Machine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.

The control of a wing-in-ground craft (WIG) usually allows for many needs, like cruising, speed, survival and stealth. Various degrees of emphasis on these requirements result in different trajectories, but there has not been a way of integrating and quantifying them yet. Moreover, most previous studies on other vehicles’ multi-objective trajectory is planned globally, lacking for local planning. For the multi-objective trajectory planning of WIGs, this paper proposes a multi-objective function in a polynomial form, in which each item represents an independent requirement and is adjusted by a linear or exponential weight. It uses the magnitude of weights to demonstrate how much attention is paid relatively to the corresponding demand. Trajectories of a virtual WIG model above the wave trough terrain are planned using reward shaping based on the introduced multi-objective function and deep reinforcement learning (DRL). Two conditions are considered globally and locally: a single scheme of weights is assigned to the whole environment, and two different schemes of weights are assigned to the two parts of the environment. Effectiveness of the multi-object reward function is analysed from the local and global perspectives. The reward function provides WIGs with a universal framework for adjusting the magnitude of weights, to meet different degrees of requirements on cruising, speed, stealth and survival, and helps WIGs guide an expected trajectory in engineering.

The paper addresses the six-degree-of-freedom coupled control problem for spacecraft formation flying subject to actuator saturation and input quantisation whilst considering limited communication resources. Firstly, a novel event-triggered distributed observer without continuous communications is presented to recover the information of the virtual leader. Remarkably, by embedding a hyperbolic tangent function-based nonlinear term into the triggering condition, the event-based observer realises a more reasonable trigger threshold. Subsequently, an adding-a-power-integrator-based fixed-time control algorithm is proposed for the follower spacecraft. Further, the control scheme ingeniously compensates for the actuator saturation and the input quantisation problems without embedding auxiliary systems. Finally, numerical simulations are carried out to highlight the advantages of the theoretical results.

The Hall–Vinen–Bekharevich–Khalatnikov (HVBK) model is widely used to numerically study quantum turbulence in superfluid helium. Based on the two-fluid model of Tisza and Landau, the HVBK model describes the normal (viscous) and superfluid (inviscid) components of the flow using two Navier–Stokes type of equations, coupled through a mutual friction force term. We derive transport equations for the third-order moments for each component of velocity involving the fourth-order moments, which are classical probes for internal intermittency at any scale, and revealing the probability of rare and strong fluctuations. Budget equations are assessed through direct numerical simulations of the HVBK flow. We simulate a forced homogeneous isotropic turbulent flow with Reynolds number of the normal fluid (based on Taylor's microscale) close to 100. Values from 0.1 to 10 are considered for the ratio between the normal and superfluid densities. For these flows, an inertial range is not discernible and the restricted scaling range approach is used to take into account the finite Reynolds number (FRN) effect. We analyse the importance of each term in budget equations and emphasize their role in energy exchange between normal and superfluid components. Some interesting features are observed: (i) transport and pressure-related terms are dominant, similarly to single-fluid turbulence; and (ii) the mathematical signature of the FRN effect is weak despite the low value of the Reynolds number. The flatness of the velocity derivatives is finally studied through the transport equations and their limit for very small scales, and it is shown to gradually increase for lower and lower temperatures, for both normal fluid and superfluid. This similarity highlights the strong locking of the two fluids. The flatness factors are also found in reasonable agreement with classical turbulence.

As a typical plasma-based optical element that can sustain ultra-high light intensity, plasma density gratings driven by intense laser pulses have been extensively studied for wide applications. Here, we show that the plasma density grating driven by two intersecting driver laser pulses is not only nonuniform in space but also varies over time. Consequently, the probe laser pulse that passes through such a dynamic plasma density grating will be depolarized, that is, its polarization becomes spatially and temporally variable. More importantly, the laser depolarization may spontaneously take place for crossed laser beams if their polarization angles are arranged properly. The laser depolarization by a dynamic plasma density grating may find application in mitigating parametric instabilities in laser-driven inertial confinement fusion.

This study proposes a newly developed deep-learning-based method to generate turbulent inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilised to predict velocity fields of a spatially developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, $Re_\theta = 661.5\unicode{x2013}1502.0$, are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. Furthermore, applying the generated turbulent inflow conditions to an inflow–outflow simulation reveals a negligible development distance for the TBL to reach the target statistics. The results demonstrate for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. They also show that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.

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.

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.

This article examines the fertility desires, intentions and outcomes of 406 alumni of a middle school in Dalian city, Liaoning province, when they were surveyed in 2014–2015 (during their late 20s and early 30s). It also looks at how and why the fertility desires, plans and outcomes of a 47-member representative subsample and their spouses changed from year to year as they moved through their 20s and 30s between 2008 and 2021. Although most respondents started out wanting two children, longitudinal interviews with the representative subsample and their spouses showed that most gradually resigned themselves to having no more than one child. This was because they felt unable to provide two children with the high standard of living and childcare that their own experiences as singletons had led them to consider essential for proper childrearing.

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.

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.

