Politecnica Salesiana University

Caesalpinia spinosa is a plant species present in South America, capable of adapting to different climatic and edaphic conditions. It is considered a multipurpose species due to its environmental benefits, contributing to the restoration of degraded soils and containing active compounds intended for various industries. Consequently, this study aimed to evaluate the viability, germination and in vitro growth of C. spinosa from seeds in different phenological stages. The development of this research was carried out in parts. First, for the viability study, seeds were classified as immature, mature and overripe, from which embryos were extracted and placed in a 1% tetrazolium salt aqueous solution for 5 hours. Second, the water content in the seeds was determined using thermogravimetric analysis to classify the seeds. Third, the seeds were disinfected with Tween 20 for 10 minutes, 70% EtOH for 1 minute and 1.5% NaClO for 10 minutes. Then, germination pre-treatment was performed with a cut in the distal region of the cotyledon (CT) and removal of the seed coat (ST) and they were cultivated in a basal MS medium supplemented with Gamborg vitamins. The highest viability was observed in immature seeds (SI) and mature seeds (SM), with values of 97.72% and 73.45% and corresponding water content of 52.23% and 7.60%, respectively. Regarding germination, the SI-ST treatment achieved a germination speed of 48.55% at 4 days, while in the interaction of phenological state and scarification, the SI-ST treatment achieved the highest percentage at 93.16%. In terms of growth, the treatments showed no statistically significant differences, with values between 52.26 mm and 64.79 mm. These results indicate that the phenological state of the seeds and the type of scarification are important for obtaining quality seedlings in vitro in a shorter time, which can be used in future reforestation and ecosystem restoration programmes.

This study investigates the effect of foreign direct investment (FDI) on renewable energy consumption in 13 Latin American countries during the period 2000–2021. In a context where the region faces significant challenges in transitioning to a cleaner energy matrix, FDI emerges as a key tool to facilitate the adoption of renewable technologies. However, the relationship between FDI and renewable energy consumption has not been sufficiently explored, particularly within the Latin American context. To address this gap, a panel data regression model was employed, including interactions with moderating variables such as economic growth and CO2 emissions. The main findings indicate that FDI positively impacts renewable energy consumption, especially in economies with high economic growth. However, in countries with high levels of CO2 emissions, the positive effect of FDI is diminished, suggesting the need for public policies that direct FDI toward more sustainable projects. These results highlight the importance of a favorable macroeconomic environment and a robust regulatory framework to maximize the benefits of FDI in the energy transition of Latin America.

Urban air pollution, primarily driven by industrial activities and vehicle traffic, is a pressing global concern. The automotive sector must urgently reduce CO2 and CO emissions due to their significant impact on climate change and human health. In Latin America, the heavy reliance on fossil fuels exacerbates air quality degradation, highlighting the need for alternative fuel research. This study evaluates the impact of oxyhydrogen (HHO) gas-gasoline blends on the emissions of an M2 vehicle using static tests. A 2023 Cherry Practivan Karry 1.2L equipped with an HHO generator, featuring a wet cell with 316 stainless steel plates and a 1.5% KOH electrolyte, was used. Three HHO flow levels were tested and measured with an MF5712 mass flow meter, under idle (780 rpm) and 2500 rpm conditions. A Brain Bee AGS-680 gas analyzer assessed the emissions of CO, CO2, and HC. The results demonstrated that blending HHO gas with 87-octane gasoline at a maximum flow of 1.7 slpm significantly reduced HC emissions, while CO2 levels slightly increased. This research underscores the potential of HHO gas in reducing pollutants and the importance of ongoing optimization to balance emissions, contributing to global efforts in combating air pollution and climate change.

