Traditional ewe’s cheese producers face certain challenges caused by fluctuating environmental parameters inside the ripening room, which lead to lack of homogeneity in the final product. The present research discusses the application of computer fluid dynamics for simulating the distribution of environmental parameters, predicting the airflow pattern, and identifying critical areas where such parameters could cause reduced cheese quality. A new monitoring system was developed including presence sensors, temperature and humidity dataloggers, pneumatic actuators, microcontrollers, and microcomputers connected remotely for control, data visualization, and processing. The validation of the computer simulation and monitoring system was made with a batch of 40 ewe’s cheeses distributed in three different zones inside a prototype ripening room and ripened for 35 days. At 35 days, a physical, chemical, and microbiological characterization of cheeses was made for evaluation of the influence of environmental conditions on cheese quality. The comparison between simulated and local measurements showed close agreement, especially concerning air velocity inside the stacks of cheese. The results of Pearson’s correlation analysis and PCA concluded that temperature affected the appearance of the rind, hardness, number and area occupied by holes. Humidity affected a_w and mFeret. Air velocity affected pH and the circularity of gas holes.

This paper describes a set of PHP based tools created to develop applications for instrumentation remote control through Web browsers. These tools include a PHP module for GPIB (general purpose interface bus) control in a Win32 platform and a set of PHP classes that make easier the creation of GUIs in Web browsers for instrument control and data visualization. The Web AJAX technology allows asynchronous data loading making it possible to have in the same browser view different panels that are able to interact and to be updated independently.

The accurate modelling of the human visual system, particularly of the retina, would be a great achievement and a big step in the development of visual prostheses. Several methods and algorithms have been proposed to accomplish such a difficult task, mainly to what concerns the adaptation and nonlinear mechanisms of the retina. This paper presents the results obtained by the employment of additive logistic regression techniques to model the nonlinear block of a canonical Linear-Nonlinear-Poisson retina model, considering the spike triggering process from a statistical point of view, complemented with the PCA of the stimuli covariance matrix. The displayed results were obtained by modelling real retina data using different forms for the nonlinear block and are assessed with different error measures.

Improving the energy efficiency of traditional production methods while preserving their cultural and economic value is a challenge aligned with the Sustainable Development Goals of the 2030 agenda. Refrigeration during cheese maturation is particularly energy-intensive, contributing significantly to greenhouse gas emissions and operating costs. An approach to make traditional cheese production more sustainable, through the development of a prototype ripening chamber with a natural refrigerant-based refrigeration system powered by renewable energy was studied. A dedicated system based on an Internet of Things architecture was developed using low-cost sensors, microcontroller units, and single-board computers to enable real-time measurement and monitoring of environmental variables and energy consumption throughout the ripening process. A comparative analysis was conducted using ewe’s milk cheese, produced and ripened with Protected Designation of Origin conditions, in both the prototype and the conventional chambers over four weeks, quantifying energy consumption and evaluating product quality. Results demonstrate the technical feasibility of energy efficient and sustainable refrigeration systems, as well as the possibility of retrofitting installed cheese ripening chambers with affordable IoT monitoring systems, while maintaining traditional cheese quality standards.

A new prototype of a ripening chamber was proposed in the CFD4CHEESE project aiming to evaluate the application of computational fluid dynamics in the optimization of traditional cheeses ripening conditions. With the purpose of evaluating the possible impact on the energy cost of cheese ripening in three Portuguese regions (Setúbal, Évora, and Beja), we demonstrated the potential economic benefits of the prototype. The proposed automation process would imply higher productive efficiency, reducing the consumption of water and electricity. The simple consideration of the reduction of electricity costs is enough to recover the investment needed to have the new ripening chambers, meaning that the investment is economically viable. In addition to the economic impact in firms, which are mostly small and medium firms, our results also contribute to an increase on the sustainable use of natural resources.