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The present study aims to investigate the association of X-ray solar flares with the magnetic complexity of sunspot groups in active regions for the period from 1996 to 2023 (solar cycles 23 to 24 and ascending phase of solar cycle 25). Statistically, we have found that (1) the total 37,166 sunspots are affiliated with the 5412 active regions. Following the Hale magnetic complexity categorization method, a total occurring sunspot groups, comprised of 31.01% α, 57.13% β, 0.01% γ, 0% δ, 0.027% γδ, 0.54% βδ, 8.45% βγ, and 2.83% βγδ magnetic groups. (2) We also found that a total of 44,667 solar flares were observed in selected time intervals. Out of that total of 44,667 solar flares, only 24,031 (60.40%) flares have their solar active region, remaining flares have an unknown solar origin. Further investigation also shows that, out of 24,031 solar flares only 23,531 solar flares are associated with sunspot groups of different magnetic complexity (according to Hale magnetic classification). These sunspot groups are affiliated with the 2499 active regions. The investigated flares (sunspot-associated) database consists of 34.11% B-, 57.87% C-, 7.36% M-, and 0.63% X-class flares. Finally, we found that a higher percentage of the intense flares (M- and X-Class) are associated with the βγδ type sunspot groups in solar cycles 23 and 24. We also observed that maximum X-class flares are associated with βγδ sunspot groups during the peak of solar cycles 23 and 24. Whereas maximum B- and C-class flares are associated with β sunspot groups during the ascending phase of each solar cycle. [ABSTRACT FROM AUTHOR]

In order to deeply analyze the drought and flood disaster response patterns of Tuojiang River Basin under the background of climate anomalies, this paper reconstructs the time series of drought and flood disaster of Tuojiang River Basin in the Qing Dynasty, and analyzed its related data with mathematical statistics, mutation test, power spectrum cycle analysis, ADF and Granger causality test, etc., to explore the occurrence patterns of drought and flood disasters in the Tuojiang River Basin in the Qing Dynasty and its climate driving force factors. The results show that, during the Qing Dynasty, 208 drought and flood disaster occurred in the Tuojiang River Basin, with an average of one drought and flood disasters occurring every 1.29 years. There were more flood disasters than drought disasters, with a total of 21 persistent flood and drought disasters and an overall high level of severity of disasters. Drought and flood disasters were interspersed frequently, and there were also drought and flood disasters coexisting during the same year. Drought and flood disasters were concentrated in a chain-like distribution from 1800 to 1910.The number of disasters peaked in the middle of the Qing Dynasty, and mega-disasters were concentrated in the middle and late Qing Dynasty. The year 1863 was an important mutation point for drought and flood disasters, after which the number of disasters increased significantly. Drought and flood disasters in the Qing Dynasty were significantly correlated with the average annual sunspot number, and continuous disaster events were more frequent around sunspot extreme years.The change cycle of drought and flood disasters is about 2.5 a, which is strongly consistent with the ENSO cycle. The number of drought and flood disasters in the Tuo River Basin was significantly higher during ENSO event years. With a lag period of 1a, sunspot activity and ENSO events are the main climatic triggers of drought and flood disasters changes. The research results not only fill the research gap of drought and flood disasters mechanism in the Tuojiang River Basin under climate change, but also enrich the knowledge of spatial and temporal distribution pattern and regularity of drought and flood disasters in the Yangtze River Basin. It provides a useful reference for strengthening the scientific use of water resources and disaster prevention and mitigation, and also provides an important reference for the flood and drought prevention and ecological civilization construction in the Tuojiang River Basin. [ABSTRACT FROM AUTHOR]

Space weather profoundly impacts Earth and its surrounding space environment, necessitating improved prediction to safeguard critical infrastructure such as communication and satellites. Solar flares can disrupt communications and pose radiation risks to airline passengers. While traditional methods offer rough estimates of solar activity trends, the potential of artificial intelligence in this domain warrants exploration. This study addresses this research gap by evaluating the performance of recurrent neural networks (RNNs) for sunspot forecasting and assessing the suitability of extreme gradient boosting (XGBoost) for solar event classification. Two publicly available datasets serve as the foundation for this research. To enhance algorithm performance through optimal hyperparameter selection, metaheuristic optimizers are employed. A unique contribution is the introduction of a modified particle swarm optimization algorithm, specifically tailored to the study's needs. Two experiments were conducted: In the first, RNNs predicted sunspot occurrence up to three steps ahead. The best-performing model, optimized using the introduced modified metaheuristic, achieved an impressive R 2 value of 0.840448, surpassing competing algorithms. In the second experiment, XGBoost models assessed solar flare severity, with the top model again optimized by the modified metaheuristic, achieving an accuracy of 0.981565. This novel approach highlights the potential for enhancing solar activity forecasting techniques and offers valuable insights into feature impacts on model decisions, thereby advancing our understanding of space weather. [ABSTRACT FROM AUTHOR]

Solar radio bursts are intense radio radiation sources that occur during the energy-release process and represent a hot topic in solar-physics and space-weather research. In this paper, we present a multimode prediction model for daily solar radio bursts. The model uses deep learning and machine learning to obtain data information from different dimensions and to establish the relationship between the characteristics of the solar active region on the solar surface and solar radio bursts. For this model, we use data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) total solar magnetic map, the Royal Observatory of Belgium World Data Centre in Brussels, and NOAA sunspot parameters (including number, area, and type of sunspots) as inputs. The output results are then compared with the list of solar radio bursts recorded by the Radio Solar Telescope Network (RSTN) to determine whether solar radio bursts are present and to determine the key parameters for determining radio bursts. Based on 5449 days of observational data, we find that the prediction accuracy of the model is 0.898 ± 0.011, and that the number of sunspots is a key parameter in determining the occurrence of solar radio bursts. Specifically, when the number of sunspots is greater than 15, the probability of occurrence of solar radio bursts is greater than 90%. We have identified the key parameters and thresholds for determining solar radio bursts and highlighted the key parameters for space-weather prediction. In addition, the prediction model can also be used for predicting in other fields. [ABSTRACT FROM AUTHOR]

