131,569 research outputs found
Determining the size of lightning-induced electron precipitation patches
No full text[1] We analyze Trimpi signatures during 23 and 24 April 1994 at four sites on or near the Antarctic Peninsula (Palmer, Faraday, Rothera, and Halley) on subionospheric VLF signals received from four U. S. naval transmitters (NAA, NSS, NLK, and NPM). Electron precipitation patches are found to be large, i.e., similar to1500 km x 600 km, with the longer axis orientated east-west. Calculations using a three-dimensional Born scattering model, where patch densities are 1.5 electrons cm(-3) above ambient at the center at similar to84 km altitude, provides results that are consistent with this picture. A high proportion (38%) of the Trimpi events were associated with strong lightning flashes in eastern United States. When lightning discharges had currents >65 kA (positive or negative), there was a >80% chance of seeing an associated Trimpi event. The chance of seeing any Trimpi events fell to near zero for discharges of <45 kA. The largest Trimpi perturbations occur when the center of the precipitation patch is 700-800 km from the receivers. This result is consistent with the modeling calculations for large patches. The equatorward edge of the precipitation patch was estimated to be at &SIM;60&DEG;S, close to the magnetic conjugate of the lightning. The close association of the equatorward edge of the precipitation patch with the conjugate location of the causative lightning is consistent with a quasi-ducted whistler-induced precipitation mechanism. Nonducted whistler-induced precipitation mechanisms would predict a 5&DEG;-10&DEG; latitudinal gap between the lightning and the equatorward edge of the patch. However, the lack of observed whistlers at the time of the Trimpi events is consistent with the nonducted whistler mechanism and is not consistent with the quasi-ducted mechanism, although the distances from duct exit point to receiver may have been too large (&SIM;700-1000 km) for the signals to be detectable. Using the significantly larger patch dimensions determined in this study, it is estimated that lightning may well be 10-100 times more effective at depleting the radiation belts than hiss
Type-1 fuzzy forecasting functions with elastic net regularization
No full textFuzzy functions have recently been used for forecasting problems. The main concepts behind a fuzzy functions are to cluster the inputs using a fuzzy clustering method and to include the obtained membership grades and their non-linear transformations as new variables in the input matrix. Then, multiple linear regression models are solved for different clusters. However, adding related variables to the input matrix leads to the multicollinearity problem. Thus, the main contribution of the proposed method is to employ an elastic net in fuzzy functions to overcome the aforementioned problem. Two regularization terms occur in an elastic net that come from the ridge and the lasso regression. These regularization terms are optimized using the nested cross-validation approach to overcome the multicollinearity problem in the fuzzy functions method. Twelve practical time-series datasets are analyzed to evaluate the performance of the proposed fuzzy functions. The outstanding performance of the proposed method has been verified in terms of root mean squared errors and mean absolute percentage errors for the selected datasets
Binary particle swarm optimization as a detection tool for influential subsets in linear regression
No full textAn influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications
A novel method as a diagnostic tool for the detection of influential observations in the Cox proportional hazards model
No full textIt is important that the process of studying and modelling the prognosis of disability should be conducted using time-to-event data, as the dynamic nature of disability could cause intervention on the modifiable (prognostic) factors, thus changing the course to a more favourable outcome. In disability research, the Cox PH model is frequently used to identify prognostic factors for the life expectancy of people with disabilities and to evaluate the treatment effects on the time to event. Accurate detection of influential observations is an important factor when fitting the Cox PH model, as influential observations in the Cox PH model can cause model misspecification, inaccurately determined factors, missed valuable biological information and/or violation of the proportional hazard assumption. In this paper, a novel multiple case detection method for influential observations is recommended in the Cox model. The aim of the paper is to inform clinicians and researchers who use the Cox PH model for describing the survival time as a function of multiple prognostic factors, regarding the importance of the detection of influential observations that can lead to misleading conclusions if they are present in the data set. The efficiency of the proposed method is presented through the real dataset. Additionally, in the specific case of North Cyprus, the aim is emphasize the importance of survival modelling studies that determine the prognostic factors affecting the lives of people with disabilities, to improve life quality and to develop a plan for healthier and higher quality life styles programmes for people with disabilities. As a first step, it is recommended that a system of database records of disabilities should be established and maintained by the government to raise public awareness. © 2018, Springer Science+Business Media B.V., part of Springer Nature
A new alternative estimation method for Liu-type logistic estimator via particle swarm optimization: an application to data of collapse of Turkish commercial banks during the Asian financial crisis
No full textIn the existence of multicollinearity problem in the logistic model, some important problems may occur in the analysis of the model, such as unstable maximum likelihood estimator with very high standard errors, false inferences. The Liu-type logistic estimator was proposed as two-parameter estimator to overcome multicollinearity problem in the logistic model. In the existing previous studies, the (k, d) pair in this shrinkage estimator is estimated by two-phase methods. However, since the different estimators can be utilized in the estimation of d, optimal choice of the (k, d) pair provided using the two-phase approaches is not guaranteed to overcome multicollinearity. In this article, a new alternative method based on particle swarm optimization is suggested to estimate (k, d) pair in Liu-type logistic estimator, simultaneously. For this purpose, an objective function that eliminates the multicollinearity problem, provides minimization of the bias of the model and improvement of the model's predictive performance, is developed. Monte Carlo simulation study is conducted to show the performance of the proposed method by comparing it with existing methods. The performance of the proposed method is also demonstrated by the real dataset which is related to the collapse of commercial banks in Turkey during Asian financial crisis
Between fear and despair: Human nature in realism
No full textThis chapter reveals the emotional dimension of realist views on human nature, which fit firmly into the anthropological camp of approaches to theorizing humanity and world politics. Much of the realist anthropology is well known and need not be rehearsed here. But often overlooked is realist man’s emotional side. This chapter focuses on the centrality of fear and despair as “emotional motifs” in the psychology of International Relations (IR) realism, and on how they relate to more widely discussed realist notions such as rationality and will to power. I show how these emotional motifs link the political-philosophical “tradition of despair” via twentieth-century classical realist views on enduring human features of international politics to structural realism, which sports implicit (but no less fundamental) anthropological foundations. I show how these foundations have affected the realist ontology through the shifting emanations of diverse realist approaches, and how they support two contradictory theoretical postures – fatalism and defense – between which realism is suspended with little hope of escape
Prediction of Inner Grooved Circular Jet Flow with Artificial Neural Networks
No full textIn this study, an artificial neural network model was established by using experimental measurement values obtained from a low-speed subsonic wind tunnel, with the length of 75 cm and experiment test section of 32× 32 cm². Model results were compared with experimental values and then, the prediction was made for the unmeasured tunnel stream values. In the wind tunnel, the jet velocity of 25 m/s and four tunnel velocities of 0, 5, 10 and 20 m/s were used. At four measurement stations x/D=0.3, x/D=12.5, x/D=31.2 and x/D=50, experimental measurements were made using a hot wire anemometer. This study is the continuation of the work done by Inan and Sisman [T. Inan, T. Sisman, Acta Phys. Pol. A 127, 1145 (2015)]. Inner grooved circular jet flows at x/D=0.3 and x/D=50 stations with average tunnel flow velocities of 7.5 m/s and 15 m/s were studied by using artificial neural networks
Prediction of Inner Grooved Circular Jet Flow with Artificial Neural Networks
No full textIn this study, an artificial neural network model was established by using experimental measurement values obtained from a low-speed subsonic wind tunnel, with the length of 75 cm and experiment test section of 32 x 32 cm(2). Model results were compared with experimental values and then, the prediction was made for the unmeasured tunnel stream values. In the wind tunnel, the jet velocity of 25 m/s and four tunnel velocities of 0, 5, 10 and 20 m/s were used. At four measurement stations x/D = 0.3, x/D = 12.5, x/D = 31.2 and x/D = 50, experimental measurements were made using a hot wire anemometer. This study is the continuation of the work done by Inan and Sisman [T. Inan, T. Sisman, Acta Phys. Pol. A 127, 1145 ( 2015)]. Inner grooved circular jet flows at x/D = 0.3 and x/D = 50 stations with average tunnel flow velocities of 7.5 m/s and 15 m/s were studied by using artificial neural networks
- …
