Polytechnic José Antonio Echeverría

The valorization of plant biomass is one of the main strategies for sustainable development. However, its use as energy, biofuels, fertilizers, value-added products, or even food is severely affected by the complexity of the plant cell wall. Therefore, the evaluation of fungi with high production of lignocellulolytic enzymes capable of efficiently degrading these substrates constitutes a viable, clean, and eco-friendly solution, allowing, for example, an increase in the digestibility and nutritional quality of alternative animal feed sources. For these reasons, the present study evaluated the ability of the mutant strain Trichodema viride M5-2 to improve the nutritional composition of the forage legumes Lablab purpureus and Mucuna pruriens through solid-state fermentation. Endo- and exoglucanase cellulolytic activity was assessed, as well as the effect of fermentation on the fiber’s physical properties and chemical composition. Molecular changes in the structure of plant fiber were analyzed using infrared spectroscopy. Increased production of the cellulolytic complex of the enzymes endoglucanase (3.29 IU/mL) and exoglucanase (0.64 IU/mL) was achieved in M. pruriens. The chemical composition showed an increase in true protein and a decrease in neutral fiber, hemicellulose, and cellulose, with a consequent improvement in nutritional quality. Fiber degradation was evident in the infrared spectrum with a significant decrease in the signals associated with cellulose and, to a lesser extent, with lignin. It can be concluded that the mutant strain T. viride M5-2 produced chemical, physical, and molecular changes in the fibrous and protein fractions of L. purpureus and M. pruriens through SSF, which improved their nutritional value as an alternative feed for animal nutrition. By promoting the use of this fungus, the nutritional quality of this source is increased through an effective and eco-friendly process, which contributes to mitigating the environmental impact of food production, in accordance with sustainability objectives and the need for more responsible agricultural practices.

This paper examines the ability of ChatGPT to generate synthetic comment datasets that mimic those produced by humans. To this end, a collection of datasets containing human comments, freely available in the Kaggle repository, was compared to comments generated via ChatGPT. The latter were based on prompts designed to provide the necessary context for approximating human results. It was hypothesized that the responses obtained from ChatGPT would demonstrate a high degree of similarity with the human-generated datasets with regard to vocabulary usage. Two categories of prompts were analyzed, depending on whether they specified the desired length of the generated comments. The evaluation of the results primarily focused on the vocabulary used in each comment dataset, employing several analytical measures. This analysis yielded noteworthy observations, which reflect the current capabilities of ChatGPT in this particular task domain. It was observed that ChatGPT typically employs a reduced number of words compared to human respondents and tends to provide repetitive answers. Furthermore, the responses of ChatGPT have been observed to vary considerably when the length is specified. It is noteworthy that ChatGPT employs a smaller vocabulary, which does not always align with human language. Furthermore, the proportion of non-stop words in ChatGPT’s output is higher than that found in human communication. Finally, the vocabulary of ChatGPT is more closely aligned with human language than the similarity between the two configurations of ChatGPT. This alignment is particularly evident in the use of stop words. While it does not fully achieve the intended purpose, the generated vocabulary serves as a reasonable approximation, enabling specific applications such as the creation of word clouds.

Probabilistic Boolean Networks (PBN) can model the dynamics of complex biological systems, as well as other non-biological systems like manufacturing systems and smart grids. In this proof-of-concept paper, we propose a PBN architecture with a learning process that significantly enhances fault and failure prediction in manufacturing systems. This concept was tested using a PBN model of an ultrasound welding process and its machines. Through various experiments, the model successfully learned to maintain a normal operating state. Leveraging the complex properties of PBNs, we utilize them as an adaptive learning tool with positive feedback, demonstrating that these networks may have broader applications than previously recognized. This multi-layered PBN architecture offers substantial improvements in fault detection performance within a positive feedback network structure that shows greater noise tolerance than other methods.

