198 research outputs found
Operation Ajax : Studie om USA:s och Storbritanniens involvering i statskuppen, Iran 1953
No full textUniversity of Växjö, School of Social Sciences Course: PO 5363, Political Science, G3 Title: the Role of the USA’s and Great Britain in the Coup d'Etat, Iran 1953 Author: Ashkan Panahirad Supervisor: Lennart Bergfeldt The purpose of this study is to examine Great Britain’s and US’ motives and action alternatives in regards to the Coup d'état against the iranian regime under Mossadegh in 1953. The method used is motive analysis (investigates the actors motives). The theories used are Rational actors model and Governmental politics. Rational actor model allows states to choose among a set of alternatives displayed in a particular situation in order to achieve their goals. Governmental politics explains what happens in states as a result of bargaining games between important actors in the government. Analysis from the rational actor model shows that the motives behind the Coup d'état were oil, economical reasons, Iran and communism. Coup d'état was the most rational action for them to achieve their goals. Governmental politics reveal the shifting of policies from one administration to another. While Clement Attlee’s government and Harry Truman’s administration where more moderate, Winston Churchill’s and Eisenhower’s where more eager to replace Mossadegh, which finally lead to a Coup d'éta
Operation Ajax : Studie om USA:s och Storbritanniens involvering i statskuppen, Iran 1953
No full textUniversity of Växjö, School of Social Sciences Course: PO 5363, Political Science, G3 Title: the Role of the USA’s and Great Britain in the Coup d'Etat, Iran 1953 Author: Ashkan Panahirad Supervisor: Lennart Bergfeldt The purpose of this study is to examine Great Britain’s and US’ motives and action alternatives in regards to the Coup d'état against the iranian regime under Mossadegh in 1953. The method used is motive analysis (investigates the actors motives). The theories used are Rational actors model and Governmental politics. Rational actor model allows states to choose among a set of alternatives displayed in a particular situation in order to achieve their goals. Governmental politics explains what happens in states as a result of bargaining games between important actors in the government. Analysis from the rational actor model shows that the motives behind the Coup d'état were oil, economical reasons, Iran and communism. Coup d'état was the most rational action for them to achieve their goals. Governmental politics reveal the shifting of policies from one administration to another. While Clement Attlee’s government and Harry Truman’s administration where more moderate, Winston Churchill’s and Eisenhower’s where more eager to replace Mossadegh, which finally lead to a Coup d'éta
Regression results for average number of articles per author model.
No full text<p>Regression results for average number of articles per author model.</p
Consistency between descriptors, author-supported keywords and tags in the ERIC and Mendeley databases
Full text linkThe purpose of this study was to identify the language consistency between indexers, authors and taggers in the ERIC and Mendeley databases. This survey was conducted using content analysis methods and techniques to evaluate the language consistency between indexers, authors and taggers in the ERIC and Mendeley databases and also to determine common keywords. The sample for this study was comprised of top twenty journals in the field of Educational Research based on the Journal Citation Reports (JCR) of Web of Science, indexed in the ERIC database in 2014. Finally 499 articles published in the above-mentioned journals in 2014 were chosen as the sample base for the dataset. Note that only articles with author-supported keywords, indexed in the ERIC database and also tagged in the Mendeley database from January 2014 to August 2016 were eligible to be assessed. Descriptors assigned to the articles on the ERIC database and tags associated to the articles on the Mendeley database for the period from January 2014 to August 2016 were extracted. Also author-assigned keywords assigned to all 499 articles were collected. Finally we created a software based on object-oriented programming (OOP) in C++ to analyze the search results. Descriptive statistics and measures, and thesaural term comparison show that there are important differences in the context of keywords from the three groups.
This study demonstrated that there were differences between the tagger, author and professional indexer views of the words used as tags, descriptors, or author-assigned keywords. The results showed that the consistency between the author-supported keywords and user tags of the 499 articles in the Mendeley was 15 percent; while the consistency between descriptors designated to the articles in the ERIC database and user tags associated to the articles on the Mendeley were three percent. On the other hand, the consistency between descriptors assigned to the articles in the ERIC database and the author-assigned keywords were 4 percent. Finally, the language consistency between the three above-mentioned groups was 1.1 percent. Also note that the presence of descriptors in the ERIC thesaurus was 34 percent, which were more than the author-supported keywords and tags.
