Abstract: A factorial design of experiments and a response surface methodology were implemented to investigate the liquid-liquid extraction process of zinc (II) from acetate medium using the 1-Butyl-imidazolium di(2-ethylhexyl) phosphate [BIm+][D2EHP-]. The optimization process of extraction parameters such as the initial pH effect (2.5, 4.5, and 6.6), ionic liquid concentration (1, 5.5, and 10 mM) and salt effect (0.01, 5, and 10 mM) was carried out using a three-level full factorial design (33). The results of the factorial design demonstrate that all these factors are statistically significant, including the square effects of pH and ionic liquid concentration. The results showed that the order of significance: IL concentration > salt effect > initial pH. Analysis of variance (ANOVA) showing high coefficient of determination (R2 = 0.91) and low probability values (P < 0.05) signifies the validity of the predicted second-order quadratic model for Zn (II) extraction. The optimum conditions for the extraction of zinc (II) at the constant temperature (20 °C), initial Zn (II) concentration (1mM) and A/O ratio of unity were: initial pH (4.8), extractant concentration (9.9 mM), and NaCl concentration (8.2 mM). At the optimized condition, the metal ion could be quantitatively extracted.
Abstract: Egg yolk oil is a concentrated source of egg bioactive compounds, such as fat-soluble vitamins, phospholipids, cholesterol, carotenoids and others. To extract lipids and other fat-soluble nutrients from liquid egg yolk, a two-step extraction process involving polar (ethanol) and non-polar (hexane) solvents were used. This extraction technique was based on egg yolk bioactive compounds polarities, where non-polar compound was extracted into non-polar hexane, but polar in to polar alcohol/water phase. But many egg yolk bioactive compounds are not strongly polar or non-polar. Egg yolk phospholipids, cholesterol and pigments are amphipatic (have both polar and non-polar regions) and their behavior in ethanol/hexane solvent system is not clear. The aim of this study was to clarify the behavior of phospholipids, cholesterol and carotenoids during extraction of egg yolk oil with ethanol and hexane and determine the loss of these compounds in egg yolk oil. Egg yolks and egg yolk oil were analyzed for phospholipids (phosphatidylcholine (PC) and phosphatidylethanolamine (PE)), cholesterol and carotenoids (lutein, zeaxanthin, canthaxanthin and β-carotene) content using GC-FID and HPLC methods. PC and PE are polar lipids and were extracted into polar ethanol phase. Concentration of PC in ethanol was 97.89% and PE 99.81% from total egg yolk phospholipids. Due to cholesterol’s partial extraction into ethanol, cholesterol content in egg yolk oil was reduced in comparison to its total content presented in egg yolk lipids. The highest amount of lutein and zeaxanthin was concentrated in ethanol extract. The opposite situation was observed with canthaxanthin and β-carotene, which became the main pigments of egg yolk oil.
Abstract: The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.
Abstract: Hyperspectral imagery (HSI) typically provides a
wealth of information captured in a wide range of the
electromagnetic spectrum for each pixel in the image. Hence, a
pixel in HSI is a high-dimensional vector of intensities with a
large spectral range and a high spectral resolution. Therefore, the
semantic interpretation is a challenging task of HSI analysis. We
focused in this paper on object classification as HSI semantic
interpretation. However, HSI classification still faces some issues,
among which are the following: The spatial variability of spectral
signatures, the high number of spectral bands, and the high cost
of true sample labeling. Therefore, the high number of spectral
bands and the low number of training samples pose the problem of
the curse of dimensionality. In order to resolve this problem, we
propose to introduce the process of dimensionality reduction trying
to improve the classification of HSI. The presented approach is a
semi-supervised band selection method based on spatial hypergraph
embedding model to represent higher order relationships with
different weights of the spatial neighbors corresponding to the
centroid of pixel. This semi-supervised band selection has been
developed to select useful bands for object classification. The
presented approach is evaluated on AVIRIS and ROSIS HSIs
and compared to other dimensionality reduction methods. The
experimental results demonstrate the efficacy of our approach
compared to many existing dimensionality reduction methods for
HSI classification.
Abstract: In recent years, tendency to use of natural antimicrobial agents in food industry has increased. Pomegranate peels containing phenolic compounds and anti-microbial agents, are counted as valuable source for extraction of these compounds. In this study, the extraction of pomegranate peel extract was carried out at different ethanol/water ratios (40:60, 60:40, and 80:20), temperatures (25, 40, and 55 ˚C), and time durations (20, 24, and 28 h). The extraction yield, phenolic compounds, flavonoids, and anthocyanins were measured. Antimicrobial activity of pomegranate peel extracts were determined against some food-borne microorganisms such as Salmonella enteritidis, Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, Aspergillus niger, and Saccharomyces cerevisiae by agar diffusion and MIC methods. Results showed that at ethanol/water ratio 60:40, 25 ˚C and 24 h maximum amount of phenolic compounds (349.518 mg gallic acid/g dried extract), flavonoids (250.124 mg rutin/g dried extract), anthocyanins (252.047 mg cyanidin3glucoside/100 g dried extract), and the strongest antimicrobial activity were obtained. All extracts’ antimicrobial activities were demonstrated against every tested microorganisms. Staphylococcus aureus showed the highest sensitivity among the tested microorganisms.
Abstract: Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.
Abstract: The correct estimation of reference evapotranspiration (ETₒ) is required for effective irrigation water resources planning and management. However, there are some variables that must be considered while estimating and modeling ETₒ. This study therefore determines the multivariate analysis of correlated variables involved in the estimation and modeling of ETₒ at Vaalharts irrigation scheme (VIS) in South Africa using Principal Component Analysis (PCA) technique. Weather and meteorological data between 1994 and 2014 were obtained both from South African Weather Service (SAWS) and Agricultural Research Council (ARC) in South Africa for this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind speed (m/s) were the inputs to the PCA-based model, while ETₒ is the output. PCA technique was adopted to extract the most important information from the dataset and also to analyze the relationship between the five variables and ETₒ. This is to determine the most significant variables affecting ETₒ estimation at VIS. From the model performances, two principal components with a variance of 82.7% were retained after the eigenvector extraction. The results of the two principal components were compared and the model output shows that minimum temperature, maximum temperature and windspeed are the most important variables in ETₒ estimation and modeling at VIS. In order words, ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity are less important and cannot be used to provide enough information about ETₒ estimation at VIS. The outcome of this study has helped to reduce input variable dimensionality from five to the three most significant variables in ETₒ modelling at VIS, South Africa.
Abstract: In Sri Lanka, the Jaffna area is a high drought affected area and depends mainly on groundwater aquifers for water needs. Water for daily activities is extracted from wells. As households manually extract water from the wells, it is not drawn from mid evening to early morning. The water inflow at night provides the maximum water level that decreases during the daytime due to extraction. The storage volume of water in wells is limited or at its lowest level during the dry season. This study analyzes the domestic water budget during the dry season in the Jaffna area. In order to evaluate the water inflow rate into wells, storage volume and extraction volume from wells over time, water pressure is measured at the bottom of three wells, which are located in coastal area denoted as well A, in nonspecific area denoted as well B, and agricultural area denoted as well C. The water quality at the wells A, B, and C, are mostly fresh, modest fresh, and saline respectively. From the monitoring, we can find that the daily inflow amount of water into the wells and daily water extraction depend on each other, that is, higher extraction yields higher inflow. And, in the dry season, the daily inflow volume and the daily extraction volume of each well are almost in balance.
Abstract: The performance of organophosphorus extractants Cyanex 272 and D2EHPA on the purification of concentrate nickel sulfate solutions was evaluated. Batch scale tests were carried out at pH range of 2 to 7 using a laboratory solution simulating concentrate nickel liquors as those typically obtained when sulfate intermediates from nickel laterite are re-leached and treated for the selective removal of cobalt, zinc, manganese and copper with Cyanex 272 ([Ca] = 0.57 g/L, [Mg] = 3.2 g/L, and [Ni] = 88 g/L). The increase on the concentration of D2EHPA favored the calcium extraction. The extraction of magnesium is dependent on the pH and of ratio of extractants D2EHPA and Cyanex 272 in the organic phase. The composition of the investigated organic phase did not affect nickel extraction. The number of stages is dependent on the magnesium extraction. The most favorable operating condition to selectively remove calcium and magnesium was determined.
Abstract: The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.
Abstract: The study assessed the effect of crude oil applied at rates, 0, 2, 5, and 10% on the fractional chemical forms and availability of some metals in soils from Usen, Edo State, with no known crude oil contamination and soil from a crude oil spill site in Ubeji, Delta State, Nigeria. Three methods were used to determine the bioavailability of metals in the soils: maize (Zea mays) plant, EDTA and BCR sequential extraction. The sequential extract acid soluble fraction of the BCR extraction (most labile fraction of the soils, normally associated with bioavailability) were compared with total metal concentration in maize seedlings as a means to compare the chemical and biological measures of bioavailability. Total Fe was higher in comparison to other metals for the crude oil contaminated soils. The metal concentrations were below the limits of 4.7% Fe, 190mg/kg Cu and 720mg/kg Zn intervention values and 36mg/kg Cu and 140mg/kg Zn target values for soils provided by the Department of Petroleum Resources (DPR) guidelines. The concentration of the metals in maize seedlings increased with increasing rates of crude oil contamination. Comparison of the metal concentrations in maize seedlings with EDTA extractable concentrations showed that EDTA extracted more metals than maize plant.
Abstract: The Metropolitan Region of Sao Paulo (MRSP) has suffered from serious water scarcity. Consequently, the most convenient solution has been building wells to extract groundwater from local aquifers. However, it requires constant vigilance to prevent over extraction and future events that can pose serious threat to the population, such as subsidence. Radar imaging techniques (InSAR) have allowed continuous investigation of such phenomena. The analysis of data in the present study consists of 23 SAR images dated from October 2007 to March 2011, obtained by the ALOS-1 spacecraft. Data processing was made with the software GMTSAR, by using the InSAR technique to create pairs of interferograms with ground displacement during different time spans. First results show a correlation between the location of 102 wells registered in 2009 and signals of ground displacement equal or lower than -90 millimeters (mm) in the region. The longest time span interferogram obtained dates from October 2007 to March 2010. As a result, from that interferogram, it was possible to detect the average velocity of displacement in millimeters per year (mm/y), and which areas strong signals have persisted in the MRSP. Four specific areas with signals of subsidence of 28 mm/y to 40 mm/y were chosen to investigate the phenomenon: Guarulhos (Sao Paulo International Airport), the Greater Sao Paulo, Itaquera and Sao Caetano do Sul. The coverage area of the signals was between 0.6 km and 1.65 km of length. All areas are located above a sedimentary type of aquifer. Itaquera and Sao Caetano do Sul showed signals varying from 28 mm/y to 32 mm/y. On the other hand, the places most likely to be suffering from stronger subsidence are the ones in the Greater Sao Paulo and Guarulhos, right beside the International Airport of Sao Paulo. The rate of displacement observed in both regions goes from 35 mm/y to 40 mm/y. Previous investigations of the water use at the International Airport highlight the risks of excessive water extraction that was being done through 9 deep wells. Therefore, it is affirmed that subsidence events are likely to occur and to cause serious damage in the area. This study could show a situation that has not been explored with proper importance in the city, given its social and economic consequences. Since the data were only available until 2011, the question that remains is if the situation still persists. It could be reaffirmed, however, a scenario of risk at the International Airport of Sao Paulo that needs further investigation.
Abstract: Green chemistry for plant extraction of active principles is the main interest of many researchers concerned with climate change. While classical organic solvents are detrimental to our environment, greener alternatives to ionic liquids are very promising for sustainable organic chemistry. This study focused on the determination of functional groups observed in the main constituents from the ionic liquid extracts of Coleus aromaticus Benth leaves using FT-IR Spectroscopy. Moreover, this research aimed to determine the best ionic liquid that can separate functionalized plant constituents from the leaves Coleus aromaticus Benth using Fourier Transform Infrared Spectroscopy. Coleus aromaticus Benth leaf extract in different ionic liquids, elucidated pharmacologically important functional groups present in major constituents of the plant, namely, rosmarinic acid, caffeic acid and chlorogenic acid. In connection to distinctive appearance of functional groups in the spectrum and highest % transmittance, potassium chloride-glycerol is the best ionic liquid for green extraction.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: A problem of complex mineral resources development is urgent and priority, it is aimed at realization of the processes of their ecologically safe development, one of its components is revealing the influence of the forms of element compounds in raw materials and in the processing products. In view of depletion of the precious metal reserves at the traditional deposits in the XXI century the large-size open cast deposits, localized in black shale strata begin to play the leading role. Carbonaceous (black) shales carry a heightened metallogenic potential. Black shales with high content of carbon are widely distributed within the scope of Bureinsky massif. According to academician Hanchuk`s data black shales of Sutirskaya series contain generally PGEs native form. The presence of high absorptive towards carbonaceous matter gold and PGEs compounds in crude ore results in decrease of valuable components extraction because of their sorption into dissipated carbonaceous matter.
Abstract: This research presents the main ideas to implement an
intelligent system composed by communicating wireless sensors
measuring environmental data linked to drought indicators (such as
air temperature, soil moisture , etc...). On the other hand, the setting
up of a spatio temporal database communicating with a Web mapping
application for a monitoring in real time in activity 24:00 /day, 7
days/week is proposed to allow the screening of the drought
parameters time evolution and their extraction. Thus this system
helps detecting surfaces touched by the phenomenon of drought.
Spatio-temporal conceptual models seek to answer the users who
need to manage soil water content for irrigating or fertilizing or other
activities pursuing crop yield augmentation. Effectively, spatiotemporal
conceptual models enable users to obtain a diagram of
readable and easy data to apprehend. Based on socio-economic
information, it helps identifying people impacted by the phenomena
with the corresponding severity especially that this information is
accessible by farmers and stakeholders themselves. The study will be
applied in Siliana watershed Northern Tunisia.
Abstract: In addition to environmental parameters like rain,
temperature diseases on crop is a major factor which affects
production quality & quantity of crop yield. Hence disease
management is a key issue in agriculture. For the management of
disease, it needs to be detected at early stage. So, treat it properly &
control spread of the disease. Now a day, it is possible to use the
images of diseased leaf to detect the type of disease by using image
processing techniques. This can be achieved by extracting features
from the images which can be further used with classification
algorithms or content based image retrieval systems. In this paper,
color image is used to extract the features such as mean and standard
deviation after the process of region cropping. The selected features
are taken from the cropped image with different image size samples.
Then, the extracted features are taken in to the account for
classification using Fuzzy Inference System (FIS).
Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.