Sustainable personalisation and explainability in Dyadic Data Systems

[Abstract]: Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others. However, these systems have had a tendency to be black-box systems, despite their objective of influencing people's decisions. There is a lack of research on providing personalised explanations to the outputs of systems that make use of such data, that is, integrating the idea of Explainable Artificial Intelligence into the field of dyadic data. Moreover, the existing approaches rely heavily on Deep Learning models for their training, reducing their overall sustainability. In this work, we propose a computationally efficient model which provides personalisation by generating explanations based on user-created images. In the context of a particular dyadic data system, the restaurant review platform TripAdvisor, we predict, for any (user, restaurant) pair, the review of the restaurant that is most adequate to present it to the user, based on their personal preferences. This model exploits the usage of efficient Matrix Factorisation techniques combined with feature-rich embeddings of the pre-trained Image Classification models, developing a method capable of providing transparency to dyadic data systems while reducing as much as 80% the carbon emissions of training compared to alternative approaches

Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using visual content created by the users is one particularly promising option, showing a potential to maximize transparency and user trust. Existing models for explaining recommendations in this context face limitations: sustainability has been a critical concern, as they often require substantial computational resources, leading to significant carbon emissions comparable to the Recommender Systems where they would be integrated. Moreover, most models employ surrogate learning goals that do not align with the objective of ranking the most effective personalized explanations for a given recommendation, leading to a suboptimal learning process and larger model sizes. To address these limitations, we present BRIE, a novel model designed to tackle the existing challenges by adopting a more adequate learning goal based on Bayesian Pairwise Ranking, enabling it to achieve consistently superior performance than state-of-the-art models in six real-world datasets, while exhibiting remarkable efficiency, emitting up to 75% less CO${_2}$ during training and inference with a model up to 64 times smaller than previous approaches

Estudio de prefactibilidad para la instalación de una planta procesadora de loche (Cucurbita Moschata Duchesne) rallado y deshidratado, en la región Lambayeque

La tesis tiene como finalidad realizar un estudio de prefactibilidad para la instalación de una planta procesadora de loche rallado y deshidratado en la región Lambayeque, dicha idea surgió a partir de la investigación que antecede la tesis, donde se identificó un público objetivo potencial, para realizar la comercialización del producto con una ventaja competitiva frente a sus competidores. Se consideró como variable de investigación independiente al loche rallado y deshidratado y como variable dependiente a la planta procesadora del mismo. Se realizó bajo un enfoque del tipo cuantitativo, recolectando datos numéricos objetivos y con diseño no experimental – transversal, buscando describir un determinado fenómeno en base a un conocimiento ya previo en un momento específico de tiempo. Los resultados muestran que el mercado objetivo corresponde a la población entre 18- 55 años del sector C, D y E; una ubicación de planta ideal en la ciudad de Chiclayo, cerca al parque Industrial. Asimismo, acorde al proceso productivo se obtuvo una capacidad de planta del 81.89%, una productividad del 83.50% y una eficiencia del 85.83% para el primer año. Por último, la viabilidad económica con un TMAR del 8.11%, se obtuvo un VAN de $. 535,462.87, un TIR del 35$. 2.07.Campus Chiclay

Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial, and Brick Production)

In recent years, the interest in aerosol particles has increased due to concerns about the effects on human health. The study of the chemical characterization (organic matter, sulfates, nitrates, and black carbon) has improved the knowledge about the negative contribution of chemicals to the environment. The identification of secondary processes (from pollutants such as SO2, NOx, and PAHs) and their role when combined with environmental factors such as humidity, solar radiation, and temperature are also of interest. With this background, this chapter seeks to highlight the most recent findings on the chemical composition of aerosol particles in the ambient air of one of the main cities of Mexico: the Metropolitan Area of Guadalajara. This megalopolis has almost 60% of the population of the Jalisco state, approximately 2.2 million vehicles and an extensive artisan brick production. Furthermore, due to its geographical position, it experiences frequent episodes of thermal inversion and exposition to high levels of solar radiation, mainly during the first half of the year

