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Plasma turbulence on disparate spatial and temporal scales plays a key role in defining the level of confinement achievable in tokamaks, with the development of reduced numerical models for cross-scale turbulence effects informed by experimental measurements an essential step. MAST-U is a well-equipped facility having instruments to measure ion and electron scale turbulence at the plasma edge. However, measurement of core electron scale turbulence is challenging, especially in H mode. Using a novel synthetic diagnostic approach, we present simulated measurement specifications of a proposed mm-wave based collective scattering instrument optimised for measuring both normal and binormal electron scale turbulence in the core and edge of MAST-U. A powerful modelling framework has been developed that combines beam-tracing techniques with gyrokinetic simulations to predict the sensitivity, localisation and spectral range of measurement. For the reconstructed MAST 022769 shot, a maximum measurable normalised bi-normal wavenumber of $k_{\perp} \rho_{e} \sim 0.6$ was predicted in the core and $k_{\perp} \rho_{e} \sim 0.79$ near the pedestal, with localisation lengths $L_{FWHM}$ ranging from $\sim$ 0.4 m in the core at $k_{\perp} \rho_{e} \sim 0.1$ to ~0.08m at $k_{\perp} \rho_{e} \sim 0.45$. Synthetic diagnostic analysis for the 022769 shot using CGYRO gyrokinetic simulation spectra reveal that ETG turbulence wavenumbers of peak spectral intensity comfortably fall within the measurable range of the instrument from the core to the pedestal. The proposed diagnostic opens up opportunities to study new regimes of turbulence and confinement in association with upcoming non-inductive, microwave based current drive experiments on MAST-U and can provide insight into cross-scale turbulence effects, while having suitability to operate during burning plasma scenarios on future reactors such as STEP., Comment: 22 pages, 19 figures, journal submission

The phenomenon of beam focusing of microwaves in a plasma near a turning-point caustic is discussed in the context of the analytical solution to the Gaussian beam-tracing equations in the 2D linear-layer problem. The location of maximum beam focusing and the beam width at that location are studied in terms of the beam initial conditions. The analytic solution is used to study the effect of this focusing on Doppler backscattering (DBS). We find that the filter function that characterises the scattering intensity contributions along the beam path through the plasma is inversely proportional to the beam width, predicting enhanced scattering contributions from the beam focusing region. We show that the DBS signal enhancement for small incident angles between the beam path and the density gradient is due to beam focusing and not due to forward scattering. The analytic beam model is used to predict the measurement of the $k_y$ density-fluctuation wavenumber power spectrum via DBS, showing that the spectral exponent of the turbulent, intermediate-to-high $k_y$ density-fluctuation spectrum might be quantitatively measurable via DBS, but not the spectral peak corresponding to the driving scale of the turbulent cascade.

The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the linearized DC optimal power flow (DC-OPF) problem, which can be solved to global optimality, but whose optimal solution is always infeasible in the original AC-OPF problem. Recently, neural networks (NN) have been introduced for solving the AC-OPF problem at significantly faster computation times. However, these methods necessitate extensive datasets, are difficult to train, and are often viewed as black boxes, leading to resistance from operators who prefer more transparent and interpretable solutions. In this paper, we introduce a novel learning-based approach that merges simplicity and interpretability, providing a bridge between traditional approximation methods and black-box learning techniques. Our approach not only provides transparency for operators but also achieves competitive accuracy. Numerical results across various power networks demonstrate that our method provides accuracy comparable to, and often surpassing, that of neural networks, particularly when training datasets are limited.

We report the first experimental detection of a zero-frequency fluctuation that is pumped by an Alfv\'en mode in a magnetically confined plasma. Core-localized bidirectional Alfv\'en modes of frequency inside the toroidicity-induced gap (and its harmonics) exhibit three-wave coupling interactions with a zero-frequency fluctuation. The observation of the zero-frequency fluctuation is consistent with theoretical and numerical predictions of zonal modes pumped by Alfv\'en modes, and is correlated with an increase in the deep core ion temperature, temperature gradient, and confinement factor $H_{89,P}$. Despite the energetic particle transport induced by the Alfv\'en eigenmodes, the generation of a zero-frequency fluctuation that can suppress the turbulence leads to an overall improvement of confinement.

Toroidal Alfv\'en eigenmodes (TAEs) can transport fusion-born energetic particles out of the plasma volume, thereby decreasing plasma self-heating efficiency and possibly damaging reactor walls. Therefore, understanding TAE destabilisation and identifying saturation mechanisms is crucial to achieving burning plasma. While TAEs have been studies extensively in the past using kinetic-MHD codes, here a fully gyrokinetic study is employed which allows for additional physics. In the case studied, the primary drive mechanism is identified as the resonance between the magnetic drifts and the TAE, and this is seen to be disrupted by equilibrium flow shear which can stabilize the mode by rotating it in the the poloidal plane. It is found that zonal flows do not play a significant role in the saturation of these TAEs, and there are no saturation mechanisms present in the local gyrokinetic picture that are able to saturate the mode at physically relevant transport levels in the case of TAE-only turbulence. Instead, we confirm that the global profile flattening of fast-ion density is the key saturation mechanism. The nonlinear excitation of TAE travelling along the electron diamagnetic direction and its beating with the ion diamagnetic TAE, resulting in large amplitude oscillations that may help detect TAEs more easily in tokamaks, is also reported.

Introductory courses on electric circuits at undergraduate level are usually presented in quite abstract terms, with questions and problems quite far from practical problems. This causes the students have difficulties to apply that theory to solve practical technical problems. On the other hand, electric circuits are everywhere in our lives, so we have plenty of real practical problems. Here we compile a selection of practical contexts suited for implementing Problem Based Learning approach in an introductory course on circuit analysis. And some examples describing the gamification process that uses these problems to build single-player role-playing games that fulfil the course contents and scheduling. The key point of the assessment and how it is related to the progress in the game is also described., Comment: 10 pages and 1 Table

Providing stable and clean energy sources is a necessity for the increasing demands of humanity. Energy produced by fusion reactions, in particular in tokamaks, is a promising path towards that goal. However, there is little experience with plasmas under conditions close to those expected in future fusion reactors, because it requires the fusion of Deuterium (D) and Tritium (T), while most of the experiments are currently performed in pure D. After more than 20 years, the Joint European Torus (JET) has carried out new D-T experiments with the aim of exploring the unique characteristics of burning D-T plasmas, such as the presence of highly energetic ions. A new stable, high confinement and impurity-free D-T regime, with strong reduction of energy losses with respect to D, has been found. Multiscale physics mechanisms critically determine the thermal confinement and the fusion power yield. These crucial achievements importantly contribute to the establishment of fusion energy generation as an alternative to fossil fuels.

The COVID-19 pandemic and its multiple outbreaks have challenged governments around the world. Much of the epidemiological modeling was based on pre-pandemic contact information of the population, which changed drastically due to governmental health measures, so called non-pharmaceutical interventions made to reduce transmission of the virus, like social distancing and complete lockdown. In this work, we evaluate an ensemble-based data assimilation framework applied to a meta-population model to infer the transmission of the disease between different population agegroups. We perform a set of idealized twin-experiments to investigate the performance of different possible parameterizations of the transmission matrix. These experiments show that it is not possible to unambiguously estimate all the independent parameters of the transmission matrix. However, under certain parameterizations, the transmission matrix in an age-compartmental model can be estimated. These estimated parameters lead to an increase of forecast accuracy in agegroups compartments assimilating age-dependent accumulated cases and deaths observed in Argentina compared to a single-compartment model, and reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission may be important for an accurate prediction and diagnosis of health care demand.

We use the Hamiltonian formulation of the geodesic equation in the Schwarzschild space-time so as to get the variational equation as the counterpart of the Jacobi equation in this approach. In this context we are able to apply the Morales-Ramis theorem to link the integrability of the geodesic equation to the integrability, in the sense of differential Galois theory, of the variational equation. This link is strong enough to hold even on geodesics for which the usual conserved quantities fail to be independent, as is the case of circular geodesics. We show explicitly the particular cases of some null geodesics and their variational equations., Comment: 12 pages

We estimate explicit lower bounds for the isoperimetric profiles of the Riemannian product of a compact manifold and the Euclidean space with the flat metric, $(M^m\times \mathbb{R}^n,g+g_E)$, $m,n>1$. In particular, we introduce a lower bound for the isoperimetric profile of $M^m\times \mathbb{R}^n$ for regions of large volume and we improve on previous estimates of lower bounds for the isoperimetric profiles of $S^2 \times \mathbb{R}^2$, $S^3 \times \mathbb{R}^2$, $S^2 \times \mathbb{R}^3$. We also discuss some applications of these results in order to improve known lower bounds for the Yamabe invariant of certain manifolds.

