Non-oxidized porous silicon-based power AC switch peripheries

We present in this paper a novel application of porous silicon (PS) for low-power alternating current (AC) switches such as triode alternating current devices (TRIACs) frequently used to control small appliances (fridge, vacuum cleaner, washing machine, coffee makers, etc.). More precisely, it seems possible to benefit from the PS electrical insulation properties to ensure the OFF state of the device. Based on the technological aspects of the most commonly used AC switch peripheries physically responsible of the TRIAC blocking performances (leakage current and breakdown voltage), we suggest to isolate upper and lower junctions through the addition of a PS layer anodically etched from existing AC switch diffusion profiles. Then, we comment the voltage capability of practical samples emanating from the proposed architecture. Thanks to the characterization results of simple Al-PS-Si(P) structures, the experimental observations are interpreted, thus opening new outlooks in the field of AC switch peripheries.     

Randomized controlled study of the urinary excretion of biophenols following acute and chronic intake of olive leaf supplements

Olive leaf supplement was characterised by HPLC and administered to healthy young adults over 28 d (three tablets or equivalent liquid dose per day), or in a single bolus dose of three tablets (or liquid equivalent). Oleuropein was the major biophenol in the extracts. There are no data on the excretion of urinary end-products of the metabolism of the olive leaf biophenols. Following both chronic and acute ingestion neither oleuropein, nor its hydrolysis product, hydroxytyrosol, were detected in urine samples. However, glucuronic acid conjugates, derived from oleuropein aglycone were detected in all urine samples up to 6 h following acute ingestion. The data suggest that oleuropein is bioavailable, which is a necessary pre-condition for bioactivity.     

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.     

A time-series prediction approach for feature extraction in a brain-computer interface.

This paper presents a feature extraction procedure (FEP) for a brain-computer interface (BCI) application where features are extracted from the electroencephalogram (EEG) recorded from subjects performing right and left motor imagery. Two neural networks (NNs) are trained to perform one-step-ahead predictions for the EEG time-series data, where one NN is trained on right motor imagery and the other on left motor imagery. Features are derived from the power (mean squared) of the prediction error or the power of the predicted signals. All features are calculated from a window through which all predicted signals pass. Separability of features is achieved due to the morphological differences of the EEG signals and each NNs specialization to the type of data on which it is trained. Linear discriminant analysis (LDA) is used for classification. This FEP is tested on three subjects off-line and classification accuracy (CA) rates range between 88% and 98%. The approach compares favorably to a well-known adaptive autoregressive (AAR) FEP and also a linear AAR model based prediction approach.     

Bandgap nanoengineering of graphene tunnel diodes and tunnel transistors to control the negative differential resistance

By means of numerical simulation based on the Green’s function formalism on a tight binding Hamiltonian, we investigate different possibilities of achieving a strong effect of negative differential resistance in graphene tunnel diodes, the operation of which is controlled by the interband tunneling between both sides of the PN junction. We emphasize on different approaches of bandgap nanoengineering, in the form of nanoribbons (GNRs) or nanomeshes (GNMs), which can improve the device behaviour. In particular, by inserting a small or even zero bandgap section in the transition region separating the doped sides of the junction, the peak current and the peak-to-valley ratio (PVR) are shown to be strongly enhanced and weakly sensitive to the length fluctuations of the transition region, which is an important point regarding applications. The study is extended to the tunneling FET which offers the additional possibility of modulating the interband tunneling and the PVR. The overall work suggests the high potential of GNM lattices for designing high performance devices for either analog or digital applications.     

On the evaluation of Geographic Information Retrieval systems

Search engines for Digital Libraries allow users to retrieve documents according to their contents. They process documents without differentiating the manifold aspects of information. Spatial and temporal dimensions are particularly dismissed. These dimensions are, however, of great interest for users of search engines targeting either the Web or specialized Digital Libraries. Recent studies reported that nearly 20% queries convey spatial and temporal information in addition to topical information. These three dimensions were referred to as parts of “geographic information.” In the literature, search engines handling those dimensions are called “Geographic Information Retrieval (GIR) systems.” Although several initiatives for evaluating GIR systems were undertaken, none was concerned with evaluating these three dimensions altogether. In this article, we address this issue by designing an evaluation framework, usefulness of which is highlighted through a case study involving a test collection and a GIR system. This framework allowed the comparison of our GIR system to state-of-the-art topical approaches. We also performed experiments for measuring performance improvement stemming from each dimension or their combination. We show that combining the three dimensions yields improvement in effectiveness (+73.9%) over a common topical baseline. Moreover, rather than conveying redundancy, the three dimensions complement each other.