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C14H14AsNO5, monoclinic, P21 (no. 4), a = 8.5830(5) Å, b = 7.0799(5) Å, c = 11.6011(7) Å, β = 100.059(2)∘, V = 694.13(8) Å3, Z = 2, R gt(F) = 0.0288, wR ref(F 2) = 0.0603, T = 150.0 K.

The traditional reinforcement learning method has the problems of overestimation of value function and partial observability in the field of machine motion planning, especially in the obstacle avoidance problem of UAV, which lead to long training time and difficult convergence in the process of network training. This paper proposes an obstacle avoidance algorithm for UAVs based on a deep recurrent double Q network. By transforming the single-network structure into a dual-network structure, the optimal action selection and action value estimation are decoupled to reduce the overestimation of the value function. The fully connected layer introduces the GRU recurrent neural network module, and uses the GRU to process the time dimension information, enhance the analyzability of the real neural network, and improve the performance of the algorithm in some observable environments. On this basis, combining with the priority experience playback mechanism, the network convergence is accelerated. Finally, the original algorithm and the improved algorithm are tested in the simulation environment. The experimental results show that the algorithm has better performance in terms of training time, obstacle avoidance success rate and robustness.

This paper studied the trajectory tracking control of a failure satellite that has actuator jumping fault. It proposed a design method based on the jumping control input for the model reference tracking controller. By comb-ining the stochastic stability definition with model reference tracking, it gave the mathematical descriptions of the trajectory tracking control of the failure satellite. It used the linear matrix inequality method and the parametric solution method of the nonhomogeneous generalized Sylvester matrix equation to design the robust H∞ state feedback control law and the complete parametric feed-forward tracking compensator respectively, with the disturbance of the failure satellite and its thrust constraint considered simultaneously. The numerical simulation results on the model of the satellite rendezvous system show that the design method proposed in this paper is effective.

C23H14IN4O7La, triclinic, P1‾ $P\overline{1}$ (no. 2), a = 8.2438(3) Å, b = 12.3786(5) Å, c = 12.6230(5) Å, α = 111.802(1)°, β = 91.144(1)°, γ = 100.6470(10)°, V = 1169.86(8) Å3, Z = 2, R gt(F) = 0.0313, wR ref(F 2) = 0.1051, T = 296 K.

C18H22N6O18U2, monoclinic, P2/c (no. 13), a = 10.894(2) Å, b = 7.9674(16) Å, c = 16.787(3) Å, β = 100.823(2)°, V = 1399.1(5) Å3, Z = 2, R gt(F) = 0.0307, wR ref(F 2) = 0.0728, T = 296(2) K.

C16H11NO10U, monoclinic, P21/c (no. 14), a = 16.4675(12) Å, b = 11.7192(8) Å, c = 9.6663(7) Å, β = 100.823(2)°, V = 1832.3(2) Å3, Z = 4, R gt(F) = 0.0277, wR ref(F2) = 0.0673, T = 170.0 K.

By comprehensive analysis of macroscopic characteristics of hardness, electrical conductivity and tensile properties and features of microcosmic morphology, the influence mechanism of process parameters of laser selective melting and following aging treatment on the microstructure, supersaturated solid solution and second-phase precipitation behavior of Al-3.4Mg-1.08Sc alloy fabricated by SLM was discussed. By utilizing the change rules of density and hardness,finally the optimum SLM process parameters and aging system were obtained.The results show that the microstructure of the Al-Mg-Sc alloy fabricated in this experiment is composed of ultrafine equiaxed grains and relatively coarser columnar grains around. The optimal fabrication parameter of Al-Mg-Sc alloy fabricated at platform temperature of 35 ℃ is conducted with scanning speed of 1600 mm/s, laser power of 370 W. The optimum aging time is 12 h at 300 ℃, the yield strength of studied alloy can reach 479.0 MPa through peak aging treatment. Under rapid cooling rate of SLM process, supersaturated solid solution is formed in Al-Mg-Sc alloy, and a large number of nanometer Al3(Sc,Zr) particles are precipitated during the fabrication and heat treatment process, which makes the Al-Mg-Sc alloy present excellent potential of mechanical properties. The fine grain strengthening and the second phase strengthening are the main factors for exhibiting properties of Al-Mg-Sc alloy fabricated by SLM.

C13H13N2O8, triclinic, P1̄ (no. 2), a = 7.8590(6) Å, b = 9.3108(8) Å, c = 11.0816(8) Å, α = 66.950(3)°, β = 71.558(3)°, γ = 77.203(4)°, V = 703.38(10) Å3, Z = 2, Rgt(F) = 0.0389, wRref(F2) = 0.1075, T = 149(2) K.

Nano-SiO2 surface grafted APTES (T-SiO2) was realized by covalent functionalization technology, and functionalized nano-SiO2 modified epoxy resin composite (T-SiO2/EP) was prepared. The surface functional groups and chemical elements of the functionalized nano-SiO2were analyzed and the mechanical and tribological properties of the T-SiO2/EP were tested. The results show that the mechanical and tribological properties of the epoxy resin are effectively improved due to the introduction of functionalized nano-SiO2. When the content of functionalized nano-SiO2 is 2%(mass fraction, same as below), the microhardness and fracture toughness of the composites (2%T-SiO2/EP) can reach the maximum, which are 70.2HD and 1.02MPa·m1/2 respectively, moreover, in dry friction condition, the friction coefficient and the wear loss reach the minimum, which are 0.49mg and 1.7mg respectively. Compared with pure epoxy resin, they are reduced by 31.9% and 34.6%, and compared with 2% unmodified nano-SiO2 reinforced epoxy resin composite, they are reduced by 14% and 10.5%,and the corresponding wear mechanism is analyzed.

