Wet-white shavings as a potential source for leather retanning bioagents

The tanning industry generates a high quantity of solid wastes, so there is a need to create ways to value these wastes with aim to reduce environmental impact. A lot of research work has been done recently and some authors have shown the potential for obtaining protein hydrolysates from solid wastes and its application. The present work had a main objective the wet-white shavings valorization by production of hydrolysed protein and biopolymrs for leather retanning.The authors would like to acknowledge IAPMEI for the support of the Project LSW2Chem 45319 by Portugal 2020 ProgrammeN/

Exploring events and distributed representations of text in multi-document summarization

In this article, we explore an event detection framework to improve multi-document summarization. Our approach is based on a two-stage single-document method that extracts a collection of key phrases, which are then used in a centrality-as-relevance passage retrieval model. We explore how to adapt this single-document method for multi-document summarization methods that are able to use event information. The event detection method is based on Fuzzy Fingerprint, which is a supervised method trained on documents with annotated event tags. To cope with the possible usage of different terms to describe the same event, we explore distributed representations of text in the form of word embeddings, which contributed to improve the summarization results. The proposed summarization methods are based on the hierarchical combination of single-document summaries. The automatic evaluation and human study performed show that these methods improve upon current state-of-the-art multi-document summarization systems on two mainstream evaluation datasets, DUC 2007 and TAC 2009. We show a relative improvement in ROUGE-1 scores of 16% for TAC 2009 and of 17% for DUC 2007.info:eu-repo/semantics/submittedVersio

MARTA: A high-energy cosmic-ray detector concept with high-accuracy muon measurement

A new concept for the direct measurement of muons in air showers is presented. The concept is based on resistive plate chambers (RPCs), which can directly measure muons with very good space and time resolution. The muon detector is shielded by placing it under another detector able to absorb and measure the electromagnetic component of the showers such as a water-Cherenkov detector, commonly used in air shower arrays. The combination of the two detectors in a single, compact detector unit provides a unique measurement that opens rich possibilities in the study of air showers.Comment: 11 page

Semantically Aware Text Categorisation for Metadata Annotation

In this paper we illustrate a system aimed at solving a longstanding and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.</p

Gastronomy and Wine in the Alentejo Portuguese Region: Motivation and Satisfaction of Turists from Évora

Food and winemaking are a recognized tangible and intangible culturalheritage of Portugal. From the relationshipbetween these twocomponents, astrategic product emerged with a considerable potential for tourism industry, which is notignored bymany of tourism organizations. This chapter intends to analyze food and winemaking from atourism demand perspective. Particularly, this study describes visitors’ profi le, including, their motivations, their knowledgeabout theenological and gastronomicresourcesand the degreeof satisfaction. A total of 308 questionnaires were collected between February and May of 2012, from the visitors that visited the historic center of Évora (Alentejo-Portugal). Results reveal a visitor profi le associated with regional cuisine and wine products from Portugal. Moreover, visitors’ evidenced a high level of knowledge regarding the Portuguese cuisine and regional wines; although this not matches with their primary motivation for visit the city of Évora

Effects of the electronic threshold on the performance of the RPC system of the CMS experiment

Resistive Plate Chambers have a very important role for muon triggering both in the barrel and in the endcap regions of the CMS experiment at the Large Hadron Collider (LHC). In order to optimize their performance, it is of primary importance to tune the electronic threshold of the front-end boards reading the signals from these detectors. In this paper we present the results of a study aimed to evaluate the effects on the RPC efficiency, cluster size and detector intrinsic noise rate, of variations of the electronics threshold voltage

Machine Learning based tool for CMS RPC currents quality monitoring

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future