GraftFast Surface Engineering to Improve MOF Nanoparticles Furtiveness

Controlling the outer surface of nanometric metal–organic frameworks (nanoMOFs) and further understanding the in vivo effect of the coated material are crucial for the convenient biomedical applications of MOFs. However, in most studies, the surface modification protocol is often associated with significant toxicity and/or lack of selectivity. As an alternative, how the highly selective and general grafting GraftFast method leads, through a green and simple process, to the successful attachment of multifunctional biopolymers (polyethylene glycol (PEG) and hyaluronic acid) on the external surface of nanoMOFs is reported. In particular, effectively PEGylated iron trimesate MIL‐100(Fe) nanoparticles (NPs) exhibit suitable grafting stability and superior chemical and colloidal stability in different biofluids, while conserving full porosity and allowing the adsorption of bioactive molecules (cosmetic and antitumor agents). Furthermore, the nature of the MOF–PEG interaction is deeply investigated using high‐resolution soft X‐ray spectroscopy. Finally, a cell penetration study using the radio‐labeled antitumor agent gemcitabine monophosphate (³H‐GMP)‐loaded MIL‐100(Fe)@PEG NPs shows reduced macrophage phagocytosis, confirming a significant in vitro PEG furtiveness.     

A computer implementation of the partition of the unity procedure and its application to arthropod population dynamics. A case study on the European grape berry moth

This paper presents a new procedure to construct families of spatial approximation-prediction functions which depend on several parameters. The method is based on partitions of the unity. In order to find optimal functions in these families we introduce a Vietoris simplicial set associated to an influence radius. We consider error estimators induced by these Vietoris simplicial sets. A computational implementation of these mathematical models has been developed to be applied to any kind of finite discrete data. Our methods provide a useful tool for the analysis of the spatial distribution of species, which is imperative for developing accurate and efficient management of endangered and invasive species, biological conservation programs, etc. We demonstrate the application of our method to the analysis of the spatial distribution of the grape moth Lobesia botrana (Lepidoptera: Tortricidae) in the vineyards of La Rioja obtaining useful results for the management of this pest.     

Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain

This paper addresses the cooperative localization and visual mapping problem with multiple heterogeneous robots. The approach is designed to deal with the challenging large semi-structured outdoors environments in which aerial/ground ensembles are to evolve. We propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in such environments. A large-scale SLAM algorithm is generalized to handle multiple robots, in which a global graph maintains the relative relationships between a series of local sub-maps built by the different robots. The key issue when dealing with multiple robots is to find the link between them, and to integrate these relations to maintain the overall geometric consistency; the events that introduce these links on the global graph are described in detail. Monocular cameras are considered as the primary extereoceptive sensor. In order to achieve the undelayed initialization required by the bearing-only observations, the well-known inverse-depth parametrization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parametrization based on anchored Plücker coordinates, to which extensible endpoints are added. Extensive simulations show the proposed developments, and the overall approach is illustrated using real-data taken with a helicopter and a ground rover.     

Barrier Lyapunov Functions for the control of output-constrained nonlinear systems

In this paper, we present control designs for single-input single-output (SISO) nonlinear systems in strict feedback form with an output constraint. To prevent constraint violation, we employ a Barrier Lyapunov Function, which grows to infinity when its arguments approach some limits. By ensuring boundedness of the Barrier Lyapunov Function in the closed loop, we ensure that those limits are not transgressed. Besides the nominal case where full knowledge of the plant is available, we also tackle scenarios wherein parametric uncertainties are present. Asymptotic tracking is achieved without violation of the constraint, and all closed loop signals remain bounded, under a mild condition on the initial output. Furthermore, we explore the use of an Asymmetric Barrier Lyapunov Function as a generalized approach that relaxes the requirements on the initial conditions. We also compare our control with one that is based on a Quadratic Lyapunov Function, and we show that our control requires less restrictive initial conditions. A numerical example is provided to illustrate the performance of the proposed control.     

Application of the OBD method for optimization of neural state variable estimators of the two-mass drive system

This paper presents neural estimators of the mechanical state variables of the electrical drive system with elastic joints. The non-measurable state variables, as the torsional torque and the load machine speed are estimated using multilayer feed-forward neural networks. The main stages of the design methodology of these neural estimators are presented. The optimal brain damage method is implemented for the structure optimization of each neural network. Then signals estimated by neural estimators are tested in the electrical drive control structure with additional feedbacks from the estimated shaft torque and the difference between the motor and the load speeds. The simulation results show good accuracy of both presented neural estimators for the wide range of changes of the reference speed and the load torque. The simulation results are then verified by laboratory experiments.     

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.     

基于USB的卫星通用化地面测试软件的设计

针对当前卫星地面测试设备研制任务剧增、研制需求周期短、密度大、技术更新快的问题,将软件工程中的组件技术引入卫星测试软件领域,提出利用测试功能组件构建通用化测试软件的设计思路;首先,对现有卫星型号的地面测试任务做共性研究;然后,分析研究现有卫星数传分系统和测控分系统单机地面测试涵盖的所有测试功能,描述通用化测试软件的研制需求;以通用化和模块化的设计思路进行软件概要设计;通过分析软件组成、设计流程图、搭建软件界面等步骤进行软件详细设计;最后,软件实现;提出的基于USB接口的卫星通用化地面测试软件,通过某型号功放级地面测试和某型号Ka应答机地面测试,经验证,通用性强,覆盖功能全面,能较好地满足多个分系统单机设备的测试要求。     

Convergence analysis of convex incremental neural networks

Recently, a convex incremental algorithm (CI-ELM) has been proposed in Huang and Chen (Neurocomputing 70:3056–3062, 2007), which randomly chooses hidden neurons and then analytically determines the output weights connecting with the hidden layer and the output layer. Though hidden neurons are generated randomly, the network constructed by CI-ELM is still based on the principle of universal approximation. The random approximation theory breaks through the limitation of most conventional theories, eliminating the need for tuning hidden neurons. However, due to the random characteristic, some of the neurons contribute little to decrease the residual error, which eventually increase the complexity and computation of neural networks. Thus, CI-ELM cannot precisely give out its convergence rate. Based on Lee’s results (Lee et al., IEEE Trans Inf Theory 42(6):2118–2132, 1996), we first show the convergence rate of a maximum CI-ELM, and then systematically analyze the convergence rate of an enhanced CI-ELM. Different from CI-ELM, the hidden neurons of the two algorithms are chosen by following the maximum or optimality principle under the same structure as CI-ELM. Further, the proof process also demonstrates that our algorithms achieve smaller residual errors than CI-ELM. Since the proposed neural networks remove these “useless” neurons, they improve the efficiency of neural networks. The experimental results on benchmark regression problems will support our conclusions. The work is under the funding of Singapore MOE AcRF Tier 1 grant WBS No: R 252-000-221-112.     

Improved MCMAC with momentum, neighborhood, and averagedtrapezoidal output

An improved modified cerebellar articulation controller (MCMAC) neural control algorithm with better learning and recall processes using momentum, neighborhood learning, and averaged trapezoidal output, is proposed in this paper. The learning and recall processes of MCMAC are investigated using the characteristic surface of MCMAC and the control action exerted in controlling a continuously variable transmission (CVT). Extensive experimental results demonstrate a significant improvement with reduced training time and an extended range of trained MCMAC cells. The improvement in recall process using the averaged trapezoidal output (MCMAC-ATO) are contrasted against the original MCMAC using the square of the Pearson product moment correlation coefficient. Experimental results show that the new recall process has significantly reduced the fluctuations in the control action of the MCMAC and addressed partially the problem associated with the resolution of the MCMAC memory array.