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Abstract Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer.

Dynamic nitrification and denitrification processes are affected by changes in soil redox conditions, and they play a vital role in regulating soil N2O emissions in rice-based cultivation. It is imperative to understand the influences of different upland crop planting systems on soil N2O emissions. In this study, we focused on two representative rotation systems in Central China: rapeseed–rice (RR) and wheat–rice (WR). We examined the biotic and abiotic processes underlying the impacts of these upland plantings on soil N2O emissions. The results revealed that during the rapeseed-cultivated seasons in the RR rotation system, the average N2O emissions were 1.24±0.20 and 0.81±0.11 kg N ha−1 for the first and second seasons, respectively. These values were comparable to the N2O emissions observed during the first and second wheat-cultivated seasons in the WR rotation system (0.98±0.25 and 0.70±0.04 kg N ha−1, respectively). This suggests that upland cultivation has minimal impacts on soil N2O emissions in the two rotation systems. Strong positive correlations were found between N2O fluxes and soil ammonium (NH4+), nitrate (NO3−), microbial biomass nitrogen (MBN), and the ratio of soil dissolved organic carbon (DOC) to NO3− in both RR and WR rotation systems. Moreover, the presence of the AOA-amoA and nirK genes were positively associated with soil N2O fluxes in the RR and WR systems, respectively. This implies that these genes may have different potential roles in facilitating microbial N2O production in various upland plantation models. By using a structural equation model, we found that soil moisture, mineral N, MBN, and the AOA-amoA gene accounted for over 50% of the effects on N2O emissions in the RR rotation system. In the WR rotation system, soil moisture, mineral N, MBN, and the AOA-amoA and nirK genes had a combined impact of over 70% on N2O emissions. These findings demonstrate the interactive effects of functional genes and soil factors, including soil physical characteristics, available carbon and nitrogen, and their ratio, on soil N2O emissions during upland cultivation seasons under rice-upland rotations.

The article presents a novel control strategy for cable-driven aerial manipulators (UAMs) aimed at enhancing impedance control during contact operations in complex environments. A fractional-order nonsingular terminal sliding mode control (FONTSMC) integrated with a disturbance observer (DOB) is proposed to improve the robustness and precision of the UAM under lumped disturbances. This developed approach utilizes the flexibility of fractional calculus, the finite-time stability of nonsingular terminal sliding mode, and the real-time disturbance estimation capabilities of the DOB to ensure smooth and compliant contact interactions. The effectiveness of the proposed control strategy is validated through comprehensive simulation studies, which demonstrate significant improvements in control performance, stability, and disturbance rejection when compared to traditional methods. The results indicate that the FONTSMC-DOB framework is highly suitable for complex aerial manipulation tasks, offering both theoretical and practical insights into the design of advanced control systems for UAMs.

Abstract Precipitation or dissolution due to geochemical reactions has been observed in the caprocks for CO2 geosequestration. Geochemical reactions modify the caprock sealing efficiency with self‐limiting or self‐enhancement. However, the effect of this modification on the caprock sealing efficiency has not been fully investigated through multiphysical‐geochemical coupling analysis. In this study, a multiphysical‐geochemical coupling model was proposed to analyze caprock sealing efficiency. This coupling model considered the full couplings of caprock deformation, two‐phase flow, CO2 concentration diffusion, geochemical reaction, and CO2 sorption. The two‐phase flow only occurs in the fracture network and the CO2 may partially dissolve into water and diffuse through the concentration difference. The dissolved CO2 has geochemical reactions with some critical minerals, thus altering flow channels. The CO2 in the fracture network diffuses into matrix, causing the matrix swelling. This fully coupling model was validated with a penetration experiment on a cement cube and compared with two other models for CO2 storage plumes. Finally, the effects of geochemical reactions on penetration depth and pore pressure were studied through parametric study. The numerical simulations reveal that the coupling of geochemical reactions and matrix diffusion significantly affect the caprock sealing efficiency. Geochemical reactions occur at a short time after the arrival of CO2 concentration and modify the fracture porosity. The CO2 diffusion into the matrix requires a much longer time and mainly induces matrix swelling. These effects may produce self‐enhancement or self‐limiting depending on the flow rate in the fracture network, thus significantly modifying caprock sealing efficiency.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country.MethodsWe employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model.ResultsOverall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models).ConclusionThe hybrid CNN-LSTM model seamlessly integrates CNN’s data feature extraction and LSTM’s recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.

Piezoelectric micropositioning systems (PMSs) have been widely utilized in the high-precision manipulation applications, but are also subjected to undesired nonlinearities, like hysteresis, and parameter uncertainties. To solve this problem, this paper proposes a new robust sliding mode control scheme for a class of nonlinear PMSs with time-varying uncertainties. Different from the conventional sliding mode control (SMC), the proposed controller further combines the Fourier series-based function estimation technique, fuzzy logic system and adaptive learning algorithm to realize online estimation and compensation of system time-varying uncertainties without their boundary information. The adaptive laws of Fourier coefficients and fuzzy adjustable parameters are obtained via the Lyapunov stability theory. Compared with the existing SMC methods, the proposed control effectively eliminates the control chattering problem, and guarantees the convergence of the tracking error in finite time in the presence of time-varying uncertainties. Theoretical analysis and numerical simulation results show that the proposed control strategy can meet the high-speed, high-precision robust tracking performance requirements of PMSs for micro/nano-manipulation applications.

Abstract Background Classical swine fever and porcine reproductive and respiratory syndrome have seriously affected the development of the swine breeding industry in China. Vaccine immunization remains the main way to prevent these infections. The aim of this study was to establish an optimized protocol for vaccine immunization against classical swine fever virus (CSFV) and porcine reproductive and respiratory syndrome virus (PRRSV). Methods Blood samples were collected from the anterior vena cava of pigs after immunization, and blood indices, secreted levels of specific antibodies and neutralizing antibodies associated with humoral immunity, the proliferation capacity of T lymphocytes as a measure of cellular immunity, and secreted levels of IFN-γ and TNF-α were determined. Results The results showed that simultaneous immunization against CSFV and PRRSV infections induced strong and specific humoral and T-cellular immune responses, high levels of cytokine IFN-γ secretion and delayed secretion of cytokine TNF-α. Moreover, significantly higher lymphocyte percentages and red blood cell and leukocyte counts were found in the group simultaneously immunized against CSFV and PRRSV. However, no statistically significant differences were observed in hemoglobin values, neutrophil counts, and median cell percentages among the S + PRRS, PRRS-S, and S-PRRS groups. Conclusion This study demonstrated that simultaneous immunization against CSFV and PRRSV had the advantages of inducing a rapid, enhanced, and long-lasting immune response. These findings provide a theoretical basis for the establishment of a reasonable and optimized vaccine immunization protocol against CSFV and PRRSV in combination with a variety of other vaccine inoculations.

Abstract Background Brain tumor segmentation plays a significant role in clinical treatment and surgical planning. Recently, several deep convolutional networks have been proposed for brain tumor segmentation and have achieved impressive performance. However, most state-of-the-art models use 3D convolution networks, which require high computational costs. This makes it difficult to apply these models to medical equipment in the future. Additionally, due to the large diversity of the brain tumor and uncertain boundaries between sub-regions, some models cannot well-segment multiple tumors in the brain at the same time. Results In this paper, we proposed a lightweight hierarchical convolution network, called LHC-Net. Our network uses a multi-scale strategy which the common 3D convolution is replaced by the hierarchical convolution with residual-like connections. It improves the ability of multi-scale feature extraction and greatly reduces parameters and computation resources. On the BraTS2020 dataset, LHC-Net achieves the Dice scores of 76.38%, 90.01% and 83.32% for ET, WT and TC, respectively, which is better than that of 3D U-Net with 73.50%, 89.42% and 81.92%. Especially on the multi-tumor set, our model shows significant performance improvement. In addition, LHC-Net has 1.65M parameters and 35.58G FLOPs, which is two times fewer parameters and three times less computation compared with 3D U-Net. Conclusion Our proposed method achieves automatic segmentation of tumor sub-regions from four-modal brain MRI images. LHC-Net achieves competitive segmentation performance with fewer parameters and less computation than the state-of-the-art models. It means that our model can be applied under limited medical computing resources. By using the multi-scale strategy on channels, LHC-Net can well-segment multiple tumors in the patient’s brain. It has great potential for application to other multi-scale segmentation tasks.

Surface electromyography (sEMG) control interface is a common method for human-centered robotics. Researchers have frequently improved the recognition accuracy of sEMG through multichannel or high-precision signal acquisition devices. However, this increases the cost and complexity of the control system. Therefore, this study developed a control interface based on the sEMG enveloped signal for a collaborative wearable robot to improve the accuracy of sEMG recognition based on the time-domain (TD) features. Specifically, an acquisition device is developed to obtain the sEMG envelope signal, and 11 types of TD features are extracted from the sEMG envelope signal acquired from the upper limb. Furthermore, a dimension reduction method based on the correlation coefficient is proposed, transforming the 11-dimensional feature into a five-dimensional envelope feature set without decreasing the accuracy. Moreover, a recognition algorithm based on a neural network has also been proposed for gesture classification. Finally, the recognition accuracy of the proposed method, principal component analysis (PCA) feature set, and Hudgins TD feature set is compared, with their accuracy at 84.39%, 72.44%, and 70.89%, respectively. Therefore, the results indicate that the envelope feature set performs better than the common gesture recognition method based on signal channel sEMG envelope signal.

Abstract Rheumatoid arthritis (RA) is an autoimmune disease associated with advanced joint dysfunction. Madhuca indica J. F. Gmel, from the family Sapotaceae, is an Indian medicinal plant reported to have an array of pharmacological properties. The aim of present investigation was to determine the anti-arthritic potential of an isolated phytoconstituent from methanolic leaf extract of Madhuca indica (MI-ALC) against FCA-induced experimental arthritis. Polyarthritis was induced in female rats (strain: Wistar) via an intradermal injection of FCA (0.1 mL) into the tail. Polyarthritis developed after 32 days of FCA administration. Then rats were treated orally with an isolated phytoconstituent from MI-ALC at doses of 5, 10, and 20 mg/kg. Findings suggested that High-Performance Thin-Layer Chromatography, Fourier-Transform Infrared Spectroscopy, and Liquid Chromatography-Mass Spectrometry spectral analyses of the phytoconstituent isolated from MI-ALC confirmed the structure as 3,5,7,3′,4′-Pentahydroxy flavone (i.e., QTN). Treatment with QTN (10 and 20 mg/kg) showed significant (p

Nanofibers (NFs) have the advantages of tremendous flexibility, small size and a high surface-to-weight ratio and are widely used in sensors, drug carriers and filters. Patterned NFs have expanded their application fields in tissue engineering and electronics. Electrospinning (ES) is widely used to prepare nonwoven NFs by stretching polymer solution jets with electric forces. However, patterned NFs cannot be easily fabricated using ordinary ES methods: the process gradually deteriorates them as repulsion effects between the deposited NFs and the incoming ones increase while residual charges in the fibers accumulate. Repulsion effects are unavoidable because charges in the polymer solution jets are the fundamental forces that are meant to stretch the jets into NFs. TRIZ theory is an effective innovation method for resolving conflicts and eliminating contradictions. Based on the material–field model and the contradiction matrix of TRIZ theory, we propose a strategy to improve ES devices, neutralizing the charges retained in NFs by alternating the current power of the correct frequency, thus successfully fabricating patterned NFs with clear boundaries and good continuity. This study demonstrates a strategy for resolving conflicts in innovation processes based on TRIZ theory and fabricating patterned NFs for potential applications in flexible electronics and wearable sensors.

Abstract Background The size of lime material is vital for the efficiency of ameliorating soil acidity, thereby influencing soil biochemical processes. However, the effects of different sized lime material application on soil organic carbon (SOC) mineralization are yet to be elucidated. Therefore, a 35-day incubation experiment was conducted to determine the effects of three particle size fractions (0.5 to 0.25, 0.25 to 0.15, and < 0.15 mm) of dolomite on SOC mineralization of two acidic paddy soils. Results CO2 emission was increased by 3–7%, 11–21%, and 32–49% for coarse-, medium-, and fine-sized dolomite treatments, respectively, compared to the control in both soils. They also well conformed to a first-order model in all treatments, and the estimated decomposition rate constant was significantly higher in the fine-sized treatment than that of other treatments (P < 0.05), indicating that SOC turnover rate was dependent on the dolomite size. The finer particle sizes were characterized with higher efficiencies of modifying soil pH, consequently resulting in higher dissolved organic carbon contents and microbial biomass carbon, eventually leading to higher CO2 emissions. Conclusions The results demonstrate that the size of dolomite is a key factor in regulating SOC mineralization in acidic paddy soils when dolomite is applied to manipulate soil pH.

A novel model-free robust control is developed for cable-driven manipulators in this work. Using the time-delay estimation (TDE) to estimate system dynamics, an attractive model-free structure is ensured. Then, a novel PID-nonsingular fast terminal sliding mode (PID-NFTSM) surface is proposed to achieve high control performance. Afterwards, a combined adaptive reaching law is designed to further enhance the control performance in the reaching phase. The developed control requires no system dynamics and is proper to apply under complex practical situations. At the same time, satisfactory comprehensive performance is still acquired thanks to the newly proposed PID-NFTSM surface and combined adaptive reaching law. Stability is proved through Lyapunov approach. Comparative simulation studies show that our control can provide better comprehensive performance than the recently reported PID-NFTSM control.

Robot-assisted rehabilitation therapy has become an important technology applied to recover the motor functions of disabled individuals. In the present paper, an adaptive admittance control strategy combined with a neural-network-based disturbance observer (AACNDO) is developed for a therapeutic robot to provide upper extremity movement assistance. Firstly, a comprehensive overview of the robot hardware and real-time control system is introduced. Then, the dynamics-based adaptive admittance controller is designed to improve human-robot interaction compliance and induce the active participation of the patients during rehabilitation training. A disturbance observer with a radial basis function network is designed to guarantee the control performance with external uncertainties and dynamics error. Besides, an adaption law is integrated into the admittance model to adjust the interaction compliance in different working areas based on the motion intention and recovery phase of the patient. Further experimental investigations, including sinusoidal trajectory tracking experiments, circular trajectory tracking experiments with admittance adjustment, and intention-based resistive training experiments, are conducted by three volunteers. Finally, the experimental results validate the feasibility and effectiveness of the rehabilitation robot and AACNDO scheme in providing patient-passive and patient-cooperative rehabilitation training.

Robot-assisted cooperative rehabilitation training has shown superiority in helping the individuals with motion impairment problems to regain their motor functions. This paper presents the development and evaluation of a new cooperative training control scheme for an end-effector-type rehabilitation robot to provide upper extremity rehabilitation training with desired compliance and intensity. Firstly, the overall mechanical structure and real-time control system of rehabilitation robot are introduced. Secondly, an integral fuzzy sliding mode impedance control strategy combined with time-delay estimation (IFSMIC-TDE) is proposed to induce the active participation of patients and suppress impedance error. The IFSMIC-TDE approach is free from nonlinear robot dynamics, and it is designed to be robust to the external uncertainties and inherent chattering characteristics. After that, the closed-loop system stability with IFSMIC-TDE is proved based on the Lyapunov stability theory. Finally, experimental investigations are conducted to illustrate the effectiveness of the proposed rehabilitation robot and control algorithm. The comparison results indicate that the proposed IFSMIC-TDE can achieve better control performance and less impedance error. Besides, the interaction compliance and training intensity can be qualitatively adjusted via appropriate impedance parameters.

The Preisach model, which is formulated as a weighted superposition of hysteresis kernels, has been widely used for hysteresis modeling, especially in smart-material-based actuators. However, in the classical Preisach model, a trade-off is always required between the model accuracy and the number of the hysteresis kernels. To deal with this problem, a model order reduction technique based on the discrete empirical interpolation method (DEIM) has recently been proposed. The method can largely reduce the number of the hysteresis kernels while barely losing the model accuracy. It is noted that the kernel weight in the reduced DEIM-based model can be both positive and negative, which means that the monotonicity of the Preisach model could be lost. The monotonicity is a very important property especially when constructing the inverse Preisach model. Furthermore, the loss of the monotonicity can also deteriorate the model predictability. In the current paper, a modification strategy is proposed. In the modified reduced Preisach model, the DEIM is only employed to select the dominant hysteresis kernels while the corresponding weights of the selected hysteresis kernels are re-identified by solving a constraint optimization problem. Systematic simulation studies and experimental validation are carried out to demonstrate the effectiveness of the proposed strategy.

An anti-mixing cover is presented to restrain the generation of unsteady flow and back-flow phenomena in the separation chamber of a cyclone separator with reduced height. Structure of the anti-mixing cover is proposed based on the Barth balance orbit theory hypothesis. In order to clarify the effect of the anti-mixing cover on the separation efficiency of the cyclone, a numerical model was established by means of the Euler-Lagrange approach. The simulation results are in good agreement with the experimental data and several empirical formulas in the range of inlet air velocity 18 m/s~23 m/s. As results, the anti-mixing cover effectively enlarged the centrifugal force acting on particles and reduced the vortex intensity at the bottom of the cyclone. Moreover, the rising dust-laden airflow is also blocked by the anti-mixing cover which leads to less back-mixing phenomena.

In recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for upper-limb exoskeleton, which can help the human operator perform repetitive rehabilitation training. Firstly, a comprehensive overview about the therapeutic exoskeleton hardware and real-time control system is introduced. Then, the neural adaptive backstepping sliding mode controller (NABSMC) is developed based on radial basis function network (RBFN) to improve the trajectory tracking accuracy with external disturbances and dynamics errors. Next, the closed-loop stability of the proposed controller is demonstrated according to the Lyapunov stability theory. Finally, further experimental investigation are conducted on three volunteers to compare the control performance of NABSMC strategy with an optimal backstepping sliding mode control (OBSMC) strategy. The comparison results show that the proposed NABSMC algorithm is capable of achieving higher trajectory tracking accuracy and better step response characteristic during repetitive passive rehabilitation training.

Singularity research is carried out. The problem, which is about six-dimensional parameters of position and orientation can not realize three-dimensional visualization for 6DOF parallel robot, has been solved. Firstly, according to the structural characteristics of the 6DOF parallel robot with the planar platform, the position and orientation of the mobile platform are described, respectively, and the six equations of forward kinematics are established by choosing the natural coordinates of three representative points as parameters. Then, the singularities of the 6DOF parallel robot with a planar platform are divided into input singularity and output singularity. Aiming at the output singularity, in combination with six constraint equations among the position vectors of three representative points, an analytical algorithm is proposed to express the coupling singularity of position and orientation and the analytical expression is derived. In further research, three kinds of output singularities are found, the spatial distribution of the output singular trajectory is determined, and a unified three-dimensional fully visualized description of six-dimensional coupling variables is realized for the first time. The problems of finding the singular orientation at a given position or the singular position at a given orientation are solved. The analysis of the singularity lays a solid foundation for the description of the three-dimensional complete visualization of a six-dimensional singularity-free workspace based on forward kinematics. What is more, it has great significance for both trajectory planning and control design of the parallel robot.

To effectively reduce the mass and simplify the structure of traditional aerial manipulators, we propose novel light cable-driven manipulator for the aerial robots in this article. The drive motors and corresponding reducers are removed from the joints to the base; meanwhile, force and motion are transmitted remotely through cables. Thanks to this design, the moving mass has been greatly reduced. In the meantime, the application of cable-driven technology also brings about extra difficulties for high-precise control of cable-driven manipulators. Hence, we design a nonsingular terminal sliding mode controller using time-delay estimation. The time-delay estimation is applied to obtain lumped system dynamics and found an attractive model-free scheme, while the nonsingular terminal sliding mode controller is utilized to enhance the control performance. Stability is analyzed based on Lyapunov theory. Finally, the designed light cable-driven manipulator and presented time-delay estimation-based nonsingular terminal sliding mode controller are analyzed. Corresponding results show that (1) our proposed cable-driven manipulator has high load to mass ratio of 0.8 if we only consider the moving mass and (2) our proposed time-delay estimation-based nonsingular terminal sliding mode is model-free and can provide higher accuracy than the widely used time-delay estimation-based proportional–derivative (PD) controller.

