Search results for " extract"
showing 10 items of 2021 documents
A no-carrier-added72Se/72As radionuclide generator based on solid phase extraction
2005
Summary72As-labelled radiopharmaceuticals could be a valuable resource for Positron Emission Tomography (PET). In particular, the long half-life of72As (T1/2= 26 h) facilitates the observation of long-term physiological or metabolic processes, such as the enrichment and distribution of antibodies in tumor tissue. This work describes the primary radiochemical separation of no-carrier-added (nca)72Se from cyclotron irradiated germanium targets and the development of a polystyrene type solid-phase extraction based72Se/72As radionuclide generator, avoiding the addition of any selenium carrier. The irradiated germanium target is dissolved in HFconcand selenium is reduced with hydrazine dihydroch…
Gas assisted mechanical expression (GAME) for the selective recovery of lipophilic and hydrophilic compounds from olive kernel
2017
Abstract This work is devoted to valorize olive kernel; a by-product produced during olive oil extraction process. For this purpose, aqueous liquid solid extraction (LSE), mechanical expression (ME), supercritical CO2 (SC-CO2) and gas assisted mechanical expression (GAME), processes were compared when applied separately or consequently (ME + GAME), in terms of total phenolic compound (TPC) and oil recovery yields. Results showed that although the high extraction yields of TPC using LSE (61.4 ± 1.3%), the extraction process is economically not viable. However, it was demonstrated that applying ME (1 h at 30 MPa) followed by GAME (1 h at 30 MPa ME and 10 MPa SC-CO2 pressures) allowed recoveri…
Herbicidal potential of aqueous extracts from Melia azedarach L., Artemisia arborescens L., Rhus coriaria L. and Lantana camara L.
2017
In the search for new strategies for weed management in agricultural systems, a great interest is to use the plant extracts to replace or integrate, chemical weed control. Two experiments were done to test the effects of plant water extracts from Chinaberry (Melia azedarach L.), Tree Wormwood (Artemisia arborescens (Vaill.) L.), Sicilian Sumac (Rhus coriaria L.) and Lantana (Lantana camara L.) on seed germination of Rocket (Eruca sativa Mill.), Rapeseed (Brassica napus L.), Bladderflower (Araujia sericifera Brot.) and Psyllium (Plantago psyllium L). The water extracts (pure and 50% mixtures) from the donor species were applied on seeds of recipient plants. In second experiment in pots, thes…
Effect of nitrogen limitation and nature of the feed upon Oenococcus oeni metabolism and extracellular protein production
2005
Aims: The aim of the study was to characterize the effect of various nitrogen sources on Oenococcus oeni growth, carbon source utilization, extracellular protease activity and extracellular proteins. More generally, the goal is to understand how nitrogen-based additives might act to enhance malolactic fermentation in wine. Methods and Results: Five yeast extracts were used. As the amino acid and nitrogen analyses revealed, they were similar in global amino acid composition, except for arginine level. Nevertheless the ratio of amino acids between free/bound, and low/high molecular weight fractions were highly different. One of the yeast extracts led to a significant protease activity in th…
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
2018
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error…
A Review of Kernel Methods in Remote Sensing Data Analysis
2011
Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastruc…
State classification for autonomous gas sample taking using deep convolutional neural networks
2017
Despite recent rapid advances and successful large-scale application of deep Convolutional Neural Networks (CNNs) using image, video, sound, text and time-series data, its adoption within the oil and gas industry in particular have been sparse. In this paper, we initially present an overview of opportunities for deep CNN methods within oil and gas industry, followed by details on a novel development where deep CNN have been used for state classification of autonomous gas sample taking procedure utilizing an industrial robot. The experimental results — using a deep CNN containing six layers — show accuracy levels exceeding 99 %. In addition, the advantages of using parallel computing with GP…
Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks
2017
Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…
Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine
2020
The rapid deployment in information and communication technologies and internet-based services have made anomaly based network intrusion detection ever so important for safeguarding systems from novel attack vectors. To this date, various machine learning mechanisms have been considered to build intrusion detection systems. However, achieving an acceptable level of classification accuracy while preserving the interpretability of the classification has always been a challenge. In this paper, we propose an efficient anomaly based intrusion detection mechanism based on the Tsetlin Machine (TM). We have evaluated the proposed mechanism over the Knowledge Discovery and Data Mining 1999 (KDD’99) …
Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning
2021
Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence o…