One-step ahead prediction of <i>fo</i>F2 using time series forecasting techniques

In this paper the problem of one-step ahead prediction of the critical frequency (<i>fo</i>F2) of the middle-latitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58000 observations of <i>fo</i>F2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Penteli (38&deg; N, 23.5&deg; E), for the period from October 2002 to May 2004. First, the embedding dimension of the dynamical system that generates the above sample is estimated using the false nearest neighbor method. This information is then utilized for the training of the predictors employed in this study, which are the linear predictor, the neural network predictor, the persistence predictor and the <i>k</i>-nearest neighbor predictor. The results obtained by the above predictors suggest that, as far as the mean square error is considered as performance criterion, the first two predictors are significantly better than the latter two predictors. In addition, the results obtained by the linear and the neural network predictors are not significantly different from each other. This may be taken as an indication that a linear model suffices for one step ahead prediction of <i>fo</i>F2

Rationale and study design of the prospective, longitudinal, observational cohort study “rISk strAtification in end-stage renal disease” (ISAR) study

Background: The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro-and macrocirculation and to determine autonomic function. Methods/design: We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality. Discussion/conclusion: We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study

Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry

OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc). METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers. RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group. CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies

QUALITATIVE-ANALYSIS OF THE PARALLEL AND ASYNCHRONOUS MODES OF THE HAMMING NETWORK

In this paper convergence analysis of the parallel and deterministic asynchronous modes of operation for the Hamming network is carried out. Conditions ensuring convergence to a stable state in a finite number of steps are derived. An upper bound of the maximum number of steps that is required to reach a stable state is obtained. Finally, a geometrical interpretation of our results is obtained

Generalized hamming networks and applications

In this paper the classical Hamming network is generalized in various ways. First, for the Hamming maxnet, a generalized model is proposed, which covers under its umbrella most of the existing versions of the Hamming Maxnet. The network dynamics are time varying while the commonly used ramp function may be replaced by a much more general non-linear function. Also, the weight parameters of the network are time varying. A detailed convergence analysis is provided. A bound on the number of iterations required for convergence is derived and its distribution functions are given for the cases where the initial values of the nodes of the Hamming maxnet stem from the uniform and the peak distributions. Stabilization mechanisms aiming to prevent the node(s) with the maximum initial value diverging to infinity or decaying to zero are described. Simulations demonstrate the advantages of the proposed extension. Also, a rough comparison between the proposed generalized scheme as well as the original Hamming maxnet and its variants is carried out in terms of the time required for convergence, in hardware implementations. Finally, the other two parts of the Hamming network, namely the competitors generating module and the decoding module, are briefly considered in the framework of various applications such as classification/clustering, vector quantization and function optimization. © 2005 Elsevier Ltd. All rights reserved

Neural network architectures for selecting the maximum input

In this paper two neural network architectures for selecting the maximum among a set of numbers are introduced. The first architecture is recurrent and relies on the Hamming MaxNet. The second architecture is feedforward, featuring modularity and pipelineability

Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics

High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to Hα chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature