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An integrated modeling framework for investigating the application of solid boron powder injection for real-time surface conditioning of plasma-facing components in tokamak environments is presented. Utilizing the DIII-D impurity powder dropper setup, this study simulates B powder injection scenarios ranging from mg/s to tens of mg/s, corresponding to B flux rates of \\(10^{20}-10^{21}\\) B/s in standard L-mode conditions. The comprehensive modeling approach combines EMC3-EIRENE for simulating the D plasma background and DIS for the ablation and transport of the B powder particles. The results show substantial transport of B to the inboard lower divertor, predominantly influenced by the main ion plasma flow. The dependency on powder particle size (5-250 \\(\\mu\\)m) was found to be insignificant for the scenario considered. The effects of erosion and redeposition were considered to reconcile the discrepancies with experimental observations, which saw substantial deposition on the outer divertor PFCs. For this purpose, the WallDYN3D code was updated to include B sources within the plasma domain and integrated into the modeling framework. The mixed-material migration modeling shows evolving B deposition patterns, suggesting the formation of mixed B-C layers or predominantly B coverage depending on the powder mass flow rate. While the modeling outcomes at lower B injection rates tend to align with experimental observations, the prediction of near-pure B layers at higher rates has yet to be experimentally verified in the C environment of the DIII-D tokamak. The extensive reach of B layers found in the modeling suggests the need for modeling that encompasses the entire wall geometry for more accurate experimental correlations. This integrated approach sets a precedent for analyzing and applying real-time in-situ boron coating techniques in advanced tokamak scenarios, potentially extendable to ITER.

Experiments with low-Z powder injection in DIII-D high confinement discharges demonstrated increased divertor dissipation and detachment while maintaining good core energy confinement. Lithium (Li), boron (B), and boron nitride (BN) powders were injected in high-confinement mode plasmas (\\(I_p=\\)1 MA, \\(B_t=\\)2 T, \\(P_{NB}=\\)6 MW, \\(\\langle n_e\\rangle=3.6-5.0\\cdot10^{19}\\) m\\(^{-3}\\)) into the upper small-angle slot (SAS) divertor for 2-s intervals at constant rates of 3-204 mg/s. The multi-species BN powders at a rate of 54 mg/s showed the most substantial increase in divertor neutral compression by more than an order of magnitude and lasting detachment with minor degradation of the stored magnetic energy \\(W_{mhd}\\) by 5%. Rates of 204 mg/s of boron nitride powder further reduce ELM-fluxes on the divertor but also cause a drop in confinement performance by 24% due to the onset of an \\(n=2\\) tearing mode. The application of powders also showed a substantial improvement of wall conditions manifesting in reduced wall fueling source and intrinsic carbon and oxygen content in response to the cumulative injection of non-recycling materials. The results suggest that low-Z powder injection, including mixed element compounds, is a promising new core-edge compatible technique that simultaneously enables divertor detachment and improves wall conditions during high confinement operation.

Turbulence in fluids and plasmas is a complex phenomena which couples structures at different scales. Turbulent flows possess spectral cascades, as well as coherent structures. When a flowing system can be considered as two-dimensional, the coupling through nonlinear interaction generates large-scale structures which extend to the system size in an inverse energy cascade. Plasmas confined by the dipole magnetic configuration in the Collisionless Terrella Experiment (CTX) display two-dimensional, intense interchange-mode dynamics. The plasma fluctuations are driven by gas injection and microwave heating, which produces a plasma maintained near marginal stability. The turbulence in CTX is investigated with respect to both local and global measurements. When viewed locally, the intense fluctuations exhibit characteristics of fully developed turbulence, with a broad power-law spectrum and finite correlation length. When viewed globally, the dynamics are found to be describable by the chaotic temporal variation of a limited number of simple spatial modes. The fluctuation energy spectrum is calculated to be consistent with the power-law trends for the inverse energy cascade. Using analysis techniques for determining spectral energy flow, it is found that three-wave interaction transfers energy to low wavenumbers, as predicted for two-dimensional turbulence. A fully parallelized, self-consistent simulation including a conserving source and sink is used to test the model equations for interchange mode dynamics in a dipolar magnetic field. The model reproduces the driven fluctuations observed in CTX, producing the rotating, radially broad, large-scale structures.

The concept of the \"Creative University\" signals that higher education stands at the center of the creative economy indicating the growing significance of intellectual capital and innovation for economic growth and cultural development. Increasingly economic activity is socialised through new media and depends on immaterial and digital goods.

This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now features not only the disruption score, as an indicator of the probability of an imminent disruption, but also a sensitivity score in real-time to indicate the underlying reasons for the imminent disruption. This adds valuable physics-interpretability for the deep-learning model and can provide helpful guidance for control actuators now that it is fully implemented into a modern Plasma Control System (PCS). The advance is a significant step forward in moving from modern deep-learning disruption prediction to real-time control and brings novel AI-enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII-D vetted in the earlier NATURE publication. In addition to when a shot is predicted to disrupt, this paper addresses reasons why by carrying out sensitivity studies. FRNN is accordingly extended to use many more channels of information, including measured DIII-D signals such as (i) the n1rms signal that is correlated with the n =1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics, (ii) the bolometer data indicative of plasma impurity content, and (iii) q-min, the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a plasma control system.

In a cross-sectional study of 140 homosexual men attending a sexually transmissible diseases clinic, the association between the presence of antibody to the human immunodeficiency virus (HIV) and the presence of proctitis, as determined by histologic examination, as well as past or present exposure to other pathogens and details of sexual practices was analyzed. Significant associations with HIV seropositivity were found with the number of lifetime partners, positive treponemal serology, and evidence of previous infection with herpes simplex virus. However the major and unique finding was the strong and independent association between proctitis diagnosed by histologic criteria and seropositivity for HIV. Whether this is cause or effect awaits further elucidation.