Abstract
Previously, two municipal solid waste commercial composts (MSW1 and MSW2) were characterized. Although sharing the same type of raw material, most of their physicochemical, stability and maturity properties differed. The present study aimed to characterize them at a microbiological level, and to infer on possible relationships between the composts properties and the structure of their bacterial communities. Both the 16S rRNA gene-based PCR-DGGE profiling and 454-pyrosequencing technology showed that the structure of the bacterial communities of these composts was distinct. The bacterial community of MSW1 was more diverse than that of MSW2. Multivariate analyses revealed that the high electrical conductivity, Cu content as well as the low phytotoxity of compost MSW1, when compared to MSW2, contributed most to shape its bacterial community structure. Indeed, high abundance of halophilic (Halomonadaceae and Brevibacteriaceae) and metal resistant organisms (Brevibacteriaceae and Bacillaceae) were found in MSW1. In addition, Pseudonocardiaceae, Streptomycetaceae, Bacillaceae, and Brevibacteriaceae may have contributed to the high humic-like acids content and low phytotoxicity of MSW1. In contrast, the high organic matter content and the high density of the cultivable fungi population were the parameters most correlated with the structure of the bacterial community of compost MSW2, dominated by Corynebacteriaceae and mainly Aerococcaceae, taxonomic groups not commonly found in composts.

Abstract
The use of poly (butylene adipate-co-terephthalate) (PBAT) in tissue engineering, more specifically in bone regeneration, has been underexplored to date due to its poor mechanical resistance. In order to overcome this drawback, this investigation presents an approach into the preparation of electrospun nanocomposite fibers from PBAT and low contents of superhydrophilic multi-walled carbon nanotubes (sMWCNT) (0.1-0.5 wt.%) as reinforcing agent. We employed a wide range of characterization techniques to evaluate the properties of the resulting electrospun nanocomposites, including Field Emission Scanning Electronic Microscopy (FE-SEM), Transmission Electronic Microscopy (TEM), tensile tests, contact angle measurements (CA) and biological assays. FE-SEM micrographs showed that while the addition of sMWCNT increased the presence of beads on the electrospun fibers' surfaces, the increase of the neat charge density due to their presence reduced the fibers' average diameter. The tensile test results pointed that sMWCNT acted as reinforcement in the PBAT electrospun matrix, enhancing its tensile strength (from 1.3 to 3.6 MPa with addition of 0.5 wt.% of sMWCNT) and leading to stiffer materials (lower elongation at break). An evaluation using MG63 cells revealed cell attachment into the biomaterials and that all samples were viable for biomedical applications, once no cytotoxic effect was observed. MG-63 cells osteogenic differentiation, measured by ALP activity, showed that mineralized nodules formation was increased in PBAT/0.5%CNTs when compared to control group (cells). This investigation demonstrated a feasible novel approach for producing electrospun nanocomposites from PBAT and sMWCNT with enhanced mechanical properties and adequate cell viability levels, which allows for a wide range of biomedical applications for these materials.

Abstract
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.

Abstract
For Anosov flows obtained by suspensions of Anosov diffeomorphisms on surfaces, we show the following type of rigidity result: if a topological conjugacy between them is differentiable at a point, then the conjugacy has a smooth extension to the suspended 3-manifold. This result generalizes the similar ones of Sullivan and Ferreira-Pinto for 1-dimensional expanding dynamics and also a result of Ferreira-Pinto for 2-dimensional hyperbolic dynamics.

Abstract
Modern search engines are evolving beyond ad hoc document retrieval. Nowadays, the information needs of the users can be directly satisfied through entity-oriented search, by ranking the entities or attributes that better relate to the query, as opposed to the documents that contain the best matching terms. One of the challenges in entity-oriented search is efficient query interpretation. In particular, the task of semantic tagging, for the identification of entity types in query parts, is central to understanding user intent. We compare two approaches for semantic tagging, within a single domain, one based on a Sesame triple store and another one based on a Lucene index. This provides a segmentation and annotation of the query based on the most probable entity types, leading to query classification and its subsequent interpretation. We evaluate the run time performance for the two strategies and find that there is a statistically significant speedup, of at least four times, for the index-based strategy over the triple store strategy.

Abstract
This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.