Recent Submissions

  • Sustainable development and fair trade. A systematic review of the main research published between 2010-2022

    Cerna, Martin Leonardo Aranda; de Jesús Castro Cruzado, Esperanza; Jiménez, Daniela Díaz; Herrera, Dusant Q.ente Dongo; Vega, Estefanía Milagros Velarde; Morán, Carlos Alberto Azabache; Cuaresma, Julio Ricardo Moscoso (Latin American and Caribbean Consortium of Engineering Institutions, 2023-01-01)
    urrently, the impacts caused by sustainable development and fair trade in agriculture are favorable for the growth of production. The objective of this research was to determine the current trends on the commercial, environmental, and economic impact of sustainable development and fair trade in the agricultural sector during the period 2010-2022. The systematic review of literature (SRL) was used as a methodology, giving as a result, the choice of 40 sources for the analysis of this research. The results had determined that there is a greater use of the qualitative approach in refereed publications on the field of study. In addition, concluded that the effort of economic, public, private, and socioeconomic actors is required to achieve a synergy to induce a joint transformation of the agricultural sector. Likewise, research trends determine that there is a positive commercial impact of the usage of fair trade and sustainable development in the world that caused a greater commercial growth in the agricultural sector.
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  • Policy to Accentuate Social Resilience: Disaster Risk Management

    Morán, Roberto Carlos Dávila; Dávila, Leonardo Velarde; Gómez, Henri Emmanuel López (Universidad del Zulia, 2022-09-05)
    The reduction of social vulnerability to disaster risk as a process of political management is presented with high criticality in all areas of application, therefore, the article seeks to determine-through heuristic documentation-guidelines related to management of risks in the face of natural and anthropogenic disasters, which allow minimizing the impact and effects on the social and economic spheres, while building resilience in the communities. Sometimes, the political and technological capacities of governance do not assume risk management from a preventive perspective, rather reactive, therefore, from this research a perspective is proposed from four cosmogonic dimensions: risk identification, management and monitoring, mitigation actions and response capabilities in the event of an event. Finally, it is considered that the good practices of public management complemented with the capacities of the business sector promote social resilience to disasters.
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  • Digital Transformation and Technological Innovation on Higher Education Post-COVID-19

    Deroncele-Acosta, Angel; Palacios-Núñez, Madeleine Lourdes; Toribio-López, Alexander (MDPI, 2023-02-01)
    The university is an important pillar in sustainable development; however, COVID-19 imposed new dynamics that called for rethinking university praxis to achieve this mission, and although the systematization of good practices is a powerful mechanism for understanding educational success, this perspective of positive change has been little developed. Hence, the present study aimed to identify positive cores of faculty in their successful post-COVID-19 performance. A qualitative methodological approach was deployed, with the Netnography method, complemented with elements of positive psychology, appreciative inquiry, and management of formative potentialities. The online community consisted of 1238 university teachers from 10 Latin American countries, who participated for two months in an appreciative interview as an asynchronous journey of constructive proposals, for the active co-construction of post-COVID-19 success factors. The findings reveal multiple affirmative topics grouped into nine positive cores, identifying two target categories: digital transformation and technological innovation, as well as the processes directly associated with their dynamization. Finally, the epistemic implications of the findings in theory and practice, and their relevance in the creation of a formative agenda of positive change for Latin American Higher Education, are presented.
    Acceso abierto
  • Analyzing sustainability indicator for Chinese mining sector

    Li, Yongbo; Barrueta Pinto, Mark Christhian; Kumar, D. Thresh (Elsevier Ltd, 2023-01-01)
    Mining sector always comes under severe scrutiny due to their negative impacts towards society and environment. Several studies contributed to reduce these impacts exists in the mining operations, in the development, studies also started to explore various assessment mechanism to understand the mining firm's sustainability impact. Among such assessment strategies, sustainability indicators gained huge momentum in recent years specifically with mining operations. This study considers one such area to focus, sustainability indicator analysis in mining. There are several sustainability indicators were introduced in the literature, which makes the consideration of sustainability indicators as a chaotic process for practitioners. Considering the fact, this study sought to explore the influential sustainability indicator and their corresponding sustainability dimension with the case context of China. As a major global manufacturer, China explores different ways to do a sustainable mining business for their long-term growth and this study could impact on their sustainable development goals roadmap. Different sustainability indicators considering mining were collected from the existing studies and further validated and categorized with expert opinions under their respective dimensions of sustainability (economy, environment, and society). The validated sustainability indicators were evaluated through a multi criteria decision making tool, DEMATEL. The inputs for the analysis were collected from a Chinese mining case company. The results revealed the influential sustainability indicator for Chinese mining sector. By understanding the most and least influential indicators, the Chinese mining practitioners can eliminate the strategies to motivate the least influential indicator and improve the strategies to motivate most influential indicator.
    Acceso restringido temporalmente
  • An application of Six Sigma DMAIRC model: case study of a manufacturing organisation