Anatomical changes associated with intra‐uterine growth restriction (IUGR) have been observed in different age groups and linked to cardiovascular complications. This study analysed the electrocardiogram (ECG) in pre‐adolescents with severe IUGR, comparing QRS complex and T‐wave biomarkers with controls. Computer simulations explored links between anatomical re‐modelling and ECG biomarkers, providing insights into the potential cardiovascular risk associated with IUGR‐induced re‐modelling. Clinical recordings were analysed using principal component analysis (PCA) to compute spatially transformed leads, enhancing QRS complex and T‐wave delineation for depolarization and repolarization assessment. Transformed leads analysis revealed a 4‐ms increase in QRS complex duration (QRS ) and a 2‐ms increase in the T peak‐to‐end interval (T ) in IUGR subjects compared to controls. We conducted electrophysiological in silico simulations using anatomical models based on clinical IUGR data. These models, derived from a reference control, incorporated key geometric changes associated with IUGR, the apex‐base length, basal diameter, wall thickness (W$\mathcal {W}$) and ventricular tissue volume, to assess their impact on depolarization and repolarization intervals. In silico PCA leads showed increased QRS , QRS amplitude and T in globular models, consistent with clinical data. Despite the QRS increase, the QT interval increases but is not linearly related to the W$\mathcal {W}$ change. These findings suggest that cardiac re‐modelling primarily influences the depolarization cycle, notably QRS , while repolarization intervals increase but are not directly related to the W$\mathcal {W}$ increase. The study highlights the impact of geometric and volumetric changes in IUGR‐related cardiac re‐modelling, also emphasizing the need for further research on electrophysiological re‐modelling and its effects on cardiac function. image Key points Intrauterine growth restriction (IUGR) is associated with long‐term cardiovascular complications, including changes in the heart's electrical activity. Cardiac re‐modelling as a consequence of IUGR can lead to electrical changes that can be assessed through an electrocardiogram (ECG). This study analysed ECGs in pre‐adolescents with severe IUGR, revealing prolonged depolarization duration (QRS complex duration) and repolarization (T peak‐to‐end interval) compared to healthy controls. Computational models incorporating clinically observed anatomical changes, such as increased ventricular wall thickness and altered heart geometry, were used to assess their impact on electrical function, and determine whether these structural modifications contribute to the ECG alterations observed in clinical data. Both clinical data analysis and simulation findings showed significant shifts in depolarization‐based biomarkers and smaller, and non‐linear changes to geometrical changes, in repolarization intervals, highlighting how cardiac re‐modelling in IUGR affects heart function as measured by ECG.

This study analyzes the intersection of energy, urban planning, decarbonization, and sustainability as a central axis for addressing urban development challenges in Latin America. A systematic search of the Scopus database selected 509 articles published between 2019 and 2024. The documents were thematically classified into urban planning (274), energy (79), and decarbonization (147), identifying only 10 studies that simultaneously integrate at least two of these dimensions in Latin American contexts. While this sample of 10 articles does not allow for generalizations about the region, the article selects representative cases to contextualize the type of research conducted, rather than offering extrapolable results. An exploratory multivariate analysis was applied to identify patterns, thematic gaps, and convergence trends, including Principal Component Analysis (PCA) to reduce the dimensionality of the set of key concepts and Hierarchical Clustering (HCC) to group terms according to their semantic proximity. These results are complemented by co-occurrence and thematic concentration maps generated from keywords extracted from the selected articles. The findings reveal a low level of integration among the topics analyzed, justifying the need to establish new lines of interdisciplinary research. The study proposes a replicable analytical tool that guides future regional research and contributes to the achievement of the Sustainable Development Goals, especially SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).