Polyaniline (PANI) and PANI-MnO2 composites were synthesized via a chemical route with varying manganese dioxide (MnO2) content, specifically 5wt% and 15wt%. X-ray diffraction (XRD) confirmed the structural formation of both PANI and PANI-MnO2 composites. The direct current conductivity was measured, showing an increase with temperature: at 393K, pure PANI had a conductivity of 2.25 × 10⁻⁴ S/cm, which increased significantly in the composites, reaching 9.03 × 10⁻⁴ S/cm for the 15wt% MnO2 composite. The Seebeck coefficient also increased with temperature and MnO2 concentration, achieving a maximum value of 52 mV K⁻¹ at 373K for the 15wt% MnO2 composite. These results indicate that the synthesized PANI- MnO2 composites exhibit semiconducting behavior with improved thermoelectric properties, making them promising candidates for applications in thermoelectric devices such as generators and thermopiles. The study highlights the potential of these materials in enhancing the efficiency of thermoelectric energy conversion.

The performance of machine learning algorithms can be optimized through the implementation of methodologies that facilitate the development of autonomous and adaptive behaviors. In this context, the incorporation of goal-oriented analysis is proposed as a means of effecting a transformation in the behavior of traditionally “passive" algorithms, such as Random Forest, through the endowment of proactivity. The aforementioned analysis, represented using the i* modeling language, identifies strategies that increase the diversity of generated trees and optimize their total number while preserving the original model’s effectiveness. In addition to the outcomes achieved, it is crucial to highlight that the goal-oriented methodology plays a pivotal role in the development and comprehension of novel algorithmic variants. Based on this analysis, two proactive variants were designed: the Proactive Forest and the Progressive Forest. These variants balance simplicity and effectiveness, maintaining the original algorithm’s performance while exploring more efficient configurations. This work introduces new variants of the Random Forest algorithm and demonstrates the potential of goal-oriented analysis as a methodology for guiding the design of more adaptive and effective algorithms.

In an organization’s digital messaging environment, information is exchanged both internally and externally, resulting in a continuous evolution of the message stream at both the conceptual and feature levels. Feature-level changes occur due to the introduction of new attributes or the evolving meaning and relevance of existing features over time. Nevertheless, the prevailing approaches to addressing this issue frequently entail the initialization of a fixed set of parameters, which can be both intricate and expensive. Furthermore, maintaining these parameters in a fixed state over time may result in solutions becoming less adaptable, efficient, or effective as the message stream evolves. This paper introduces a goal-oriented, self-tuning solution for dynamic feature selection, based on approaches from the literature. The proposed solution is applied to the classification of messages within a multi-agent system. The approach employs the iStar modeling language to integrate proactive strategies and adaptive tuning plans, which are then operationalized through the multi-agent system. The effectiveness of the solution is evaluated through test cases and an experimental studies, comparing the results of the dynamic feature selection method from the literature with the variants introduced in this work. In spam detection, the proposed solution demonstrates improvements in several of the quality measures assessed. In news classification, the proposed solution was assessed, a variant was proposed and it demonstrated improved adaptability and effectiveness. This highlights the proposed solution potential for enhanced adaptability in dynamic messaging environments.

Probabilistic Boolean Networks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a Probabilistic Boolean Network architecture with a learning process that significantly improves the prediction of the occurrence of faults and failures in smart-grid systems. This idea was tested in a Probabilistic Boolean Network model of the WSCC nine-bus system that incorporates Intelligent Power Routers on every bus. The model learned the equality and negation functions in the different experiments performed. We take advantage of the complex properties of Probabilistic Boolean Networks to use them as a positive feedback adaptive learning tool and to illustrate that these networks could have a more general use than previously thought. This multi-layered PBN architecture provides a significant improvement in terms of performance for fault detection, within a positive-feedback network structure that is more tolerant of noise than other techniques.

