This research proposes the assessment of banking and financial services using AI to monitor how banks apply AI approaches and the feedback they receive from customers. In order to better track, anticipate, and react to customer behavior, the financial information generated by the growing utilization of digital pay and banking can be mined by banks and other financial institutions. Based on research, secondary sources are reviewed to ascertain the data used in banking and financial services. A methodical survey is designed to gather the main information clients have regarding AI applications. The study is that private banks and other financial institutions use a variety of artificial intelligence (AI) services to better serve their clients and ensure that they are happy with their financial services, as some clients are not happy with banking services. Additionally, the results indicated that clients would be more committed to banking and financial services representatives by providing creative preparation for the advancement of AI practices in the workplace.

The Internet of Things (IoT) with smart cities and smart homes enables us to progressively sense and alter our surroundings. However, Artificial Intelligence (AI) must leverage such distinguishing abilities and detected data. We are heading in that way with sophisticated IoT technologies. As connected items become more authoritative, we are seeing a smart city movement away from cloud-based IoT systems and toward edge AI and embedded AI. Lower latency, better privacy, and the necessity to process data close to the source have all influenced this decision. According to a current report, smart cities can have 70 billion linked things by 2023. These cities will become smarter as a result of these networked things and introduce hazards and privacy concerns. As a result of the numerous smart city initiatives and plans that have been implemented in current years, we will not only see the anticipated aids but also the hazards that have been presented. The present and future movements in smart cities and IoT are discussed. This chapter provides an overview of several smart city techniques and elements that can be or are now automated with the help of the Internet of Things (IoT), and Artificial Intelligence (AI). Novel components of smart cities of smart parking and smart governance are determined through Artificial Intelligence (AI) in the Internet of Things (IoT). Finally, the IoT's flaws and solutions in the context of smart cities are determined. This work focuses on identifying the transformation and challenges faced in developing a smart city in AI.

A magnetised flux rope model, "Flux Rope in 3D" (FRi3D) is used in the framework of European Heliospheric Forecasting Information Asset (EUHFORIA) for studying the evolution and propagation of coronal mass ejections (CME). In this paper, we rectify the mistake in the mentioned magnetic field profile of the FRi3D model used in Maharana et al., 2022, and we clarify the actual profile used in that work. In addition, we provide the recent updates introduced to the FRi3D implementation in EUHFORIA like optimising the "ai.fri3d" python package to reduce computational time and exploring different CME leg disconnection methods to make the numerical implementation more stable.

We present observations and modeling results of the propagation and impact at Earth of a high-latitude, extended filament channel eruption that commenced on 2015 July 9. The coronal mass ejection (CME) that resulted from the filament eruption was associated with a moderate disturbance at Earth. This event could be classified as a so-called "problem storm" because it lacked the usual solar signatures that are characteristic of large, energetic, Earth-directed CMEs that often result in significant geoeffective impacts. We use solar observations to constrain the initial parameters and therefore to model the propagation of the 2015 July 9 eruption from the solar corona up to Earth using 3D magnetohydrodynamic heliospheric simulations with three different configurations of the modeled CME. We find the best match between observed and modeled arrival at Earth for the simulation run that features a toroidal flux rope structure of the CME ejecta, but caution that different approaches may be more or less useful depending on the CME-observer geometry when evaluating the space weather impact of eruptions that are extreme in terms of their large size and high degree of asymmetry. We discuss our results in the context of both advancing our understanding of the physics of CME evolution and future improvements to space weather forecasting.

The “Flux Rope in 3D” (FRi3D, Isavnin, 2016), a coronal mass ejection (CME) model with global three-dimensional (3D) geometry, has been implemented in the space weather forecasting tool EUHFORIA (Pomoell and Poedts, 2018). By incorporating this advanced flux rope model in EUHFORIA, we aim to improve the modelling of CME flank encounters and, most importantly, the magnetic field predictions at Earth. After using synthetic events to showcase FRi3D’s capabilities of modelling CME flanks, we optimize the model to run robust simulations of real events and test its predictive capabilities. We perform observation-based modelling of the halo CME event that erupted on 12 July 2012. The geometrical input parameters are constrained using the forward modelling tool included in FRi3D with additional flux rope geometry flexibilities as compared to the pre-existing models. The magnetic field input parameters are derived using the differential evolution algorithm to fit FRi3D parameters to the in situ data at 1 AU. An observation-based approach to constrain the density of CMEs is adopted, in order to achieve a better estimation of mass corresponding to the FRi3D geometry. The CME is evolved in EUHFORIA’s heliospheric domain and a comparison of FRi3D’s predictive performance with the previously implemented spheromak CME in EUHFORIA is presented. For this event, FRi3D improves the modelling of the total magnetic field magnitude and Bz at Earth by ∼30% and ∼70%, respectively. Moreover, we compute the expected geoeffectiveness of the storm at Earth using an empirical Dst model and find that the FRi3D model improves the predictions of minimum Dst by ∼20% as compared to the spheromak CME model. Finally, we discuss the limitations of the current implementation of FRi3D in EUHFORIA and propose possible improvements.

