Statistical study of chorus wave distributions in the inner magnetosphere using Ae and solar wind parameters

Energetic electrons within the Earth's radiation belts represent a serious hazard to geostationary satellites. The interactions of electrons with chorus waves play an important role in both the acceleration and loss of radiation belt electrons. The common approach is to present model wave distributions in the inner magnetosphere under different values of geomagnetic activity as expressed by the geomagnetic indices. However, it has been shown that only around 50% of geomagnetic storms increase flux of relativistic electrons at geostationary orbit while 20% causes a decrease and the remaining 30% has relatively no effect. This emphasizes the importance of including solar wind parameters such as bulk velocity (V), density (n), flow pressure (P), and the vertical interplanetary magnetic field component (Bz) that are known to be predominately effective in the control of high energy fluxes at the geostationary orbit. Therefore, in the present study the set of parameters of the wave distributions is expanded to include the solar wind parameters in addition to the geomagnetic activity. The present study examines almost 4 years (1 January 2004 to 29 September 2007) of Spatio-Temporal Analysis of Field Fluctuation data from Double Star TC1 combined with geomagnetic indices and solar wind parameters from OMNI database in order to present a comprehensive model of wave magnetic field intensities for the chorus waves as a function of magnetic local time, L shell (L), magnetic latitude (λm), geomagnetic activity, and solar wind parameters. Generally, the results indicate that the intensity of chorus emission is not only dependent upon geomagnetic activity but also dependent on solar wind parameters with velocity and southward interplanetary magnetic field Bs (Bz < 0), evidently the most influential solar wind parameters. The largest peak chorus intensities in the order of 50 pT are observed during active conditions, high solar wind velocities, low solar wind densities, high pressures, and high Bs. The average chorus intensities are more extensive and stronger for lower band chorus than the corresponding upper band chorus

Potential of Mycobacterium tuberculosis resuscitation-promoting factors as antigens in novel tuberculosis sub-unit vaccines

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Smoking Knowledge, Attitude and Behavior of Child Labor Who Live in Tehran during 2013-2014

Background: Children and adolescent smoking is one of the most important health problems in the world. There is a major concern that child labor may generate a pseudo maturity syndrome, including smoking.The current survey focus on smoking behavior, knowledge and attitude of child labor are working in Tehran.Materials and Methods: The study adopted a cross-sectional design, based on a primary pilot descriptive cross sectional study, using GYTS self-administered questionnaire. 816 child labor, which were student of work labor schools or worked as child labor on Tehran parks and crossing roads, were randomly selected using multi stage cluster sampling. DATA analyzed using SPSS v.22 (IBM statistic) software and chi square test to compare the frequency of variables in different groups.Results: 50.6% of our participants were boy and child laboring age varied from 11 to 17 years old. 18.6% of child labor had smoking experience (Confident Interval 95%=17.3-20.1). 9.8% of them were current smoker (CI 95%=8.6-10.9) and 1.2% were current regular smoker (CI 95%=0.9-2.1). Child labor smoking hazard knowledge was evaluated by considering the minimum and maximum score of 10 to 30. Results demonstrated that the mean score of knowledge, attitude and behavior were 17.1±6.2, 36.5±16.1 (range 15-45) and 46.1±3.0 (range 25-75), respectively.Conclusion: Considering to our findings, planning tobacco control program for these specific groups is required, aiming at preventing cigarette smoking by increasing the knowledge and correcting their attitude

Outer radiation belt electron lifetime model based on combined Van Allen Probes and Cluster VLF measurements

The flux of energetic electrons in the outer radiation belt shows a high variability. The interactions of electrons with very low frequency (VLF) chorus waves play a significant role in controlling the flux variation of these particles. Quantifying the effects of these interactions is crucially important for accurately modeling the global dynamics of the outer radiation belt and to provide a comprehensive description of electron flux variations over a wide energy range (from the source population of 30 keV electrons up to the relativistic core population of the outer radiation belt). Here, we use a synthetic chorus wave model based on a combined database compiled from the Van Allen Probes and Cluster spacecraft VLF measurements to develop a comprehensive parametric model of electron lifetimes as a function of L‐shell, electron energy, and geomagnetic activity. The wave model takes into account the wave amplitude dependence on geomagnetic latitude, wave normal angle distribution, and variations of wave frequency with latitude. We provide general analytical formulas to estimate electron lifetimes as a function of L‐shell (for L = 3.0 to L = 6.5), electron energy (from 30 keV to 2 MeV), and geomagnetic activity parameterized by the AE index. The present model lifetimes are compared to previous studies and analytical results and also show a good agreement with measured lifetimes of 30 to 300 keV electrons at geosynchronous orbit

System identification of local time electron fluencies at geostationary orbit

The electron fluxes at geostationary orbit measured by Geostationary Operational Environmental Satellite (GOES) 13, 14, and 15 spacecraft are modeled using system identification techniques. System identification, similar to machine learning, uses input‐output data to train a model, which can then be used to provide forecasts. This study employs the nonlinear autoregressive moving average exogenous technique to deduce the electron flux models. The electron fluxes at geostationary orbit are known to vary in space and time, making it a spatiotemporal system, which complicates the modeling using system identification/machine learning approach. Therefore, the electron flux data are binned into 24 magnetic local time (MLT), and a separate model is developed for each of the 24 MLT bins. MLT models are developed for six of the GOES 13, 14, and 15 electron flux energy channels (75 keV, 150 keV, 275 keV, 475 keV, >800 keV, and >2 MeV). The models are assessed on separate test data by prediction efficiency (PE) and correlation coefficient (CC) and found these to vary by MLT and electron energy. The lowest energy of 75 keV at the midnight sector had a PE of 36.0 and CC of 59.3, which increased on the dayside to a PE of 66.9 and CC of 81.6. These metrics increased to the >2 MeV model, which had a low PE and CC of 63.0 and 81.8 on the nightside to a high of 80.3 and 90.8 on the dayside

Equatorial magnetosonic waves observed by cluster satellites: the Chirikov resonance overlap criterion

Magnetosonic waves play an important role on the overall dynamics of relativistic radiation belt electrons. Numerical codes modeling the evolution of the radiation belts often account for wave-particle interaction with magnetosonic waves. The diffusion coefficients incorporated in these codes are generally estimated based on the results of statistical surveys of the occurrence and amplitude of these waves. These statistical models assume that the spectrum of the magnetosonic waves can be considered as continuous in frequency space. This assumption can only be valid if the discrete nature of the waves satisfy the Chirikov overlap criterion. Otherwise, the assumption of a continuous frequency spectrum could produce erroneous results in wave models and hence estimates of the electron diffusion coefficients used in numerical models of the inner magnetosphere. Recently, it was demonstrated, through a case study conducted on a single short (10 s) period snapshot within a longer wave event, that the discrete nature of the equatorial magnetosonic waves do satisfy the Chirikov overlap criterion and so the assumption of a continuous frequency spectrum is valid for the calculation of diffusion coefficients. This paper expands this study to a broader range of time with many magnetosonic wave events to determine whether the discrete nature of the waves always satisfy the Chirikov overlap criterion. The results show that most, but not all, discrete magnetosonic emissions satisfy the Chirikov overlap criterion. Therefore, the use of the continuous spectrum, employed in quasi-linear theory, may not always be justified

Electrochemical-based biosensors for microRNA detection: Nanotechnology comes into view

Nanotechnology plays an undeniable significant role in medical sciences, particularly in the field of biomedicine. Development of several diagnostic procedures in medicine has been possible through the beneficial application of nano-materials, among which electrochemical nano-biosensors can be mentioned. They can be employed to quantify various clinical biomarkers in detection, evaluation, and follow up stages of the illnesses. MicroRNAs, a group of regulatory short RNA fragments, added a new dimension to the management and diagnosis of several diseases. Mature miRNAs are single-stranded RNA molecules approximately 22 nucleotides in length, which regulate a vast range of biological functions from cellular proliferation and death to cancer development and progression. Recently, diagnostic value of miRNAs in various diseases has been demonstrated. There are many traditional methods for detection of miRNAs including northern blotting, quantitative real time PCR (qRT-PCR), microarray technology, nanotechnology-based approaches, and molecular biology tools including miRNA biosensors. In comparison with other techniques, electrochemical nucleic acid biosensor methods exhibit many interesting features, and could play an important role in the future nucleic acid analysis. This review paper provides an overview of some different types of nanotechnology-based biosensors for detection of miRNAs. © 201

The influence of solar wind and geomagnetic indices on lower band chorus emissions in the inner magnetosphere

