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Two common methods exist for solving indoor autonomous navigation and obstacle-avoidance problems using monocular vision: the traditional simultaneous localization and mapping (SLAM) method, which requires complex hardware, heavy calculations, and is prone to errors in low texture or dynamic environments; and deep-learning algorithms, which use the fully connected layer for classification or regression, resulting in more model parameters and easy over-fitting. Among the latter ones, the most advanced indoor navigation algorithm divides a single image frame into multiple parts for prediction, resulting in doubled reasoning time. To solve these problems, we propose a multi-task deep network based on feature map region division for monocular indoor autonomous navigation. We divide the feature map instead of the original image to avoid repeated information processing. To reduce model parameters, we use convolution instead of the fully connected layer to predict the navigable probability of the left, middle, and right parts. We propose that the linear velocity is determined by combining three prediction probabilities to reduce collision risk. Experimental evaluation shows that the proposed method is nine times smaller than the previous state-of-the-art methods; further, its processing speed and navigation capability increase more than five and 1.6 times, respectively.

Background Pancreatic ductal adenocarcinoma (PDAC) is a devastatingly malignant tumor with a high mortality. However, current strategies to treat PDAC generally have low efficacy and high side-effects, therefore, effective treatment against PDAC remains an urgent need. Results We report a semiconducting polymer nano-radiopharmaceutical with intrinsic photothermal capability and labeling with therapeutic radioisotope 177Lu (177Lu-SPN-GIP) for combined radio- and photothermal therapy of pancreatic tumor. 177Lu-SPN-GIP endowed good stability at physiological conditions, high cell uptake, and long retention time in tumor site. By virtue of combined radiotherapy (RT) and photothermal therapy (PTT), 177Lu-SPN-GIP exhibited enhanced therapeutic capability to kill cancer cells and xenograft tumor in living mice compared with RT or PTT alone. More importantly, 177Lu-SPN-GIP could suppress the growth of the tumor stem cells and reverse epithelial mesenchymal transition (EMT), which may greatly reduce the occurrence of metastasis. Conclusion Such strategy we developed could improve therapeutic outcomes over traditional RT as it is able to ablate tumor with relatively lower doses of radiopharmaceuticals to reduce its side effects. Graphical abstract

The autopilot of aerobridge has attracted considerable attention recently since human error and high labor costs. To estimate the pose of an aerobridge, this paper proposes a navigation algorithm with the aid of pairwise distances and inertial measurements. We first locate the joints of the aerobridge with the distance measurement. Then the pose of the aerobridge is calculated using its kinematic in closed-form and the associated covariance is derived. A dual quaternion based multiplicative extended Kalman filter is designed to fuse inertial measurements with the estimated pose, which produces a smooth pose. To verify the precision of this algorithm, a simulation is constructed based on Robotics Toolbox and the experiment results are positive.

At present, the main methods of solving the monocular depth estimation for indoor drones are the simultaneous localization and mapping (SLAM) algorithm and the deep learning algorithm. SLAM requires the construction of a depth map of the unknown environment, which is slow to calculate and generally requires expensive sensors, whereas current deep learning algorithms are mostly based on binary classification or regression. The output of the binary classification model gives the decision algorithm relatively rough control over the unmanned aerial vehicle. The regression model solves the problem of the binary classification, but it carries out the same processing for long and short distances, resulting in a decline in short-range prediction performance. In order to solve the above problems, according to the characteristics of the strong order correlation of the distance value, we propose a non-uniform spacing-increasing discretization-based ordinal regression algorithm (NSIDORA) to solve the monocular depth estimation for indoor drone tasks. According to the security requirements of this task, the distance label of the data set is discretized into three major areas&mdash, the dangerous area, decision area, and safety area&mdash, and the decision area is discretized based on spacing-increasing discretization. Considering the inconsistency of ordinal regression, a new distance decoder is produced. Experimental evaluation shows that the root-mean-square error (RMSE) of NSIDORA in the decision area is 33.5% lower than that of non-uniform discretization (NUD)-based ordinal regression methods. Although it is higher overall than that of the state-of-the-art two-stream regression algorithm, the RMSE of the NSIDORA in the top 10 categories of the decision area is 21.8% lower than that of the two-stream regression algorithm. The inference speed of NSIDORA is 3.4 times faster than that of two-stream ordinal regression. Furthermore, the effectiveness of the decoder has been proved through ablation experiments.

In view of complex modeling and inefficient calculation at existing stage finite element analysis method in bus body skeleton analysis. This paper presents a method on simulating complex large section beams by equivalent stiffness element. Equivalent stiffness element model is analyzed theoretically. Experiments are carried out to verify that the equivalent stiffness element method can be used in rapid analysis and calculation for girder, body frame and body optimization. It not only reduces modeling complexity, but also improves calculation efficiency. To a certain extent, it can provide a better analysis method for large-scale vehicle modeling and simulation.