Quantifying forecast uncertainty is a key aspect of state-of-the-art numerical weather prediction and data assimilation systems. Ensemble-based data assimilation systems incorporate state-dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work a machine learning method is presented based on convolutional neural networks that estimates the state-dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heterodastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman-like analysis update and the machine learning based estimation of a state-dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof-of-concept. The promising results show that the machine learning method is able to predict precise values of the forecast covariance matrix in relatively high-dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter outperforming it when the ensembles are relatively small.

The Doppler backscattering (DBS) diagnostic, also referred to as Doppler reflectometry, measures turbulent density fluctuations of intermediate length scales. However, when the beam's wavevector is not properly aligned perpendicular to the magnetic field, the backscattered power is attenuated. In previous work, we used beam tracing and reciprocity to derive this mismatch attenuation quantitatively. In this paper, we applied our model, in the small but finite mismatch limit, to a several new cases. We compared our predictions with multiple O-mode channels for the first time. We then identified a $\sim 3^{\circ}$ error in the MAST Q-band's quasioptics, showing that our model is useful for commissioning DBS diagnostics. For both O- and X-mode, we compared experimental data with our model's predictions at multiple times during the shots, unlike our previous work, where only a single time was analysed. Finally, we analysed other contributions to the backscattered signal, evaluating how much they affect our measurements of mismatch attenuation, giving comparisons with data from both MAST and MAST-U. This paper's detailed study systematically validates and demonstrates the usefulness of our model for quantitatively interpreting DBS data from spherical tokamaks.

We use the beam model of Doppler backscattering (DBS), which was previously derived from beam tracing and the reciprocity theorem, to shed light on mismatch attenuation. This attenuation of the backscattered signal occurs when the wavevector of the probe beam's electric field is not in the plane perpendicular to the magnetic field. Correcting for this effect is important for determining the amplitude of the actual density fluctuations. Previous preliminary comparisons between the model and Mega-Ampere Spherical Tokamak (MAST) plasmas were promising. In this work, we quantitatively account for this effect on DIII-D, a conventional tokamak. We compare the predicted and measured mismatch attenuation in various DIII-D, MAST, and MAST-U plasmas, showing that the beam model is applicable in a wide variety of situations. Finally, we performed a preliminary parameter sweep and found that the mismatch tolerance can be improved by optimising the probe beam's width and curvature at launch. This is potentially a design consideration for new DBS systems.

Second order accurate Cartesian grid methods have been well developed for interface problems in the literature. However, it is challenging to develop third or higher order accurate methods for problems with curved interfaces and internal boundaries. In this paper, alternative approaches based on two different grids are developed for some interface and internal layer problems, which are different from adaptive mesh refinement (AMR) techniques. For one dimensional, or two-dimensional problems with straight interfaces or boundary layers that are parallel to one of the axes, the discussion is relatively easy. One of challenges is how to construct a fourth order compact finite difference scheme at boarder grid points that connect two meshes. A two-grid method that employs a second order discretization near the interface in the fine mesh and a fourth order discretization away from the interface in the coarse and boarder grid points is proposed. For two dimensional problems with a curved interface or an internal layer, a level set representation is utilized for which we can build a fine mesh within a tube $|\varphi({\bf x}) | \le \delta h$ of the interface. A new super-third seven-point discretization that can guarantee the discrete maximum principle has been developed at hanging nodes. The coefficient matrices of the finite difference equations developed in this paper are M-matrices, which leads to the convergence of the finite difference schemes. Non-trivial numerical examples presented in this paper have confirmed the desired accuracy and convergence., Comment: 22 pages