Effects of milk high pressure homogenization on biogenic amine accumulation during ripening of ovine and bovine Italian cheeses

The aim of this work was to evaluate the biogenic amine (BA) content during the ripening of both bovine and ovine cheeses obtained using milk subjected to a homogenization treatment at 100 MPa before cheese-making. The data obtained were compared with those from cheeses produced by the same milks without any treatment or thermized. The results showed that both microbial ecology and BA concentrations of cheeses during ripening were significantly influenced by the type of milk used for cheese-making and by the treatment applied to the raw materials. In particular, the microbial counts found in Caciotta indicated that the high pressure homogenization (HPH) of milk significantly reduced the presence of the yeasts, Micrococcaceae and lactobacilli at the end of ripening. On the other hand, the HPH treatment of milk favoured the proliferation of yeasts in ovine cheese. Moreover, the ovine cheeses were characterized by a remarkably higher accumulation of BA than bovine cheeses. However, the HPH treatment of milk was able to drastically reduce the biogenic amine concentrations in both cheese typologies at the end of ripening.     

Effect of surface modification on NO2 sensing properties of SnO2 varistor-type sensors

NO₂ sensing properties of SnO₂-based varistor-type sensors have been investigated in the temperature range of 400-650°C and in the NO₂ concentration range of 15–30 ppm. Pure SnO₂ exhibited a weak nonlinear I–V characteristic in air, but clear nonlinearity in NO₂ at 450°C. The breakdown voltage of SnO₂ shifted to a high electric field upon exposure to NO₂ and the magnitude of the shift was well correlated with NO₂ concentration. Thus, SnO₂ exhibited some sensitivity to NO₂ as a varistor-type sensor. When SnO₂ particles coated with a SiO₂ thin film were used as a raw material for fabricating a varistor, the breakdown voltage in air was approximately the double that of pure SnO₂ and the sensitivity to 15 ppm NO₂ was enhanced slightly. However, the sensitivity to 30 ppm NO₂ decreased. The Cr₂O₃-loading on SnO₂ also led to an increase in the breakdown voltage in air, but the Cr₂O₃ addition was not effective for promoting the NO₂ sensitivity under the present experimental conditions.     

Usage of SoS methodologies in production system design

Production systems design is a multifaceted task, due to a variety of aspects such as the mutual interdependency between the sub-systems, the variety of configurations and alternative system control strategies, the multiple managerial and “soft” aspects that cast an influence on the behavior of the system. A reasonable number of modeling tools can be applied to production system design, but they tend to divide the problem into unconnected sub-problems whose individual solutions may result in a poor global one. This is despite the fact that production design encompasses all aspects of manufacturing operations, and needs a systemic approach, as clearly shown in practitioner-oriented literature. This paper proposes to apply the “System of Systems” approach to production system design in order to represent their main aspects and support the rational definition of the path leading from corporate strategy to system (re)design.     

A comparison of graph-based word sense induction clustering algorithms in a pseudoword evaluation framework

This article presents a comparison of different Word Sense Induction (wsi) clustering algorithms on two novel pseudoword data sets of semantic-similarity and co-occurrence-based word graphs, with a special focus on the detection of homonymic polysemy. We follow the original definition of a pseudoword as the combination of two monosemous terms and their contexts to simulate a polysemous word. The evaluation is performed comparing the algorithm’s output on a pseudoword’s ego word graph (i.e., a graph that represents the pseudoword’s context in the corpus) with the known subdivision given by the components corresponding to the monosemous source words forming the pseudoword. The main contribution of this article is to present a self-sufficient pseudoword-based evaluation framework for wsi graph-based clustering algorithms, thereby defining a new evaluation measure (top2) and a secondary clustering process (hyperclustering). To our knowledge, we are the first to conduct and discuss a large-scale systematic pseudoword evaluation targeting the induction of coarse-grained homonymous word senses across a large number of graph clustering algorithms.     

On the use of formal methods to model and verify neuronal archetypes

Having a formal model of neural networks can greatly help in understanding and verifying their properties, behavior, and response to external factors such as disease and medicine. In this paper, we adopt a formal model to represent neurons, some neuronal graphs, and their composition. Some specific neuronal graphs are known for having biologically relevant structures and behaviors and we call them archetypes. These archetypes are supposed to be the basis of typical instances of neuronal information processing. In this paper we study six fundamental archetypes (simple series, series with multiple outputs, parallel composition, negative loop, inhibition of a behavior, and contralateral inhibition), and we consider two ways to couple two archetypes: (i) connecting the output(s) of the first archetype to the input(s) of the second archetype and (ii) nesting the first archetype within the second one. We report and compare two key approaches to the formal modeling and verification of the proposed neuronal archetypes and some selected couplings. The first approach exploits the synchronous programming language Lustre to encode archetypes and their couplings, and to express properties concerning their dynamic behavior. These properties are verified thanks to the use of model checkers. The second approach relies on a theorem prover, the Coq Proof Assistant, to prove dynamic properties of neurons and archetypes.     

Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution

Wireless Body Area Sensor Networks (WBASN) are an emerging technology enabling the design of natural human–computer interfaces (HCI). Automatic recognition of human motion, gestures, and activities is studied in several contexts. For example, mobile computing technology is being considered as a replacement of traditional input systems. Moreover, body posture and activity monitoring can be used for entertainment and health-care applications. However, until now, little work has been done to develop flexible and efficient WBASN solutions suitable for a wide range of applications. Their requirements pose new challenges for sensor network designs, such as optimizing traditional solutions for use as environmental monitoring-like applications and developing on-the-field stress tests. In this paper, we demonstrate the flexibility of a custom-designed WBASN called WiMoCA with respect to a wide range of posture and activity recognition applications by means of practical implementation and on-the-field testing. Nodes of the network mounted on different parts of the human body exploit tri-axial accelerometers to detect its movements. The advanced digital Micro-electro-mechanical system (MEMS) based inertial sensor has been chosen for WiMoCA because it demonstrated high flexibility of use in many different situations, providing the chance to exploit both static and dynamic acceleration components for different purposes. Furthermore, the sensibility and accuracy of the sensing element is perfectly adequate for monitoring human movement, while keeping cost low and size compact, thus meeting our requirements. We implemented three types of applications, stressing the WBASN in many aspects. In fact, they are characterized by different requirements in terms of accuracy, timeliness, and computation distributed on sensing nodes. For each application, we describe its implementation, and we discuss results about performance and power consumption.