Recent publications
For an arbitrary ring A , we study the abelianization of the elementary group . In particular, we show that for a commutative ring A there exists an exact sequence \begin{equation*} {\rm K}_2(2,A)/{\rm C}(2,A) \rightarrow A/M \rightarrow \mathit{{\rm E}}_2(A)^{\rm ab} \rightarrow 1, \end{equation*}
where is the central subgroup of the Steinberg group generated by the Steinberg symbols and M is the additive subgroup of A generated by and 3(b+1)(c+1) , with .
In the context of massive open online courses (MOOCs), searching and retrieving information can be challenging because there is a huge amount of unstructured content, which creates a problem and makes it difficult for users to quickly find relevant lessons or resources. As a result, learners and teachers face significant barriers to accessing the most useful and up-to-date information. This study focuses on the use of representation learning to improve named entity recognition (NER) by creating an innovative search engine for MOOCs, leveraging recent breakthroughs in natural language processing (NLP) and deep learning. We provide an automated NER dataset generation process that minimizes the need for extensive manual annotation and makes building scaled NER systems easier. The primary aim is to develop a cohesive and modular search platform specifically designed for MOOC environments. We intend to improve the precision and effectiveness of entity recognition in various settings by employing advanced machine-learning algorithms. Key components of this advanced search engine include Siamese networks emphasizing contrastive learning and Long Short-Term Memory (LSTM) networks for contextual language representation, Graph Neural Networks (GNN), and Convolutional Neural Networks (CNNs) to enhance the platform’s capacity, analysis, and interpretation of complex data. The findings indicate that integrating representation learning with traditional supervised classification methods can significantly improve the performance of NER systems, ultimately contributing to more personalized and context-aware educational experiences. Experimental findings showcase the enhanced accuracy of the modular search engine, laying a robust groundwork for future advancements in MOOC search technologies.
Background and Objective: A previous study investigated the in vitro release of methylene blue (MB), a widely used cationic dye in biomedical applications, from nanocellulose/nanoporous silicon (NC/nPSi) composites under conditions simulating body fluids. The results showed that MB release rates varied significantly with the nPSi concentration in the composite, highlighting its potential for controlled drug delivery. To further analyze the relationship between diffusion dynamics and the MB concentration, this study developed a finite element (FE) method to solve Fick’s equations governing the drug delivery system. Methods: Release profiles of MB from NC/nPSi composites with varying nPSi concentrations (0%, 0.1%, 0.5%, and 1.0%) were experimentally measured in triplicate using phosphate-buffered saline (PBS) at 37 °C, pH 7.4, and 100 rpm. Mathematical models incorporating linear and quadratic dependencies of the diffusion coefficient on the MB concentration were developed and tested using the FE method. Model parameters were refined by minimizing the error between simulated and experimental MB release profiles. Results: The proposed FE method closely matched experimental data, validating its accuracy and robustness in simulating the diffusion and release processes. Conclusions: This study emphasizes the significant impact of the nPSi concentration on enhancing release control and highlights the importance of material composition in designing drug delivery systems. The findings suggest that the FE method can be effectively applied to model other complex systems, paving the way for advancements in precision drug delivery and broader biomedical applications.
Analysis and forecasting of the industrial sector electricity consumption is important for energy planning and control, in addition to being essential for the developing of a country or region. In this context, electricity consumption projections are highly relevant for the decision-making of companies operating in energy systems with the aim of optimizing such operations. This paper proposes a methodology for forecasting electricity consumption by integrating a time series filtering method with any classical forecasting model. In particular, in order to evaluate the performance of the proposed methodology it was used the Seasonal and Trend decomposition using Loess (STL) decomposition method integrated with several classical statistical models (Holt-Winters, seasonal autoregressive integrated moving average, dynamic linear model, and TBATS) and the artificial neural networks approach (NNAR - neural network autoregression, MLP - multi-layer perceptron, and LSTM - long short-term memory). The methodology was applied to the electricity consumption of Brazil’s cement industry. Based on the MAPE and RMSE precision metrics, the proposed methodology obtained the best performance for the time series under analysis via introducing the filtering method. The LSTM model integrated with the STL decomposition presented the best results among the models considered.
Background
Background. According to the World Health Organization, dementia is one of the leading causes of disability in the elderly, imposing a significant burden on global public health. It is estimated that dementia affects over 55 million people worldwide, with 60% of them living in low‐ and middle‐income countries. Early detection of cognitive impairment is crucial for timely intervention and management. In this context, neuropsychological assessment tools play a vital role in identifying individuals at risk. The Memory Alteration Test (M@T) has emerged as a promising tool due to its high sensitivity for early detection of dementia and MCI. To build on this, our study aimed to analyze the psychometric properties of the M@T, in order to ensure its validity and reliability in clinical settings.
Method
The study was conducted among 352 participants over 60 years of age, recruited from Municipal Elderly Care Centers in four socioeconomically distinct districts of Lima, Peru. The M@T captures five domains of memory: encoding, temporal orientation, semantic memory, free recall and cued recall. For this, we proposed a unidimensional model and a multidimensional model with 5 factors, using a confirmatory factor analysis and a Rasch modeling.
Result
The majority of participants were women (82.9%), with a notable proportion reporting a clinical history of hypertension (33.2%). The internal structure analysis and Rasch modeling exhibited a unidimensional structure with good fit and adequate reliability (GY = .897, ord = .934). On the other hand, the 5‐factor multidimensional model showed a poor fit with unreliable scores (GY = .174‐.785, ord = .576‐.898). Likewise, the Rasch analysis demonstrated that the test measures a general memory ability reliably (Rp = .813), while the multidimensional model showed that the scores in each domain were not consistent (Rp = .358‐.707).
Conclusion
The findings suggest that the M@T operates as a unidimensional factor, emphasizing the importance of considering the total score rather than individual dimensions. This underscores the need for caution when interpreting results, particularly when employing multidimensional models, which may not accurately capture the underlying structure of the test. Ultimately, a unidimensional approach offers a more parsimonious and reliable means of assessing cognitive function using the M@T.
This chapter aims to describe the strategies and implementation of retail companies dedicated to the marketing of fashion brands in Peru to achieve sustainable business logistics with technology and, thus, have a better reach to the consumer. In addition, the different levels of communication and content analysis of these companies on the sustainable topic were analyzed through extensive fieldwork and documentation.
Finally, the chapter reflects on the problems, challenges, and future directions of the Peruvian fashion industry in the era of artificial intelligence, where integration with the sustainable world is far from being far away and both complement each other synergistically.
This study aimed to evaluate the effects of salt addition and different thermal‐assisted pressure processing (TAPP) conditions (temperature and pressure levels) on technological, chromatic, and textural parameters and lipid oxidation of Superficial pectoralis beef muscle. A factorial design with three factors was applied: KCl/NaCl marination (marinated samples MS; non‐marinated samples, NMS), temperature during high‐pressure processing (50, 70°C), and pressure level (0.1, 200, and 300 MPa). All factors affect the water‐holding capacity of beef, which is important to ensure both high yields and optimal tenderness and juiciness in the final product. MS treated at 50°C had the highest yield values, regardless of applied pressure level. TAPP modified the color parameter values of raw samples, resulting in brighter and less reddish. After cooking, color differences remained, indicating that this process did not fully reverse the changes induced by TAPP treatments. MS had lower shear force values than NMS. The presence of salts slightly diminished shear force values. A similar texture profile was obtained for NMS treated at 70°C and 300 MPa and MS treated at 50°C and 200 MPa. NMS and MS treated at 70°C and 0.1 MPa had the highest thiobarbituric acid reactive substance values. Based on the results, marinated samples treated at 200 MPa and 50°C were selected for treatment. TAPP could be an innovative technology for the development of value‐added beef products with assured texture.
Practical Application
Beef tenderness is an essential attribute in consumer satisfaction and purchase decisions. However, several factors affect tenderness, such as the amount of connective tissue, muscle contraction in rigor mortis, and proteolysis. The development of ready‐to‐cook products with guaranteed tenderness by applying thermally assisted pressure processing would benefit both the industry and consumers.
Prediabetes represents a significant public health challenge in Latin America. Its prevalence varies considerably depending on the diagnostic criteria used, which hinders a precise understanding of its magnitude in the region.
To estimate the prevalence and incidence of prediabetes in Latin America through a systematic review (SR).
A SR and meta-analysis was conducted searching through October 25, 2024 in SCOPUS, EMBASE, Web of Science, and PubMed. Studies were included if they: (1) used probabilistic sampling methods, (2) included adult participants (≥ 18 years), (3) assessed prediabetes using WHO criteria, fasting glucose, postprandial glucose, or HbA1c, and (4) were published in English, Spanish, or Portuguese. Studies using non-probabilistic sampling, focusing on specific medical conditions, or lacking sufficient data to calculate prevalence or incidence were excluded. Random-effect models were used to estimate pooled prevalence, with heterogeneity assessed using I² statistics and publication bias through funnel plots.
Twenty-five studies from 9 countries published between 1992 and 2023 were analyzed. The pooled prevalence of prediabetes was 24% (95% CI: 18–30%). According to specific criteria, the prevalences were: WHO 11% (95% CI: 5–18%), FG 18% (95% CI: 10–27%), PPG 20% (95% CI: 3–46%), and HbA1c 32% (95% CI: 21–52%). High heterogeneity was observed among studies (I² = 99–100%, p < 0.001). Only one study analyzed the incidence, which was 12.8%.
Prediabetes prevalence in Latin America is high, with significant variations by diagnostic criteria. The limited number of incidence studies and high heterogeneity highlight the need for standardized approaches in future research. Implementation of preventive strategies and strengthening of epidemiological surveillance systems are crucial for addressing this public health challenge.
Los envases utilizados en alimentos tienen la capacidad de mejorar la calidad de los productos debido a que conservan y, comunican el valor y composición nutricional. Por otro lado, los envases inteligentes permiten optimizar su funcionalidad, mejorar la vida útil y abordar problemas de sostenibilidad. El objetivo de la revisión sistemática es analizar los aspectos asociados al diseño de envases inteligentes utilizados en la industria de alimentos y como se relaciona con la preservación del medio ambiente, a partir de la literatura científica en el período 2017-2023. Las fuentes de información se recopilaron en la base de datos Scopus a partir de la ecuación de búsqueda que contenía términos asociados a envases inteligentes, industria y medio ambiente. En la primera búsqueda se tuvo 4540 artículos, de los cuales mediante el método PRISMA se seleccionaron 55 artículos. Además, se encontró que más del 20% de los registros provenían de China, seguido de Italia y Brasil, incrementándose a partir del 2020, siendo los descriptores más frecuentes: “packaging materials” y “food packaging”. El 50% de artículos seleccionados abordan temas sobre los beneficios ambientales que tiene el diseño de los envases inteligentes como el uso de materiales reciclables y/o residuos agroindustriales. Además, los aspectos más relevantes a tener en cuenta durante el diseño son la biodegradabilidad, la compatibilidad con alimentos y el costo. En conclusión, el uso de tecnologías avanzadas y residuos orgánicos en la producción de envases mejora la calidad del producto y la experiencia del consumidor, asimismo promueve una gestión sostenible.
El presente reporte de caso tiene como objetivo analizar el diagnóstico precoz sobre la hemorragia postparto con la finalidad que las obstetras reconozcan oportunamente. La metodología de la investigación se enmarca en revisión bibliográficas de base de datos, un estudio descriptivo analítico, la recolección de la información es la historia clínica estructurada en base a la Guía de comprobación CARE. El caso es paciente gestante de 21 años, sin factores de riesgo en el embarazo y parto, con hemoglobina 12.2 gr/dl previos al parto, episiotomía al nacimiento y ocurrencia de desgarro vaginal de tercer grado, que cursa un control de puerperio inmediato con parámetros normales, presentando a las 6 horas postparto palidez marcada, cefalea, mareos y Hb 6.9 gr/dl. Se plantea la estimación de la cuantía de pérdida sanguínea y valoración de la severidad de la pérdida sanguínea sobre el estado hemodinámico de la paciente a través del índice shock.
Improper solid waste management in Lima, particularly glass, leads to severe environmental, social, and public health problems. The low recycling rate and waste accumulation contaminate soils and groundwater, impacting long-term quality of life. This research aims to evaluate the inclusion of residual glass powder (RGP) in concrete to enhance the sustainability of concrete design, focusing on San Juan de Lurigancho, where the highest amount of waste per person in Lima is generated. The proposed solution involves developing a waterproof concrete design by incorporating residual glass powder (RGP). This approach includes replacing 5%, 10%, and 15% of the cement in the mix to achieve a strength of 280 kg/cm², thereby reducing pollution from glass waste and CO2 emissions. Fresh concrete properties were evaluated and found to improve flow and temperature. The slump of fresh concrete increased gradually with the percentage of residual glass powder (RGP), reaching up to 16.5%. Regarding the properties in the hardened state, in terms of strength, replacing 15% of the cement with RGP resulted in a 2.57% increase in compressive strength. The tensile strength at 28 days increased by 21.53% and 16.8% when replacing 10% and 15% of the cement, respectively. However, replacing 15% of the cement resulted in a 0.4% decrease in flexural strength, while a 10% replacement resulted in a 1.44% increase. On the other hand, replacing cement with 15% RGP reduced CO2 emissions to 53.79 kg/m³. Additionally, a higher percentage of RGP in the concrete allows for cost savings of up to 12.2%, demonstrating a progressive reduction. From the analyses, it was found that the mix including 10% RGP stands out as the optimal option. It shows significant improvements in strength and profitability, reducing production costs by 3.4% and CO2 emissions by 10.83%. This design achieves an ideal balance between performance, cost, and environmental sustainability.
The present research aimed to evaluate the relationships between cognitive, behavioral, and social factors that influence career goals among Peruvian engineering students using Social Cognitive Career Theory (SCCT). To this end, a total of 1195 engineering students were recruited from three universities in the north of Lima, Peru, and completed several questionnaires related to SCCT variables, mastery experience, emotional state, and gender stereotypes. A multigroup analysis was employed to evaluate gender-based differences in the variables. The findings indicate that individual, social, and contextual factors influence the academic and career trajectories of Peruvian women engineering students within the SCCT framework.
Objectives: to analyze the relationship between religion and professional experience with spiritual intelligence in nurses
Methods: cross-sectional and analytical study carried out in 2021, with the participation of 544 nursing professionals working in health facilities in Peru during the COVID-19 pandemic. Multiple regression analysis and Pearson’s correlation were used to analyze the data.
Results: in nurses, a healthy level of spiritual intelligence predominated (42.8%). Those who did not profess a religion were more likely to have a lower spiritual intelligence score (global scale and dimensions); however, experienced nurses were more likely to have higher spiritual intelligence (global scale and dimensions) than novice nurses (p<0.05).
Conclusions: spiritual intelligence in nurses was predicted by religion and professional experience. This finding suggests that spiritual intelligence in nursing is consolidated through religious practices and during professional practice.
Descriptors: Intelligence; Religion; Spirituality; Nurses; Daily Activities.
This research aims to analyse the indirect effect of pluralistic attitudes and political distrust on the relationship between relative deprivation and populist attitudes. We conducted a survey on a sample of 3,800 adults from Chile, Colombia, and Peru and performed a mediation analysis using structural equation modelling. The findings showed that political distrust and pluralism have positive and statistically significant indirect effects on the relation between relative deprivation and populist attitudes in these three countries. These findings suggest that perceptions of inequality may contribute to increased political distrust and be associated with pluralistic demands to listen to those perceived as marginalised from power, which ultimately predicts populist attitudes. The study posits that this is a model that may explain the inclusive populism that is often prevalent in Latin American countries.
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