worry, rumination, metacognition, cognitive reappraisal and expressive suppression) over time to explore differences in clinical and performance variables in primary care patients with emotional symptoms.
Methods
We compared the effect of adding transdiagnostic cognitive-behavioural therapy (TD-CBT) to treatment as usual (TAU) according to cluster membership and sought to determine the variables that predicted cluster membership. 732 participants completed scales about cognitive-emotional processes, anxiety and depressive symptoms, functioning, and quality of life (QoL) at baseline, posttreatment, and at 12 months. Longitudinal cluster analysis and logistic regression analyses were carried out.
Results
A two-cluster solution was chosen as the best fit, named as “less” or “more” improvement in cognitive-emotional processes. Individuals who achieved more improvement in cognitive-emotional processes showed lower emotional symptoms and better QoL and functioning at all three time points. TAU+TD-CBT, income level, QoL and anxiety symptoms were significant predictors of cluster membership.
Conclusions
These results underscore the value of adding TD-CBT to reduce maladaptive cognitive-emotional regulation strategies. These findings highlight the importance of the processes of change in therapy and demonstrate the relevance of the patient’s cognitive-emotional profile in improving treatment outcomes
Detecting Cyberattacks to Federated Learning on Software-Defined Networks
No full textFederated learning is a distributed machine-learning technique that enables multiple devices to learn a shared model while keeping their local data private. The approach poses security challenges, such as model integrity, that must be addressed to ensure the reliability of the learned models. In this context, software-defined networking (SDN) can play a crucial role in improving the security of federated learning systems; indeed, it can provide centralized control and management of network resources, enforcement of security policies, and detection and mitigation of network-level threats. The integration of SDN with federated learning can help achieve a secure and efficient distributed learning environment. In this paper, an architecture is proposed to detect attacks on Federated Learning using SDN; furthermore, the machine learning model is deployed on a number of devices for training. The simulation results are carried out using the N-BaIoT dataset and training models such as Random Forest achieves 99.6%, Decision Tree achieves 99.8%, and K-Nearest Neighbor achieves 99.3% with 20 features