The findings showed that the consistency between the keywords used by authors and taggers were more than the keywords chosen by indexers and authors, and by indexers and taggers. This means that three sides of the information representation triangle, i.e., indexer, author and tagger are unfamiliar with each other’s language. It is worth noting that tags are useful supplements to controlled vocabularies, since the former provide a means for social organization of knowledge outside the framework of the latter. The low consistency between tags and descriptors in this research indicates that Mendeley users do not use the same terminology as subject specialists who maintain descriptors in the ERIC thesaurus. Further research involving semantic analysis of Mendeley tags may reveal an emerging vocabulary suitable for inclusion in the ERIC thesaurus as a controlled vocabulary
Effect of wind speed distribution and site assessment on pitch bearing loads and life
Full text linkIn this paper, the wind speed distribution at thirteen onshore and offshore wind sites has been studied. Different probability distributions are used to estimate the wind speed distribution. Several goodness-of-fit indicators were used to assess the suitability of the fitting. The highest results were achieved by Kernel distribution in both onshore and offshore wind sites. Onshore wind sites did not fit well compared to offshore wind sites. Rayleigh distribution results at onshore wind sites were worse than at offshore wind sites. Onshore and offshore wind distributions result in various load duration distributions in pitch bearing. The concept of life ratio was introduced to compare the long-term fatigue life of the pitch bearing in different wind speed conditions. It is observed that the fatigue life of the pitch bearings in some wind sites is less than that of related IEC classes, and the risk of failure of the pitch bearing before the end of its expected designed fatigue life exists.publishedVersio
A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications
Full text linkCurrent segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available results, they heavily require high memory GPUs. In this paper, we customize a memory-efficient (GPU) brain structure segmentation framework, named FLBS, based on nnU-nets which enables our framework to adapt its architecture based on memory constraints dynamically. To further reduce the need for memory, we also reduce multi-label brain segmentation to the fusion of sequential single-label segmentations. In the first step, single label patches are extracted from the T1w and segmentation maps by locating the approximate area of each structure on the MNI305 template, including the safety margin. These considerations not only decrease the hardware usage but also maintains comparable computational time. Moreover, the target brain structures are customizable based on the specific clinical applications. We evaluate the performance in terms of Dice coefficient, runtime, and GPU requirement on OASIS-3 and CoRR-BNU1 datasets. The validation results show our comparable accuracies with state-of-the-arts and confirm the generalizability on unseen datasets while significantly reducing GPU requirements and maintaining runtime duration. Our framework is also executable on a budget GPU with a minimum requirement of 4G RAM. We develop a memory-efficient deep Brain MRI segmentation tool that significantly reduces the hardware requirement of MRI segmentation while maintaining comparable accuracy and time. These advantages make FLBS suitable for widespread use in clinical applications, especially for clinics with a limited budget. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available on https://github.com/arnejad/FLBS
The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research
No full textBackground: Investigating low-prevalence diseases such as multiple sclerosis is challenging because of the rather small number of individuals affected by this disease and the scattering of real-world data across numerous data sources. These obstacles impair data integration, standardization, and analysis, which negatively impact the generation of significant meaningful clinical evidence.Objective: This study aims to present a comprehensive, research question-agnostic, multistakeholder-driven end-to-end data analysis pipeline that accommodates 3 prevalent data-sharing streams: individual data sharing, core data set sharing, and federated model sharing.Methods: A demand-driven methodology is employed for standardization, followed by 3 streams of data acquisition, a data quality enhancement process, a data integration procedure, and a concluding analysis stage to fulfill real-world data-sharing requirements. This pipeline's effectiveness was demonstrated through its successful implementation in the COVID-19 and multiple sclerosis global data sharing initiative.Results: The global data sharing initiative yielded multiple scientific publications and provided extensive worldwide guidance for the community with multiple sclerosis. The