OBJECTIVES/GOALS: Diffusion basis spectrum imaging (DBSI) allows for detailed evaluation of white matter microstructural changes present in cervical spondylotic myelopathy (CSM). Our goal is to utilize multidimensional clinical and quantitative imaging data to characterize disease severity and predict long-term outcomes in CSM patients undergoing surgery. METHODS/STUDY POPULATION: A single-center prospective cohort study enrolled fifty CSM patients who underwent surgical decompression and twenty healthy controls from 2018-2021. All patients underwent diffusion tensor imaging (DTI), DBSI, and complete clinical evaluations at baseline and 2-years follow-up. Primary outcome measures were the modified Japanese Orthopedic Association score (mild [mJOA 15-17], moderate [mJOA 12-14], severe [mJOA 0-11]) and SF-36 Physical and Mental Component Summaries (PCS and MCS). At 2-years follow-up, improvement was assessed via established MCID thresholds. A supervised machine learning classification model was used to predict treatment outcomes. The highest-performing algorithm was a linear support vector machine. Leave-one-out cross-validation was utilized to test model performance. RESULTS/ANTICIPATED RESULTS: A total of 70 patients – 20 controls, 25 mild, and 25 moderate/severe CSM patients – were enrolled. Baseline clinical and DTI/DBSI measures were significantly different between groups. DBSI Axial and Radial Diffusivity were significantly correlated with baseline mJOA and mJOA recovery, respectively (r=-0.33, p<0.01; r=-0.36, p=0.02). When predicting baseline disease severity (mJOA classification), DTI metrics alone performed with 38.7% accuracy (AUC: 72.2), compared to 95.2% accuracy (AUC: 98.9) with DBSI metrics alone. When predicting improvement after surgery (change in mJOA), clinical variables alone performed with 33.3% accuracy (AUC: 0.40). When combining DTI or DBSI parameters with key clinical covariates, model accuracy improved to 66.7% (AUC: 0.65) and 88.1% (AUC: 0.95) accuracy, respectively. DISCUSSION/SIGNIFICANCE: DBSI metrics correlate with baseline disease severity and outcome measures at 2-years follow-up. Our results suggest that DBSI may serve as a valid non-invasive imaging biomarker for CSM disease severity and potential for postoperative improvement.

Frequent freezing injury greatly influences winter wheat production; thus, effective prevention and a command of agricultural production are vital. The freezing injury monitoring method integrated with ‘3S’ (geographic information systems (GIS), global positioning system (GPS) and remote sensing (RS)) technology has an unparalleled advantage. Using HuanJing (HJ)-1A/1B satellite images of a winter wheat field in Shanxi Province, China plus a field survey, crop types and winter wheat planting area were identified through repeated visual interpretations of image information and spatial analyses conducted in GIS. Six vegetation indices were extracted from processed HJ-1A/1B satellite images to determine whether the winter wheat suffered from freezing injury and its degree of severity and recovery, using change vector analysis (CVA), the freeze injury representative vegetation index and the combination of the two methods, respectively. Accuracy of the freezing damage classification results was verified by determining the impact of freezing damage on yield and quantitative analysis. The CVA and the change of normalized difference vegetation index (ΔNDVI) monitoring results were different so a comprehensive analysis of the combination of CVA and ΔNDVI was performed. The area with serious freezing injury covered 0.9% of the total study area, followed by the area of no freezing injury (3.5%), moderate freezing injury (10.2%) and light freezing injury (85.4%). Of the moderate and serious freezing injury areas, 0.2% did not recover; 1.2% of the no freezing injury and light freezing injury areas showed optimal recovery, 15.6% of the light freezing injury and moderate freezing injury areas showed poor recovery, and the remaining areas exhibited general recovery.

The aerodynamics of a stationary wing in a turbulent wake are investigated. Force and velocity measurements are used to describe the unsteady flow. Various wakes are studied with different dominant frequencies and length scales. In contrast to the pre-stall angles of attack, the time-averaged lift increases substantially at post-stall angles of attack as the wing interacts with the von Kármán vortex street and experiences temporal variations of the effective angle of attack. At an optimal offset distance from the wake centreline, the time-averaged lift becomes maximum despite of small amplitude oscillations in the effective angle of attack. The stall angle of attack can reach 20° and the maximum lift coefficient can reach 64 % higher than that in the freestream. Whereas large velocity fluctuations at the wake centreline cause excursions into the fully attached and separated flows during the cycle, small-amplitude oscillations at the optimal location result in periodic shedding of leading edge vortices. These vortices may produce large separation bubbles with reattachment near the trailing-edge. Vorticity roll-up, strength and size of the vortices increase with increasing wavelength and period of the von Kármán vortex street, which also coincides with an increase in the spanwise length scale of the incident wake, and all contribute to the remarkable increase in lift.

For the safety problems caused by the limited landing space of the deck during the arresting process of the carrier-based aircraft, a dynamic model of the carrier-based aircraft’s landing and arresting is built. Based on the batch simulation method, the lateral dynamics safety envelope of the aircraft during the arresting was defined, and the dynamic response of the key points in the envelope during the arresting process was investigated. Subsequently, the influence of engine thrust and aircraft quality on the arresting safety envelope was studied based on reasonable safety evaluation indicators, and the safety status envelope of the deck arresting was given. Then, the particular Hamilton-Jacobi partial differential equation is used to obtain the lateral dynamics safety envelope of the carrier-based aircraft in the process of landing and arresting by backward inversion. Results indicate that engine thrust and landing quality have little effect on the yaw angle in the arresting safety boundary during the arresting. Additionally, with the engine thrust and landing quality increase, the maximum safe off-centre distance gradually decreases, and the safety boundary decreases accordingly. During the phase of landing glide, the engine thrust and quality have little effect on the maximum safe eccentric distance. When the engine thrust is increased by 40%, the maximum safe yaw angle is reduced from 0.3°, and the safety boundary is reduced by 4.2%. When the aircraftquality increases by 40%, the maximum safe yaw angle is reduced by 0.4°, and the safety boundary is reduced by 2.8%. The findings of this paper can provide framework for the research on theaircraft-to-carrier dynamic matching characteristics of the carrier-based system, and is of great significance to the research on improving the safety of the carrier-based aircraft landing arresting.