    Sharma, Pallavi; Gupta, Anshu; Malik, S. C.; Jha, P. C.; Pinto, Mark Christhian Barrueta (Inderscience Publishers, 2022-01-01)
    Global competition extends several challenges to micro, small and medium enterprises (MSMEs) organisations. There is an increasing need for these firms to adopt strategies for productivity improvement. Six Sigma is a data driven methodology supported by statistical, mathematical and managerial techniques for improving the bottom line effectively. In this paper, an application of DMAIRC model of Six Sigma is presented in context to a quality improvement initiative in an MSME organisation. DMAIRC is an effective alternative to linear DMAIC model, as it provides opportunity to review results and diagnose additional improvements. The additional improvements are checked for feasibility and implemented through additional improvement cycles. The study also demonstrates integration of the quality management techniques such as Pareto analysis, cause and effect analysis, current reality tree, design of experiments, and integrated AHP-TOPSIS in the DMAIRC model stages. The paper provides a roadmap for real life applications of Six Sigma methodology following the DMAIRC model.
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  • Design of green infrastructure for the revaluation of the Ventanilla-Peru wetlands and the protection of the environment

    Esenarro, Doris; Quijano, Joseline; Rodriguez, Ciro; Arteaga, Jennifer; Hinojosa, Karina (Springer Science and Business Media Deutschland GmbH, 2022-01-01)
    The purpose of this research is the design of a green infrastructure that allows a regional conservation area to revalue the Ventanilla wetlands to promote ecotourism through spaces for the conservation of natural resources, turning it into a tourist attraction. The proposal considers design and construction criteria with adequate technology, biodegradable, and sustainable materials where environmental impact is minimized in this context. The collection of information through field visits and the use of different software for the topographic survey. Results show that the infrastructure design proposed was validated by a survey of potential users of the place, with 75% of the interviewees agreeing with the design proposal that allows interaction and harmony with nature, giving it a landscape value, generating local, national, and international visitors. The value is in the ecosystem services that the landscape provides to the city due to the design and construction criteria with adequate technology, biodegradable, and sustainable materials minimizing the environmental impact and promoting the cultural exchange, preservation, and ecological awareness wetland.
    Acceso restringido temporalmente
  • Method for Collecting Relevant Topics from Twitter supported by Big Data

    Silva, Jesús; Senior Naveda, Alexa; Gamboa Suarez, Ramiro; Hernández Palma, Hugo; Niebles Núẽz, William (Institute of Physics Publishing, 2020-01-07)
    There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.
  • Time Series Decomposition using Automatic Learning Techniques for Predictive Models

    Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ovallos-Gazabon, David; Varela, Noel (Institute of Physics Publishing, 2020-01-07)
    This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.
    Acceso abierto
  • Neural Networks for the Web Services Classification

    Silva, Jesús; Senior Naveda, Alexa; Solórzano Movilla, José; Niebles Núẽz, William; Hernández Palma, Hugo (Institute of Physics Publishing, 2020-01-07)
    This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.
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  • Forecasting Electric Load Demand through Advanced Statistical Techniques

    Silva, Jesús; Senior Naveda, Alexa; García Guliany, Jesús; Niebles Núẽz, William; Hernández Palma, Hugo (Institute of Physics Publishing, 2020-01-07)
    Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.
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  • Parallel Algorithm for Reduction of Data Processing Time in Big Data

    Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ovallos-Gazabon, David; Varela, Noel (Institute of Physics Publishing, 2020-01-07)
    Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.
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  • Temporary Variables for Predicting Electricity Consumption Through Data Mining

