SEeD for Change: The Systemic Event Design Project Applied to Terra Madre Salone del Gusto for the Development of Food Communities

In the contemporary era, food plays a key role in balancing environmental, social, and economic balances, not only due to its primary identity as a resource that nourishes living beings and the planet but also through the processes triggered by stakeholders who act at the internal local food systems. In the latter, an orientation towards sustainability is increasingly urgently required, capable of achieving a widespread creation of shared value. In this scenario, the International Slow Food Association operates, which also, through the Terra Madre Salone del Gusto initiative, coordinates communities and events located throughout the world on the theme of “good, clean and fair” food. This article aims to analyze, through the lens of the systemic approach, the interesting and multifaceted impacts of this event, as an opportunity to disseminate and contagion of ideas, attitudes, and behaviors around the themes of sustainability and biodiversity, but also as a moment of consolidation and creation of relationships between and within local food systems and local communities. The research project presented, entitled “SEeD for Change”, was coordinated by the University of Gastronomic Sciences of Pollenzo with the University of Turin and helped to focus on the actors, relationships and contexts that actually and virtually hosted the event: places in which through a common and shared language, change has been generated

SEeD for Change: The Systemic Event Design Project Applied to Terra Madre Salone del Gusto for the Development of Food Communities

In the contemporary era, food plays a key role in balancing environmental, social, and economic balances, not only due to its primary identity as a resource that nourishes living beings and the planet but also through the processes triggered by stakeholders who act at the internal local food systems. In the latter, an orientation towards sustainability is increasingly urgently required, capable of achieving a widespread creation of shared value. In this scenario, the International Slow Food Association operates, which also, through the Terra Madre Salone del Gusto initiative, coordinates communities and events located throughout the world on the theme of “good, clean and fair” food. This article aims to analyze, through the lens of the systemic approach, the interesting and multifaceted impacts of this event, as an opportunity to disseminate and contagion of ideas, attitudes, and behaviors around the themes of sustainability and biodiversity, but also as a moment of consolidation and creation of relationships between and within local food systems and local communities. The research project presented, entitled “SEeD for Change”, was coordinated by the University of Gastronomic Sciences of Pollenzo with the University of Turin and helped to focus on the actors, relationships and contexts that actually and virtually hosted the event: places in which through a common and shared language, change has been generated

Triple molybdates one-, one - and three(two)valence metals

The authors thank Ph. D. M. K. Alibaeva, Ph. D. I. A. Gudkova and Ph. D. I. V. Korolkova for participation in the research.The review summarizes experimental data on the phase formation, structure and properties of new complex oxide compounds group - triple molybdates containing tetrahedral molybdate ion, two different singly charged cation, together with tri- or divalent cation. The several structural families of these compounds were distinguished and it shown that many of them are of interest as luminescent, laser, ion-conducting or nonlinear optical materials.The work is executed at partial support of the Russian Foundation for basic research (projects No. 08-03-00384, 13-03-01020 and 14-03-00298)

Deep learning for semantic segmentation of 3D point cloud.

Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests

Vismodegib resistant mutations are not selected in multifocal relapses of locally advanced basal cell carcinoma after vismodegib discontinuation.

Hedgehog pathway inhibitors (HPI) inactivating SMO 1, have become first line treatment for patients with locally advanced BCC (laBCC). HPI safety and efficacy have been shown in clinical trials2,3. Nevertheless, common adverse events lead to treatment discontinuation

A machine learning approach for knee injury detection from magnetic resonance imaging

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model’s effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively

The temperature and chronology of heavy-element synthesis in low-mass stars

Roughly half of the heavy elements (atomic mass greater than that of iron) are believed to be synthesized in the late evolutionary stages of stars with masses between 0.8 and 8 solar masses. Deep inside the star, nuclei (mainly iron) capture neutrons and progressively build up (through the slow-neutron-capture process, or s-process) heavier elements that are subsequently brought to the stellar surface by convection. Two neutron sources, activated at distinct temperatures, have been proposed: 13C and 22Ne, each releasing one neutron per alpha-particle (4He) captured. To explain the measured stellar abundances, stellar evolution models invoking the 13C neutron source (which operates at temperatures of about one hundred million kelvin) are favoured. Isotopic ratios in primitive meteorites, however, reflecting nucleosynthesis in the previous generations of stars that contributed material to the Solar System, point to higher temperatures (more than three hundred million kelvin), requiring at least a late activation of 22Ne. Here we report a determination of the s-process temperature directly in evolved low-mass giant stars, using zirconium and niobium abundances, independently of stellar evolution models. The derived temperature supports 13C as the s-process neutron source. The radioactive pair 93Zr-93Nb used to estimate the s-process temperature also provides, together with the pair 99Tc-99Ru, chronometric information on the time elapsed since the start of the s-process, which we determine to be one million to three million years.Comment: 30 pages, 10 figure

Dynamical Jahn-Teller Effect and Berry Phase in Positively Charged Fullerene I. Basic Considerations

We study the Jahn-Teller effect of positive fullerene ions $^2$C$_{60}^{+}$ and $^1$C$_{60}^{2+}$. The aim is to discover if this case, in analogy with the negative ion, possesses a Berry phase or not, and what are the consequences on dynamical Jahn-Teller quantization. Working in the linear and spherical approximation, we find no Berry phase in $^1$C$_{60}^{2+}$, and presence/absence of Berry phase for coupling of one $L=2$ hole to an $L=4$/$L=2$ vibration. We study in particular the special equal-coupling case ($g_2=g_4$), which is reduced to the motion of a particle on a 5-dimensional sphere. In the icosahedral molecule, the final outcome assesses the presence/absence of a Berry phase of $\pi$ for the $h_u$ hole coupled to $G_g$/$H_h$ vibrations. Some qualitative consequences on ground-state symmetry, low-lying excitations, and electron emission from C$_{60}$ are spelled out.Comment: 31 pages (RevTeX), 3 Postscript figures (uuencoded

Spacial and temporal dynamics of the volume fraction of the colloidal particles inside a drying sessile drop

Using lubrication theory, drying processes of sessile colloidal droplets on a solid substrate are studied. A simple model is proposed to describe temporal dynamics both the shape of the drop and the volume fraction of the colloidal particles inside the drop. The concentration dependence of the viscosity is taken into account. It is shown that the final shapes of the drops depend on both the initial volume fraction of the colloidal particles and the capillary number. The results of our simulations are in a reasonable agreement with the published experimental data. The computations for the drops of aqueous solution of human serum albumin (HSA) are presented.Comment: Submitted to EPJE, 7 pages, 8 figure