Comparison of formulas for resonant interactions between energetic electrons and oblique whistler-mode waves

Test particle simulation is a useful method for studying both linear and nonlinear wave-particle interactions in the magnetosphere. The gyro-averaged equations of particle motion for first-order and other cyclotron harmonic resonances with oblique whistler-mode waves were first derived by Bell [J. Geophys. Res. 89, 905 (1984)] and the most recent relativistic form was given by Ginet and Albert [Phys. Fluids B 3, 2994 (1991)], and Bortnik [Ph.D. thesis (Stanford University, 2004), p. 40]. However, recently we found there was a (- 1) l - 1 term difference between their formulas of perpendicular motion for the lth-order resonance. This article presents the detailed derivation process of the generalized resonance formulas, and suggests a check of the signs for self-consistency, which is independent of the choice of conventions, that is, the energy variation equation resulting from the momentum equations should not contain any wave magnetic components, simply because the magnetic field does not contribute to changes of particle energy. In addition, we show that the wave centripetal force, which was considered small and was neglect in previous studies of nonlinear interactions, has a profound time derivative and can significantly enhance electron phase trapping especially in high frequency waves. This force can also bounce the low pitch angle particles out of the loss cone. We justify both the sign problem and the missing wave centripetal force by demonstrating wave-particle interaction examples, and comparing the gyro-averaged particle motion to the full particle motion under the Lorentz force. ? 2015 AIP Publishing LLC.SCI(E)[email protected]; [email protected]

Report of the President, Bowdoin College 1914-1915

https://digitalcommons.bowdoin.edu/presidents-reports/1023/thumbnail.jp

Revealing the source of Jupiter’s x-ray auroral flares

Jupiter’s rapidly rotating, strong magnetic field provides a natural laboratory that is key to understanding the dynamics of high-energy plasmas. Spectacular auroral x-ray flares are diagnostic of the most energetic processes governing magnetospheres but seemingly unique to Jupiter. Since their discovery 40 years ago, the processes that produce Jupiter’s x-ray flares have remained unknown. Here, we report simultaneous in situ satellite and space-based telescope observations that reveal the processes that produce Jupiter’s x-ray flares, showing surprising similarities to terrestrial ion aurora. Planetary-scale electromagnetic waves are observed to modulate electromagnetic ion cyclotron waves, periodically causing heavy ions to precipitate and produce Jupiter’s x-ray pulses. Our findings show that ion aurorae share common mechanisms across planetary systems, despite temporal, spatial, and energetic scales varying by orders of magnitude

Rotating auroral current system and reconnection sites on Saturn

Energetic charged particles trapped in a planet’s magnetosphere can produce stunning aurorae when they travel along the magnetic field lines and eventually collide with the planet’s atmosphere. Magnetic reconnection is one of the key processes in driving plasma and energy transport in the magnetosphere, and also a fundamental plasma process in energizing charged particles. Here, using in-situ measurements from the Cassini spacecraft, we report multiple small-scale reconnection sites that rotated with the magnetosphere. The spatial distribution of the identified long-standing multiple small reconnection site sequences shows no significant preference on local times. A chain of field- aligned currents is also found in Saturn’s magnetosphere that generates separated auroral patches. Both the multiple currents and their energy sources in the magnetodisc are azimuthally separated and rotate with the planet. The generation of the rotating azimuthally distributed field-aligned currents might be associated with the rotating long-standing small-scale reconnection processes

Power Line Communication with Robust Timing and Carrier Recovery against Narrowband Interference for Smart Grid

Power line communication (PLC) is an important interconnection technology for the smart grid, but the robustness of PLC transmission is faced with a great challenge due to strong non-Gaussian noise and interference. In this paper, a narrowband interference (NBI) resistant preamble is designed, and an effective timing and frequency synchronization method is proposed for OFDM-based PLC systems in the smart grid, which is capable of simultaneously conveying some bits of transmission parameter signaling (TPS) as well. In the time domain, the cyclic extension of the training OFDM symbol is scrambled, which makes it feasible to combat against NBI contamination. More accurate timing detection and sharper correlation peak can be implemented under the power line channel and the AWGN channel in the presence of NBI, compared with the conventional Schmidl’s and Minn’s methods with the same preamble length. Furthermore, the TPS transmitted using the proposed method is also immune from the NBI. The proposed method is capable of improving the synchronization performance of the PLC transmission significantly, which is verified by theoretical analysis and computer simulations

L-leucine stimulates glutamate dehydrogenase activity and glutamate synthesis by regulating mTORC1/SIRT4 pathway in pig liver