In this paper, the vibration characteristics of the designed hybrid truss-type movable heavy-load pouring robot (pouring robot) and the pose control of the parallel working arm are studied. The dynamic-static method is used to simplify the truss and parallel working arm of the pouring robot into a forced vibration system composed of spring, damping, and mass body, based on this the 9-degree-of-freedom vibration equation of the pouring robot is established, the analysis of the amplitude–frequency characteristics shows that the four input vibrations of the pavement have basically the same influence on the ladle, which will cause the ladle to resonate. Kane’s method is used to establish the lower mobility dynamic equation of the parallel working arm, the Jacobian matrix of force-driven and torque-driven coupling are given. Based on the vibration characteristics of the pouring robot and the dynamic model of the parallel working arm, an adaptive sliding mode control method with radial basis function neural network compensator and Newton–Euler iterative estimator are proposed to realize the pose control of the ladle, the Lyapunov theorem proves the stability of the control method, the simulation results show that adaptive sliding mode control has better control performance, faster response speed, higher convergence accuracy, and better robustness than traditional sliding mode control algorithm. The paper provides a reference and research basis for the suppression of ladle vibration when the pouring robot is transferred, which is affected by the road roughness.

Abstract Background The abnormal expression of non-coding RNAs (ncRNAs), such as microRNAs and long ncRNAs, often contribute to the development of cancers. miR-200c functions as a tumour suppressor that impacts the growth of bladder cancer cells and the epithelial-to-mesenchymal transition (EMT). LncRNA X inactive specific transcript (XIST) is highly expressed in tumour tissues, promotes cancer progression and might act as an miRNA molecular sponge. This study aimed to examine the relationship between lncRNA XIST and miR-200c and to assess their functions in the regulation of the stemness properties and tumourigenicity of human bladder cancer stem cell (BCSC)-like cells. Methods Biological effects including cell clone formation, sphere formation, self-renewal properties and mouse tumourigenesis were examined in BCSC-like cells with miR-200c overexpression or XIST knockdown. Real-time PCR and western blotting were used to detect the expression changing of related factors in BCSC-like cells gene models. Dual luciferase reporter assay was used to examine the changes of XIST and miR-200c expression levels. Results The results indicated that miR-200c overexpression and XIST knockdown could inhibit cell clone formation, self-renewal ability and EMT in BCSC-like cells. miR-200c knockdown could restore the tumour growth inhibition caused by XIST knockdown. Conclusion LncRNA XIST may act as an inhibitor of miR-200c to regulate the stemness properties and tumourigenicity of bladder cancer cells, and our findings might reveal a potential strategy of targeting XIST for bladder cancer therapy.

In this paper, a novel robust control scheme is developed for the trajectory tracking control purpose of cable-driven robots under complex lumped uncertainties. The proposed control scheme utilizes time-delay estimation (TDE) technique to obtain the lumped unknown system dynamics; and therefore ensures an attractive model-free advantage. A novel adaptive nonsingular fast terminal sliding mode (ANFTSM) manifold is designed considering its uncertainties, which is estimated by a newly designed adaptive law. Afterward, corresponding TDE-based ANFTSM control scheme is proposed. The proposed control scheme requires no system dynamics and therefore is easy to use in complicated practical applications benefitting from TDE technique. Meanwhile, high tracking accuracy and fast dynamical response can be effectively ensured using the proposed ANFTSM manifold. Stability of the closed-loop control system is proved through Lyapunov method. Finally, comparative simulations and experiments were conducted to demonstrate the superiorities of our proposed method over the existing ones.

To ensure high-performance tracking control of cable-driven manipulators under complex uncertainties, we propose a novel optimal nonsingular terminal sliding mode (ONTSM) control scheme using super-twisting algorithm (STA) and time-delay estimation (TDE) technique. The TDE technique is applied as framework to take advantage of its model-free nature. Meanwhile, by introducing a quadratic performance index, we design an ONTSM manifold to achieve high dynamical performance in the sliding mode phase. Afterward, the modified STA is utilized to provide good performance in the reaching phase. Thanks to above three elements, the proposed ONTSM control with STA and TDE schemes is model-free and easy to apply in complicated practical applications. The stability of the closed-loop control system is analyzed considering the ONTSM and STA dynamics. Finally, comparative experiments were performed to demonstrate the effectiveness and superiorities of our newly proposed control scheme over two existing control schemes.

To ensure high control performance of cable-driven robots under complicated lumped uncertainties, we propose a novel time-delay control (TDC) scheme with adaptive fractional-order nonsingular terminal sliding mode (AFONTSM) in this paper. The proposed control scheme utilizes time-delay estimation (TDE) to estimate and compensate the lumped unknown system dynamics and therefore ensures a fascinating model-free feature. A novel AFONTSM manifold is designed by considering the existing uncertainties using an adaptive algorithm. Then, the TDC scheme is designed using TDE technique and AFONTSM manifold and fast-TSM-type reaching law. Thanks to TDE technique, the proposed control scheme is model-free and can be easily applied in complex practical applications. Meanwhile, the high control accuracy and fast dynamical response can still be guaranteed benefitting from the AFONTSM dynamics and fast-TSM-type reaching law. Stability of the closed-loop control system is analyzed using Lyapunov method. Corresponding simulation and experimental results effectively demonstrate the validity and superiorities of our proposed TDC scheme with AFONTSM over the other three existing TDC schemes.

