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Inverse Synthetic Aperture Radar (ISAR) images of spacecraft are composed of discrete scatterers that exhibit weak texture, high dynamics, and discontinuity. These characteristics result in sparse point clouds obtained using traditional algorithms for the Three-Dimensional (3D) reconstruction of spacecraft ISAR images. Furthermore, using point clouds to comprehensively describe the complete shape of targets is difficult, which consequently hampers the accurate extraction of the structural and pose parameters of the target. To address this problem, considering that space targets usually have specific modular structures, this paper proposes a method for abstracting parametric structural primitives from space target ISAR images to represent their 3D structures. First, the energy accumulation algorithm is used to obtain the sparse point cloud of the target from ISAR images. Subsequently, the point cloud is fitted using parameterized primitives. Finally, primitives are projected onto the ISAR imaging plane and optimized by maximizing their similarity with the target image to obtain the optimal 3D representation of the target primitives. Compared with the traditional point cloud 3D reconstruction, this method can provide a more complete description of the three-dimensional structure of the target. Meanwhile, primitive parameters obtained using this method represent the attitude and structure of the target and can directly support subsequent tasks such as target recognition and analysis. Simulation experiments demonstrate that this method can effectively achieve the 3D abstraction of space targets based on ISAR sequential images.

Summary: Trained immunity is classically characterized by long-term functional reprogramming of innate immune cells to combat infectious diseases. Infection-induced organ injury is a common clinical severity phenotype of sepsis. However, whether the induction of trained immunity plays a role in protecting septic organ injury remains largely unknown. Here, through establishing an in vivo β-glucan training and lipopolysaccharide (LPS) challenge model in zebrafish larvae, we observe that induction of trained immunity could inhibit pyroptosis of hepatocytes to alleviate septic liver injury, with an elevated trimethyl-histone H3 lysine 4 (H3K4me3) modification that targets mitophagy-related genes. Moreover, we identify a C-type lectin domain receptor in zebrafish, named DrDectin-1, which is revealed as the orchestrator in gating H3K4me3 rewiring-mediated mitophagy activation and alleviating pyroptosis-engaged septic liver injury in vivo. Taken together, our results uncover tissue-resident trained immunity in maintaining liver homeostasis at the whole-animal level and offer an in vivo model to efficiently integrate trained immunity for immunotherapies.

The further developments of additive manufacturing (AM) provides a possibility for Ti6Al4V and its functionally graded materials (FGMs), but limits further applications due to its low ductility, low microhardness and poor wear resistance. The key lies in the presence of microstructures such as epitaxial columnar β-grain orientation and heterogeneous phase distribution in AM Ti6Al4V, as well as the interfacial bonding problems (intermetallic phases, delamination, cracking) in AM Ti6Al4V FGMs. In order to address these fundamental intra and inter-layer problems, firstly the microstructure and phase evolution process of AM Ti6Al4V columnar-to-equiaxed transformation (CET) promoted by some alloying strategies was summarized in this paper according to alloying elements and reinforcing particle types. Subsequently, controlled changes in the composition and microstructure of AM Ti6Al4V FGMs were achieved by adding interlayers/introducing gradient interfaces, and the metallurgical reactions and phase transformation mechanisms that inhibit the formation of intermetallic compounds and interfacial defects were explored. Finally, the difficulties and future research directions for alloying strategies to achieve high strength and ductility of AM Ti6Al4V were presented. This review aims to improve the reliability of AM Ti6Al4V and its FGMs for applications in critical load-bearing structures through in-situ alloying and multi-material for the microstructure design of AM Ti6Al4V.

AbstractDual-specificity phosphatase 12 (DUSP12) is abnormally expressed under various pathological conditions and plays a crucial role in the pathological progression of disorders. However, the role of DUSP12 in cerebral ischaemia/reperfusion injury has not yet been investigated. This study explored the possible link between DUSP12 and cerebral ischaemia/reperfusion injury using an oxygen-glucose deprivation/reoxygenation (OGD/R) model. Marked decreases in DUSP12 levels have been observed in cultured neurons exposed to OGD/R. DUSP12-overexpressed neurons were resistant to OGD/R-induced apoptosis and inflammation, whereas DUSP12-deficient neurons were vulnerable to OGD/R-evoked injuries. Further investigation revealed that DUSP12 overexpression or deficiency affects the phosphorylation of apoptosis signal-regulating kinase 1 (ASK1), c-Jun NH2-terminal kinase (JNK), and p38 mitogen-activated protein kinase (MAPK) in neurons under OGD/R conditions. Moreover, blockade of ASK1 diminished the regulatory effect of DUSP12 deficiency on JNK and p38 MAPK activation. In addition, DUSP12-deficiency-elicited effects exacerbating neuronal OGD/R injury were reversed by ASK1 blockade. In summary, DUSP12 protects against neuronal OGD/R injury by reducing apoptosis and inflammation through inactivation of the ASK1-JNK/p38 MAPK pathway. These findings imply a neuroprotective function for DUSP12 in cerebral ischaemia/reperfusion injury.

The backless cable-stayed bridge has the advantages of beautiful shape and reasonable force, but due to the low overall stiffness of the bridge pylon during cantilever construction, it is susceptible to the effect of solar temperature. To reveal the temperature deformation laws and achieve accurate alignment prediction during the installation process of steel–concrete composite pylons in complex environments, a refined numerical simulation model for the 3D bridge temperature field was established based on the proposed automatic sunshine-shadow recognition method. Subsequently, the optimal time periods for construction control are provided. The results of the study show that, during the cantilever construction of the bridge pylon, one pylon column will shade the other pylon column, resulting in asynchronous deformation that can reach 7.6 mm. The effect of solar temperature on the displacement of the bridge pylon is significant, where the maximum daily change in transverse displacement in the cantilevered state of the pylon can reach 33.6 mm, and the maximum change in cable force value can reach 52 kN. In order to mitigate the effect of solar radiation, the best construction time for the bridge pylon is 19:30~9:30, while the tensioning and measurement of the cable should be avoided from 6:00~18:00. This strategy ensures that the control of the pylon top displacement is maintained within 1/4000 of the pylon height, and the error in cable force is kept within 5%.

The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance.