Background Alcohol consumption during adolescence constitutes a significant risk factor for the development of long-term problems, underscoring the need for effective preventive strategies. Objective To analyze the effectiveness of alcohol prevention programs among young populations based on the underlying theoretical model of intervention. Methods A systematic review was conducted following PRISMA 2020 guidelines. A total of 52 articles reporting 83 randomized controlled trials involving individuals aged 12 to 25 were included. Random-effects models, sensitivity analyses, and meta-analyses of effect sizes were performed to compare the theoretical models employed. Results Motivational Interviewing emerged as the most consistent and effective theoretical model (d = 0.39; I² = 24.51%) with no evidence of publication bias. Its application in face-to-face modalities showed greater effectiveness compared to web-based formats. Sensitivity analysis reduced initial heterogeneity (I² = 18.01% for web-based; I² = 41% for in-person), highlighting the need for methodological standardization to enhance the reliability and replicability of interventions. Conclusion Theoretical models such as Motivational Interviewing and the Theory of Planned Behavior demonstrate strong efficacy and consistency. Integrating these with complementary approaches—such as Cognitive Behavioral Therapy, Social Learning Theory, and Social Norms Systems—within hybrid delivery formats (combining in-person methods with digital tools) may enhance program standardization and amplify their preventive impact.

The expanding integration of wind and photovoltaic (PV) energy is disrupting the power system planning processes. Their incorporation poses limitations to forecasting due to their inherent variability. This review compiles a total of ninety studies conducted and published between 2019 and 2025, presenting for the first time an integrated approach that simultaneously optimizes the generation, transmission, storage, and flexibility of resources given high ratios of renewable generation. We present a systematic taxonomy of conflicting optimization approaches—deterministic, stochastic, robust, and AI-enhanced optimization—outlining meaningful mathematical formulations, real-world case studies, and the achieved trade balance between optimality, scale, and runtime. Emerging international cooperation clusters are identified through quantitative bibliometric analysis, and method selection in practice is illustrated using a table with concise snapshots of case study excerpts. Other issues analyzed include long-duration storage, centralized versus decentralized roadmap delineation, and regulatory and market drivers of grid expansion. Finally, we identified gaps in the literature—namely, resilience, sector coupling, and policy uncertainty—that warrant further investigation. This review provides critical insights for researchers and planners by systematically integrating methodological perspectives to tackle real-world, application-oriented problems related to generation and transmission expansion models amid significant uncertainty.

This study aimed to assess the power capacity of electrical conductors under the long-term expansion of distribution systems over a 10-year horizon by considering voltage quality constraints and reliability indicators. The MATPOWER library in Matlab was employed, along with the IEEE 15-bus and 33-bus distribution system test cases. A 5% annual load growth was simulated for each system, which involved analyzing key parameters, such as the power loss, voltage deviation, and average failure rate. An algorithm was developed to perform a multi-criteria analysis, which provided optimal solutions for the system behavior in response to increasing demand. Given the close relationship between distribution systems and load growth, voltage quality and reliability indicators were evaluated annually to identify improvement opportunities by taking into account economic factors, implementation timelines, the replacement of electrical components, and medium- and long-term investments. The proposed algorithm recommended upgrades to electrical conductors without significantly affecting the system costs. For the initial year of the IEEE 15-bus system, enhancements were suggested for lines L1–L2, L2–L3, L3–L4, L2–L6, L3–L11, and L11–L12, which allowed the system to operate without further modifications for five years, maintained the minimum voltages above 0.95 p.u., and reduced the average failure rate while demand continued to grow.

Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement.

This research addresses IL-28B gene polymorphisms (rs12979860 and rs8099917) to determine their association with HTLV-1-related diseases; it aims to compare genotypic frequencies to identify predisposition or protection, considering population, disease, and controls. Given HTLV-1’s impact on immunity, this study seeks biomarkers for early diagnosis and intervention. A systematic search met inclusion criteria, such as open access bibliographic and experimental studies published in English between 2010 and 2024, and genetic factors linked to susceptibility to pathologies. Regarding exclusion criteria, bibliographic or experimental studies in organisms other than humans, unofficial sources, non-indexed journals, and scientific articles in languages other than English were ruled out. Statistical data analyses were assessed using meta-analysis, including forest plot and Q test of heterogeneity based on the I² statistics. The analyzed data indicate associations between genotypes, such as CT, GG, CC, and TT of the rs12979890 and rs8099917 polymorphisms and the predisposition to various diseases, such as HCV, arthropathy, HAM/TSP, cytomegalovirus and Crimean–Congo hemorrhagic fever associated with HTLV-1; however, the observed inconsistencies, such as high heterogeneity, and deficiency of related information limit the consolidation of the findings. Further research is needed to clarify IL-28B genotype interactions and disease susceptibility in HTLV-1 infections.