The emergence of active regions on the Sun is an integral feature of the solar dynamo mechanism. However, details about the generation of active-region-scale magnetism and the journey of this magnetic flux from the interior to the photosphere are still in question. Shifting paradigms are now developing for the source depth of the Sun's large-scale magnetism, the organization of this magnetism into fibril flux tubes, and the role of convection in shaping active-region observables. Here we review the landscape of flux emergence theories and simulations, highlight the role flux emergence plays in the global dynamo process, and make connections between flux emergence on the Sun and other cool stars. As longer-term and higher fidelity observations of both solar active regions and their associated flows are amassed, it is now possible to place new constraints on models of emerging flux. We discuss the outcomes of statistical studies which provide observational evidence that flux emergence may be a more passive process (at least in the upper convection zone); dominated to a greater extent by the influence of convection and to a lesser extent by buoyancy and the Coriolis force acting on rising magnetic flux tubes than previously thought. We also discuss how the relationship between stellar rotation, fractional convection zone depth, and magnetic activity on other stars can help us better understand the flux emergence processes. Looking forward, we identify open questions regarding magnetic flux emergence that we anticipate can be addressed in the next decade with further observations and simulations. [ABSTRACT FROM AUTHOR]

Introduction: The external use treatment in the handling of psoriasis is first option in the light and light moderated psoriasis and as complementary therapy in severe or intense psoriasis. Calendula officinalis L. has anti-inflammatory, healing and antibacterial properties. Objective: To determine the Efficacy of marigold pomade to 20 % in the treatment of psoriasis. Methodology: A quasi experimental study was carried out in 40 patients from the dermatology service at Juan Bruno Zayas Hospital in the period corresponding to January - May of 2019; divided in a control group (pomade without Marigold) and a study group (Marigold pomade to 20 %). Injuries in the first week were classified and evolution was assessed when concluding fifth and tenth weeks. Finally, response to treatment was also assessed. Results: In the fifth week the 55 % of patients from study group showed light flakes while in the control group predominated intense erythema (75 %) and in the study group with equal frequency (35 %) to light and moderated, while the control group predominated intense (75 %). In the tenth week in the study group, flakes between absentee and light represented 80 % and 75 % of erythema; while in the control group the light and missing 15 %, as for flakes as for erythema. The treatment proved to be effective in 80 % of the patients from the study group, in contrast to the control group where the excellence response was in 15 %. Conclusion: Marigold pomade to 20 % is effective for the treatment of psoriasis and succeeds in attaining the maximum efficiency among the fifth and tenth weeks of treatment. [ABSTRACT FROM AUTHOR]

Abstract Individual sunspot observations have formed a ground basis of international sunspot number, a unique reference for long‐term solar variability in the centennial timescale. The original datasets were subjected to exploitations and analyses upon the recalibrations of the sunspot number series. In this context, this study reviewed and analysed original sunspot records and their databases in the Kawaguchi Science Museum (KSM) in Japan. KSM hosts sunspot drawings and logbooks from 1972 to 2013. This dataset has a longer chronological coverage than what was known to the scientific community (1981–2010). These records have been digitized and publicized in a museum database, which allows users to access individual sunspot drawings and numerical data in KSM logbooks. These records are highly homogeneous as a single observer's dataset (Hitoshi Takuma), who used a 15‐cm refractor at the Kawaguchi Juvenile Museum in 1972–2003 and a 20‐cm refractor at KSM in 2003–2013. We also reviewed the Takuma data series, his monthly observation days (21.3 days/month), sunspot number in the whole disk and each hemisphere, and sunspot positions in a butterfly diagram. We also assessed Takuma's data stability in comparison with the international sunspot number and reference datasets of the SILSO. Takuma's data appear stable until 2003, when he changed the observation site and instrument. His data stability was quantitatively compared with the SILSO reference datasets, confirming the substantial long‐term stability of the data and establishing its reliability as an alternative reference for sunspot number recalibration.

Locally weighted regression (LOESS) is a modern non-parametric regression method designed for treating cases where classical procedures are not highly efficient or cannot applied efficiently. Sunspots are the darker areas of the solar sphere's surface relative to other regions and are an important indicator of solar activity .The aim of this paper is to model and predict the number of sunspots because of their very importance to understanding the terrestrial consequences of solar activity and its direct impact on weather and communication systems on Earth, which may lead to damage to satellites. In this paper, the number of sunspots represented by annual data for the period from 1900 to 2021 (122 years) as well as monthly data for the period from January 1900 to January 2022 (1465 months) was obtained from the global data center (Sunspot Index and Long-term Solar Observations) (SILSO). The LOESS regression used for estimating and predicting the number of monthly and annual sunspots. The smoothing parameter, as well as the degree of the polynomial that fulfills the lowest for Akaike corrected information criterion. The analysis showed the ability of the LOESS to represent sunspot data by passing diagnostic tests as well as its high predictive ability. From the predictive values for the monthly data, it found that the maximum average number of sunspots will be 123.7 in July 2022, and the lowest average will be in February with 61.3 sunspots. Regarding the annual data, it found from the predictive values that the maximum average number of sunspots will be in the year 2023 with an average of 161.7 sunspots, and the lowest average will be in the year 2029 with an average of 16.1. Keywords: Locally weighted regression; sunspot; solar cycle; prediction.

Recent interest of solar physicists in the analysis of the coronal mass ejections and circular solar flares in fan-spine magnetic configurations (FSCs) necessitates measurements of the corresponding coronal magnetic fields. A dominant sunspot with the circumjacent magnetic flux of opposite polarity produces a specific coronal region of the quasi-transverse (QT-) propagation of microwaves. We make use of the theory of QT-propagation to evaluate the strengths of coronal magnetic fields in the active regions NOAA 11579, 12242, and 12488 while they are non-flaring. Microwave polarization changes were observed with the RATAN-600 radio telescope, the Siberian Solar Radio Telescope, and the Nobeyama Radioheliograph. Changes in the sign of circular radio polarization provide the strengths of coronal fields in a QT-region if the coronal plasma density Ne and the length scale of magnetic field divergence Ld are known. We evaluate the length scale by means of the potential-field source-surface (PFSS) model and the coronal density from the Gaussian inversion of the differential emission measure (from the Solar Dynamics Observatory observational data), obtaining NeLd = (0.46–0.64)×1010 m−2. The resulting coronal fields of 1.4×10−3 T and 2.34×10−3 T are attributed to the heights of 100 Mm and 50.2 Mm. We discuss the validity and consistency of the involved values to draw conclusions on the feasibility of coronal radio magnetography of FSCs. [ABSTRACT FROM AUTHOR]

Active regions on the surface of the Sun are potential sources of solar flares. These include active regions that are not directly facing Earth, such as those on the Sun's far-side. However, unlike Earth-facing active regions, far-side active regions including the east limb are difficult to observe. Therefore, east limb active region observation and analysis capability are necessary to predict the occurrences of east limb flares. The purpose of this study is to develop an automatic detection system of active regions occurring in the solar east limb using EUV intensity data from the EUVI instrument onboard the STEREO-A and STEREO-B spacecraft. Random forest and support vector machine algorithms were used in the system development. Active regions will be detected as bright regions in these EUV data. This detection system supports routine space weather forecasting activities at the Research Center for Space, part of the Space Weather Information and Forecast Services (SWIFtS). The system combines STEREO/EUVI with SDO/AIA data into a single composite map in the heliographic coordinate system, and then automatically detects the presence of east limb bright regions based on their intensities in near real-time. It also provides the estimated time of arrival (ETA) of the east limb bright regions on the east limb of the solar disk. The system also calculates the bright regions' mean intensity, total intensity, area and angular width. A machine-learning model has also been developed based on eight years of data to classify bright regions as different types of photospheric entities according to the calculated parameters. A validation test using an operational dataset produced accuracy scores of 79% and 64% for the random forest and support vector machine algorithms, respectively. [ABSTRACT FROM AUTHOR]

Halo coronal mass ejections (CMEs) from the Sun are the subset of CMEs, which are more energetic than the normal CMEs. Only the more energetic CMEs are observable in white-light coronagraphs when they are traveling along the line of sight (LOS) and a CME is better observed when it is traveling perpendicularly to the LOS. We investigated the flare associated front side halo CMEs, their active regions (ARs) and flare ribbon properties observed during the period of 2010 – 2016 in Solar Cycle 24. The aim of this study is to investigate the statistical relationship between the kinematic properties of CMEs and their source active region and flare ribbon properties. The properties of the solar source region (sunspot classification according to Hale and McIntosh, sunspot area, and the number of spots) and flare ribbons (total unsigned magnetic flux, total unsigned reconnection flux, active region area, and ribbon area) are obtained for the selected events and analyzed to find the dependence of the halo CME properties (linear speed, kinetic energy, space speed, and mass) on the source region characteristics. From the preliminary analysis, 13% of halo CMEs are found to be associated with X-class flares, 52% with M-class flares, and 35% with C-class flares. It is found that many events (45/60) were produced by β γ δ and β γ Hale-type sunspot groups and Ekc/Dkc/Fkc McIntosh sunspot classes, which implies a degree of complexity of the involved sunspots. We found moderate correlations between the active region properties and CME properties, but strong correlation between the flare ribbons and CME properties. This result indicates that the kinematics of CMEs are determined by the role of magnetic reconnection and the flux related to it. We estimated the synthetic CME mass (using a synthetic CME generated based on a full ice-cream cone structure proposed recently by Na et al., Astrophys. J.906, 46, 2021). It is demonstrated that the estimated mass of halo CMEs is 1.44 times (without occulted area) and 2 times (with occulted area) that of the observed CME mass. Further, the relations between the halo CME synthetic mass with the active region and flare ribbon properties have been obtained. [ABSTRACT FROM AUTHOR]

Large scale climatic phenomenon with lag time may be used as essential variables for stepwise prediction of rainfall, but the interaction of these signals on the occurrence of rainfall leads to non-linear, complex nature of relations. A model tree is a promising tool for modeling complex systems and recognizing the most significant variables. The model tree approach uses a greedy algorithm in which the increased number of variables does not necessarily improve the model's accuracy; hence the model should be run stepwise. This study employed a stepwise M5 model tree to predict annual rainfall in Hashem Abad station, north of Iran, using observational data and 17 climatic signals during the 1985–2019 period to determine the most significant variables. For this purpose, 131,017 subsets consisting of 17 members were produced, and the M5 model tree was fitted on each of them. The best combination of variables with the highest accuracy simulated the rainfall with a 36-mm error (less them 5–6%) and a correlation coefficient of 94%. Among the climatic signals, the Sun Spot (SP) was placed in the tree root (most significant), while the Nino 4, EA, and NAO were ranked as the other significant predictors, respectively. The results also indicated that due to the nature of rainfall variations and the greedy algorithm of the M5 model, it is necessary to perform stepwise modeling. The lag time in teleconnections may be considered a suitable feature for early prediction of next year's rainfall and capturing the inter-annual variation. [ABSTRACT FROM AUTHOR]