Positive-unlabelled learning for identifying new candidate dietary restriction-related genes among ageing-related genes

Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, the existing ML approach naively labels genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence. This hinders the reliability of the negative examples (non-DR-related genes) and the method’s ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role

ESTRATEGIA EDUCATIVA PARA LA INCORPORACIÓN DE LA DIMENSIÓN AMBIENTAL EN LOS ESTUDIANTES DE LA CARRERA DE AGRONOMÍA

La protección del ambiente debe tenerse en cuenta por los sistemas educativos desde todos sus ángulos de influencia. Al respecto, es importante que los educadores instruyan y desarrollen valores sobre la protección del medio ambiente para promover el desarrollo sostenible no solo desde las aulas sino desde otros contextos universitarios. El objetivo del presente artículo es proponer una estrategia que contribuya a la educación ambiental en los estudiantes de la carrera de Agronomía desde la Extensión Universitaria, de manera que se aporte a la solución del problema objeto de estudio. Se ofrece información sobre el tema así como una caracterización del problema en la población elegida. Para su realización, se utilizaron métodos de la investigación educativa en los niveles teóricos, empíricos y matemáticos, así como instrumentos asociados a ellos. Los resultados alcanzados fueron satisfactorios y se evaluaron a través del criterio de expertos. En los anexos se muestran los modelos de: encuesta, prueba pedagógica y guía de observación a clases utilizados en el proceso de investigación

Hacia la excelencia en la docencia de producción animal: empleo de técnicas de aprendizaje activo para la gestión integral de explotaciones ganaderas

El objetivo de este Proyecto de Innovación Docente es crear un equipo de trabajo orientado al desarrollo de metodologías de aprendizaje activo para la gestión de explotaciones ganaderas en la ETS de Ingenierías Agrarias de la Universidad de Valladolid (Campus de Palencia). Se pretende que los alumnos adquieran la competencia en gestión de explotaciones ganaderas desde un enfoque profesional. Se incluye una guía para el manejo del programa GID (MSD Animal Health), un modelo de informe técnico profesional y la rúbrica de evaluación del informeDepartamento de Ciencias Agroforestale

Predictors of Global Non-Motor Symptoms Burden Progression in Parkinson’s Disease. Results from the COPPADIS Cohort at 2-Year Follow-Up

Background and Objective: Non-motor symptoms (NMS) progress in different ways between Parkinson's disease (PD) patients. The aim of the present study was to (1) analyze the change in global NMS burden in a PD cohort after a 2-year follow-up, (2) to compare the changes with a control group, and (3) to identify predictors of global NMS burden progression in the PD group. Material and Methods: PD patients and controls, recruited from 35 centers of Spain from the COPPADIS cohort from January 2016 to November 2017, were followed-up with after 2 years. The Non-Motor Symptoms Scale (NMSS) was administered at baseline (V0) and at 24 months ± 1 month (V2). Linear regression models were used for determining predictive factors of global NMS burden progression (NMSS total score change from V0 to V2 as dependent variable). Results: After the 2-year follow-up, the mean NMS burden (NMSS total score) significantly increased in PD patients by 18.8% (from 45.08 ± 37.62 to 53.55 ± 42.28; p < 0.0001; N = 501; 60.2% males, mean age 62.59 ± 8.91) compared to no change observed in controls (from 14.74 ± 18.72 to 14.65 ± 21.82; p = 0.428; N = 122; 49.5% males, mean age 60.99 ± 8.32) (p < 0.0001). NMSS total score at baseline (β = -0.52), change from V0 to V2 in PDSS (Parkinson's Disease Sleep Scale) (β = -0.34), and change from V0 to V2 in NPI (Neuropsychiatric Inventory) (β = 0.25) provided the highest contributions to the model (adjusted R-squared 0.41; Durbin-Watson test = 1.865). Conclusions: Global NMS burden demonstrates short-term progression in PD patients but not in controls and identifies worsening sleep problems and neuropsychiatric symptoms as significant independent predictors of this NMS progression

