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Journal articles on the topic 'Error log clustering'
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1
Yelamanchili, Rama Krishna. "Modeling Stock Market Monthly Returns Volatility Using GARCH Models Under Different Distributions." International Journal of Accounting & Finance Review 5, no. 1 (March 18, 2020): 42–50. http://dx.doi.org/10.46281/ijafr.v5i1.425.
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This papers aims to uncover stylized facts of monthly stock market returns and identify adequate GARCH model with appropriate distribution density that captures conditional variance in monthly stock market returns. We obtain monthly close values of Bombay Stock Exchange’s (BSE) Sensex over the period January 1991 to December 2019 (348 monthly observations). To model the conditional variance, volatility clustering, asymmetry, and leverage effect we apply four conventional GARCH models under three different distribution densities. We use two information criterions to choose best fit model. Results reveal positive Skewness, weaker excess kurtosis, no autocorrelations in relative returns and log returns. On the other side presence of autocorrelation in squared log returns indicates volatility clustering. All the four GARCH models have better information criterion values under Gaussian distribution compared to t-distribution and Generalized Error Distribution. Furthermore, results indicate that conventional GARCH model is adequate to measure the conditional volatility. GJR-GARCH model under Gaussian distribution exhibit leverage effect but statistically not significant at any standard significance levels. Other asymmetric models do not exhibit leverage effect. Among the 12 models modeled in present paper, GARCH model has superior information criterion values, log likelihood value, and lowest standard error values for all the coefficients in the model.
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Grigorieva, Maria, and Dmitry Grin. "Clustering error messages produced by distributed computing infrastructure during the processing of high energy physics data." International Journal of Modern Physics A 36, no. 10 (April 10, 2021): 2150070. http://dx.doi.org/10.1142/s0217751x21500706.
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Large-scale distributed computing infrastructures ensure the operation and maintenance of scientific experiments at the LHC: more than 160 computing centers all over the world execute tens of millions of computing jobs per day. ATLAS — the largest experiment at the LHC — creates an enormous flow of data which has to be recorded and analyzed by a complex heterogeneous and distributed computing environment. Statistically, about 10–12% of computing jobs end with a failure: network faults, service failures, authorization failures, and other error conditions trigger error messages which provide detailed information about the issue, which can be used for diagnosis and proactive fault handling. However, this analysis is complicated by the sheer scale of textual log data, and often exacerbated by the lack of a well-defined structure: human experts have to interpret the detected messages and create parsing rules manually, which is time-consuming and does not allow identifying previously unknown error conditions without further human intervention. This paper is dedicated to the description of a pipeline of methods for the unsupervised clustering of multi-source error messages. The pipeline is data-driven, based on machine learning algorithms, and executed fully automatically, allowing categorizing error messages according to textual patterns and meaning.
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He, Ruiquan, Haihua Hu, Chunru Xiong, and Guojun Han. "Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory." Micromachines 12, no. 8 (July 27, 2021): 879. http://dx.doi.org/10.3390/mi12080879.
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The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.
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Xie, Shu-tong, Qiong Chen, Kun-hong Liu, Qing-zhao Kong, and Xiu-juan Cao. "Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses." Scientific Programming 2021 (June 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/9977977.
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In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.
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Lee, Jinhyung. "Factors Affecting Health Information Technology Expenditure in California Hospitals." International Journal of Healthcare Information Systems and Informatics 10, no. 2 (April 2015): 1–13. http://dx.doi.org/10.4018/ijhisi.2015040101.
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This paper investigates the factors affecting health information technology (IT) investment. Different from previous studies, health IT was measured as the dollar amount of hardware, software and labor related health IT. This study employed Hospital and Patient level data of the Office of Statewide Health Planning and Development (OSHPD) from 2000 to 2006. The generalized linear model (GLM) was employed with log link and normal distribution and controlled for clustering error. This study found that not-for-profit and government hospital, teaching hospitals, competition, health IT expenditure of neighborhood hospitals were positively associated with health IT expenditure. However, rural hospitals were negatively associated with health IT expenditure. Moreover, this study found a significant increase in health IT investment over seven years resulted from increased clinical IT adoption.
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Sharma, Abhishek, and Tarun Gulati. "Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3395. http://dx.doi.org/10.11591/ijece.v7i6.pp3395-3401.
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The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K<sub>c</sub>). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.
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FERRO, A., G. PIGOLA, A. PULVIRENTI, and D. SHASHA. "FAST CLUSTERING AND MINIMUM WEIGHT MATCHING ALGORITHMS FOR VERY LARGE MOBILE BACKBONE WIRELESS NETWORKS." International Journal of Foundations of Computer Science 14, no. 02 (April 2003): 223–36. http://dx.doi.org/10.1142/s0129054103001698.
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Mobile Backbone Wireless Networks (MBWN) [10] are wireless networks in which the base stations are mobile. Our strategy is the following: mobile nodes are dynamically grouped into clusters of bounded radius. In the very large wireless networks we deal with we deal with, several hundreds of clusters may be generated. Clustering makes use of a two dimensional Euclidean version of the Antipole Tree data structure [5]. This very effective structure was originally designed for finite sets of points in an arbitrary metric space to support efficient range searching. It requires only a linear number of pair-wise distance calculations among nodes. Mobile Base Stations occupy an approximate centroid of the clusters and are moved according to a fast practical bipartite matching algorithm which tries to minimize both total and maximum distance. We show that the best known computational geometry algorithms [1] become infeasible for our application when a high number of mobile base stations is required. On the other hand our proposed 8% average error solution requires O (k log k) running time instead of the approximatively O (k2) exact algorithm [1]. Communication among nodes is realized by a Clusterhead Gateway Switching Routing (CGSR) protocol [15] where the mobile base stations are organized in a suitable network. Other efficient clustering algorithms [11, 17] may be used instead of the Antipole Tree. However the nice hierarchical structure of the Antipole Tree makes it applicable to other types of mobile wireless (Ad-Hoc) and wired networks but this will be subject of future work.
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Yu, Yanxiang, Chicheng Xu, Siddharth Misra, Weichang Li, Michael Ashby, Wen Pan, Tianqi Deng, et al. "Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, no. 4 (August 1, 2021): 393–406. http://dx.doi.org/10.30632/pjv62n4-2021a4.
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Compressional and shear sonic traveltime logs (DTC and DTS, respectively) are crucial for subsurface characterization and seismic-well tie. However, these two logs are often missing or incomplete in many oil and gas wells. Therefore, many petrophysical and geophysical workflows include sonic log synthetization or pseudo-log generation based on multivariate regression or rock physics relations. Started on March 1, 2020, and concluded on May 7, 2020, the SPWLA PDDA SIG hosted a contest aiming to predict the DTC and DTS logs from seven “easy-to-acquire” conventional logs using machine-learning methods (GitHub, 2020). In the contest, a total number of 20,525 data points with half-foot resolution from three wells was collected to train regression models using machine-learning techniques. Each data point had seven features, consisting of the conventional “easy-to-acquire” logs: caliper, neutron porosity, gamma ray (GR), deep resistivity, medium resistivity, photoelectric factor, and bulk density, respectively, as well as two sonic logs (DTC and DTS) as the target. The separate data set of 11,089 samples from a fourth well was then used as the blind test data set. The prediction performance of the model was evaluated using root mean square error (RMSE) as the metric, shown in the equation below: RMSE=sqrt(1/2*1/m* [∑_(i=1)^m▒〖(〖DTC〗_pred^i-〖DTC〗_true^i)〗^2 + 〖(〖DTS〗_pred^i-〖DTS〗_true^i)〗^2 ] In the benchmark model, (Yu et al., 2020), we used a Random Forest regressor and conducted minimal preprocessing to the training data set; an RMSE score of 17.93 was achieved on the test data set. The top five models from the contest, on average, beat the performance of our benchmark model by 27% in the RMSE score. In the paper, we will review these five solutions, including preprocess techniques and different machine-learning models, including neural network, long short-term memory (LSTM), and ensemble trees. We found that data cleaning and clustering were critical for improving the performance in all models.
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CARABIN, H., S. T. McGARVEY, I. SAHLU, M. R. TARAFDER, L. JOSEPH, B. B. DE ANDRADE, E. BALOLONG, and R. OLVEDA. "Schistosoma japonicum in Samar, the Philippines: infection in dogs and rats as a possible risk factor for human infection." Epidemiology and Infection 143, no. 8 (October 2, 2014): 1767–76. http://dx.doi.org/10.1017/s0950268814002581.
