Abstract
This paper presents a platform for real and simulated autonomous sailing competitions, which can also be used as a research tool to test and assess navigation algorithms. The platform provides back-end services – competition server, boat modelling and data storage – and supports external browsers and software agents as front-end clients. The back-end adopts the Multi-Agent System (MAS) paradigm for the internal modelling of sailing boats and offers a Web Service Application Programming Interface (API) for the external software agents and a Web application for Web browsers. As a whole, the platform offers tracking (real competitions) and simulation (simulated competitions) modes. The testing and assessment of navigation algorithms and boat models correspond to private simulated competitions. In simulation mode, the back-end internal boat agent implements a simplified physical model, including the weight, sail area, angle of the sail and rudder, velocity and direction of the wind and position and velocity of the hull, whereas the front-end external boat agent implements the navigation algorithm on the team side, ensuring the privacy of strategic knowledge. The Web application allows the configuration and launching of competitions, the registration of teams and researchers, the uploading of boat physical features for simulation as well as the live or playback viewing of real and simulated competitions. The simulation mode is illustrated with the help of a case study. The proposed platform, which is open, scalable, modular and distributed, was designed for the research community to prepare, run and gather data from real and simulated autonomous sailing competitions.

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master-worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances.

Abstract
Sustainability is gaining interest among academics and practitioners due to increased stakeholder awareness of environmental and social issues. However, relatively little research has been conducted on the extent to which firms have integrated social sustainability aspects into the management of their supply chains in emerging economies. The purpose of this article is to explore the social issues pertinent to suppliers and to identify measures and dimensions related to social sustainability in emerging economies. Further, it explores the benefits suppliers and buyers gain by effectively managing such social issues. For this purpose, first, in-depth interviews were conducted with 27 supply chain managers. Further, a survey was conducted in Indian manufacturing industries and co-variance-based structural equation modeling was used to test the hypothesized model. The findings reveal that there are 18 validated supplier social sustainability measures underlying five social dimensions: labor rights, safety and health, societal responsibility, diversity, and product responsibility. The results also suggest a positive relationship between supplier social sustainability practices and supply chain performance mediated by supplier performance. In addition, the role of the buyer's commitment and investment moderates both suppliers and supply chain performance. These results are relevant because they not only identify the social issues plaguing supply chains in emerging economies, but also have practical implications for organizations trying to build socially sustainable supply chains for competitive advantage.

Abstract
Firms are increasingly under pressure to incorporate social sustainability practices into their operations and supply chain management strategies. The purpose of this research is to develop a taxonomy of the supply chain social sustainability (SCSS) practices adopted by firms. The methodology involves two steps. First, the taxonomy is built based on literature review. Second, our empirical analysis, using non-financial (sustainability) reports with a sample of 55 listed companies, identifies standard adoption practices. We used content analysis software to unearth the influential words in the sustainability reports from different industries, sizes, and geographical regions. The content analysis reveals three different themes that provide a snapshot of how Portuguese firms integrate social sustainability into their supply chain and operations. Firms emphasize diverse facets of social sustainability practices in upstream and downstream supply chain based on different industries. The results assume significance and provide unique insights on adoption practices to supply chain practitioners who otherwise have no information on what constitutes supply chain social sustainability in this region.

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