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This study aims to couple two codes, one able to perform icing simulations and another one capable to simulate the performance of an electrothermal anti-icing system in an integrated fashion.

The classical tool chain of icing simulation (aerodynamics, water catch and impact, mass and energy surface balance) is coupled to the thermal analysis through the surface substrate and the ice thickness. In the present approach, the ice protection simulation is not decoupled from the ice accretion simulation, but a single computational workflow is considered.

A fast approach to simulate advanced anti-icing systems is found in this study.

This study shows the validation of present procedure against literature data, both experimental and numerical.

The purpose of this paper is to verify whether, besides the traditional organisational models mainly implemented by wine-making cooperatives, more modern and hybrid organisational forms can be profitably applied within an increasingly competitive wine market.

The study outlined in this paper deployed a mixed method. Specifically, an archived analysis, a survey and a descriptive case study (including visits, interviews and documentary analysis) were the methodological techniques used in this study, which were “in series but integrated” between themselves. In this paper, the landscape of Sicilian wine cooperatives is described by collating and processing different types of statistical sources, which have been integrated by direct surveys undertaken in 2017. Thereafter, the study focussed on a wine cooperative with a specific business model and a strategic edge by analysing its strategic choices and main structural and governance characteristics. Within this case study, a financial ratio analysis, which was based on 2011-2017 financial statements, was conducted to analyse the profitability, financial balance, capital structure and debt relationships of the wine cooperative.

The Sicilian wine cooperative system is still predominantly characterised by partial and vertical integration, implemented by cooperatives which elect to sell mainly bulk wine to wine merchants. In such a context, there is scope for other degrees of integration and strategic inter-firm alliances; the latter includes “vertical quasi-integration”. The study demonstrated how the wine cooperative under investigation is overcoming the structural problems of the regional wine sector and why it is retaining such a strategic alliance with one of the most important Italian wine conglomerates. Indeed, it has acquired greater strength and reliability since its collaboration with the aforementioned wine company. Thus, total revenue and the company’s market share of packaged wine have increased. However, there are still margins for improving sales’ profitability.

This study has territorial limitations but Sicilian wine cooperatives generally play an important role in the regional, Italian and European wine industries. As such, this research should be considered as an exploratory study, deserving further investigation into different strategic choices within the wine cooperative system by performing cross-case comparisons. Results may also be useful in orienting cooperative strategies in Sicily (or further afield) to small-to-medium wine cooperatives, often lacking specific abilities relating to the distribution, marketing and selling of their wine. Public agricultural policies may also be enlightened by these research pathways.

The authors contend that their study provides hitherto missing information relating to inter-firm strategic alliances, which wine cooperatives might implement to enhance their competitiveness and survive in the long-run.

The purpose of this paper is to propose a framework for intelligent analysis of Twitter data. The purpose of the framework is to allow users to explore a collection of tweets by extracting topics with semantic relevance. In this way, it is possible to detect groups of tweets related to new technologies, events and other topics that are automatically discovered.

The framework is based on a three-stage process. The first stage is devoted to dataset creation by transforming a collection of tweets in a dataset according to the vector space model. The second stage, which is the core of the framework, is centered on the use of non-negative matrix factorizations (NMF) for extracting human-interpretable topics from tweets that are eventually clustered. The number of topics can be user-defined or can be discovered automatically by applying subtractive clustering as a preliminary step before factorization. Cluster analysis and word-cloud visualization are used in the last stage to enable intelligent data analysis.

The authors applied the framework to a case study of three collections of Italian tweets both with manual and automatic selection of the number of topics. Given the high sparsity of Twitter data, the authors also investigated the influence of different initializations mechanisms for NMF on the factorization results. Numerical comparisons confirm that NMF could be used for clustering as it is comparable to classical clustering techniques such as spherical k-means. Visual inspection of the word-clouds allowed a qualitative assessment of the results that confirmed the expected outcomes.

The proposed framework enables a collaborative approach between users and computers for an intelligent analysis of Twitter data. Users are faced with interpretable descriptions of tweet clusters, which can be interactively refined with few adjustable parameters. The resulting clusters can be used for intelligent selection of tweets, as well as for further analytics concerning the impact of products, events, etc. in the social network.