We applied the Grad‐Shafranov reconstruction (GSR) technique to Martian magnetic flux ropes observed downstream from strong crustal magnetic fields in the southern hemisphere. The GSR technique can ...provide a two‐dimensional axial magnetic field map as well as the axial orientation of flux ropes from single‐spacecraft data under assumptions that the structure is magnetohydrostatic and time independent. The reconstructed structures, including their orientation, allowed us to evaluate possible formation processes for the flux ropes. We reconstructed 297 magnetic flux ropes observed by Mars Global Surveyor between April 1999 and November 2006. Based on characteristics of their geometrical axial orientation and transverse magnetic field topology, we found that they can be mainly distinguished according to whether draped interplanetary magnetic fields overlaying the crustal magnetic fields are involved or not. Approximately two thirds of the flux ropes can be formed by magnetic reconnection between neighboring crustal magnetic fields attached to the surface. The remaining events seem to require magnetic reconnection between crustal and overlaid draped magnetic fields. The latter scenario should allow planetary ions to be transferred from closed magnetic flux tube to flux tubes connected to interplanetary space, allowing atmospheric ions to escape from Mars. We quantitatively evaluate lower limits on potential ion escape rates from Mars owing to magnetic flux ropes.
Key Points
Mars flux rope structures are recovered via the Grad‐Shafranov (GS) equationOne third of flux ropes may be formed via merging of crustal and draped fieldsIon escape rates via flux ropes are estimated to be at least 1022–1023 ions/s
Here, in this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known ...Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. A detailed description of the numerical methods of the code, including a novel formulation of the boundary conditions for free-boundary equilibria, and validation of the implementation of those methods using both analytic test cases and cross-code validation is shown. Results show expected convergence across tested polynomial degree for analytic and cross-code test cases.
FINDING A THESIS TOPIC Franks, Peter J.S.
Oceanography (Washington, D.C.),
09/2022, Letnik:
35, Številka:
2
Journal Article
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Finding a thesis topic is hard. It may be the hardest thing you do during your graduate degree. But there are commonalities to thesis topics—and the approaches to finding them—that might help you ...focus your efforts during your thesis-topic quest. Here I offer my advice and experience to help you find your way, and perhaps shorten your journey.
A new reconstruction method is developed for two‐dimensional (2‐D), steady, magnetohydrostatic structures with anisotropic plasma pressure, which is assumed to be solely dependent on magnetic field ...strength. This dependence leads to a Poisson‐like partial differential equation that can be solved as a spatial initial‐value problem by use of data taken from a single spacecraft passing through a coherent structure. However, the resulting partial differential equation cannot be reduced to the ordinary Grad‐Shafranov equation with isotropic pressure. The numerical code for new reconstruction is developed and successfully validated against an exact analytical solution. This new reconstruction method is first applied to examine 2‐D geometry of magnetic mirror structures observed by the Magnetospheric Multiscale (MMS) spacecraft in the Earth's magnetosheath. The observed mirror structures satisfy the magnetohydrostatic conditions and are comoving with the average ion bulk flow. Using MMS1 measurements, the reconstruction produces a 2‐D magnetic field map and distribution maps of pressures perpendicular and parallel to the magnetic field. The reconstructed field map reveals magnetic bottle‐like structures as predicted by the mirror‐mode theory. A very good agreement is achieved between observation and reconstruction for the other three MMS spacecraft not used for reconstruction. It is concluded that this new reconstruction is suitable for examining 2‐D geometry of mirror structures.
