Polarisation-dependent single-pulse ultrafast optical switching of an elementary ferromagnet

The ultimate control of magnetic states of matter at femtosecond (or even faster) timescales defines one of the most pursued paradigm shifts for future information technology. In this context, ultrafast laser pulses developed into extremely valuable stimuli for the all-optical magnetization reversal in ferrimagnetic and ferromagnetic alloys and multilayers, while this remains elusive in elementary ferromagnets. Here we demonstrate that a single laser pulse with sub-picosecond duration can lead to the reversal of the magnetization of bulk nickel, in tandem with the expected demagnetization. As revealed by realistic time-dependent electronic structure simulations, the central mechanism involves ultrafast light-induced torques that act on the magnetization. They are only effective if the laser pulse is circularly polarized on a plane that contains the initial orientation of the magnetization. We map the laser pulse parameter space enabling the magnetization switching and unveil rich intra-atomic orbital-dependent magnetization dynamics featuring transient inter-orbital non-collinear states. Our findings open further perspectives for the efficient implementation of optically-based spintronic devices.

Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method.

Feasibility Study for the Fast Periodic Pulsed Reactor with UO2 Fuel

In order to study the feasibility of the fast periodic pulsed reactor with UO2 as fuel (abbreviated as FPPRU), the core models with different load schemes are designed. Neutronic characteristics of two typical design schemes are compared, and the better design scheme is determined. The critical search method is established for analyzing the reactor dynamics. Furthermore, the theoretical estimation formulas are derived to study the factors affecting the reactor dynamics clearly and intuitively. The reactor dynamics of the fast periodic pulsed reactor with UO2 and PuO2 as fuel are compared. The thermal hydraulic characteristic of FPPRU is studied with the sub-channel model. The results show that the design scheme of the FPPRU meets the demand of neutronics and thermal hydraulics safety. Meanwhile, the pulse parameter quality of the FPPRU with UO2 as fuel is not as good as that of IBR-2 with PuO2 as fuel.

Development of a thermal model for irreversible electroporation: an approach to estimate and optimize the IRE protocols

Purpose Irreversible electroporation (IRE) is an emerging technique that has drawn attention in the field of cancer treatment. IRE uses non-thermal electric pulses to induce death of cancerous cells. However, recent studies have shown that the application of this technique may result in heating of the tissue. There is still room for improving its efficiency and defining better treatment protocols. This study investigates the optimal IRE protocols that avoiding the thermal damage during the IRE treatment. Methods Electrode and pulse parameter are investigated. Finite element models are created to evaluate the ablation area and the temperature changes in the tissue. The model is validated experimentally in bovine liver tissue, while the parameters were optimized using response surface method (RSM). Results From analysis of variance, the parameter of electrode distance and input voltage has significant effect to the temperature rise in the IRE treatment of bovine liver (P = 0.020 and P = 0.003 respectively). Meanwhile, only the input voltage significantly affects the ablation area (P < 0.001). The optimal result from RSM showed that for maximum ablation area 250.82mm2 with no thermal damage, the IRE protocol consisted of an active electrode length of 10 mm, a distance between electrodes of 10 mm, and the delivery of 50 pulses of 41.21 µs and 3000 V. Conclusions The approach presented in this study allows the optimization of the IRE protocols. An optimal IRE protocol that maximizes the ablation area was successfully calculated which can be applied with no risk of thermal damage to the tissue.

Polarisation-dependent single-pulse ultrafast optical switching of an elementary ferromagnet

The ultimate control of magnetic states of matter at femtosecond (or even faster) timescales defines one of the most pursued paradigm shifts for future information technology. In this context, ultrafast laser pulses developed into extremely valuable stimuli for the all-optical magnetisation reversal in ferrimagnetic and ferromagnetic alloys and multilayers, while this remains elusive in elementary ferromagnets. Here we demonstrate that a single laser pulse with sub-picosecond duration can lead to the reversal of the magnetisation of bulk nickel, in tandem with the expected demagnetisation. As revealed by realistic time-dependent electronic structure simulations, the central mechanism is ultrafast light-induced torques acting on the magnetisation, which are only effective if the laser pulse is circularly polarised on a plane that contains the initial orientation of the magnetisation. We map the laser pulse parameter space enabling the magnetisation switching and unveil rich intra-atomic orbital-dependent magnetisation dynamics featuring transient inter-orbital non-collinear states. Our findings open further perspectives for the efficient implementation of optically-based spintronic devices.

Estimation of trapezoidal-shaped overlapping nuclear pulse parameters based on a deep learning CNN-LSTM model

The Long Short-Term Memory neural network (LSTM) has excellent learning ability for the time series of the nuclear pulse signal. It can accurately estimate the parameters (such as amplitude, time constant, etc.) of the digitally shaped nuclear pulse signal (especially the overlapping pulse signal). However, due to the large number of pulse sequences, the direct use of these sequences as samples to train the LSTM increases the complexity of the network, resulting in a lower training efficiency of the model. The convolution neural network (CNN) can effectively extract the sequence samples by using its unique convolution kernel structure, thus greatly reducing the number of sequence samples. Therefore, the CNN-LSTM deep neural network is used to estimate the parameters of overlapping pulse signals after digital trapezoidal shaping of exponential signals. Firstly, the estimation of the trapezoidal overlapping nuclear pulse is considered to be obtained after the superposition of multiple exponential nuclear pulses followed by trapezoidal shaping. Then, a data set containing multiple samples is set up; each sample is composed of the sequence of sampling values of the trapezoidal overlapping nuclear pulse and the set of shaping parameters of the exponential pulse before digital shaping. Secondly, the CNN is used to extract the abstract features of the training set in these samples, and then these abstract features are applied to the training of the LSTM model. In the training process, the pulse parameter set estimated by the present neural network is calculated by forward propagation. Thirdly, the loss function is used to calculate the loss value between the estimated pulse parameter set and the actual pulse parameter set. Finally, a gradient-based optimization algorithm is applied to update the weight by getting back the loss value together with the gradient of the loss function to the network, so as to realize the purpose of training the network. After model training was completed, the sampled values of the trapezoidal overlapping nuclear pulse were used as input to the CNN-LSTM model to obtain the required parameter set from the output of the CNN-LSTM model. The experimental results show that this method can effectively overcome the shortcomings of local convergence of traditional methods and greatly save the time of model training. At the same time, it can accurately estimate multiple trapezoidal overlapping pulses due to the wide width of the flat top, thus realizing the optimal estimation of nuclear pulse parameters in a global sense, which is a good pulse parameter estimation method.