Introduction: The sun is the main source of Earth energy and its climate changes. Changes in the amount of energy emitted from the sun or in its surface temperature can cause fluctuations in the atmosphere. Solar spots are among the factors that can affect the climatic conditins of the Earth periodically and ultimately lead to climate changes. The wavelet transform (WT) has been a major development in the field of data analysis since the 1980s. It is an effective means of processing time-series data, especially those with nonstationary characteristics. Iran has been experiencing high climatic variations during the past decades.Wavelet analysis can be a powerful tool to analyze the processes that are nonstationary in nature and occur over finite spatiotemporal domains. In particular, it is used to study oscillatory behavior and reveal the periodicities present in signals. To obtain the possible correlations among the periodic behaviors represented in environmental datasets, wavelet analyses have to be performed. A wavelet is a wave-like oscillatory function which is localized in a time and frequency space and must have a mean of zero. The output is a graphical representation of the 95% confidence level contour, which shows the correlation between two time series, as a function of the signal period and its time evolution Methodology: Wavelets are flexible functions that are resolved in the time and frequency domains and, thus, ideal to quantify the changes in the contribution of each period (or frequency) over time to the overall variance (or power) of a signal (e.g., a time series). Because wavelets can be scaled (i.e., contracted or dilated), they can efficiently accommodate both high and low-frequency structures in nonstationary signals, whose statistical characteristics vary over time. In this study, the Morlet wavelet was used, and the spectral analysis of precipitation time series was done through using the wavelet theory with the aim of identifying the effect of sunspots on the spectral behavior of precipitation. The precipitation data as well as the data on the sunspots for a period of 50 year (1961-2010) were used at two synoptic stations of Isfahan and Kashan. To this end, the researcher benefited from statistical analysis and wavelet analysis using the MATLAB software. Sunspots are the most important indicators of the variability in solar radiation; in the majority of studies, the relative number of sunspots is mainly used to analyze the sunspot changes. Results and Discussion: Many studies have analysed the variability in precipitation, since climate change has become a major global issue. We investigated the trends of precipitation in two synoptic stations of Isfahan and Kashan and explored the changes in local water resources to improve our understanding of the impacts of climate change on water resources in the area sunspots in different years. Also, the correlation coefficients of these fluctuations at the mentioned stations were calculated. Based on the wavelet analysis, it was obvious that during all the months of the year sunspots activity left behind an 11-year-old cycle. Of course, in different months, the extremes of activities and fluctuations were found to be different. The plotted wavelets for different months of the year were evaluated. It was revealed that, during the statistical period of 60 years, there have been four cycles in the sunspot activity with a different degree of intensity in each cycle. According to the results, there was no significant relationship between precipitation and the activity of solar spots. Based on wavelet analysis, there was a poor negative correlation between them. Considering the analysis of a 50-year period, four 11-year cycles were observed in the sunspot activity. Conclusion: Sunspots are the most important indicators of variability in solar radiation. In the majority of studies, the relative number of sunspots is mainly used to analyze the sunspot changes. The application of wavelet transform and regression analysis helps to interpret the periodic cycles, trends and non-stationarities on different timescales in the climate records examined. The present study was conducted based on the annual, seasonal, and monthly averages of the sunspots during a statistical period of 50 years derived from the American Geophysical Union. The aim was to investigate the connection among the 11-year sunspot activity cycles and precipitation patterns in two synoptic stations of Isfahan and Kashan from 1961 to 2010. It was observed that the peak activity was in the second and the third cycles, and the least one was in the first and the fourth cycles. Additionally, the range of rainfall cycles (7-12 years) was higher in the fall than in the other seasons at the studied stations. The findings of this study can be beneficial for policymakers to consider future potential droughts and wet years based on sunspot activities so that water resources can be more properly managed.

Abstract The 11 year sunspot number cycle has been a fascinating phenomenon for many scientists in the last three centuries. Various mathematical functions have been used for modelling the 11 year sunspot number cycles. In this paper, we present a new model, which is derived from the well known Maxwell-Boltzmann probability distribution function. A modification has been carried out by introducing a new parameter, called area parameter to model sunspot number cycle using Maxwell-Boltzmann probability distribution function. This parameter removes the normality condition possessed by probability density function, and fits an arbitrary sunspot cycle of any magnitude. The new model has been fitted in the actual monthly averaged sunspot cycles and it is found that, the Hathaway-Wilson-Reichmann measure, goodness of fit is high. The estimated parameters of the sunspot number cycles 1 to 24 has been presented in this paper. A Monte Carlo based simple random search is used for nonlinear parameter estimation. Prediction has been carried out for the next sunspot number cycles 25 through a model by averaging of recent cycle’s model parameters. This prediction can be used for simulating a more realistic sunspot cycle profile. Through extensive Monte Carlo simulations, large number of sunspot cycle profile could be generated and these can be used in the orbital dynamics studies.

All of us encounter our unique difficulties as scientists. Although the kind of difficulty varies by scientist, I describe my own in this program, and explain how I responded, together with a sort of moral. They are: (a) When theorists seemed to encounter a deadlock, (b) When a well-accepted theory is confronted with a seriously contradictory observational fact, (c) When there is a generally believed morphology of a phenomenon that does not agree with my observations, (d) When a single approach or theory prevails, but I have a different idea. Obviously, my responses are just an attempt, which will be judged by future developments in space physics.

• TRNSYS-FLUENT coupling method for sunspot simulation. • Sunspot impact on cooling floor conditions for different localization and solar heat flux. • Dynamic thermal behavior of indoor air regarding to the sunspot displacement. The dynamic thermal simulation of buildings is a real challenge, in terms of regulations and labelling but also of decisions (implementation, materials choice, architectural design, rehabilitation ...). Today, we lack reference models of the dynamic thermal behaviour of the cooling floor under solar radiation effect. In fact, the simulation models developed in literature are represented in a very simplified method and cannot be used for a detailed analysis of thermal comfort. The present study aims to reduce the knowledge gap and resolve the limitations such as (i) studying the effect of sunspot for a real weather conditions, (ii) visualize the dynamic thermal behaviour of indoor air and surface cooling floor relative to the sunspot shifting and (iii) predict different sunspot shape as a function of geographic location. A 3D model is presented in this paper to evaluate the sunspot impact on cooling floor coupled to underground tank under climatic conditions in Algeria. The sunspot is calculated using a coupled TRNSYS-FLUENT dynamic simulation. Simulations are run for different periods and weather conditions. The results have shown that the developed model can provide a real simulation of thermal performance of the cooling floor taking into account the sunspot shifting. The incident solar heat flux on cooling floor reaches a maximum of 300 W/m2 for Bechar city and 120 W/m2 for Oran city. A wide surface temperature ranges of 13 °C between the umbrageous and illuminated areas. For Oran weather conditions, the indoor air temperature in the umbrageous area is around 24 °C, while that of the illuminated area is 28 °C which can cause a temperature heterogeneity in the room. [ABSTRACT FROM AUTHOR]

We present a study of wave processes occurring in solar active region NOAA 11131 on 10 December 2010, captured by the Solar Dynamics Observatory in the 1600 Å, 304Å and 171Å channels. For spectral analysis, we employed pixelized wavelet filtering together with a developed digital technique based on empirical mode decomposition. We studied the ~3-minute wave dynamics to obtain relationships with the magnetic structuring of the underlying sunspot. We found that during development of wave trains the motion path occurred along a preferential direction, and that the broadband wavefronts can be represented as a set of separate narrowband oscillation sources. These sources become visible as the waves pass through the umbral inhomogeneities caused by the differing magnetic field inclination angles. We found the spatial and frequency fragmentation of wavefronts, and deduced that the combination of narrowband spherical and linear parts of the wavefronts provide the observed spirality. Maps of the magnetic field inclination angles confirm this assumption. We detect the activation of umbral structures as the increasing of oscillations in the sources along the front ridge. Their temporal dynamics are associated with the occurrence of umbral flashes. Spatial localization of the sources is stable over time and depends on the oscillation period. We propose that these sources are the result of wave paths along the loops extending outwards from the magnetic bundles of the umbra. [ABSTRACT FROM AUTHOR]

This paper is aimed to provide a new theory for the formation of the solar surface eruptions and sunspots. The key ingredient of the study is the new anti-diffusive effect of heat, based on the recently developed statistical theory of heat by the authors [4]. The anti-diffusive effect of heat states that due to the higher rate of photon absorption and emission of the particles with higher energy levels, the photon flux will move toward to the higher temperature regions from the lower temperature regions. This anti-diffusive effect of heat leads to a modified law of heat transfer, which includes a reversed heat flux counteracting the heat diffusion. It is this anti-diffusive effect of heat and thereby the modified law of heat transfer that lead to the temperature blow-up and consequently the formation of sunspots, solar eruptions, and solar prominences. This anti-diffusive effect of heat may be utilized to design a plasma instrument, directly converting solar energy into thermal energy. This may likely offer a new form of fuel much more efficient than the photovoltaic devices. [ABSTRACT FROM AUTHOR]

This study is aimed at defining the relationship between a set of environmental factors and childhood HFMD and then at estimating the related effect. The 16 environmental factors included meteorological, air pollution, and sunspot. A traditional TSR modified by using susceptible-infectious-recovery models and distribution lag nonlinear model was applied to estimate the short-term effects of daily environmental factors on children HFMD occurrence in 2014–2018 with adjustment of potential confounding factors. A total of 70,027 children aged 0–15 years with HFMD were enrolled. No significant effect was observed for daily sunspot numbers and average visibility. We found positive effects of the ambient average temperature, with an approximately m-shaped curve of the overall cumulative relationship, peaking at 25.6 °C with a relative risk (RR) of 1.45 (95% confidence intervals 1.21–1.73). The largest RR value of hot effect was achieved on the current day and then decreased by 2 days (total group, male group, and scatter group) or 1 day (female group and nursery group), and the effect lasted about 6 to 8 days from the lag 4 or lag 6 day. A greater association of temperature with HFMD for the female group and the scattered group was observed. This study suggests that ambient average temperature might be a risk factor for children HFMD in Tianjin. Further studies are warranted to confirm these findings. [ABSTRACT FROM AUTHOR]