To guarantee high-performance tracking control of cable-driven manipulators under complex lumped uncertainties, we propose a novel practical adaptive integral terminal sliding mode (AITSM) control scheme in this paper. The proposed control scheme utilizes time-delay estimation (TDE) to estimate and compensate the system dynamics and therefore ensures an attractive model-free control structure. Meanwhile, a novel adaptive algorithm is designed to timely and appropriately update the gains for the AITSM manifold and combined adaptive reaching law (ARL). High control accuracy, fast dynamical response, and strong robustness can be effectively ensured, thanks to the proposed AITSM manifold and combined ARL. The stability of the closed-loop control system is analyzed using the Lyapunov stability theory. Comparative experiments were conducted, and the corresponding results show that the newly proposed TDEbased AITSM control scheme can provide a better and comprehensive control performance than the existing AITSM control method.

Exoskeletons composed of rigid frames and joints have been widely implemented in rehabilitation application. However, the rigid mechanical structure introduces many problems, such as heavy weight, large inertia, and joint misalignment. To overcome these undesirable inherent disadvantages of rigid exoskeletons, the innovative contributions of this paper are to investigate the design and fuzzy sliding mode admittance (FSMCA) control of a soft wearable exoskeleton with compliance tendon-sheath actuation (CTSA) for multi-mode elbow rehabilitation training. The CTSA system is developed on the base of the Hill-based muscle model to imitate the working characteristics of human muscle and improve the compliance and coordination of human-robot cooperation. The FSMCA controller is capable of providing patient-passive and patient-active therapies under different training intensities and encouraging the active participation of the patients with various weakness levels. Further experimental investigations, including the trajectory tracking experiments with/without admittance regulation, the step response experiments with disturbance, the frequency response experiments, and the patient-active training experiments, are carried out by three healthy volunteers. Experimental results demonstrate the effectiveness of the proposed FSMCA control scheme in achieving high control accuracy and favorable frequency response characteristic. Besides, the training intensity can be qualitatively adjusted via appropriate admittance parameters.

Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti‐noise performance, over‐segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over‐segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti‐noise performance, highly precise segmentation result, and are useful in effectively segmenting low‐contrast images.

This study is mainly about the designation of a new type of haptic device and an asymmetric teleoperation robot system. Aiming at the problems of tracking and transparency of an asymmetric teleoperation system, a robust control algorithm based on a state observer was proposed. The Haptic Device was designed and was chosen as the master-robot of the system. The Baxter dual-arm robot was chosen as the slave-robot of the system. The simulation experiment of robust control based on a state observer of the asymmetric teleoperation robot was carried out. The experiment results showed that the maximum values of displacement tracking errors in three directions x, y, and z are 0.02 m, 0.01 m, and 0.015 m, respectively. Compared with single- joint PID control, the performance of the new control algorithm is improved. The force feedback experiment on the real asymmetric teleoperation robot system was carried out. The results showed that the force feedback wave is consistent with the actual situation and showed that the robust control algorithm proposed is superior to PID. Therefore, the algorithm perfectly satisfied the system. The experiment parameters also demonstrate that the haptic device satisfies the design requirements of the asymmetric teleoperation robots system and the industry standards.

A new nonlinear robust control scheme is proposed and investigated for the trajectory tracking control problem of an underwater vehicle-manipulator system (UVMS) using the discrete time delay estimation (DTDE) technique. The proposed control scheme mainly has two parts: the DTDE part and the desired dynamics part. The former one is applied to properly estimate and compensate the complex unknown lumped dynamics of the system, using the intentionally time-delayed system's information. The latter one is used to obtain the desired dynamic characteristic of the closed-loop control system. Thanks to the DTDE technique, the proposed control scheme no longer requires the detailed system dynamic information or the acceleration signals, bringing in good feasibility for actual applications and satisfactory control performance. The stability of the closed-loop control system is analyzed and proved using the bounded input bounded out stability theory. Finally, nine degree of freedoms (DOFs) simulation and seven DOFs pool experiment studies were conducted to demonstrate the effectiveness of the proposed control scheme with an UVMS developed in our laboratory. Corresponding results show that our proposed control scheme can ensure satisfactory control performance with relative small control gains and obtain a precision of 0.064 m for the end effector in the task space.

To overcome the problems of structural parametric uncertainty and cable transmission model complexity, a nonlinear controller based on time-delay estimation and fuzzy self-tuning is proposed. The unknown dynamics and disturbances are estimated by time delaying the state of motion immediately before. The control gains are self-tuned by a fuzzy controller, which can reduce the errors caused by system’s uncertainties and external disturbances. Compared with the conventional Proportional-derivative (PD) and time-delay control, the result shows that the proposed control scheme based on time-delay estimation can improve the joint trajectory tracking accuracy of cable-driven robot by significantly reducing the control gains. With the PD gains self-tuned by fuzzy strategy, the mean square errors of trajectory tracking are decreased approximately by 5–20% more than the conventional time-delay control with constant gains. In addition, the experimental result shows that the proposed method has an effective inhibitory effect on dead zone in cable-driven joints. Experiment performed on position tracking control of a 2-degree-of-freedom cable-driven robot is presented to illustrate that the controller has the advantages of simple and reliable structure, model-free, strong robustness, and high tracking accuracy.

