Review and Analysis on Main Technology of Exoskeletal Robot System for Upper Limbs Rehabilitation

Major function of exoskeletal robot system for upper limbs rehabilitation is to assist patient to carry out upper limbs’ rehabilitation training. Main technology of exoskeletal robot system for upper limbs rehabilitation includes design of mechanical structure of exoskeletal robot, design of control system of exoskeletal robot and implemention of data and information transmission between exoskeletal robot and upper limbs of human body. Currently implemention of data and information transmission rely mainly on methods of acquiring sEMG signal and force feedback. Reviewing and analyzing the specific technical development and deficiency in field of exoskeletal robot system for upper limbs rehabilitation will be important way in improving and upgrading the technology in future

Degeneralization Algorithm for Generation of Büchi Automata Based on Contented Situation

We present on-the-fly degeneralization algorithm used to transform generalized Büchi automata (GBA) into Büchi Automata (BA) different from the standard degeneralization algorithm. Contented situation, which is used to record what acceptance conditions are satisfiable during expanding LTL formulae, is attached to the states and transitions in the BA. In order to get the deterministic BA, the Shannon expansion is used recursively when we expand LTL formulae by applying the tableau rules. On-the-fly degeneralization algorithm is carried out in each step of the expansion of LTL formulae. Ordered binary decision diagrams are used to represent the BA and simplify LTL formulae. The temporary automata are stored as syntax directed acyclic graph in order to save storage space. These ideas are implemented in a conversion algorithm used to build a property automaton corresponding to the given LTL formulae. We compare our method to previous work and show that it is more efficient for four sets of random formulae generated by LBTT

The General Ensemble Biogeochemical Modeling System (GEMS) and its Applications to Agricultural Systems in the United States

The General Ensemble Biogeochemical Modeling System (GEMS) (Liu, 2009; Liu et al., 2004c) was developed to integrate well-established ecosystem biogeochemical models with various spatial databases for the simulations of biogeochemical cycles over large areas. Figure 18.1 shows the overall structure of the GEMS. Some of the key components are described below. General Ensemble Biogeochemical Modeling System (GEMS) 310 Multiple Underlying Biogeochemical Models 310 Monte Carlo Simulations 311 Model Inputs: Management Practices and Others 311 Model Outputs 311 Data Assimilation 311 Simulation of Agricultural Practices: EDCM as an Example 312 Net Primary Production (NPP) and Improvements in Crop Genetics and Agronomics 312 Soil Carbon Dynamics 312 Impacts of Soil Erosion and Deposition 313 CH4 and N2O Fluxes 313 Study Areas and Modeling Design 314 Study Areas 314 Nebraska Eddy Flux Tower Sites 314 Regional Applications: Mississippi Valley and Prairie Potholes 315 Modeling Design 315 Results 316 Impacts of Management Practices on SOC at Site Scale 316 Quantification of Regional Carbon Stocks and GHG Fluxes 317 Prairie Pothole Region 317 Mississippi Valley 319 Discussion 32

Co-delivery of resveratrol and docetaxel via polymeric micelles to improve the treatment of drug-resistant tumors

Co-delivery of anti-cancer drugs is promising to improve the efficacy of cancer treatment. This study was aiming to investigate the potential of concurrent delivery of resveratrol (RES) and docetaxel (DTX) via polymeric nanocarriers to treat breast cancer. To this end, methoxyl poly(ethylene glycol)-poly(d,l-lactide) copolymer (mPEG-PDLA) was prepared and characterized using FTIR and 1H NMR, and their molecular weights were determined by GPC. Isobologram analysis and combination index calculation were performed to find the optimal ratio between RES and DTX to against human breast adenocarcinoma cell line (MCF-7 cells). Subsequently, RES and DTX were loaded in the mPEG-PDLA micelles simultaneously, and the morphology, particle size distribution, in vitro release, pharmacokinetic profiles, as well as cytotoxicity to the MCF-7 cells were characterized. IC50 of RES and DTX in MCF-7 cells were determined to be 23.0 µg/ml and 10.4 µg/ml, respectively, while a lower IC50 of 4.8 µg/ml of the combination of RES and DTX was obtained. The combination of RES and DTX at a ratio of 1:1 (w/w) generated stronger synergistic effect than other ratios in the MCF-7 cells. RES and DTX loaded mPEG-PDLA micelles exhibited prolonged release profiles, and enhanced cytotoxicity in vitro against MCF-7 cells. The AUC(0→t) of DTX and RES in mPEG-PDLA micelles after i.v. administration to rats were 3.0-fold and 1.6-fold higher than that of i.v. injections of the individual drugs. These findings indicated that the co-delivery of RES and DTX using mPEG-PDLA micelles could have better treatment of tumors. Keywords: Resveratrol, Docetaxel, Methoxyl poly(ethylene glycol)-poly(d,l-lactide) copolymer (mPEG-PDLA), Micelles, Drug resistance tumo

The Representer Method, the Ensemble Kalman Filter and the Ensemble Kalman Smoother: A Comparison Study Using a Nonlinear Reduced Gravity Ocean Model

This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the truth and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2-4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method. (c) 2005 Elsevier Ltd. All rights reserved

Study on CO

CO2 emission control is of great urgency, and the decrease of heavy-duty diesel engines’ CO2 emission is one of the significant methods to reduce CO2 emission. This paper uses a full-flow constantvolume dilution sampling system to experiment on the China VI heavy-duty diesel engine in order to measure CO2 emissions under WHTC and WHSC cycles and different loads, with studying the instantaneous emissions characteristics of CO2, post-processing effects on CO2 emissions, influence factors of CO2 emissions. The study found that the CO2 emissions before the post-treatment of the WHSC cycle are 37% higher than that of the WHTC cycle, while emissions of CO2 are 3.45% higher than that before post-treatment. Simultaneously, cold start increases the CO2 emissions of the heavy-duty diesel engines by more than 1%. Post-treatment still increases the CO2 emissions of heavy-duty diesel engines by 3.5%. In addition, CO2 emissions have different trends with power at different speeds. CO2 emissions get an incremental within 600rpm and 900rpm, which gradually becomes slower until it reaches the peak, as engine power increases; the CO2 emissions initially increase, followed by a decrease, and then continue to increase within 1000rpm and 1400rpm; the CO2 emissions are almost not affected by the speed within 1500rpm and 1900rpm