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SUMMARYThe role that animals play in the transmission of Schistosoma japonicum to humans in the Philippines remains uncertain and prior studies have not included several species, adjustment for misclassification error and clustering, or used a cohort design. A cohort study of 2468 people providing stool samples at 12 months following praziquantel treatment in 50 villages of Western Samar, the Philippines, was conducted. Stool samples from dogs, cats, rats, and water buffaloes were collected at baseline (2003–2004) and follow-up (2005). Latent-class hierarchical Bayesian log-binomial models adjusting for misclassification errors in diagnostic tests were used. The village-level baseline and follow-up prevalences of cat, dog, and rat S. japonicum infection were associated with the 12-month cumulative incidence of human S. japonicum infection, with similar magnitude and precision of effect, but correlation between infection levels made it difficult to divide their respective effects. The cumulative incidence ratios associated with a 1% increase in the prevalence of infection in dogs at baseline and in rats at follow-up were 1·04 [95% Bayesian credible interval (BCI) 1·02–1·07] and 1·02 (95% BCI 1·01–1·04), respectively, when both species were entered in the model. Dogs appear to play a role in human schistosomiasis infection while rats could be used as schistosomiasis sentinels.
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Wei, Chunzhu, Qianying Zhao, Yang Lu, and Dongjie Fu. "Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China." Remote Sensing 13, no. 16 (August 6, 2021): 3123. http://dx.doi.org/10.3390/rs13163123.
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Pearl River Delta (PRD), as one of the most densely populated regions in the world, is facing both natural changes (e.g., sea level rise) and human-induced changes (e.g., dredging for navigation and land reclamation). Bathymetric information is thus important for the protection and management of the estuarine environment, but little effort has been made to comprehensively evaluate the performance of different methods and datasets. In this study, two linear regression models—the linear band model and the log-transformed band ratio model, and two non-linear regression models—the support vector regression model and the random forest regression model—were applied to Landsat 8 (L8) and Sentinel-2 (S2) imagery for bathymetry mapping in 2019 and 2020. Results suggested that a priori area clustering based on spectral features using the K-means algorithm improved estimation accuracy. The random forest regression model performed best, and the three-band combinations outperformed two-band combinations in all models. When the non-linear models were applied with three-band combination (red, green, blue) to L8 and S2 imagery, the Root Mean Square Error (Mean Absolute Error) decreased by 23.10% (35.53%), and the coefficient of determination (Kling-Gupta efficiency) increased by 0.08 (0.09) on average, compared to those using the linear regression models. Despite the differences in spatial resolution and band wavelength, L8 and S2 performed similarly in bathymetry estimation. This study quantified the relative performance of different models and may shed light on the potential combination of multiple data sources for more timely and accurate bathymetry mapping.
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Gholanlo, H. Heydari. "Analysis of permeability based on petrophysical logs: comparison between heuristic numerical and analytical methods." Journal of Petroleum Exploration and Production Technology 11, no. 5 (April 16, 2021): 2097–111. http://dx.doi.org/10.1007/s13202-021-01163-9.
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AbstractA series of novel heuristic numerical tools were adopted to tackle the setback of permeability estimation in carbonate reservoirs compared to the classical methods. To that end, a comprehensive data set of petrophysical data including core and log in two wells was situated in Marun Oil Field. Both wells, Well#1 and Well#2, were completed in the Bangestan reservoir, having a broad diversity of carbonate facies. In the light of high Lorenz coefficients, 0.762 and 0.75 in Well#1 and Well#2, respectively, an extensive heterogeneity has been expected in reservoir properties, namely permeability. Despite Well#1, Well#2 was used as a blinded well, which had no influence on model learning and just contributed to assess the validation of the proposed model. An HFU model with the aim of discerning the sophistication of permeability and net porosity interrelation has been developed in the framework of Amaefule’s technique which has been modified by newly introduced classification and clustering conceptions. Eventually, seven distinct pore geometrical units have been distinguished through implementing the hybridized genetic algorithm and k-means algorithm. Furthermore, a K-nearest neighbors (KNN) algorithm has been carried out to divide log data into the flow units and assigns them to the pre-identified FZI values. Besides, a cross between the ε-SVR model, a supervised learning machine, and the Harmony Search algorithm has been used to estimate directly permeability. To select the optimum combination of the involved logging parameters in the ε-SVR model and reduce the dimensionality problem, a principle component analysis (PCA) has been implemented on Well#1 data set. The result of PCA illustrates parameters, such as permeability, the transit time of sonic wave, resistivity of the unflashed zone, neutron porosity, photoelectric index, spectral gamma-ray, and bulk density, which possess the highest correlation coefficient with first derived PC. In line with previous studies, the findings will be compared with empirical methods, Coates–Dumanior, and Timur methods, which both have been launched into these wells. Overall, it is obvious to conclude that the ε -SVR model is undeniably the superior method with the lowest mean square error, nearly 4.91, and the highest R-squared of approximately 0.721. On the contrary, the transform relationship of porosity and permeability has remarkably the worst results in comparison with other models in error (MSE) and accuracy (R2) of 128.73 and 0.116, respectively.
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Liu, Ling, and Sang-Bing Tsai. "Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data." Wireless Communications and Mobile Computing 2021 (July 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1170622.
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In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.
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Kshivets, O. "Lung cancer prediction: Phase transitions and cell ratio factors." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): e22170-e22170. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e22170.
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e22170 Background: Search of precise prognostic factors for non-small lung cancer (LC) patients (LCP) was realized. Methods: In trial (1985–2008) the data of consecutive 490 LCP after complete resections R0 (age=56.7±8 years; m=439, f=51; tumor diameter: D=4.5±2.1 cm; pneumonectomies=206, lobectomies=284, combined procedures with resection of pericardium, atrium, aorta, VCS, carina, diaphragm, ribs=130; squamous=308, adenocarcinoma=147, large cell=35; T1=143, T2=217, T3=107, T4=23; N0=282, N1=115, N2=93; G1=114, G2=140, G3=236; early LC: LC till 2 cm in D with N0=58, invasive LC=432) was reviewed. Variables selected for 5-year survival (5YS) study were input levels of blood cell subpopulations, TNMG, D. Neural networks computing, Cox regression, clustering, structural equation modeling, Monte Carlo and bootstrap simulation were used to determine any significant regularity. Results: For total of 490 LCP overall life span (LS) was 1824±1304 days (median=1879) and real 5YS reached 62%, 10 years - 50.3%, 20 years - 45.3%. 304 LCP (LS=2597.3±1037 days) lived more than 5 years without LC progressing. 186 LCP (LS=559.8±383.1 days) died because of LC during first 5 years after surgery. 5YS of early LCP was significantly superior (100%) compared with invasive LCP (56.9%) (P=0.000 by log-rank test). 5YS of LCP with N0 was significantly better (78.4%) compared with LCP with N1–2 (39.9%) (P=0.000). Cox modeling displayed that 5YS significantly depended on: phase transition (PT) in terms of synergetics “early-invasive LC”, PT N0-N12, histology, G1–3, cell ratio factors: ratio between the total populations of leucocytes, lymphocytes, neutrophils and LC cells (P=0.000–0.044). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT “early-invasive LC”, (rank=1), PT N0-N12 (2), histology (3), G1–3 (4), T1–4 (5), ratio of lymphocytes/LC cells (6), healthy cells/LC cells (7), erythrocytes/LC cells (8), thrombocytes/LC cells (9), eosinophils/LC cells (10), neutrophils/LC cells (11). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; urea under ROC curve=1.0). Conclusions: 5YS of LCP after radical procedures depended on: 1) PT “early-invasive LC”; 2) PT N0-N12; 3) cell ratio factors; 4) LC characteristics. No significant financial relationships to disclose.
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Muhima, Rani Rotul, Muchamad Kurniawan, and Oktavian Tegar Pambudi. "A LOF K-Means Clustering on Hotspot Data." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 1 (July 1, 2020): 29. http://dx.doi.org/10.25139/ijair.v2i1.2634.
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K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method. The object detected outliers are then removed. Then new centroid for each group is obtained using the K-Means method again. This dataset was taken from the FIRM are provided by the National Aeronautics and Space Administration (NASA). Clustering was done by varying the number of clusters (k = 10, 15, 20, 25, 30, 35, 40, 45 and 50) with cluster optimal is k = 20. The result based on the value of Sum of Squared Error (SSE) shown the LOF K-Means method was better than the K-Means method.
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Zhou, Kun, Mingmin Zhang, Jiaoying Shi, and Zhigeng Pan. "A New Simplification Algorithm for Colored or Textured Polygonal Models." International Journal of Virtual Reality 4, no. 4 (January 1, 2000): 1–20. http://dx.doi.org/10.20870/ijvr.2000.4.4.2653.
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Many applications in computer graphics require complex and highly detailed models. However, the level of detail actually necessary may vary considerably. It is often desirable to use approximations in place of excessively detailed models to control processing time. A new polygonal mesh simplification algorithm is presented for colored or textured models based on vertex clustering, and a more accurate error-measuring method for vertex clustering is introduced. The algorithm can produce high quality approximations of polygonal models. It makes adaptive subdivision of the bounding box in the original model using octree structure and performs vertex clustering in an error range specified by users. The color or texture information defined over the mesh can be preserved during simplification by constructing a texture map for the simplified mesh. To make a continuous transition between level of detail (LoD) models possible, an efficient interpolating method is also proposed. The efficiency of the algorithm is demonstrated in the experimental results.
