The control of refrigeration in the food chain is fundamental at all stages, with special emphasis on the retail stage. The implementation of information and communication technologies (IoT, open-source hardware and software, cloud computing, etc.) is representing a revolution in the operational paradigm of food control. This paper presents a low-cost IoT solution, based on free hardware and software, for monitoring the temperature in refrigerated retail cabinets. Specifically, the use of the ESP-8266-Wi-Fi microcontroller with DS18B20 temperature sensors is proposed. The ThingSpeak IoT platform is used to store and process data in the cloud. The solution presented is robust, affordable, and flexible, allowing to extend the scope of supervising other relevant parameters in the operating process (light control, energy efficiency, consumer presence, etc.).

A concern in Mathematics Education is the professional development of the teacher and to promote effective training programs. The literature provides principles guiding the design of such programs, which were considered for an instruction intended to strengthen the teacher’s practice in relation to the development of mathematical skills. The objective of this work was to study if the designed program was effective, in relation to the impact on the teacher’s teaching. A case study on a group of in-service teachers focused on the ability of mathematical modeling (MM) and their perspectives of this skill was carried out. This was divided into three moments: before, during, and after the program. The findings show that, before the program, teachers conceived modeling from epistemological, pragmatic and conceptual perspectives; during the program, they focused on the pragmatic and educational perspectives; and at the end of the program, the group was stripped of the pragmatic, epistemological and conceptual perspectives to move towards the educational and socio-critical. They were also able to justify the choice or disassociation of one or more of them. The study advances towards the concretion of more specific and robust professional development programs for mathematics teachers.

Years of research have demonstrated the ability of near infrared (NIR) spectroscopy to predict the chemical composition and percentage of included ingredients in intact finished compound feeds. However, before reaching the end product, the mixing stage—where a large number of highly-varied ingredients are mixed together to obtain the desired product—is a critical point in feed manufacture. Detection of errors at this stage is crucial, since the process can still be corrected. This study sought to demonstrate the ability of NIR equations developed for finished compound feeds to be used at the mixing stage and ensure that the product meets specifications for chemical and ingredient labelling. When equations developed with intact compound feeds for prediction of crude protein (CP), crude fibre (CF), percentage of sunflower meal (SFM) and percentage of soybean meal (SBM) were applied to samples taken at the mixing stage, the results for CP and CF showed that NIR predictions at this stage provide information very similar to that obtained for the final product (CP_mixer = 15.65% CP_final = 15.87%; CF_mixer = 7.69% CF_final = 8.02%). The minor differences observed for the prediction of SFM (%) and SBM (%) at the two stages (SFM_mixer = 6.94% SFM_final = 7.99%; SBM_mixer = 11.04% SBM_final = 9.31%) may be due to the special features of the validation samples used. Although a number of technical and scientific problems remain to be solved, results confirm NIR spectroscopy as a viable tool at the mixing stage. This technique would enable manufacturers to make any corrections required at this point of the process.

This study presents an experience that combines problem-posing and Math Trails in the context of future teachers’ instruction. Pre-service teachers in the third year of their studies were faced with the design of tasks to be included in Math Trails for primary school students. The study analyzes, from a quantitative approach, 117 tasks contained in 11 Math Trails. The analysis was performed on the basis of classification variables (grade, mathematical content and object or real element involved in every task) and research variables which provide information about the nature of the tasks (procedural vs. problem-solving, level of cognitive demand, degree of contextualization, openness and creativity). Additionally, relationships between the different categories of analysis have been studied. The results reveal certain biases in the tasks in relation to the contents addressed (an abundance of tasks with a geometric component and a scarcity of tasks involving algebra or probability concepts). Most of the tasks are presented in a real context. However, a higher percentage of procedural tasks than problem-solving tasks is observed, with a predominance of low openness, creativity and cognitive demand. These results provide useful lines of work to address difficulties faced by future teachers in the STEAM field.