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

Context: Soils reinforced with natural fibers such as banana fibers (BF) constitute a promising alternative for improving the geotechnical properties of the soil, especially in rapidly growing urban contexts like Peru. Methods: This study was structured into four stages: the extraction and preparation of soil samples; the evaluation of the physical characteristics of the fibers; mixing with proportions of 0.5, 1, 1.5, and 2% BF relative to the soil dry weight; and physical and mechanical tests to assess the effects on geotechnical properties. Results: The addition of 1% BF optimized the properties of the modified soil: the maximum dry density remained stable, the California bearing ratio increased by 5.95%, and the unconfined compressive strength increased by 23.81% compared to natural soil. Conclusions: The use of BF-treated soil meets the local standards for application in infrastructure such as roads and pavements, thus promoting sustainable construction practices and contributing to the development of resilient and environmentally responsible infrastructure.

Context: Accurate dental arch shape prediction is crucial for orthodontic treatment and personalized dental appliance creation. This study introduces a computer vision-based tool for predicting arch shapes in 3D dental models.   Objective: To automate the selection of dental arch shapes through mathematical model analysis.  Method: A dataset of 484 digital dental models was narrowed to 50 through specific criteria. Experts classified these into ovoid, square, and tapered shapes using 3M templates. An automated 3D dental arch shape prediction tool was developed, incorporating automatic alignment, cusp detection, curve fitting with a sixth-order polynomial, and model comparison. Our validations employed attribute agreement analysis, the root mean squared error, the sum of squared errors, and a Gage R&R Study. Results: This study achieved a 90% agreement rate in the evaluator vs. standard comparison for the lower jaw, as well as 78% for that of the upper jaw. The Gage R&R study confirmed measurement reliability, and the sixth-order polynomial model was identified as optimal for arch shape description. The tool’s predictive accuracy was validated through comparative analysis. Conclusion: This research introduces an effective automated method for selecting dental arch shapes. The tool demonstrated substantial accuracy, with the potential to significantly enhance orthodontic diagnostic and treatment planning processes. Future research could further refine this methodology by exploring advanced mathematical models and incorporating machine learning techniques to optimize the selection process.

Context: Pavement condition data are a fundamental component of pavement management systems (PMS) and play a critical role in structural evaluation. The quality of these data directly influences decision-making processes at the network, project, or research level particularly regarding the pavement project life cycle. Method: This study aimed to assess 18 techniques for evaluating the structure of flexible pavements, utilizing both non-destructive (NDT) and destructive (DT) testing. Following a comprehensive review of the consulted techniques, proprietary models were developed and implemented across multiple projects to structurally evaluate in-service pavements. Statistical analysis was employed to determine the relationships between parameters, distinguishing between those based on empirical and mechanistic approaches. Results: The application of evaluation techniques revealed that parameters such as radial strain (εrca), vertical strain (εzsr), and the structural number exhibit a strong correlation when categorized within the same approach. Conversely, their correlation is moderately strong when differing approaches are used. Additionally, models relying solely on deflection basin data demonstrated high correlation with rigorous methods that incorporate thickness data. Conclusions: These findings underscore the practical value of the developed models in pavement management at the network level, offering cost-effective solutions that enhance the detection of structural deficiencies and inform maintenance and rehabilitation strategies.

Context: Quantum many-body systems have been a prominent topic over the past two decades, underpinning advancements in superconductors, ultracold atoms, and quantum computing, among other fields. This bibliometric analysis explores key concepts, influential authors, and thecurrent significance of a powerful family of algorithms in computational physics, i.e., density matrix renormalization group (DMRG) algorithms. Special emphasis is placed on the use of tensor product states in developing classical simulations of quantum systems.Method: This paper presents a literature review sourced from the SCOPUS database. It analyzes trends and approaches related to uncertainty in numerical developments for quantum many-body systems, with a focus on the Bose-Hubbard Model, in order to better understand the imposition of additional constraints to ensure the validity of the results.Results: The increasing number of publications on this topic over the last decade indicates a growing interest in solutions for many-body quantum systems, driven by promising advances in superconductive materials, quantum computing, and other impactful areas.Conclusions: This work explored essential foundational works to help beginners understand a well-established technique that aims to overcome the limitations of classical computing. The use of matrix product states in DMRG algorithms is gaining significant traction in various fields, including quantum computing, machine learning, and statistical mechanics, with the purpose of addressing the challenges related to quantum many-body systems.

Context: Electronic sensors play a crucial role in different applications such as robotics or industrial or home automation. Sensors can measure essential environmental variables in order to feed digital signal processing algorithms and perform actions more efficiently. The sensors used for distance measurements follow different approaches. However, it is difficult to find a study with performance comparisons. Method: An empirical study was performed to evaluate and compare the performance of ultrasonic and infrared sensors in frontal and lateral detection situations while considering distance and angle variations. Results: The results show that the ultrasonic sensor detects the distance with good accuracy along its operational range. However, the distance measure is inaccurate when the obstacle is not orthogonal to the sensor. The ultrasonic sensor showed high accuracy in long-range, frontal obstacle detection, while the infrared sensor performed better at short distances with angled obstacles. Statistical analysis confirmed strong linear correlations, especially for the ultrasonic sensor, supporting the complementary use of both sensors in distance measurement applications. Conclusions: An evaluation of ultrasonic and infrared sensors for distance measurement in applications involving robotics and the Internet of Things revealed that the former are more reliable for distant, orthogonal obstacles, while the latter perform better at short distances on angled surfaces, highlighting their complementary strengths and the need for future improvements to address environmental sensitivity and detection limitations.

Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises.Method: This study addressed a significant gap by proposing a novel approach for the automated selection of the most relevant intrinsic mode functions (IMFs) using empirical mode decomposition and discrimination metrics such as the Minkowski distance, the mean square error, cross-correlation, and the entropy function. The main objective was to address the challenge of determining the optimal number of IMFs required to accurately reconstruct brain activity signals.Results:The results were promising, as they facilitated the identification of IMFs that contained the most relevant information, marking a significant advancement in the field. To validate these findings, standard methods including the correlation coefficient, the p-value, and the Wasserstein distance were employed. Additionally, an EEGLAB-based brain mapping was conducted, adding robustness and credibility to the results obtained. Conclusions: Our method is a fundamental tool that enhances epileptic seizure identification from EEG signals, with significant clinical implications in the diagnosis and treatment of epilepsy.

Context: This paper proposes an energy management system (EMS) for battery energy storage systems (BESS) to reduce operating costs in AC microgrids (MGs) operating in grid-connected (GON) and islanded (GOFF) mode, considering energy purchase, conventional generation, and maintenance costs while accounting for all the operational constraints of the system and its components. Method: A master-slave methodology based on particle swarm optimization (PSO) and an hourly power flow based on the successive approximations method (SAM) is used as a smart BESS operation strategy. This proposal is validated in a 33-bus AC-MG operating in GON and GOFF modes, in comparison with two methods utilizing the vortex search algorithm (VSA) and conitnuos version of the Chu & Beasley genetic algorithm (CBGA) and the same power flow. Results: The PSO-based EMS achieved the lowest costs i.e., 6897.59 USD/day (GON) and 17 527.42 USD/day (GOFF), with cost reductions of 1.45 and 0.13 %, and low standard deviation values (0.067 and 0.014 %), which confirms its efficiency, robustness, and constraint compliance. Conclusions: The EMS based on PSO/SAM delivers superior solution quality and processing times in both modes of operation. In GON mode, it reduces the mean costs by 0.0287%compared to the VSA and 0.2252%vs. the CBGA, whereas, in GOFF mode, the reductions are 0.0191 and 0.0355 %, respectively. These results reflect a more effective cost reduction than exact methods, which constitutes this paper’s main contribution.

Context: This study developed an energy dispatch model (EDM) using the Cauchy-based distribution optimizer (CbDO) for coordinating battery energy storage units (BESUs) and photovoltaic (PV) sources in medium-voltage distribution networks, aiming to minimize energy losses and operating costs while observing to network constraints. Method: The CbDO was implemented in MATLAB and benchmarked against the continuous genetic algorithm (CGA), the parallel particle swarm optimizer, the parallel vortex search algorithm, and a semidefinite programming (SDP) approach. The analyzed scenarios included unitary and variable power factor operation in order to test optimization performance. Results: The CbDO outperformed traditional methods, achieving lower energy losses and CO2 emissions, closely matching the SDP method's results in variable power factor scenarios. The most significant gains were observed when all DERs operated flexibly, validating our proposal's effectiveness in complex non-convex problems.Conclusions: The CbDO is a viable and efficient solution for EDM, providing near-SDP performance with a simpler implementation. BESU integration and flexible power factor operation can notably enhance grid efficiency. 

Context: This work applies an experimental methodology to the design of a control system based on a non-conventional Mamdani fuzzy controller that regulates the speed of an encoder-based DC motor.Method: The proposed methodology consists of four steps: i) fuzzy controller input/output selection, ii) fuzzy controller design, iii) controller hardware implementation, and iv) membership function parameterization. This methodology generates seven pairs of unique error and control signalsthat are differentiated by experimentally adjusting the parameters of the triangular membership functions used for the fuzzy controller design, which was implemented in an Atmega328P micro-controller. For each of the seven approaches defined, an experiment was performed, performing a control action to obtain the transient response of the DC motor speed when the reference was a step-type signal.Results: The motor response and the reference signal were used to calculate the error, whose squared error integral was estimated to determine which experimental approach yielded the best fuzzy control results, i.e., with the lowest possible error.Conclusions: The proposed methodology ensures the minimization of the squared error integral between the signal to be controlled and the reference signal. For fitting 6, the performance index obtained was J = 0.0002, whichrepresents a decrease of ≈ 99.99 % with respect to the worst case (fitting 1), whose performance index was J = 4.10.

The RIBIERSE-CYTED network, i.e., the Network for the Large-Scale Integration of Renewable Energies in Electrical Systems (723RT0150; 2023-2026), is a hub for researchers and technologists attached to Ibero-American universities, companies, and local administrations. From a scientific and technical perspective, this network contributes to the decarbonization of the electricity sector by favoring the large-scale integration of renewable sources into electric power systems. It promotes and articulates a framework for joint university-business cooperation and scientific research with a shared Ibero-American vision, and it encourages knowledge of the renewable context from service providers to end users.