High-temperature coupling of high-speed GPC with continuous viscometry. II. Ethylene–vinyl acetate copolymers

An application of the high temperature coupling of a gel permeation chromatograph with a home-made continuous viscometric detector is described. It concerns the comprehensive characterization of ethylene–vinyl acetate copolymers. Suitable chromatographic conditions are chosen to enable a correct use of the universal calibration concept. Through analysis of poly(vinyl acetate) fractions and commercial polyethylene samples, a comparison is made with the results of classical measurements. Average molecular weights as well as intrinsic viscosity appear to be in good agreement within experimental error, which proves the system for the characterization of random ethylene–vinyl acetate copolymers. In attempting to obtain a reliable estimate of long chain branching frequency λ, a series of commercial samples has been selected, with the vinyl acetate weight fraction within the range 0–45%. As a rule, experimental viscosity law exhibits two parts, a straight line with a Mark–Houwink exponent 0.7 in the low molecular weight region and a curvature, well smoothed by a third-degree polynomial regression. Consequently, long chain branching does not appear before a limiting molecular weight of about 50,000. Beyond this limit, λ is 0.5 × 10^?4, with no dependence on molecular weight, which resembles low density polyethylene.     

Reinforcement of single-walled carbon nanotube bundles by intertube bridging

During their production, single-walled carbon nanotubes form bundles. Owing to the weak van der Waals interaction that holds them together in the bundle, the tubes can easily slide on each other, resulting in a shear modulus comparable to that of graphite. This low shear modulus is also a major obstacle in the fabrication of macroscopic fibres composed of carbon nanotubes. Here, we have introduced stable links between neighbouring carbon nanotubes within bundles, using moderate electron-beam irradiation inside a transmission electron microscope. Concurrent measurements of the mechanical properties using an atomic force microscope show a 30-fold increase of the bending modulus, due to the formation of stable crosslinks that effectively eliminate sliding between the nanotubes. Crosslinks were modelled using first-principles calculations, showing that interstitial carbon atoms formed during irradiation in addition to carboxyl groups, can independently lead to bridge formation between neighbouring nanotubes.     

Composes definis dans le systeme AgInSe structure cristalline de □2AgIn5Se8

The usual quasibinary representation obeying Parthe's laws seems too restrictive in the case of the Ag-In-Se system. According to our D.T.A. and X ray analyses, a ternary system representation has to be adopted. The crystal structure of the defect compound AgIn₅Se₈ has been refined to an agreement factor R (F) = 0.0315 in the P42m space group with unit cell parameters: $\text{a}\text{= 5.7934 (4)}\text{A}\text{?}$, $\text{c}\text{= 11.6223 (2)}\text{A}\text{?}$ and $\text{c}\text{a}\text{= 2.0061 ± 0.0003}$.     

A 65-nm Dual-Core Multithreaded Xeon® Processor With 16-MB L3 Cache

This paper describes a dual-core 64-b Xeon MP processor implemented in a 65-nm eight-metal process. The 435-mm² die has 1.328-B transistors. Each core has two threads and a unified 1-MB L2 cache. The 16-MB shared, 16-way set-associative L3 cache implements both sleep and shut-off leakage reduction modes. Long channel transistors are used to reduce subthreshold leakage in cores and uncore (all portions of the die that are outside the cores) control logic. Multiple voltage and clock domains are employed to reduce power     

Reliability and performance optimization of pipelined real-time systems

We consider pipelined real-time systems that consist of a chain of tasks executing on a distributed platform. The processing of the tasks is pipelined: each processor executes only one interval of consecutive tasks. We are interested in minimizing both the input–output latency and the period of application mapping. For dependability reasons, we are also interested in maximizing the reliability of the system. We therefore assign several processors to each interval of tasks, so as to increase the reliability of the system. Both processors and communication links are unreliable and subject to transient failures. We assume that the arrival of the failures follows a constant parameter Poisson law, and that the failures are statistically independent events. We study several variants of this multiprocessor mapping problem, with several hypotheses on the target platform (homogeneous/heterogeneous speeds and/or failure rates). We provide NP-hardness complexity results, and optimal mapping algorithms for polynomial problem instances. Efficient heuristics are presented to solve the general case, and experimental results are provided.     

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.     

Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

In semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation’s dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated.     

Robust output feedback stabilization of uncertain time-varying state-delayed systems with saturating actuators

The robust output feedback stabilization problem for state-delayed systems with time-varying delays and saturating actuators is addressed here. The systems considered are continuous-time, with parametric uncertainties entering all the matrices in the system representation. A saturating control law is designed and a region of initial conditions is specified within which local asymptotic stability of the closed-loop system is ensured. The least conservative approach that employs the Lyapunov–Krasovskii functional is adopted to ensure stabilization. The designed controller is dependent on the time-delay and its rate of change. The controller is constructed in terms of the solution to a set of matrix inequalities.