Repositorio Universidad Europea del Atlántico
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La subjetividad de las preconcepciones en la correlación de los métodos de traducción y el enfoque domesticante y extranjerizante: estudio de caso en traducción humanística y literaria
No full textCNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification
No full textIntroduction: 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
Constraint of Lignin–Carbohydrate Complex Orchestrated on Polyphenol in Oil–Water Interface Targeting Ulcerative Colitis Therapy
No full textThe therapeutic potential of polyphenols in ulcerative colitis (UC), mediated through immune modulation and gut microbiota homeostasis. To enhance the oral bioavailability of polyphenols, we architected a colon–targeted W1/O/W2 emulsion system featuring a rationally designed lignin–carbohydrate complex (LCC) as a dual–functional emulsifier system for the first time. Based on the innate structural duality of LCC, which comprising hydrophobic lignin and hydrophilic carbohydrates, we employed LCC for O/W emulsifier. This inherent amphiphilicity was further engineered via laccase–mediated grafting of isovanillin, yielding a modified LCC with tailored lipophilicity for effective W/O interfacial stabilization. The W1/O/W2 emulsion ensured the stability of the encapsulated polyphenols with divergent polarity but also enabled pH–responsive payload release under colonic conditions (pH >7.0). In DSS–induced colitis, the system demonstrated a synergistic effect, the LCC itself acted as a prebiotic to modulate the gut microbiota, specifically enriching short chain fatty acid–producing bacteria, while the released polyphenols reinforced the intestinal barrier, which collectively accelerated mucosal healing. This research proposes a carbon–neutral therapeutic strategy for colitis, not only establishing a proof–of–concept for replacing synthetic emulsifiers with engineered biomass, but also as a multi–functional platform to stabilize colon–targeted co–delivery system and microbiome regulation in colitis
Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students
No full textBackground/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
Enhancing fault detection in new energy vehicles via novel ensemble approach
No full textNew energy vehicles (NEVs) has emerged as a sustainable alternative to conventional vehicles, however have unresolved reliability challenges due to their complex electronic systems and varying operating conditions. Faults in drivetrain and battery systems, occurring at rates up to 12% annually, present significant barriers to the widespread adoption of NEVs. This study proposes a robust fault detection framework that applies multiple machine learning and deep learning models to address these challenges. The research utilizes the benchmark NEV fault diagnosis dataset, which contains real-world sensor data from NEVs. The models tested include logistic regression, passive-aggressive classifier, ridge classifier, perceptron, gated recurrent unit (GRU), convolutional neural network, and artificial neural network. The proposed ensemble GRULogX model stands out among the implemented model, leveraging GRU with logistic regression and other key classifiers, and achieved 99% accuracy, demonstrating high precision and recall. Cross-validation and hyperparameter optimization were adopted to further ensure the model’s generalizability and reliability. This research enhances the fault detection capabilities of NEVs, thereby improving their reliability and supporting the wider adoption of clean energy transportation solutions
Thermal activation in oxidative model drives catechin assembly into theaflavins with enhanced antioxidant capacity
No full textTheaflavins are important bioactive pigments in tea, but their non-enzymatic formation pathways under heating remain incompletely understood. This study investigated the temperature-dependent formation of four theaflavins using aqueous catechin model systems. Potassium ferricyanide was used as a controlled oxidant to initiate catechin oxidation and subsequent coupling reactions. Liquid chromatography-mass spectrometry (LC-MS), ultraviolet-visible spectroscopy (UV–Vis), DPPH and ABTS assays were applied to track product profiles and antioxidant capacity. Moderate temperatures promoted catechin oxidation to ortho-quinones and favored the accumulation of theaflavins, leading to higher antioxidant activity. Low temperatures limited conversion, whereas high temperatures accelerated precursor consumption and theaflavin degradation. Across all model systems, theaflavin levels peaked at 30 min and then declined with prolonged heating. Among the four products, TF3 showed the strongest thermoresponsive and the highest antioxidant capacity. These findings reveal a thermally activated catechin self-assembly mechanism in an oxidant-assisted model system
Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision
Full text linkHistopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256
256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools
The Health Benefits of Tamarindus indica: A Focus on the Relationship Between Phytochemical Composition and Physiological Effects
No full textBackground/Objectives: Conventional pharmacotherapy for the most prevalent human diseases still has limited efficacy. Natural medicines are recognized for their therapeutic efficacy and low side effects. Tamarindus indica is a tropical tree of the Fabaceae family, valued for its multiple uses and the nutritional properties of its fruits. The purpose of this review is to provide an overview of the nutraceutical value of T. indica, focusing on its phytochemical composition and main health benefits. Methods: For this purpose, a bibliography search was performed in PubMed, Scopus, and ScienceDirect databases, including all articles published between 2000 and December 2025. Results: The T. indica fruit contains different phytochemical compounds, such as flavonoids, tannins, alkaloids, and saponins, with therapeutic potential. These compounds exert free radical scavenging activity, improve antioxidant and detoxification enzyme activities, exert antimicrobial effects, attenuate the activation of pro-inflammatory mediators, and regulate the expression of lipid metabolism genes. Conclusions: This article presents an integrated analysis summarizing the phytochemical characteristics, mode of action, medical utility, and safe use of T. indica, thereby contributing to a greater understanding of its potential health benefits
Inflammatory potential of the diet and self-rated quality of life in Italian adults
No full textBackground: Dietary quality is widely acknowledged as a key factor in maintaining good health. Recommendations that promote plant-based eating patterns are largely grounded in evidence showing that dietary choices can modulate the immune function. In line with such a hypothesis, diet may be considered as a potential driver of persistent low-grade inflammation. Quality of life (QoL), on the other hand, serves as a broad indicator that encompasses both physical and psychological wellbeing.Aim: The purpose of this cross-sectional study was to examine the relationship between the inflammatory potential of the diet and QoL in a population sample of Italian adults.Design: A total of 1,936 participants completed a 110-item food frequency questionnaire to assess eating habits. The inflammatory potential of their diet was calculated using the dietary inflammatory score (DIS). Quality of life was measured with the Manchester Short Appraisal (MANSA).Results: Higher DIS values, reflecting a more pro-inflammatory diet, were linked to reduced likelihood of reporting high QoL (OR = 0.56; 95% CI: 0.40–0.78). Several specific domains of QoL, including general life satisfaction, social relationships, personal safety, satisfaction with cohabitation, physical health, and mental health, also showed significant associations with DIS.Conclusion: The findings suggest an association between the inflammatory potential of the diet and QoL
A scalable and secure federated learning authentication scheme for IoT
No full textSecure 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