In this paper, a finite element method of simulating complex section beam element with approximate stiffness element is presented. By establishing an approximate stiffness element, the basic mechanical properties of the element are equivalent to the original complex section beam element’s. The approximate stiffness element has several sub-section beam elements, which intersect with other beam elements in space. The problem that missing of intersection points among complex section beam and other beams, which is caused by using conventional beam element, is solved. The error between the approximate stiffness element and the original complex section beam element is analyzed by theoretical deduction, and the feasibility and accuracy of the approximate stiffness element method are verified by the physical test of a bus body.

Renal cell carcinoma (RCC) is a common tissue tumor that occurs across all age groups and has become one of the types of cancer with the fastest increasing incidence. Due to the resistance of RCC chemo- and radiotherapy, surgery is the only currently effective treatment. Therefore, specific markers for the diagnosis and prognosis of RCC are expected to result in novel methods of treatment. Ecto-5′-nucleotidase, also termed cluster of differentiation (CD)73, is a protein that is activated in several types of aggressive cancer and may promote cancer progression. CD73 was examined in the present study to determine the association between the protein and RCC. The expression levels of CD73 in 159 RCC tissue sections and 30 paratumorous normal renal tissue samples obtained from 235 patients that underwent nephrectomy were examined by immunohistochemical staining. By contrast, the expression level of P-glycoprotein (P-gp), a potential prognostic factor in RCC, was also examined in 85 RCC and 13 normal tissue samples. Intense CD73 expression was identified in 75 out of 159 RCC cell membranes compared with normal renal tissues. In contrast, there was high P-gp expression in the blood vessels of 42 out of 85 RCC tissues and there was no significant difference between the P-gp expression identified in RCC cells (34 out of 85) and the cell membrane of normal renal cells (2 out of 13). The expression level of CD73 in RCC cells was significantly associated with tumor type, tumor node metastasis (TNM) stage, and tumor grade. However, the expression of P-gp in RCC cells was only associated with the TNM stage and tumor grade. Using a multivariable Cox regression analysis, it was found that the median survival rate of RCC patients with intense CD73 expression in RCC cells was 62.06±5.35 months, which was drastically shorter compared with rare CD73 expression (103.72±3.67 months). In conclusion, the expression level of CD73 is significantly associated with RCC tumor progression and may serve as a favorable marker for the diagnosis and prognosis of RCC, in addition to being a therapeutic target for the treatment of RCC.

During a long-term exploration in space, astronauts will be constantly exposed to space radiation which contains various types of low dose-rate heavy particles. Although the fluxes of these heavy ions are very small, the heavy ions can cause severe biological effects including early effects and late effects. This short review will discuss the biological effects induced by space radiation and introduce a new anti-radiation agent we have developed. We synthesized a class of reactive oxygen species scavengers called GANRA-5 with lowly toxic but high radio-protective effect to both heavy ions and X-rays. GANRA-5 can protect cells from X-rays and carbon ions induced cell killing by significantly reducing the amount of free radical production therefor reduce double-strand breaks and genomic instability. In vivo, GANRA-5 can effectively protect the mice from the damage induced by X-rays irradiation by enhancing superoxide dismutase activities. In brief, GANRA-5 is potentially applicable in space flight.

This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes.

Identification of a specific biomarker for cancer stem cells (CSCs) is of potential applications in the development of effective therapeutic strategies for renal cell carcinoma (RCC). In this study, both the RCC cell line 786-O and surgically removed clear cell RCC (ccRCC) tissues were implemented to grew as spheroids in serum-free medium supplemented with mitogens. This subpopulation possessed key characteristics defining CSCs. We also identified that surgically removed ccRCC tissues were heterogenic and there was a subpopulation of cells that was highly stained with rhodamine-123. Based on membrane-proteomic analyses, CD73 was identified as a candidate biomarker. We further found that CD73high cells were highly tumorigenic. As few as 100 CD73high cells were capable of forming xenograft tumors in non obese diabetic/severe combined immunodeficiency disease mice, whereas 1 × 105 CD73low cells did not initiate tumor formation. During successive culture, the CD73high population regenerated both CD73high and CD73low cells, whereas the CD73low population remained low expression level of CD73. Furthermore, the CD73high cells were more resistant to radiation and DNA-damaging agents than the CD73low cells, and expressed a panel of 'stemness' genes at a higher level than the CD73low cells. These findings suggest that a high level of CD73 expression is a bona fide biomarker of ccRCC stem-like cells. Future research will aim at the elucidation of the underlying mechanisms of CD73 in RCC development and the distinct aspects of ccRCC stem-like cells from other tumor types.

