After achieving significant research results on laser-driven boron fusion, the essential facts are presented how the classical very low-energy gains of the initially known thermal ignition conditions for fusion of hydrogen (H) with the boron isotope 11 (HB11 fusion) were bridged by nine orders of magnitudes in agreement with experiments. This is possible under extreme non-thermal equilibrium conditions for ignition by >10 PW-ps laser pulses of extreme power and nonlinear conditions. This low-temperature clean and low-cost fusion energy generation is in crucial contrast to local thermal equilibrium conditions with the advantage to avoid the difficulties of the usual problems with extremely high temperatures.

A new scheme is proposed to improve the quality of proton beams via ultra-intense laser pulse interacting with double plasma targets, which consist of a pre-target with relatively low density and a main target with high density. Both one- and two-dimensional Particle-in-Cell simulations show that, the using of an appropriate pre-target can help to obtain a much stronger longitudinal charge separation field in contrast to using only the main target. And proton beam with lower momentum divergence, better monochromaticity and collimation, as well as higher current density is generated. Moreover, due to the strengthened coupling between the laser pulse and targets, the energy conversion from laser pulse to protons is also increased.

By using two-dimensional particle-in-cell simulations, plasma block acceleration via radiation pressure from an ultraintense circularly polarized laser pulse with intensity I≈1022W/cm2 is investigated based on a double-target scheme, in which the targets are composed of a pre-target with a relatively low plasma density and a main target with a high plasma density. It has been demonstrated that an appropriately selected pre-target can help to greatly enhance the charge separation field in the main target, which then leads to generation of a strongly accelerated and well directed plasma block with proton energy in GeV magnitude. This result can have potential applications in the plasma block ignition of proton-born fusion.

The possibility of enhancing inner-shell x-ray emission, especially Kα emission, by femtosecond-laser irradiation of solid cones instead of foils was investigated theoretically. In a model for hot electron (HE) transport and Kα x-ray generation, Kα emission from laser-irradiated solid cones and foils is investigated. As a complementarity to the model, the contributions from electric and magnetic fields generated by the HE current in solid cones and foils are discussed. The results indicate that the efficiency of HE energy conversion to Kα photons is improved and the optimum HE temperature is increased.

With the exponential increase in malware, homology analysis has become a hot research topic in the malware detection field. This paper proposes MHAS, a malware homology analysis system based on ensemble learning and multifeatures. MHAS generates grayscale images from malware binary files and then uses the opcode tool IDA Pro to extract opcode sequences and system call graphs. Thus, RGB images and M-images are generated on the image matrix. Then, MHAS uses convolutional neural networks (CNNs) as base learners to perform bagging ensemble learning to learn features from the grayscale images, RGB images and M-images. Next, MHAS integrates the nine base learners using voting, learning and selective ensemble (in that order) and maps the integration results to the result matrix. Finally, the result matrix is again integrated using the learning method to obtain the final malware classification result. To verify the accuracy of MHAS, we performed a malware family classification experiment, that included samples of 10 malware families. The results showed that MHAS can reach an accuracy rate of 99.17%, meaning that it can effectively analyze and identify malware families.