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Revillon, Guillaume, and Ali Mohammad-Djafari. "A Complete Classification and Clustering Model to Account for Continuous and Categorical Data in Presence of Missing Values and Outliers †." Proceedings 33, no. 1 (December 9, 2019): 23. http://dx.doi.org/10.3390/proceedings2019033023.
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Classification and clustering problems are closely connected with pattern recognition where many general algorithms have been developed and used in various fields. Depending on the complexity of patterns in data, classification and clustering procedures should take into consideration both continuous and categorical data which can be partially missing and erroneous due to mismeasurements and human errors. However, most algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification and clustering framework that handles both outliers and missing values. Here, an approach based on mixture models is preferred since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. More precisely, a scale mixture of Normal distributions is updated to handle outliers and missing data issues for any types of data. Then a variational Bayesian inference is used to find approximate posterior distributions of parameters and to provide a lower bound on the model log evidence used as a criterion for selecting the number of clusters. Eventually, experiments are carried out to exhibit the effectiveness of the proposed model through an application in Electronic Warfare.
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Ma, Wenping, Xiaoting Li, Yue Wu, Licheng Jiao, and Dan Xing. "Data Fusion and Fuzzy Clustering on Ratio Images for Change Detection in Synthetic Aperture Radar Images." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/403095.
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The unsupervised approach to change detection via synthetic aperture radar (SAR) images becomes more and more popular. The three-step procedure is the most widely used procedure, but it does not work well with the Yellow River Estuary dataset obtained by two synthetic aperture radars. The difference of the two radars in imaging techniques causes severe noise, which seriously affects the difference images generated by a single change detector in step two, producing the difference image. To deal with problem, we propose a change detector to fuse the log-ratio (LR) and the mean-ratio (MR) images by a context independent variable behavior (CIVB) operator and can utilize the complement information in two ratio images. In order to validate the effectiveness of the proposed change detector, the change detector will be compared with three other change detectors, namely, the log-ratio (LR), mean-ratio (MR), and the wavelet-fusion (WR) operator, to deal with three datasets with different characteristics. The four operators are applied not only in a widely used three-step procedure but also in a new approach. The experiments show that the false alarms and overall errors of change detection are greatly reduced, and the kappa and KCC are improved a lot. And its superiority can also be observed visually.
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Örenbaş, Halit, and Muharrem Mercimek. "Clustered Exact Daum-Huang Particle Flow Filter." Mathematical Problems in Engineering 2019 (May 13, 2019): 1–8. http://dx.doi.org/10.1155/2019/8369565.
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Unlike the conventional particle filters, particle flow filters do not rely on proposal density and importance sampling; they employ flow of the particles through a methodology derived from the log-homotopy scheme and ensure successful migration of the particles. Amongst the efficient implementations of particle filters, Exact Daum-Huang (EDH) filter pursues the calculation of migration parameters all together. An improved version of it, Localized Exact Daum-Huang (LEDH) filter, calculates the migration parameters separately. In this study, the main objective is to reduce the cost of calculation in LEDH filters which is due to exhaustive calculation of each migration parameter. We proposed the Clustered Exact Daum-Huang (CEDH) filter. The main impact of CEDH is the clustering of the particles considering the ones producing similar errors and then calculating the same migration parameters for the particles within each cluster. Through clustering and handling the particles with high errors, their engagement and influence can be balanced, and the system can greatly reduce the negative effects of such particles on the overall system. We implement the filter successfully for the scenario of high dimensional target tracking. The results are compared to those obtained with EDH and LEDH filters to validate its efficiency.
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Wu, Jiann-Ming. "Natural Discriminant Analysis Using Interactive Potts Models." Neural Computation 14, no. 3 (March 1, 2002): 689–713. http://dx.doi.org/10.1162/089976602317250951.
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Natural discriminant analysis based on interactive Potts models is developed in this work. A generative model composed of piece-wise multivariate gaussian distributions is used to characterize the input space, exploring the embedded clustering and mixing structures and developing proper internal representations of input parameters. The maximization of a log-likelihood function measuring the fitness of all input parameters to the generative model, and the minimization of a design cost summing up square errors between posterior outputs and desired outputs constitutes a mathematical framework for discriminant analysis. We apply a hybrid of the mean-field annealing and the gradient-descent methods to the optimization of this framework and obtain multiple sets of interactive dynamics, which realize coupled Potts models for discriminant analysis. The new learning process is a whole process of component analysis, clustering analysis, and labeling analysis. Its major improvement compared to the radial basis function and the support vector machine is described by using some artificial examples and a real-world application to breast cancer diagnosis.
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Krishnamoorthy, Yuvaraj, and Karthika Ganesh. "Spatial Pattern and Determinants of Tobacco Use Among Females in India: Evidence From a Nationally Representative Survey." Nicotine & Tobacco Research 22, no. 12 (July 29, 2020): 2231–37. http://dx.doi.org/10.1093/ntr/ntaa137.
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Abstract Introduction Tobacco use has been steadily increasing among the females in developing countries. It has led to rise in tobacco–related morbidity and mortality among females. Knowing the geographic distribution of the habit is essential to identify high–priority areas and direct the healthcare intervention. Hence, this study was done to assess the spatial patterns and determinants of tobacco consumption among females in India. Aims and Methods Univariate and bivariate Moran’s I statistic and local indicators for spatial association maps were generated to determine the spatial clustering of tobacco consumption (smoked and smokeless form). Ordinary least-square regression, spatial-lag and spatial-error models were performed to assess the determinants. Poverty (belonging to poorest and poorer quintile of wealth index), illiteracy (no formal education), marital status, ST population, tobacco use by family members, and alcohol use were the explanatory variables. Results Univariate Moran’s I was .691 suggesting positive spatial autocorrelation. High–prevalence clustering (hotspots) was maximum in the central, eastern, and northeastern states such as Chhattisgarh, Madhya Pradesh, Odisha, Bihar, Manipur, Tripura, Meghalaya, Mizoram, and Assam. This pattern was similar for both smokeless and smoked form. Results of spatial-lag and spatial-error model suggested that alcohol use, scheduled tribes, illiteracy, poverty, marital status, and tobacco use by family members were significant determinants of female tobacco consumption. The coefficient of spatial association was maximum for alcohol use (β = .20, p < .001) followed by widowed/separated/divorced (β = .12, p < .001). Conclusions Tobacco consumption among females in India is spatially clustered. Multisectoral coordination and targeted interventions are required in the geographical hotspots of tobacco consumption. Implications This is the first study to explore the geospatial pattern of tobacco consumption among females in India. We found that the pattern of tobacco use among females is spatially clustered in India. Clustering was predominantly found in central, eastern, and northeastern regions of the country. Tribal population in these areas and complementarities between alcohol and tobacco use contributed significantly to the high–prevalence clustering. These findings will be helpful for policymakers and planners to devise specific intervention package targeting the high–risk regions.
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Diego, Vincent P., Bernadette W. Luu, Alexis Garza, Marco Hofmann, Marcio A. Almeida, Jerry S. Powell, Long V. Dinh, et al. "Disentangling the Effects of HLA DRB1*15:01 and DQB1*06:02 to Establish the True HLA Risk Allele for Inhibitor Development in the Treatment of Hemophilia A." Blood 136, Supplement 1 (November 5, 2020): 1–2. http://dx.doi.org/10.1182/blood-2020-142716.
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The HLA haplotype DRB1*15:01/DQB1*06:02 has been shown to be associated with the development of inhibitory antibodies ("inhibitors") against therapeutic FVIII (tFVIII) proteins in Hemophilia A (HA) patients. Moreover, this same haplotype has also been implicated with an increased risk for the development of multiple sclerosis, narcolepsy, and drug-induced-hypersensitivity and -liver-injury. Recent reports in the literature have indicated that the alleles of this haplotype may have differential effects on immunogenicity despite the fact that they are in strong linkage disequilibrium (LD). Thus, the question arises as to which of the two constituent alleles of this haplotype is the true HLA risk allele. To address this question we analyzed peptidomic profiling data on HLA-class-II (HLAcII)-presented peptides from dendritic cell (DC)-protein processing and presentation assays (PPPAs) performed in two independent experiments performed by Peyron et al. (2018) and Diego et al. (2020) which reported results on both the HLA-DQ and -DR isomers. The tFVIII in both experiments is full-length recombinant FVIII (FL-rFVIII). We performed log-linear mixed model analyses under two models, where the dependent variable in both models is the logarithm of the expected peptide count. Under Model 1, we analyzed in the fixed effect component of the model a single HLA allele predictor variable consisting of 10 levels represented by distinct DRB1 and DQB1 alleles (4 and 6 levels, respectively). Under this model, we analyzed in the random effects component of the model variables for individuals (8 and 9 levels from Peyron et al. and Diego et al., respectively), experiments (2 levels for Peyron et al. and Diego et al.), and HLA isomers (2 levels for DQ and DR). The random effects component of the model serves to reduce the error (or "noise") in our estimation of the predictive effect of HLA alleles for the peptide counts by accounting for the clustering due to individuals, experiments, and isomers. This approach therefore can be understood as maximizing the signal-to-noise ratio. Model 2 was more focused in that the single HLA allele predictor variable in the fixed effect component consisted of only DQB1*06:02 and DRB1*15:01. Model 2 was also simpler in that we only accounted for two random effect variables, namely for individuals (in this case 2 and 6 levels for Peyron et al. and Diego et al., respectively) and experiments. Thus, Model 2 is a direct head-to-head comparison of the two main HLA alleles of interest. The Model 1 results are reported in Figure 1A and B, where it can be seen that relative to the baseline reference allele provided by DQB1*02:01, both DQB1*06:02 and DRB1*15:01 are at significantly increased risk of contributing to the overall peptide count. Results are reported as risk ratios (RRs) and their associated 95% confidence interval lower and upper bounds (95% CI LB and 95% CI UB). For DQB1*06:02 and DRB1*15:01 respectively, we found a RR (95% CI LB, 95% CI UB) of 1.76 (1.24, 2.50) and 14.16 (10.38, 19.33). Because they are compared to the same baseline, the two RRs may also be directly compared, thus showing that DRB1*15:01 contributes significantly more to the overall peptide count than DQB1*06:02. Under Model 2 (the head-to-head comparison), the RR for the DRB1*15:01 allele against the baseline DQB1*06:02 allele is 7.00 (5.80, 8.44) (Figure 2A and B). These results contribute to the field of precision medicine because they demonstrate that even in the case of alleles in tight LD, their effects can be effectively disentangled by a DC-PPPA and a log-linear mixed model analysis. Our results support the conclusion that in regard to the well-known risk haplotype of DQB1*06:02/DRB1*15:01, the true HLA risk allele seems to be DRB1*15:01. Disclosures Luu: Haplogenics Corporation: Current Employment. Hofmann:CSL Behring: Current Employment. Powell:Haplogenics Corporation: Membership on an entity's Board of Directors or advisory committees. Dinh:Haplogenics Corporation: Current Employment. Escobar:National Hemophilia Foundation: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novo Nordisk: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Maraskovsky:CSL Behring: Current Employment. Howard:Haplogenics Corporation: Membership on an entity's Board of Directors or advisory committees.
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Blom, Philip, Garrett Euler, Omar Marcillo, and Fransiska Dannemann Dugick. "Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification." Geophysical Journal International 221, no. 3 (March 9, 2020): 1750–64. http://dx.doi.org/10.1093/gji/ggaa105.
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SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.
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Mainali, Janardan, Heejun Chang, and Yongwan Chun. "A review of spatial statistical approaches to modeling water quality." Progress in Physical Geography: Earth and Environment 43, no. 6 (June 26, 2019): 801–26. http://dx.doi.org/10.1177/0309133319852003.
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We review different regression models related to water quality that incorporate spatial aspects in their model. Spatial aspects refer to the location of different sites and are usually characterized by the distance between different points and directions by which they are related to each other. We focus on spatial lag and error, spatial eigenvector-based, geographically weighted regression, and spatial-stream-network-based models. We evaluated different studies using these methods based on how they dealt with clustering (spatial autocorrelation) of response variables, incorporated those clustering in the error (residual spatial autocorrelation), used multi-scale processes, and improved the model performance. The water-quality-based regression modeling approaches are shifting from straight-line distance-based spatial relations to upstream–downstream relations. Calculation of spatial autocorrelation and residual spatial autocorrelation was dependent upon the type of spatial regression used. The weights matrix is used as available in the software and most of the studies did not attempt to modify it. Different scale processes like certain distance from rivers versus consideration of entire watersheds are dealt with separately in most of the studies. Generally, the capacity of the predictor variables to predict the response variable significantly improves when spatial regressions are used. We identify new research directions in terms of spatial considerations, weights matrix construction, inclusion of multi-scale processes, and identification of predictor variables in such models.
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Cottle, David, and Euan Fleming. "Do price premiums for wool characteristics vary for different end products, processing routes and fibre diameter categories?" Animal Production Science 56, no. 12 (2016): 2146. http://dx.doi.org/10.1071/an14744.
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No Australian wool price hedonic studies have separated auction data into different end product-processing groups (PPR) on the basis of all fibre attributes that affect the suitability of wool sale lots for PPR. This study was conducted to assess: (1) whether including information about PPR groupings is more useful in understanding price than clustering by broad fibre diameter (FD) categories, and (2) if the ‘noise’ of macroeconomic effects on price can be reduced by using a clean price relative to the market indicator (RelPrice) formula or a log RelPrice formula compared with log price or clean price. Hedonic models using data derived from 369 918 Australian auction sale lots in 2010–2011 were estimated for these four dependent price variables. Linear FD models predicted less of price’s variance than quadratic or exponential models. Segmenting wool sale lots into 10 PPR before wool price analyses was found to increase the proportion of price variance explained and thus be worthwhile. The change in price with a change in FD, staple length and staple strength differs significantly between PPR. Calculating RelPrice or log RelPrice appears a better price parameter than clean price or log price. Comparing the RelPrice and clean price models, the mean absolute percentage errors were 6.3% and 16.2%, respectively. The differences in price sensitivity to FD, staple length and staple strength across PPR implies a complex set of price-setting mechanisms for wool as different users place different values on these wool properties. These price-setting mechanisms need to be incorporated in hedonic models for agricultural products that possess this characteristic. The wool price premiums can be used to estimate relative economic values when constructing sheep breeding selection indexes and can help determine the most profitable wool clip preparation strategies.
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Wu, Ping, Luca Agnelli, Brian A. Walker, Katia Todoerti, David C. Johnson, Martin Kaiser, Christopher P. Wardell, et al. "Improved Risk Stratification in Myeloma Using Microrna-Based Classifier." Blood 120, no. 21 (November 16, 2012): 932. http://dx.doi.org/10.1182/blood.v120.21.932.932.
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Abstract Abstract 932 Introduction Multiple myeloma (MM) is a heterogeneous disease. The discovery of a class of small non-coding RNAs (miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. It may be possible to use this interesting new biology to improve our ability to risk stratify patients in the clinic. Methods We performed global miRNA expression profiling analysis of 163 primary tumors included in the UK Myeloma IX clinical trial. miRNA expression profiling was carried out using Affymetrix GeneChip miRNA 2.0; expression values for 847 hsa-miRNAs were extracted using Affymetrix miRNA QC tool and RMA-normalized. There are also 153 matching samples with gene expression profiles (GEP) and 72 matching cases with genotyping data available for integrative analyses. GEP was generated on Affymetrix HG-U133 Plus 2.0 and the expression values were RMA normalized; genotyping was performed on Affymetrix GeneChip Mapping 500K Array and the copy number values were obtained using GTYPE and dChip and were inferred against normal germ-line counterpart for each sample. Results Firstly we have defined 8 miRNAs linked to 3 Translocation Cyclin D (TC) subtypes of MM with distinct prognoses, including miR-99b/let-7e/miR-125a upregulation and miR-150/miR-155/miR-34a upregulation in unfavourable 4p16 and MAF cases respectively as well as miR-1275 upregulation and miR-138 downregulation in favourable 11q13 cases. The expression levels of the miRNA cluster miR-99b/let-7e/miR-125a at 13q13 have been shown to be associated with shorter progression free survival in our dataset. Interestingly unsupervised hierarchical clustering analysis using these 8 miRNAs identified two subclusters among 11q13 cases, which have differential effect on overall survival (OS). We then evaluated the association of miRNA expression with OS and identified 3 significantly associated miRNAs (miR-17, miR-18 and miR-886-5p) after multiple testing corrections, either per se or in concerted fashion. We went on to develop an “outcome classifier” based on the expression of two miRNAs (miR-17 and miR-886-5p), which is able to stratify patients into three risk groups (median OS 19.4 months vs 40.6 months vs 65.3 months, log-rank test p = 0.001). The robustness of the miRNA-based classifier has been validated using 1000 bootstrap replications with an estimated error rate of 1.6%. The miRNA-stratified risk groups are independent from main adverse fluorescence in situ hybridization (FISH) abnormalities (1q gain, 17p deletion and t(4;14)), International Staging System (ISS) and Myeloma IX treatment arm (intensive or non-intensive). Using the miRNA-based classifier in the context of ISS/FISH risk stratification showed that it can significantly improves the predictive power (likelihood-ratio test p = 0.0005) and this classifier is also independent from GEP-derived prognostic signatures including UAM, IFM and Myeloma IX 6-gene signature (p < 0.002). Integrative analyses didn't show enough evidence that the miRNAs comprising the classifier were deregulated via copy number changes; however, our data supported that the mir-17-92 cluster was activated by Myc and E2F3, highlighting the potential importance of Myc/E2F/miR-17-92 negative feedback loop in myeloma pathogenesis. We developed an approach to identify the putative targets of the OS-associated miRNAs and show that they regulate a large number of genes involved in MM biology such as proliferation, apoptosis, angiogenesis and drug resistance. Conclusion In this study we developed a simple miRNA-based classifier to stratify patients into three risk groups, which is independent from current prognostic approaches in MM such as ISS, FISH abnormalities and GEP-derived signatures. The miRNAs comprising the classifier are biologically relevant and have been shown to regulate a large number of genes involved in MM biology. This is the first report to show that miRNAs can be built into molecular diagnostic strategies for risk stratification in MM. Disclosures: No relevant conflicts of interest to declare.
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Lee, Hyeong-Tak, Jeong-Seok Lee, Hyun Yang, and Ik-Soon Cho. "An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm." Applied Sciences 11, no. 2 (January 15, 2021): 799. http://dx.doi.org/10.3390/app11020799.
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As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.
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Scherer, Moritz, Christine Jungk, Michael Götz, Philipp Kickingereder, David Reuss, Martin Bendszus, Klaus Maier-Hein, and Andreas Unterberg. "Early postoperative delineation of residual tumor after low-grade glioma resection by probabilistic quantification of diffusion-weighted imaging." Journal of Neurosurgery 130, no. 6 (June 2019): 2016–24. http://dx.doi.org/10.3171/2018.2.jns172951.
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OBJECTIVEIn WHO grade II low-grade gliomas (LGGs), early postoperative MRI (epMRI) may overestimate residual tumor on FLAIR sequences. Consequently, MRI at 3–6 months follow-up (fuMRI) is used for delineation of residual tumor. This study sought to evaluate if integration of apparent diffusion coefficient (ADC) maps permits an accurate estimation of residual tumor early on epMRI.METHODSFrom a consecutive cohort, 43 cases with an initial surgery for an LGG, and complete epMRI (< 72 hours after resection) and fuMRI including ADC maps, were retrospectively identified. Residual FLAIR hyperintense tumor was manually segmented on epMRI and corresponding ADC maps were coregistered. Using an expectation maximization algorithm, residual tumor segments were probabilistically clustered into areas of residual tumor, ischemia, or normal white matter (NWM) by fitting a mixture model of superimposed Gaussian curves to the ADC histogram. Tumor volumes from epMRI, clustering, and fuMRI were statistically compared and agreement analysis was performed.RESULTSMean FLAIR hyperintensity suggesting residual tumor was significantly larger on epMRI compared to fuMRI (19.4 ± 16.5 ml vs 8.4 ± 10.2 ml, p < 0.0001). Probabilistic clustering of corresponding ADC histograms on epMRI identified subsegments that were interpreted as mean residual tumor (7.6 ± 10.2 ml), ischemia (8.1 ± 5.9 ml), and NWM (3.7 ± 4.9 ml). Therefore, mean tumor quantification error between epMRI and fuMRI was significantly reduced (11.0 ± 10.6 ml vs −0.8 ± 3.7 ml, p < 0.0001). Mean clustered tumor volumes on epMRI were no longer significantly different from the fuMRI reference (7.6 ± 10.2 ml vs 8.4 ± 10.2 ml, p = 0.16). Correlation (Pearson r = 0.96, p < 0.0001), concordance correlation coefficient (0.89, 95% confidence interval 0.83), and Bland-Altman analysis suggested strong agreement between both measures after clustering.CONCLUSIONSProbabilistic segmentation of ADC maps facilitates accurate assessment of residual tumor within 72 hours after LGG resection. Multiparametric image analysis detected FLAIR signal alterations attributable to surgical trauma, which led to overestimation of residual LGG on epMRI compared to fuMRI. The prognostic value and clinical impact of this method has to be evaluated in larger case series in the future.
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Dogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences Discussions 11, no. 9 (September 10, 2014): 10179–233. http://dx.doi.org/10.5194/hessd-11-10179-2014.
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Abstract. In operational hydrology, estimation of predictive uncertainty of hydrological models used for flood modelling is essential for risk based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analyzing and predicting uncertainty. However, case studies comparing performance of these methods, most particularly predictive uncertainty methods, are limited. This paper focuses on two predictive uncertainty methods that differ in their methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC), aiming at identifying possible advantages and disadvantages of these methods (both estimating residual uncertainty) based on their comparative performance. We test these two methods on several catchments (from UK) that vary in its hydrological characteristics and models. Special attention is given to the errors for high flow/water level conditions. Furthermore, normality of model residuals is discussed in view of clustering approach employed within the framework of UNEEC method. It is found that basin lag time and forecast lead time have great impact on quantification of uncertainty (in the form of two quantiles) and achievement of normality in model residuals' distribution. In general, uncertainty analysis results from different case studies indicate that both methods give similar results. However, it is also shown that UNEEC method provides better performance than QR for small catchments with changing hydrological dynamics, i.e. rapid response catchments. We recommend that more case studies of catchments from regions of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features be tested.
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Wu, Xing, Linlin Wang, Fan Feng, and Suyan Tian. "Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma." Journal of International Medical Research 48, no. 3 (December 19, 2019): 030006051989383. http://dx.doi.org/10.1177/0300060519893837.
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Objective To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. Methods Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. Results The five-gene diagnostic signature including keratin 5 ( KRT5), mucin 1 ( MUC1), triggering receptor expressed on myeloid cells 1 ( TREM1), complement C3 ( C3) and transmembrane serine protease 2 ( TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide ( ADH1C), alpha-2-glycoprotein 1, zinc-binding ( AZGP1), clusterin ( CLU), cyclin dependent kinase 1 ( CDK1) and paternally expressed 10 ( PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. Conclusions Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.
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Moroke, Ntebogang Dinah. "An Optimal Generalized Autoregressive Conditional Heteroscedasticity Model for Forecasting the South African Inflation Volatility." Journal of Economics and Behavioral Studies 7, no. 4(J) (August 30, 2015): 134–49. http://dx.doi.org/10.22610/jebs.v7i4(j).600.
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Abstract: In most cases, financial variables are explained by leptokurtic distribution and often fail the assumption of normal distribution. This paper sought to explore the robustness of GARCH–type models in forecasting inflation volatility using quarterly time series data spanning 2002 to 2014. The data was sourced from the South African Reserve Bank database. SAS version 9.3 was used to generate the results. The initial analyses of data confirmed non-linearity, hereroscedasticity and non-stationarity in the series. Differencing was imposed in a log transformed series to induce stationarity. Further findings confirmed that ð´ð‘… (1)_ð¼ðºð´ð‘…ð¶ð» (1, 1)model suggested a high degree persistent in the conditional volatility of the series. However, theð´ð‘… (1)_ð¸ðºð´ð‘…ð¶ð» (2, 1)model was found to be more robust in forecasting volatility effects than the ð´ð‘… (1)_ð¼ðºð´ð‘…ð¶ð» (1, 1) and ð´ð‘… (1)_ðºð½ð‘… − ðºð´ð‘…ð¶ð» (2, 1)models. This model confirmed that inflation rates in South Africa exhibits the stylised characteristics such as volatility clustering, leptokurtosis and asymmetry effects. These findings may be very useful to the industry and scholars who wish to apply models that capture heteroscedastic and non-linear errors. The findings may also benefit policy makers and may be referred to when embarking on strategies in-line with inflation rate.
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Hennessy, Lachlan, and James Macnae. "Source-dependent bias of sferics in magnetotelluric responses." GEOPHYSICS 83, no. 3 (May 1, 2018): E161—E171. http://dx.doi.org/10.1190/geo2017-0434.1.
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The predominant signals of audio-frequency magnetotellurics (AMT) are called sferics, and they are generated by global lightning activity. When sferic signals are small or infrequent, measurement noise in electric and magnetic fields causes errors in estimated apparent resistivity and phase curves, leading to great model uncertainty. To reduce bias in apparent resistivity and phase, we use a global propagation model to link sferic signals in time series AMT data with commercially available lightning source information including strike time, location, and peak current. We then investigate relationships between lightning strike location, peak current, and the quality of the estimated apparent resistivity and phase curves using the bounded influence remote reference processing code. We use two empirical approaches to preprocessing time-series AMT data before estimation of apparent resistivity and phase: stitching and stacking (averaging). We find that for single-site AMT data, bias can be reduced by processing sferics from the closest and most powerful lightning strikes and omitting the lower amplitude signal-deficient segments in between. We hypothesized that bias can be further reduced by stacking sferics on the assumptions that lightning dipole moments are log-normally distributed whereas the superposed noise is normally distributed. Due to interference between dissimilar sferic waveforms, we tested a hybrid stitching-stacking approached based on clustering sferics using a wavelet-based waveform similarity algorithm. Our results indicate that the best approach to reduce bias was to stitch the closest and highest amplitude data.