The liver is the most essential organ for the metabolism of ammonia, in where most of ammonia is removed by urea and glutamine synthesis. Regulated by leucine, glutamate dehydrogenase (GDH) catalyzes the reversible inter-conversion of glutamate to ammonia. To determine the mechanism of leucine regulating GDH, pigs weighing 20 ± 1 kg were infused for 80 min with ammonium chloride or alanine in the presence or absence of leucine. Primary pig hepatocytes were incubated with or without leucine. In the in vivo experiments with either ammonium or alanine as the nitrogen source, addition of leucine significantly inhibited ureagenesis and promoted the production of glutamate and glutamine in the perfused pig liver (P  0.05), while mTORC1 signaling was activated. Leucine exerted no significant changes in both GDH activity and SIRT4 gene expression in rapamycin treated hepatocytes (P > 0.05). In conclusion, L-leucine increases GDH activity and stimulates glutamate synthesis from different nitrogen sources by regulating mTORC1/SIRT4 pathway in the liver of pigs. Keywords: Glutamate dehydrogenase activity, Glutamate synthesis, L-leucine, mTORC1/SIRT4 pathway, Pig live

Combining spatial response features and machine learning classifiers for landslide susceptibility mapping

Reliable landslide susceptibility mapping (LSM) is essential for disaster prevention and mitigation. This study develops a deep learning framework that integrates spatial response features and machine learning classifiers (SR-ML). The method has three steps. First, depthwise separable convolution (DSC) extracts spatial features to prevent confusion of multi-factor features. Second, spatial pyramid pooling (SPP) extracts response features to obtain features under different scales. Third, the high-level features are fused into prepared ML classifiers for more effective feature classification. This framework effectively extracts and uses different-dimension features of samples, explores ML classifiers for beneficial feature classification, and breaks through the limitation of fixed input sample sizes. In the Yarlung Zangbo Grand Canyon region, data on 203 landslides and 11 conditioning factors were prepared for availability verification and LSM. The evaluation indicated that the area under the receiver operating characteristic curve (AUC) for the proposed SR and SR-ML achieved 0.920 and 0.910, which were 6.6% and 5.6% higher than the random forest (RF, with the highest AUC in ML group) method, respectively. Furthermore, the framework using 64×64 size inputs had the lowest mean error of 0.01, revealing that samples considering landslide scales could improve performance for LSM

Study Of The Energy Budget During Isolated Auroral Substorms

The solar atmosphere permanently releases ionized material forming the solar wind, which carries the frozen-in interplanetary magnetic field (IMF). When the solar wind reaches the space environment of the Earth, the IMF and the geomagnetic field can reconfigure their topology in the process of magnetic reconnection. Geomagnetic field lines are therefore opened by the interplanetary medium and dragged anti-sunward by the solar wind flow, which gives the Earth magnetosphere an elongated shape. This process results in the accumulation of open magnetic flux and energy in the geomagnetic tail. Eventually, when a significant amount of open magnetic flux has been accumulated and convected downtail, intense magnetic reconnection also occurs inside of the magnetotail, in the central plasma sheet, and the magnetic field lines return to a closed configuration, which reduces the amount of open magnetic flux. This flux closure process releases a significant amount of energy often estimated to be of the order 10^15 - 10^16 J stored in the tail, which can trigger auroral substorms, as a result of the solar wind - magnetosphere interaction. The released energy is distributed between the ionosphere, the ring current, the plasma sheet, and the formation of a plasmoid. In this work, we combine data from the ESA Cluster and the NASA IMAGE spacecraft to investigate three reconnection events occurring in 2001. We compare in-situ measurement from Cluster and auroral FUV imaging from IMAGE complemented by SuperDARN radar measurement of the ionospheric convection. The auroral hemispheric power is computed using the IMAGE-FUV images of the electron and proton aurora. The amount of open geomagnetic flux is estimated using the imaging of the proton aurora and the magnetic reconnection rates are derived from both missions and the SuperDARN data. We analyze the energy circulation by assessing the energy conversion and dissipation for each individual process during different substorm periods. We compare the hemispheric power, open magnetic flux and reconnection rates and search for a possible relation between them

Automated Classification of Auroral Images with Deep Neural Networks

Terrestrial auroras are highly structured that visualize the perturbations of energetic particles and electromagnetic fields in Earth’s space environments. However, the identification of auroral morphologies is often subjective, which results in confusion in the community. Automated tools are highly valuable in the classification of auroral structures. Both CNNs (convolutional neural networks) and transformer models based on the self-attention mechanism in deep learning are capable of extracting features from images. In this study, we applied multiple algorithms in the classification of auroral structures and performed a comparison on their performances. Trans-former and ConvNeXt models were firstly used in the analysis of auroras in this study. The results show that the ConvNeXt model can have the highest accuracy of 98.5% among all of the applied algorithms. This study provides a direct comparison of deep learning tools on the application of classifying auroral structures and shows promising capability, clearly demonstrating that auto-mated tools can help to minimize the bias in future auroral studies