Previous studies of Coronal Mass Ejections (CMEs) have shown the importance of understanding their geometrical structure and internal magnetic field configuration for improving forecasting at Earth. The precise prediction of the CME shock and the magnetic cloud arrival time, their magnetic field strength and the orientation upon impact at Earth is still challenging and relies on solar wind and CME evolution models and precise input parameters. In order to understand the propagation of CMEs in the interplanetary medium, we need to understand their interaction with the complex features in the magnetized background solar wind which deforms, deflects and erodes the CMEs and determines their geo-effectiveness. Hence, it is important to model the internal magnetic flux-rope structure in the CMEs as they interact with CIRs/SIRs, other CMEs and solar transients in the heliosphere. The spheromak model (Verbeke et al. 2019) in the heliospheric wind and CME evolution simulation EUHFORIA (Pomoell and Poedts, 2018), fits well with the data near the CME nose close to its axis but fails to predict the magnetic field in CME legs when these impact Earth (Scolini et al. 2019). Therefore, we implemented the FRi3D stretched flux-rope CME model (Isavnin, 2016) in EUHFORIA to model a more realistic CME geometry. Fri3D captures the three-dimensional magnetic field structure with parameters like skewing, pancaking and flattening that quantify deformations experienced by an interplanetary CME. We perform test runs of real CME events and validate the ability of FRi3D coupled with EUHFORIA in predicting the CME geo-effectiveness. We have modeled two real events with FRi3D. First, a CME event on 12 July 2012 which was a head-on encounter at Earth. Second, the flank CME encounter of 14 June 2012 which did not leave any magnetic field signature at Earth when modeled with Spheromak. We compare our results with the results from non-magnetized cone simulations and magnetized simulations employing the spheromak flux-rope model. We further discuss how constraining observational parameters using the stretched flux rope CME geometry in FRi3D affects the prediction of the magnetic field strength in our simulations, highlighting improvements and discussing future perspective. This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870405 (EUHFORIA 2.0)

Abstract Coronal mass ejections (CMEs) and solar energetic particles (SEPs) are two phenomena that can cause severe space weather effects throughout the heliosphere. The evolution of CMEs, especially in terms of their magnetic structure, and the configuration of the interplanetary magnetic field (IMF) that influences the transport of SEPs are currently areas of active research. These two aspects are not necessarily independent of each other, especially during solar maximum when multiple eruptive events can occur close in time. Accordingly, we present the analysis of a CME that erupted on May 11, 2012 (SOL2012‐05‐11) and an SEP event following an eruption that took place on May 17, 2012 (SOL2012‐05‐17). After observing the May 11 CME using remote‐sensing data from three viewpoints, we evaluate its propagation through interplanetary space using several models. Then, we analyze in‐situ measurements from five predicted impact locations (Venus, Earth, the Spitzer Space Telescope, the Mars Science Laboratory en route to Mars, and Mars) in order to search for CME signatures. We find that all in‐situ locations detect signatures of an SEP event, which we trace back to the May 17 eruption. These findings suggest that the May 11 CME provided a direct magnetic connectivity for the efficient transport of SEPs. We discuss the space weather implications of CME evolution, regarding in particular its magnetic structure, and CME‐driven IMF preconditioning that facilitates SEP transport. Finally, this work remarks the importance of using data from multiple spacecraft, even those that do not include space weather research as their primary objective.

These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects.See AAREADME_insitu_ML.txt for more explanation.If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin, D. Stansby): christian.moestl@oeaw.ac.at or twitter @chrisoutofspaceMade with https://github.com/cmoestl/heliocatsLoad in python with e.g. for Parker Solar Probe data:> import pickle> filepsp='psp_2018_2019_sceq_ndarray.p'> [psp,hpsp]=pickle.load(open(filepsp, "rb" ) )plot time vs total field> import matplotlib.pyplot as plt> plt.plot(psp['time'],psp['bt'])Times psp[:,0 ] or psp['time'] are in matplotlib format. Variable 'hpsp' contains a header with the variable names and units for each column. Coordinate systems for magnetic field components are RTN (Ulysses), SCEQ (Parker Solar Probe, STEREO-A/B, VEX, MESSENGER), HEEQ (Wind)available parameters:bt = total magnetic fieldbxyz = magnetic field componentsvt = total proton speedvxyz = velocity components (only for PSP)np = proton densitytp = proton temperaturexyz = spacecraft position in HEEQr, lat, lon = spherical coordinates of position in HEEQ

These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.See AAREADME_insitu.txt for more explanation.If you use these data for peer reviewed scientific publications, please get in touch by christian.moestl@oeaw.ac.at or twitter @chrisoutofspace concerning usage and possible co-authorship.Made with https://github.com/cmoestl/heliocats

