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Abstract Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

Rosa persica is considered a clonal plant because it is mainly propagated by clonal growth. Due to environmental degradation and habitat devastation, R. persica has been listed as a national second-class protected plant in China. However, the absence of research on wild populations of R. persica has impeded progress in formulating efficient conservation strategies. In this study, we investigated the clonal dispersal distance of R. persica to accurately determine the genetic diversity and population structure of the wild population in Xinjiang. We suggested that 20 m was the threshold distance with which to distinguish between different genets of plants. Based on this, we collated sequencing data from a total of 70 different genets of plants from 117 test samples. Eight populations of R. persica were primarily categorized into three subgroups: BL (Bole), TC (Tacheng) and CG (Changji). Of these, the CG subgroup exhibited the most genetic diversity. This research is the first to illustrates the clonal dispersal distance of R. persica, thus providing valuable reference guidelines for understanding the reproductive characteristics of clonal plants. In addition, the genetic diversity of R. persica provides a theoretical foundation for the formulation of conservation policies.

Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- ( ${R}^{{2}}$ ) and cluster-wise ( ${R}_{\textit {over}\textit {lap}}$ ) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability ( ${R}^{{2}}$ up to 0.69, ${R}_{\textit {over}\textit {lap}}$ up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent ( $0.60< $ ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.

Simultaneous Location and Mapping (SLAM) is a common algorithm for position estimation in GNSS-denied environments. However, the high structural consistency and low lighting conditions in tunnel environments pose challenges for traditional visual SLAM and LiDAR SLAM. To this end, this paper presents LiDAR and optical flow fusion odometry (LOFF), which uses a direction-separated data fusion method to fuse optical flow odometry into the degenerate direction of the LiDAR SLAM without sacrificing the accuracy. Moreover, LOFF incorporates detectors and a compensator, allowing for a smooth transition between general environments and degeneracy environments. This capability facilitates the stable flight of unmanned aerial vehicles (UAVs) in GNSS-denied tunnel environments, including corners and long-distance consistency. Through real-world experiments conducted in a GNSS-denied pedestrian tunnel, we demonstrate the superior position accuracy and trajectory smoothness of LOFF compared to state-of-the-art visual SLAM and LiDAR SLAM.

[Introduction] The strategic goal of "carbon peaking and carbon neutrality" has increased the demand for new energy power grid integration, so it is necessary to improve the flexible operation capacity of coal-fired generating units. [Method] This paper introduced the flexible transformation technology and common evaluation indicators of existing coal-fired units in detail. Flexibility transformation mainly included condensate throttling technology, coupled biomass co-combustion transformation technology of coal-fired units, flexibility technology of pulverizing system of coal-fired units, etc.; Common evaluation indicators for the flexibility of coal-fired generating units included: the power consumption rate of the generator set plant, the thermal efficiency of the boiler, the standard coal consumption rate of the generator set, etc. On this basis, this paper summarized and analyzed the flexibility transformation technology and evaluation indexes. [Result] Finally, the paper puts forward seven kinds of the technology development direction and relevant suggestions of coal-fired generating units. [Conclusion] The structural improvement of original generating units, the introduction of multi-form new energies and the coordinated regulation design of multi-heat storage will be the main direction of subsequent development. It is expected that paper can provide reference for subsequent coal-fired generating units to adapt to "dual carbon" energy planning.

[Introduction] The primary frequency modulation technology of thermal power units has been considered essential to ensure the power grid's safe and stable operation. [Method] The current primary frequency modulation technology, the auxiliary primary frequency modulation technology, the coupled frequency modulation control strategy, and the potential development direction of primary frequency modulation technology were reviewed. [Result] The results show that the primary frequency modulation technology for the unit mainly includes regulating valve pre-throttling, adjustable regenerative steam extraction frequency modulation, and condensate water throttling frequency modulation. Among them, the condensate water throttling frequency modulation technology should be the main mode. Auxiliary primary frequency modulation technology is mainly based on the fast-response rate characteristics of flywheel energy storage and battery to meet the unit input and output requirements. The focus and difficulty of coupled auxiliary frequency modulation technology are then analyzed. The system modeling and control strategy research based on frequency modulation technology can optimize the matching between unit parameters. [Conclusion] Thus, the application status of primary frequency modulation technology in recent years is compared, and it is considered that flywheel energy storage primary frequency modulation and sliding pressure operation optimization will be the key development direction.

