Repositorio Universidad Internacional Iberoamericana
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices
No full textIn the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost
Advancing Nutritional Science: Contemporary Perspectives on Diet’s Role in Metabolic Health and Disease Prevention
No full textThis Special Issue of Diet and Nutrition: Metabolic Diseases showcases cutting-edge research exploring the intersection between nutrition, dietary patterns, and public health. The contributions in this collection involve both fundamental and applied research, offering new insights into how nutrition can combat the growing global burden of non-communicable diseases [1]. The studies in this issue emphasize the critical role that diet plays in promoting metabolic health, preventing chronic diseases, and improving overall quality of life.
In recent years, nutrition has become a central focus in global health efforts, with a growing body of evidence demonstrating its impact on both individual and population-level outcomes [2,3]. This Special Issue encompasses several key themes, including the role of dietary interventions in managing metabolic disorders, the importance of nutrient timing and quality, and the broader implications of sustainable dietary practices
Efectos en salud mental de docentes de primaria en escuelas de jornada extendida en Villa Hermosa, La Romana, RD.
No full textEl objetivo de este estudio es explorar cómo el trabajo docente afecta la salud mental de los profesores en escuelas de jornada escolar extendida en Villa Hermosa, La Romana, República Dominicana. La investigación emplea un método mixto con un enfoque de triangulación concurrente (DITRIAC), que involucra a una muestra cuantitativa de 139 docentes y una muestra cualitativa de 13 docentes. Los resultados revelan que los profesores que participaron en el estudio están expuestos a factores personales y laborales que pueden tener un impacto negativo en su salud mental y en la creación de un ambiente de aprendizaje adecuado. Se sugiere la necesidad de implementar estrategias para mejorar la calidad de vida de los docentes. Se anticipa que se encontrarán diversos elementos desfavorables que limitan el desempeño de los docentes, como por ejemplo la falta de recursos y la infraestructura inadecuada del lugar; condiciones laborales insatisfactorias, como bajos salarios, aulas sobrepobladas, múltiples clases y actividades, así como la sensación de desigualdad en las condiciones de trabajo, la inestabilidad laboral, así como la intensidad y prolongación del trabajo. También se pueden mencionar factores ambientales desfavorables, como ruido en el aula que obliga a elevar la voz, además de la presencia de corrientes de aire y fluctuaciones de temperatura en la sala, así como la exposición al ruido del entorno. También se tienen en cuenta aspectos negativos en las relaciones interpersonales, como dificultades con los estudiantes, requerimientos emocionales, casos de violencia y una escasa calidad en las relaciones sociales dentro del ámbito laboral
Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection
No full textSchizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and accurate diagnosis of schizophrenia remains a challenge due to the disorder’s complex nature and the limitations of state-of-the-art techniques. It is evident from the literature that electroencephalogram (EEG) signals provide valuable insights into brain activity, but their high dimensionality and complexity pose remain key challenges. Thus, our research introduces a novel approach by integrating the multichannel EGG, Crossover-Boosted Archimedes Optimization Algorithm (CAOA), and Rough Set Theory (RST) for schizophrenia detection. It is a four-stage model. In the first stage, Raw EGG data is collected. The data is passed to the next stage, which is called data preprocessing. This is used for artifact removal, band-pass filtering, and data normalization. The preprocessed data passed to the next stage. In the feature extraction stage, feature selection is performed using CAOA. In addition, classification is performed using a Support Vector Machine (SVM) based on features extracted through Multivariate Empirical Mode Function (MEMF) and entropy measures. The data interpretation stage displays the results to the end user using the data interpretation stage. We experimented and tested our proposed model using real EEG datasets. The simulation results prove that the proposed model achieved an average accuracy of 94.9%, sensitivity of 93.9%, specificity of 96.4%, and precision of 92.7%. Thus, our proposed model demonstrates significant improvements over state-of-the-art methods. In addition, the integration of CAOA and RST effectively addresses the challenges of high-dimensional EEG data, helps optimize the feature selection process, and increases accuracy. In future work, we suggest incorporating large-size datasets that include more diverse patient groups and refining the model with advanced machine-learning models and techniques
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
Evaluación del riesgo de los medicamentos veterinarios presentes en los alimentos y su relación con la seguridad alimentaria. Estudio de caso: Huevos.
