Search Results (8)

The design of differentiated fiscal instruments for industrial sustainability requires robust, data-driven tools capable of capturing the heterogeneity of environmental performance across manufacturing units—a challenge that conventional econometric approaches address only partially, given the non-linear nature of operational–environmental interactions in reconfigurable production systems. This study introduces a two-phase computational framework that integrates unsupervised machine learning and supervised classification to generate evidence-based sustainability profiles for fiscal policy targeting. Its principal contribution is the combination of K-Means clustering with a binary artificial neural network (ANN) classifier, operationalized through an accessible decision-support interface that enables differentiated incentive allocation without requiring programming expertise from policymakers. A dataset of 1000 manufacturing records comprising seven operational and technological input variables—material usage, production capacity, reconfiguration time, downtime, AI optimization, IoT connectivity, and predictive maintenance—and three environmental output indicators—energy consumption, carbon emissions, and waste generation—was analyzed. In Phase One, K-Means segmentation with k = 6, selected through multi-criteria convergence (Silhouette = 0.102; Elbow, Davies–Bouldin, and Calinski–Harabasz indices), identified six distinct sustainability profiles with marked environmental differentiation. In Phase Two, a binary ANN classifier (architecture: 7 → 64 → 32 → 1 neurons; ReLU and sigmoid activations) was trained to distinguish the reference cluster C0 (low environmental impact: energy 145.1 kWh, emissions 45.2 CO₂-eq) from the high-impact cluster C1 (emissions 67.8 CO₂-eq, waste 41.5 kg). The trained classifier achieved an overall accuracy of 75.4% and an AUC-ROC of 0.774 on the held-out test set, with a macro-averaged F1-score of 0.753 and a Cohen’s kappa coefficient of 0.508, indicating moderate-to-substantial agreement beyond chance. Class C1 (high-impact establishments) achieved a precision of 0.794 and a recall of 0.730, supporting reliable identification of manufacturing units that would most benefit from targeted fiscal support. The framework is deployed through a Gradio-based graphical interface incorporating a traffic-light sustainability classification (green/yellow/red), enabling direct and interactive application by tax authorities and industrial policymakers. The modular architecture supports adaptation to larger or sector-specific datasets, making it transferable across industrial policy contexts. Full article

This article examines the unique depiction of the Visitation in the Book of Hours of Juana Enriquez, housed in the Biblioteca del Palacio Real de Madrid and also known as the Book of Hours of Isabella the Catholic. While the Visitation is a common theme in other Books of Hours, this manuscript’s rendition stands out for its inclusion of visual elements not found in other works by the same illuminator. Through a detailed iconographic analysis, the article emphasizes the significance of the Visitation scene, exploring its visual components and their implications for understanding the spiritual and cultural context of the era. The study aims to highlight the Visitation miniature as a prime example of imagery crafted to serve the inner devotion of its first owner, Juana Enriquez. Ultimately, this research offers a deeper appreciation of the Book of Hours of Juana Enriquez as a product of its time, designed for meditation and contemplation. Full article

Malnutrition is common in chronic obstructive pulmonary disease (COPD) patients and is associated with worse lung function and greater severity. This review by the Andalusian Group for Nutrition Reflection and Investigation (GARIN) addresses the nutritional management of adult COPD patients, focusing on Morphofunctional Nutritional Assessment and intervention in clinical practice. A systematic literature search was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, followed by critical appraisal based on Scottish Intercollegiate Guidelines Network (SIGN) guidelines. Recommendations were graded according to the European Society for Clinical Nutrition and Metabolism (ESPEN) system. The results were discussed among GARIN members, with consensus determined using a Likert scale. A total of 24 recommendations were made: 2(A), 6(B), 2(O), and 14(GPP). Consensus exceeded 90% for 17 recommendations and was 75–90% for 7. The care of COPD patients is approached from a nutritional perspective, emphasizing nutritional screening, morphofunctional assessment, and food intake in early disease stages. Nutritional interventions include dietary advice, recommendations on food group intake, and the impact of specialized nutritional treatment, particularly oral nutritional supplements. Other critical aspects, such as physical activity and quality of life, are also analyzed. These recommendations provide practical guidance for managing COPD patients nutritionally in clinical practice. Full article

This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process with simulated annealing within each solution that each process works. The highlight of this work is presented in the algorithmic design for optimizing the problem by applying cooperative processes. In each iteration of the proposed heuristics, collective communication allows the master process to identify the process with the best solution and point-to-point communication allows the best solution to be sent to the master process so that it can be distributed to all the processes in progress in order to direct the search toward a space of solutions which is close to the best solution found at the time. This search is performed by applying simulated annealing. On the other hand, the mathematical representation of an optimization model present in the literature of the university course timing problem is performed. The results obtained in this work show that the proposed metaheuristics improves the results of other metaheuristics for all test instances. Statistical analysis shows that the proposed metaheuristic presents a different behavior from the other metaheuristics with which it is compared. Full article

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases. Full article

This work presents an optimization proposal to better the computational convergence time in convection-diffusion and driven-cavity problems by applying a simulated annealing (SA) metaheuristic, obtaining optimal values in relaxation factors (RF) that optimize the problem convergence during its numerical execution. These relaxation factors are tested in numerical models to accelerate their computational convergence in a shorter time. The experimental results show that the relaxation factors obtained by the SA algorithm improve the computational time of the problem convergence regardless of user experience in the initial low-quality RF proposal. Full article

Coastal zones are important areas for the development of diverse ecosystems. The analysis of chlorophyll a (Chl a), as an indicator of primary production in these regions, is crucial for the quantification of phytoplankton biomass, which is considered the main food chain base in the oceans and an indicator of the trophic state index. This variable is greatly important for the analysis of the oceanographic variability, and it is crucial for determining the tendencies of change in these areas with the objective of determining the effects on the ecosystem and the population dynamics of marine resources. In this study, we analysed the Chl a concentration distribution on the mainland coast of the Gulf of California based on the monthly data from July 2002 to July 2019, obtained from remote sensing (Moderate-Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) with a 9 km resolution). The results showed a clear distribution pattern of Chl a observed along this area with the maximum levels in March and minimum levels in August. A four-region characterisation on this area was used to make a comparison of the Chl a concentrations during warm and cold periods. The majority of the results were statistically significant. The spectral analysis in each of the four regions analysed in this study determined the following variation frequencies: annual, semi-annual, seasonal, and inter-annual; the last was related to the macroscale climatological phenomena El Niño-La Niña affecting the variability of the Chl a concentration in the study region. Full article

The coastal zones are areas with a high flow of energy and materials where diverse ecosystems are developed. The study of coastal oceanography is important to understand the variability of these ecosystems and determine their role in biogeochemical cycles and climate change. Sea surface temperature (SST) analysis is indispensable for the characterization of physical and biological processes, and it is affected by processes at diverse timescales. The purpose of this work is to analyze the oceanographic variability of the Eastern Coastal Zone of the Gulf of California through the study of the SST from time series analysis of monthly data obtained from remote sensors (AVHRR-Pathfinder Version 5.1 and Version 5 resolution of 4 km, MODIS-Aqua, resolution of 4 km) for the period 1981 to 2016. The descriptive analysis of SST series showed that the values decrease from south to north, as well as the amplitude of the warm period decrease from south to north (cold period increase from south to north). The minimum values occurred during January and February, and ranged between 18 and 20 °C; and maximum values, of about 32 °C, arose in August and September. Cluster analysis allowed to group the data in four regions (south, center, midriff islands and north), the spectral analysis in each region showed frequencies of variation in scales: Annual (the main), seasonal, semiannual, and interannual. The latter is associated with the El Niño and La Niña climatological phenomena. Full article