The robust controller is designed for certain BTT missile which is equipped with grid fin. In order to design the controller of BTT missile, the aerodynamic force of grid fin is analyzed when the grid fin rotates in flying, then the aerodynamic force and aerodynamic moment along with movement equations are linearized based on the small perturbation linear theory, the beginning state of the cruise is chosen as the feature point, and then the dynamical coefficient are calculated, then the longitudinal and lateral perturbation equations are established. The dynamic responses of each motion deviation under the longitudinal and lateral free disturbance are obtained through solving the perturbation equations. The transfer functions and the dynamic responses under the step deflection of grid fin are derived, then the dynamic characteristics of the missile can be obtained. The results indicate that the grid fin can provide favorable stability. The H ∞ controller of pitch and lateral bank channels are designed based on dynamic characteristics analysis, the simulation results indicate that the system dynamic performance can satisfy the requirement, and H ∞ controller has a fine robustness for the parametric variation.

This study considers the leader-following formation problem for the nonholonomic mobile robots, where the dynamics of the leader robot is unknown to the follower. First, the leader-following formation is transformed into a special trajectory tracking. Then neural network observer by the follower robot is designed to estimate the dynamics of the leader robot and based on multiple sliding surface technique control laws are designed for the follower robot to track the leader robot in the desired separation and bearing. The efficiency of the control law design is illustrated by formal proof and simulation results. Finally, conclusions are discussed.

Background B cell translocation gene 1 (BTG1) has long been recognized as a tumor suppressor gene. Recent reports demonstrated that BTG1 plays an important role in progression of cell cycle and is involved in cellular response to stressors. However, the microRNAs mediated regulatory mechanism of BTG1 expression has not been reported so far. MicroRNAs can effectively influence tumor radiosensitivity by preventing cell cycle progression, resulting in enhancement of the cytotoxicity of radiotherapy efficacy. This study aimed to demonstrating the effects of microRNAs on the BTG1 expression and cellular radiosensitivity. Methods The human renal carcinoma 786-O cells were treated with 5 Gy of X-rays. Expressions of BTG1 gene and miR-454-3p, which was predicted to target BTG1 by software algorithm, were analyzed by quantitative polymerase chain reaction. Protein expressions were assessed by Western blot. Luciferase assays were used to quantify the interaction between BTG1 3′-untranslated region (3′-UTR) and miR-454-3p. The radiosensitivity was quantified by the assay of cell viability, colony formation and caspase-3 activity. Results The expression of the BTG1 gene in 786-O cells was significantly elevated after treatments with X-ray irradiation, DMSO, or serum starvation. The up-regulation of BTG1 after irradiation reduced cellular radiosensitivity as demonstrated by the enhanced cell viability and colony formation, as well as the repressed caspase-3 activity. In comparison, knock down of BTG1 by siRNA led to significantly enhanced cellular radiosensitivity. It was found that miR-454-3p can regulate the expression of BTG1 through a direct interaction with the 3′-UTR of BTG1 mRNA. Decreasing of its expression level correlates well with BTG1 up-regulation during X-ray irradiation. Particularly, we observed that over-expression of miR-454-3p by transfection inhibited the BTG1 expression and enhanced the radiosensitivity. In addition, cell cycle analysis showed that over-expression of miR-454-3p shifted the cell cycle arrest from G2/M phase to S phase. Conclusions Our results indicate that BTG1 is a direct target of miR-454-3p. Down-regulation of BTG1 by miR-454-3p renders tumor cells sensitive to radiation. These results may shed light on the potential application in tumor radiotherapy. Electronic supplementary material The online version of this article (doi:10.1186/1748-717X-9-179) contains supplementary material, which is available to authorized users.

In order to realize the localization demand of mobile robot, the motion model and observation model of mobile robot were analyzed, and the mobile robot real-time localization algorithm of given map environment based on extended Kalman filtering was described. Besides, the algorithm was simulated. The landmark model of map was built in the process of simulation, and the landmark that was observed under control action was used to correct the location of mobile robot, by which the positioning of robot was realized. The result of simulation shows that the positional accuracy of this algorithm is very high. In addition, the construction method of real-time map of mobile robot was described on extended Kalman filtering under unknown environment based on this. Experimental result shows that this algorithm can identify the location of landmark accurately so as to give reference for the real-time positioning and mapping construction of mobile robot.

In this paper, Universal Kriging (UK) technology combined with Model Predictive Control (MPC) algorithm is exploited to estimate the unknown situation of oil dispersion for just one unmanned aerial vehicle (UAV) to predict and track the objective boundary automatically. The advantage of the Kriging method is that it is an optimal interpolator in the sense that the estimates are unbiased and the minimum variance is known, so that it can relatively accurately construct the environment map. In addition, the advantage of the MPC is its constraint handling capacity. These two uncorrelated technologies, however, can be combined to realize some special functions. The simulation based on an advection diffusion equation demonstrates the proposed method is feasible and effective.

This paper presents a hierarchical inner-outer loop-based approach to the longitudinal and lateral control of a small unmanned helicopter in the presence of parameter uncertainties and exogenous disturbances. The outer loop employs robust H ∞ output feedback to attenuate the wind disturbance and track the reference trajectory, while the inner loop controller is proposed to solve the parameter uncertainties problems using robust H ∞ static output feedback. The obtained control method takes advantage of the feature of hierarchical structure ensuring the stability of the flight attitude. Finally, the validity of the proposed control strategy is verified through simulation results.

This paper considers the robust static output feedback control of a miniature helicopter with parameter uncertainties and wind gust disturbances. A sufficient H 2 /LMI condition is derived for suboptimal controllers of uncertain system. Then by iterative LMI algorithm, the robust static output feedback controller is designed for the helicopter system with uncertainties. In the end, simulation results are given to validate the control method.

This paper considers the environmental boundary tracking problem where the objective is to steer a mobile robot to track the desired environmental boundary. Based on the kinematic model of the mobile robot, we design a kinematic control law for the robot to track the desired boundary. Kinematic control law needs perfect velocity tracking, which doesn't always hold for a mobile robot. So, dynamic control law is designed for the nonholonomic mobile robot to track a designated reference velocity, so as to track the desired environmental boundary. The stability of the designed control laws is verified by Lyapunov theory and Barbalat's Lemma and illustrated by simulation results.

A linear physical model with uncertainties is used to describe the lateral dynamics of a miniature helicopter. The structured parameters with uncertainties are identified through a lot of experiments. Then a robust H ∞ static output feedback (RHSOF) control is presented to guarantee the stability of the system and the influence of the unmodeled dynamics and disturbance in a given level. At last, simulation results validates the designed control law.

This paper examines the fault tolerant control problem for a generic class of incipient failure modes that grow in severity as a function of component loading. Assuming that a prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component loads to minimize risk of failure, while not overly degrading nominal performance. A methodology is proposed for posing this problem as a finite horizon constrained optimization, where constraints correspond to maximum risk of failure and maximum allowable deviation from nominal performance. Simulation results are given for an example system with an active redundant DC motor configuration. The fault mode simulated in this system is temperature dependent motor winding insulation degradation.

This paper attempts to make a conceptual illustration of fault prognosis-enhanced reconfiguration control. The definition of fault propagation and the proposed control adaptation algorithm ensuring the mission completion are discussed in this paper, along with some illustrative examples on prognosis-enhanced reconfiguration control of servomechanisms.

In this paper, decentralized controllers are developed to drive a swarm of mobile agents with high-order (n > 2) nonlinear dynamics in strict feedback form into a moving target region while avoiding collisions among themselves. It is important to consider coordination of multiple high-order agent dynamics which generalize the existing simple single-integrator/double-integrator ones because, in practice, we may need to incorporate actuator dynamics into the vehicle dynamics in order to achieve better performance, thus increasing the order of the system dynamics. The control design is based on a fusion of two kinds of new potential functions (target potential functions and collision avoidance potential functions), backstepping technique and Lyapunov synthesis. The presence of parametric uncertainties is handled by adaptive control techniques. The framework does not depend on a fixed ordering of agents, and is robust to individual agent failures. Therefore, flexibility and scalability are improved. Simulation results illustrate the performance of the proposed approach.

In this paper, we present an autonomous helicopter with vision based navigation called South China University of Technology unmanned aerial vehicle (SCUAV). A GPS/INS system has been designed and implemented for getting stable navigation information. A Kalman filtering has been used in this system for data fusion. A real-time computer vision system is presented in this paper as the complement of the GPS/INS system. The vision algorithm is designed and implemented in this paper, which is integrated with algorithms for tracking a known landmark and estimating the helicopter positions. A method of image processing is designed for tracking and recognizing known land marks. At the end of the paper, we will present the experiment results to demonstrate our efficacious algorithm.

A small unmanned helicopter is constructed in South China University of Technology. It has already gained some rudimental capability of autonomously flying. In this paper we introduce the further design of its multi-variable control strategy, a robust gain-scheduling algorithm. The feasibility of the proposed design is proven via simulation results.