Objective Hypertension is one of the leading causes of human death and disability. MTHFR and MTRR regulate folate metabolism and are closely linked to hypertension, although the relationship is inconsistent among different ethnic groups. The present study aims to investigate the effects of MTHFR C677T (rs1801133), MTHFR A1298C (rs1801131), and MTRR A66G (rs1801394) polymorphisms on hypertension susceptibility in the Bai nationality of the Yunnan Province, China. Methods This case–control study included 373 hypertensive patients and 240 healthy controls from the Chinese Bai population. The genotyping of MTHFR and MTRR gene polymorphisms was carried out by using the KASP method. The effects of genetic variations of MTHFR and MTRR genes on hypertension risk were evaluated with odds ratios (OR) and 95% confidence intervals (95% CI). Results The present study revealed that the CT and TT genotypes and T allele of MTHFR C677T locus were considerably associated with an increased risk of hypertension. In addition, MTHFR A1298C locus CC genotype could significantly increase the hypertension risk. The T-A and C–C haplotypes of MTHFR C677T and MTHFR A1298C could increase the risk of hypertension. Further stratified analysis by risk rank of folate metabolism indicated that people with poor utilization of folic acid were more prone to develop hypertension. In the hypertension group, the MTHFR C677T polymorphism was significantly associated with fasting blood glucose, fructosamine, apolipoprotein A1, homocysteine, superoxide dismutase, and malondialdehyde levels. Conclusion Our study suggested that genetic variations of MTHFR C677T and MTHFR A1298C were significantly associated with susceptibility to hypertension in the Bai population from Yunnan, China.

In traditional centralized machine learning frameworks, the consolidation of all data in a central data center for processing poses significant concerns related to data privacy breaches and data sharing complexities. In contrast, federated learning presents a privacy-preserving paradigm by training models on local devices, thus circumventing the need for data transfer. However, in the case of non-IID (non-independent and identically distributed) data distribution, the performance of federated learning will drop. Addressing this predicament, this study introduces the FedSC algorithm as a remedy. The FedSC algorithm initially partitions clients into clusters based on the distribution of their data types. Within each cluster, clients exhibit comparable local optimal solutions, thus facilitating the aggregation of a superior global model. Moreover, the global model trained by the previous cluster serves as the initial model parameter for subsequent clusters, enabling the incorporation of data contributions from each cluster to foster the development of an enhanced global model. Experimental results corroborate the superiority of the FedSC algorithm over alternative federated learning approaches, particularly in non-IID data distributions, thereby establishing its capacity to achieve heightened accuracy.

Abstract Retraction: [Jiazheng Yuan, Zhuang Wang, Cheng Xu, Hongtian Li, Songyin Dai, Hongzhe Liu, Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks, IET Circuits, Devices & Systems 2022 (https://doi.org/10.1049/cds2.12140)].The above article from IET Circuits, Devices & Systems, published online on 29 December 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer revie process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Abstract Due to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language text, and the missing signal is filled by the BERT model. Since the number of input data in BERT model cannot exceed 512 words, the structure of BERT model is not suitable for WLAN signals with large data volume. Therefore, we redefine the model structure based on the original BERT model and fill in the missing signals in the radio map in parallel. In addition, the loss function is redefined. Except that each segment has a loss function, the weighted average value of all segment loss functions is defined as the total loss function. The experimental results show that the BERT model is better than the traditional linear interpolation method, compressed sensing algorithm and matrix completion algorithm in filling the missing signals in the fingerprint database, and the probability of error within 2 m reaches about 94%.

Abstract Self-adaptability is highly envisioned for artificial devices such as robots with chemical noses. For this goal, seeking catalysts with multiple and modulable reaction pathways is promising but generally hampered by inconsistent reaction conditions and negative internal interferences. Herein, we report an adaptable graphitic C6N6-based copper single-atom catalyst. It drives the basic oxidation of peroxidase substrates by a bound copper-oxo pathway, and undertakes a second gain reaction triggered by light via a free hydroxyl radical pathway. Such multiformity of reactive oxygen-related intermediates for the same oxidation reaction makes the reaction conditions capable to be the same. Moreover, the unique topological structure of CuSAC6N6 along with the specialized donor-π-acceptor linker promotes intramolecular charge separation and migration, thus inhibiting negative interferences of the above two reaction pathways. As a result, a sound basic activity and a superb gain of up to 3.6 times under household lights are observed, superior to that of the controls, including peroxidase-like catalysts, photocatalysts, or their mixtures. CuSAC6N6 is further applied to a glucose biosensor, which can intelligently switch sensitivity and linear detection range in vitro.

Phosphogypsum (PG), a byproduct during the phosphoric acid production process, also known as the wet process, contains complex and diverse impurities, resulting in low utilization and considerable accumulation. This leads to a massive waste of land resources and a series of environmental pollution problems. Given the current urgent ecological and environmental situation, developing impurity removal processes with low energy consumption and high efficiency, exploring valuable resource recovery, preparing high value-added PG products, and broadening the comprehensive utilization ways of PG are significant strategies to promote the sustainable consumption of PG and sustainable development of the phosphorus chemical industry. This review comprehensively summarizes the advantages and disadvantages of existing PG impurity removal and utilization technologies and probes into the future development direction, which provides references and ideas for subsequent PG research.

Knowledge regarding the response of soil invertebrate communities to typhoon disturbance is limited, although it is known that soil invertebrates are sensitive to forest disturbances and that tropical cyclones (typhoons/hurricanes) are the most destructive natural disasters affecting the structure and function of forest ecosystems. To fill this knowledge gap, soil invertebrates in both litter and topsoil layers were investigated in four representative subtropical coastal forests of eastern China one week before the first typhoon (Hinnamnor) (T1), one day after the first typhoon (Hinnamnor) (T2), one day after the second typhoon (Muifa) (T3), and one week after the second typhoon (Muifa) (T4) in September 2022. Typhoon disturbances decreased the density and taxa abundance of soil invertebrate communities in litter layer, but the first typhoon disturbance increased these values in the topsoil layer. One week after the second typhoon disturbance, soil invertebrate communities in the litter layer showed a gradual recovery trend. Meanwhile, the soil invertebrate communities in the litter layer were more sensitive to typhoon disturbances than those in the topsoil layer. Furthermore, the responses of the soil invertebrate communities to the typhoon disturbances varied greatly with the forest types. The invertebrate densities in the litter layer decreased by 62.1%, 63.53%, 47.01%, and 46.92% in Chinese fir, second broad-leaved, mixed, and bamboo forests, respectively. Particularly, these two non-catastrophic typhoons significantly altered the functional group composition of detrital food webs in the short term, and the proportion of phytophages in detrital food webs in the litter layer increased after the typhoon disturbances. In conclusion, the effects of typhoon disturbances on soil invertebrate communities vary greatly with forest type and soil layer, and soil invertebrate communities can gradually recover after typhoon disturbances. The legacy effects of typhoon disturbances on the functional group composition of detrital food webs may influence carbon and nutrient cycling in forest ecosystems.

