Database activity in the Italian Astronet: DIRA 2

The development and utilization of informational archives and databases started, in the Italian Astronet Project, in the middle of 1983. In that year, a small group of astronomers and some more technical people met together in an Astronet working group, with a common, painful experience in managing astronomical catalogues and archives with computers. Nowadays, some years later, some software packages and the contents of both, a relative general database and several local databases represent the work and the effort of the group. The systems have been conceived and developed keeping in mind the original goal of the group: to allow the single atronomer to make a free use of original data. The main package (DIRA) was rewritten, after some years of use, to fully take advantage of the several suggestions of the astronomer that used it and gathered experiences in the astronomical catalog's management. A more technical goal was to install the whole system, born and developed in the vms environment, on unix and unix-like systems. This new version, DIRA2, has a new user interface, a query language with SQL style commands supporting numerical and character functions also and a set of commands to create new catalogues from existing data. The graphics commands are also more powerful with respect to the previous version. DIRA (and DIRA2 of course) philosophy and design are very simple and proved to be very appreciated by astronomers, namely, to normalize and homogenize, at minimum, astronomical catalogues, to collect satisfactory astronomical documentation on their contents and, finally, to allow an astronomical approach to the dialogue with the database. DIRA2 is currently used in most Italian astronomical institutes to retrieve data from a still growing database of about 140 well documented and controlled astronomical catalogues, for the identification of objects and the preparation of a 'medium size' survey, in astrometry and in the creation of new catalogues

Effect of lairage duration on some blood constituents and beef quality in bulls after long journey

Al fine di contribuire alla individuazione di un tempo adeguato di sosta al termine di un lungo trasporto, sono stati esaminati gli effetti di diverse durate della sosta al macello su alcuni parametri ematici e sulla qualit\ue0 della carne in bovini maschi interi sottoposti a viaggi commerciali di lunga durata. Lo studio \ue8 stato condotto su 39 vitelloni Limousine allevati nelle medesime condizioni presso un unica azienda situata nelle vicinanze di Saragoza (Spagna). Gli animali sono stati esaminati al termine di 5 viaggi commerciali dopo un tragitto di 2500 km, presso lo stabilimento di macellazione \u201cSan Giorgio\u201d di Gangi (Palermo). Il tempo di trasporto \ue8 stato pari a ore 53,6\ub110,9. Per i soggetti della prima, della terza e della quinta consegna, la durata della sosta \ue8 risultata compresa tra 24 e 36 ore, con una media pari a 31 ore (gruppo \u201cShort Lairage\u201d), mentre nella terza e nella quinta consegna la sosta \ue8 stata pari, rispettivamente, a 57 e 59 ore (gruppo \u201cLong Lairage\u201d). I risultati relativi all\u2019esame emocromo-citometrico, hanno evidenziato un effetto significativo (P>0,001) della durata della sosta pre-macellazione sia sul numero dei leucociti che sulle piastrine. La durata della sosta non ha mostrato avere nessun effetto significativo su globuli rossi, emoglobina ed ematocrito anche se l\u2019analisi della varianza per misure ripetute ha mostrato che, indipendentemente dalla durata della sosta, l\u2019ematocrito \ue8 variato significativamente dal momento dello scarico a quello della macellazione, aumentando durante tale periodo. L\u2019analisi statistica effettuata sui parametri ematochimici ha evidenziato un effetto significativo (P>0,05) della durata della sosta solo sull\u2019enzima CK e sul cortisolo. L\u2019enzima CK ha mostrato un incremento nel gruppo \u201cShort Lairage\u201d (33,2% vs 14,3%) mentre il Cortisolo ha mostrato una diminuzione nel gruppo \u201cLong Lairage\u201d (36,3% vs 3,8%). La durata della sosta non ha influenzato significativamente (P>0,05) l\u2019incidenza di lesioni lievi e gravi registrate sulle carcasse. Per quanto concerne la qualit\ue0 della carne, la durata della sosta ha influenzato significativamente il pHu, risultato pi\uf9 elevato (P>0,01) nel muscolo dei soggetti del gruppo \u201cLong Lairage\u201d; la luminosit\ue0 a 24h post mortem \ue8 risultata significativamente pi\uf9 elevata (P>0,05) nei soggetti del gruppo \u201cShort Lairage\u201d rispetto a quelli del gruppo \u201cLong Lairage\u201d, mentre gli indici del rosso e del giallo sono risultati essere maggiori in quest\u2019ultimo gruppo. Il calo peso dopo cottura \ue8 risultato significativamente minore (P>0,01) nel gruppo \u201cShort Lairage\u201d, lo stesso gruppo \u201cShort Lairage\u201d ha fatto registrare carni significativamente pi\uf9 tenere (P>0,01). Dai risultati ottenuti emerge come la durata della sosta pre-macellazione dopo un trasporto di lunga durata pu\uf2 influenzare il quadro ematologico e la qualit\ue0 della carne. Nel complesso \ue8 emerso che prolungare la sosta oltre le 36 ore non provoca alcun beneficio per il benessere dell\u2019animale e rischia di peggiorare la qualit\ue0 della carne. Nel caso di trasporti cos\uec lunghi come quelli esaminati sarebbe opportuno una migliore organizzazione della logistica al fine di ridurre il tempo di attesa degli animali prima della macellazione.With the aim to contribute to determine an adequate resting time for cattle after long transportation, the effects of different lairage time on some haematic parameters and meat quality of bulls subjected to long commercial journeys were investigated. Thirty-nine Limousine bulls supplied by one farm located near to Saragoza (Spain) were examined after 5 consignments at the final destination, after a journey of 2.550 km, of the \u201cSan Giorgio\u201d abattoir (Palermo, Italy). Transport time was of 53.6 \ub1 10.9 h; lairage duration for bulls of the 1st, 3rd and 5th consignments was of 31 h on average (\u201cShort Lairage\u201d group), whereas, for those of the 2nd and 4th consignments, was of 59 and 57 h, respectively (\u201cLong Lairage\u201d group). As regards the blood cell counts, data showed a significant effect (P< 0.001) of the lairage duration on leukocyte and platelet counts. No significant effect was observed for erythrocyte count, haemoglobin and hematocrit in relation to the lairage duration, even if the repeated measure analysis of variance showed that, irrespectively to the lairage duration, the hematocrit increased significantly from the unloading to the slaughter. Haematological parameters showed a significant (P< 0.05) effect of the lairage time only on CK and Cortisol. CK enzyme showed an increase in the \u201cShort Lairage\u201d group (33.2% vs. 14.3%) whereas, Cortisol showed a decrease in the \u201cLong Lairage\u201d group (36.3% vs. 3.8%). The different lairage duration did not significantly (P> 0.05) affect the incidence of slight and severe bruises of carcass. As regard meat quality, lairage duration has significantly influenced the pHu which was higher (P< 0.01) in the muscle of the animals of the \u201cLong Lairage\u201d group, the luminosity at 24h post mortem which was significantly higher (P< 0.05) in animals of the \u201cShort Lairage\u201d group and the red and yellow indices which were higher in the \u201cLong Lairage\u201d group. \u201cShort Lairage\u201d group showed lower (P< 0.01) value of cooking loss and higher (P< 0.01) value of tenderness. Data show that pre-slaughter lairage duration after a long transport may influence the blood parameters as well as meat quality. On the whole, the increase of the lairage duration over 36 h does not determine any benefit for the animal well-being whereas it can cause a reduction of the beef quality. For so long transports, it should be better an adequate organization of the facilities in order to diminish the pre-slaughter lairage duration

