Inserción del análisis financiero en PyMes colombianas como mecanismo para promover la sostenibilidad empresarial

La sostenibilidad empresarial surge a partir de la integración del desarrollo sostenible a la operación empresarial cuyo fin es generar un valor económico, ambiental y social para incrementar el bienestar y progreso de las generaciones presentes y futuras; es así como, con base en este planteamiento, se planteó como propósito fundamental indagar como el análisis financiero contribuye a la sostenibilidad empresarial de las PyMes. Para este estudio, se empleó una metodología cualitativa, de tipo descriptiva basada en una revisión documental de publicaciones científicas y académicas de relevancia sobre las variables objeto de estudio como: análisis financiero, sostenibilidad y Pymes colombianas. Se realizó un análisis de los principales aspectos que definen el desarrollo sostenible, así como también los principales factores que inciden en su eficacia, y, se detalló las contribuciones que aportan los análisis financieros a la mejora de los resultados esperados. Los hallazgos obtenidos permitieron observar la importancia de las decisiones de inversión en la sostenibilidad y los factores que influyen en el éxito de estas. Finalmente, se puede mencionar que el análisis financiero juega un papel fundamental en la lectura oportuna de las variables del entorno y la toma de decisiones estratégicas para el desarrollo de la sostenibilidad

Electrical consumption patterns through machine learning

Electricity distribution companies have been incorporating new technologies that allow them to obtain complete information in real time about their customers´ consumption. Thus, a new concept called "Smart Metering" has been adopted, giving way to new types of meters that interact in an interconnected system. This will allow to make data analysis, accurate forecasts and detecting consumption patterns that will be relevant for the decision-making process. This research focuses on discovering common patterns among customers from data collected by smart meters

Impact of leadership on the development of organizational communication

Fundamental leadership in communication has been approached from science as a thematic focus of great relevance both academically and for the industry. In this way, the present study is developed in order to identify the bibliometric applications of the impact of leadership in the development of organizational communication. At the methodological level, a documentary research based on scientometric processes is presented, where the Scopus databases are consulted during the period from 1958 to 2022. The results allow us to show 512 results within the database, observing a significant growth of the scientific prediction, where the most relevant sources are Journal of Business Communication, Corporate Communications, Journal of Communication Management, International Journal of Business Communication and Business Communication Quarterly and in turn observing that 47% of the publications come from the United State

Project-Based Learning (PBL) With Virtual Mediations And Computer Tools In An Animal Genetics Course In An Animal Science Program

The project-based learning (PBL) strategy, supported by virtual mediations and computer tools, is an effective method of learning complex subjects based on the use of problems as a starting point for the acquisition or reinforcement of knowledge. In this study, it is proposed to adapt the methodology and approach of PBL to use it as a method of approaching all the curricular contents of the subject Animal Genetics, of the Animal Science programme of the University of Sucre-Colombia. The strategy was developed in the time allotted for the course and was applied to 32 students. The degree of familiarization, academic productivity, asynchronous mediation through e-mail, spreadsheet mastery and appreciation of the methodology implemented were determined. The results showed that 100% of the students were unaware of the application of the strategy, initially showing fears at the beginning of the activity. On analyzing the reports submitted throughout the semester, it was found that 50.0% of the students achieved an average mark of 4.2±0.14, 40.6% achieved an average mark of 3.70±0.09 and only 9.4% obtained a failing mark. 55.2% of the students who achieved a passing grade had an average of more than 4.0 and 45.8% of the students were in the range 3.0- 3.9. The products delivered and the degree of completion were influenced by the degree of mastery of the spreadsheet and the level of email participation recorded. It was found that the learning strategy generated motivation in the students, reflected in the fulfilment of the goals and objectives set at the beginning of the course, and increased student-teacher interaction

Retraction: using Big Data to determine potential dropouts in higher education

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics

Temporary variables for predicting electricity consumption through data mining

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

Neural networks for tea leaf classification

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

Retraction: time series decomposition using automatic learning techniques for predictive models

This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author's institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction

Big data and automatic detection of topics: social network texts

This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts