Rapid submarine ice melting in the grounding zones of ice shelves in West Antarctica.

Enhanced submarine ice-shelf melting strongly controls ice loss in the Amundsen Sea embayment (ASE) of West Antarctica, but its magnitude is not well known in the critical grounding zones of the ASE's major glaciers. Here we directly quantify bottom ice losses along tens of kilometres with airborne radar sounding of the Dotson and Crosson ice shelves, which buttress the rapidly changing Smith, Pope and Kohler glaciers. Melting in the grounding zones is found to be much higher than steady-state levels, removing 300-490 m of solid ice between 2002 and 2009 beneath the retreating Smith Glacier. The vigorous, unbalanced melting supports the hypothesis that a significant increase in ocean heat influx into ASE sub-ice-shelf cavities took place in the mid-2000s. The synchronous but diverse evolutions of these glaciers illustrate how combinations of oceanography and topography modulate rapid submarine melting to hasten mass loss and glacier retreat from West Antarctica

Computer-aided diagnosis of gynaecological abnormality using B-mode ultrasound images

Ultrasound scan is one of the most reliable imaging for detecting/diagnosing of gynaecological abnormalities. Ultrasound imaging is widely used during pregnancy and has become central in the management of the problems of early pregnancy, particularly in miscarriage diagnosis. Also ultrasound is considered as the most important imaging modality in the evaluation of different types of ovarian tumours. The early detection of ovarian carcinoma and miscarriage continues to be a challenging task. It mostly relies on manual examination, interpretation by gynaecologists, of the ultrasound scan images that may use morphology features extracted from the region of interest. Diagnosis depends on using certain scoring systems that have been devised over a long time. The manual diagnostic process involves multiple subjective decisions, with increased inter- and intra-observer variations which may lead to serious errors and health implications. This thesis is devoted to developing computer-based tools that use ultrasound scan images for automatic classification of Ovarian Tumours (Benign or Malignant) and automatic detection of Miscarriage cases at early stages of pregnancy. Our intended computational tools are meant to help gynaecologists to improve accuracy of their diagnostic decisions, while serving as a tool for training radiology students/trainees on diagnosing gynaecological abnormalities. Ultimately, it is hoped that the developed techniques can be integrated into a specialised gynaecology Decision Support System. Our approach is to deal with this problem by adopting a standard image-based pattern recognition research framework that involve the extraction of appropriate feature vector modelling of the investigated tumours, select appropriate classifiers, and test the performance of such schemes using sufficiently large and relevant datasets of ultrasound scan images. We aim to complement the automation of certain parameters that gynaecologist experts and radiologists manually determine, by image-content information attributes that may not be directly accessible without advanced image transformations. This is motivated by, and benefit from, advances in computer vision that led the emergence of a variety of image processing/analysis techniques together with recent advances in data mining and machine learning technologies. An expert observer makes a diagnostic decision with a level of certainty, and if not entirely certain about their diagnostic decisions then often other experts’ opinions are sought and may be essential for diagnosing difficult “Inconclusive cases”. Here we define a quantitative measure of confidence in decisions made by automatic diagnostic schemes, independent of accuracy of decision. In the rest of the thesis, we report on the development of a variety of innovative diagnostic schemes and demonstrate their performances using extensive experimental work. The following is a summary of the main contributions made in this thesis. 1. Using a combination of spatial domain filters and operations as pre-processing procedures to enhance ultrasound images for both applications, namely miscarriage identification and ovarian tumour diagnosis. We show that the Non-local means filter is effective in reducing speckle noise from ultrasound images, and together with other filters we succeed in enhancing the inner border of malignant tumours and reliably segmenting the gestational sac. 2. Developing reliable automated procedures to extract several types of features to model gestational sac dimensional measurements, few of which are manually determined by radiologist and used by gynaecologists to identify miscarriage cases. We demonstrate that the corresponding automatic diagnostic schemes yield excellent accuracy when classified by the k-Nearest Neighbours. 3. Developing several local as well as global image-texture based features in the spatial as well as the frequency domains. The spatial domain features include the local versions of image histograms, first order statistical features and versions of local binary patterns. From the frequency domain, we propose a novel set of Fast Fourier Geometrical Features that encapsulates the image texture information that depends on all image pixel values. We demonstrate that each of these features define Ovarian Tumour diagnostic scheme that have relatively high power of discriminating Benign from Malignant tumours when classified by Support Vector Machine. We show that the Fast Fourier Geometrical Features are the best performing scheme achieving more than 85% accuracy. 4. Introducing a simple measure of confidence to quantify the goodness of the automatic diagnostic decision, regardless of decision accuracy, to emulate real life medical diagnostics. Experimental work in this theis demonstrate a strong link between this measure and accuracy rate, so that low level of confidence could raise an alarm. 5. Conducting sufficiently intensive investigations of fusion models of multi-feature schemes at different level. We show that feature level fusion yields degraded performance compared to all its single components, while score level fusion results in improved results and decision level fusion of three sets of features using majority rule is slightly less successful. Using the measure of confidence is useful in resolving conflicts when two sets of features are fused at the decision level. This leads to the emergence of a Not Sure decision which is common in medical practice. Considering the Not Sure label is a good practice and an incentive to conduct more tests, rather than misclassification, which leads to significantly improved accuracy. The thesis concludes with an intensive discussion on future work that would go beyond improving performance of the developed scheme to deal with the corresponding multi-class diagnostics essential for a comprehensive gynaecology Decision Support System tool as the ultimate goal

