Plasma vertical displacement control is essential for the stable operation of tokamak devices. The traditional plasma vertical displacement calculation method is not suitable for balancing speed and accuracy simultaneously, which is necessary for real-time feedback control. In this study, neural networks are used to rapidly detect vertical displacement recognition. Based on a fully connected neural network, the vertical displacement calculation model is trained and tested using magnetic data of approximately 2000 shots. To compare the effects of different inputs on vertical displacement calculation, different magnetic measurement diagnostic signals are used to train and test the model. Compared with a full magnetic measurement dataset, 39 magnetic measurement signals (38 magnetic probes and plasma current) show better accuracy with mean square error <0.0005. The model is tested using historical experimental data, and it demonstrates accurate vertical displacement calculation even in the case of a vertical displacement event. In general, neural network algorithm has great application potential in vertical displacement calculation.

In the present study, we investigated the influence of different mid-stage N compensation timings on agronomic and physiological traits associated with grain yield and quality in field experiments. Two japonica rice cultivars with a good tasting quality (Nangeng 9108 and Nangeng 5055) were examined under eight N compensation timings (N1–N6: one-time N compensation at 7-2 weeks before heading; N7: split N compensation at 5 and 3 weeks before heading; N8: split N compensation at 4 and 2 weeks before heading) and a control with no N compensation. The highest yield was obtained with N7, followed by N3. The yield advantage is mainly attributable to the improved population structure (higher productive tiller rate with a stable number of effective panicles), higher total number of spikelets per unit area (large panicles with more grains per panicle), larger leaf area index in the late period and higher photosynthetic production capacity (more dry matter accumulation and transportation in the middle and late periods). Delaying N compensation timing improved the processing and nutritional quality of rice, but decreased the quality of appearance and cooking/eating traits. Our results suggest that, from the perspective of achieving relative coordination between high yield and high quality of japonica rice, the optimal N compensation should be divided equally at 5 and 3 weeks before heading. However, if simplifying the number of operations and the pursuit of eating quality were considered, one-time N compensation should be conducted at 5 weeks before heading.

People often assess the reasonableness of another person’s judgments. When doing so, the evaluator should set aside knowledge that would not have been available to the evaluatee to assess whether the evaluatee made a reasonable decision, given the available information. But under what circumstances does the evaluator set aside information? On the one hand, if the evaluator fails to set aside prior information, not available to the evaluatee, they exhibit belief bias. But on the other hand, when Bayesian inference is called for, the evaluator should generally incorporate prior knowledge about relevant probabilities in decision making. The present research integrated these two perspectives in two experiments. Participants were asked to take the perspective of a fictitious evaluatee and to evaluate the reasonableness of the evaluatee’s decision. The participant was privy to information that the fictitious evaluatee did not have. Specifically, the participant knew whether the evaluatee’s decision judgment was factually correct. Participants’ judgments were biased (Experiments 1 and 2) by the factuality of the conclusion as they assessed the evaluatee’s reasonableness. We also found that the format of information presentation (Experiment 2) influenced the degree to which participants’ reasonableness ratings were responsive to the evaluatee’s Bayesian rationality. Specifically, responsivity was greater when the information was presented in an icon-based, graphical, natural-frequency format than when presented in either a numerical natural-frequency format or a probability format. We interpreted the effects of format to suggest that graphical presentation can help organize information into nested sets, which in turn enhances Bayesian rationality.

Planting patterns have significant effects on rice growth. Nonetheless, little is known about differences in annual crop yield and resource utilization among mechanized rice planting patterns in a rice–wheat cropping system. Field experiments were conducted from 2014 to 2017 using three treatments: pot seedling transplanting for rice and row sowing for wheat (PST-RS), carpet seedling transplanting for rice and row sowing for wheat (CST-RS) and row sowing for both crops (RS-RS). The results showed that, compared with RS-RS, PST-RS and CST-RS prolonged annual crop growth duration by 25–26 and 13–15 days, increased effective accumulated temperature by 399 and 212°C days and increased cumulative solar radiation by 454 and 228 MJ/m2 because of the earlier sowing of rice by 28 and 16 days in PST-RS and CST-RS, respectively. Compared with RS-RS, the annual crop yield of PST-RS and CST-RS increased by 3.1–3.8 and 2.0–2.6 t/ha, respectively, because of the increase in the number of spikelets/kernels per hectare, aboveground biomass, mean leaf area index and grain–leaf ratio. In addition, temperature production efficiency, solar radiation production efficiency and solar radiation use efficiency were higher in PST-RS, followed by CST-RS and RS-RS. These results suggest that mechanized rice planting patterns such as PST-RS increase annual crop production in rice–wheat cropping systems by increasing yield and solar energy utilization.

