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The aim of this article is to provide a brief introduction and description of SBN (Servizio Bibliotecario Nazionale), the Italian national network which has been working as a full‐scale union catalogue and shared cataloguing system since June 1992 and now connects 104 libraries. The gradual development of SBN over the past 10 years is described as well as its structure and architecture and its future possible developments.

The 26th annual seminar of the European Library Automation Group (ELAG) was held in Rome in April 2002. This report provides an overview of the papers presented, including those describing relevant developments in Italy (such as the Italian Digital Library), those covering the theme of the seminar (semantic Web and libraries), as well as papers on functional requirements for bibliographic records and others. Workshops are a key feature of the ELAG seminars and a brief mention is made of these.

To critically assess the contribution of community-based logging, low-carbon emission non-timber activities, and direct payment for environmental services in building sustainable rural livelihoods in the Amazon.

Fieldwork undertaken in 2008, 2010, and 2013 on sources of income for 110 interviewed families living in and around three different types of conservation units located on the advancing frontier in western Pará State.

Three scenarios identified with very different socio-environmental outcomes, (1) the multi-functional combination of agricultural and non-agricultural activities replaces frontier farming, reduces deforestation and carbon emissions, increases income, and promotes social inclusion, (2) the mere juxtaposition of green alternative activities alongside unsustainable frontier farming has limited regional impact, and (3) environmental restrictions interfere with rural livelihoods to the point that people to leave the countryside.

Evaluation of the effectiveness of GO and NGO policies in one of the poorest and environmentally problematic regions of Brazil.

First-hand information at the family farmer level concerning sources of income from conventional and green land use systems which is essential for formulating viable socio-environmental policy capable of reducing deforestation and carbon emissions which negatively impact global climate change.

The purpose of this paper is to evaluate the shelf-life of minimally processed pineapple when subjected to the use of different edible coatings.

The pineapples were peeled and cut into cubes. The gums were prepared by dissolving them in distilled water and then heated to total dissolution. After calcium chloride, citric acid and ascorbic acid and glycerol were added in the solutions. The pieces of pineapple were completely submerged in the respective solutions and then drained. Four treatments were obtained, namely: T1 – control treatment (pineapple without coating); T2 – pectin; T3 – tara; T4 – xanthan. The cubes were stored in PET by 12 days at 4±1°C. Analyzes were carried out of mass loss, pH, titratable acidity, soluble solids, microbiological and sensory analysis.

It was possible to observe that the use of evaluated coatings was efficient to maintain the conservation of minimally processed pineapple in all analyzes, when compared with the control sample. The treatment with tara gum showed the best results to those obtained by the other studied gums.

The study may help small-scale establishments to increase the shelf-life of minimally processed pineapple.

Tara gum reduced the mass loss, delayed the microbial growth and maintained the sensorial quality of minimally processed pineapples for a longer time.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

The goal of this paper is to model the activated carbon adsorption capacity of cadmium using a polynomial regression model. The properties that influence the removal of heavy metals by activated carbon must be taken into consideration in order to synthesize materials specific to the characteristics of the pollutant.

Multivariable analysis applications allow a comprehensive description of the relationship between activated carbon surface properties and heavy metal adsorption.

The authors use a second-grade polynomial regression model to determine the influence of porosity and surface acidity in cadmium adsorption by activated carbon.

The authors propose a statistic analysis to correlate the carbon properties with its cadmium adsorption capacity. Model coefficient analysis will give a better comprehension of the influence of activated carbon porosity and surface acidity of cadmium removal.