In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

To investigate the influence of external prestressing with carbon fiber-reinforced polymer (CFRP) tendons on the flexural and shear performance of reinforced concrete beams, a total of six simply supported beams with a length of 3.5 m and two two-span continuous beams with a length of 7 m were studied. The influences of the prestressing level of CFRP tendons, the pattern of straight-line and flexural reinforcement of externally prestressed CFRP tendons, and the existence of initial cracks in the beams on the flexural bearing capacity of the reinforced beams were explored through modeling tests. The calculating equation for anticipating the ultimate flexural bearing capacity of concrete beams reinforced with externally prestressed CFRP tendons was modified based on the test results. The findings indicate that the flexural bearing capacity of the test beams was significantly enhanced through both flexural and straight-line reinforcement. Specifically, the ultimate flexural bearing capacity of the simply supported beams exhibited improvements of approximately 160% and 170%, respectively, whereas the ultimate flexural bearing capacity of the continuous beams was enhanced by approximately 113% through the reinforcement of flexural arrangement. Additionally, the shear resistance capacity of the concrete beams was notably improved through the flexural arrangement reinforcement, and the shear resistance capacity of the beams exhibited a substantial increase as the prestressing level of the CFRP tendons increased. The flexural moments in the vicinity of the support point of the reinforced beam exhibited an increase ranging from 41.7% to 112.5% when the stirrup strains started to escalate rapidly. Additionally, the rigidity of the test beams showed a close correlation with both the prestressing level and the existence of initial cracks.

Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective way to improve controller training efficiency and reduce training costs. To achieve this, we propose a deep reinforcement learning model, RoBERTa-RL (RoBERTa with Reinforcement Learning), for generating pilot repetitions. RoBERTa-RL is based on the pre-trained language model RoBERTa and is optimized through transfer learning and reinforcement learning. Transfer learning is used to address the issue of scarce data in the ATC domain, while reinforcement learning algorithms are employed to optimize the RoBERTa model and overcome the limitations in model generalization caused by transfer learning. We selected a real-world area control dataset as the target task training and testing dataset, and a tower control dataset generated based on civil aviation radio land-air communication rules as the test dataset for evaluating model generalization. In terms of the ROUGE evaluation metrics, RoBERTa-RL achieved significant results on the area control dataset with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.9962, 0.992, and 0.996, respectively. On the tower control dataset, the scores were 0.982, 0.954, and 0.982, respectively. To overcome the limitations of ROUGE in this field, we conducted a detailed evaluation of the proposed model architecture using keyword-based evaluation criteria for the generated repetition instructions. This evaluation criterion calculates various keyword-based metrics based on the segmented results of the repetition instruction text. In the keyword-based evaluation criteria, the constructed model achieved an overall accuracy of 98.8% on the area control dataset and 81.8% on the tower control dataset. In terms of generalization, RoBERTa-RL improved accuracy by 56% compared to the model before improvement and achieved a 47.5% improvement compared to various comparative models. These results indicate that employing reinforcement learning strategies to enhance deep learning algorithms can effectively mitigate the issue of poor generalization in text generation tasks, and this approach holds promise for future application in other related domains.

Abstract Traditionally, radar‐based localisation systems are designed to deal with targets in line‐of‐sight (LOS) scenarios. However, the performance of those radar systems is limited by multipath echoes reflected from non‐line‐of‐sight (NLOS) targets and walls in the urban areas. In recent years, there has been an increasing interest in multipath exploitation methods aided by urban environmental knowledge. Based on the exploitation of space diversity provided by the multipath effect, this article proposes a novel two‐stage localisation method using a single monostatic radar via association of multipath time‐of‐arrivals (TOAs). In the offline stage, reference TOA data at different locations is generated by a ray‐tracing method to avoid the heterogeneity of multipath propagation at different locations, assuming that the environment information is completely known prior. In the online stage, the TOA set of multipath echoes from the target is estimated by a sparse reconstruction method to achieve super‐resolution rather than the matched filter. Then based on the finite‐set statistics (FISST) theory, a grid‐based multi‐observation data association (GMODA) algorithm is present as the likelihood function of the multipath measurement given a candidate location, which is followed by a maximum likelihood estimation to derive the location of the target. Furthermore, the global nearest neighbour approximation of GMODA is introduced to avoid combinatorial explosion in the association algorithm. Simulation and experiment results on a single antenna millimetre‐wave radar in various scenarios show that the proposed method achieves high localisation accuracy in both LOS and NLOS conditions while maintaining low computational cost at the same time. The presented results suggest that our method can be applied to target localisation applications in the complex urban environment.

Research on theoretical prediction methods for the mixture toxicity of engineered nanoparticles (ENPs) faces significant challenges. The application of in silico methods based on machine learning is emerging as an effective strategy to address the toxicity prediction of chemical mixtures. Herein, we combined toxicity data generated in our lab with experimental data reported in the literature to predict the combined toxicity of seven metallic ENPs for Escherichia coli at different mixing ratios (22 binary combinations). We thereafter applied two machine learning (ML) techniques, support vector machine (SVM) and neural network (NN), and compared the differences in the ability to predict the combined toxicity by means of the ML-based methods and two component-based mixture models: independent action and concentration addition. Among 72 developed quantitative structure–activity relationship (QSAR) models by the ML methods, two SVM-QSAR models and two NN-QSAR models showed good performance. Moreover, an NN-based QSAR model combined with two molecular descriptors, namely enthalpy of formation of a gaseous cation and metal oxide standard molar enthalpy of formation, showed the best predictive power for the internal dataset (R2test = 0.911, adjusted R2test = 0.733, RMSEtest = 0.091, and MAEtest = 0.067) and for the combination of internal and external datasets (R2test = 0.908, adjusted R2test = 0.871, RMSEtest = 0.255, and MAEtest = 0.181). In addition, the developed QSAR models performed better than the component-based models. The estimation of the applicability domain of the selected QSAR models showed that all the binary mixtures in training and test sets were in the applicability domain. This study approach could provide a methodological and theoretical basis for the ecological risk assessment of mixtures of ENPs.

Soliton explosions are among the most striking soliton phenomena in nonlinear dissipative systems. In this regime, quasi-stable solitons suffer abrupt structural collapses and then recover to their original state. Here, we report on the first experimental observation of soliton explosions triggered by vector dissipative soliton collisions in a polarization-multiplexed ultrafast fiber laser. The fiber laser simultaneously generates two orthogonally polarized pulse trains with different repetition rates, which enables periodic soliton collisions in the cavity. Based on the dispersive Fourier transform technique, the spectral dynamics of collision-induced soliton explosions are identified in real time. The corresponding time-domain measurements demonstrate opposite temporal shifts in the two output pulse trains. Numerical simulations validate the experimental observations and give more insights into the physical mechanism for soliton explosions. Our findings can shed new light on the dynamics of soliton explosions.

A single-row pile dam is a type of rigid debris flow barrier structure. In order to reveal the mutual interaction effects between debris flow and a single-row pile dam and the distribution law of debris flow impact forces, an analysis of the correlation between debris flow density, characteristic particle size, and relative pile spacing (the ratio of pile gap size to debris flow characteristic particle diameter) and debris flow impact forces was conducted to obtain dimensionless parameters related to debris flow impact force and climbing height. These parameters were verified through flume model experiments to provide theoretical support for the engineering design of single-row pile dams. The results show that the impact force of the debris flow on the pile dam is mainly affected by the flow velocity, debris flow density, and relative pile spacing. The dimensionless parameters are introduced, and the semi-empirical expression of the impact load coefficient is established. The impact pressure coefficient of debris flow is classified into two categories, and the calculation model of impact load of a single-row pile dam is constructed. The research results can provide a theoretical basis and technical parameters for designing a single-row pile dam.

The increased permeability of the lung microvascular endothelium is one critical initiation of acute lung injury (ALI). The disruption of vascular-endothelium integrity results in leakiness of the endothelial barrier and accumulation of protein-rich fluid in the alveoli. During ALI, increased endothelial-cell (EC) permeability is always companied by high frequency and amplitude of cytosolic Ca2+ oscillations. Mechanistically, cytosolic calcium oscillations include calcium release from internal stores and calcium entry via channels located in the cell membrane. Recently, numerous publications have shown substantial evidence that calcium-permeable channels play an important role in maintaining the integrity of the endothelium barrier function of the vessel wall in ALI. These novel endothelial signaling pathways are future targets for the treatment of lung injury. This short review focuses on the up-to-date research and provide insight into the contribution of calcium influx via ion channels to the disruption of lung microvascular endothelial-barrier function during ALI.

A type of manipulator configuration with four-fingers is put forward, for grasping fragile hollow workpieces, with adjustable finger length and finger pad shape. Within a certain range, the manipulator can be used to grasp the internal cross section of fragile objects with different size of circular and oval shape. The design idea and structure of the manipulator are introduced, while the join positions between the finger pad and the finger body, as determined by uniform interpolation or Chebyshev interpolation, are comparatively analyzed and researched regarding forming force contact with workpiece. During the process of grasping, the internal forces and deformations between finger pad and workpiece are analyzed, based on a constructed finite element analysis model. The calculation example shows that, under the same grasping parameters, the maximum impact force on the workpiece is reduced by 63%, when the curvature adjustment points for the finger pad are distributed according to the Chebyshev interpolation, compared to their equal spacing distribution. Research has provided a theoretical basis for the design optimization of the finger pad structure and the connection point positions. For using manipulator to grasp objects with different size of circular and oval shape, the working space of the proposed manipulator is studied. The experiments show that, the manipulator structure, as presented in this article, can meet the requirements of relevant tasks.

MicroRNAs (miRNAs) are crucial post-transcriptional regulators of gene expression in ubiquitous biological processes, including immune-related pathways. This review focuses on the miR-183/96/182 cluster (miR-183C), which contains three miRNAs, miR-183, -96, and -182, having almost identical seed sequences with minor differences. The similarity among seed sequences allows these three miRNAs to act cooperatively. In addition, their minor differences permit them to target distinct genes and regulate unique pathways. The expression of miR-183C was initially identified in sensory organs. Subsequently, abnormal expression of miR-183C miRNAs in various cancers and autoimmune diseases has been reported, implying their potential role in human diseases. The regulatory effects of miR-183C miRNAs on the differentiation and function of both innate and adaptive immune cells have now been documented. In this review, we have discussed the complex role of miR-183C in the immune cells in both normal and autoimmune backgrounds. We highlighted the dysregulation of miR-183C miRNAs in several autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), and ocular autoimmune disorders, and discussed the potential for utilizing miR-183C as biomarkers and therapeutic targets of specific autoimmune diseases.

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives.

Approaching space target tracking is a typical and challenging mission in the space situational awareness (SSA) field. As the space-based radar is able to monitor the space targets of interest full-weather all-time, the space-based radar system is utilized in this paper. However, most multi-target tracking (MTT) filters in target tracking studies merely utilize the location or narrow measurements, and many potentially valuable electromagnetic scattering characteristics are missed, which leads to space target false tracking problems. The space-based radar transmits a wide-band signal, and the measured high-resolution range profile (HRRP) information is an effective characteristic for different target discrimination. Therefore, the HRRP characteristics of space targets are implemented into the update recursion of the MTT filter, which can be utilized to improve the tracking performance. Then, to predict the target HRRP sequence, the geometrical theory of diffraction (GTD) model is utilized. Additionally, a modified spatial spectrum method with a novel covariance matrix is designed to improve the scattering parameter estimation accuracy. Finally, an adapting threshold is devised for merging the Gaussian mixture (GM) components weights. The proposed threshold is on the basis of the proposed HRRP characteristic-aided probability hypothesis density (PHD) filter, and it can tackle the problem of space target discrimination. Simulation results validate the effectiveness and robustness of the proposed probability hypothesis density (HGI-PHD) filter aided by HRRP information and improved with GM weights.

Reasonable allocation of space-based radar resources is a crucial aspect of improving the accuracy of space multi-target tracking and enhancing spatial awareness. The conventional resource allocation algorithm fails to exploit the high dynamic radar cross-section (RCS) characteristics, resulting in poor tracking robustness, tracking divergence, or even loss of tracking. However, the RCS of space targets fluctuates considerably in actual tracking scenarios, which cannot be disregarded for space target tracking tasks. To address this issue, we propose an adaptive allocation method that considers the dynamic RCS fluctuation characteristic for space-based radar tracking assignments. The proposed method exploits the predictable orbital information of space target to calculate the real-time observation angle of radar, and then obtains the multi-target dynamic RCS through the target RCS dataset. By combining the obtained RCS sequence, radar power, and bandwidth, an optimal model for radar tracking accuracy is established based on the multi-target posterior Cramér–Rao lower bound (PCRLB) to evaluate the tracking performance. By resolving the aforementioned multivariance optimization problem, we eventually acquire the results of power and bandwidth pre-allocation for tracking multiple space targets. Simulation results validate that, compared with the traditional methods, the proposed joint dynamic RCS power and bandwidth allocation (JRPBA) method can achieve superior tracking accuracy and minimize instances of missed tracking.

After Wenchuan earthquake, many large debris flow have occur in Banzi Gully. Especially in 2019, the "8·20" debris flow caused serious damage to the roads，bridges and villages in the gully mouth, mail river was seriously squeeaed to the other side and there is a big risk of blocking the river in the future. Based on the field investigation and comprehensive analysis of the basic characteristics and formation conditions of the debris flow, the characteristics of river blockage are analyzed, the parameters of river blockage under different debris flow frequency are calculated, and the possible impact of dam-breaking flood under each frequency on Miansi Town is predicted. The results show that debris flow with frequencies of 2%, 5% and 10% are less likely to cause river blockage of Min River, and if there was a river blocking event , the town of Miansi would not be exposed to dam-breaking flood. The debris flow with a frequency of 1% are likely to cause river blockage. The debris flow material with a volume of 57.38×104 m3 can reach the Min River to form a dam with a height of about 51.61 m. Under the action of the main river flood (1890.0 m3/s), the dam will be broken, and the peak flow rate of the flood was 5935.49 m3/s, which dropped to 2312.25 m3/s while arrive at Miansi Town. Since the flood depth (4.00 m) is higher than the height of the protective embankment (3.5 m), the flood is likely to damage the houses near the protective embankment of Miansi Town.This paper provided a reference for the analysis of the characteristics of large-scale debris flow blockage in the future, as well as the possible impact of the collapse flood on downstream towns.

ObjectivesThis paper aims to study the coupling interaction between a truss box floating raft and submarine hull, and proposes a design approach for enhancing the vibration isolation effect of the truss box floating raft-hull coupling system. MethodsFirst, modal analyses of the aforementioned coupling system and individual floating raft and hull structures are carried out respectively, and comparisons are made of the modal shapes between the floating raft of the coupling system and individual floating raft structure, as well as between the hull structure of the coupling system and individual hull structure. The effects of the thickness of the floating raft panel and isolator stiffness on the isolation of the coupling system in the full-frequency range are then investigated. ResultsThe floating raft of the coupling system shows little difference in natural frequency or modal shape compared with the individual floating raft, and the vibration isolation effects are also the same. In addition, the increase in the thickness of the panel and the weakening of the stiffness of the lower vibration isolator will improve the vibration isolation capability of the coupling system. ConclusionsThe results of this study indicate that the mutual influence between the floating raft and hull is small, and can be considered a weak coupling relationship.

Abstract Purpose Double-endobutton technique, as a widely accepted strategy for the treatment of acromioclavicular joint dislocation, is undergoing constant improvement. This study aims to assess the clinical effect of a modified single-endobutton combined with the nice knot in the fixation of Rockwood type III or V acromioclavicular joint dislocation. Methods From January 2016 to June 2019, 16 adult patients (13 males and 3 females) with Rockwood type III or V acromioclavicular joint dislocation were treated with a modified single-endobutton technique combined with the nice knot in our department. The age ranged from 18 to 64 years old with an average of 32.8 years old. Operative time, intraoperative blood loss, post-operative clinical outcomes and radiographic results were recorded and analyzed. Preoperative and last follow-up scores in the Constant-Murley Scale, Neer score, Rating Scale of the American Shoulder and Elbow Surgeons and VAS scale and complications such as infection, re-dislocation, implant loosening, medical origin fracture and hardware pain were recorded and evaluated. Results Sixteen patients were followed up for 6 to 18 months with an average of 10.3 months. The operative time was 50–90 min with an average of (62.5 ± 3.10) min. The intraoperative blood loss was 30–100 ml, with an average of (55.0 ± 4.28) ml. The complications, such as wound infection, internal fixation failure and fractures, were not found in these cases. According to Karlsson criteria, there were excellent in 14 cases, good in 2 cases at the final follow-up. The mean VAS score of the patients was 5.88 ± 0.26 preoperatively, compared with 0.19 ± 0.14 at the final follow-up evaluation. The difference was statistically significant (P

Abstract Extracting traffic information from images plays an increasingly significant role in Internet of vehicle. However, due to the high-speed movement and bumps of the vehicle, the image will be blurred during image acquisition. In addition, in rainy days, as a result of the rain attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even have a bearing on traffic accidents. In this paper, we propose a motion-blurred restoration and rain removal algorithm for IoV based on generative adversarial network and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical research directions in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leaky-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblocks, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, as an image de-raining task based on transfer learning, we can fine-tune the pre-trained model with less training data and show good results on several datasets used for image rain removal.

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability.

The goal of the current study was to quantify the trophic transfer of copper nanoparticles (CuNPs) in a food chain consisting of the microalga Pseudokirchneriella subcapitata as the representative of primary producer, the grazer Daphnia magna, and the omnivorous mysid Limnomysis benedeni. To quantify the size and number concentration of CuNPs in the biota, tissue extraction with tetramethylammonium hydroxide (TMAH) was performed and quantification was done by single particle inductively coupled plasma mass spectrometry (sp-ICP-MS). The bioconcentration factor (BCF) of the test species for CuNPs varied between 102 – 103 L/kg dry weight when expressing the internal concentration on a mass basis, which was lower than BCF values reported for Cu2+ (103 – 104 L/kg dry weight). The particle size of CuNPs determined by sp-ICP-MS ranged from 22 to 40 nm in the species. No significant changes in the particle size were measured throughout the food chain. Moreover, the measured number of CuNPs in each trophic level was in the order of 1013 particles/kg wet weight. The calculated trophic transfer factor (mass concentration basis) was > 1. This indicates biomagnification of particulate Cu from P. subcapitata to L. benedeni. It was also found that the uptake of particulate Cu (based on the particle number concentration) was mainly from the dietary route rather than from direct aqueous exposure. Furthermore, dietary exposure to CuNPs had a significant effect on the feeding rate of mysid during their transfer from daphnia to mysid and from alga through daphnia to mysid. This work emphasizes the importance of tracing the particulate fraction of metal-based engineered nanoparticles when studying their uptake and trophic transfer.

Abstract Recent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

Abstract To study the effect of the invasion of Bursaphelenchus xylophilus on the functional relationship between woody plants and insect communities, the populations of tree species and insect communities were investigative in the Masson pine forests with different infestation durations of B. xylophilus. In this study, the number of Pinus massoniana began to decrease sharply, whereas the total number of other tree species in the arboreal layer increased gradually with the infestation duration of B. xylophilus. The principal component analysis ordination biplot shows that there was a significant change in the spatial distribution of woody plant species in different Masson pine forest stands. Additionally, a total of 7,188 insect specimens were obtained. The insect population showed an upward trend in stand types with the increase of pine wilt disease infection periods, which demonstrated that the insect community had been significantly affected by the invasion of B. xylophilus. However, the insect diversity indexes were not significantly different among Masson pine forest stands. The structure of insect functional groups changed from herbivorous (He) > omnivorous (Om) > predatory (Pr) > parasitic (Pa) > detritivorous (De) in the control stand to He > Pa > Om, De > Pr after B. xylophilus infestation in the forests. The results showed that the populations of He, Pa, and De increased after the invasion of B. xylophilus, but the populations of Pr decreased. Moreover, the redundancy analysis ordination biplots reflected the complicated functional relationship between woody plant communities and insects after the invasion of B. xylophilus. The present study provides insights into the changes in the community structure of woody plants and insects, as well as the functional relationship between woody plant communities and insect communities after invasion of B. xylophilus.

Dysregulated miRNAs have been implicated in the pathogenesis of systemic lupus erythematosus (SLE). Our previous study reported a substantial increase in three miRNAs located at the miR-183-96-182 cluster (miR-183C) in several autoimmune lupus-prone mice, including MRL/lpr and C57BL/6-lpr (B6/lpr). This study reports that in vitro inhibition of miR-182 alone or miR-183C by specific antagomirs in activated splenocytes from autoimmune-prone MRL/lpr and control MRL mice significantly reduced lupus-related inflammatory cytokines, interferon-gamma (IFNγ), and IL-6 production. To further characterize the role of miR-182 and miR-183C cluster in vivo in lupus-like disease and lymphocyte phenotypes, we used hCD2-iCre to generate B6/lpr mice with conditional deletion of miR-182 or miR-183C in CD2+ lymphocytes (miR-182−/−B6/lpr and miR-183C−/-B6/lpr). The miR-182−/−B6/lpr and miR-183C−/−B6/lpr mice had significantly reduced deposition of IgG immunocomplexes in the kidney when compared to their respective littermate controls, although there appeared to be no remarkable changes in renal pathology. Importantly, we observed a significant reduction of serum anti-dsDNA autoantibodies in miR-183C−/−B6/lpr mice after reaching 24 weeks-of age compared to age-matched miR-183Cfl/flB6/lpr controls. In vitro activated splenocytes from miR-182−/−B6/lpr mice and miR-183C−/−B6/lpr mice showed reduced ability to produce lupus-associated IFNγ. Forkhead box O1(Foxo1), a previously validated miR-183C miRNAs target, was increased in the splenic CD4+ cells of miR-182−/−B6/lpr and miR-183C−/−B6/lpr mice. Furthermore, in vitro inhibition of Foxo1 with siRNA in splenocytes from miR-182−/−B6/lpr and miR-183C−/-B6/lpr mice significantly increased IFNγ expression following anti-CD3/CD28 stimulation, suggesting that miR-182 and miR-183C miRNAs regulate the inflammatory IFNγ in splenocytes via targeting Foxo1. The deletion of either miR-182 alone or the whole miR-183C cluster, however, had no marked effect on the composition of T and B cell subsets in the spleens of B6/lpr mice. There were similar percentages of CD4+, CD8+, CD19+, as well as Tregs, follicular helper T (TFH), germinal center B (GCB), and plasma cells in the miR-183C−/−B6/lpr and miR-182−/−B6/lpr mice and their respective littermate controls, miR-183Cfl/flB6/lpr and miR-182fl/flB6/lpr mice. Together, our data demonstrate a role of miR-183C in the regulation of anti-dsDNA autoantibody production in vivo in B6/lpr mice and the induction of IFNγ in in vitro activated splenocytes from B6/lpr mice.

Commensal bacteria and the immune system have a close and strong relationship that maintains a balance to control inflammation. Alterations of the microbiota, known as dysbiosis, can direct reactivity to self-antigens not only in the intestinal mucosa but also at the systemic level. Our laboratory previously reported gut dysbiosis, particularly lower abundance of bacteria in the family Lactobacillaceae, in lupus-prone MRL/lpr mice, a model of systemic autoimmunity. Restoring the microbiota with a mix of 5 different Lactobacillus species (spp.), L. reuteri, L. oris, L. johnsonii, L. gasseri and L. rhamnosus, attenuated lupus-liked clinical signs, including splenomegaly and lymphadenopathy. However, our understanding of the mechanism was limited. In this study, we first investigated the effects of individual species. Surprisingly, none of the species individually recapitulated the benefits of the mix. Instead, Lactobacillus spp. acted synergistically to attenuate splenomegaly and renal lymphadenopathy through secreted factors and a CX3CR1-dependent mechanism. Interestingly, oral administration of MRS broth exerted the same benefits likely through increasing the relative abundance of endogenous Lactobacillus spp. Mechanistically, we found increased percentages of FOXP3-negative type 1 regulatory T cells with administration of the mix in both spleen and mesenteric lymph nodes. In addition, oral gavage of Lactobacillus spp. decreased the percentage of central memory T cells while increasing that of effector memory T cells in the lymphoid organs. Furthermore, a decreased percentage of double negative T cells was observed in the spleen with the mix. These results suggest that Lactobacillus spp. might act on T cells to attenuate splenomegaly and lymphadenopathy. Together, this study advances our understanding of how Lactobacillus spp. attenuate lupus in MRL/lpr mice. The synergistic action of these bacteria suggests that multiple probiotic bacteria in combination may dampen systemic autoimmunity and benefit lupus patients.