We describe a new low-frequency wideband radio survey of the southern sky. Observations covering 72–231 MHz and Declinations south of $+30^\circ$ have been performed with the Murchison Widefield Array “extended” Phase II configuration over 2018–2020 and will be processed to form data products including continuum and polarisation images and mosaics, multi-frequency catalogues, transient search data, and ionospheric measurements. From a pilot field described in this work, we publish an initial data release covering 1,447 $\mathrm{deg}^2$ over $4\,\mathrm{h}\leq \mathrm{RA}\leq 13\,\mathrm{h}$ , $-32.7^\circ \leq \mathrm{Dec} \leq -20.7^\circ$ . We process twenty frequency bands sampling 72–231 MHz, with a resolution of 2′–45′′, and produce a wideband source-finding image across 170–231 MHz with a root mean square noise of $1.27\pm0.15\,\mathrm{mJy\,beam}^{-1}$ . Source-finding yields 78,967 components, of which 71,320 are fitted spectrally. The catalogue has a completeness of 98% at ${{\sim}}50\,\mathrm{mJy}$ , and a reliability of 98.2% at $5\sigma$ rising to 99.7% at $7\sigma$ . A catalogue is available from Vizier; images are made available via the PASA datastore, AAO Data Central, and SkyView. This is the first in a series of data releases from the GLEAM-X survey.

The great demographic pressure brings tremendous volume of beef demand. The key to solve this problem is the growth and development of Chinese cattle. In order to find molecular markers conducive to the growth and development of Chinese cattle, sequencing was used to determine the position of copy number variations (CNVs), bioinformatics analysis was used to predict the function of ZNF146 gene, real-time fluorescent quantitative polymerase chain reaction (qPCR) was used for CNV genotyping and one-way analysis of variance was used for association analysis. The results showed that there exists CNV in Chr 18: 47225201-47229600 (5.0.1 version) of ZNF146 gene through the early sequencing results in the laboratory and predicted ZNF146 gene was expressed in liver, skeletal muscle and breast cells, and was amplified or overexpressed in pancreatic cancer, which promoted the development of tumour through bioinformatics. Therefore, it is predicted that ZNF146 gene affects the proliferation of muscle cells, and then affects the growth and development of cattle. Furthermore, CNV genotyping of ZNF146 gene was three types (deletion type, normal type and duplication type) by Real-time fluorescent quantitative PCR (qPCR). The association analysis results showed that ZNF146-CNV was significantly correlated with rump length of Qinchuan cattle, hucklebone width of Jiaxian red cattle and heart girth of Yunling cattle. From the above results, ZNF146-CNV had a significant effect on growth traits, which provided an important candidate molecular marker for growth and development of Chinese cattle.

Bragg scattering of nonlinear surface waves over a wavy bottom is studied using two-dimensional fully nonlinear numerical wave tanks (NWTs). In particular, we consider cases of high nonlinearity which lead to complex wave generation and transformations, hence possible multiple Bragg resonances. The performance of the NWTs is well verified by benchmarking experiments. Classic Bragg resonances associated with second-order triad interactions among two surface (linear incident and reflected waves) and one bottom wave components (class I), and third-order quartet interactions among three surface (linear incident and reflected waves, and second-order reflected/transmitted waves) and one bottom wave components (class III) are observed. In addition, class I Bragg resonance occurring for the second-order (rather than linear) transmitted waves, and Bragg resonance arising from quintet interactions among three surface and two bottom wave components, are newly captured. The latter is denoted class IV Bragg resonance which magnifies bottom nonlinearity. It is also found that wave reflection and transmission at class III Bragg resonance have a quadratic rather than a linear relation with the bottom slope if the bottom size increases to a certain level. The surface wave and bottom nonlinearities are found to play opposite roles in shifting the Bragg resonance conditions. Finally, the results indicate that Bragg resonances are responsible for the phenomena of beating and parasitic beating, leading to a significantly large local free surface motion in front of the depth transition.