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Dogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences 19, no. 7 (July 23, 2015): 3181–201. http://dx.doi.org/10.5194/hess-19-3181-2015.
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Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.
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Veenstra, Christine Marie, Andrew J. Epstein, Craig Evan Pollack, and Katrina Armstrong. "Does hospital academic status impact colon cancer care value?" Journal of Clinical Oncology 32, no. 30_suppl (October 20, 2014): 6. http://dx.doi.org/10.1200/jco.2014.32.30_suppl.6.
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6 Background: Given the high cost of cancer care, delivery of high-value care is crucial. The effect of hospital academic status on value of care for patients with stage II and III colon cancer is unknown. Methods: SEER-Medicare cohort study of 20,118 patients age 66+ with stage II or III colon cancer diagnosed 2000-2005 and followed through December 31, 2007. Patients were assigned to a treating hospital based on hospital affiliation of the primary oncologist. We constructed Kaplan-Meier curves to assess unadjusted overall survival. We estimated a Cox proportional hazards model to assess adjusted overall survival. To examine associations between hospital academic status and mean cost of care we estimated a generalized linear model (GLM) with log link and gamma family. We estimated quantile regression models to examine associations between hospital teaching status and cost at various quantiles (25th, 50th, 75th, 90th, 95th, 99th, 99.5th, 99.9th). Standard errors were adjusted to account for clustering of patients within hospitals. Results: 4449/20,118 (22%) patients received care from providers affiliated with academic hospitals. There was no significant difference in unadjusted median survival based on hospital academic status for patients with stage II (academic 6.4 yrs vs. non-academic 6.3 yrs, p=0.711) or stage III disease (academic 4.2 yrs vs. non-academic 4.2 yrs, p=0.81). After adjustment, treatment at academic hospitals was not associated with significantly reduced risk of death from colon cancer (stage II HR 1.05, 95% CI: 0.97 - 1.13; p=0.23; stage III HR 0.99, 95% CI: 0.94-1.07; p=0.98). Excepting stage III patients at the 99.9th percentile of costs, there were no significant differences in adjusted costs between academic and non-academic hospitals. Conclusions: We find no difference in overall survival for patients with stage II or stage III colon cancer based on academic status of the treating hospital. Furthermore, costs of care are similar between academic and non-academic hospitals across virtually the full range of the cost distribution. Most colon cancer patients do not receive cancer care at academic hospitals. Our findings indicate that for patients with stage II or III disease, this inequity does not impact the value of care.
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Aprikyan, Andrew A. G., David Pritchard, Conrad W. Liles, Steve Schwartz, and David C. Dale. "Gene Expression Profiles for Normal Human Bone Marrow-Derived CD34+ Stem Cells and CD34−/CD33+ Myeloid-Committed Progenitor Cells in Response to Daily Granulocyte-Colony-Stimulating Factor (G-CSF) Treatment." Blood 104, no. 11 (November 16, 2004): 4162. http://dx.doi.org/10.1182/blood.v104.11.4162.4162.
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Abstract G-CSF is widely used to accelerate marrow recovery after cancer chemotherapy, to facilitate collection of hematopoietic progenitor cells, and to treat severe chronic neutropenia. Although G-CSF was originally defined as a stimulus for myeloid cell proliferation, it has potent anti-apoptotic properties, affects synthesis of proteins stored in neutrophil granules, and has many other effects on cells of the myeloid lineage. To improve understanding of the molecular and cellular effects of G-CSF, particularly related to its use for the treatment of severe chronic neutropenia, we performed gene expression profile studies using Affymetrix oligonucleotide arrays and purified bone marrow cell sub-populations from normal volunteers treated with daily subcutaneous G-CSF (300 mcg/sc/qd) for five days. Under local anaesthesia, paired marrow aspirates were obtained from the posterior iliac crest before and after 5 daily doses of G-CSF. CD34+ and CD34−/CD33+ cells were purified using Miltenyi immunomagnetic beads. Two rounds of amplification of total RNA isolated from purified CD34+ or CD33+cells was used to obtain sufficient cRNA for hybridization. Expression data from scanned chips were first analyzed using the RMA algorithm. The limma package of the Bioconductor project was used to identify differentially expressed genes. Limma uses an empirical Bayes method to moderate the standard errors of the estimated log-fold changes. The statistical analysis of CD33+ cells revealed that 150 of more than 12,000 genes examined were up- or down-regulated >2-fold in response to G-CSF treatment. The top 10 genes with up- or down-regulated level of expression include clusterin, neutrophil elastase, two transcription factors, gelsolin, Grb2, phospholipase D3, protein kinase C, the major vault protein, and serine-threonine kinase. In the myeloid-committed CD34-/CD33+ progenitor cells, genes with altered expression level represent those with gene products involved in the cell cycle, regulation of apoptosis, the cytoskeleton, the inflammatory response, or serine proteases and transcription factors. Most of the genes up-regulated in CD33+ cells (e.g. neutrophil elastase, phospholipase D, protein kinase C) were down-regulated in CD34-positive cells in response to G-CSF. The results of the comparative analyses revealed the normal signature gene expression profiles for CD34+ and CD34−/CD33+ cells and identified genes that may mediate specific G-CSF effects.
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Siraj, T., and W. Zhou. "Quantification of Measurement Errors in the Lengths of Metal-Loss Corrosion Defects Reported by Inline Inspection Tools." Journal of Pressure Vessel Technology 141, no. 6 (August 2, 2019). http://dx.doi.org/10.1115/1.4044211.
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Abstract This paper proposes a framework to quantify the measurement error associated with lengths of corrosion defects on oil and gas pipelines reported by inline inspection (ILI) tools based on a relatively large set of ILI-reported and field-measured defect data collected from different in-service pipelines in Canada. A log-logistic model is proposed to quantify the likelihood of a given ILI-reported defect being a type I defect (without clustering error) or a type II defect (with clustering error). The measurement error associated with the ILI-reported length of the defect is quantified as the average of those associated with the types I and II defects, weighted by the corresponding probabilities obtained from the log-logistic model. The implications of the proposed framework for the reliability analysis of corroded pipelines given the ILI information are investigated using a realistic pipeline example.
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Miltersen, Peter Bro. "Error Correcting Codes, Perfect Hashing Circuits, and Deterministic Dynamic Dictionaries." BRICS Report Series 4, no. 17 (January 17, 1997). http://dx.doi.org/10.7146/brics.v4i17.18813.
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We consider dictionaries of size n over the finite universe U ={0, 1}^w and introduce a new technique for their implementation: error correcting codes. The use of such codes makes it possible to replace the use of strong forms of hashing, such as universal hashing, with much weaker forms, such as clustering.<br />We use our approach to construct, for any epsilon > 0, a deterministic solution to the dynamic dictionary problem using linear space, with worst case time O(n) for insertions and deletions, and worst case time O(1) for lookups. This is the first deterministic solution to the dynamic dictionary problem with linear space, constant query time, and non-trivial update time. In particular, we get a solution to the static dictionary problem with O(n) space, worst case query time O(1), and deterministic initialization time O(n^(1+epsilon)). The best previous deterministic initialization time for such dictionaries, due to Andersson, is O(n^(2+epsilon)). The model of computation for these bounds is a unit cost RAM with word size w (i.e. matching the universe), and a standard instruction set. The constants in the big-O's are independent upon w. The solutions are weakly non-uniform in w, i.e. the code of the algorithm contains word sized constants, depending on w, which must be computed at compile-time, rather than at run-time, for the stated run-time bounds to hold. <br />An ingredient of our proofs, which may be interesting in its own right, is the following observation: A good error correcting code for a bit vector fitting into a word can be computed in O(1) time on a RAM with unit cost multiplication. <br />As another application of our technique in a different model of computation, we give a new construction of perfect hashing circuits, improving a construction by Goldreich and Wigderson. In particular, we show that for any subset S of {0;1}w of size n, there is a Boolean circuit C of size O(w log w) with w inputs and 2 log n outputs so that the function defined by C is 1-1 on S. The best previous bound on the size of such a circuit was O(w log w log log w).
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Orisa, Mira, and Michael Ardita. "Web Usage Mining Menggunakan Algoritma Clastering K-Mean." Jurnal Teknologi Informasi dan Terapan 8, no. 1 (June 30, 2021). http://dx.doi.org/10.25047/jtit.v8i1.179.
