Systematically monitoring the baseline sensitivity of troublesome weeds to herbicides is crucial for the early detection of a premature market lifespan of herbicides. Florpyrauxifen-benzyl is one of the most important rice herbicides across the world, which has been used for five years in China. Barnyardgrass is one of the main targeted weed species of florpyrauxifen-benzyl. Totally 114 barnyardgrass populations were collected from rice fields in Jiangsu Province, China, and screened for susceptibility of each population to florpyrauxifen-benzyl by whole-plant bioassays. For all populations, the GR50 doses (the dose causing a 50% reduction in fresh weight of aboveground parts) of florpyrauxifen-benzyl ranged from 1.0 to 34.5 g ai ha-1, with an average of 6.8 g ai ha-1, a baseline sensitivity dose of 3.3 g ai ha-1, and a baseline sensitivity index of 34.5. Twenty-one days after treatment with florpyrauxifen-benzyl at the labeled dose (36 g ai ha-1), 90% of the barnyardgrass populations showed reductions in fresh weight of aboveground parts > 95%. Compared with the baseline sensitivity dose, 63, 44, and seven populations had no resistance (55%), low resistance (39%), and moderate resistance (6%) to florpyrauxifen-benzyl, respectively. Furthermore, the GR50 distribution of barnyardgrass populations did not show a significant correlation with city, planting methods (direct-seeding or transplanting), and rice species (ssp. indica or ssp. japonica) at the seed-collection rice fields (P > 0.05). In conclusion, florpyrauxifen-benzyl remains effective for barnyardgrass control in rice fields despite serious resistance challenges.

The emerging era of big data in radio astronomy demands more efficient and higher-quality processing of observational data. While deep learning methods have been applied to tasks such as automatic radio frequency interference (RFI) detection, these methods often face limitations, including dependence on training data and poor generalisation, which are also common issues in other deep learning applications within astronomy. In this study, we investigate the use of the open-source image recognition and segmentation model, Segment Anything Model (SAM), and its optimised version, HQ-SAM, due to their impressive generalisation capabilities. We evaluate these models across various tasks, including RFI detection and solar radio burst (SRB) identification. For RFI detection, HQ-SAM (SAM) shows performance that is comparable to or even superior to the SumThreshold method, especially with large-area broadband RFI data. In the search for SRBs, HQ-SAM demonstrates strong recognition abilities for Type II and Type III bursts. Overall, with its impressive generalisation capability, SAM (HQ-SAM) can be a promising candidate for further optimisation and application in RFI and event detection tasks in radio astronomy.

Glaciers play a crucial role in the Asian Water Tower, underscoring the necessity of accurately assessing their mass balance and ice volume to evaluate their significance as sustainable freshwater resources. In this study, we analyzed ground-penetrating radar (GPR) measurements from a 2020 survey of the Xiao Dongkemadi Glacier (XDG) to determine ice thickness, and we extended the glacier’s volume-change record to 2020 by employing multi-source remote-sensing data. Our findings show that the GPR-derived mean ice thickness of XDG in 2020 was 54.78 ± 3.69 m, corresponding to an ice volume of 0.0811 ± 0.0056 km3. From 1969 to 2020, the geodetic mass balance was −0.19 ± 0.02 m w.e. a−1, and the glacier experienced area and ice volume losses of 16.38 ± 4.66% and 31.01 ± 4.59%, respectively. The long-term mass-balance reconstruction reveals weak fluctuations occurred from 1967 to 1993 and that overall mass losses have occurred since 1994. This ongoing shrinkage and ice loss are mainly associated with the temperature increases in the warm season since the 1960s. If the climate trend across the central Tibetan Plateau follows to the SSP585 scenario, then XDG is at risk of disappearing by the end of the century.

Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model.

This study demonstrates a kilowatt-level, spectrum-programmable, multi-wavelength fiber laser (MWFL) with wavelength, interval and intensity tunability. The central wavelength tuning range is 1060–1095 nm and the tunable number is controllable from 1 to 5. The wavelength interval can be tuned from 6 to 32 nm and the intensity of each channel can be adjusted independently. Maximum output power up to approximately 1100 W has been achieved by master oscillator power amplifier structures. We also investigate the wavelength evolution experimentally considering the difference of gain competition, which may give a primary reference for kW-level high-power MWFL spectral manipulation. To the best of our knowledge, this is the highest output power ever reported for a programmable MWFL. Benefiting from its high power and flexible spectral manipulability, the proposed MWFL has great potential in versatile applications such as nonlinear frequency conversion and spectroscopy.

