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In this study, we propose the study of an innovative precipitation prototype designed by ResourSEAs, guided by a CFD-PBM (Computational Fluid Dynamics and Population Balance Model) approach, aiming to understand the influence of reactant concentration and nozzle orientation on precipitation processes. The first part of the study examines the effect of reactant concentration on supersaturation and the zeroth-order moment (m0) within a controlled flow and turbulence fields. Three different concentrations of Mg2+ (0.1, 0.3, and 0.6 M) and OH− (0.005, 0.01, and 0.02 M) were tested, resulting in varying supersaturation profiles and m0 fields. Our results show that, under equal turbulence conditions, increasing the concentration of reactants beyond a certain point actually slows down mixing, which in turn hinders the generation of supersaturation. As a result, supersaturation profiles become nearly identical to those of lower concentrations, despite having consumed more reactants. The second part of this study focuses on the effect of nozzle orientation and positioning along the prototype axis on reactant mixing and particle formation. The simulations reveal that nozzle orientation has a significant impact on the formation of primary particles, especially when positioned in low-velocity regions, leading to slower mixing and greater particle growth. Conversely, high-velocity regions promote faster mixing and more intense aggregation. These findings highlight the interplay between concentration, nozzle orientation, and flow conditions in determining precipitation efficiency, offering insights for optimizing reactor design in industrial applications.

Students’ performance is a crucial aspect for university programs effectiveness and organization. In this paper, we introduce and analyze a performance index for the first-year students of a private Italian university, namely the Libera Università Maria Ss. Assunta. We use administrative data on 532 undergraduate students enrolled in any of the eight available bachelor degrees in 2015. Our aim is to improve the general understanding of performance linking it with personal student’s characteristics and with degree-specific aspects. A beta inflated latent class approach is employed to identify clusters of performance establishing a link with all available explanatory variables. The empirical analysis unveils that a good and balanced degree organization may improve students’ performance. The student’s ability plays a crucial role in discriminating between good and bad performances, and also strongly depends on individual-specific characteristics, such as the final mark obtained at high school.

University evaluation is a topic of increasing concern in Italy as well as in other countries. In empirical analysis, university activities and performances are generally measured by means of indicator variables, summarizing the available information under different perspectives. In this paper, we argue that the evaluation process is a complex issue that can not be addressed by a simple descriptive approach and thus association between indicators and similarities among the observed universities should be accounted for. Particularly, we examine faculty-level data collected from different sources, covering 55 Italian Economics faculties in the academic year 2009/2010. Making use of a clustering framework, we introduce a biclustering model that accounts for both homogeneity/heterogeneity among faculties and correlations between indicators. Our results show that there are two substantial different performances between universities which can be strictly related to the nature of the institutions, namely the Private and Public profiles . Each of the two groups has its own peculiar features and its own group-specific list of priorities, strengths and weaknesses. Thus, we suggest that caution should be used in interpreting standard university rankings as they generally do not account for the complex structure of the data.