Cheese whey constitutes one of the most polluting by-products of the food industry, due to its high organic load. Thus, in order to mitigate the environmental concerns, a large number of valorization approaches have been reported; mainly targeting the recovery of whey proteins and whey lactose from cheese whey for further exploitation as renewable resources. Most studies are predominantly focused on the separate implementation, either of whey protein or lactose, to configure processes that will formulate value-added products. Likewise, approaches for cheese whey valorization, so far, do not exploit the full potential of cheese whey, particularly with respect to food applications. Nonetheless, within the concept of integrated biorefinery design and the transition to circular economy, it is imperative to develop consolidated bioprocesses that will foster a holistic exploitation of cheese whey. Therefore, the aim of this article is to elaborate on the recent advances regarding the conversion of whey to high value-added products, focusing on food applications. Moreover, novel integrated biorefining concepts are proposed, to inaugurate the complete exploitation of cheese whey to formulate novel products with diversified end applications. Within the context of circular economy, it is envisaged that high value-added products will be reintroduced in the food supply chain, thereby enhancing sustainability and creating “zero waste” processes.

This study aimed at investigating the issue of domain specificity of motivation across specific science disciplines (chemistry and physics) in combination with influencing factors related with students’ academic major and gender. A clustered sampling was used to select 281 participants who were undergraduate students (105 males and 176 females) attending three different academic majors (Environmental Technology, Conservation of Cultural Heritage, Food Technology) at a Greek tertiary education institution. Motivation to learn chemistry and physics was evaluated concurrently using the discipline-specific (Chemistry and Physics) versions of Science Motivation Questionnaire II (SMQ II) denoted as CMQ II and PMQ II, respectively. Discipline-based comparisons showed the existence of domain specificity in motivation with the whole student sample displaying higher motivation to learn chemistry relative to physics in all five motivational scales of SMQ II (self-efficacy, self-determination, intrinsic motivation, career motivation and grade motivation). Multivariate analysis of variance (MANOVA) and discriminant function analysis showed that students’ motivation to learn chemistry and physics depends strongly on their academic major. Furthermore, MANOVA provided evidence for a small gender effect on motivation for either chemistry or physics learning. The implications of the current study for effective tailor-made educational practice and research across different science disciplines are critically discussed.

This work examines if chaos and long memory exist in PM₁₀ concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months’ duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM₁₀ time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz’s algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM₁₀ time series were combined in this manner.

This study aims at investigating students’ awareness of green chemistry principles (GCPs) which are infused in two context-based projects that are related to socio-scientific issues, namely climate change and materials’ recycling. At first, content analysis of the projects’ activities showed the infusion of four GCPs. Subsequently, the students’ written reports associated with these activities were evaluated in order to examine the incorporation of different GCPs in their argumentation. The four principles identified in the first part of the study, were also shown to be incorporated in students’ argumentation, although with variable frequency. The specific themes which manifest students’ awareness of the different GCPs were also identified. These themes may guide the effective design of educational material dedicated to the infusion of GCPs in chemistry education, thus contributing to the promotion of students’ environmental awareness and the development of skills which characterise socially responsible citizenship.