Background: Piezoelectric actuators are widely used in many micro/nano-manipulation applications, but their positioning accuracy are badly affected by their inherent nonlinear hysteresis and creep. To solve this problem, this paper presents a new robust motion-control method for piezoelectric actuators with fast nonsingular terminal sliding mode based on time delay estimation. Method: The proposed controller needs no detailed information about the hysteresis and other nonlinearities of the system, leading to a simple and model-free characteristic due to the time delay estimation and ensures fast convergence and high tracking accuracy thanks to the nonsingular terminal sliding-mode surface and fast terminal sliding mode -type reaching law. A robust exact differentiator is adopted to estimate the velocity and accelerationinformation online, and it overcomes the limitation of only available position measurements. The finite-time convergence and stability of the closed-loop system are proved by using a Lyapunov function. Results: Experimental results show that the proposed control strategy has faster convergence and higher tracking precision compared with a traditional time delay control. Conclusion: The proposed control strategy can be widely used as an effective control method for high-precision motion control of piezoelectric actuators.

The robust control issues in trajectory tracking of an unmanned aerial robot (UAR) are challenging tasks due to strong parametric uncertainties, large nonlinearities, and high couplings in robot dynamics. This paper investigates the dynamical modelling and robust control of an aerial robot using a hexarotor with a 2-degrees-of-freedom (DOF) manipulator in a complex aerial environment. Firstly, the kinematic model and dynamic model of the aerial robot are developed by the Euler-Lagrange method. Afterwards, a linear active disturbance rejection control is designed for the robot to achieve a high-accuracy trajectory tracking goal under heavy lumped disturbances. In this control scheme, the modelling uncertainties and external disturbances are estimated by a linear extended state observer, and the high tracking precision is guaranteed by a proportion-differentiation (PD) feedback control law. Meanwhile, an artificial intelligence algorithm is applied to adjust the control parameters and ensure that the state variables of the robot converge to the references smoothly. Furthermore, it requires no detailed knowledge of the bounds on unknown dynamical parameters. Lastly, numerical simulations and experiments validate the efficiency and advantages of the proposed method.

This article presents an integrated approach for the parameter identification of a small-scale unmanned helicopter. With the flight experiment data collection and preprocessing, a hybrid identified algorithm combining the improved artificial bee colony algorithm and prediction error method is proposed to obtain the unknown dynamical parameters of the linear model. The proposed algorithm is valid to use thanks to an adaptive search equation, a novel probability-scaling method, and a chaotic operator and has a good performance in search speed and quality. Afterwards, we design a wind tunnel test to modify the main rotor time constant of the identified model. The identified accuracy and feasibility of the proposed approach are verified by making a time-domain comparison with three other algorithms. Results show that the dynamical characteristics of the helicopter can be determined accurately by the identified model. And the proposed approach is propitious to enhance the reliability and availability of the identified dynamical model.

In this paper, a numerical method for forward kinematics of general Stewart manipulator using natural coordinates is presented. The kinematic equations are in quadratic forms and the corresponding Jacobian matrix is a linear function of coordinates because of using natural coordinates. According to the characteristics of the kinematic equations, the Newton-Raphson algorithm is simplified to decrease the renewal time of iterations between equations and Jacobian matrix, and used to solve the kinematic equations. The singularity and convergence problems of the algorithm are discussed. Furthermore, the method using natural coordinates is compared with the traditional method using rotation matrix through numerical examples. Comparison results show that the method using natural coordinates is very accurate, more efficient, and has a greater convergence domain.

Many assembly modes exist due to multi-solutions for forward kinematics of 6-UPS parallel robot. However, the existing theoretical research results have not been well applied to the real-time control, because of the complexity of the forward kinematics. This paper proposes an analytic algorithm to establish the forward kinematics model of a 6-UPS parallel robot with an extra displacement sensor mounted on the centers of upper and lower platform ((6+1)-UPS). Based on the unit quaternion method, the improved forward kinematics model of the proposed (6+1)-UPS system is theoretically derived to be four equations about four components of unit quaternion. The whole computation process is analytical. The method of measurement of the 7th link length and the utilization of unit quaternion reduces the complexity of computation process of forward kinematics, avoids the computer memory spillover and increases the computation efficiency to be suitable for the real-time control application. Then, the analytical solution of the position vector and orientation matrix of the (6+1)-UPS parallel robot are obtained. Finally, the correctness and effectiveness of the proposed approach are illustrated with a numerical example.

The total alkaloids extracted from the leaves of Alstonia scholaris (ASAs) have been reported to reduce fever, remove phlegm, and relieve coughs. However, their drug half-lives are short. Thus, to obtain sustained-release preparations of total alkaloids from ASAs, mandelic acid oxyanhydride (mandelic acid OCA) was synthesized by the reaction of L-mandelic acid (MA) with triphosgene, and subsequent copolymerization with polyethylene glycol monomethyl ether (mPEG) of different molecular weights yielded the corresponding mPEG poly-MA (mPEG-PMA) copolymers. ASAs-loaded microspheres were then prepared using the double emulsion method, and their in vitro release (15 d, 37 °C) and in vitro degradation behaviors were studied. The morphology, size, embedding efficiency, and drug loading efficiency were investigated for the prepared microspheres, and screening was carried out using the mPEG _10K -PMA drug-loaded microspheres to analyze their biological characteristics. Anti-inflammatory experiments using Kunming mice and Sprague Dawley rats showed that the microspheres exhibited good anti-inflammatory properties. Moreover, the ASAs-loaded microspheres exhibited a good biocompatibility, and the hemolysis rate was

The solution of the dynamic equations of the six-axis accelerometer is a prerequisite for sensor calibration, structural optimization, and practical application. However, the forward dynamic equations (FDEs) and inverse dynamic equations (IDEs) of this type of system have not been completely solved due to the strongly nonlinear coupling relationship between the inputs and outputs. This article presents a comprehensive study of the FDEs and IDEs of the six-axis accelerometer based on a parallel mechanism. Firstly, two sets of dynamic equations of the sensor are constructed based on the Newton–Euler method in the configuration space. Secondly, based on the analytical solution of the sensor branch chain length, the coordination equation between the output signals of the branch chain is constructed. The FDEs of the sensor are established by combining the coordination equations and two sets of dynamic equations. Furthermore, by introducing generalized momentum and Hamiltonian function and using Legendre transformation, the vibration differential equations (VDEs) of the sensor are derived. The VDEs and Newton–Euler equations constitute the IDEs of the system. Finally, the explicit recursive algorithm for solving the quaternion in the equation is given in the phase space. Then the IDEs are solved by substituting the quaternion into the dynamic equations in the configuration space. The predicted numerical results of the established FDEs and IDEs are verified by comparing with virtual and actual experimental data. The actual experiment shows that the relative errors of the FDEs and the IDEs constructed in this article are 2.21% and 7.65%, respectively. This research provides a new strategy for further improving the practicability of the six-axis accelerometer.

Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.

In this study, the poly(lactide-ε-caprolactone) (P(LA-CL)) copolymer is synthesized by ring-opening polymerization with glycol used as a molecular weight regulator to adjust the molecular weight of the polymer. The proton nuclear magnetic resonance spectroscopy and gel permeation chromatography (GPC) results demonstrate that the P(LA-CL) copolymer is successfully synthesized, and that the molecular weight can be controlled by the glycol content. The thermoplastic plate is processed with triallyl isocyanurate as a cross-linking agent by a single-screw extruder followed by γ-ray irradiation. Shape memory test results show that the material had the desired shape memory effect, with deformation recovery rates reaching 100%. After secondary stretching of samples, deformation recovery rates are unchanged. The results of mechanical property measurements indicate that with added lactide, the tensile strength is improved and shore hardness is increased by 20%−30%. Data from clinical trials also reveal that the material has good clinical effects in thermoplastic membrane fixation.

This article presents design and optimization results from an implementation of a novel disturbance decoupling control strategy for a small-scale unmanned helicopter. Such a strategy is based on the active disturbance rejection control (ADRC) method. It offers an appealing alternative to existing control approaches for helicopters by combining decoupling and disturbance rejection without a detailed plant dynamics. The tuning of the control system is formulated as a function optimization problem to capture various design considerations. In comparison with several different iterative search algorithms, an artificial bee colony (ABC) algorithm is selected to obtain the optimal control parameters. For a fair comparison of control performance, a well-designed LQG controller is also optimized by the proposed method. Comparison results from an attitude tracking simulation against wind disturbance show the significant advantages of the proposed optimization control for this control application.

In this paper, a new approach for the forward kinematics of nearly general Stewart platform (i.e., both the fixed base and moving platform are restricted to planar platforms) with an extra sensor is presented. Fifteen compatible algebra equations which express the coupling relationships between the position and orientation variables of the moving platform are obtained. Because the magnitude of the position vector is measured by the extra sensor, the fifteen compatible algebra equations can be transformed into a set of linear algebraic equations with two unknown variables with the help of elimination by substitution, from which a unique solution can be obtained. In the end, a numerical example is presented to verify the correctness of the proposed approach. The proposed approach is simple and does not need any iteration, is expected to be used in real time applications.

Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.

The spatial-based method has become the most widely used method in improving the visibility of images. The visibility improving is mainly to remove the noise in the image, in order to trade off denoising and detail maintaining. A novel adaptive non-local means-based nonlinear fitting method is proposed in this paper. Firstly, according to the smoothness of the intensity around the central pixel, eight kinds of templates with different precision are exploited to approximate the central pixel through a novel adaptive non-local means filter design; the approximate weight coefficients of templates are derived from the approximation credibility. Subsequently, the fractal correction is used to smooth the denoising results. Eventually, the Rockafellar multiplier method is employed to generalize the smooth plane fitting to any geometric surface, thus yielding the optimal fitting of the center pixel approximation. Through a large number of experiments, it is clearly elucidated that compared with the classical spatial iteration-based methods and the recent denoising algorithms, the proposed algorithm is more robust and has better effect on denoising, while keeping more original details during denoising.

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

To deal with the problem of obstacle avoidance for redundant robots, an obstacle avoidance algorithm based on the internal motion of the 7-DOF redundant anthropomorphic arm is presented. The motion of that critical points move away from the closest points on the obstacles is defined as obstacle avoiding motion. Two transitioning variables were used to make a smooth, continuous transition between the primary and the secondary tasks. Using this approach, the robot can get the target configuration while avoiding the obstacles. Finally, the validity of the obstacle avoidance algorithm based on transitioning between tasks is manifested by simulation. The results show that, for the obstacle avoiding problem, the redundant robot not only can realize the obstacle avoidance, but also prevents the conflict between tasks by the proposed approach.

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.