Comparison of nonlinear growth models and factors affecting body weight at different ages in Toy Poodles

Limited information is available to evaluate optimal growth in Toy Poodles. This work aimed at comparing three growth curves, proposing centile charts and developing a model to estimate the adult body weight (BW) in Toy Poodles. A total of 65 puppies (male = 30, female = 35) born in the same breeding centre were used. BW at birth and, weekly BW, for 53 weeks, BW of parents, litter size, type of management, daily activity and neutering were recorded. Forty-six puppies were sold, and their data were reported by the new owners. Three growth curves (i.e. Hawthorne, Brody and Gompertz) were constructed and compared; Linear Mixed Models including demographic characteristics and management habits were built. The BW at birth was 154 \ub1 35 g and adult BW was 3208 \ub1 860 g. Based on the goodness-of-fit and accuracy indices, Gompertz was the best growth model and was selected to plot centile curves based on sex. Toy Poodles achieved 50% of their adult weight at 11\u201312 weeks, with an overall growth rate of 11.8%. Adult BW was affected by birth BW (p <.01), sex (p <.05) and mother\u2019s BW (p <.01) and their effects varied depending on the age. Extrinsic factors, including litter size, type of management and daily activity were less significant, probably due to the standardised and high-level management of these Toy Poodles. These new and applicable tools for monitoring the growth and predicting adult BW could be useful for veterinarians, breeders and owners for early diagnosis of poor health and welfare. Subject classification codes: companion animals sectionsHighlights Performance of three logistic models for describing the growth curve in Italian Toy Poodles were compared Based on the goodness of fit and accuracy indices, Gompertz was the best growth model The centile growth curves were constructed for males and females using the Gompertz Adult body weight (BW) was mainly affected by the sex and birth BW, and less by BW of the parents and litter size Monitoring BW of puppies may be useful to enhance their health and welfare

Spectrogram classification using dissimilarity space

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs

Animal sound classification using dissimilarity spaces

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four different backbones with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, experiments process the spectrograms using the heterogeneous auto-similarities of characteristics. Once the similarity spaces are computed, each pattern is \u201cprojected\u201d into the space to obtain a vector space representation; this descriptor is then coupled to a support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best standalone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset

La responsabilit\ue0 del notaio nei procedimenti di fusione societaria

Esame delle lquestioni che possono insorgere quando la delibera assunta con la partecipazione del notaio concerne una fusione societari

Postprocessing for skin detection

Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences

Feature transforms for image data augmentation

A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature