How alternative food networks work in a metropolitan area? An analysis of Solidarity Purchase Groups in Northern Italy

Our paper focuses on Solidarity Purchase Group (SPG) participants located in a highly urbanized area, with the aim to investigate the main motivations underlining their participation in a SPG and provide a characterization of them. To this end, we carried out a survey of 795 participants involved in 125 SPGs in the metropolitan area of Milan (Italy). Taking advantage of a questionnaire with 39 questions, we run a factor analysis and a two-step cluster analysis to identify different profiles of SPG participants. Our results show that the system of values animating metropolitan SPG practitioners does not fully conform to that traditionally attributed to an alternative food network (AFN). In fact, considerations linked to food safety and healthiness prevail on altruistic motives such as environmental sustainability and solidarity toward small producers. Furthermore, metropolitan SPGs do not consider particularly desirable periurban and local food products. Observing the SPGs from this perspective, it emerges as such initiatives can flourish also in those places where the lack of connection with the surrounding territory is counterbalanced by the high motivation to buy products from trusted suppliers who are able to guarantee genuine and safe products, not necessarily located nearby

Optimizing Antenna Arrays for Spatial Multiplexing: Towards 6G Systems

In this paper we discuss the design of antenna arrays to be used for multiplexing applications. In particular, we introduce a suitable performance index to analyze the effect of the antenna geometry and the distribution of users for the overall performance of Multi-User Multiple Input Multiple Output systems. By means of such performance index, antenna arrays can be designed so as to increase the number of multiplexed parallel sub-channels. Numerical results show that a proper design could allow to double the contemporary served users and the overall system throughput

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches

The role of network connectivity on epileptiform activity

A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models

Interference by cortisone with endotoxin's adjuvator action on transplantation of a mouse tumour.

Observations on the in vivo plating of mouse mammary tumour are extended by making counts of tumours at a significantly earlier phase of development than in previously reported work. In the experiments now described, most of the growth of the tumours has been without benefit of stroma. The noteworthy economy of the experimental method is discussed. The persistence of endotoxin's adjuvator effect on such tumour counts is tested in the face of gamma irradiation and cortisone. Cortisone, it is found, offsets endotoxin's adjuvator action; irradiation does not. Antagonism between endotoxin and cortisone, in this system with tumour cells plated in vivo, seems to indicate that endotoxin's enhancing effect depends more on inflammatory than on immunological factors

A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization

A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm and it exploits some a-priori information on the antenna under test (AUT) to generate an over-complete representation basis. Accordingly, the problem at hand is reformulated in a compressive sensing (CS) framework as the retrieval of a maximally-sparse distribution (with respect to the overcomplete basis) from a reduced set of measured data and then it is solved by means of a Bayesian strategy. Representative numerical results are presented to, also comparatively, assess the effectiveness of the proposed approach in reducing the "burden/cost" of the acquisition process as well as to mitigate (possible) truncation errors when dealing with space-constrained probing systems.Comment: Submitted to IEE

Radar array diagnosis from undersampled data using a compressed sensing/sparse recovery technique

A Compressed Sensing/Sparse Recovery approach is adopted in this paper for the accurate diagnosis of fault array elements from undersampled data. Experimental validations on a slotted waveguide test array are discussed to demonstrate the effectiveness of the proposed procedure in the failures retrieval from a small set of measurements with respect to the number of radiating elements. Due to the sparsity feature of the proposed formulation, the method is particularly appealing for the diagnostics of large arrays, typically adopted for radar applications