Sliding mode closed-loop control of FES: controlling the shank movement

Functional electrical stimulation (FES) enables restoration of movement in individuals with spinal cord injury. FES-based devices use electric current pulses to stimulate and excite the intact peripheral nerves. They produce muscle contractions, generate joint torques, and thus, joint movements. Since the underlying neuromuscular-skeletal system is highly nonlinear and time-varying, feedback control is necessary for accurate control of the generated movement. However, classical feedback/closed-loop control algorithms have so far failed to provide satisfactory performance and were not able to guarantee stability of the closed-loop system. Because of this, only open-loop controlled FES devices are in clinical use in spite of their limitations. The purpose of the reported research was to design a novel closed-loop FES controller that achieves good tracking performance and guarantees closed-loop stability. Such a controller was designed based on a mathematical neuromuscular-skeletal model and is founded on a sliding mode control theory. The controller was used to control shank movement and was tested in computer simulations as well as in actual experiments on healthy and spinal cord injured subjects. It demonstrated good robustness, stability, and tracking performance properties.     

Analysis of direct solar ultraviolet irradiance measurements in the French Alps. Retrieval of turbidity and ozone column amount

Direct ultraviolet spectral solar irradiance is regularly obtained by the difference between global and diffuse irradiances at the French Alpine station of Brian?on; the data of years 2001 and 2002 are analyzed in this paper. Comparison with modeled values is used for cloud screening, and an average UV-A aerosol optical depth is used as an index of turbidity; it is found to be around 0.05 for the clear winter days and around 0.2 in summer. Langley plots are used to verify the instrument calibration; they confirm the expected uncertainty smaller than 5%. The ozone total column amount is estimated with an uncertainty between -3 and Dobson units; comparisons with TOMS (Total Ozone Mapping Spectrometer) overpass values shows agreement within the expected uncertainties of both instruments.     

Putative dynamics of vasopressin in its V1a receptor binding site

The molecular architecture of the GPCRs, including the dynamic set of interactions between the receptor and the ligand, is one of the key structural questions of biophysical approaches. In the present study, molecular dynamics (MD) simulations were performed on the well-validated molecular model of the vasopressin V1a receptor applying different parameters (i.e., force fields, time variation, use of constraints) in order to sample the conformational space of the endogenous ligand arginine vasopressin (AVP), to explore different putative binding modes, and to analyze the simulation results with respect to experimental data. Noteworthy, it is to mention that for the first time a model of the vasopressin receptor remained stable in a 500 ps MD simulation run under vacuo boundary conditions using the Kollman all-atom FF even though no constraints were imposed. Conclusively, we determined an optimized experimental procedure for studying the dynamics and structure-functionship of this highly important family of GPCRs: the use of MD simulations with the Kollman all-atom force-field parameters on a constrained receptor. Our simplified model may be used as a basis for structure based design of new GPCR ligands and for in silico screening of virtual combinatorial chemistry libraries.     

Effects of 1,4-Benzoxazin-3-One Derivatives from Maize on Survival and Fecundity of Metopolophium dirhodum (Walker) on Artificial Diet

Effects of 2--D-glucopyranosyloxy-4-hydroxy-7-methoxy-1,4benzoxazin-3-one (DIMBOA-Glc) and DIMBOA-Glc N-O-methylated (HDMBOA-Glc), two compounds present in high concentration in maize, were tested on the aphid Metopolophium dirhodum reared on artificial diet. HDMBOA-Glc and DIMBOA-Glc decrease survival of adults with an LD₅₀ of 1 mM and 5.6 mM, respectively, after 72 hr of feeding. These compounds also decrease the fecundity of the aphids at concentrations of 2 mM and 1 mM, respectively. At concentrations of 2 mM HDMBOA-Glc and 8 mM DIMBOA-Glc in the diet, the average lifetime fecundity of 10 females is near zero. Offspring mortality on diet with 2 mM DIMBOA-Glc is significantly higher than with the control diet. In contrast, HDMBOA-Glc has no effect on the survival of offspring. The possibility that these compounds protect Poaceae against aphids is discussed.     

升温速率对氧化铝粉末烧结行为的影响

采用不同的升温速率,在膨胀计中对脱脂后的氧化铝粉末射成形坯件进行一系列的烧结试验.结果表明,烧结致密化过程主要发生在升温阶段,快速升温有利于致密化的进行和抑止晶粒长大,但由于烧结时间较短和烧结炉最高温度的限制,产品的最终致密化程度不高.在低温时快速升温,高温时缓慢加热,可以获得较好的致密化效果和微观结构.试验和分析结果将为建立非等温烧结模型和烧结工艺参数的优化方法提供依据.     

Embedded holonic fault diagnosis of complex transportation systems

The use of electronic equipment and embedded computing technologies in modern complex transportation systems continues to grow in a highly competitive market, in which product maintainability and availability is vital. These technological advances also make fault diagnosis and maintenance interventions much more challenging, since these operations require a deep understanding of the entire system. This paper proposes a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

In semi-arid African regions (annual rainfall between 200 and 600 mm), variability of vegetative activity is mainly due to the rainfall of the current rainy season. In most of South Africa, the rainy season occurs from October to March. On average, vegetative activity lags rainfall by 1 to 2 months. The interannual variability in early summer (December to September) normalized difference vegetation index (NDVI) depends primarily on precipitation at the beginning (October to November) of the rainy season. However, once this primary control is removed, the residual interannual variability in NDVI highlights a double memory effect: a 1-year effect, referred to as Mem₁, and a 7- to 10-month effect, referred to as Mem₂. This article aims at better describing the influence of soil and vegetation characteristics on these two memory effects. The data sets used in this study are as follows: (1) a 19-year NDVI time series from National Oceanic and Atmospheric Administration (NOAA) satellites, (2) rainfall records from a network of 1160 rain-gauge stations compiled by the Water Research Commission (WRC), (3) vegetation types from Global Land Cover (GLC) 2000 and (4) soil characteristics from the soil and terrain database for Southern Africa (SOTERSAF). Results indicate that among 20–30% of NDVI variance that is not explained by the concurrent rainfall, one-third is explained by the two memory effects. Mem₁ is found to have maximum effect in the northwest of our study domain, near the Botswana boundary, in the South Kalahari. Associated conditions are open grasslands growing on Arenosols. Mem₁ is less important in the southeast, particularly in open grassland with shrubs growing on Cambisols. Thus, Mem₁ mainly depends on soil texture. Mem₂ is more widespread and its influence is the greatest in the centre, the south and the east of our domain. It is related to rainfall from January to April, which controls, beyond the intervening dry season, the interannual variations of NDVI (December to September) at the beginning of the next rainy season. Through these new findings, this article emphasizes again the high potential of remote-sensing techniques to monitor and understand the dynamics of semi-arid environments.