Search results for: green extraction

Authors: D. Berdous, H. Ferfera-Harrar

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Superabsorbent polymers (SAPs) or hydrogels with three-dimensional hydrophilic network structure are high-performance water absorbent and retention materials. The in situ synthesis of metal nanoparticles within polymeric network as antibacterial agents for bio-applications is an approach that takes advantage of the existing free-space into networks, which not only acts as a template for nucleation of nanoparticles, but also provides long term stability and reduces their toxicity by delaying their oxidation and release. In this work, SAP/nanosilver nanocomposites were successfully developed by a unique green process at room temperature, which involves in situ formation of silver nanoparticles (AgNPs) within hydrogels as a template. The aim of this study is to investigate whether these AgNPs-loaded hydrogels are potential candidates for antimicrobial applications. Firstly, the superabsorbents were prepared through radical copolymerization via grafting and crosslinking of acrylamide (AAm) onto chitosan backbone (Cs) using potassium persulfate as initiator and N,N’-methylenebisacrylamide as the crosslinker. Then, they were hydrolyzed to achieve superabsorbents with ampholytic properties and uppermost swelling capacity. Lastly, the AgNPs were biosynthesized and entrapped into hydrogels through a simple, eco-friendly and cost-effective method using aqueous silver nitrate as a silver precursor and curcuma longa tuber-powder extracts as both reducing and stabilizing agent. The formed superabsorbents nanocomposites (Cs-g-PAAm)/AgNPs were characterized by X-ray Diffraction (XRD), UV-visible Spectroscopy, Attenuated Total reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR), Inductively Coupled Plasma (ICP), and Thermogravimetric Analysis (TGA). Microscopic surface structure analyzed by Transmission Electron Microscopy (TEM) has showed spherical shapes of AgNPs with size in the range of 3-15 nm. The extent of nanosilver loading was decreased by increasing Cs content into network. The silver-loaded hydrogel was thermally more stable than the unloaded dry hydrogel counterpart. The swelling equilibrium degree (Q) and centrifuge retention capacity (CRC) in deionized water were affected by both contents of Cs and the entrapped AgNPs. The nanosilver-embedded hydrogels exhibited antibacterial activity against Escherichia coli and Staphylococcus aureus bacteria. These comprehensive results suggest that the elaborated AgNPs-loaded nanomaterials could be used to produce valuable wound dressing.

Keywords: antibacterial activity, nanocomposites, silver nanoparticles, superabsorbent Hydrogel

Procedia PDF Downloads 242

Authors: S. Jurado, E. Pazmino

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Determination of the medial axis of a porous media sample is a non-trivial problem of interest for several disciplines, e.g., hydrology, fluid dynamics, contaminant transport, filtration, oil extraction, etc. However, the computational tools available for researchers are limited and restricted. The primary aim of this work was to develop a series of algorithms to extract porosity, medial axis structure, and pore-throat size distributions from porous media domains. A complementary objective was to provide the algorithms as free computational software available to the academic community comprising researchers and students interested in 3D data processing. The burn algorithm was tested on porous media data obtained from High-Resolution X-Ray Microtomography (HRXMT) and idealized computer-generated domains. The real data and idealized domains were discretized in voxels domains of 550³ elements and binarized to denote solid and void regions to determine porosity. Subsequently, the algorithm identifies the layer of void voxels next to the solid boundaries. An iterative process removes or 'burns' void voxels in sequence of layer by layer until all the void space is characterized. Multiples strategies were tested to optimize the execution time and use of computer memory, i.e., segmentation of the overall domain in subdomains, vectorization of operations, and extraction of single burn layer data during the iterative process. The medial axis determination was conducted identifying regions where burnt layers collide. The final medial axis structure was refined to avoid concave-grain effects and utilized to determine the pore throat size distribution. A graphic user interface software was developed to encompass all these algorithms, including the generation of idealized porous media domains. The software allows input of HRXMT data to calculate porosity, medial axis, and pore-throat size distribution and provide output in tabular and graphical formats. Preliminary tests of the software developed during this study achieved medial axis, pore-throat size distribution and porosity determination of 100³, 320³ and 550³ voxel porous media domains in 2, 22, and 45 minutes, respectively in a personal computer (Intel i7 processor, 16Gb RAM). These results indicate that the software is a practical and accessible tool in postprocessing HRXMT data for the academic community.

Keywords: medial axis, pore-throat distribution, porosity, porous media

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Authors: Alina Gilmanova

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China’s dependency on coal for energy is causing pollution in China and abroad. To supply the increasing energy demand and being under the pressure from international society to reduce the emissions, China was pushed to develop renewable energy. The increasing subsidies in Renewable energy sources (RES) led not only to the price-cutting but also affecting the international trade in green technology sector. In order to evaluate the role of RES in foreign policy of China, I am going to give an (i) overview of RES development in China and examine the cooperation between China and (ii) developed, (ii) developing and emerging countries. The conclusive remarks are intended to address the question of how the present Chinese renewable energy development is impacting its foreign policy and international society.

Keywords: renewable energy, China, foreign affairs, brics, cooperation

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Authors: Shuo Liu

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In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

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Authors: Evgeny E. Tereshatov

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Astatine-211 is considered one of the most promising radionuclides for Targeted Alpha Therapy. In order to develop reliable procedures to label biomolecules and utilize efficient delivery vehicle principles, one should understand the main chemical characteristics of astatine. The short half-life of 211At (~7.2 h) and absence of any stable isotopes of this element are limiting factors towards studying the behavior of astatine. Our team has developed a procedure for rapid and efficient isolation of astatine from irradiated bismuth material in nitric acid media based on 3-octanone and 1-octanol extraction chromatography resins. This process has been automated and it takes 20 min from the beginning of the target dissolution to the At-211 fraction elution. Our next step is to consider commercially available chromatography resins and their applicability in astatine purification in the same media. Results obtained along with the corresponding sorption mechanisms will be discussed.

Keywords: astatine-211, chromatography, automation, mechanism, radiopharmaceuticals

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Authors: Saif Alomari

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The paper aims at giving physical and mathematical descriptions of how the structural parameters of a resonant tunnelling diode (RTD) affect its output characteristics. Specifically, the value of the peak voltage, peak current, peak to valley current ratio (PVCR), and the difference between peak and valley voltages and currents ΔV and ΔI. A simulation-based approach using the Non-Equilibrium Green Function (NEGF) formalism based on the Silvaco ATLAS simulator is employed to conduct a series of designed experiments. These experiments show how the doping concentration in the emitter and collector layers, their thicknesses, and the width of the barriers and the quantum well influence the above-mentioned output characteristics. Each of these parameters was systematically changed while holding others fixed in each set of experiments. Factorial experiments are outside the scope of this work and will be investigated in future. The physics involved in the operation of the device is thoroughly explained and mathematical models based on curve fitting and underlaying physical principles are deduced. The models can be used to design devices with predictable output characteristics. These models were found absent in the literature that the author acanned. Results show that the doping concentration in each region has an effect on the value of the peak voltage. It is found that increasing the carrier concentration in the collector region shifts the peak to lower values, whereas increasing it in the emitter shifts the peak to higher values. In the collector’s case, the shift is either controlled by the built-in potential resulting from the concentration gradient or the conductivity enhancement in the collector. The shift to higher voltages is found to be also related to the location of the Fermi-level. The thicknesses of these layers play a role in the location of the peak as well. It was found that increasing the thickness of each region shifts the peak to higher values until a specific characteristic length, afterwards the peak becomes independent of the thickness. Finally, it is shown that the thickness of the barriers can be optimized for a particular well width to produce the highest PVCR or the highest ΔV and ΔI. The location of the peak voltage is important in optoelectronic applications of RTDs where the operating point of the device is usually the peak voltage point. Furthermore, the PVCR, ΔV, and ΔI are of great importance for building RTD-based oscillators as they affect the frequency response and output power of the oscillator.

Keywords: peak to valley ratio, peak voltage shift, resonant tunneling diodes, structural parameters

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Authors: Kemal Polat

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In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This proposal aims for semantic enrichment between glossaries using the Simple Knowledge Organization System (SKOS) vocabulary to discover synonyms, hyponyms and hyperonyms semiautomatically, in Brazilian Portuguese, generating new semantic relationships based on WordNet. To evaluate the quality of this proposed model, experiments were performed by the use of two sets containing new relations, being one generated automatically and the other manually mapped by the domain expert. The applied evaluation metrics were precision, recall, f-score, and confidence interval. The results obtained demonstrate that the applied method in the field of Oil Production and Extraction (E&P) is effective, which suggests that it can be used to improve the quality of terminological mappings. The procedure, although adding complexity in its elaboration, can be reproduced in others domains.

Keywords: ontology matching, mapping enrichment, semantic web, linked data, SKOS

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Authors: L. N. Shubha, P. Madhusudana Rao

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The Polyaniline / Copper Oxide(PANI / CuO) nanocomposite was prepared by solution mixing of prepared Polyaniline and copper Oxide in Dimethyl sulfoxide (DMSO). The synthesis involved the formation of dark green colored Polyaniline-Copper Oxide nanocomposite. The synthesized polymer nano composites were characterized by XRD, FTIR, SEM and UV-Visible Spectroscopy. The characteristic peaks in XRD, FTIR and UV-Visible spectra confirmed the presence of CuO in the polymer structure. SEM analysis revealed formation of PANI/CuO nano composite The D.C. conductivity measurements were performed using two probe method for various temperatures.

Keywords: polyaniline/copper oxide (PANI/CuO) nanocomposite, XRD, SEM, FTIRand DC- conductivity, UV-visible spectra

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Authors: Mary Booth

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Biophotovoltaics is a method of green energy generation derived from exposing plants to lights. Its vast potential is hampered by the public’s relative ignorance of its existence. This work aims to formalize the principles of the physical processes of biophotovoltaics into a comprehensible visual software model, thus amplifying the human thought process. The methods used involve initially crafting a scale model of a working biophotovoltaic system from household materials inspired by the work of Paolo Bombelli. The scale model is then programmed into a system-level simulation, wherein a 3D animation dissects the system and its general energy generation process. The completed 3D system-level simulation ultimately creates a simplified visual understanding of the complex principles of the biophotovoltaic system.

Keywords: 3D, biophotovoltaics, render

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Yik Sin Chan, Bee Ling Chuah, Wei Quan Chan, Ri Jin Cheng, Yan Hang Oon, Kong Soo Khoo, Nam Weng Sit

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Traditionally, medicinal plants have been used to treat different kinds of ailments including infectious diseases. They serve as a good source of lead compounds for the development of new and safer anti-infective agents. This study aimed to investigate the antimicrobial potential of the leaves of three medicinal plants, namely Catunaregam spinosa (Rubiaceae; Mountain pomegranate), Houttuynia cordata (Saururaceae; "fishy-smell herb") and Rhapis excelsa (Arecaceae; “broadleaf lady palm”). The leaves extracts were obtained by sequential extraction using hexane, chloroform, ethyl acetate, ethanol, methanol and water. The antibacterial and antifungal activities were assessed using a colorimetric broth microdilution method against a panel of human pathogenic bacteria (Gram-positive: Bacillus cereus and Staphylococcus aureus; Gram-negative: Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa) and fungi (yeasts: Candida albicans, Candida parapsilosis and Cryptococcus neoformans; Moulds: Aspergillus fumigatus and Trichophyton mentagrophytes) respectively; while antiviral activity was evaluated against the Chikungunya virus on monkey kidney epithelial (Vero) cells by neutral red uptake assay. All the plant extracts showed bacteriostatic activity, however, only 72% of the extracts (13/18) were found to have bactericidal activity. The lowest minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were given by the hexane extract of C. spinosa against S. aureus with the values of 0.16 and 0.31 mg/mL respectively. All the extracts also possessed fungistatic activity. Only the hexane, chloroform and ethyl acetate extracts of H. cordata exerted inhibitory activity against A. fumigatus, giving the lowest fungal susceptibility index of 16.7%. In contrast, only 61% of the extracts (11/18) showed fungicidal activity. The ethanol extract of R. excelsa exhibited the strongest fungicidal activity against C. albicans, C. parapsilosis and T. mentagrophytes with minimum fungicidal concentration (MFC) values of 0.04–0.08 mg/mL, in addition to its methanol extract against T. mentagrophytes (MFC=0.02 mg/mL). For anti-Chikungunya virus activity, only chloroform and ethyl acetate extracts of R. excelsa showed significant antiviral activity with 50% effective concentrations (EC50) of 29.9 and 78.1 g/mL respectively. Extracts of R. excelsa warrant further investigations into their active principles responsible for antifungal and antiviral properties.

Keywords: bactericidal, Chikungunya virus, extraction, fungicidal

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Authors: Mekha Mathew, Varun P Gopi

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Wireless Capsule Endoscopy (WCE) has become a great device in Gastrointestinal (GI) tract diagnosis, which can examine the entire GI tract, especially the small intestine without invasiveness and sedation. Bleeding in the digestive tract is a symptom of a disease rather than a disease itself. Hence the detection of bleeding is important in diagnosing many diseases. In this paper we proposes a novel method for distinguishing bleeding regions from normal regions based on Contourlet transform and Local Binary Pattern (LBP). Experiments show that this method provides a high accuracy rate of 96.38% in CIE XYZ colour space for k-Nearest Neighbour (k-NN) classifier.

Keywords: Wireless Capsule Endoscopy, local binary pattern, k-NN classifier, contourlet transform

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Authors: Thel Phyu Phyu Soe

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Myanmar started its new political movement in 2011, opening up to trade, encouraging foreign investment, deepening its financial sectors. The tourism is one of the key sectors to make reform process from the perspective of green economy and green growth. The Inle Lake, second largest lake, famous for broad diversity of cultural and natural assets, become one of the country’s main tourism destination. In the study area, local livelihoods are based on a combination of farming (mainly floating garden) wage labor, tourism, and small business. But the Inle lake water body or water surface area decreased by 96.44 km² within 20 years, from 67.98 km² in 1990 to 56.63 km² in 2010. Floating garden cultivation (hydro phonic farm) is a distinguished characteristic of Inle Lake. Two adjacent villages (A and B) were selected to compare the relationship between tourism access and agricultural production. Ground truthing, focus group discussion, and in-depth questionnaires with floating gardeners were carried out. In A village, 57% of the respondents relied tourism as their major income sources, while almost all the households in B village relied floating gardens as major livelihood. Both satellite image interpretation and community studies highlighted that around 80% of the floating garden become fallow after severe drought in 2010 and easy income access to tourism related activities. The villagers can get 20-30 US$ for round trip guiding to major tourist attraction places.Even though tourism is the major livelihood options for the A village, the poorest households (less than 1500 US$ per year) are those who didn’t own transportation property for tourism related activities. In B village, more than 70% of the households relied floating gardens as their major income sources and less participated in tourism related activities because they don’t have motorboat stand connected to the major tourist attraction areas. Access to tourism related activities (having boat stand where they can guide tourists by boat and sell local products and souvenirs) have much impacted on changes in local people livelihood options. However, tourism may have impacts that are beneficial for one group of a society, but which are negative for another. Income inequality and negative impacts can only be managed effectively if they have been identified, measured and evaluated. The severe drought in 2010, instability of lake water level, high expenses for agriculture assisted the local people to participate in easy access tourism related activities.

Keywords: diminishing, floating garden, livelihood, tourism-related income

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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MicroRNAs (miRNA) are a class of endogenous, single-stranded, small, and non-protein coding RNA molecules typically 20-25 nucleotides long. They are thought to regulate the expression of other genes in a broad range by binding to 3’- untranslated regions (3’-UTRs) of specific mRNAs. The detection of miRNAs is very important for understanding of the function of these molecules and in the diagnosis of variety of human diseases. However, detection of miRNAs is very challenging because of their short length and high sequence similarities within miRNA families. So, a simple-to-use, low-cost, and highly sensitive method for the detection of miRNAs is desirable. In this study, we demonstrate a novel bi-directional extension (BDE) assay. In the first step, a specific linear RT primer is hybridized to 6-10 base pairs from the 3’-end of a target miRNA molecule and then reverse transcribed to generate a cDNA strand. After reverse transcription, the cDNA was hybridized to the 3’-end which is BDE sequence; it played role as the PCR template. The PCR template was amplified in an SYBR green-based quantitative real-time PCR. To prove the concept, we used human brain total RNA. It could be detected quantitatively in the range of seven orders of magnitude with excellent linearity and reproducibility. To evaluate the performance of BDE assay, we contrasted sensitivity and specificity of the BDE assay against a commercially available poly (A) tailing method using miRNAs for let-7e extracted from A549 human epithelial lung cancer cells. The BDE assay displayed good performance compared with a poly (A) tailing method in terms of specificity and sensitivity; the CT values differed by 2.5 and the melting curve showed a sharper than poly (A) tailing methods. We have demonstrated an innovative, cost-effective BDE assay that allows improved sensitivity and specificity in detection of miRNAs. Dynamic range of the SYBR green-based RT-qPCR for miR-145 could be represented quantitatively over a range of 7 orders of magnitude from 0.1 pg to 1.0 μg of human brain total RNA. Finally, the BDE assay for detection of miRNA species such as let-7e shows good performance compared with a poly (A) tailing method in terms of specificity and sensitivity. Thus BDE proves a simple, low cost, and highly sensitive assay for various miRNAs and should provide significant contributions in research on miRNA biology and application of disease diagnostics with miRNAs as targets.

Keywords: bi-directional extension (BDE), microRNA (miRNA), poly (A) tailing assay, reverse transcription, RT-qPCR

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Authors: Vibha Devi, Shabina Khanam

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Hemp (Cannabis sativa) possesses a rich content of ω-6 linoleic and ω-3 linolenic essential fatty acid in the ratio of 3:1, which is a rare and most desired ratio that enhances the quality of hemp oil. These components are beneficial for the development of cell and body growth, strengthen the immune system, possess anti-inflammatory action, lowering the risk of heart problem owing to its anti-clotting property and a remedy for arthritis and various disorders. The present study employs supercritical fluid extraction (SFE) approach on hemp seed at various conditions of parameters; temperature (40 - 80) °C, pressure (200 - 350) bar, flow rate (5 - 15) g/min, particle size (0.430 - 1.015) mm and amount of co-solvent (0 - 10) % of solvent flow rate through central composite design (CCD). CCD suggested 32 sets of experiments, which was carried out. As SFE process includes large number of variables, the present study recommends the application of resampling techniques for cross-validation of the obtained data. Cross-validation refits the model on each data to achieve the information regarding the error, variability, deviation etc. Bootstrap and jackknife are the most popular resampling techniques, which create a large number of data through resampling from the original dataset and analyze these data to check the validity of the obtained data. Jackknife resampling is based on the eliminating one observation from the original sample of size N without replacement. For jackknife resampling, the sample size is 31 (eliminating one observation), which is repeated by 32 times. Bootstrap is the frequently used statistical approach for estimating the sampling distribution of an estimator by resampling with replacement from the original sample. For bootstrap resampling, the sample size is 32, which was repeated by 100 times. Estimands for these resampling techniques are considered as mean, standard deviation, variation coefficient and standard error of the mean. For ω-6 linoleic acid concentration, mean value was approx. 58.5 for both resampling methods, which is the average (central value) of the sample mean of all data points. Similarly, for ω-3 linoleic acid concentration, mean was observed as 22.5 through both resampling. Variance exhibits the spread out of the data from its mean. Greater value of variance exhibits the large range of output data, which is 18 for ω-6 linoleic acid (ranging from 48.85 to 63.66 %) and 6 for ω-3 linoleic acid (ranging from 16.71 to 26.2 %). Further, low value of standard deviation (approx. 1 %), low standard error of the mean (< 0.8) and low variance coefficient (< 0.2) reflect the accuracy of the sample for prediction. All the estimator value of variance coefficients, standard deviation and standard error of the mean are found within the 95 % of confidence interval.

Keywords: resampling, supercritical fluid extraction, hemp oil, cross-validation

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Authors: Zoi Konsoula

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The increasing prevalence of Alzheimer’s disease (AD) and diabetes mellitus (DM) constitutes them major global health problems. Recently, the inhibition of key enzyme activity is considered a potential treatment of both diseases. Specifically, inhibition of acetylcholinesterase (AChE), the key enzyme involved in the breakdown of the neurotransmitter acetylcholine, is a promising approach for the treatment of AD, while inhibition of α-amylase retards the hydrolysis of carbohydrates and, thus, reduces hyperglycemia. Unfortunately, commercially available AChE and α-amylase inhibitors are reported to possess side effects. Consequently, there is a need to develop safe and effective treatments for both diseases. In the present study, pomegranate peel (PP) was extracted using various solvents of increasing polarity, while two extraction methods were employed, the conventional maceration and the ultrasound assisted extraction (UAE). The concentration of bioactive phytoconstituents, such as total phenolics (TPC) and total flavonoids (TFC) in the prepared extracts was evaluated by the Folin-Ciocalteu and the aluminum-flavonoid complex method, respectively. Furthermore, the anti-neurodegenerative and anti-hyperglycemic activity of all extracts was determined using AChE and α-amylase inhibitory activity assays, respectively. The inhibitory activity of the extracts against AChE and α-amylase was characterized by estimating their IC₅₀ value using a dose-response curve, while galanthamine and acarbose were used as positive controls, respectively. Finally, the kinetics of AChE and α-amylase in the presence of the most inhibitory potent extracts was determined by the Lineweaver-Burk plot. The methanolic extract prepared using the UAE contained the highest amount of phytoconstituents, followed by the respective ethanolic extract. All extracts inhibited acetylcholinesterase in a dose-dependent manner, while the increased anticholinesterase activity of the methanolic (IC₅₀ = 32 μg/mL) and ethanolic (IC₅₀ = 42 μg/mL) extract was positively correlated with their TPC content. Furthermore, the activity of the aforementioned extracts was comparable to galanthamine. Similar results were obtained in the case of α-amylase, however, all extracts showed lower inhibitory effect on the carbohydrate hydrolyzing enzyme than on AChE, since the IC₅₀ value ranged from 84 to 100 μg/mL. Also, the α-amylase inhibitory effect of the extracts was lower than acarbose. Finally, the methanolic and ethanolic extracts prepared by UAE inhibited both enzymes in a mixed (competitive/noncompetitive) manner since the Kₘ value of both enzymes increased in the presence of extracts, while the Vmax value decreased. The results of the present study indicate that PP may be a useful source of active compounds for the management of AD and DM. Moreover, taking into consideration that PP is an agro-industrial waste product, its valorization could not only result in economic efficiency but also reduce the environmental pollution.

Keywords: acetylcholinesterase, Alzheimer’s disease, α-amylase, diabetes mellitus, pomegranate

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Authors: Olga Kononova, Sergey Lyapin

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Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools.

Keywords: contextual knowledge, contextual search, e-library services, frequency-ranked query, paragraph-oriented query, technologies of the contextual knowledge extraction

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Authors: Mounir Baccar, Imen Jmal

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Because of the rise in energy costs, thermal storage systems designed for the heating and cooling of buildings are becoming increasingly important. Energy storage can not only reduce the time or rate mismatch between energy supply and demand but also plays an important role in energy conservation. One of the most preferable storage techniques is the Latent Heat Thermal Energy Storage (LHTES) by Phase Change Materials (PCM) due to its important energy storage density and isothermal storage process. This paper presents a numerical study of the solidification of a PCM (paraffin RT27) in a rectangular thermal storage exchanger for air conditioning systems taking into account the presence of natural convection. Resolution of continuity, momentum and thermal energy equations are treated by the finite volume method. The main objective of this numerical approach is to study the effect of natural convection on the PCM solidification time and the impact of fins number on heat transfer enhancement. It also aims at investigating the temporal evolution of PCM solidification, as well as the longitudinal profiles of the HTF circling in the duct. The present research undertakes the study of two cases: the first one treats the solidification of PCM in a PCM-air heat exchanger without fins, while the second focuses on the solidification of PCM in a heat exchanger of the same type with the addition of fins (3 fins, 5 fins, and 9 fins). Without fins, the stratification of the PCM from colder to hotter during the heat transfer process has been noted. This behavior prevents the formation of thermo-convective cells in PCM area and then makes transferring almost conductive. In the presence of fins, energy extraction from PCM to airflow occurs at a faster rate, which contributes to the reduction of the discharging time and the increase of the outlet air temperature (HTF). However, for a great number of fins (9 fins), the enhancement of the solidification process is not significant because of the effect of confinement of PCM liquid spaces for the development of thermo-convective flow. Hence, it can be concluded that the effect of natural convection is not very significant for a high number of fins. In the optimum case, using 3 fins, the increasing temperature of the HTF exceeds approximately 10°C during the first 30 minutes. When solidification progresses from the surfaces of the PCM-container and propagates to the central liquid phase, an insulating layer will be created in the vicinity of the container surfaces and the fins, causing a low heat exchange rate between PCM and air. As the solid PCM layer gets thicker, a progressive regression of the field of movements is induced in the liquid phase, thus leading to the inhibition of heat extraction process. After about 2 hours, 68% of the PCM became solid, and heat transfer was almost dominated by conduction mechanism.

Keywords: heat transfer enhancement, front solidification, PCM, natural convection

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Authors: Y. Xu, L. Xiong, Z. Xu

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In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to the cloud. However, this causes difficulties in image retrieval and data management. A secure image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that the user can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms. Experimental results prove that the proposed method can achieve better security and better retrieval precision.

Keywords: secure image retrieval, secure search, orthogonal decomposition, secure cloud computing

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Authors: Tamara Trček Pečak, Andrej Mohar, Denis Trček

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This article investigates the latest technological means, which deploy smart structures that are based on (advanced) wireless sensors technologies and ubiquitous computing in general in order to support the above mentioned decision making. Based on two years of in-field research experiences it gives their analysis for these kinds of purposes and provides appropriate architectures and architectural solutions. Moreover, the directions for future research are stated, because these technologies are currently the most promising ones to enable cost-effective preservation of cultural heritage not only in uncontrolled places, but also in general.

Keywords: smart structures, wireless sensors, sensors networks, green computing, cultural heritage preservation, monitoring, cost effectiveness

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

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The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: bibliometrics, data analysis, extraction and data integration, scientometrics

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Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Eman Ghoneim

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The Arabian deserts include some of the driest areas on Earth. Yet, its landforms reserved a record of past wet climates. During humid phases, the desert was green and contained permanent rivers, inland deltas and lakes. Some of their water would have seeped and replenished the groundwater aquifers. When the wet periods came to an end, several thousand years ago, the entire region transformed into an extended band of desert and its original fluvial surface was totally covered by windblown sand. In this work, radar and thermal infrared images were used to reveal numerous hidden surface/subsurface features. Radar long wavelength has the unique ability to penetrate surface dry sands and uncover buried subsurface terrain. Thermal infrared also proven to be capable of spotting cooler moist areas particularly in hot dry surfaces. Integrating Radarsat images and GIS revealed several previously unknown paleoriver and lake basins in the region. One of these systems, known as the Kufrah, is the largest yet identified river basin in the Eastern Sahara. This river basin, which straddles the border between Egypt and Libya, flowed north parallel to the adjacent Nile River with an extensive drainage area of 235,500 km2 and massive valley width of 30 km in some parts. This river was most probably served as a spillway for an overflow from Megalake Chad to the Mediterranean Sea and, thus, may have acted as a natural water corridor used by human ancestors to migrate northward across the Sahara. The Gilf-Kebir is another large paleoriver system located just east of Kufrah and emanates from the Gilf Plateau in Egypt. Both river systems terminate with vast inland deltas at the southern margin of the Great Sand Sea. The trends of their distributary channels indicate that both rivers drained to a topographic depression that was periodically occupied by a massive lake. During dry climates, the lake dried up and roofed by sand deposits, which is today forming the Great Sand Sea. The enormity of the lake basin provides explanation as to why continuous extraction of groundwater in this area is possible. A similar lake basin, delimited by former shorelines, was detected by radar space data just across the border of Sudan. This lake, called the Northern Darfur Megalake, has a massive size of 30,750 km2. These former lakes and rivers could potentially hold vast reservoirs of groundwater, oil and natural gas at depth. Similar to radar data, thermal infrared images were proven to be useful in detecting potential locations of subsurface water accumulation in desert regions. Analysis of both Aster and daily MODIS thermal channels reveal several subsurface cool moist patches in the sandy desert of the Arabian Peninsula. Analysis indicated that such evaporative cooling anomalies were resulted from the subsurface transmission of the Monsoonal rainfall from the mountains to the adjacent plain. Drilling a number of wells in several locations proved the presence of productive water aquifers confirming the validity of the used data and the adopted approaches for water exploration in dry regions.

Keywords: radarsat, SRTM, MODIS, thermal infrared, near-surface water, ancient rivers, desert, Sahara, Arabian peninsula

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Authors: Muslim Khan, Kenneth B. Jensen, Kevin A. Francesconi

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Trace amounts (ca 1 µg/L, 13 nM) of arsenic are present in sea water mostly as the oxyanion arsenate. In contrast, arsenic is present in marine biota (animals and algae) at very high levels (up to100,000 µg/kg) a significant portion of which is present as lipid-soluble compounds collectively termed arsenolipids. The complex nature of sea water presents an analytical challenge to detect trace compounds and monitor their environmental path. We developed a simple method using liquid-liquid extraction combined with HPLC-High Resolution Mass Spectrometer capable of detecting trace of arsenolipids (99 % of the sample matrix while recovering > 80 % of the six target arsenolipids with limit of detection of 0.003 µg/L.)

Keywords: arsenolipids, sea water, HPLC-high resolution mass spectrometry

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Authors: N. Ibrahim-Rassoul, A. Kessi, E. K. Si-Ahmed, N. Djilali, J. Legrand

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The static and dynamic formation of liquid water bridges is analyzed using a combination of visualization experiments in a microchannel with a mathematical model. This paper presents experimental and theoretical findings of water plug/capillary bridge formation in a 250 μm squared microchannel. The approach combines mathematical and numerical modeling with experimental visualization and measurements. The generality of the model is also illustrated for flow conditions encountered in manipulation of polymeric materials and formation of liquid bridges between patterned surfaces. The predictions of the model agree favorably the observations as well as with the experimental recordings.

Keywords: green energy, mathematical modeling, fuel cell, water plug, gas diffusion layer, surface of revolution

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Authors: A. N. Paithane, D. S. Bormane, S. D. Shirbahadurkar

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It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database.

Keywords: intrinsic mode function (IMF), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), emotion detection, electrocardiogram (ECG)

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Authors: Tudor Barbu

Abstract:

An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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