Pumping systems are among the most electrical energy-consuming applications. To improve energy savings in such systems, new efficient pump’s models have been proposed and developed from studies that are mainly focused on the behavior of the internal flow and the optimization of the impeller’s geometrical parameters. However, this useful approach is insufficient to achieve satisfactory and efficient operation and, thus, to guarantee a reduction in the current level of energy consumption in these systems. To achieve this, the pump must not only operate efficiently but also consume low energy. In this paper, a mathematical criterion was developed for selecting low-energy-consumption pumps among pumps previously selected through an efficiency criterion. The analytical solution obtained was implemented in MATLAB®and relies on two databases. The first one comprises information on 52 pumps from different manufacturers and operating ranges and includes numerical coefficients describing the flow rate dependence, Q, on the head, H(Q), shaft power, N(Q), and efficiency, $\eta (Q)$. The second database contains system parameters, such as piping system’s length, diameter, and material, the different tube fittings and accessories, and the information about the suction and discharge reservoirs, e.g., free surface velocity and pressure, and the static heads. Typical residential and commercial systems were considered. The results show that the developed model accurately identifies pumps with the lowest energy consumption for a given system. First, the model identifies efficiently operating pumps. Later, using the low-energy-consumption criterion, it identifies the most energy-saving pumps among those operating efficiently. Finally, a numerical indicator of the energy consumption, defined relative to the pump with the highest value, allows for determining which pump has the lowest consumption. This methodology also facilitates the evaluation and understanding of why inefficient pumps may have low energy consumption, thus providing a rationale for their exclusion.

An essential requirement for modern industrial plants in the Industry 4.0 vision is to guarantee cybersecurity and safety related to the presence of faults. Additionally, the increasing complexity of the control systems and the digital transformation in industries demand a more integrative vision in supervision services improving interoperability. Regularly, research in fault diagnosis and cybersecurity has been developed separately despite having many elements in common. This paper presents a novel fuzzy-based strategy that integrates the early detection and location of cyber-attacks and faults. This holistic approach promotes the necessary and important interrelation between the technical groups of Operational Technology (OT) and Information Technology (IT) in industrial plants allowing for the simplification of the computational solution of the condition monitoring system. The proposal was assessed with two known benchmarks showing robust behavior in the presence of noise and disturbances in the measurements and outstanding performance.

The Variable Size and Cost Bin Packing Problem (VSCBPP) focuses on minimizing the overall cost of containers used to pack a specified set of items. This problem has significant applications across various fields, including energy, cargo transport, and informatics, among others. Most research conducted on this problem has concentrated on enhancing solution methodologies. Recently, some studies have investigated the use of fuzzy approaches to VSCBPP, which allow for the relaxation of certain constraints. In this paper, we introduce a metaheuristic method for solving the fuzzy version of VSCBPP, facilitating the simultaneous relaxation of two constraints: the overloading of containers and the exclusion of specific items from the packing process. Consequently, this two-dimensional fuzzy relaxation of the VSCBPP enables us to derive a range of solutions that present varying trade-offs between cost and the satisfaction levels of the original constraints. We employ mechanisms from the multi-objective metaheuristic approach to maximize the degrees of relaxation while minimizing the original cost function. To demonstrate the efficacy of our proposed solution, we utilized two well-known multi-objective evolutionary P-metaheuristics (Multi-Objective Genetic Algorithm and NSGA-II) and two S-metaheuristics (Multi-Objective Local Search and Ulungu Multi-Objective Simulated Annealing) specifically tailored for the fuzzy version of the VSCBPP. Computational experiments were conducted on 39 instances to validate the effectiveness of this approach.

The large-scale introduction of renewable energy, replacing fossil fuels, is presented as an essential part of the energy transition; this substitution is being observed in electrical systems, but its introduction will also be necessary in other sectors, such as transportation, either by incorporating renewable energy sources in the sector’s facilities, including automotive service centers, or through the electrification of transportation technology. The introduction of electromobility in a country is associated with a group of technologies that are required to make this introduction viable, such as electric vehicles themselves, charging stations and workshops for the repair and maintenance of this technology. Taking the above as a point of reference, this article addresses the basic elements of a proposal for an energy transition in the transport sector, identifying the limitations and barriers existing in the country for the introduction of electric mobility and, finally, arriving at a roadmap proposal to achieve the required synergy between energy transition and electromobility.

Housing supply is a controversial topic across the globe. In Europe, for example, housing provision is fundamentally characterised by capitalist market economy. However, the intensity of state intervention differs significantly in the respective European countries. Germany is certainly one of the countries in which the state traditionally intervenes considerably in the housing market and attempts to control it through various measures. In addition, the stakeholders in the housing market are very different, and the housing market is highly segmented. In Cuba, as a rather atypical example for Latin America, where the housing market is largely liberalised, the housing market is very strongly regulated by the socialist Cuban state. In this article, we address the topics of housing supply by analysing the institutional framework conditions and the different levels of intervention in the very different systems of Germany and Cuba. In doing so, we find that the objectives at the planning and political levels in these two case studies are quite similar despite the major political and social differences. It has become clear, that there is a gap between the aspiration of the political-administrative planning system and the planning expectations of the citizens in both systems. However, it is also emphasised that the state's claim to intervene in the housing market and the regulations, production and financing conditions are very different. Using Vaus’s ‘most-different-case approach’, we emphasise for the two cities of Hamburg and Havana that although considerable successes have been achieved in both systems in terms of adequate housing provision, but major challenges still exist in both cities. The analysis has also made clear how important it is to combine housing policy demands with the realities of housing industry, real estate markets and urban planning perspectives.

Sugarcane wax is a co‐product with strong potential. It contains several bioactive components that can be used for pharmaceutical and cosmetic purposes. It is also considered a substitute for leading vegetable waxes in the international market. The extraction and refining technologies that have been reported in Cuba do not always manage to deliver a wax that meets the quality demanded by the market. This problem can be solved with the development of innovative technologies that allow increasing yields, higher quality, and the reduction of costs. The main objective of this work was to achieve a validated model of the Cuban sugarcane wax‐refining process using the SuperPro Designer simulator, and to evaluate the alternatives in order to reduce consumption rates and increase stability in the final product quality, as customers demand. The model successfully replicated 39 out of 44 variables used for process validation, with values falling within the historical range of the process. Deviations from historical values were less than 4 percentage points. Among the proposed modifications, the second of the two alternatives proposed resulted in lower consumption rates and more consistent quality of refined wax.

Rapid drawdown has been identified as one of the most frequent causes of slope failures due to the effects associated with drought and operational changes when incorporating hydroelectric plants, which influence the filling level of earth dams. The main goal of this research is to obtain predictive models based on Artificial Neural Networks that return the factor of safety of the upstream slope in homogeneous earth dams in the face of the effect of rapid drawdown. Three geometries and 40 soils were defined to form the embankment, from which hybrid numerical models of transient water flow with unsaturated soils were built, considering three discharge speeds. From these results, a database was built to develop the predictive models, by means of the KNIME program and an algorithm based on Artificial Neural Networks. The behavior of the factor of safety as a function of time is also analyzed to establish its recovery intervals. Main results show that the minimum factor of safety is obtained between 52 % and 88 % of the total drawdown time. Regarding the predictive models, the adjusted R2 determination coefficients were greater than 95 % in all cases and the errors remained below 10 %. This demonstrates a high effectiveness of this algorithm and the validity of its application to geotechnical problems.

The main objective of this research is to identify current trends in Industry 4.0 within the manufacturing sector through bibliometrics. A dataset of 1069 documents from 2020 to 2024 obtained from the Web of Science is processed. Using the R-Bibliometrix package, research trends, leading authors, and institutional contributions are identified. The accelerated growth rate of 30.77% in publications confirms research interest. Thematic exploration reveals the convergence of Industry 4.0 with sustainability, AI, the Internet of Things, smart manufacturing, and digitalization as dominant themes. The transition towards smarter and more efficient systems is evident, with an emphasis on integrating sustainability into Industry 4.0 practices. Challenges persist in management adjustment, technological integration, and strategy for digital transformation. The study identifies sustainability and machine learning as critical enabling factors for Industry 4.0, while security and collaboration have emerged as key focus areas in recent years. A wide geographic distribution of research contributions with substantial international cooperation is observed, highlighting India, Italy, and China. Major journals like Sustainability and Journal of Manufacturing Systems emerge as influential platforms for disseminating research on the topic. The analysis of citation networks, co-occurrence, and thematic evolution underscores the multidimensional impact of Industry 4.0 technologies on manufacturing.

Sun position sensors are used in space applications as part of the attitude determination and control system. The aim of this work is to describe the design process of single and dual axis sun position sensors based on a photodiode array detector and a window to limit and direct the light that reaches the detector. The design process involves choosing the sensor architecture, modeling its output, and then evaluating the model through simulation. Six architectures, with different configurations and geometries for both the detector and window, were modeled and compared. To evaluate the model and performance of the sensor, a program was developed, and simulations were made varying the window height and size as well as the photodiode size. From the simulations performed, we concluded that for each design, the key factors that influence the sensitivity and field of view performance are the window height and window or photodiode size, depending on the sensor configuration. From the comparison across architectures, the detector configuration, as well as the window geometry, influences the sensitivity and linearity of the response for similar conditions of operation. The two-quadrant detector configuration offers a better sensitivity than the triangular photodiode configuration. For a better linearity of the output, a square window is preferred. This comparative study is part of the development of new products for space applications by the National Atomic Energy Commission and contributes to the Argentinian National Space Plan.

La cáscara de arroz se ha empleado sin procesar como desgrasante de pastas cerámicas, pero no se ha investigado su empleo de forma micronizada, opción que podría facilitar la homogenización y cohesión de sus componentes, ahorrando la utilización de otros materiales convencionales. El trabajo persiguió como objetivo evaluar la influencia del empleo de cáscara de arroz micronizada como desgrasante en las propiedades de ladrillos cerámicos. La investigación se desarrolló en dos etapas: una primera, en que se diseñaron briquetas cocidas en mufla determinando el porcentaje óptimo de cáscara de arroz micronizada; y en una segunda etapa, el control de calidad de la dosificación del ladrillo cocido en horno industrial con adición de cascara de arroz micronizada. Se evaluaron formulaciones con 10% y 20% de cáscara de arroz, llegando a la conclusión que con la primera es factible producir ladrillos cerámicos para tabiques, cumpliendo estos las propiedades especificadas.

(Bi0.5Na0.5)1−xBaxTiO3 lead-free ferroelectric ceramics were synthesized via the conventional solid-state reaction method. Structural and dielectric properties were investigated as a function of the doping concentration, considering x = 0, 2, 5, 8, 10, 12, 16, and 18 at. % Ba. The structural analyses were carried out from the x-ray diffraction technique, including the Rietveld refinement method, and Raman spectroscopy. Results confirmed the formation of the perovskite structure, revealing different crystalline symmetries, depending on the Ba²⁺ concentration: the single rhombohedral ferroelectric phase (R3c) for x = 0 and 2 at. %; coexistence of both rhombohedral ferroelectric (R3c) and tetragonal antiferroelectric (P4bm) phases for x = 5 at. % Ba; the single tetragonal antiferroelectric phase (P4bm) for x = 8 at. % Ba; coexistence of two tetragonal phases (antiferroelectric P4bm and ferroelectric P4mm) for x = 10 at. % Ba; and the single tetragonal ferroelectric phase (P4mm) for x = 12, 16, and 18 at. % Ba. The characteristics of the phases’ transition, investigated from dielectric analysis, revealed the presence of two dielectric anomalies, which indeed have been associated to different phases’ transitions, one of them showing relaxor-like characteristics. The obtained results offer new insights for a better understanding on the features of the phase diagram for the studied ceramic system, according to the different observed crystalline symmetries (ferroelectric and antiferroelectric) in a very wide doping concentration. In the light of the obtained results, a new phase diagram has been proposed considering a wider compositional range than those reported in the literature.

Medium voltage motors must be protected more rigorously than low voltage motors considering that they take longer to transfer the heat generated in their windings to the environment. It is essential to have the following minimum information: stator damage curve under normal working conditions, hot and cold locked rotor tests of the motor, results of vacuum and short circuit tests, type of load on the motor shaft to evaluate the heating of the engine during its acceleration at start-up, especially in the case of heavy starting. In many cases it turns out that users do not have the information related above and the problem to be solved is how to proceed to estimate thermal protection in the event that this information is not available? This work proposes the steps to follow when this situation arises.

The objectives are the analysis of the benefits and risks of LED lighting systems in the economic, social and environmental fields; as well as the evaluation of the political, economic and social conditions for the use of LED lighting systems in Cuba. The relationship between the advantages of this technology and the benefits offered by its application in different sectors of society and economy is deepened. Also, its impact on the lighting industry and off-grid communities in developing countries is discussed. In addition, the main risks involved in their use are exposed, identifying the related open research fields in this direction, and it demonstrates the importance of considering the risks of LED lighting in projects. Finally, the existing conditions in Cuba for the development of LED lighting and its contribution to sustainability and energy independence are presented.