The prediction of a time series such as climate indices and the sunspot number (SSN) with long-term oscillatory behaviors has been a challenging task due to the complex combination of oscillations. Frequency extraction algorithms have been developed to separate a time series into different oscillation components according to frequency, such as empirical model decomposition (EMD) and wavelet analysis. In the current study, the deep learning long short-term memory (LSTM) model was employed to predict the oscillation components extracted using EMD. The SSN series was modeled with the hybrid EMD-LSTM model. The simulation study results indicate that the LSTM model reproduces the smooth cyclic pattern of the sine function, and only a few hidden units are needed to model it. The EMD-LSTM model achieves better performance than does the LSTM model for mid-range SSN predictions while the LSTM achieves better performance within the first few time lags. However, the cyclic prediction of the SSN requires mid-range lags; thus, the superior performance of the EMD-LSTM model for these lags cannot be ignored. Furthermore, the remaining components from the significant EMD signals can be modeled to reveal the variability (or uncertainty) in the prediction. The summed residual is fitted by k-nearest neighbor resampling. The final SSN prediction results show that the EMD-LSTM model predicts a later and larger SSN for Solar Cycle 25 than does the LSTM model. Overall, the results lead to the conclusion that the EMD-LSTM model might be a suitable alternative for modeling complex sunspot time series with cyclic patterns. [ABSTRACT FROM AUTHOR]

Sunspot prediction is an important task for space weather and solar physics. Traditional point forecast may not be sufficiently satisfactory and reliable. To quantify the uncertainty of point prediction, a hybrid interval prediction model has been proposed for sunspot forecasting. Three major steps are taken: (1) the complementary ensemble empirical mode decomposition (CEEMD), to decompose the sunspot sequence into a series of modal components, (2) the fuzzy information granulation (FIG), to extract the minimum, average and maximum value of each window, and (3) the extreme learning machine (ELM), to conduct point prediction and interval prediction, superimposing the prediction values of all components as the final forecast results. The empirical study focus on the 13-month smoothed monthly sunspot number recorded by Solar Influences Data Analysis Center (SIDC) and show that the mixed model with the filtered CEEMD is more effective than the unfiltered one. It also enables us to track changes of the sunspot number with fast calculating speed and high accuracy both in point prediction and interval prediction. [ABSTRACT FROM AUTHOR]

The Total Solar Irradiance (TSI) primarily varies on an 11-year time scale and is governed by features such as sunspots and associated decay products such as plage and faculae. These short-lived physical features can also modulate the solar irradiance at intermediate and short temporal scales. Here we investigate the periodic variations, at solar-surface-rotation time scales, of photometric indices derived from images obtained at the San Fernando Observatory (SFO), and we compare them to the properties of the contemporaneous TSI as measured by the Total Irradiance Monitor (TIM) onboard the SOlar Radiation and Climate Experiment (SORCE) spacecraft. Both the daily ground- and space-based data, which span from early 2003 to late 2018, present missing pixels. We use an autoregressive gap-filling method to construct continuous time series to be analyzed via Fourier and wavelet spectral techniques. Lomb–Scargle periodograms, which can handle time series with missing data, are used for comparison. Both the Fourier spectral power and the periodograms yield compatible results with statistically significant periodicities in the range 25 – 35 days. All of the time series have maximum power at 27 days. Significant secondary periods are found at 29 – 30 days and 34 – 35 days. Wavelet analyses of the full time series show that the photometric index resulting from the red-continuum photometric sum [ Σ r ] and the TSI exhibit common high power at surface-solar-rotation scales during the active part of the solar cycle. The phase relation at the surface-solar-rotation scales is not definite. During the solar minimum interval between Solar Cycles (SCs) 23 and 24, variations in the TSI are found to be related to variations both in the photometric index Σ K , calculated from Ca ii K-line photometric sums and in the magnetic flux in the solar activity latitudinal band (as found in previous work). This suggests that the TSI changes during the minimum are caused by the reduced line-blanketing effect of diffused magnetic field. [ABSTRACT FROM AUTHOR]

This paper explores the role of bubbles in an overlapping-generations economy where some agents are more productive than the others. Due to missing credit markets, productive and unproductive agents cannot trade their financial assets to achieve mutual benefit, making the economy to perform below its potential. Introducing bubbles on the one hand crowds out some productive resources as it does in the current literature. It on the other hand may divert resources away from unproductive agents to productive ones. We find that bubbles are growth enhancing when the unproductive agents' productivity level lies within an interval. Furthermore, in sharp contrast to the existing literature, the equilibrium bubble size is stable. This finding enables us to construct a two-state stationary sunspot equilibrium in which one state is associated with small bubbles and low growth whereas the other one has large bubbles and high growth. • Bubbles crowd out productive resources in an OLG model with financial frictions. • Bubbles re-allocate resources as productivity level of agents lies in an interval. • When the re-allocative effect dominates, bubbles are growth enhancing. • The equilibrium bubble size is unique and stable. • A two-state stationary sunspot equilibrium is found. [ABSTRACT FROM AUTHOR]

The Carrington event is considered to be one of the most extreme space weather events in observational history within a series of magnetic storms caused by extreme interplanetary coronal mass ejections from a large and complex active region that emerged on the solar disk. In this article, we study the temporal and spatial evolutions of the source sunspot active region and visual aurorae and compare this storm with other extreme space weather events on the basis of their auroral spatial evolution. Sunspot drawings by Schwabe, Secchi, and Carrington describe the position and morphology of the source active region at that time. Visual auroral reports from the Russian Empire, Iberia, Ireland, Oceania, and Japan fill the spatial gap of auroral visibility and revise the time series of auroral visibility in middle to low magnetic latitudes. The reconstructed time series is compared with magnetic measurements and shows the correspondence between low‐latitude to mid‐latitude aurorae and the phase of magnetic storms. The spatial evolution of the auroral oval is compared with those of other extreme space weather events in 1872, 1909, 1921, and 1989 as well as their storm intensity and contextualizes the Carrington event, as one of the most extreme space weather events, but likely not unique. Plain Language Summary: The Carrington event is considered to be one of the most extreme space weather events in observational history. In this article, we have studied the temporal and spatial evolutions of the source active region and visual low‐latitude aurorae. We have also compared this storm with other extreme space weather events on the basis of the spatial evolution. We have compared the available sunspot drawings to reconstruct the morphology and evolution of sunspot groups at that time. We have surveyed visual auroral reports in the Russian Empire, Ireland, Iberian Peninsula, Oceania, and Japan and fill the spatial gap of auroral visibility and revised its time series. We have compared this time series with magnetic measurements and shown the correspondence between low‐latitude to midlatitude aurorae and the phase of magnetic storms. We have compared the spatial evolution of the auroral oval with those of other extreme space weather events in 1872, 1909, 1921, and 1989 as well as their storm intensity and concluded that the Carrington event is one of the most extreme space weather events but is likely not unique. Key Points: Original sunspot drawings during the 1859 storms are revealed and analyzedNew auroral reports from Eurasia and Oceania fill the spatial and temporal gaps of the auroral visibility during the 1859 stormsThe 1859 storms are compared and contextualized with the other extreme space weather events [ABSTRACT FROM AUTHOR]

A huge collection of solar images to visualize sunspot are acquired by various solar observatories spread across the globe. This necessitates efficient tools for detecting and analyzing the sunspots encompassing diverse solar features. One such contribution is delivered in this work by exploiting the intrinsic intensity variations of solar images associated with sunspots and their attributes. The presented mechanism initially, pre-processes the acquired solar images by correcting the intensity variations introduced while profiling from the sun center to the limb followed by smoothening using a localized window. The resultant is then differenced from the global threshold that is obtained as a result of the statistical analysis computed over the probability distribution function of the input image. This arrangement offers higher discerning variations concerned with the local contextual structures related to sunspot, umbra, and penumbra. Also, it captures the major gradient variation between these regions that adds to the pixel heterogeneity surrounding them to finally render an automatic sunspot detection mechanism distinguishing the diverse solar regions. Receiver Operating Characteristics (ROC) investigation on annual solar images in Flexible Image Transport System (FITS) format reveals the presented method's efficacy. Also, Pearson correlation analysis of the evaluated sunspot numbers from the detected sunspots with the solar catalog reveals the scheme's detection closeness. Moreover, the model's simplicity analyzed along the time and space dimensions affirms its extension to real-time analysis • Fully automated adaptive thresholding sunspot detection framework delineating umbra & penumbra. • Local-Global thresholding method exploiting intensity variations across multiple levels. • ROC analysis in both manual and automatic modes affirms scheme's performance. • Higher correlation values attained for sunspot numbers with the HFC and SILSO catalogs. • Complexity analysis reveals the involved minimal operations without compromising performance. [ABSTRACT FROM AUTHOR]

Based on the daily sunspot number (SN) data (1954–2011) from the Purple Mountain Observatory, the extreme value theory (EVT) is employed for the research of the long-term solar activity. It is the first time that the EVT is applied on the Chinese SN. Two methods are used for the research of the extreme events with EVT. One method is the block maxima (BM) approach, which picks the maximum SN value of each block. Another one is the peaks-over-threshold (POT) approach. After a declustering process, a threshold value (here it is 300) is set to pick the extreme values. The negative shape parameters are obtained by the two methods, respectively, indicating that there is an upper bound for the extreme SN value. Only one value of the N-year return level (RL) is estimated: N = 19 years. For N = 19 years, the RL values of SN obtained by two methods are similar with each other. The RL values are found to be 420 for the POT method and the BM method. Here, the trend of 25th solar cycle is predicted to be stronger, indicating that the length of meridional forms of atmospheric circulation will be increased.

We describe a new tool developed for solar flare forecasting on the base of some sunspot group properties. Assuming that the flare frequency follows the Poisson statistics, this tool uses a database containing the morphological characteristics of the sunspot groups daily observed by the Equatorial Spar of INAF – Catania Astrophysical Observatory since January 2002 up today. By means of a linear combination of the flare rates computed on the base of some properties of the sunspot groups, like area, number of pores and sunspots, Zurich class, relative importance between leading spot and density of the sunspot population, and type of penumbra of the main sunspot, we determine the probability percentages that a flare of a particular energy range may occur. Comparing our forecasts with the flares registered by GOES satellites in the 1–8 Å X-ray band during the subsequent 24 h we measured the performance of our method. We found that this method, which combines some morphological parameters and a statistical technique, has the best performances for the strongest events, which are more interesting for their implications in the Earth environment.

In nonconvex environments, a sunspot equilibrium can sometimes be destroyed by the introduction of new extrinsic information. We provide a simple test for determining whether or not a particular equilibrium survives, or is robust to, all possible re…nements of the state space. We use this test to provide a characterization of the set of robust sunspot-equilibrium allocations of a given economy: it is equivalent to the set of equilibrium allocations of the associated lottery economy.

