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Background Depression is a chronic illness that generally requires lifelong therapy. Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive technique with few side effects that has been reported to be useful in the treatment of depression. However, no studies to date have evaluated in a randomized controlled trial (RCT) the efficacy of rTMS for maintenance treatment of depression. Methods/design In this article, we report the design and protocol of a randomized, single-blind, placebocontrolled, parallel-group, multicenter study in China to evaluate the efficacy of rTMS in the prevention of relapse of depressive symptoms. In total, 540 patients, aged 18 to 60 years, diagnosed with depression and experiencing an acute exacerbation of depressive symptoms, will be enrolled. The study will consist of four phases: a screening/tolerability phase of up to 7 days; an open-label, flexible-dose lead-in phase of 8 weeks; an open-label, fixed-dose stabilization phase of 6 weeks; and a single-blind relapse prevention phase of 12 months. During the open-label phase, all patients will be treated with venlafaxine. Remitters with Hamilton Rating Scale for Depression (HAM-D17) score ⩽7 will be eligible to enter the single-blind phase and will be randomly assigned to one of three groups: group 1 on active rTMS and venlafaxine; group 2 on sham rTMS and venlafaxine; and group 3 on venlafaxine alone. Efficacy will be evaluated during the study using relapse assessment (time between subject randomization to treatment and the first occurrence of relapse). Secondary outcome measures will include: symptom changes, measured by the HAM-D17; illness severity changes, measured by the Clinical Global Impression of Severity for Depression (CGI-SDEP); and changes in subject functioning, assessed with the Personal and Social Performance (PSP)scale. Safety will be assessed throughout the study by monitoring of adverse events, clinical laboratory tests, electrocardiography (ECG), and measurements of vital signs (temperature, pulse, and blood pressure) and weight. Suicidality will be assessed by the Columbia Suicide Severity Rating Scale (C-SSRS). Discussion The result of this trial will assess the efficacy of rTMS in the prevention of relapse of symptoms of depression by determining whether rTMS in combination with an antidepressant is more efficacious than the antidepressant alone for maintenance of the clinical response [ABSTRACT FROM AUTHOR]

A short-term solar flare prediction model is built using predictor teams rather than an individual set of predictors. The information provided by the set of predictors could be redundant. So it is necessary to generate subsets of predictors which can keep the information constant. These subsets are called predictor teams. In the framework of rough set theory, predictor teams are constructed from sequences of the maximum horizontal gradient, the length of neutral line and the number of singular points extracted from SOHO/MDI longitudinal magnetograms. Because of the instability of the decision tree algorithm, prediction models generated by the C4.5 decision tree for different predictor teams are diverse. The flaring sample, which is incorrectly predicted by one model, can be correctly forecasted by another one. So these base prediction models are used to construct an ensemble prediction model of solar flares by the majority voting rule. The experimental results show that the predictor team can keep the distinguishability of the original set, and the ensemble prediction model can obtain better performance than the model based on the individual set of predictors. [ABSTRACT FROM AUTHOR]

Solar flares are powered by the energy stored in magnetic fields, so evolutionary information of the magnetic field is important for short-term prediction of solar flares. However, the existing solar flare prediction models only use the current information of the active region. A sequential supervised learning method is introduced to add the evolutionary information of the active region into a prediction model. The maximum horizontal gradient, the length of the neutral line, and the number of singular points extracted from SOHO/MDI longitudinal magnetograms are used in the model to describe the nonpotentiality and complexity of the photospheric magnetic field. The evolutionary characteristics of the predictors are analyzed by using autocorrelation functions and mutual information functions. The analysis results indicate that a flare is influenced by the 3-day photospheric magnetic field information before flare eruption. A sliding-window method is used to add evolutionary information of the predictors into machine learning algorithms, then C4.5 decision tree and learning vector quantization are employed to predict the flare level within 48 hours. Experimental results indicate that the performance of the short-term solar flare prediction model within the sequential supervised learning framework is significantly improved. [ABSTRACT FROM AUTHOR]

From a large number of SOHO/MDI longitudinal magnetograms, three physical measures including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are computed. These measures are used to describe photospheric magnetic field properties including nonpotentiality and complexity, which is believed to be closely related to solar flares. Our statistical results demonstrate that solar flare productivity increases with nonpotentiality and complexity. Furthermore, the relationship between the flare productivity and these measures can be well fitted with a sigmoid function. These results can be beneficial to future operational flare forecast models. [ABSTRACT FROM AUTHOR]

The boundary integral equation (BIE) is a representation for the force-free magnetic field in the solar atmosphere, which is proposed by Yan & Sakurai. By using the force-free solutions given by Low & Lou, the validity of this method for magnetic extrapolation in open space above a spherical surface is checked. The magnetic fields selected for the examination have different ranges of α values that represent the extents to which the force-free fields deviate from the potential field. The field points selected for calculation cover the altitude ranging from 1.1 R0 to 2.5 R0, where R0 denotes the radius of the spherical surface. The results of the examination support the application of the BIE method to large-scale and global extrapolation of the force-free magnetic field in open space above a spherical surface. The calculations also show that, for a complex force-free field with a large range of α values, the volume integral equation in the BIE method has a poor convergence property and more computing time is needed to obtain a meaningful result, whereas the altitude of the field point does not influence the convergence speed. [ABSTRACT FROM AUTHOR]