    Silva, Jesús; Senior Naveda, Alexa; Hernández Palma, Hugo; Niebles Núẽz, William; Niebles Núẽz, Leonardo (Institute of Physics Publishing, 2020-01-07)
    In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.
    Acceso abierto
  • Natural Language Explanation Model for Decision Trees

    Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ruiz-Lazaro, Alex; Varela, Noel (Institute of Physics Publishing, 2020-01-07)
    This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.
    Acceso abierto
  • Neural Networks for Tea Leaf Classification

    Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ruiz-Lazaro, Alex; Varela, Noel (Institute of Physics Publishing, 2020-01-07)
    The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.
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  • Identification of Patterns of Fatal Injuries in Humans through Big Data

    Silva, Jesus; Romero, Ligia; Pineda, Omar Bonerge; Herazo-Beltran, Yaneth; Zilberman, Jack (Elsevier BV, 2020)
    External cause injuries are defined as intentionally or unintentionally harm or injury to a person, which may be caused by trauma, poisoning, assault, accidents, etc., being fatal (fatal injury) or not leading to death (non-fatal injury). External injuries have been considered a global health problem for two decades. This work aims to determine criminal patterns using data mining techniques to a sample of patients from Mumbai city in India.
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  • Analyzing the critical success factor of CSR for the Chinese textile industry

    Li, Y. (Elsevier Ltd, 2020-07-01)
    Increasing population and urbanization motivates the capability of consuming more fashion goods than ever. This push creates more momentum on global companies to focus on clothing sectors. Recent advancements, including globalization and e-commerce, have made this sector as one of the top businesses worldwide. Top clothing brands made several strategies to satisfy the stakeholders to sustain in this hot, profitable business. This results in practicing more sustainable strategies, including corporate social responsibility in their clothing business throughout their operations, including the supply chain. However, most of the developed nations are consumers of textiles, which are produced and processed by any of the developing and under developing nations. Meanwhile, achieving sustainability in clothing business includes promoting sustainability in the whole chain of suppliers. Pressure from developed nations urges developing nations to promote sustainable practices in their operations. Several studies discussed the CSR related strategies in textile sectors but failed to explore their critical success factors based on their region. With this concern, this study attempts to study the critical success factors of CSR in textile industries situated in one of the developing nations, China. This study collected the critical success factors from literature and validated with the field experts; then the same were evaluated with the assistance of Chinese textile case industrial managers. Decision-making trial and evaluation laboratory tool has been used to evaluate the influential critical success factors of CSR to promote CSR through motivating those most influential success factors. These results could help the Chinese textile industrial managers to further extend strong roots on CSR implementation. Finally, this study sheds some light on future opportunities that exist within Chinese contexts with the implementation of CSR.
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  • Perceptual map teaching strategy

    Chipoco Quevedo, Mario; [email protected] (Universidad Peruana de Ciencias Aplicadas (UPC), 2016-11-15)
    Este documento contiene el diseño de una estrategia para enseñar mapas perceptuales en un curso de gerencia de marca, con la adición de una técnica de modelado para elaborarlos. Los mapas perceptuales son herramientas para el análisis del posicionamiento de marca, y se enseñan en cursos de pregrado y postgrado. Sin embargo, es muy usual utilizar un marco puramente descriptivo y teórico, sin explicar los mecanismos para construirlos. Se presentan métodos basados en regresión multilineal y en análisis factorial como herramientas de modelado, para explicar en clase y proporcionar una mejor comprensión de esta materia.
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  • Pricing and spread components at the Lima Stock Exchange

    Universidad Peruana de Ciencias Aplicadas (UPC) (United Nations Publications, 2015-08-18)
    This paper analyses three aspects of the share market operated by the Lima Stock Exchange: (i) the short-term relationship between the pricing, direction and volume of order flows; (ii) the components of the spread and the equilibrium point of the limit order book per share, and (iii) the pricing, order direction and trading volume dynamic resulting from shocks in the same variables when lagged. The econometric results for intraday data from 2012 show that the short-run dynamic of the most and least liquid shares in the General Index of the Lima Stock Exchange is explained by the direction of order flow, whose price impact is temporary in both cases.
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