SMR analysis using 3DPC datasets

Datasets and results of the paper: Characterization of rock slopes through slope mass rating using 3D point clouds, Riquelme et al 2016, IJRMMS.

Proposta di traduzione in italiano commentata di alcuni estratti del libro "Viajante Chic" di Gloria Kalil e analisi del blog dell'autrice brasiliana

Il seguente elaborato si pone come obiettivo quello di fornire una proposta di traduzione in italiano con relativo commento di alcuni estratti del libro "Viajante Chic", selezionati personalmente, dell'autrice brasiliana Gloria Kalil. Inoltre, è presente un'analisi del blog che viene curato dalla stilista stessa.

Datasets for the graphs of the paper (Fig. 2 to Fig. 7)

Investigating the relationship between factors (climate change, atmospheric CO2 concentrations enrichment, and vegetation structure) and hydrological processes is important for understanding and predicting the interaction between the hydrosphere and biosphere. The Integrated Biosphere Simulator (IBIS) was used to evaluate the effects of climate change, rising CO2, and vegetation structure on hydrological processes in China at the end of the 21st century. Seven simulations were implemented using the assemblage of the IPCC climate and CO2 concentration scenarios, SRES A2 and SRES B1. Analysis results suggest that (1) climate change will have increasing effects on runoff evapotranspiration (ET), transpiration (T), and transpiration ratio (transpiration/evapotranspiration, T/E) in most hydrological regions of China except in the southernmost regions; (2) elevated CO2 concentrations will have increasing effects on runoff at the national scale, but at the hydrological region scale, the physiology effects induced by elevated CO2 concentration will depend on the vegetation types, climate conditions, and geographical background information with noticeable decreasing effects shown in the arid Inland region of China; (3) leaf area index (LAI) compensation effect and stomatal closure effect are the dominant factors on runoff in the arid Inland region and southern moist hydrological regions, respectively; (4) the magnitudes of climate change (especially the changing precipitation pattern) effects on the water cycle are much larger than those of the elevated CO2 concentration effects; however, increasing CO2 concentration will be one of the most important modifiers to the water cycle; (5) the water resource condition will be improved in northern China but depressed in southernmost China under the IPCC climate change scenarios, SRES A2 and SRES B1.

Summarizing level-two topological relations in large spatial datasets

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.

Multiscale histograms: Summarizing topological relations in large spatial datasets

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this paper, we present several novel techniques to eectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important topological relations: contains, contained, overlap, and disjoint. We rst present a novel framework to construct a multiscale histogram composed of multiple Euler histograms with the guarantee of the exact summarization results for aligned windows in constant time. Then we present an approximate algorithm, with the approximate ratio 19/12, to minimize the storage spaces of such multiscale Euler histograms, although the problem is generally NP-hard. To conform to a limited storage space where only k Euler histograms are allowed, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy. Finally, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. Our extensive experiments against both synthetic and real world datasets demonstrated that the approximate mul- tiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost effciency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for the real datasets.

O BLOG CORPORATIVO COMO DESAFIO PARA OS PROFISSIONAIS DE COMUNICAÇÃO

As características desejadas a um blog corporativo eficiente do ponto de vista da comunicação são o tema desta dissertação. Buscou-se compreendê-las, assim como listá-las, verificar a aplicabilidade de tais características e relacionar ganhos à imagem pública de uma empresa através da ferramenta. A metodologia empregada foi o estudo múltiplo de casos, com análise de cinco blogs que contêm a maior parte das competências sugeridas em protocolo de avaliação construído a partir de leituras, entrevistas e visitas a blogs. Cada blog estudado possui pontos fortes distintos, mas também melhorias que precisam ser consideradas. Comportamentos de abertura e transparência totais ao diálogo ainda são mais discursos que práticas nas organizações brasileiras, que precisam aproveitar melhor as oportunidades de relacionamento que as mídias sociais trouxeram.

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.