Microbiological and chemical characterization of Fiore Sardo, a traditional Sardinian cheese made from ewe's milk

The purpose of this study was to assess the chemical and microbial characteristics of 12 batches of artisanal Fiore Sardo, a protected designation of origin (PDO) hard cheese made from raw ewe's milk without addition of starters, during maturation. High standard deviations were observed for moisture percentage, total solids percentage and NaCl percentage content, possibly owing to differences in manufacturing processes and/or milk composition. Total mesophilic bacteria varied between 10 log₁₀ cfu/g in 48-h-old cheese samples and 3 log₁₀ cfu/g in 9-month-old samples. Total coliforms and staphylococci showed the highest counts at 48 h of ripening then decreased significantly, dropping to levels below 2 log₁₀ cfu/g at 3 months of maturation. Lactic acid bacteria and enterococci were the dominant micro-organisms throughout maturation. They were mainly represented by the species Lactococcus lactis ssp. lactis, Enterococcus faecium, Lactobacillus plantarum and Lactobacillus casei group. Low levels of yeasts were detected throughout the maturation period of the cheese. Debaryomyces hansenii and Kluyveromyces lactis var. lactis were the prevalent yeast species isolated.     

MODELING OF SOLAR DOMESTIC WATER HEATING SYSTEMS USING ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can be trained to predict results from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly useful in system modeling and for system identification. The objective of this work was to train an ANN to learn to predict the useful energy extracted and the temperature rise in the stored water of solar domestic water heating (SDHW) systems with the minimum of input data. An ANN has been trained based on 30 known cases of systems, varying from collector areas between 1.81 m² and 4.38 m². Open and closed systems have been considered both with horizontal and vertical storage tanks. In addition to the above, an attempt was made to consider a large variety of weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were the collector area, storage tank heat loss coefficient (U-value), tank type, storage volume, type of system, and ten readings from real experiments of total daily solar radiation, mean ambient air temperature, and the water temperature in the storage tank at the beginning of a day. The network output is the useful energy extracted from the system and the temperature rise in the stored water. The statistical R²-value obtained for the training data set was equal to 0.9722 and 0.9751 for the two output parameters respectively. Unknown data were subsequently used to investigate the accuracy of prediction. These include systems considered for the training of the network at different weather conditions and completely unknown systems. Predictions within 7.1% and 9.7% were obtained respectively. These results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored water. The advantages of this approach compared to the conventional algorithmic methods are the speed, the simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network. Additionally, actual weather data have been used for the training of the network, which leads to more realistic results as compared to other modeling programs, which rely on TMY data that are not necessarily similar to the actual environment in which a system operates.     

Lin2k: A Novel Web‐Based Collaborative Tool‐Application to Engineering Education

In this paper we present the educational process of Lin2k, a Web‐based tool, which supports distant asynchronous, written, peer‐collaboration in a case study. The tool constitutes an open learning environment that endows engineering students with collaborative competencies, necessary for their successful shift to professional practice. Students are engaged in a process of experiential learning of collaboration while, in parallel, we follow up their interactions and collect communication data of interest. Lin2k collaboration evaluating and adaptive feedback mechanisms are aimed at equipping students with the necessary conceptual knowledge that underlies collaborative activity. Lin2k experimental uses in the Civil Engineering Department of Aristotle University of Thessaloniki, Greece, proved the efficacy of its pedagogy. The Lin2k educational process serves as a prototype that may contribute to the revision of engineering curricula so as to face the challenges that the new technologies impose, along with the necessity of relating academic to professional life.