Global estimates suggest that over a billion people worldwide—more than 15% of the global population—live with some form of mobility disability, underscoring the pressing need for innovative technological solutions. Recent advancements in artificial vision systems, driven by deep learning and image processing techniques, offer promising avenues for detecting mobility aids and monitoring gait or posture anomalies. This paper presents a systematic review conducted in accordance with ProKnow-C guidelines, examining key methodologies, datasets, and ethical considerations in mobility impairment detection from 2015 to 2025. Our analysis reveals that convolutional neural network (CNN) approaches, such as YOLO and Faster R-CNN, frequently outperform traditional computer vision methods in accuracy and real-time efficiency, though their success depends on the availability of large, high-quality datasets that capture real-world variability. While synthetic data generation helps mitigate dataset limitations, models trained predominantly on simulated images often exhibit reduced performance in uncontrolled environments due to the domain gap. Moreover, ethical and privacy concerns related to the handling of sensitive visual data remain insufficiently addressed, highlighting the need for robust privacy safeguards, transparent data governance, and effective bias mitigation protocols. Overall, this review emphasizes the potential of artificial vision systems to transform assistive technologies for mobility impairments and calls for multidisciplinary efforts to ensure these systems are technically robust, ethically sound, and widely adoptable.

The transition to electric public transportation is crucial for reducing the carbon footprint and promoting environmental sustainability. However, successful implementation requires strong public policies, including tax incentives and educational programs, to encourage widespread adoption. This study identifies the optimal electric bus model for Cuenca, Ecuador, using the multicriteria decision-making methods PROMETHEE and TOPSIS. The evaluation considers four key dimensions: technical (autonomy, passenger capacity, charging time, engine power), economic (acquisition, operation, and maintenance costs), social (community acceptance and accessibility), and environmental (reduction of pollutant emissions). The results highlight passenger capacity as the most influential criterion, followed by autonomy and engine power. The selected electric bus model emerges as the most suitable option due to its energy efficiency, low maintenance costs, and long service life, making it a cost-effective long-term investment. Additionally, its adoption would enhance air quality and improve the overall user experience. Beyond its relevance to Cuenca, this study provides a replicable methodology for evaluating electric bus feasibility in other cities with different geographic and socioeconomic contexts.

Wildfires represent a growing concern worldwide, and their frequency has increased due to climate change and human activities, posing risks to biodiversity and human safety. In the Metropolitan District of Quito (DMQ), the combination of flammable vegetation and steep slopes increases the wildfire susceptibility. Although there are no formally designated firebreaks in these areas, many natural and artificial elements, such as roads, water bodies, and rocky terrain, can effectively function as firebreaks if properly adapted. This study aimed to evaluate the wildfire behavior and assess the effectiveness of both adapted existing barriers and proposed firebreaks using FlamMap simulations. Geospatial and meteorological data were integrated to generate landscape and weather inputs for simulating wildfires in nine high-susceptibility areas within the DMQ. Fuel vegetation models were obtained by matching the national land-cover data with Scott and Burgan fuel models, and OpenStreetMap data were used to identify the firebreak locations. The simulation results show that adapting existing potential firebreaks could reduce the burned area by an average of 42.6%, and the addition of strategically placed firebreaks could further reduce it by up to 70.2%. The findings suggest that implementing a firebreak creation and maintenance program could be an effective tool for wildfire mitigation.