The Latin American experience of allografting patients with severe aplastic anaemia: real-world data on the impact of stem cell source and ATG administration in HLA-identical sibling transplants

We studied 298 patients with severe aplastic anaemia (SAA) allografted in four Latin American countries. The source of cells was bone marrow (BM) in 94 patients and PBSCs in 204 patients. Engraftment failed in 8.1% of recipients with no difference between BM and PBSCs (P = 0.08). Incidence of acute GvHD (aGvHD) for BM and PBSCs was 30% vs 32% (P = 0.18), and for grades III–IV was 2.6% vs 11.6% (P = 0.01). Chronic GvHD (cGvHD) between BM and PBSCs was 37% vs 59% (P = 0.002) and extensive 5% vs 23.6% (P = 0.01). OS was 74% vs 76% for BM vs PBSCs (P = 0.95). Event-free survival was superior in patients conditioned with anti-thymocyte globulin (ATG)-based regimens compared with other regimens (79% vs 61%, P = 0.001) as excessive secondary graft failure was seen with other regimens (10% vs 26%, P = 0.005) respectively. In multivariate analysis, aGvHD II–IV (hazard ratio (HR) 2.50, confidence interval (CI) 1.1–5.6, P = 0.02) and aGvHD III–IV (HR 8.3 CI 3.4–20.2, Po0.001) proved to be independent negative predictors of survival. In conclusion, BM as a source of cells and ATG-based regimens should be standard because of higher GvHD incidence with PBSCs, although the latter combining with ATG in the conditioning regimen could be an option in selected high-risk patient

Predictors of Global Non-Motor Symptoms Burden Progression in Parkinson’s Disease. Results from the COPPADIS Cohort at 2-Year Follow-Up

COPPADIS Study Group.[Background and Objective] Non-motor symptoms (NMS) progress in different ways between Parkinson’s disease (PD) patients. The aim of the present study was to (1) analyze the change in global NMS burden in a PD cohort after a 2-year follow-up, (2) to compare the changes with a control group, and (3) to identify predictors of global NMS burden progression in the PD group.[Material and Methods] PD patients and controls, recruited from 35 centers of Spain from the COPPADIS cohort from January 2016 to November 2017, were followed-up with after 2 years. The Non-Motor Symptoms Scale (NMSS) was administered at baseline (V0) and at 24 months ± 1 month (V2). Linear regression models were used for determining predictive factors of global NMS burden progression (NMSS total score change from V0 to V2 as dependent variable).[Results] After the 2-year follow-up, the mean NMS burden (NMSS total score) significantly increased in PD patients by 18.8% (from 45.08 ± 37.62 to 53.55 ± 42.28; p < 0.0001; N = 501; 60.2% males, mean age 62.59 ± 8.91) compared to no change observed in controls (from 14.74 ± 18.72 to 14.65 ± 21.82; p = 0.428; N = 122; 49.5% males, mean age 60.99 ± 8.32) (p < 0.0001). NMSS total score at baseline (β = −0.52), change from V0 to V2 in PDSS (Parkinson’s Disease Sleep Scale) (β = −0.34), and change from V0 to V2 in NPI (Neuropsychiatric Inventory) (β = 0.25) provided the highest contributions to the model (adjusted R-squared 0.41; Durbin-Watson test = 1.865).[Conclusions] Global NMS burden demonstrates short-term progression in PD patients but not in controls and identifies worsening sleep problems and neuropsychiatric symptoms as significant independent predictors of this NMS progression.This research was funded by Fundación Española de Ayuda a la Investigación en Parkinson y otras Enfermedades Neuro-degenerativas (Curemos el Parkinson; www.curemoselparkinson.org).Peer reviewe