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Systematic Literature Review on Smart Specialization: Future Prospects and Opportunities

Beatriz Rosas Michael Demmler (2023, [Artículo])

"Smart specialisation (SS) has been the new cohesion policy in the European Union during the last two periods. The present study aims to analyse the most relevant existing state-of-the-art literature on smart specialisation through a systematic and bibliometric review. Using the Web of Science bibliographic database, we analysed the content of 207 articles under the TCCM methodology and constructed a network of citations in order to summarize theories, characteristics, context and methods presented in existing studies on the topic. Our results show the theoretical and methodological gaps of the past, such as Entrepreneurial Discovery Process and SS indicators. These remain to the present day. The context analysis showed that the scope of smart specialisation extended beyond the frontiers of the European Union, given how it has been adopted by other countries as well. These results suggest the importance of developing a more robust theoretical, conceptual and methodological framework. Consequently, the guides need to be more accurate and should be continuously updated. Our results are valuable for the EDP actors and have policymaking implications".

Especialización inteligente Estrategias de innovación regional Revisión de literatura sistemática Métodos de especialización inteligente Smart specialization Smart specialization methods CIENCIAS SOCIALES CIENCIAS SOCIALES

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding