Análisis exergético y modelado matemático del proceso de secado de soya en climas cálidos-húmedos
Tesis Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Español Recientemente la investigación en secadores solares se enfoca en determinar la cinética de secado y su descripción a partir de modelos matemáticos, sin embargo, los estudios exergéticos son relativamente limitados. Se requieren estudios teóricos y experimentales para romper la barrera del conocimiento con este enfoque, que permitan evaluar el máximo potencial de trabajo útil desperdiciado por un sistema (pérdida de exergía) a medida que se equilibra con un entorno de referencia (medio ambiente). Este trabajo presenta, mediante la instrumentación de secadores solares tipo gabinete, el modelado matemático y análisis exergético, para evaluar los procesos de transferencia de calor y masa del secado de soya en climas cálidos-húmedos, específicamente en la ciudad de San Francisco de Campeche, Campeche. El paradigma de investigación es cuantitativo puesto que se recolectaron datos para probar una hipótesis con base en la medición numérica y análisis estadístico, con el fin de establecer pautas de comportamiento. Se analizó el comportamiento de la irradiancia, temperatura ambiente y humedad relativa y su relación con la pérdida de peso. Se comparó el desempeño térmico de secadores solares directos (tipo gabinete) con convección natural y convección forzada con un horno convencional eléctrico a temperaturas controladas (55 °C y 65 °C). Se obtuvieron datos de contenido de humedad para realizar la cinética de secado y evaluar los modelos matemáticos propuestos. De igual manera, se realizó un análisis de colorimetría para estudiar el efecto del secado sobre las características visuales del producto. Las eficiencias exergéticas promedio fueron 47.40 % y 53.67 % para la convección natural y convección forzada, respectivamente. Se estimaron los indicadores de sustentabilidad exergética como son: potencial de mejora, índice de sustentabilidad y relación de energía residual. metadata Acosta Pech, Israel del Jesús mail israel.acosta@doctorado.unini.edu.mx (2022) Análisis exergético y modelado matemático del proceso de secado de soya en climas cálidos-húmedos. Doctoral thesis, SIN ESPECIFICAR.
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Recientemente la investigación en secadores solares se enfoca en determinar la cinética de secado y su descripción a partir de modelos matemáticos, sin embargo, los estudios exergéticos son relativamente limitados. Se requieren estudios teóricos y experimentales para romper la barrera del conocimiento con este enfoque, que permitan evaluar el máximo potencial de trabajo útil desperdiciado por un sistema (pérdida de exergía) a medida que se equilibra con un entorno de referencia (medio ambiente). Este trabajo presenta, mediante la instrumentación de secadores solares tipo gabinete, el modelado matemático y análisis exergético, para evaluar los procesos de transferencia de calor y masa del secado de soya en climas cálidos-húmedos, específicamente en la ciudad de San Francisco de Campeche, Campeche. El paradigma de investigación es cuantitativo puesto que se recolectaron datos para probar una hipótesis con base en la medición numérica y análisis estadístico, con el fin de establecer pautas de comportamiento. Se analizó el comportamiento de la irradiancia, temperatura ambiente y humedad relativa y su relación con la pérdida de peso. Se comparó el desempeño térmico de secadores solares directos (tipo gabinete) con convección natural y convección forzada con un horno convencional eléctrico a temperaturas controladas (55 °C y 65 °C). Se obtuvieron datos de contenido de humedad para realizar la cinética de secado y evaluar los modelos matemáticos propuestos. De igual manera, se realizó un análisis de colorimetría para estudiar el efecto del secado sobre las características visuales del producto. Las eficiencias exergéticas promedio fueron 47.40 % y 53.67 % para la convección natural y convección forzada, respectivamente. Se estimaron los indicadores de sustentabilidad exergética como son: potencial de mejora, índice de sustentabilidad y relación de energía residual.
| Tipo de Documento: | Tesis (Doctoral) |
|---|---|
| Palabras Clave: | Análisis exergético, Modelado matemático, Tecnologías de secado solar, Convección natural, Convección Forzada, Indicadores de sostenibilidad. |
| Clasificación temática: | Materias > Ingeniería |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales |
| Depositado: | 22 Sep 2023 23:30 |
| Ultima Modificación: | 22 Sep 2023 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/1990 |
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A scalable and secure federated learning authentication scheme for IoT
Secure and scalable authentication remains a fundamental challenge in Internet of Things (IoT) networks due to constrained device resources, dynamic topology, and the absence of centralized trust infrastructures. Conventional password-based and certificate-driven authentication schemes incur high computation, storage, and communication overhead, limiting their suitability for large-scale deployments. To address these limitations, this paper proposes ScLBS, a federated learning (FL)–based self-certified authentication scheme for distributed and sustainable IoT environments. ScLBS integrates self-certified public key cryptography with FL-driven trust adaptation, enabling decentralized public key derivation without reliance on third-party certificate authorities or exposure of private credentials. A zero-knowledge mechanism combined with location-aware authentication strengthens resistance to impersonation, Sybil, and replay attacks. Hierarchical key management supported by a -tree enables efficient group rekeying and preserves forward and backward secrecy under dynamic membership. Formal security verification is conducted under the Dolev–Yao adversary model using ProVerif, confirming secrecy of private and session keys (SKs) and correctness of authentication. Extensive NS-3 simulations and ablation analysis demonstrate that ScLBS achieves lower authentication delay, reduced message overhead, improved network utilization, and decreased energy consumption compared to representative IoT authentication schemes, while maintaining bounded FL overhead. These results indicate that ScLBS provides a balanced trade-off between security strength, scalability, and resource efficiency for constrained IoT networks.
Premkumar Chithaluru mail , B. Veera Jyothi mail , Fahd S. Alharithi mail , Wojciech Ksiazek mail , M. Ramchander mail , Aman Singh mail aman.singh@uneatlantico.es, Ravi Kumar Rachavaram mail ,
Chithaluru
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Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.
Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,
Elío Pascual
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Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model
Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, a hybrid feature engineering approach is proposed that combines term frequency-inverse document frequency and the bag of words. For experimental evaluation, “Suicide Watch” and “Depression” subreddits on the Reddit platform are used. Results indicate that the proposed SVEM model achieves an accuracy of 94%, better than existing approaches. The model also shows robust performance concerning precision, recall, and F1, each with a 0.93 score. ERT and deep learning models are also used, and performance comparison with these models indicates better performance of the SVEM model. Gated recurrent unit, long short-term memory, and recurrent neural network have an accuracy of 92% while the convolutional neural network obtains an accuracy of 91%. SVEM’s computational complexity is also low compared to deep learning models. Further, this study highlights the importance of explainability in healthcare applications such as suicidal ideation detection, where the use of LIME provides valuable insights into the contribution of different features. In addition, k-fold cross-validation further validates the performance of the proposed approach.
Erol KINA mail , Jin-Ghoo Choi mail , Abid Ishaq mail , Rahman Shafique mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,
KINA
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Human metapneumovirus (hMPV) is one of the potential pandemic pathogens, and it is a concern for elderly subjects and immunocompromised patients. There is no vaccine or specific antiviral available for hMPV. We conducted an in-silico study to predict initial antiviral candidates against human metapneumovirus. Our methodology included protein modeling, stability assessment, molecular docking, molecular simulation, analysis of non-covalent interactions, bioavailability, carcinogenicity, and pharmacokinetic profiling. We pinpointed four plant-derived bio-compounds as antiviral candidates. Among the compounds, apigenin showed the highest binding affinity, with values of − 8.0 kcal/mol for the hMPV-F protein and − 7.6 kcal/mol for the hMPV-N protein. Molecular dynamic simulations and further analyses confirmed that the protein-ligand docked complexes exhibited acceptable stability compared to two standard antiviral drugs. Additionally, these four compounds yielded satisfactory outcomes in bioavailability, drug-likeness, and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) and STopTox analyses. This study highlights the potential of apigenin and xanthoangelol E as an initial antiviral candidate, underscoring the necessity for wet-lab evaluation, preclinical and clinical trials against human metapneumovirus infection.
Hasan Huzayfa Rahaman mail , Afsana Khan mail , Nadim Sharif mail , Wasifuddin Ahmed mail , Nazmul Sharif mail , Rista Majumder mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Isabel De la Torre Díez mail , Shuvra Kanti Dey mail ,
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Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods: This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results: The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN–LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial–temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion: Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design enables real-time and edge-device deployment, addressing a major limitation of existing deep-learning techniques. The findings highlight the potential of CNNAttLSTM for scalable, efficient, and accurate agricultural disease monitoring and broader precision agriculture applications.
Gaurav Tuteja mail , Fuad Ali Mohammed Al-Yarimi mail , Amna Ikram mail , Rupesh Gupta mail , Ateeq Ur Rehman mail , Jeewan Singh mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es,
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