Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management. A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of waste produced. The ML models had greater predictive capacity than a linear model with stepwise parameter selection. Then, uncertainty is included in the predictions using a normal distribution based on the residuals of the model. Furthermore, we also demonstrate that ML models can be used as a tool to identify possible production anomalies. This research shows innovative ways to deal with uncertainty in production planning using modern methods in the field of operation research. These tools improve classical methods and provide production managers with valuable information to assess the economic benefits of improved machinery or process controls. As a consequence, accurate predictive models can potentially improve the profitability of food companies, also reducing their environmental impact.</p

From the Editors: CLIL at university: research and developments

In recent times, the relevance of CLIL (Content and Language Integrated Learning) at most educational levels, especially in the university world, has experienced an exponential increase, as recent publications show (Doiz et al. 2013, Fortanet-Gómez 2013, Llinares et al. 2012, or Smit and Dafouz 2012a, among others). Teaching in English seems to be a popular topic nowadays, but it is also a need. The articles included in this issue show three main common features of CLIL and its role in today’s higher education: the process of internationalization of the educational system, the need for a language policy, and the fact that English for Specific Purposes (ESP) as a field of research and teaching as well as ESP practitioners are all very much concerned with CLIL

Cyberbullying in the University Setting. Relationship With Emotional Problems and Adaptation to the University

Little scientific attention has been paid to the problem of cyberbullying in the university environment, compared to similar studies conducted on adolescents. This study attempts to analyze the predictive capacity of certain emotional problems (anxiety, depression, and stress) and university adaptation with respect to cyberbullying in victims and aggressors. The European Cyberbullying Intervention Project Questionnaire, the Depression Anxiety Stress Scale-21 and the Student Adaptation to College Questionnaire were administered to a sample of 1282 university students (46.33% male) aged between 18 and 46. The results suggest that high levels of depression and stress increase the probability of being a cyberbullying victim, while high levels of depression increase the probability of being a cyberbullying aggressor. Similarly, the personal–emotional and social adaptation of students are found to be predictor variables of being a cyberbullying victim, in that high levels of personal–emotional and social adaptation decrease the probability of being a victim, while high levels of personal–emotional, academic and institutional adaptation decrease the probability of being a cyberbullying victim. The results of this study are of special relevance, since they indicate that intervention programs should consider the influence of emotional intelligence, as well as the relevance students’ adaptation to university

Trace elements contamination in an abandoned mining site in a semiarid zone

The distribution of trace elements throughout mining areas is an important issue because abandoned tailings can be a major source of environmental pollution. The aim of this study was to identify the trace element content, hydric dispersion ways and its reception areas in selected zones of the abandoned mining district of Sierra Minera Cartagena-La Unión. The results obtained allowed to establish points affected by primary, secondary and tertiary contamination, according to their proximity to contamination sources, as a function of its chemical and mineralogical composition. Applied GIS methodology allowed visualisation and confirmation of established conceptual model