Key Points
A new reconstruction method for 2‐D, steady, magnetohydrostatic structures with anisotropic plasma pressure is developed and validated
The reconstruction is first applied to in situ observations of magnetic mirror structures by MMS spacecraft in the Earth's magnetosheath
The reconstructed magnetic field map shows magnetic bottle‐like structures as predicted by the mirror‐mode theory
Electroencephalography (EEG) based Brain-Computer Interface (BCI) enables subjects to communicate with the outside world or control equipment using brain signals without passing through muscles and ...nerves. Many researchers in recent years have studied the non-invasive BCI systems. However, the efficiency of the intention decoding algorithm is affected by the random non-stationary and low signal-to-noise ratio characteristics of the EEG signal. Furthermore, channel selection is another important issue in BCI systems intention recognition. During intention recognition in BCI systems, the unnecessary information produced by redundant electrodes affects the decoding rate and deplete system resources. In this paper, we introduce a recurrent-convolution neural network model for intention recognition by learning decomposed spatio-temporal representations. We apply the novel Gradient-Class Activation Mapping (Grad-CAM) visualization technology to the channel selection. Grad-CAM uses the gradient of any classification, flowing into the last convolutional layer to produce a coarse localization map. Since the pixels of the localization map correspond to the spatial regions where the electrodes are placed, we select the channels that are more important for decision-making. We conduct an experiment using the public motor imagery EEG dataset EEGMMIDB. The experimental results demonstrate that our method achieves an accuracy of 97.36% at the full channel, outperforming many state-of-the-art models and baseline models. Although the decoding rate of our model is the same as the best model compared, our model has fewer parameters with faster training time. After the channel selection, our model maintains the intention decoding performance of 92.31% while reducing the number of channels by nearly half and saving system resources. Our method achieves an optimal trade-off between performance and the number of electrode channels for EEG intention decoding.
•Automatic detection and targeting system developed for futuristic cotton weed management.•YOLOv8 performed better with CBAM and C3Ghost modules in the backbone architecture.•Heatmap visualization ...showed accurate interpretability with consistent patterns.•Bot-SORT tracking algorithm reduced computational load in software and hardware.•Higher intersection over union resulted in correct localization of targeting system.
Traditional means of weed removal, such as human work or the use of pesticides, frequently require significant amounts of effort, incur high expenses, and can negatively impact the environment. This study introduces a modified version of the YOLOv8 nano architecture that is suitable for running on edge devices for real-time applications. The proposed model uses an augmented version of the well-known CottonWeedDet12 dataset consisting of a total of 16,944 images with characteristic annotations to develop a model capable of correctly distinguishing 12 different cotton weed classes with an increased mean average precision of 97.6 % that is about 1.2 % more than the model trained using original, unaugmented dataset. The final selected model uses a convolutional block attention module (CBAM) and a unique C3Ghost block within the YOLOv8 backbone, which together increase the model's reliability for more accurate predictions with reduced computational complexity. Upon training with the augmented dataset, the proposed model with only 3.6 million parameters was able to achieve an mAP@50 score of 97.6 %, which surpasses all previous studies conducted using this dataset. Additionally, a high F1 score of 94.4 % proves that the model has a good balance between recall and precision. Class Activation Map (CAM) approaches such as EigenCAM, Grad-CAM++, and LayerCAM explainable AI (XAI) showed promising results for each of the customized models upon testing their interpretability for cotton weed detection. Furthermore, based on this model, a fast and cost-efficient targeting system was developed using a yaw-pitch mechanism for automatic weed tracking and herbicide spraying.
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The development of a generalized two dimensional MHD equilibrium solver within the nimrod framework Sovinec, et al., J. Comput. Phys. 195 (2004) 355 is discussed. Spectral elements are used to ...represent the poloidal plane. To permit the generation of spheromak and other compact equilibria, special consideration is given to ensure regularity at the geometric axis (R=0). The scalar field Λ=ψ/R2 is used as the dependent variable to express the Grad–Shafranov operator as a total divergence. With the correct gauge, regularity along the geometric axis is satisfied. The convergence properties of the spectral elements are investigated by comparing numerically generated equilibria against known analytic solutions. Equilibria accurate to double precision error are generated with sufficient resolution. Depending on the equilibrium, either geometric or algebraic convergence is observed as the polynomial degree of the spectral-element basis is increased.
The rapid migration to remote instruction during the Covid-19 pandemic has expedited the need for more research, expertise, and practical guidelines for online and blended learning. A theoretical ...grounding of approaches and practices is imperative to support blended learning and sustain change across multiple levels in education organizations, from leadership to classroom. The Community of Inquiry is a valuable framework that regards higher education as both a collaborative and individually constructivist learning experience. The framework considers the interdependent elements of social, cognitive, and teaching presence to create a meaningful learning experience. In this volume, the authors further explore and refine the blended learning principles presented in their first book, Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry, with an added focus on designing, facilitating, and directing collaborative blended learning environments by emphasizing the concept of shared metacognition.
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