A catalogue of open cluster radii determined from Gaia proper motions

In this work we improve a previously published method to calculate in a reliable way the radius of an open cluster. The method is based on the behaviour of stars in the proper motion space as the sampling changes in the position space. Here we describe the new version of the method and show its performance and robustness. Additionally, we apply it to a large number of open clusters using data from Gaia DR2 to generate a catalogue of 401 clusters with reliable radius estimations. The range of obtained apparent radii goes from Rc=1.4+-0.1 arcmin (for the cluster FSR 1651) to Rc=25.5+-3.5 arcmin (for NGC~2437). Cluster linear sizes follow very closely a lognormal distribution with a mean characteristic radius of Rc=3.7 pc, and its high radius tail can be fitted by a power law as N \propto Rc^(-3.11+-0.35). Additionally, we find that number of members, cluster radius and age follow the relationship Nc \propto Rc^(1.2+-0.1) Tc^(-1.9+-0.4) where the younger and more extensive the cluster, the more members it presents. The proposed method is not sensitive to low density or irregular spatial distributions of stars and, therefore, is a good alternative or complementary procedure to calculate open cluster radii not having previous information on star memberships.Comment: 13 pages including 9 figures and 2 tables (main table will be available online). Accepted for publication in MNRA

Trade-off between optimal design and operation in district cooling networks

Especially in densely populated areas, district cooling represents an opportunity to reduce energy consumption and emissions. Nevertheless, this technology is characterised by large capital costs which impede its diffusion. As a consequence, optimization tools can significantly help to unleash their potential. In this paper, a methodology is proposed to combinedly optimize the design and operation of a district cooling system based on a Mixed Integer Quadratic Programming. The model is compared to the design only optimization, based on a properly tailored heuristic approach. The models, when applied to a case study characterized by seasonal demand, provide similar solutions, which differ by 0.5 % in terms of objective value for a standard scenario. The simultaneous design and operation optimization does not provide sensible savings with respect to optimizing solely the design. A sensitivity analysis is performed to prove the robustness of the results. The results showed that the simulta- neous operation and design optimization would be limited to 1 % of total costs in the case of seasonal cooling demand. On the other hand, if the cooling demand persists throughout the year, as in tropical climates, the combined optimization provides significant benefits, since these savings reach 4.7 % of total costs

Technology Forecasting Using Data Mining and Semantics: First Annual Report

The planning and management of research and development is a challenging process which is compounded by the large amounts of information which is available. The goal of this project is to mine science and technology databases for patterns and trends which facilitate the formation of research strategies. Examples of the types of information sources which we exploit are diverse and include academic journals, patents, blogs and news stories. The intended outputs of the project include growth forecasts for various technological sectors (with an emphasis on sustainable energy), an improved understanding of the underlying research landscape, as well as the identification of influential researchers or research groups. This paper focuses on the development of techniques to both organize and visualize the data in a way which reflects the semantic relationships between keywords. We studied the use of the joint term frequencies of pairs of keywords, as a means of characterizing this semantic relationship – this is based on the intuition that terms which frequently appear together are more likely to be closely related. Some of the results reported herein describe: (1) Using appropriate tools and methods, exploitable patterns and information can certainly be extracted from publicly available databases, (2) Adaptation of the Normalized Google Distance (NGD) formalism can provide measures of keyword distances that facilitate keyword clustering and hierarchical visualization, (3) Further adaptation of the NGD formalism can be used to provide an asymmetric measure of keyword distances to allow the automatic creation of a keyword taxonomy, and (4) Adaptation of the Latent Semantic Approach (LSA) can be used to identify concepts underlying collections of keywords

Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods

Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities

A fast and cost-effective approach to develop and map EST-SSR markers: oak as a case study

Background: Expressed Sequence Tags (ESTs) are a source of simple sequence repeats (SSRs) that can be used to develop molecular markers for genetic studies. The availability of ESTs for Quercus robur and Quercus petraea provided a unique opportunity to develop microsatellite markers to accelerate research aimed at studying adaptation of these long-lived species to their environment. As a first step toward the construction of a SSR-based linkage map of oak for quantitative trait locus (QTL) mapping, we describe the mining and survey of EST-SSRs as well as a fast and cost-effective approach (bin mapping) to assign these markers to an approximate map position. We also compared the level of polymorphism between genomic and EST-derived SSRs and address the transferability of EST-SSRs in Castanea sativa (chestnut). Results: A catalogue of 103,000 Sanger ESTs was assembled into 28,024 unigenes from which 18.6% presented one or more SSR motifs. More than 42% of these SSRs corresponded to trinucleotides. Primer pairs were designed for 748 putative unigenes. Overall 37.7% (283) were found to amplify a single polymorphic locus in a reference fullsib pedigree of Quercus robur. The usefulness of these loci for establishing a genetic map was assessed using a bin mapping approach. Bin maps were constructed for the male and female parental tree for which framework linkage maps based on AFLP markers were available. The bin set consisting of 14 highly informative offspring selected based on the number and position of crossover sites. The female and male maps comprised 44 and 37 bins, with an average bin length of 16.5 cM and 20.99 cM, respectively. A total of 256 EST-SSRs were assigned to bins and their map position was further validated by linkage mapping. EST-SSRs were found to be less polymorphic than genomic SSRs, but their transferability rate to chestnut, a phylogenetically related species to oak, was higher. Conclusion: We have generated a bin map for oak comprising 256 EST-SSRs. This resource constitutes a first step toward the establishment of a gene-based map for this genus that will facilitate the dissection of QTLs affecting complex traits of ecological importance