The simultaneous analysis of electrophysiological signals from various physiological systems, such as the brain, skeletal muscles, and cardiac muscles, has become increasingly necessary in both clinical and research settings. However, acquiring multiple modalities of electrophysiological data often necessitates the use of diverse, specialized technological tools, which can complicate the establishment of a comprehensive multimodal experimental setup. This paper introduces a prototype system, named the Multimodal–Multichannel Acquisition Module—MADQ, designed for the simultaneous acquisition of multimodal and multichannel electrophysiological and general-purpose signals. The MADQ comprises three distinct capturing blocks, each equipped with separate reference circuits, supporting a total of up to 40 electrophysiological input channels, alongside 4 channels of analog input and 4 channels of digital input signal. The system is capable of sampling frequencies up to 16 kHz. Key features of the MADQ include individually configurable bipolar recording, lead-off detection capability, and real-time online filtering. The system’s functional performance was characterized through metrics such as Input-Referred Noise (IRN), Noise-Free Bits (NFB), and Effective Number of Bits (ENOB) across varying gain and sampling frequencies. Preliminary experiments, conducted in a setup emulating a sleep study with auditory evoked potential detection, demonstrate the system’s potential for integration into multimodal experimental scenarios.

Proposal: This article proposes a mathematical behavioural model implemented in MCalibration™ to capture the mechanical response of specimens made of glass fibre-filled polylactic acid (PLA/GF). The aim was to validate the influence of different printing parameters on the resulting mechanical properties through simulations. Method: The specimens were manufactured according to the geometric specifications of the ASTM D638 tensile testing standard using various densities and packing patterns. The experiments were carried out at a quasi-static strain rate, obtaining experimental stress-strain curve data, which revealed behaviour typical of semi-crystalline polymers. A viscoelastic/viscoplastic mathematical model implemented in MCalibration™ was subsequently calibrated, achieving a prediction of 94.26%, compared to the experimental data, with a mean absolute difference (NMAD) of 8.18 and a coefficient of determination (R²) of 0.942. Finally, curves were fitted for the model constants for each packing pattern based on increasing density. Results: The curve fits achieved a prediction rate of over 90.39%. Using the resulting equations, simulations were performed and compared with experimental data, achieving a prediction rate of over 90%. This validates the proposed model for predicting the mechanical behaviour of elements printed with PLA/GF.

This work focuses on the problem of optimal reactive power adjustment (ORPA) in electric power systems (EPSs) by implementing the Mean-Variance Mapping Algorithm (MVMO) focusing on the control of static devices such as taps in transformers and static capacitor banks. The study focuses on IEEE test systems of 39 and 118 buses using MATLAB R2024b together with the MATPOWER toolbox. The main novelty lies in the application of the MVMO algorithm to solve the ORPA problem considering only static control elements, which allows an efficient and practical solution with lower computational complexity; through statistical analysis, the performance of each of the algorithms was evaluated where it was experimentally shown that MVMO presents a better performance in terms of reducing active power losses and improving voltage profiles compared to the PSO algorithm.

Measuring solar irradiance is key to assessing the conversion efficiency of photovoltaic (PV) modules. Also, PV modules can be used to estimate irradiance through their electrical response to solar radiation using closed-form models (CFMs). This paper presents a prototype design for irradiance estimation based on evaluating three CFMs by implementing a maximum power point tracking (MPPT) system and a surface temperature measurement system. The system employs an incremental conductance (IC)-based control algorithm, which is optimized to eliminate oscillations at the maximum power point (MPP) and ensure efficient MPP tracking. Experimental validation of the implemented circuits is carried out using Arduino Nano, calibrated sensors, and low-cost electronic devices. Tests in real conditions were performed for four days under different irradiance scenarios, using two monocrystalline PV modules: one with 10 years of use and one new one. The accuracy of the CFMs was evaluated using the mean absolute percentage error (MAPE) and root mean squared error indicators, comparing their estimates with measurements from a Davis Instruments pyranometer. The most accurate CFM obtained a MAPE of 4.38% with the 10-year module and 3.26% with the new module. The results show that the proposed methodology provides estimates with an error of less than 5%, which validates its applicability under various climatic conditions, even with old PV modules.