In order to address the growing need for more accurate space-weather predictions, a new model named EUHFORIA (EUropean Heliospheric FORecasting Information Asset) was recently developed. We present the first results of the performance assessment for the solar-wind modeling with EUHFORIA and identify possible limitations of its present setup. Using the basic EUHFORIA 1.0.4 model setup with the default input parameters, we modeled background solar wind (no coronal mass ejections) and compared the obtained results with Advanced Composition Explorer (ACE) in-situ measurements. For the purposes of statistical study we developed a technique of combining daily EUHFORIA runs into continuous time series. The combined time series were derived for the years 2008 (low solar activity) and 2012 (high solar activity), from which in-situ speed and density profiles were extracted. We find for the low-activity phase a better match between model results and observations compared to the high-activity time interval considered. The quality of the modeled solar-wind parameters is found to be rather variable. Therefore, to better understand the results obtained we also qualitatively inspected characteristics of coronal holes, i.e. the sources of the studied fast streams. We discuss how different characteristics of the coronal holes and input parameters to EUHFORIA influence the modeled fast solar wind, and suggest possibilities for the improvement of the model.

This code is concerned with data interpretation and prediction for space weather research. It is a method for forward modeling of solar storm magnetic flux ropes, called 3-Dimensional Coronal Rope Ejection (3DCORE). 3DCORE is able to produce synthetic in situ observations of the magnetic cores of solar coronal mass ejections sweeping over planets and spacecraft. Near Earth, these data are taken currently by the Wind, ACE and DSCOVR spacecraft. Other suitable spacecraft making these kind of observations carrying magnetometers in the solar wind were MESSENGER, Venus Express, MAVEN, and even Helios, and in the future this will be done by Parker Solar Probe, Solar Orbiter and possibly interplanetary CubeSats. It is distributed with an MIT license and has this DOI: https://doi.org/10.6084/m9.figshare.5450341 If this research is used for peer-reviewed scientific publications, the paper below needs to cited and in any case, please also contact me either atchristian.moestl@oeaw.ac.ator through https://twitter.com/chrisoutofspace This code was used for producing the figures and results in the publication: Möstl et al. (2018), in press at AGU Space Weather, https://arxiv.org/abs/1710.00587 The 3DCORE code is also on github: https://github.com/cmoestl/3DCORE

Forecasting the geomagnetic effects of solar storms, known as coronal mass ejections (CMEs), is currently severely limited by our inability to predict the magnetic field configuration in the CME magnetic core and by observational effects of a single spacecraft trajectory through its 3-D structure. CME magnetic flux ropes can lead to continuous forcing of the energy input to the Earth's magnetosphere by strong and steady southward-pointing magnetic fields. Here we demonstrate in a proof-of-concept way a new approach to predict the southward field B z in a CME flux rope. It combines a novel semiempirical model of CME flux rope magnetic fields (Three-Dimensional Coronal ROpe Ejection) with solar observations and in situ magnetic field data from along the Sun-Earth line. These are provided here by the MESSENGER spacecraft for a CME event on 9-13 July 2013. Three-Dimensional Coronal ROpe Ejection is the first such model that contains the interplanetary propagation and evolution of a 3-D flux rope magnetic field, the observation by a synthetic spacecraft, and the prediction of an index of geomagnetic activity. A counterclockwise rotation of the left-handed erupting CME flux rope in the corona of 30° and a deflection angle of 20° is evident from comparison of solar and coronal observations. The calculated Dst matches reasonably the observed Dst minimum and its time evolution, but the results are highly sensitive to the CME axis orientation. We discuss assumptions and limitations of the method prototype and its potential for real time space weather forecasting and heliospheric data interpretation.

Predicting the magnetic field within an Earth‐directed coronal mass ejection (CME) well before its arrival at Earth is one of the most important issues in space weather research. In this article, we compare the intrinsic flux rope type, that is, the CME orientation and handedness during eruption, with the in situ flux rope type for 20 CME events that have been uniquely linked from Sun to Earth through heliospheric imaging. Our study shows that the intrinsic flux rope type can be estimated for CMEs originating from different source regions using a combination of indirect proxies. We find that only 20% of the events studied match strictly between the intrinsic and in situ flux rope types. The percentage rises to 55% when intermediate cases (where the orientation at the Sun and/or in situ is close to 45°) are considered as a match. We also determine the change in the flux rope tilt angle between the Sun and Earth. For the majority of the cases, the rotation is several tens of degrees, while 35% of the events change by more than 90°. While occasionally the intrinsic flux rope type is a good proxy for the magnetic structure impacting Earth, our study highlights the importance of capturing the CME evolution for space weather forecasting purposes. Moreover, we emphasize that determination of the intrinsic flux rope type is a crucial input for CME forecasting models.