Online Mesh Refinement for Parallel Atmospheric Models

Forecast precisions of climatological models are limited by computing power and time available for the executions. As more and faster processors are used in the computation, the resolution of the mesh adopted to represent the Earth’s atmosphere can be increased, and consequently the numerical forecast is more accurate. However, a finer mesh resolution, able to include local phenomena in a global atmosphere integration, is still not possible due to the large number of data elements to compute in this case. To overcome this situation, different mesh refinement levels can be used at the same time for different areas of the domain. Thus, our paper evaluates how mesh refinement at run time (online) can improve performance for climatological models.The online mesh refinement (OMR) increases dynamically mesh resolution in parts of a domain,when special atmosphere conditions are registered during the execution. Experimental results show that the execution of a model improved by OMR provides better resolution for the meshes, without any significant increase of execution time. The parallel performance of the simulations is also increased through the creation of threads in order to explore different levels of parallelism.     

Reference spectra to classify Amazon water types

Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3–7); and waters dominated by chlorophyll-a (chl-a) (classes 8–10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.     

Quad‐Mesh Generation and Processing: A Survey

Triangle meshes have been nearly ubiquitous in computer graphics, and a large body of data structures and geometry processing algorithms based on them has been developed in the literature. At the same time, quadrilateral meshes, especially semi‐regular ones, have advantages for many applications, and significant progress was made in quadrilateral mesh generation and processing during the last several years. In this survey we discuss the advantages and problems of techniques operating on quadrilateral meshes, including surface analysis and mesh quality, simplification, adaptive refinement, alignment with features, parametrisation and remeshing.     

A multimodal feature learning approach for sentiment analysis of social network multimedia

In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.     

Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.     

New insights on multi-state component criticality and importance

In this paper, new importance measures for multi-state systems with multi-state components are introduced and evaluated. These new measures complement and enhance current work done in the area of multi-state reliability. In general, importance measures are used to evaluate and rank the criticality of component or component states with respect to system reliability. The focus of the study is to provide intuitive and clear importance measures that can be used to enhance system reliability from two perspectives: (1) how a specific component affects multi-state system reliability and (2) how a particular component state or set of states affects multi-state system reliability. The first measure unsatisfied demand index, provides insight regarding a component or component state contribution to unsatisfied demand. The second measure multi-state failure frequency index, elaborates on an approach that quantifies the contribution of a particular component or component state to system failure. Finally, the multi-state redundancy importance identifies where to allocate component redundancy as to improve system reliability. The findings of this study indicate that both perspectives can be used to complement each other and as an effective tool to assess component criticality. Examples illustrate and compare the proposed measures with previous multi-state importance measures.     

Potent and Selective Non‐hydroxamate Histone Deacetylase 8 Inhibitors

Specific inhibition of histone deacetylase 8 (HDAC8) has been suggested as a promising option for the treatment of neuroblastoma and T‐cell malignancies. A novel class of highly potent and selective HDAC8 inhibitors with a pyrimido[1,2‐c][1,3]benzothiazin‐6‐imine scaffold was studied that is completely different from the traditional concept of HDAC inhibitors comprising a zinc binding group (ZBG), in most cases a hydroxamate group, a spacer, and a capping group that may interact with the surface of the target protein. Although lacking a ZBG, some of the new compounds were shown to have outstanding potency against HDAC8 in the single‐digit nanomolar range. The pyrimido[1,2‐c][1,3]benzothiazin‐6‐imines also inhibited the growth of solid and hematological tumor cells. The small size and beneficial physicochemical properties of the novel HDAC inhibitor class underline the high degree of drug likeness. This and the broad structure–activity relationship suggest great potential for the further development of compounds with the pyrimido[1,2‐c][1,3]benzothiazin‐6‐imine scaffold into innovative and highly effective therapeutic drugs against cancer.     

A model approach to assess the long-term trends of indirect photochemistry in lake water. The case of Lake Maggiore (NW Italy)

A model-based approach is here developed and applied to predict the long-term trends of indirect photochemical processes in the surface layer (5 m water depth) of Lake Maggiore, NW Italy. For this lake, time series of the main parameters of photochemical importance that cover almost two decades are available. As a way to assess the relevant photochemical reactions, the modelled steady-state concentrations of important photogenerated transients (^•OH, ³CDOM* and CO₃^-•) were taken into account. A multivariate analysis approach was adopted to have an overview of the system, to emphasise relationships among chemical, photochemical and seasonal variables, and to highlight annual and long-term trends. Over the considered time period, because of the decrease of the dissolved organic carbon (DOC) content of water and of the increase of alkalinity, a significant increase is predicted for the steady-state concentrations of the radicals ^•OH and CO₃^−•. Therefore, the photochemical degradation processes that involve the two radical species would be enhanced. Another issue of potential photochemical importance is related to the winter maxima of nitrate (a photochemical ^•OH source) and the summer maxima of DOC (^•OH sink and ³CDOM* source) in the lake water under consideration. From the combination of sunlight irradiance and chemical composition data, one predicts that the processes involving ^•OH and CO₃^−• would be most important in spring, while the reactions involving ³CDOM* would be most important in summer.