pipeline facilitated gathering pertinent data from various sources, accommodating distinct sharing streams and assimilating them into a unified data set for subsequent statistical analysis or secure data examination. This pipeline contributed to the assembly of the largest data set of people with multiple sclerosis infected with COVID-19.Conclusions: The proposed data analysis pipeline exemplifies the potential of global stakeholder collaboration and underlines the significance of evidence-based decision-making. It serves as a paradigm for how data sharing initiatives can propel advancements in health care, emphasizing its adaptability and capacity to address diverse research inquiries.The author(s) have disclosed that they received financial support for the research, authorship, or publication of this paper from the following sources: the operational costs associated with this study were funded by the Multiple Sclerosis International Federation and the Multiple Sclerosis Data Alliance (MSDA) operating under the European Charcot Foundation. The MSDA is a global not-for-profit multistakeholder collaboration acting under the umbrella of the European Charcot Foundation, financially supported by a combination of industry partners, including Novartis, Merck, Biogen, Janssen, Bristol-Myers Squibb, and Roche. Additionally, this work was supported by the Flemish government through the Onderzoeksprogramma Artificiële Intelligentie Vlaanderen program and the Research Foundation Flanders for ELIXIR Belgium. QMENTA provided the central platform, while Amazon supplied the computational resources utilized in this work. The statistical analysis was conducted at the Clinical Outcomes Research Unit, The University of Melbourne, with support from National Health and Medical Research Council (1129189 and 1140766). The authors wish to extend their sincere appreciation to Nikola Lazovski for his invaluable guidance and collaboration throughout the global data sharing initiative project, especially concerning the central platform. They are also profoundly grateful to Dr Ilse Vermeulen for her unwavering support and encouragement throughout the various stages of drafting and conceptualizing the manuscript
A Memory-efficient Deep Framework for Multi-Modal MRI-based Brain Tumor Segmentation
Full text linkAutomatic Brain Tumor Segmentation (BraTS) from MRI plays a key role in diagnosing and treating brain tumors. Although 3D U-Nets achieve state-of-the-art results in BraTS, their clinical use is limited due to requiring high-end GPU with high memory. To address the limitation, we utilize several techniques for customizing a memory-efficient yet ac-curate deep framework based on 2D U-nets. In the framework, the simultaneous multi-label tumor segmentation is decomposed into fusion of sequential single-label (binary) segmentation tasks. In addition to reducing the memory consumption, it may also improve the segmentation accuracy since each U-net focuses on a sub-task, simpler than whole BraTS segmentation task. Extensive data augmentations on multi-modal MRI and the batch dice-loss function are also employed to further increase the generalization accuracy. Experiments on BraTS 2020 demonstrate that our framework almost achieves state-of-the-art results. Dice scores of 0.905, 0.903, and 0.822 for whole tumor, tumor core, and enhancing tumor are accomplished on the testing set. Moreover, our customized framework is executable on budget-GPUs with minimum requirement of only 2G RAM. Clinical relevance— We develop a memory-efficient deep Brain tumor segmentation tool that significantly reduces the hardware requirement of tumor segmentation while maintaining comparable accuracy and time. These advantages make our framework suitable for widespread use in clinical applications, especially in low-income regions. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available:https://github.com/Nima-Hs/BraTS
Evaluation and Comparison of Scheduling Strategies for the Scheduling of Electric Vehicles at Capacity-Constrained Charging Stations
No full textAs the popularity of electric vehicles (EVs) increases, congestion at charging becomes a more imminent problem. Congestion at a charging station can lead to long waiting queues and failure of EV owners to charge their vehicles fully before their departure from the station. To combat this issue, this paper explores several candidate scheduling strategies that can be applied for the charging prioritization of EVs at a single station. Through extensive simulations, the efficacy of these strategies is studied under three performance metrics. From the set of strategies studied, we find that earliest deadline first (EDF) and shortest job first (SJF) are the best options in the case that adherence to deadline or a shorter waiting time is most valued, respectively.CSE3000 Research ProjectComputer Science and Engineerin
The surveying of the effect of the incentive pays to the degree of the attraction of resources in bank branches through the data mining technique
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