Statistical wave models, describing the distribution of wave amplitudes as a function of location, geomagnetic activity, and other parameters, are needed as the basis to describe the wave-particle interactions within numerical models of the radiation belts. In this study, we widen the scope of the statistical wave models by investigating which of the solar wind parameters or geomagnetic indices and their time lags have the greatest influence on the amplitudes of lower band chorus (LBC) waves in the inner magnetosphere. The solar wind parameters or geomagnetic indices with the greatest control over the waves were found using the error reduction ratio (ERR) analysis, which plays a key role in system identification modeling techniques. In this application, the LBC magnitudes at different locations are considered as the output data, while the lagged solar wind parameters are the input data. The ERR analysis automatically determines a set of the most influential parameters that explain the variations in the emissions. Both linear and nonlinear applications of the ERR analysis are compared using solar wind inputs and show that the linear ERR analysis can be misleading. The linear results show that the interplanetary magnetic field (IMF) factor has the most influence on at each magnetic local time (MLT) sector. However, the nonlinear ERR analysis shows that the IMF factor coupled with the solar wind velocity has the main contribution to the LBC wave magnitudes. When geomagnetic indices are included as inputs with the solar wind parameters to the nonlinear ERR analysis, the results show that the majority of the variation in emissions may be attributed to the Auroral Electrojet (AE) index. In the dawn sectors between 00 and 12 MLT and 5 < L < 7, the AE index multiplied by the solar wind velocity with zero time lag has the most influence on the amplitudes of LBC. For 5 < L < 7, the parameters with the highest ERR are the AE index multiplied by the solar wind velocity with a 2-hr time lag at 12–16 MLT, the linear AE index with a 2-hr time lag at 16–20 MLT, and AE index multiplied by the IMF factor with zero lag at 20–00 MLT. For 4 < L < 5, the parameters with the highest ERR are the AE index multiplied by the solar wind dynamic pressure with zero time lag at 00–04 MLT, the AE index multiplied by the solar wind velocity with zero time lag between 14 and 12 MLT, the AE index multiplied by the solar wind velocity with a 2-hr time lag at 12–16 MLT, the Dst index with a 6-hr time lag at 12–16 MLT, and the AE index multiplied by the IMF factor with zero lag at 20–00 MLT

A dynamical model of equatorial magnetosonic waves in the inner magnetosphere: a machine learning approach

Equatorial magnetosonic waves (EMS), together with chorus and plasmaspheric hiss, play key roles in the dynamics of energetic electron fluxes in the magnetosphere. Numerical models, developed following a first principles approach, that are used to study the evolution of high energy electron fluxes are mainly based on quasilinear diffusion. The application of such numerical codes requires statistical models for the distribution of key magnetospheric wave modes to estimate the appropriate diffusion coefficients. These waves are generally statistically modeled as a function of spatial location and geomagnetic indices (e.g., AE, Kp, or Dst). This study presents a novel dynamic spatiotemporal model for EMS wave amplitude, developed using the Nonlinear AutoRegressive Moving Average eXogenous machine learning approach. The EMS wave amplitude, measured by the Van Allen Probes, are modeled using the time lags of the solar wind and geomagnetic indices as inputs as well as the location at which the measurement is made. The resulting model performance is assessed on a separate Van Allen Probes data set, where the prediction efficiency was found to be 34.0% and the correlation coefficient was 56.9%. With more training and validation data the performance metrics could potentially be improved, however, it is also possible that the EMS wave distribution is affected by stochastic factors and the performance metrics obtained for this model are close to the potential maximum

Diagnostic and prognostic significance of exercise-induced premature ventricular complexes in men and women: A four year follow-up

Two hundred eighty patients (197 men and 83 women) with normal rest electrocardiograms and no history of prior myocardial infarction were referred for evaluation of chest pain. It was found that exercise-induced premature ventricular complexes had a lower sensitivity, specificity, positive predictive value and negative predictive value in predicting significant coronary artery disease than exercise-induced ST segment depression greater than or equal to 1 mm. The incidence of exercise-induced premature ventricular complexes was not significantly different in patients with no significant coronary artery disease, single vessel disease or multivessel disease. The site of origin of exercise-induced premature ventricular complexes was not helpful in predicting the presence or severity of coronary artery disease. At a mean follow-up period of 47.1 months, exercise-induced premature ventricular complexes did not predict coronary events (cardiac death or nonfatal myocardial infarction) in men or women