With the continuous construction of large-scale Low Earth Orbit (LEO) constellations, their potential for Global Navigation Satellite System (GNSS) applications has been emphasized. This study aims to derive an optimal positioning configuration formula based on the ratio of high-elevation and low-elevation satellites, which can improve the positioning accuracy and overcome the accuracy loss due to signal occlusion. A genetic algorithm is used to solve the optimal positioning configuration problem for large-scale satellite selection. Through a simulation using Starlink satellites currently in orbit, it is verified that the traditional recursive algorithm cannot be applied to satellite selection for large-scale constellations. The proposed formula has a similar accuracy to the Quasi-Optimal algorithm when there is no signal occlusion and the satellites are uniformly selected. However, the accuracy of the latter deteriorates significantly under signal occlusion. Our algorithm can effectively overcome this problem. Moreover, we discuss the effect of different types of obstructions on the accuracy loss. We find that the Quasi-Optimal algorithm is more sensitive to a single large-angle obstruction than multiple small-angle obstructions. Our proposed formula can reduce the localization accuracy degradation caused by signal occlusions in both scenarios.

Individuals with physical limb disabilities are often restricted to perform activities of daily life (ADLs). While efficacy of bilateral training has been demonstrated in improving physical coordination of human limbs, few robots have been developed in simulating people’s ADLs integrated with task-specific force field control. This study sought to develop a bilateral robot for better task rendering of general ADLs (gADLs), where gADL-consistent workspace is achieved by setting linear motors in series, and haptic rendering of multiple bimanual tasks (coupled, uncoupled and semi-coupled) is enabled by regulating force fields between robotic handles. Experiments were conducted with human users, and our results present a viable method of a single robotic system in simulating multiple physically bimanual tasks. In future, the proposed robotic system is expected to be serving as a coordination training device, and its clinical efficacy will be also investigated.

The pollution of industrial wastewater has become a global issue in terms of economic development and ecological protection. Pseudomonas oleovorans has been studied as a bacterium involved in the treatment of petroleum pollutants. Our study aimed to investigate the physicochemical properties and drug resistance of Pseudomonas oleovorans isolated from industrial wastewater with a high concentration of sulfate compounds. Firstly, Pseudomonas oleovorans was isolated and then identified using matrix-assisted flight mass spectrometry and 16S rDNA sequencing. Then, biochemical and antibiotic resistance analyses were performed on the Pseudomonas oleovorans, and a microbial high-throughput growth detector was used to assess the growth of the strain. Finally, PCR and proteomics analyses were conducted to determine drug-resistance-related genes/proteins. Based on the results of the spectrum diagram and sequencing, the isolated bacteria were identified as Pseudomonas oleovorans and were positive to reactions of ADH, MTE, CIT, MLT, ONPG, and ACE. Pseudomonas oleovorans was sensitive to most of the tested antibiotics, and its resistance to SXT and CHL and MIN and TIM was intermediate. The growth experiment showed that Pseudomonas oleovorans had a good growth rate in nutrient broth. Additionally, gyrB was the resistance gene, and mdtA2, mdtA3, mdtB2, mdaB, and emrK1 were the proteins that were closely associated with the drug resistance of Pseudomonas oleovorans. Our results show the biochemical properties of Pseudomonas oleovorans from industrial wastewater with a high concentration of sulfate compounds and provide a new perspective for Pseudomonas oleovorans to participate in biological removal of chemical pollutants in industrial wastewater.

Adjusting tillage parameters according to soil conditions can reduce energy consumption. In this study, the working parameters and soil physical parameters of plowing were determined using a designed electric suspension platform and soil instrument. The soil conditions were classified into three physical states, namely ‘hard’, ‘zero’, and ‘soft’ using a fuzzy C-means clustering algorithm, taking the soil moisture content and cone penetration resistance as the grading indexes. The Takagi–Sugeno (T–S) fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs to indirectly identify the soil’s physical state. The results show that when 280 groups of test data were used to verify the model, 264 groups were correctly identified, indicating a soil physical state identification accuracy of 94.29%. The T–S fuzzy neural network prediction model can achieve the real-time and accurate physical state identification of paddy soil during plowing.