Ridge B is one of the least studied areas in Antarctica but has been considered to be a potential location for the oldest ice on Earth. Among important parameters for calculating where very old ice may exist, geothermal heat flux (GHF) is critical but poorly understood. Here, GHF is determined by quantifying the transitions between dry and wet basal conditions using a radioglaciological method applied to airborne radio-echo sounding data. GHF is then constrained by a thermodynamic model matched to the transitions. The results show that GHF in Ridge B varies locally and ranges from 48.5 to 65.1 mW m−2, with an average value of 58.0 mW m−2, which is consistent with the current known GHF constrained by subglacial lakes and derived from Vostok ice core temperature measurements. Our work highlights the value of considering local GHF when locating the oldest ice in this potential region or other regions.

There is an unavoidable time offset between the camera stream and the inertial measurement unit (IMU) data due to the sensor triggering and transmission delays, which will seriously affect the accuracy of visual-inertial odometry (VIO). A novel online time calibration framework via double-stage EKF for VIO is proposed in this paper. First, the first-stage complementary Kalman filter is constructed by adapting the complementary characteristics between the accelerometer and the gyroscope in the IMU, where the rotation result predicted by the gyroscope is corrected through the measurement of the accelerometer so that the IMU can output a more accurate initial pose. Second, the unknown time offset is added to the state vector of the VIO system. The estimated pose of IMU is used as the prediction information, and the reprojection error of multiple cameras on the same feature point is used as the constraint information. During the operation of the VIO system, the time offset is continuously calculated and superimposed on the IMU timestamp to obtain the data synchronized by the IMU and the camera. The Schur complement model is used to marginalize the camera state that carries less information in the system state, avoiding the loss of prior information between images, and improving the accuracy of camera pose estimation. Finally, the effectiveness of proposed algorithm is verified using the EuRoC dataset and the real experimental data.

Background: The formation of pial-pial collateral network aneurysms due to carotid occlusion is a rare neurological phenomenon. This case details a 69-year-old male who developed a pial-pial collateral network aneurysm secondary to left internal carotid artery occlusion, leading to intracranial hemorrhage. Methods: The patient presented with altered consciousness due to left temporal intracerebral hemorrhage, subdural hematoma, and intraventricular hemorrhage. Cerebral angiography revealed an occluded left internal carotid artery, with superficial temporal artery (STA) and superior orbital artery anastomosis, and extensive pial-pial collaterals from the posterior temporal artery. A 4 mm aneurysm arising from this collateral network was identified. Surgical intervention involved a left temporal craniectomy and excision of the aneurysm, prioritizing the preservation of the STA. N.B., Informed patient consent was obtained in this study. Results: Successful aneurysm removal and preservation of collateral pathways were confirmed by postoperative imaging. The patient exhibited rapid neurological improvement; by postoperative day (POD) one, the patient showed limited response to stimuli. He was extubated by POD4 and discharged on POD27, where he conversed well, was independently ambulatory, and needed minimal to no assistance in activities of daily living. Conclusions: This case highlights the need for careful preoperative planning and intraoperative precision, especially in preserving vital collateral vascular pathways.

The release of GNSS raw data on Android smartphones provides the potential for high-precision smartphone positioning using multi-constellation and multi-frequency signals. However, severe multipath and low observation quality in kinematic environments make double-differenced uncombined ambiguities difficult to resolve reliably. To address this, the paper proposes an improved wide-lane (WL) integer ambiguity resolution (IAR) method that combines integer rounding and the Least-Square AMBiguity Decorrelation Adjustment (LAMBDA) methods. The proposed method achieved fix rates of 57% to 70% in challenging environments, with an average improvement of 7 · 7% in horizontal positioning accuracy compared to the float solution. The traditional partial integer rounding method only improved accuracy by 1 · 1%.

