Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments

With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has generated substantial interest over the last decade, already too vast to be summarized here, in scalable methodologies for analyzing large spatial datasets. Scalable spatial process models have been found especially attractive due to their richness and flexibility and, particularly so in the Bayesian paradigm, due to their presence in hierarchical model settings. However, the vast majority of research articles present in this domain have been geared toward innovative theory or more complex model development. Very limited attention has been accorded to approaches for easily implementable scalable hierarchical models for the practicing scientist or spatial analyst. This article is submitted to the Practice section of the journal with the aim of developing massively scalable Bayesian approaches that can rapidly deliver Bayesian inference on spatial process that are practically indistinguishable from inference obtained using more expensive alternatives. A key emphasis is on implementation within very standard (modest) computing environments (e.g., a standard desktop or laptop) using easily available statistical software packages without requiring message-parsing interfaces or parallel programming paradigms. Key insights are offered regarding assumptions and approximations concerning practical efficiency.Comment: 20 pages, 4 figures, 2 table

Öteki bilinç : gerçeküstücülük ve ikinci yeni

Ankara : Türk Edebiyatı Bölümü, İhsan Doğramacı Bilketn Üniv., 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references.In this thesis, the similarities and differences between Surrealism which influences the world poetry movements and The Second New which is an Avantgarde in Turkish poetry are tried to be analysed. The characteristics, methods and objects of both poetry movements are compared and evaluated based on the texts, interviews and essays. In various articles, The Second New is discussed together with Surrealism and it is considered as meaningless, irrelevant to reality. But mainly it is considered as a movement also exclusive to our country under the influence of Surrealism. When the objects of The Second New to write poetry in a different style and objects of Surrealism such as examining reality and regarding human in a different perspective in his unity are compared, it is observed that the two have radical differences. While for Surrealism all the techniques like imagery, metaphors are means for its objects, for The Second New the characteristics that the surrealists regarded as means have been objects to reach for a different poetry. In the end of the research, It is observed that Surrealism and the Second New are the products of different periods and social conditions. While Surrealism is an unavaidable stop for the societies of the West to make sense of the world and human, The Second New has not pursued such a goal but it aimed to build a new poetics together with the fact that It also realized to regard human and reality in a different way by the change in the poetical discourse.Yeniay, Müesser, 1983-M.S

Diagnostic accuracy of adropin as a preliminary test to exclude acute pulmonary embolism: a prospective study

Background This study aims to investigate the diagnostic accuracy of adropin as a biomarker to exclude the diagnosis of acute pulmonary embolism (PE). Methods Patients admitted to the emergency department of a tertiary health centre between August 2019 and August 2020 and diagnosed with PE were included in this prospective cohort study. The amount of serum adropin was determined in patients with (PE) and compared with that of healthy volunteers. Receiver operating characteristic analysis was performed with the obtained data, and the area under the curve (AUC) with a 95% confidence interval was determined. The parameters of diagnostic accuracy for PE were determined. Results A total of 57 participants were included in the study (28 controls and 29 PE patients). The mean adropin level in the PE group was 187.33 +/- 62.40 pg/ml, which was significantly lower than that in the control group (524.06 +/- 421.68 pg/ml) (p < 0.001). When the optimal adropin cut-off value was 213.78 pg/ml, the likelihood ratio of the adropin test was 3.4, and the sensitivity of the adropin test at this value was 82% with specificity of 75% (95% CI; AUC: 0.821). Conclusion Our results suggest that adropin may be considered for further study as a candidate marker for the exclusion of the diagnosis of PE. However, more research is required to verify and support the generalizability of our study results

Enhancement of stimulated Brillouin scattering of higher-order acoustic modes in single-mode optical fiber

This paper was published in Optics Letters and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/abstract.cfm?URI=ol-30-20-2685. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.Solving the elastic wave equation exactly for a GeO2-doped silica fiber with a steplike distribution of the longitudinal and shear velocities and density, we have obtained the dispersion, attenuation, and fields of the leaky acoustic modes supported by the fiber. We have developed a model for stimulated Brillouin scattering of these modes in a pump-probe configuration and provided their Brillouin gains and frequencies for an extended range of core sizes and GeO2 doping. Parameter ranges close to cutoff of the acoustic modes and pump depletion enhance the ratio of higher-order peaks to the main peak in the Brillouin spectrum and are suitable for simultaneous strain-temperature sensing.Shahraam Afshar V., V. P. Kalosha, Xiaoyi Bao, Liang Che

Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs

Hybrid adaptive evolutionary algorithm based on decomposition

The performance of search operators varies across the different stages of the search/optimization process of evolutionary algorithms (EAs). In general, a single search operator may not do well in all these stages when dealing with different optimization and search problems. To mitigate this, adaptive search operator schemes have been introduced. The idea is that when a search operator hits a difficult patch (under-performs) in the search space, the EA scheme “reacts” to that by potentially calling upon a different search operator. Hence, several multiple-search operator schemes have been proposed and employed within EA. In this paper, a hybrid adaptive evolutionary algorithm based on decomposition (HAEA/D) that employs four different crossover operators is suggested. Its performance has been evaluated on the well-known IEEE CEC’09 test instances. HAEA/D has generated promising results which compare well against several well-known algorithms including MOEA/D, on a number of metrics such as the inverted generational distance (IGD), the hyper-volume, the Gamma and Delta functions. These results are included and discussed in this paper

Hypothyroidism Induced Severe Rhabdomyolysis in a Hemodialysis Patient

Hypothyroidism occurs relatively common and is a significant cause of morbidity and mortality during the course of chronic kidney disease. Rhabdomyolysis is a potentially life-threatening condition characterised by necrosis of muscular tissue and rarely associates with hypothyroidism. Here we describe a case of rhabdomyolysis due to severe hypothyroidism in a 56-year-old female hemodialysis patient

Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study

PURPOSEHigh-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy.METHODSA total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed.RESULTSConsidering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant.CONCLUSIONOur artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology–pathology compatibility and by using an artificial intelligence model

Elimination of spurious modes in full-vectorial finite element method based acoustic modal solution

Finite element method is a powerful technique for solving a wide range of engineering problems. However, the existence of the spurious solutions in full-vectorial finite element method has been a major problem for both acoustic and optic modal analyses. For emerging photonic devices exploiting light-sound interactions in high index contrast waveguides, this problem is a major limitation. A penalty function is introduced to remove these unwanted spurious modes in acoustic waveguides, which also identifies the acoustic modes more easily. Numerically simulated results also show considerably improved vector mode profiles. The proposed penalty method has been applied for the characterization of low index contrast single mode fiber and also for high index contrast silicon nanowire to demonstrate its effectiveness