Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

This paper explores the characteristics of a character typical of Greek comedy of the fourth century BC, whose name -or nickname-, built by the lexical component φιλ (“love”, “attraction”, “obsession”) attached to the object towards which that love is directed, gives its name to numerous plays. We will concentrate on a selection of fragments of middle comedies, which we will analyze taking as a model the precedent of Philocleon, from Aristophanes’ Wasps, who, like these characters, also manifests a fervent and exclusive affection that impacts on his eccentric behavior.

The following article intends to present some general aspects of politics in the work of Propertius and to deepen the discussion about his usage of water formations as metaphors, understanding that there is a line connecting both subjects. There is a great number of arguments about the structure of his four books, either pointing to his intent to support the Augustan political regime, mainly in book four, or to deny it completely. The main goal of this article is to contribute to the reading of Propertius’ books and their politics, analysing the subtext of the water’s metaphors.

Multichannel Functional Electrical Stimulation (FES) technology is widely employed in artificial motor control research. This study presents the design and evaluation of a four-channel, remotely controlled surface electrical muscle stimulator prototype. The prototype introduces a modern alternative for the control block, employing a Wi-Fi-enabled solution based on the ESP32 microcontroller. This controller enables remote configuration of activation sequences for individual channels and supports extensive customization of parameters for a biphasic waveform stimulus. The current signal is demultiplexed into four outputs. Additionally, this study provides a detailed functional evaluation of the amplification stage and examines the load-dependent limitations of the output current magnitude. Preliminary experimental testing demonstrates the prototype's ability to generate controlled stimulation sequences in hand muscles. The prototype's functional and experimental performance suggests its potential application in artificial motor control research.

This study evaluates the effectiveness of Recurrent Neural Networks (RNNs) and Transformer-based models in predicting the Air Quality Index (AQI). Accurate AQI prediction is critical for mitigating the significant health impacts of air pollution and plays a vital role in public health protection and environmental management. The research compares traditional RNN models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, with advanced Transformer architectures. Data were collected from a weather station in Cuenca, Ecuador, focusing on key pollutants such as CO, NO2, O3, PM2.5, and SO2. Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). The findings reveal that the LSTM model achieved superior performance, with an R2 of 0.701, an RMSE of 0.087, and an MAE of 0.056, demonstrating superior capability in capturing temporal dependencies within complex datasets. Conversely, while Transformer-based models exhibited potential, they were less effective in handling intricate time-series data, resulting in comparatively lower accuracy. These results position the LSTM model as the most reliable approach for AQI prediction, offering an optimal balance between predictive accuracy and computational efficiency. This research contributes to improving AQI forecasting and underscores the importance of timely interventions to mitigate the harmful effects of air pollution.  

Graphene oxide (GO) has garnered significant interest due to its exceptional and tunable properties, which make it a promising candidate for a wide range of engineering applications, including composite material fabrication and water treatment. In this study, GO was synthesized from graphite flakes using a modified Hummers method involving a reduced amount of sulfuric acid. The resulting material was characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). These techniques enabled a clear differentiation between the morphology of the synthesized GO and that of the original graphite. The GO exhibited a substantially altered structure, with increased thickness likely due to the incorporation of oxygen-containing functional groups on its basal plane. FTIR analysis confirmed the presence of characteristic functional groups such as hydroxyl, carbonyl, and carboxyl. XPS analysis revealed that the elemental composition of the synthesized GO consisted of approximately 69.7% carbon and 29.9% oxygen, with a trace amount of sulfur attributed to the reagents used in the synthesis. The observed changes in morphology and composition suggest the successful synthesis of GO with potential for functionalization and application in diverse engineering contexts.

All continents are currently undergoing energy transitions toward low-carbon economies driven by renewable technologies. Africa is no exception; with its rapidly growing population and expanding economy, it represents nearly one-fifth of the global demographic. Although the African continent contributes less than 3% of global carbon emissions, it is already experiencing severe and disproportionate impacts from climate change. This manuscript aims to analyze hydropower development in Ethiopia, Tanzania, Zambia, and Zimbabwe, with the objective of assessing the current use of this renewable energy resource and its project role. These countries were selected due to their significant hydroelectric potential and ongoing investment in renewable energy infrastructure. While these nations have made substantial commitments to hydropower, climate-induced shifts in hydrological patterns, particularly increased drought risk, pose serious challenges to energy security and sustainability. Consequently, electric utilities must not only forecast project future energy generation but also implement robust mitigation and adaptation strategies to safeguard long-term investments. Given the critical role of climate change as an external variable influencing energy planning, it is essential to evaluate hydropower generation and reservoir operations through a multidimensional framework that includes parameters such as temperature, precipitation, humidity, river flow, watershed characteristics, and other related factors.

This study aims to analyze the behavior of Mach number and pressure field flow patterns in off-design planar and conical nozzles with a divergent half-angle of 10.85°. Numerical simulations of the flow field were conducted using the ANSYS-Fluent R16.2 software, employing the RANS model and the SAS turbulence model under transient flow conditions. The nozzle pressure ratios (NPR) ranged from 1.97 to 8.91. The results reveal differences in flow patterns, including Mach number and static pressure, between the two nozzle types. Notably, normal shock fronts exhibited varying positions for the same NPR values. The maximum peak flow fluctuation along the centerline of the conical nozzle's divergent section reached Mach 2.844, compared to Mach 2.011 in the planar nozzle, indicating lower flow velocity in the latter. At the nozzle outlet, the flow velocity of the conical nozzle was Mach 2.535, representing a 27.32% increase compared to the planar nozzle, which achieved Mach 1.991. Additionally, the throat area significantly influenced mass flow transit, with the planar nozzle having a larger throat area than the conical nozzle. These findings provide insights into the impact of nozzle geometry on flow characteristics under off-design conditions.

This study evaluates the impact of B10 and B20 biodiesel blends produced from waste frying oil on pollutant emissions when used in diesel-powered vehicles operating under real-world driving conditions at high altitudes, ranging from 2619 to 2877 meters above sea level, in the Metropolitan District of Quito, Ecuador. Comparative tests were conducted using two diesel vehicles: one equipped with a common rail direct injection (CRDI) system, designated as M2.5C, and another with an injection pump system, referred to as H2.5B. Both vehicles were initially fueled with conventional diesel to establish a baseline. Exhaust emissions were measured under hot-engine conditions using a Portable Emissions Measurement System (PEMS) along a 15.7 km route that included ascending, descending, and urban driving segments. The findings indicate that carbon monoxide (CO) emissions were lowest when pure diesel was used in both engine types. Hydrocarbon (HC) emissions were minimal when B20 biodiesel was employed, regardless of the vehicle. Nitrogen oxide (NOx} emissions showed no significant differences across the fuels tested, and in urban driving conditions, NOx levels remained consistently stable.

This paper presents the development of a computing platform for the real-time monitoring of cardiovascular parameters derived from bioelectrical signals. A comprehensive analysis of primary users was conducted, leading to the identification of both technical and functional requirements. The interface design was guided by Sommerville’s methodology. The system architecture is based on a microservices model, incorporating a relational database and enabling integration with data transmitted from Internet of Things (IoT) devices. The platform was evaluated through incremental stress testing, starting with zero users and increasing in steps of 100 up to 5,000. A total of 22,132 requests were processed at a peak rate of 440.4 requests per second, with an average response time of 930 ms and 95% of responses occurring within 2,300 ms. The system demonstrated error-free performance with up to 1,700 concurrent users. At 5,000 users and 26,393 total requests, a minimal error rate of 0.16% was recorded, confirming the platform’s stability under high workloads. These findings validate the feasibility of the proposed solution for remote biomedical monitoring, offering an efficient, scalable, and robust tool for real-time health supervision.

Maintenance and inspection protocols in the aerospace industry are designed to safeguard the structural integrity of aircraft and ensure pilot safety. However, the air intakes of fighter aircraft pose significant access challenges for maintenance technicians during preflight inspections. To address this limitation, this study presents an innovative solution: the implementation and evaluation of a robot equipped with a wheeled-legged locomotion system. This system enables efficient access to the air intakes, significantly enhancing the inspection protocol. The robot was developed in close alignment with the operational requirements of Peruvian Air Force (FAP) technicians, which was critical to defining its design specifications and manufacturing parameters. Its adaptive and compact architecture allows it to navigate confined intake structures effectively, optimizing inspection time and resource utilization. The prototype’s performance was rigorously assessed through standardized tests, demonstrating its capability to reliably access and inspect air intakes under preflight conditions. This advancement contributes to the modernization of conventional aircraft maintenance procedures by integrating robotic technologies into the aeronautical inspection process.