Pursuing highly efficient aerodynamic efficiency in aircraft has driven the development of morphing wing technology. However, there are still limitations to morphing wing technology, including adaptation of load and deformation, and deformation monitoring and control. This work introduces an intelligent trailing edge structure that balances deformation and load-bearing and achieves deformation monitoring and active control. Firstly, we employ a honeycomb structure for non-uniform filling of the trailing edge. The filling method is obtained through inverse design using a genetic algorithm based on neural networks, allowing the device to undergo continuous deformation while meeting load-bearing requirements. The bending deformation of the wing is achieved using shape memory alloy (SMA) wire. Additionally, we design and fabricate a metal-based multichannel flexible sensor, and based on beam bending theory, we establish the strain–displacement relationship. These sensors are affixed to the trailing edge surface, enabling real-time monitoring and active control of trailing edge deformation. Building an experimental platform to test this system, the results show that the sensors can accurately give feedback on the degree of wing deformation, and the error of active deformation control technology is less than 4%. This provides a new method for the deformation feedback control closed-loop system of intelligent variant wings.

Visual simultaneous localisation and mapping (vSLAM) has shown considerable promise in positioning and navigating across a variety of indoor and outdoor settings, significantly enhancing the mobility of robots employed in industrial and everyday services. Nonetheless, the prevalent reliance of vSLAM technology on the assumption of static environments has led to suboptimal performance in practical implementations, particularly in unstructured and dynamically noisy environments such as substations. Despite advancements in mitigating the influence of dynamic objects through the integration of geometric and semantic information, existing approaches have struggled to strike an equilibrium between performance and real-time responsiveness. This study introduces a lightweight, multi-modal semantic framework predicated on vSLAM, designed to enable intelligent robots to adeptly navigate the dynamic environments characteristic of substations. The framework notably enhances vSLAM performance by mitigating the impact of dynamic objects through a synergistic combination of object detection and instance segmentation techniques. Initially, an enhanced lightweight instance segmentation network is deployed to ensure both the real-time responsiveness and accuracy of the algorithm. Subsequently, the algorithm’s performance is further refined by amalgamating the outcomes of detection and segmentation processes. With a commitment to maximising performance, the framework also ensures the algorithm’s real-time capability. Assessments conducted on public datasets and through empirical experiments have demonstrated that the proposed method markedly improves both the accuracy and real-time performance of vSLAM in dynamic environments.

To optimize flapping foil performance, in the current study we apply deep reinforcement learning (DRL) to plan foil non-parametric motion, as the traditional control techniques and simplified motions cannot fully model nonlinear, unsteady and high-dimensional foil–vortex interactions. Therefore, a DRL training framework is proposed based on the proximal policy optimization algorithm and the transformer architecture, where the policy is initialized from the sinusoidal expert display. We first demonstrate the effectiveness of the proposed DRL-training framework, learning the coherent foil flapping motion to generate thrust. Furthermore, by adjusting reward functions and action thresholds, DRL-optimized foil trajectories can gain significant enhancement in both thrust and efficiency compared with the sinusoidal motion. Last, through visualization of wake morphology and instantaneous pressure distributions, it is found that DRL-optimized foil can adaptively adjust the phases between motion and shedding vortices to improve hydrodynamic performance. Our results give a hint of how to solve complex fluid manipulation problems using the DRL method.

Data on epidemiology trends of paediatric tuberculosis (TB) are limited in China. So, we investigated the clinical and epidemiological profiles in diagnosed TB disease and TB infection patients at Beijing Children’s Hospital. Of 3 193 patients, 51.05% had pulmonary TB (PTB) and 15.16% had extrapulmonary TB (EPTB). The most frequent forms of EPTB were TB meningitis (39.05%), pleural TB (29.75%), and disseminated TB (10.33%). PTB patients were significantly younger and associated with higher hospitalization frequency. Children aged 1–4 years exhibited higher risk of PTB and TB meningitis, and children aged 5–12 years had higher risk of EPTB. The proportion of PTB patients increased slightly from 40.9% in 2012 to 65% in 2019, and then decreased to 17.8% in 2021. The percentage of EPTB cases decreased from 18.3% in 2012 to 15.2% in 2019, but increased to 16.4% in 2021. Among EPTB cases, the largest increase was seen in TB meningitis. In conclusion, female and young children had higher risk of PTB in children. TB meningitis was the most frequent forms of EPTB among children, and young children were at high risk of TB meningitis. The distribution of different types of EPTB differed by age.