We report the experimental results of the commissioning phase in the 10 PW laser beamline of the Shanghai Superintense Ultrafast Laser Facility (SULF). The peak power reaches 2.4 PW on target without the last amplifying during the experiment. The laser energy of 72 ± 9 J is directed to a focal spot of approximately 6 μm diameter (full width at half maximum) in 30 fs pulse duration, yielding a focused peak intensity around 2.0 × 1021 W/cm2. The first laser-proton acceleration experiment is performed using plain copper and plastic targets. High-energy proton beams with maximum cut-off energy up to 62.5 MeV are achieved using copper foils at the optimum target thickness of 4 μm via target normal sheath acceleration. For plastic targets of tens of nanometers thick, the proton cut-off energy is approximately 20 MeV, showing ring-like or filamented density distributions. These experimental results reflect the capabilities of the SULF-10 PW beamline, for example, both ultrahigh intensity and relatively good beam contrast. Further optimization for these key parameters is underway, where peak laser intensities of 1022–1023 W/cm2 are anticipated to support various experiments on extreme field physics.

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.

The performance of hypersonic vehicles in the take-off stage considerably influences their capability of accomplishing the flight tasks. This study is aimed at enhancing the take-off performance of a cruise aircraft using the improved chimp optimisation algorithm. The proposed algorithm, which uses the Sobol sequence for initial population generation and a function of the weight factors, can effectively overcome the problems of premature convergence and low accuracy of the original algorithm. In particular, the Sobol sequence aims to obtain a better fitness value in the first iteration, and the weight factor aims to accelerate the convergence speed and avoid the local optimal solution. The take-off mass model of the hypersonic vehicle is constructed considering the flight data obtained using the pseudo-spectral method in the climb phase. Simulations are performed to evaluate the algorithm performance, and the results show that the algorithm can rapidly and stably optimise the benchmark function. Compared to the original algorithm, the proposed algorithm requires 28.89% less optimisation time and yields an optimised take-off mass that is 1.72kg smaller.

In this study, an active defence cooperative guidance (ADCG) law that enables cheap and low-speed airborne defence missiles with low manoeuverability to accurately intercept fast and expensive attack missiles with high manoeuverability was designed to enhance the capability of aircraft for active defence. This guidance law relies on the line-of-sight (LOS) guidance method, and it realises active defence by adjusting the geometric LOS relationship involving an attack missile, a defence missile and an aircraft. We use a nonlinear integral sliding surface and an improved second-order sliding mode reaching law to design the guidance law. This can not only reduce the chattering phenomenon in the guidance command, but it can also ensure that the system can reach the sliding surface from any initial position in a finite time. Simulations were carried out to verify the proposed law using four cases: different manoeuvering modes of the aircraft, different speed ratios of the attack and defence missiles, different reaching laws applied to the ADCG law and a robustness analysis. The results show that the proposed guidance law can enable a defence missile to intercept an attack missile by simultaneously using information about the relative motions of the attack missile and the aircraft. It is also highly robust in the presence of errors and noise.

Magnetohydrodynamic (MHD) simulations of the solar corona have become more popular with the increased availability of computational power. Modern computational plasma codes, relying upon computational fluid dynamics (CFD) methods, allow the coronal features to be resolved using solar surface magnetograms as inputs. These computations are carried out in a full three-dimensional domain and, thus, selection of the correct mesh configuration is essential to save computational resources and enable/speed up convergence. In addition, it has been observed that for MHD simulations close to the hydrostatic equilibrium, spurious numerical artefacts might appear in the solution following the mesh structure, which makes the selection of the grid also a concern for accuracy. The purpose of this paper is to discuss and trade off two main mesh topologies when applied to global solar corona simulations using the unstructured ideal MHD solver from the COOLFluiD platform. The first topology is based on the geodesic polyhedron and the second on $UV$ mapping. Focus is placed on aspects such as mesh adaptability, resolution distribution, resulting spurious numerical fluxes and convergence performance. For this purpose, first a rotating dipole case is investigated, followed by two simulations using real magnetograms from the solar minima (1995) and solar maxima (1999). It is concluded that the most appropriate mesh topology for the simulation depends on several factors, such as the accuracy requirements, the presence of features near the polar regions and/or strong features in the flow field in general. If convergence is of concern and the simulation contains strong dynamics, then grids which are based on the geodesic polyhedron are recommended compared with more conventionally used $UV$-mapped meshes.

This paper proposes an online entry trajectory planning based on predictor corrector algorithm that satisfies terminal constraints, path constraints and geographic constraints for lifting-body entry vehicles. The vehicle is considered as a 3DOF point mass. A piecewise linear bank profile in the altitude-versus-velocity (H-V) plane is used to project the longitudinal trajectory and a heading angle corridor-based bank angle reversal logic is designed to satisfy the geographic constraints simultaneously. The algorithm in longitudinal plane solves the problem of the mismatch between terminal range and height and the algorithm in lateral plane satisfies the no-fly zone constraint. Simulation results with the CAV-H model show that the proposed algorithm can generate entry trajectories satisfying multiple constraints and has certain robustness.