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We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of $^{29-33}\mathrm{Si}$ on a carbon target at $\sim$230~MeV/nucleon. The experimental data covering charge changes of $\Delta Z$ = 1-4 are reproduced well by the isospin-dependent quantum molecular dynamics (IQMD) coupled with the evaporation GEMINI (IQMD+GEMINI) model. We further explore the mechanisms underlying the single-proton removal reaction in this model framework. We conclude that the cross sections from direct proton knockout exhibit a overall weak dependence on the mass number of $\mathrm{Si}$ projectiles. The proton evaporation induced after the projectile excitation significantly affects the cross sections for neutron-deficient $\mathrm{Si}$ isotopes, while neutron evaporation plays a crucial role in the reactions of neutron-rich $\mathrm{Si}$ isotopes. It is presented that the relative magnitude of one-proton and one-neutron separation energies is an essential factor that influences evaporation processes., Comment: 7 pages, 4 figures

A nonlinear saturation mechanism for reversed shear Alfven eigenmode (RSAE) is proposed and analysed, and is shown to be of relevance to typical reactor parameter region. The saturation is achieved through the generation of high-frequency quasi-mode due to nonlinear coupling of two RSAEs, which is then damped due to coupling with the shear Alfven continuum, and leads to the nonlinear saturation of the primary RSAEs . An estimation of the nonlinear damping rate is also provided., Comment: submitted to Plasma Physics and Technology

In this study, we address the limitations inherent in most existing vehicle trajectory prediction methodologies that indiscriminately incorporate all agents within a predetermined proximity when accounting for inter-agent interactions. These approaches commonly employ attention-based architecture or graph neural networks for encoding interactions, which introduces three challenges: (i) The indiscriminate selection of all nearby agents substantially escalates the computational demands of the model, particularly in those interaction-rich scenarios. (ii) Moreover, the simplistic feature extraction of current time agents falls short of adequately capturing the nuanced dynamics of interactions. (iii) Compounded by the inherently low interpretability of attention mechanism and graph neural networks, there is a propensity for the model to allocate unreliable correlation coefficients to certain agents, adversely impacting the accuracy of trajectory predictions. To mitigate these issues, we introduce ASPILin, a novel approach that enhances the selection of interacting agents by considering their current and future lanes, extending this consideration across all historical frames. Utilizing the states of the agents, we estimate the nearest future distance between agents and the time needed to reach this distance. Then, combine these with their current distances to derive a physical correlation coefficient to encode interactions. Experiments conducted on popular trajectory prediction datasets demonstrate that our method is efficient and straightforward, outperforming other state-of-the-art methods., Comment: code:https://github.com/kkk00714/ASPILin

The magnetic components of geodesic acoustic mode (GAM) are analytically investigated under the gyrokinetic framework with both the m=1 and m=2 harmonics are considered, where m is the poloidal mode number. With the quasi-neutrality condition and Ampere's law, the amplitudes of various poloidal magnetic components are derived. It is shown that both m=1 and m=2 magnetic components exist and are dominated by the cosine and sine components, respectively. In addition, it is found that the amplitudes of all magnetic components increase with respect to the ratio of plasma pressure to magnetic pressure \b{eta} and safety factor q. Most importantly, the amplitude of m=1 magnetic component is significantly enhanced due to the coupling of magnetic drift frequency with the first and second harmonics of the distribution functions, thus it can be comparable to that of m=2 magnetic component under certain conditions., Comment: 18 pages, 1 figure

Effects of resonant magnetic perturbations (RMPs) on tangential neutral beam heating in the EAST tokamak are studied numerically. RMPs with linear resistive magnetohydrodynamics response are used in the modeling. A variety of representing configurations of RMP coil currents are examined and their effects on the NBI heating efficiency are compared, in order to find a parameter window where deleterious effects of RMPs on NBI heating efficiency are minimized. It is found that the internal redistribution of fast ions by RMPs induces local accumulation of fast ions, resulting in higher local fast ion pressure than the case without RMPs. It is also found that the toroidal phasing of the RMP with respect to the fast ion source has slight effects on the steady-state radial profile of fast ions. The dependence of fast ion loss fraction on the RMP up-down phase difference shows similar behavior as the dependence of the radial width of chaotic magnetic field on the phase difference. A statistical method of identifying resonances between RMPs and lost fast ions is proposed and the results indicate that some resonances between RMPs and lost passing particles may be of non-integer fractional order, rather than the usual integer order.

Gyrokinetic simulations in the collisionless limit demonstrate the physical mechanisms and the amplitude of the current driven by turbulence. Simulation results show the spatio-temporal variation of the turbulence driven current and its connection to the divergence of the Reynolds stress and the turbulence acceleration. Fine structures (a few ion Larmor radii) of the turbulence induced current are observed near the rational surfaces with the arbitrary wavelength solver of the quasi-neutrality equation. The divergence of the Reynolds stress plays a major role in the generation of these fine structures. The so-called "spontaneous" current is featured with large local magnitude near the rational surfaces., Comment: 21pages, 20figures

A large fraction of major waterways have dams influencing streamflow, which must be accounted for in large-scale hydrologic modeling. However, daily streamflow prediction for basins with dams is challenging for various modeling approaches, especially at large scales. Here we examined which types of dammed basins could be well represented by long short-term memory (LSTM) models using readily-available information, and delineated the remaining challenges. We analyzed data from 3557 basins (83% dammed) over the contiguous United States and noted strong impacts of reservoir purposes, degree of regulation (dor), and diversion on streamflow modeling. While a model trained on a widely-used reference-basin dataset performed poorly for non-reference basins, the model trained on the whole dataset presented a median Nash-Sutcliffe efficiency coefficient (NSE) of 0.74. The zero-dor, small-dor (with storage of approximately a month of average streamflow or less), and large-dor basins were found to have distinct behaviors, so migrating models between categories yielded catastrophic results, which means we must not treat small-dor basins as reference ones. However, training with pooled data from different sets yielded optimal median NSEs of 0.72, 0.79, and 0.64 for these respective groups, noticeably stronger than existing models. These results support a coherent modeling strategy where smaller dams (storing about a month of average streamflow or less) are modeled implicitly as part of basin rainfall-runoff processes; then, large-dor reservoirs of certain types can be represented explicitly. However, dammed basins must be present in the training dataset. Future work should examine separate modeling of large reservoirs for fire protection and irrigation, hydroelectric power generation, and flood control.