The Sound Design Toolkit

The Sound Design Toolkit is a collection of physically informed sound synthesis models, specifically designed for practice and research in Sonic Interaction Design. The collection is based on a hierarchical, perceptually founded taxonomy of everyday sound events, and implemented by procedural audio algorithms which emphasize the role of sound as a process rather than a product. The models are intuitive to control \u2013 and the resulting sounds easy to predict \u2013 as they rely on basic everyday listening experience. Physical descriptions of sound events are intentionally simplified to emphasize the most perceptually relevant timbral features, and to reduce computational requirements as well

FePO(4)NPs Are an Efficient Nutritional Source for Plants: Combination of Nano-Material Properties and Metabolic Responses to Nutritional Deficiencies

Phosphorous and iron are a macro- and micronutrient, respectively, whose low bioavailability can negatively affect crop productivity. There is ample evidence that the use of conventional P and Fe fertilizers has several environmental and economical disadvantages, but even though great expectations surround nanotechnology and its applications in the field of plant nutrition, little is known about the mechanisms underlying the uptake and use of these sub-micron particles (nanoparticles, NPs) by crop species. This work shows that cucumber and maize plants both use the nutrients borne by FePO(4)NPs more efficiently than those supplied as bulk. However, morpho-physiological parameters and nutrient content analyses reveal that while cucumber plants (aStrategy Ispecies with regard to Fe acquisition) mainly use these NPs as a source of P, maize (aStrategy IIspecies) uses them preferentially for Fe. TEM analyses of cucumber root specimens revealed no cell internalization of the NPs. On the other hand, electron-dense nanometric structures were evident in proximity of the root epidermal cell walls of the NP-treated plants, which after ESEM/EDAX analyses can be reasonably identified as iron-oxyhydroxide. It appears that the nutritional interaction between roots and NPs is strongly influenced by species-specific metabolic responses

Graphical Encoding of a Spatial Logic for the pi-Calculus

This paper extends our graph-based approach to the verification of spatial properties of π-calculus specifications. The mechanism is based on an encoding for mobile calculi where each process is mapped into a graph (with interfaces) such that the denotation is fully abstract with respect to the usual structural congruence, i.e., two processes are equivalent exactly when the corresponding encodings yield isomorphic graphs. Behavioral and structural properties of π-calculus processes expressed in a spatial logic can then be verified on the graphical encoding of a process rather than on its textual representation. In this paper we introduce a modal logic for graphs and define a translation of spatial formulae such that a process verifies a spatial formula exactly when its graphical representation verifies the translated modal graph formula

Phytochemical investigations on Artemisia alba Turra growing in the North-East of Italy

Artemisia alba Turra (Asteraceae) is an Euro-Mediterranean plant used in Veneto (North-East of Italy) as traditional medicine for the treatment of various diseases. A. alba is a taxonomically problematic species, characterized by common polymorphism leading to a quite high variability in secondary metabolites content. Nonetheless, the phytochemical knowledge on its phytoconstituents, especially non-volatile components, is limited. In the present paper, the phytochemical composition of a tincture obtained from the aerial parts of A. alba growing in Veneto is presented. Extensive chromatographic separations led to the isolation of three new sesquiterpene derivatives, whose structures were elucidated by 1D and 2D NMR experiments and mass spectrometry. Furthermore, flavonoid composition and volatile constituents of the tincture of A. alba were preliminary studied by HPLC-MSn and GC-MS, respectivel

Integrated Structure and Semantics for Reo Connectors and Petri Nets

In this paper, we present an integrated structural and behavioral model of Reo connectors and Petri nets, allowing a direct comparison of the two concurrency models. For this purpose, we introduce a notion of connectors which consist of a number of interconnected, user-defined primitives with fixed behavior. While the structure of connectors resembles hypergraphs, their semantics is given in terms of so-called port automata. We define both models in a categorical setting where composition operations can be elegantly defined and integrated. Specifically, we formalize structural gluings of connectors as pushouts, and joins of port automata as pullbacks. We then define a semantical functor from the connector to the port automata category which preserves this composition. We further show how to encode Reo connectors and Petri nets into this model and indicate applications to dynamic reconfigurations modeled using double pushout graph transformation

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach

Background: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective: This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods: The study includes 283, 468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40, 000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results: The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions: This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes

Automata for true concurrency properties

We present an automata-theoretic framework for the model checking of true concurrency properties. These are specified in a fixpoint logic, corresponding to history-preserving bisimilarity, capable of describing events in computations and their dependencies. The models of the logic are event structures or any formalism which can be given a causal semantics, like Petri nets. Given a formula and an event structure satisfying suitable regularity conditions we show how to construct a parity tree automaton whose language is non-empty if and only if the event structure satisfies the formula. The automaton, due to the nature of event structure models, is usually infinite. We discuss how it can be quotiented to an equivalent finite automaton, where emptiness can be checked effectively. In order to show the applicability of the approach, we discuss how it instantiates to finite safe Petri nets. As a proof of concept we provide a model checking tool implementing the technique

Bioraznolikost mikrobnih konzorcija izoliranih iz tradicionalnog svježeg ovčjeg sira Karakačanski skakutanac

The aim of this study was to assess the structure of indigenous microbial community associated with traditional fresh sheep cheese Karakačanski skakutanac and to preserve autochthonous microbial consortia. Eleven cheeses were sampled during production season (April-September) and subjected to microbiological analysis. Bacterial DNA was isolated by Maxwell®16 DNA system from 99 microbial consortia harvested from three culture media (M17, Rogosa, CATC) on the 1st, 2nd and 3rd day of the cheese shelf life. The extracted bulk DNA (n = 99) was used as a template for PCR-ARDRA and PCR-DGGE analysis. There were no dramatic shifts in the bacterial number and structure of the microbial consortia harvested on the 1st, 2nd or 3rd day of the cheese shelf life neither during period of sampling. Lactococcus lactis subsp. lactis reached the number of 107-108 CFU g-1, while Leuconostoc pseudomesenteroides, Enterococcus faecalis, and Lactobacillus versmoldensis were identified only at lower dilutions (10-2 - 10-3). This first polyphasic microbiological-molecular study of the Karakačanski skakutanac indicated the main LAB representatives associated with the cheese. Obtained autochthonous microbial consortia present a valuable pool of strains for further genetic and functional characterizations.Cilj ovog rada bio je analizirati strukturu mikrobnih konzorcija tradicionalnog svježeg ovčjeg sira Karakačanski skakutanac. Jedanaest sireva sakupljeno je tijekom sezone proizvodnje od travnja do rujna. Mikrobni konzorciji sakupljeni su sa 3 hranjive podloge (M17, Rogosa, CATC) od 11 sireva nakon prvog, drugog, i trećeg dana proizvodnje. Mikrobna DNA je izolirana iz 99 konzorcija, te korištena u PCR-ARDRA i PCR-DGGE analizi. Nije bilo promjene u strukturi mikrobnih konzorcija sakupljenih prvog, drugog i trećeg dana nakon proizvodnje, niti tijekom sezone. Utvrđena je dominantnost populacije Lactococcus lactis subsp. lactis (107-108 CFU g-1), dok su ostale vrste bakterija mliječne kiseline, Leuconostoc pseudomesenteroides, Enterococcus faecalis i Lactobacillus versmoldensis, identificirane samo na nižim razrjeđenjima (10-2-10-3). Ova prva mikrobiološko- molekularna analiza tradicionalnog sira Karakačanski skakutanac omogućila je uvid u strukturu njegove specifične mikrobne populacije. Sakupljeni mikrobni konzorciji predstavljaju značajan izvor sojeva za daljnju genetsku i funkcionalnu karakterizaciju