The Jordanian Muslim Brotherhood's Perceptions of the Palestinian Issue

The positions and beliefs adopted by the Muslim Brotherhood (MB) on aspects of the Palestinian issue, particularly in the 1980s and 1990s, are of major interest as they directed MB policies and enabled it to mobilize opinion against Jordan's foreign policy regarding Palestine. The framework of the Jordanian Muslim Brotherhood's views on Palestine was based on the Islamization of the Palestinian question by the prophetic claim that Jerusalem-Palestine is one 'Islamic land' and by asserting the religious duty of Jordan to play a strategic role in defending an Islamic cause. Also, they believe that the conflict with Israel is a religio-civilization conflict, not a political one, between Islam and Judaism

Representation of sharp rifts and faults mechanics in modeling ice shelf flow dynamics: Application to Brunt/Stancomb-Wills Ice Shelf, Antarctica

Ice shelves play a major role in buttressing ice sheet flow into the ocean, hence the importance of accurate numerical modeling of their stress regime. Commonly used ice flow models assume a continuous medium and are therefore complicated by the presence of rupture features (crevasses, rifts, and faults) that significantly affect the overall flow patterns. Here we apply contact mechanics and penalty methods to develop a new ice shelf flow model that captures the impact of rifts and faults on the rheology and stress distribution of ice shelves. The model achieves a best fit solution to satellite observations of ice shelf velocities to infer the following: (1) a spatial distribution of contact and friction points along detected faults and rifts, (2) a more realistic spatial pattern of ice shelf rheology, and (3) a better representation of the stress balance in the immediate vicinity of faults and rifts. Thus, applying the model to the Brunt/Stancomb-Wills Ice Shelf, Antarctica, we quantify the state of friction inside faults and the opening rates of rifts and obtain an ice shelf rheology that remains relatively constant everywhere else on the ice shelf. We further demonstrate that better stress representation has widespread application in examining aspects affecting ice shelf structure and dynamics including the extent of ice mélange in rifts and the change in fracture configurations. All are major applications for better insight into the important question of ice shelf stability

Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac

Ultrasound imaging is one of the most widely used multipurpose imaging modalities for monitoring and diagnosing early pregnancy events. The first sign and measurable element of an early pregnancy is the appearance of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurements tend to result in inter- and intraobserver variations, which may lead to difficulties in diagnosis. This paper proposes a new method for automatic identification of miscarriage cases in the first trimester of pregnancy. The proposed method automatically segments the GS and calculates the Mean Sac Diameter (MSD) and other geometric features of the segmented sac. After classifying the image based on the extracted features into either a pregnancy of unknown viability (PUV) or a possible miscarriage case, we assign the decision with a strength level to reflect its reliability. The paper argues that the level of decision strength gives more insight into decision making than other classical alternatives and makes the automated decision process closer to the diagnosis practice by exper

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered

A constitutive framework for predicting weakening and reduced buttressing of ice shelves based on observations of the progressive deterioration of the remnant Larsen B Ice Shelf

The increasing contribution of the Antarctic Ice Sheet to sea level rise is linked to reductions in ice shelf buttressing, driven in large part by basal melting of ice shelves. These ocean-driven buttressing losses are being compounded as ice shelves weaken and fracture. To date, model projections of ice sheet evolution have not accounted for weakening ice shelves. Here we present the first constitutive framework for ice deformation that explicitly includes mechanical weakening, based on observations of the progressive degradation of the remnant Larsen B Ice Shelf from 2000 to 2015. We implement this framework in an ice sheet model and are able to reproduce most of the observed weakening of the ice shelf. In addition to predicting ice shelf weakening and reduced buttressing, this new framework opens the door for improved understanding and predictions of iceberg calving, meltwater routing and hydrofracture, and ice shelf collapse

Sensitivity of the dynamics of Pine Island Glacier, West Antarctica, to climate forcing for the next 50 years

Pine Island Glacier, a major contributor to sea level rise in West Antarctica, has been undergoing significant changes over the last few decades. Here, we employ a three-dimensional, higher-order model to simulate its evolution over the next 50 yr in response to changes in its surface mass balance, the position of its calving front and ocean-induced ice shelf melting. Simulations show that the largest climatic impact on ice dynamics is the rate of ice shelf melting, which rapidly affects the glacier speed over several hundreds of kilometers upstream of the grounding line. Our simulations show that the speedup observed in the 1990s and 2000s is consistent with an increase in sub-ice-shelf melting. According to our modeling results, even if the grounding line stabilizes for a few decades, we find that the glacier reaction can continue for several decades longer. Furthermore, Pine Island Glacier will continue to change rapidly over the coming decades and remain a major contributor to sea level rise, even if ocean-induced melting is reduced

Ефективність і конкурентоспроможність підприємств

Стаття присвячена пошуку зв'язку між поняттями ефективність і конкурентоспроможність та вивченню основних чинників, що впливають на отримання і утримання конкурентоспроможності протягом тривалого часу.Статья посвящена отысканию связи между понятиями "эффективность" и "конкурентоспособность" и изучению основных факторов, влияющих на достижение и удержание конкурентоспособности в долгосрочной перспективе.The article is devoted to searching a tie between the notions of "effectiveness" and "competitiveness"; and scrutinizing main factors, which effect reaching and keeping competitiveness in a long-term perspective

Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images

Introduction: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images. Methods: We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Results: According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy). Conclusions: ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images