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Algoritma K-means merupakan salah satu algoritma yang digunakan untuk metode clustering dalam data mining. Algoritma ini hanya bisa digunakan untuk mengolah data bertipe numerik menjadi pengetahuan. Metode ini cocok digunakan untuk mengolah data log access file server web untuk bidang web usage mining. Dari sekian banyak data di log access pengunjung dapat diambil pengetahuannya setelah diolah oleh algoritma K-mean. Penelitian ini dilakukan untuk mengetahui kluster dari waktu yang digunakan oleh pengguna untuk mengakses website pada sebuah instansti. Setelah melakukan try and error dalam menetapkan jumlah k dan nilai centroid awal,maka diperoleh 4 kluster. Dengan penggunaan distance measure yaitu squared Euclidean distance. Dengan average cluster distance sama dengan 207,286. Nilai Davies boudin index untuk klaster k sama dengan 4 adalah 0,076.
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Avery, Lisa, Nooshin Rotondi, Constance McKnight, Michelle Firestone, Janet Smylie, and Michael Rotondi. "Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study." BMC Medical Research Methodology 19, no. 1 (October 29, 2019). http://dx.doi.org/10.1186/s12874-019-0842-5.
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Abstract Background It is unclear whether weighted or unweighted regression is preferred in the analysis of data derived from respondent driven sampling. Our objective was to evaluate the validity of various regression models, with and without weights and with various controls for clustering in the estimation of the risk of group membership from data collected using respondent-driven sampling (RDS). Methods Twelve networked populations, with varying levels of homophily and prevalence, based on a known distribution of a continuous predictor were simulated using 1000 RDS samples from each population. Weighted and unweighted binomial and Poisson general linear models, with and without various clustering controls and standard error adjustments were modelled for each sample and evaluated with respect to validity, bias and coverage rate. Population prevalence was also estimated. Results In the regression analysis, the unweighted log-link (Poisson) models maintained the nominal type-I error rate across all populations. Bias was substantial and type-I error rates unacceptably high for weighted binomial regression. Coverage rates for the estimation of prevalence were highest using RDS-weighted logistic regression, except at low prevalence (10%) where unweighted models are recommended. Conclusions Caution is warranted when undertaking regression analysis of RDS data. Even when reported degree is accurate, low reported degree can unduly influence regression estimates. Unweighted Poisson regression is therefore recommended.
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Amraei, Hamed, and Reza Falahat. "Improved ST-FZI method for permeability estimation to include the impact of porosity type and lithology." Journal of Petroleum Exploration and Production Technology, December 7, 2020. http://dx.doi.org/10.1007/s13202-020-01061-6.
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AbstractPermeability represents the flow conductivity of a porous media. Since permeability is one of the most vital as well as the complex properties of a hydrocarbon reservoir, it is necessary to measure/estimate accurately, rapidly and inexpensively. Routine methods of permeability calculation are through core analysis and well tests, but due to problems and weaknesses of the aforementioned methods such as excessive costs and time, these are not necessarily applied on neither in all wells of a field nor in all reservoir intervals. Therefore, log-based approaches have been recently developed. The goal of this research is to provide a flowchart to estimate permeability using well logs in one of Iranian south oil fields and finally to introduce a new algorithm to estimate the permeability more accurately. Permeability is firstly estimated using artificial neural network (ANN) employing routine well logs and core data. Subsequently, it is estimated using Stoneley-Flow Zone Index (ST-FZI) and is compared with the results of core analysis. Correlation coefficients in permeability estimation by artificial neural network and Stoneley-FZI are R2 = 0.75 and R2 = 0.85, respectively. On the next step, an improved algorithm for permeability prediction (improved ST-FZI) is presented that includes the impact of lithology and porosity type. To improve the permeability estimation by ST-FZI method, electro-facies clustering based on MRGC method is employed. For this purpose, rock pore typing utilizing VDL and NDS synthetic logs is employed that considers the porosity types and texture. The VDL log separates interparticle porosity from moldic and intra-fossil porosities and washes out and weak rock-type zones. Employing MRGC method, three main facies are considered: good-quality reservoir rock, medium-quality reservoir rock and bad-quality (non-reservoir) rocks. Permeability is then estimated for each group employing ST-FZI method. The estimated permeability log by improved ST-FZI method shows better match with the measured permeability (R2 = 0.93). The average error between estimated and measured permeability for ANN, ST-FZI method and improved ST-FZI method is 1.83, 1.18 and 0.796, respectively. The increased correlation is mainly due to involving the impact of porosity types on improved ST-FZI method. Therefore, it is recommended to apply this algorithm on variety of complicated reservoir to analyze its accuracy on different environments.
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Moore, Simon C., Bella Orpen, Jesse Smith, Chinmoy Sarkar, Chenlu Li, Jonathan Shepherd, and Sarah Bauermeister. "Alcohol affordability: implications for alcohol price policies. A cross-sectional analysis in middle and older adults from UK Biobank." Journal of Public Health, April 9, 2021. http://dx.doi.org/10.1093/pubmed/fdab095.
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Abstract Background Increasing the price of alcohol reduces alcohol consumption and harm. The role of food complementarity, transaction costs and inflation on alcohol demand are determined and discussed in relation to alcohol price policies. Methods UK Biobank (N = 502,628) was linked by region to retail price quotes for the years 2007 to 2010. The log residual food and alcohol prices, and alcohol availability were regressed onto log daily alcohol consumption. Model standard errors were adjusted for clustering by region. Results Associations with alcohol consumption were found for alcohol price (β = −0.56, 95% CI, −0.92 to −0.20) and availability (β = 0.06, 95% CI, 0.04 to 0.07). Introducing, food price reduced the alcohol price consumption association (β = −0.26, 95% CI, −0.50 to −0.03). Alcohol (B = 0.001, 95% CI, 0.0004 to 0.001) and food (B = 0.001, 95% CI, 0.0005 to 0.0006) price increased with time and were associated (ρ = 0.57, P < 0.001). Conclusion Alcohol and food are complements, and the price elasticity of alcohol reduces when the effect of food price is accounted for. Transaction costs did not affect the alcohol price consumption relationship. Fixed alcohol price policies are susceptible to inflation.
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Szabó, N. P., B. A. Braun, M. M. G. Abdelrahman, and M. Dobróka. "Improved well logs clustering algorithm for shale gas identification and formation evaluation." Acta Geodaetica et Geophysica, August 16, 2021. http://dx.doi.org/10.1007/s40328-021-00358-0.
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AbstractThe identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.
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Singh, Shri Kant, Aditi Aditi, and Jitendra Gupta. "Spatial clustering and meso-scale correlates of thyroid disorder among women in India: evidence from the National Family Health Survey (2015–16)." Journal of Public Health, June 24, 2021. http://dx.doi.org/10.1007/s10389-021-01614-x.
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Abstract Purpose Thyroid disorders are a major public health burden. Generally, women exhibit higher differentials in the prevalence of these disorders. This study focuses on the socio-economic and behavioural correlates of thyroid disorders along with their spatial clustering among women of reproductive age in India. Methods The study uses dataset from the fourth National Family Health Survey (NFHS-4) carried out in 2015–16 to assess self-reported thyroid disorders. Poor–rich ratio (PRR) and concentration index (CI) were used to study the variation in thyroid disorder among women arising out of economic inequality. Moran’s I statistics and bivariate local spatial autocorrelation (BiLISA) maps were used to understand spatial dependence and clustering of thyroid disorder. Spatial lag and error models were applied to examine the correlates of the disorder. Results Thyroid disorder prevalence was higher among women from socio-economically better-off households. Adjusted effects showed that users of iodized salt were 1.14 times more likely to suffer from a thyroid disorder as compared to non-users, which is contrary to the general belief that a higher percentage of consumption of iodide salt leads to a lower prevalence of thyroid disorder. A higher autoregressive coefficient (0.71) indicated significantly higher spatial clustering in thyroid disorders. Conclusions The prevalence of thyroid disorder in India depends appreciably on spatial and various ecological factors. Sedentary lifestyles among women may be aggravating diseases, which has strong linkage with thyroid disorders. It is strongly recommended to effectively integrate universal salt iodization with activities geared towards the elimination of iodine deficiency disorders.
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Biswas, M. "Identifying geographical heterogeneity of under-five child nutritional status in districts of India." European Journal of Public Health 30, Supplement_5 (September 1, 2020). http://dx.doi.org/10.1093/eurpub/ckaa166.954.
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Abstract The nutritional status of under-five children often cited as a sensitive indicator of household living standards as well as the economic condition and also an important determinant of child survival. Despite India has already achieved remarkable progress in reducing child malnutrition, progress toward reducing the number of malnourished children has been sluggish. improvements in nutrition still represent a massive unfinished agenda. The objective of this study identified the place-specific spatial dependencies and heterogeneities in the associations between socioeconomic and demographic factors and nutritional status among under-five children in India. The study used a geocoded database from the fourth wave of the National Family Health Survey (NFHS-4 2015-16) data for 640 districts. The dependent variables were stunting, wasting and underweight. Moran's I and univariate LISA were used to confirm the spatial autocorrelation and clustering of nutritional status. Multivariate Ordinary least square (OLS), Geographically weighted regression (GWR), spatial (lag/error) models were employed to decrypt the determinants of under-five nutritional status. Overall, the prevalence and spatial clustering (Moran's I statistics) of stunting, wasting and underweight were 38% (0.634), 21% (0.488) and 36% (0.721), respectively. GWR results disclosed that the relationships between the outcomes and its covariates were significantly place-specific and spatially clustering in terms of their respective magnitude, direction and strength. Regarding model performance and prediction accuracy, GWR better fits compared to traditional OLS models. The findings of the present study identified district-level nutritional conditions (hotspots) in India, where children are under severe risk of malnutrition can help health professionals, planners and policymakers in designing and implementing effective place-specific health policies to improve district-level under-five nutrition status in India. Key messages To identify the place-specific spatial dependencies and heterogeneities in the associations between socio-economic and demographic factors and nutritional status among under-five children in India. This study helps health professionals, planners and policymakers in designing and implementing effective place-specific health policies to improve district-level under-five nutrition status in India.
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Mishra, Prem Shankar, Pradeep Kumar, and Shobhit Srivastava. "Regional inequality in the Janani Suraksha Yojana coverage in India: a geo-spatial analysis." International Journal for Equity in Health 20, no. 1 (January 7, 2021). http://dx.doi.org/10.1186/s12939-020-01366-2.
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Abstract Introduction Although India has made significant progress in institutional delivery after the implementation of the National Rural Health Mission under which the Janani Suraksha Yojana (JSY) is a sub-programme which played a vital role in the increase of institutional delivery in public facilities. Therefore, this paper aims to provide an understanding of the JSY coverage at the district level in India. Further, it tries to carve out the factors responsible for the regional disparity of JSY coverage at district levels. Methods The study used the National Family Health Survey data, which is a cross-sectional survey conducted in 2015–16, India. The sample size of this study was 148,145 women aged 15–49 years who gave last birth in the institution during 5 years preceding the survey. Bivariate and multivariate regression analysis was used to fulfill the study objectives. Additionally, Moran’s I statistics and bivariate Local Indicator for Spatial Association (LISA) maps were used to understand spatial dependence and clustering of JSY coverage. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of JSY utilization. Results The value of spatial-autocorrelation for JSY was 0.71 which depicts the high dependence of the JSY coverage over districts of India. The overall coverage of JSY in India is 36.4% and it highly varied across different regions, districts, and even socioeconomic groups. The spatial error model depicts that if in a district the women with no schooling status increase by 10% then the benefits of JSY get increased by 2.3%. Similarly, if in a district the women from poor wealth quintile, it increases by 10% the benefits of JSY also increased by 4.6%. However, the coverage of JSY made greater imperative to understand it due to its clustering among districts of specific states only. Conclusion It is well reflected in the EAGs states in terms of spatial-inequality in service coverage. There is a need to universalize the JSY programme at a very individual level. And, it is required to revisit the policy strategy and the implementation plans at regional or district levels.
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"Image Compression Using Different Vector Quantization Algorithms and Its Comparison." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (July 10, 2019): 3459–88. http://dx.doi.org/10.35940/ijitee.i8165.078919.
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Image compression techniques are presented in this paper which can be used for storage and transmission of digital lossy images. It is mostly important in both multimedia and medical field to store huge database and data transfer. Medical images are used for diagnosis purposes. So, vector quantization is a novel method for lossy image compression that includes codebook design, encoding and decoding stages. Here, we have applied different lossy compression techniques like VQ-LBG (Vector quantization- Linde, Buzo and Gray algorithm), DWT-MSVQ (Discrete wavelet transform-Multistage Vector quantization), FCM (Fuzzy c-means clustering) and GIFP-FCM (Generalized improved fuzzy partitions-FCM) methods on different medical images to measure the qualities of compression. GIFP-FCM is an extension of classical FCM and IFP-FCM (Improved fuzzy partitions FCM) algorithm with a purpose to reward hard membership degree. The presentation is assessed based on the effectiveness of grouping output. In this method, a new objective function is reformulated and minimized so that there is a smooth transition from fuzzy to crisp mode. It is fast, easy to implement and has rapid convergence. Thus, the obtained results show that GIFP-FCM algorithm gives better PSNR performance, high CR (compression ratio), less MSE (Mean square error) and less distortion as compared to other above used methods indicating better image compression.
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Dum, Deebom, Zorle, and Tuaneh, Godwin Lebari. "Modeling Exchange Rate and Nigerian Deposit Money Market Dynamics Using Trivariate form of Multivariate GARCH Model." Asian Journal of Economics, Business and Accounting, March 4, 2019, 1–18. http://dx.doi.org/10.9734/ajeba/2019/v10i230103.
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The risks associated with exchange rate and money market indicators have drawn the attentions of econometricians, researchers, statisticians, and even investors in deposit money banks in Nigeria. The study targeted at modeling exchange rate and Nigerian deposit banks money market dynamics using trivariate form of multivariate GARCH model. Data for the period spanning from 1991 to 2017 on exchange rate (Naira/Dollar) and money market indicators (Maximum and prime lending rate) were sourced for from the central bank of Nigeria (CBN) online statistical database. The study specifically investigated; the dynamics of the variance and covariance of volatility returns between exchange rate and money market indicators in Nigeria were examine whether there exist a linkage in terms of returns and volatility transmission between exchange rate and money market indicators in Nigeria and compared the difference in Multivariate BEKK GARCH considering restrictive indefinite under the assumption of normality and that of student’s –t error distribution. Preliminary time series checks were done on the data and the results revealed the present of volatility clustering. Results reveal the estimate of the maximum lag for exchange rate and money market indicators were 4 respectively. Also, the results confirmed that there were two co-integrating equations in the relationship between the returns on exchange rate and money market indicators. The results of the diagonal MGARCH –BEKK estimation confirmed that diagonal MGARCH –BEKK in students’-t was the best fitted and an appropriate model for modeling exchange rate and Nigerian deposit money market dynamics using trivariate form of multivariate GARCH model. Also, the study confirmed presence of two directional volatility spillovers between the two sets of variables.
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Anderson, Brett R. "Abstract 104: (In)adequacy of the All Patient Refined-Diagnosis Related Groups for Neonatal Cardiac Benchmarking and Reimbursement." Circulation: Cardiovascular Quality and Outcomes 11, suppl_1 (April 2018). http://dx.doi.org/10.1161/circoutcomes.11.suppl_1.104.
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Background: The All Patient Refined-DRG (APR-DRG) system is commonly used for benchmarking and reimbursement. Little is known about the adequacy when applied to pediatric service lines. Cardiac neonates not on ECMO are billed under one of three APR-DRGs, undifferentiated by case type/complexity. Two are not cardiac specific. We hypothesized that differences in pediatric case mix not captured under the DRG/severity system may have large impacts on pediatric cardiac benchmarking and reimbursement. Methods: We utilized national administrative data from 46 pediatric tertiary hospitals from the Pediatric Health Information System Database, 2014. We included all neonates with APR-DRGs 588, 609, and 630 (Newborn <1500gm w major procedure, Newborn 1500-2499gm w major procedure, and Newborn ≥2500gm w major cardiovascular procedure). Log linear regression was used to compare adjusted cost-to-charge ratio (CCR) costs between cardiac and non-cardiac discharges and across clinical case complexity categories (Risk Adjustment for Congenital Heart Surgery, RACHS-1), controlling for DRG/severity category and clustering standard errors by center. Estimated reimbursements were calculated, multiplying New York State 2014 APR-DRG weights by a range of hospital base rates. Results: In total, 4,631 neonates met inclusion. Neonates <2500gm undergoing cardiac surgery had 32% higher costs than those undergoing non-cardiac surgeries under the same DRG/severity (CI 20-46%, p<0.001; median $283,000 vs $200,000). Neonates ≥2500gm undergoing high complexity operations (RACHS-1 class 5 or 6) had 44% higher costs than children undergoing lower complexity under the same DRG/severity (CI 26-65%, p<0.001; median $198,000 vs $120,000). Payer mix was similar for cardiac/non-cardiac patients. Assuming 2014 base rates of $6-8,000, average expenses for cardiac neonates <2500gm undergoing major procedures needed to be <45-68% of CCR costs to generate profit (vs <54-80% for non-cardiac); expenses for neonates ≥2500gm undergoing high complexity cases needed to be <42-60% of costs (vs <67-83% for lower complexity). Conclusions: The APR-DRG system is inadequate for neonatal cardiac benchmarking, and its role in reimbursement has significant potential ramifications for the revenue of pediatric cardiac service lines paid on DRG.