Abstract Korean chronicles have a large amount of observational records of natural phenomena, including astronomical and meteorological events over two thousand years. Here we examine the correlation of solar activity and climate change from historical sunspot and frost records in the Korean chronicles. There are 42 sunspot records in Goryeo Dynasty (918–1392 CE) and 13 records in Joseon Dynasty (1392–1910 CE). The sunspot records in Goryeo Dynasty show a periodicity in good agreement with the well-known solar activity of 11 years. Korean sunspot records suggest that the solar activity in Joseon Dynasty decreased compared with that in the previous ∼500 years. In order to examine the long-period variation of solar activity, we include Chinese historical sunspot records in our analysis to supplement the lack of Korean records, and find a new ∼240-yr long-period solar activity from the power spectral analysis. Korean chronicles also have about 700 frost records during the last millennium. We investigate these frost records and find a sign of cooling down that can be interpreted as climate change during the last millennium. We also find ∼240-yr cooling period from the historical frost records, which is well in accord with that of solar activity. Therefore, we conclude that the solar activity has decreased during the last one thousand years and also has a long-term variation of ∼240 years. Highlights: • The correlation of solar activity and climate change has been examined from Korean historical sunspot and frost records. • Korean sunspot records suggest that the solar activity has decreased during the last one thousand years. A new ∼240 yr long-period solar activity was found from the combined Korean and Chinese records. • Korean frost records also show a sign of cooling down and ∼240-yr cooling period. [ABSTRACT FROM AUTHOR]

In this study, we modified the previously introduced X-ray flare production potential (FPP) of each modified Zurich sunspot group based on its X-ray flare peak flux. Then, using these FPPs, the daily total sunspot number (Zurich number) calculation equation was modified; thus, new sunspot number data (CRz) were produced. We also compared ISSN and CRz with some geomagnetic indices (Dst, the maximum CME speed, and Ap indices). We found that the CRz data describe the geomagnetic indices quite well compared to ISSN. By using the regression equation between CRz and the sunspot area, the CRz dataset was recalculated back to 1874. Those reconstructed datasets were compared to observational data, and it was found that reconstructed and observed monthly datasets show very good agreement. The only small differences exist in maxima and minima of cycles. [ABSTRACT FROM AUTHOR]

We analyze the pre‐deposit game in a two‐depositor banking model. The Glass–Steagall bank is assumed to be restricted to holding only liquid assets. Depositors tolerate a panic‐based run if its probability of occurrence s is small. How s affects the allocation of assets depends on the incentive compatibility constraint (ICC). When the ICC is not binding, the sunspot allocation is not a mere randomization over the run and non‐run outcomes under the so‐called "optimal contract." We offer this paper as a contribution to both the literature on banking and financial fragility and also the broader literature on sunspot equilibrium. [ABSTRACT FROM AUTHOR]

This paper develops a general equilibrium rational expectations model with search and multiple equilibria where aggregate shocks have a permanent effect on the unemployment rate. If agents' wealth decreases, for either fundamental (productivity) or non‐fundamental (sunspot) reasons, the unemployment rate can increase for a potentially indefinite period. This makes the unemployment rate dynamics path dependent. [ABSTRACT FROM AUTHOR]

Abstract Cosmogenic radionuclides provide information about air masses exchanges between stratosphere and troposphere. The 7Be concentration in aerosols usually shows a seasonal variability, depending also of the climatic conditions. There are, however, fewer data available of the behavior of other cosmogenic radionuclides, such as 10Be and 22Na. In this work about 7 years of aerosols collected in Cáceres (Spain) were analyzed. The 7Be concentration was higher than 22Na and 10Be, being the 22Na/7Be and 10Be/7Be ratios (1.16 ± 0.02)·10−4 and (1.5 ± 0.3)·10−4 respectively. For the 22Na/7Be, a seasonal variation was observed, being higher in spring/summer. Seasonal variation of 7Be and 22Na were explained using a model taking into account local values of the solar radiation, rainfall and dry deposition. The effective residence time for 7Be and 22Na were (9.9 ± 1.0) and (11.3 ± 1.4) d respectively. Both 7Be and 22Na seemed to decrease with increasing number of sunspots, although it was not statistically significant probably due to the low solar activity reported in the analyzed period. Highlights • Activity values of 10Be, only reported once in the literature. • Seasonal variability of 7Be and 22Na was related to local meteorological variables. • 7Be and 10Be concentrations are correlated. • Similar effective residence time for 7Be and 22Na. [ABSTRACT FROM AUTHOR]

For many decades ionospheric researchers investigated the variations in the ionosphere due to solar activity. The suggested relevant models are based on singlestation data, considering regional and global geographic conditions. The present study investigated the impact of the solar cycles 21st (1976 to 1986) and 23rd (1996 to 2008) on the ionospheric F2 layer's critical frequency (f0F2) at mid-latitude over the Wakkanai region (45.39° N, 141.68° E), Japan. The statistical analyses showed that monthly median f0F2 has a significant non-linear association with high sunspot numbers (SSN) over Wakkanai, which represents a saturation effect depending on the time of the day in different months and on the magnitude of the solar cycle. Polynomial empirical models of f0F2 based on parameters such as SSN and geomagnetic index Ap were examined. Considering the rate of change in solar activity factor much improved the accuracy of our empirical model and also reduces the hysteresis effect. The most appropriate empirical model for single-station diurnal models of f0F2 was developed using Fourier series. Diurnal models incorporate Japanese standard time, months and sunspot numbers. The computed f0F2 models were compared with the IRI-2012 model's predicted f0F2 values, which demonstrated the better accuracy of the Fourier model compared to the global IRI model. The models obtained in this study are useful for researchers and organisations working in the field of sunspot performance relating to the dynamics of the ionosphere. [ABSTRACT FROM AUTHOR]