No full textLa producción avícola es uno de los sectores de mayor importancia a nivel mundial por sus grandes aportes de productos y subproductos como la carne y el huevo a la alimentación humana. Esta se ve afectada por agentes patógenos que causan enfermedades, y para asegurar y mantener la producción se utilizan antibióticos veterinarios para tratar o prevenir, sean como promotores de crecimiento o terapéuticos. El objetivo del estudio fue evaluar el riesgo de los antibióticos veterinarios de uso en la producción avícola en gallinas ponedoras de huevos y su percepción de impacto con relación con la seguridad alimentaria. La muestra fue de 44 establecimientos veterinarios y/o productores de huevos en la provincia Espaillat y 385 consumidores de la provincia Santo Domingo, República Dominicana. A los datos se le aplicó un análisis factorial por extracción de componentes principales utilizando el programa estadístico IBM SPSS Statistics versión 25. El análisis para el estudio de validación determinó, 8 factores con su consistencia interna con 22 ítems para los productores de huevos y veterinarias y para los consumidores 3 factores con 8 ítems, con un coeficiente de Cronbach de 0.799 y 0.771 en ambos cuestionarios respectivamente, permitiendo analizar la estructura de las interacciones en las variables y estableciendo las relaciones entre los ítems. La estadística descriptiva demostró que más del 32 % de los productores de huevo no obtienen medicamentos con prescripción de un veterinario. De los consumidores que ingieren huevo de mesa (70.4 %), el 58.8 % considera que el huevo es más seguro en comparación a otros alimentos. Los resultados obtenidos afirman que existe riesgo de medicamentos veterinarios en los huevos y que la escala encontrada es confiable y válida para el instrumento evaluado para el constructo de los factores de impacto a los riesgos de la seguridad alimentaria
Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet
No full textOver the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet
Efficient deep learning-based approach for malaria detection using red blood cell smears
No full textMalaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff
Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms
No full textIn contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research
Readaptación de un instrumento para la evaluación de entornos virtuales de aprendizaje en el proyecto europeo de educación inclusiva denominado LOVEDISTANCE
No full textEsta investigación tuvo por objetivo valorar la utilización de un Instrumento para la evaluación de Entornos Virtuales de Aprendizaje (EVA), específicamente el DELES (Distance Education Learning Environments Survey) para el Proyecto Europeo de Educación Inclusiva denominado LOVEDISTANCE (Learning Optimization and Academic Inclusion Via Equitative Distance Teaching and Learning). El supuesto inicial es que el instrumento puede ser útil, pero está desactualizado y no necesariamente enfocado a los objetivos del proyecto LOVEDISTANCE, en particular al de Educación Inclusiva. El ejercicio académico se llevó a cabo en la Universidad de Levinsky, en Tel Aviv, Israel, y el análisis de la información se hizo con un enfoque cuanti-cualitativo, donde se utilizó, en una primera parte, la medida del consenso entre expertos para medir la fiabilidad estadística de las respuestas de los expertos, y después se realizó un análisis de la varianza (ANOVA) para determinar si existían diferencias significativas entre las medias de los grupos; posteriormente, se hizo un análisis cualitativo pormenorizado de las observaciones a partir de tres ejes de análisis: consideraciones del ejercicio investigativo, perfil de los investigadores y análisis de cada escala del instrumento. Algunas de las conclusiones más relevantes fueron que el instrumento es, en su mayoría, útil para los propósitos del proyecto LOVEDISTANCE, pero precisa mejoras en lo referido a las siguientes escalas: relevancia del aprendizaje para el alumno, apoyo por parte del instructor y la medición en la autonomía del estudiante