In agricultural vehicles with internal combustion engines, owing to the use of rear-wheel drive or four-wheel drive, it is difficult to obtain information regarding the slip of the driving wheels. Excessive wheel slip, an inevitable phenomenon occurring during agricultural activities, can easily damage the original soil surface and result in excessive energy consumption. To solve these problems, a distributed drive agricultural vehicle (DDAV) based on multi-information fusion was proposed. The actual travel reduction of each wheel was calculated by determining the vehicle parameters in order to deliver the required torque to the four drive wheels via sliding mode control (SMC) and incremental proportional-integral (PI) control. Through this process, the vehicle always operates in a straight line. Test results show that, on a uniform surface, the travel reduction of each wheel can be maintained at the target value by using the incremental PI control strategy, with only minor fluctuations, to make the vehicle run in a straight line (R2 = 0.9999). Furthermore, on a separated surface, the travel reduction of each wheel can be maintained at the target value, and using the SMC strategy enables more identical coefficient of gross tractions for each wheel to make the vehicle run in a straight line (R2 = 0.9902). Unlike the non-control strategy, the vehicle reaches a stable state within 1 s, owing to the use of a controller that can effectively reduce the impact of road changes on vehicle velocity. This study can provide a reference for the drive control of DDAVs.

Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (μ) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity.

For the reason of enormous computational expense, although the least squares finite element method has the advantages of high accuracy, robustness and strong versatility, the application of it is limited in computational fluid dynamics. The problems solved in this article include the rewriting of branching statements and the kernel function, variable distribution and data transfer between graphic processing units, and library functions rewriting. To the best knowledge of the authors, this article is the first time to develop the parallel computing codes for single and multiple graphic processing units based on the least squares finite element method. The computational results of single and multiple graphic processing units are verified by lid-driven cavity flow. Compared with a single central processing unit on the condition of 120 3 grids, the acceleration ratios of single and dual graphic processing units are up to 70.5 times and 95.2 times, respectively, which is much higher than the previous value of 7.7. With the increase in the grid number, the acceleration ratio of single and multiple graphic processing units is expected to increase, which can greatly enhance the computational efficiency of the least squares finite element method. Therefore, it is possible to solve the massive turbulence computing by the least squares finite element method with higher efficiency.

Substantial Phanerozoic crustal growth and reworking in the eastern Central Asian Orogenic Belt has partly obscured the Precambrian geological record. Accurate identification of such Precambrian magmatism and its petrogenesis is paramount to unveiling the early crustal evolution of the eastern Central Asian Orogenic Belt. Two outcrops of Neoarchean and Paleoproterozoic magmatism in the Longjiang area of NE China, eastern Central Asian Orogenic Belt, were recently discovered. The Kaoquantun outcrop consists of monzogranites that yield a zircon 207Pb/206Pb age of 2568 ± 8 Ma and are similar to peraluminous A1-type granites. They were derived from the partial melting of a hybrid crustal source comprising newly accreted Mesoarchean crust and abundant supracrustal metapelites in a post-collisional extensional setting. Such metapelites were initially part of the upper crust but were buried in the middle-lower crust due to crustal thickening prior to ca. 2.6 Ga. In the Zhanbeitun outcrop, basaltic andesites overlie monzogranites, and both have been intruded by syenogranites. The monzogranites and syenogranites yield zircon 207Pb/206Pb ages of 1881 ± 10 Ma and 1843 ± 3 Ma, respectively, whereas the basaltic andesites are inferred to have formed at 1.88-1.84 Ga. The 1.88 Ga monzogranites have relatively enriched zircon Hf-O isotopic compositions and were derived from a hybrid melt of mafic rocks in the lower crust and ancient metabasalts that had undergone low-temperature alteration at supracrustal depths. The 1.88-1.84 Ga basaltic andesites are Nb-rich and were generated by the partial melting of a mantle wedge that was metasomatized by subduction-derived fluids, recycled sediments, and slab-derived melts. The 1.84 Ga syenogranites are peraluminous and K-rich, and record a profound mixing and assimilation process involving melts derived from supracrustal sediments and the lower crust. Three stages of Paleoproterozoic magmatism emerged from subductionrelated crustal thickening, slab breakoff, and collision, respectively. The zircon Hf isotopic data further indicate that the eastern Central Asian Orogenic Belt underwent at least one stage of proto-crustal growth at ca. 3.0 Ga, followed by multiple phases of crustal reworking during the Neoarchean and Paleoproterozoic, with dominant involvement of supracrustal components. [ABSTRACT FROM AUTHOR]