1,721,208 research outputs found
Endothelin-receptor antagonists in the management of pulmonary arterial hypertension: where do we stand?
No full textMichele Correale,1 Armando Ferraretti,2 Ilenia Monaco,3 Davide Grazioli,1 Matteo Di Biase,4 Natale Daniele Brunetti3 1Department of Cardiology, Ospedali Riuniti University Hospital, Foggia, 2Cardiology Unit, “Caduti in guerra” Hospital, Canosa di Puglia, BT, 3Department of Cardiology, University of Foggia, 4Santa Maria Hospital, GVM Care and Research, Bari, Italy Abstract: Pulmonary arterial hypertension, a disease largely neglected until a few decades ago, is presently the object of intense studies by several research teams. Despite considerable progress, pulmonary arterial hypertension remains a major clinical problem, because it is not always easy to diagnose, treat, and prevent. The disease was considered incurable until the late 1990s, when Epoprostenol was introduced as the first tool against this illness. More recently, therapy for pulmonary arterial hypertension gained momentum after publication of the SERAPHIN and AMBITION trials, which also highlighted the importance of upfront therapy. This review also focuses on recent substudies from these trials and progress in drugs targeting the endothelin pathway. Future perspectives with regard to endothelin-receptor antagonists are also discussed. Keywords: endothelin-receptor antagonists, pulmonary arterial hypertension, Bosentan, ambrisentan, sitaxentan, macitenta
Data Mining for Petroleum Geology
No full textIn petroleum geology, exploration and production wells are often analysed
using image logs, because they provide a visual representation of the
borehole surface and they are fundamental to retrieve information on
bedding and rocks characteristics. Aim of this Ph.D. work was to define and
implement a suite of automatic and semi-automatic tools for interpretation
of image logs and large datasets of subsurface data coming from geological
exploration.
This led to the development of I2AM (Intelligent Image Analysis and
Mapping), a semi-automatic system that exploits image processing
algorithms and artificial intelligence techniques to analyse and classify
borehole images. More in detail, the objectives of the I2AM approach are:
(1) to automatically extract rock properties information from all the different
types of data recorded/measured in the wells, and visual features from image
logs in particular; (2) to identify clusters along the wells that have similar
characteristics; (3) to predict class distribution over new wells in the same
area.
The main benefits of this approach are the ability to manage and use a large
amount of subsurface data simultaneously. Moreover, the automatic
identification of similar portions of wells by hierarchical clustering saves a
lot of time for the geologist (since he analyses only the previously identified
clusters). The interpretation time reduces from days to hours and
subjectivity errors are avoided. Moreover, chosen clusters are the input for
supervised learning methods which learn a classification that can be applied
to new wells. Finally, the learned models can also be studied for a cluster
characterization, in a descriptive approach
Unsupervised and Supervised Learning in cascade for Petroleum Geology
No full textCascade of unsupervised and supervised learning algorithms are suitable in all those problems where there are large unlabelled input datasets and the underlying data structure is hidden and not clearly defined. In petroleum geology the understanding and characterization of reservoirs needs integration of different subsurface data in order to create reliable reservoir models. The large amount of data for each well and the presence of different wells to be simultaneously analysed make this task both complex and time consuming. In this scenario, the development of reliable characterization methods is of prime importance in order to help the geologist and reduce the subjectivity of data interpretation. In this paper, we propose a novel interpretation system based on the use of unsupervised and supervised learning techniques in cascade. Using unsupervised algorithm the domain expert identifies relevant clusters that will be used as classes in the following step, in order to learn a classifier to be applied to new instances and wells. We test the approach over five real well dataset using different evaluating techniques. Main advantages of this approach are the ability to manage and use a large amount of data simultaneously and the reduction in interpretation time of a group of wells
Automatic Cluster Selection Using Index Driven Search Strategy
No full textClustering is the task of categorizing objects into different classes in an unsupervised way. Hierarchical clustering algorithms are usually very effective in detecting the dataset underlying structure. However, they do not create clusters, but compute only a hierarchical representation of the dataset. It is then desirable to make them a suitable automatic pre-processing step for the algorithms operating on the selected clusters. To this purpose, in this paper we present an algorithm that finds the best clustering partition according to clustering validity indexes. In particular, our automatic approach performs a validity index-driven search through a clustering tree. The best partition is then selected cutting the tree in a non-horizontal way. The algorithm was implemented in a software tool and then tested on different datasets. The overall system makes then hierarchical clustering an automatic step, where no user interaction is needed in order to select clusters from a hierarchical cluster representation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The Main Advantages to Use the Integration between Geology and Artificial Intelligence Techniques to Interpret Image Logs, an Example from Algeria
No full textImage logs hold important information about the subsurface sequences and they provide information about bedding and fault/fracture spatial distribution and characteristics. They can supply insight on the rock texture, textural organization and porosity types and distribution. To reduce the subjectivity of the interpretation and cut the interpretation time we developed and tested a new semi-automatic process for image log interpretation using a new software.
This process led to the development of an expert system (called I2AM) that exploits image processing algorithms and clustering techniques, to analyze and classify borehole images. This system extrapolates the maximum amount of information from the image logs by considering not only the surfaces that cut the borehole but also the textural features of the images.
Once the image log are analysed the application of clustering techniques to the values extracted from the borehole images supply a consistent classification of the images and the propagation of this classification along the logged section. In this way, we can automatically extract rock properties information with two main advantages: (i) avoid the subjectivity of the interpretation, (ii) reduce the interpretation time. The final results of this process is a set of “image facies” identified along the image log obtained by a largely automated log interpretation, although some level of human interaction and correction is still necessary.
We define the clustering application as semi-automatic because the interpreter can decide, based on his geological background and on the geological characteristics of the logged section, to keep the clusters/classes proposed by the system or modify the number of clusters/classes. The clustering process and the propagation of the classes along the logged section is very fast (30 seconds) allowing an interactive approach, producing several scenarios with different number of classes and/or allowing a quick update of the image log interpretation once more data/knowledge is acquired.
This approach was tested on 5 wells from north Africa where a previous image log interpretation was performed. The new interpretation based on this system made 3 years later (with more data and information) produced more refined results in very short time
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Validation of a Semi-automatic Interpretation of Image Logs Using Two Wells from a North Africa Sandstone Reservoir
No full textA new system was developed to extrapolate the maximum amount of information from the image logs by considering not only the surfaces that cut the borehole but also the textural features of the images. The main objective of developing this system was to reduce the subjectivity and the time of interpretation tasks by largely automating the log interpretation, although some level of human interaction and correction is still necessary.
This approach exploits image processing algorithms to analyze borehole images and artificial intelligence techniques to classify them. The resulting implemented system produces a semi-automatic interpretation of the image logs. This software was used over the FMI logs of four wells from the north African region in order to test the validity of the results
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