Background: Diabetic nephropathy (DN) is one of the leading causes of chronic kidney disease (CKD) worldwide, tubular injury is the driving force during the pathogenesis and progression of DN. Thus, we aim to utilize the connectivity map (CMap) with renal tubulointerstitial transcriptomic profiles of biopsy-proven DN to identify novel drugs for treating DN.Methods: We interrogated the CMap profile with tubulointerstitial transcriptomic data from renal biopsy-proven early- and late-stage DN patients to screen potential drugs for DN. Therapeutic effects of candidate drug were assessed in Murine model of diabetic kidney disease (STZ-induced CD-1 mice), and HK-2 cells and immortalized bone marrow-derived macrophages (iBMDMs).Results: We identified CAY10603, a specific inhibitor of histone deacetylase 6 (HDAC6), as a potential drug that could significantly reverse the altered genes in the tubulointerstitial component. In DN patients and mice, upregulation of HDAC6 was mainly observed in renal tubular cells and infiltrated macrophages surrounding the diluted tubules. In both early- and late-onset diabetic mice, daily CAY10603 administration effectively alleviated renal dysfunction and reduced macrophage infiltration, tubular injury and tubulointerstitial fibrosis. Mechanistically, CAY10603 suppressed NLRP3 activation in both HK-2 cells and iBMDMs.Conclusion: CAY10603 exhibited therapeutic potential for DN by suppressing NLRP3 inflammasome activation in both tubular cells and macrophages.

The communication system between the islands has the problems of high cost and the transmitted signal may require relay amplification. In order to solve this issue, a novel Free Space Optical (FSO) communication system using Manchester encoded and decoded signals is designed. It can support 10 Gbit/s optical signal transmission over 100 km submarine cable (using G. 654 standard single-mode fiber) cascaded with 1 km FSO wireless link. We evaluate the performance by measuring the time-domain waveform, optical spectrum, eye diagrams and Bit Error Rate (BER) in simulation with Manchester coded signals, under sunny, rainy and foggy day. The measured results show that the receiver sensitivity can reach -18.92 dBm at the back-to-back system. Under the 100 km submarine cable and 1 km FSO wireless link, the receiver sensitivity can reach -17.72, -16.66 and -11.16 dBm in the sunny, rainy and foggy day, respectively. The simulation results prove that the FSO communication system using Manchester code may have potential application in the field of islands information transmission in the future.

Previous studies have reported that deletion of the transcription factor, early growth response protein 2 (EGR2), in normal C57BL/6 (B6) resulted in the development of lupus-like autoimmune disease. However, increased EGR2 expression has been noted in human and murine lupus, which challenges the notion of the autoimmune suppressive role of EGR2 in B6 mice. In this study, we derived both conditional EGR2-/-B6/lpr and EGR2-/-B6 mice to elucidate the immune and autoimmune regulatory roles of EGR2 in autoinflammation (B6/lpr) versus physiologically normal (B6) conditions. We found that conditional EGR2 deletion increased spleen weight, enhanced T cell activation and IFNγ production, and promoted germinal center B cells and LAG3+ regulatory T cells development in both B6/lpr and B6 mice. Nevertheless, EGR2 deletion also showed strikingly differential effects in these two strains on T lymphocyte subsets profile, Foxp3+ Tregs and plasma cell differentiation, anti-dsDNA autoantibodies and immunoglobulins production, and on the induction of IL-17 in in vitro activated splenocytes. Specifically, EGR2 deletion in B6/lpr mice significantly decreased serum levels of anti-dsDNA autoantibodies, total IgG, IgM, IgG1, and IgG2a with reduced plasma cells differentiation. Furthermore, EGR2 deletion in B6/lpr mice had no obvious effect on IgG immunocomplex deposition, medium caliber vessel, and glomeruli inflammation but increased complement C3 immunocomplex deposition and large caliber vessel inflammation in the kidneys. Importantly, we demonstrated that EGR2 deletion in B6/lpr mice significantly reduced pathogenic CD4-CD8-CD3+B220+ double negative T cells, which correlated with the reduced anti-dsDNA autoantibodies in serum and decreased IL-17 production in splenocytes of EGR2-/-B6/lpr mice. Together, our data strongly suggest that the role of EGR2 is complex. The immunoregulatory role of EGR2 varies at normal or autoinflammation conditions and should not be generalized in differential experimental settings.

A prediction method that integrated a mixture descriptor with an established mixture toxicology method was proposed for the joint toxicity of chemical pollutants. A weighted descriptor derived from the single descriptor of each component was employed to calculate a mixture descriptor, which was successfully embedded into the generalized concentration addition (GCA) model named the extended GCA (XGCA) model. To develop and validate the proposed approach, binary antibiotic mixtures (ciprofloxacin and oxytetracycline) and metal-oxide (copper oxide and zinc oxide) nanoparticle mixtures were selected to study their toxicity to freshwater green algae. The results showed that concentration-response curve (CRC) derived from the XGCA model was closer to the observed CRC than those from the GCA, Concentration Addition (CA), and Independent Action (IA) models. The difference between effect concentrations predicted by the XGCA model and observed did not exceed a factor of 1.6. The XGCA model was relatively more accurate at predicting joint toxicity (in terms of effect concentrations and effect errors) than the reference models, independent of component types and mixture ratios. The XGCA model predicts the joint toxicity through molecular structural or nanostructural characters, thus modes of toxic action are not preconditions for predicting the toxicity of the mixtures. This result demonstrates the practicability of using the XGCA method in toxicity assessments of mixture pollutants with unknown modes of action.

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT.

In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, this paper proposes an intelligent mining road object detection (IMOD) model developed using a 5G-multi-UAV and a deep learning approach. The IMOD model employs data sensors to monitor surface data in real time within a multisystem collaborative 5G network. The model transmits data to various intelligent systems and edge devices in real time, and the unmanned mining card constructs the driving area on the fly. The IMOD model utilizes a convolutional neural network to identify obstacles in front of driverless mining trucks in real time, optimizing multisystem collaborative control and driverless mining truck scheduling based on obstacle data. Multiple systems cooperate to maneuver around obstacles, including avoiding static obstacles, such as standing and lying dummies, empty oil drums, and vehicles; continuously avoiding multiple obstacles; and avoiding dynamic obstacles such as walking people and moving vehicles. For this study, we independently collected and constructed an obstacle image dataset specific to the mining area, and experimental tests and analyses reveal that the IMOD model maintains a smooth route and stable vehicle movement attitude, ensuring the safety of driverless mining trucks as well as of personnel and equipment in the mining area. The ablation and robustness experiments demonstrate that the IMOD model outperforms the unmodified YOLOv5 model, with an average improvement of approximately 9.4% across multiple performance measures. Additionally, compared with other algorithms, this model shows significant performance improvements.