This paper presents an optimization model for wireless channel allocation in cellular networks, specifically designed for the transmission of smart meter (SM) data through a mobile virtual network operator (MVNO). The model efficiently allocates transmission channels, minimizing smart grid (SG) costs. The MVNO manages fixed and random channels through a shared access scheme, optimizing meter connectivity. Channel allocation is based on a Markovian approach and optimized through the Hungarian algorithm that minimizes the weight in a bipartite network between meters and channels. In addition, cumulative tokens are introduced that weight transmissions according to channel availability and network congestion. Simulations show that dynamic allocation in virtual networks improves transmission performance, contributing to sustainability and cost reduction in cellular networks. This study highlights the importance of inefficient resource management by cognitive mobile virtual network and cognitive radio virtual network operators (C-MVNOs), laying a solid foundation for future applications in intelligent networks. This work is motivated by the increasing demand for efficient and scalable data transmission in smart metering systems. The novelty lies in integrating cumulative tokens and a Markovian-based bipartite graph matching algorithm, which jointly optimize channel allocation and transmission reliability under heterogeneous wireless conditions.

There is a lack of knowledge concerning the interlaminar fracture toughness under mixed‐mode ratios of 3D‐printed composites. In this work, several additive manufacturing (AM) continuous Fiber Reinforced Thermoplastic (cFRT) specimens have been tested to characterize the initiation and propagation of interlaminar fracture toughness under three different mixed‐mode G II /( G I + G II ) ratios: 25, 50, and 75%. The results obtained do not exhibit the common tendency seen in traditional laminated composite materials, in which the fracture toughness increases with the mixed‐mode ratio. While the fracture toughness for the 50% mixed‐mode ratio falls between the corresponding mode I and mode II values, the fracture toughness for the 25% and 75% ratios falls outside this range. To provide a reasonable explanation, fractography and microstructure analyses were conducted to quantify fiber, matrix, and void contents. It was concluded that this uncommon behavior is probably related to the intrinsic variability of the material and manufacturing process. Highlights Continuous fibers improve mechanical properties of 3D‐printed composite parts. Delamination is a critical failure mode for laminated composite materials. Characterization of mixed‐mode delamination is key for simulation and design. First complete study of mixed‐mode delamination for 3D‐printed composites.

Adverse childhood experiences, such as abuse, are a risk factor for mental health and poor socio-emotional development in adulthood. Assessing these experiences in specific populations allows for the identification of patterns and the implementation of preventive interventions. Objective: To evaluate the psychometric properties of the abbreviated version of the Adverse Childhood Experiences Abuse Form (ACE-ASF) in Ecuadorian youth, aiming to ensure the validity, reliability, and consistency of the instrument in accurately measuring abuse dimensions in this Ecuadorian population. Methodology: An instrumental study was conducted on the psychometric properties of the eight-item ACE-ASF, applying it to a sample of 840 university students (52.1% females and 47.9% males). The evaluation focused on analyzing the factorial structure and internal consistency of the instrument in this sample. Results: The two-factor model showed a satisfactory fit across all levels of invariance (configural, metric, scalar, and strict), with acceptable fit indices (CFI, TLI, GFI, RMSEA, and SRMR). The internal consistency was adequate, as assessed using the McDonald’s omega and Cronbach’s alpha coefficients. Convergent and discriminant validity were confirmed using the AVE and HTMT indices, ensuring proper differentiation between the dimensions assessed. Conclusion: The ACE-ASF proved to be a valid and reliable instrument for assessing abuse experiences in Ecuadorian youth. Its two-factor structure reflects distinct yet related dimensions, providing a useful tool for identifying adverse childhood experiences in this population.