Magnetogram data of 517 bipolar active regions are analyzed to study latitude, magnetic flux, polarity separation dependence of tilt angle of the active regions with well-defined bipolar magnetic configurations. The data were obtained at Huairou Solar Observing Station in Beijing during 1988 to October 2001. By statistical analysis, it is found: (1) The tilt angle (ψ) is a function of the latitude (θ). Our observed result, sinψ=0.5 sin θ, is in good agreement with that obtained by Wang and Sheeley (1991). (2) The tilt angle is a function of the magnetic flux. The tilt angle increases (decreases) with flux increasing when the flux is smaller (larger) than 5×1021 Mx. (3) The tilt angle is a function of the magnetic polarity separation. The tilt angle increases (decreases) with the separation increasing when the separation is smaller (larger) than 8×109 cm. (4) The magnetic flux (φ in 1020 Mx) is correlated to the magnetic polarity separation (d in Mm), following φ20∼d1.15. The result is close to the observed result of Wang and Sheeley (1989), φ20∼d1.3. (5) The tilt fluctuations are independent of the latitude, but depend slightly on the separation, which is similar to the result obtained by Fisher, Fan, and Howard (1995). (6) The distribution function of the ratio of net magnetic flux to total magnetic flux is almost centered around zero net flux. The imbalance of magnetic flux is lower than 10% for 47% of our samples; 31% of active regions are in imbalance of the magnetic flux between 10% and 20%. [ABSTRACT FROM AUTHOR]

Three sympathetic flares were observed with the Solar Magnetic Field Telescope (SMFT) at the Huairou Solar Observing Station of Beijing Astronomical Observatory on 29 August, and 1 September 1990. Each set of sympathetic flares had three ribbons. Two ribbons appeared in active region NOAA 6233 and one ribbon occurred in NOAA 6240 embedded in a single polarity area. Photospheric vector magnetograms were simultaneously obtained from both regions as well. We use a new numerical technique to reconstruct the chromospheric and coronal magnetic fields by making use of the observed vector magnetic fields in the photosphere as boundary conditions. Magnetic field loops linking both regions were identified from the reconstructed 3-D fields. The analysis of chromospheric filtergrams and reconstructed 3-D magnetic fields indicates that interaction between a sheared lower loop in the active region NOAA 6233 and a higher loop linking the two regions resulted in sympathetic flares. The analysis of the time delay between flare ribbons in NOAA 6233 and 6240 indicates that heat conduction along the higher loop from the primary energy release site is responsible for the sympathetic flaring in NOAA 6240. The events reported in this paper represent only one alternative as the cause of sympathetic flaring in which energy transport along coronal interconnecting loops plays the major role, and no in-situ energy release is required. [ABSTRACT FROM AUTHOR]

Instantaneous predictions of the maximum monthly smoothed sunspot number in solar cycle 23 have been made with a linear regressive model, which gives the predicted maximum value as a function of the smoothed sunspot numbers corresponding to a given month from the minimum in all preceding cycles. These predictions indicate that the intensity of solar activity in the current cycle will be at an average level. [ABSTRACT FROM AUTHOR]

We extrapolated the 3-D fields above the photosphere, taking the observed photospheric magnetic fields in the active regions NOAA 6659 and 7321 as the boundary conditions of a linear force-free field model, and detected the singular points of the 2-D fields in a plane at the chromospheric level. These singular points can be described with the Poincaré index. Singular points with the index of +1 correspond to concentrations of magnetic flux, and those with the index of -1 to the saddle points in the plane. All of these singular points are connected by the lanes demarcating the 2-D magnetic cells in the plane. It has been confirmed that these saddle points are the intersections between separators and planes intersecting the 3-D fields. From comparisons between kernels of flares occurring in both regions and the saddle points, we found that there is a close morphological relationship between distributions of the saddle points and flare kernels. The main results are as follows: (a) The flare kernels tend to appear in areas with concentrating 2-D saddle points. (b) The morphology of the kernels is exactly confined by the lanes in the plane at chromospheric level. These facts seem favourable for the viewpoint that solar flares are closely related to magnetic separatrices and separators. [ABSTRACT FROM AUTHOR]

The distribution of photospheric longitudinal magnetic fields in a solar active region can be considered to be a natural surface in a mathematical sense. Just as with landscapes on the Earth, the surface may be a fractal Brownian surface (FBS). A method suggested in the paper can manifest whether the surface is an FBS or not. The method has been applied to the longitudinal magnetic fields in AR 5988 on March 24, 1990 and AR 6233 on August 30, 1990, in which the observational characteristics are quite different. The testing results indicate that the distributions of longitudinal magnetic field in both regions are not in agreement with the FBS model [ABSTRACT FROM AUTHOR]

We investigate the 3D magnetic topology in the active region NOAA 11719 nine hours before and after a flare–coronal mass ejection (CME) event on 2013 April 11. The extrapolated 3D coronal magnetic field is computed employing a boundary integrated model, and a complex magnetic topology skeleton comprising five fairly robust null points and their relevant structures are revealed with a mathematical method based on a Poincaré index of isolated 3D null points. Comparative analyses show that the magnetic topology skeleton in this active region determines geometries of post-flare loops and flare ribbons, and characterizes the initial stage of the CME. The present work demonstrates that the magnetic topology skeleton plays an important role in the process of the flare–CME eruption. [ABSTRACT FROM AUTHOR]

Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions. [ABSTRACT FROM AUTHOR]

The mass of a coronal mass ejection (CME) is calculated from the measured brightness and assumed geometry of Thomson scattering. The simplest geometry for mass calculations is to assume that all of the electrons are in the plane of the sky (POS). With additional information like source region or multiviewpoint observations, the mass can be calculated more precisely under the assumption that the entire CME is in a plane defined by its trajectory. Polarization measurements provide information on the average angle of the CME electrons along the line of sight of each CCD pixel from the POS, and this can further improve the mass calculations as discussed here. A CME event initiating on 2012 July 23 at 2:20 UT observed by the Solar Terrestrial Relations Observatory is employed to validate our method. [ABSTRACT FROM AUTHOR]