C12H21Cl2N3O2, monoclinic, P21/c (no. 14), a = 7.2704(7) Å, b = 11.4750(12) Å, c = 18.7004(19) Å, β = 95.475(2)°, V = 1553.0(3) Å3, Z = 4, Rgt(F) = 0.0372, wRref(F2) = 0.1022, T = 296(2) K.

Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes the therapeutic ratio. ART utilizes advanced imaging techniques such as CT, MRI, and PET to modify the treatment plan based on observed anatomical changes and even biological changes during the course of treatment. The narrative review provides a comprehensive guide on ART for healthcare professionals and trainees in radiation oncology and anyone else interested in the topic. The incorporation of artificial intelligence in ART has played a crucial role in improving effectiveness, particularly in contour segmentation, treatment planning, and quality assurance. This has expedited the process to render online ART feasible, lowered the burden for radiation oncology practitioners, and enhanced the precision of dynamically personalized treatment. Current technical and clinical progress on ART is discussed in this review, highlighting the ongoing development of imaging technologies and AI and emphasizing their contribution to enhancing the applicability and effectiveness of ART.

C6H5BrFNO3, orthorhombic, P212121 (no. 19), a = 3.9894(4) Å, b = 13.6128(11) Å, c = 14.7495(12) Å, V = 801.00(12) Å3, Z = 4, Rgt(F) = 0.0213, wRref(F2) = 0.0465, T = 150(2) K.

C26H25BrN2O4, triclinic, P1̄ (no. 2), a = 9.4202(5) Å, b = 9.7871(5) Å, c = 15.0135(9) Å, α = 106.775(3)°, β = 92.534(3)°, γ = 117.424(3)°, V = 1150.52(11) Å3, Z = 2, Rgt(F) = 0.0316, wRref(F2) = 0.0671, T = 150(2) K.

C6H6BrNO3, triclinic, P1̄ (no. 2), a = 7.0407(5) Å, b = 7.1597(6) Å, c = 8.3808(7) Å, α = 75.844(4)°, β = 94.562(4)°, γ = 76.306(4)°, V = 373.61(5) Å3, Z = 4, Rgt(F) = 0.0205, wRref(F2) = 0.0527, T = 150(2) K.

C7H4F3NO2, monoclinic, P21/c (no. 14), a = 6.4488(11) Å, b = 14.922(2) Å, c = 7.7868(12) Å, β = 100.941(5)°, V = 735.7(2) Å3, Z = 4, Rgt(F) = 0.0431, wRref(F2) = 0.1158, T = 150(2) K.

C8H7NO4, orthorhombic, Pccn (no. 56), a = 14.1361(10) Å, b = 15.8923(12) Å, c = 6.6410(6) Å, V = 1491.9(2) Å3, Z = 8, Rgt(F) = 0.0406, wRref(F2) = 0.1155, T = 150(2) K.

C8H8ClNO9, monoclinic, P21/n (no. 14), a = 7.1284(3) Å, b = 8.5538(5) Å, c = 18.7089(10) Å, β = 99.760(2)°, V = 1124.26(10) Å3, Z = 4, Rgt(F) = 0.0381, wRref(F2) = 0.1031, T = 150 K.

Carbon Capture, Utilization and Storage (CCUS) is one of the key technologies for realizing large-scale low-carbon utilization of coal-fired power plants in service. How to evaluate its economics is crucial to the decision-making of traditional coal-fired power enterprises. This paper analyzes the changes in the physical, emission and economic parameters of in-service coal-fired power plants without and with the CCUS retrofit. A method for evaluating the economic feasibility of coal-fired power plants retrofitting based on net cash flow is proposed, which compares the impact of CCUS retrofit on the net present value of the remaining life cycle of the power plant. The impact of uncertain parameters such as carbon dioxide sales unit price, carbon capture device operating cost, free carbon quota, and carbon emission right price on the evaluation results are analyzed.

The optimization of trans-regional electricity transmission scale of China’s Western renewable energy base is crucial to drive China's energy revolution. Currently, the regional power planning or optimization is aimed at minimizing the total economic cost, without taking the cost and benefit of transregional electricity transmission into account. In this paper, a regional planning model considering the cost and benefit of trans-regional electricity transmission was proposed, and the target of trans-regional electricity transmission scale by 2050 was optimized. This study analysed the influence of carbon price, fossil fuel scarcity value, accommodation cost for renewable power on the optimized result of trans-regional electricity transmission scale. Furthermore, the policy implication of the research was also analysed.

C14H14N2O2, monoclinic, P21/c (no. 14), a = 8.5030(9) Å, b = 7.6199(8) Å, c = 18.738(2) Å, β = 92.852(2)°, V = 1212.6(2) Å3, Z = 4, Rgt(F) = 0.0413, wRref(F2) = 0.1247, T = 296.15 K.