In order to accurately calculate the sunshine-induced temperature effect of an ultra-high bridge pylon, a refined numerical simulation model for the 3D bridge temperature field was established based on the proposed automatic sunshine shadow recognition method and pylon-height-related convection modification method. A suspension bridge H-shaped pylon with a height of 280 m was taken as an example, and the temperature field and corresponding thermal stress and deformation were calculated under typical meteorological conditions in spring, summer, autumn, and winter. The results show that the maximum temperature differences between the outer surfaces of the pylon can reach 19 °C and 16 °C for north–south walls and east–west walls, respectively, and exceed the recommended value of ±5 °C in the Chinese Specification. The maximum displacements can reach 370 mm and 110 mm at the top of the bridge pylon in the longitudinal and transverse directions of the bridge, respectively. After the modification of the convective coefficient of the outer surfaces with different wind speeds at different pylon heights, the temperature gradually decreased from the top to the bottom of the pylon, with a temperature difference of 8 °C. The significant influence of the sunshine shadow was shown on the temperature field and temperature effect of the bridge pylon. By considering the shadow effect, the maximum temperature difference can reach 12 °C between adjacent sunlit and shaded areas and can reach 14 °C between two pylon columns. A significant mutation of thermal stress existed in the shaded area, and the maximum stress could be reduced by 13 MPa compared with the adjacent sunlit area. Obvious asynchronous deformation was shown between two pylon columns, and the maximum asynchronous displacement at the top of the pylon can reach 18 mm and 45 mm in the longitudinal and transverse directions, respectively.

The assessment and prediction of the toxicity of engineered nanomaterials (NMs) present in mixtures is a challenging research issue. Herein, the toxicity of three advanced two-dimensional nanomaterials (TDNMs), in combination with an organic chemical (3,4-dichloroaniline, DCA) to two freshwater microalgae (Scenedesmus obliquus and Chlorella pyrenoidosa), was assessed and predicted not only from classical mixture theory but also from structure-activity relationships. The TDNMs included two layered double hydroxides (Mg-Al-LDH and Zn-Al-LDH) and a graphene nanoplatelet (GNP). The toxicity of DCA varied with the type and concentration of TDNMs, as well as the species. The combination of DCA and TDNMs exhibited additive, antagonistic, and synergistic effects. There is a linear relationship between the different levels (10, 50, and 90%) of effect concentrations and a Freundlich adsorption coefficient (KF) calculated by isotherm models and adsorption energy (Ea) obtained in molecular simulations, respectively. The prediction model incorporating both parameters KF and Ea had a higher predictive power for the combined toxicity than the classical mixture model. Our findings provide new insights for the development of strategies aimed at evaluating the ecotoxicological risk of NMs towards combined pollution situations.

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.

The traditional tracking and traceability system based on ARM9 and ZigBee and the tracking and traceability system based on STM32 have poor anti-interference ability, which leads to the low consistency between the cold chain food information transmitted and the actual information, resulting in poor tracking and traceability effect. In order to solve this problem, on the basis of designing SSH framework to process logistics information, genetic algorithm was combined to extract tracking and traceability features, and the cold chain logistics tracking scheme was designed. The experimental results show that in recognition experiments of 90 times, the effective recognition times of the system are available, the minimum effective times can reach 83 times, and the average success rate of traceability can reach 94.55%. The results show that the system can effectively track and trace the cold chain logistics information.

Humans have long been fascinated by the opportunities afforded through motor augmentation provided by the supernumerary robotic fingers (SRFs) and limbs (SRLs). However, the neuroplasticity mechanism induced by the motor augmentation equipment still needs further investigation. This study focused on the resting-state brain functional reorganization during longitudinal brain–computer interface (BCI)-controlled SRF training in using the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC) metrics. Ten right-handed subjects were enrolled for 4 weeks of BCI-controlled SRF training. The behavioral data and the neurological changes were recorded at baseline, training for 2 weeks, training for 4 weeks immediately after, and 2 weeks after the end of training. One-way repeated-measure ANOVA was used to investigate long-term motor improvement [F(2.805,25.24) = 43.94, p < 0.0001] and neurological changes. The fALFF values were significantly modulated in Cerebelum_6_R and correlated with motor function improvement (r = 0.6887, p < 0.0402) from t0 to t2. Besides, Cerebelum_9_R and Vermis_3 were also significantly modulated and showed different trends in longitudinal SRF training in using ReHo metric. At the same time, ReHo values that changed from t0 to t1 in Vermis_3 was significantly correlated with motor function improvement (r = 0.7038, p < 0.0344). We conclude that the compensation and suppression mechanism of the cerebellum existed during BCI-controlled SRF training, and this current result provided evidence to the neuroplasticity mechanism brought by the BCI-controlled motor-augmentation devices.

In a classical metasurface-based holographic display system, a clear holographic reconstruction image can be obtained only in the back-focal-plane of the lens. However, when the receiving plane deviates from the focal plane, the reconstructed holographic image suffers from degradations that limit the quality of the images. Hence, we propose a novel metasurface-based holographic display to realize clearly continuous imaging within the specified position range by superimposing the meta-axilens phase. It can effectively relieve the alignment requirements of the imaging system. Firstly, the parameters and properties of all-dielectric meta-atom is analyzed. Then we compare and demonstrate two sets of metasurface-based optical elements (meta-axicon, meta-lens, and meta-axilens), made by silicon meta-atoms working at 610 nm. Furthermore, we show that the metasurface hologram with the axilens phase can form a series of the relatively clear holographic images away from the focal plane. Our proposal suggests a method to reconstruct holographic wavefront in different planes simultaneously, and one can find their application domain, such as 3D biological imaging, spectroscopy, optical information storage, and encryption.

Images captured in bad weather are not conducive to visual tasks. Rain streaks in rainy images will significantly affect the regular operation of imaging equipment; to solve this problem, using multiple neural networks is a trend. The ingenious integration of network structures allows for full use of the powerful representation and fitting abilities of deep learning to complete low-level visual tasks. In this study, we propose a generative adversarial network (GAN) with multiple attention mechanisms for image rain removal tasks. Firstly, to the best of our knowledge, we propose a pretrained vision transformer (ViT) as the discriminator in GAN for single-image rain removal for the first time. Secondly, we propose a neural network training method that can use a small amount of data for training while maintaining promising results and reliable visual quality. A large number of experiments prove the correctness and effectiveness of our method. Our proposed method achieves better results on synthetic and real image datasets than multiple state-of-the-art methods, even when using less training data.