Education on Engineering urges of learning methodologies different to the current ones that would allow students to profile competences in accordance to the exigencies of the competitive exercise of engineering, in which they will perform the essential functions typical of the career and develop as an upright, mature, and reflective people. Three questions are brought up in this study: Which are the basic competences that need a contemporary engineer? How can the learning of these basic competences be improved and assessed in engineering students?The results allow to show, by comparing groups of students belonging to the course of “Direccion de proyectos en Ingenieria” (Engineering Project Management), that there exists strong evidence for improvement of basic competences: underlying science knowledge, knowledge of fundamental and advanced engineering, engineering reasoning and problem solving, systemic thinking, as well as conceiving, designing, implementing and operating systems in companies and in social context.It is considered feasible to integrate the experience in other courses of Engineering reinforcing the tutorial role of the teacher, defining evaluation criteria of the course and identifying and assessing the improvement of competences during the learning process

ObjectiveThe paper aims to apply scientific domain analysis and network analysis to research in professional competences and its relationship to technology and engineering.MethodologyNetwork analysis is a tool of scientific domain analysis. Nowadays it is supported by technology and computer sciences, which allow the generation of network maps in order to process the information.ResultsThe models of competences proposed for professionals in information technologies have been analyzed together with the importance of developing competency management systems and the support of computer technologies to this field.ConclusionsDomain analysis facilitates the structuring and organization of information, which is very useful to the analysis of competences and its relation with technology and engineering, being a new subject of study

Prior studies determined the importance of study of professional competencies under eight approaches: 1)workplace; 2)behavioral theory; 3)business strategy; 4)holistic approach; 5)engineering and technological context; 6)higher education; 7)organizational psychology; and 8)cognitive and motivational approach. The aim to delve deeper the cognitive-motivational aspect is to determine the relationship between these aspects. Thereupon we use techniques of scientific domain analysis as scientific analysis of the patterns of co-occurrence and co-citation in the research literature indexed in Scopus, bibliographic records are analyzed for relating the theories of the most representative authors and key terms found grouped in clusters research to prove that An individual's learning is proportional to the motivation provided to him or her, which means that it is necessary to know the personal aspects of each individual in the formation of professional competencies. Other factors affected by motivation are goals and achievements

The emerging paradigm of skills has been studied by different disciplines, and it has generated a broad spectrum of research literature regarding competences, both work and executive competencies, but there is not necessarily agreement among authors on the definition of the term.Through this paper we attempt to give an approximation of the intellectual structure of research related to professional competences. Our aim, through reflection on the approaches and trends of the use of this term, is to contribute to improving the work of professionals in raising awareness of the implicit and explicit aspects of the term and how subjective it can be. Using scientific domain analysis, a new paradigm of information science, we studied the set of publications in journals listed in the multidisciplinary databases: “Web of Science” and “Scopus” from 1950 to mid-2012.The maps generated using the scientific visualization software “Citespace II” allowed us to visualize the lines of research, specialties and trends of the professional competences wherein we identified eight groups formed from characteristics and common research topics

The contamination of soils by heavy metals is a current problem for agricultural production. Rapid access and reliability to heavy metal concentration such as cadmium is crucial for international trade. In the present study, visible and near infrared (VIS-NIR) spectroscopy, combined with linear and statistical methods, were used to predict the cadmium concentration of organic cocoa bean samples. Partial Least Square Regression (PLSR) and Support Vector Regression (SVR) were implemented to estimate the content of this heavy metal from hyperspectral imaging and chemical analysis. Competitive Adaptive Reweighted Sampling Method (CARS) and Jackknife method were used for selecting optimal wavelength. The SVR model performed satisfactorily with the use of 45 resulting wavelengths from optimization using CARS and the Jackknife method, with an adjusted coefficient for the test R ² of 0.9401 and an RMSEP of 0.2594. Based on the results, it was concluded that VIS-NIR spectroscopy combined with CARS-Jackknife methods seems to be a fast and effective alternative to classical methods for predicting the concentration of cadmium in organic cocoa beans.

The agribusiness in Peru has grown significantly in the last decade and has become one of the most important producers of fine and flavor cocoa. The European Union has established quality parameters for the presence of heavy metals, one of which is cadmium due to its harmful effects on human health. To improve the quality control of cocoa, this article proposes to estimate the percentage of cadmium in a cocoa sample using the hyperspectral imaging and Machine Learning algorithms, as a non-invasive, non-destructive method that can be implemented in real time. In addition, areas of the spectral signature that contribute the most to the cadmium estimate are identified in order to significantly reduce the number of spectral bands used in the model. This reduction of spectral bands allowed to increase the R2 with the validation data from 67.92% to 72.39%, reaching an average error of 0.18 ppm using the Partial Least Square (PLS) method.