In cognitive wireless networks, multi-node cooperative spectrum sensing can effectively improve the accuracy of spectrum sensing, but there is a non-linear relationship between the number of nodes and sensing accuracy. Nodes with low reliability participate in cooperative sensing, which is not conducive to the improvement of sensing accuracy, and reduces the energy efficiency of spectrum sensing, which poses challenges to the normal operation of cognitive wireless networks. In order to improve energy efficiency and sensing performance, this paper proposes the node evaluation and scheduling (NES) algorithm and the Secure Spectrum Sensing based on Blockchain (SSSB) algorithm, which can evaluate the reliability of sensing nodes in real time, and obtain the trust value of the node. The nodes information is stored in the management center of blockchain. Blockchain encrypts nodes information to ensure that a node corresponds to its own trust value without confusion. Fusion Center of cognitive wireless networks select good performance nodes to participate in cooperative spectrum sensing. Which can reduce energy consumption while improving the sensing performance. The simulation experiment results show that the new algorithm in this paper is far superior to the traditional algorithm. Under the same other conditions, the detection probability is increased by 5%, and the energy consumption is reduced by 10%, and the safety index has also been greatly improved.

GP4, a new Ralstonia solanacearum phage, is a short-tailed phage. Few structures of Ralstonia solanacearum phages have been resolved to near-atomic resolution until now. Here, we present a 3.7 Å resolution structure of the GP4 head by cryo-electron microscopy (cryo-EM). The GP4 head contains 540 copies of major capsid protein (MCP) gp2 and 540 copies of cement protein (CP) gp1 arranged in an icosahedral shell with a triangulation number T = 9. The structures of gp2 and gp1 show a canonical HK97-like fold and an Ig-like fold, respectively. The trimeric CPs stick on the surface of the head along the quasi-threefold axis of the icosahedron generating a sandwiched three-layer electrostatic complementary potential, thereby enhancing the head stability. The assembly pattern of the GP4 head provides a platform for the further exploration of the interaction between Ralstonia solanacearum and corresponding phages.

This paper investigates a simple memristor emulator consisting of a diode bridge and a capacitor. It exhibits pinched hysteresis loops, and what is more striking is the higher frequency, as it operates up to greater than 5 MHz. Based on the proposed memristor, a higher-frequency Colpitts circuit was established. According to the mathematical model of the system, the system only possesses one unstable equilibrium point. Period doubling bifurcation, reverse periodic doubling bifurcation, different types of periodic and chaotic orbits, transient chaos, coexisting bifurcations and offset boosting are depicted. More interestingly, it has coexisting multiple attractors with different topologies, such as a chaotic attractor accompanied with periodic orbits, period-1 orbits with bicuspid structure and periodic-2 orbits with tridentate structure. Moreover, a hardware circuit using discrete components was fabricated and experimental measurements were consistent with the MATLAB numerical results, further confirming the real feasibility of the proposed circuit.

Doping engineering is crucial for both fundamental science and emerging applications. While transition metal (TM) dopants exhibit considerable advantages in the tuning of magnetism and conductivity in bulk Ga₂O₃, investigations on TM-doped two-dimensional (2D) Ga₂O₃ are scarce, both theoretically and experimentally. In this study, the detailed variations in impurity levels within 3d TM-doped 2D Ga₂O₃ systems have been explored via first-principles calculations using the generalized gradient approximation (GGA) +U method. Our results show that the Co impurity tends to incorporate on the tetrahedral GaII site, while the other dopants favor square pyramidal GaI sites in 2D Ga₂O₃. Moreover, Sc³⁺, Ti⁴⁺, V⁴⁺, Cr³⁺, Mn³⁺, Fe³⁺, Co³⁺, Ni³⁺, Cu²⁺, and Zn²⁺ are the energetically favorable charge states. Importantly, a transition from n-type to p-type conductivity occurs at the threshold Cu element as determined by the defect formation energies and partial density of states (PDOS), which can be ascribed to the shift from electron doping to hole doping with respect to the increase in the atomic number in the 3d TM group. Moreover, the spin configurations in the presence of the square pyramidal and tetrahedral coordinated crystal field effects are investigated in detail, and a transition from high-spin to low-spin arrangement is observed. As the atomic number of the 3d TM dopant increases, the percentage contribution of O ions to the total magnetic moment significantly increases due to the electronegativity effect. Additionally, the formed 3d bands for most TM dopants are located near the Fermi level, which can be of significant benefit to the transformation of the absorbing region from ultraviolet to visible/infrared light. Our results provide theoretical guidance for designing 2D Ga₂O₃ towards optoelectronic and spintronic applications.

In this paper, we consider a fully discrete interpolated coefficient mixed finite element method for semilinear time fractional reaction–diffusion equations. The classic $L1$ scheme based on graded meshes and new mixed finite element based on triangulation is used for the temporal and spatial discretization, respectively. The interpolation coefficient technique is used to deal with the semilinear term, and the discrete nonlinear system is solved by a Newton-like iterative method. Stability and convergence results for both the original variable and its flux are derived. Numerical experiments confirm our theoretical analysis.

In multi-node cooperative sensing of cognitive networks, as the number of nodes increases, and the energy consumption must increase, but the sensing performance does not necessarily improve. The nodes with less information are not helpful for the sensing performance but will increase the unnecessary energy consumption. To improve the sensing performance and reduce the energy consumption of nodes, a dynamic node selection algorithm based on reinforcement learning is proposed in this paper. The algorithm can evaluate the reliability of sensing nodes in real-time, select the nodes with the highest reliability to participate in cooperative sensing, and update the reliability of nodes in real-time through the method of combining feedback energy consumption and sensing performance. In a real-time environment, nodes with high reliability are selected to participate in cooperative sensing, and the optimal balance between sensing performance and energy consumption is achieved. The experimental results show that the proposed algorithm can reduce energy consumption and improve the perception performance at the same time. Under the same conditions, the detection probability is 5% higher than that of the traditional method, while the energy consumption is only 16.7% of that of the traditional method.