Search results for: causal realtion extraction

Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd ZaizuIlyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

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This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates.

Keywords: local features modelling, face recognition system, Gaussian mixture models, Feret

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Authors: Tarikayehu Amanuel, Abraham Mohammed

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The production of biodiesel from waste materials is becoming an increasingly important research area in the field of renewable energy. One potential waste material source is avocado, a fruit with a large seed and peel that are typically discarded after consumption. This research aims to investigate the feasibility of using avocado waste as a feedstock for the production of biodiesel. The study focuses on extracting oil from the waste material using the transesterification technique and then characterizing the properties of oil to determine its suitability for conversion to biodiesel. The study was conducted experimentally, and a maximum oil yield of 11.583% (150g of oil produced from 1.295kg of avocado waste powder) was obtained from avocado waste powder at an extraction time of 4hr. An 87% fatty acid methyl ester (biodiesel) conversion was also obtained using a methanol/oil ratio of 6:1, 1.3g NaOH, reaction time 60min, and 65°C reaction temperature. Furthermore, from 145 ml of avocado waste oil, 126.15 ml of biodiesel was produced, indicating a high percentage of conversion (87%). Conclusively, the produced biodiesel showed comparable physical and chemical characteristics to that of standard biodiesel samples considered for the study. The results of this research could help to identify a new source of biofuel production while also addressing the issue of waste disposal in the food industry.

Keywords: biodiesel, avocado, transesterification, soxhlet extraction

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Authors: Javad Nosratabadi

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This paper investigates the difference between firms' actual and expected employment along with the amount of loans invested by them. In addition, it examines the relationship between the amount of loans received by firms and wages. Empirically, using a causal effect estimation and firm-level data from a province in Iran between 2004 and 2011, the results show that there is a range of the loan amount for which firms' expected employment meets their actual one. In contrast, there is a gap between firms' actual and expected employment for any other loan amount. Furthermore, the result shows that there is a positive and significant relationship between the amount of loan invested by firms and wages.

Keywords: expected employment, actual employment, wage, loan

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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Authors: Edward Schwalb, Matthias Dehmer, Michael Schlenkrich, Farzaneh Taslimi, Ketron Mitchell-Wynne, Horen Kuecuekyan

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We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure.

Keywords: mechanical design validation, FEA, supervised decision tree, convolutional neural network.

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Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

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The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: Nadia Soledad Peralta

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Within the framework of socio-cognitive interaction, argumentation is understood as a psychological process that supports and induces reasoning and learning. Most authors emphasize the great potential of argumentation to negotiate with contradictions and complex decisions. So argumentation is a target for researchers who highlight the importance of social and cognitive processes in learning. In the context of social interaction among university students, different types of arguments are analyzed according to group size (dyads and triads) and the type of task (reading of frequency tables, causal explanation of physical phenomena, the decision regarding moral dilemma situations, and causal explanation of social phenomena). Eighty-nine first-year social sciences students of the National University of Rosario participated. Two groups were formed from the results of a pre-test that ensured the heterogeneity of points of view between participants. Group 1 consisted of 56 participants (performance in dyads, total: 28), and group 2 was formed of 33 participants (performance in triads, total: 11). A quasi-experimental design was performed in which effects of the two variables (group size and type of task) on the argumentation were analyzed. Three types of argumentation are described: authentic dialogical argumentative resolutions, individualistic argumentative resolutions, and non-argumentative resolutions. The results indicate that individualistic arguments prevail in dyads. That is, although people express their own arguments, there is no authentic argumentative interaction. Given that, there are few reciprocal evaluations and counter-arguments in dyads. By contrast, the authentically dialogical argument prevails in triads, showing constant feedback between participants’ points of view. It was observed that, in general, the type of task generates specific types of argumentative interactions. However, it is possible to emphasize that the authentically dialogic arguments predominate in the logical tasks, whereas the individualists or pseudo-dialogical are more frequent in opinion tasks. Nerveless, these relationships between task type and argumentative mode are best clarified in an interactive analysis based on group size. Finally, it is important to stress the value of dialogical argumentation in educational domains. Argumentative function not only allows a metacognitive reflection about their own point of view but also allows people to benefit from exchanging points of view in interactive contexts.

Keywords: sociocognitive interaction, argumentation, university students, size of the grup

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Authors: Othman Saleh Al-Razgan

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Obesity establishes an epidemic along with an array of comorbidities and this call for careful clinical assessment, to identify causal factors and comprehensive management. In Kuwait, this epidemic reflects the progressive, socio-economic and age-related issues, along with the shift of nutrition from traditional to modern-style. The current research attempts to narrate the obesity and related health issues in Kuwait, with a special emphasis on the magnitude of the issue in Kuwait, nutrition transition over the past three decades, change in life-style, and possible solution for this issue.

Keywords: clinical assessment, comorbidities, obesity, socio-economic

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Authors: Yindi Zhao, Yun Zhang, Silu Xia, Lixin Wu

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The contours and areas of water surfaces, especially lakes, often change due to natural disasters and construction activities. It is an effective way to extract and update water contours from satellite images using image processing algorithms. However, to produce optimal water surface contours that are close to true boundaries is still a challenging task. This paper compares the performances of three different level set models, including the Chan-Vese (CV) model, the signed pressure force (SPF) model, and the region-scalable fitting (RSF) energy model for extracting lake contours. After experiment testing, it is indicated that the RSF model, in which a region-scalable fitting (RSF) energy functional is defined and incorporated into a variational level set formulation, is superior to CV and SPF, and it can get desirable contour lines when there are “holes” in the regions of waters, such as the islands in the lake. Therefore, the RSF model is applied to extracting lake contours from Landsat satellite images. Four temporal Landsat satellite images of the years of 2000, 2005, 2010, and 2014 are used in our study. All of them were acquired in May, with the same path/row (121/036) covering Xuzhou City, Jiangsu Province, China. Firstly, the near infrared (NIR) band is selected for water extraction. Image registration is conducted on NIR bands of different temporal images for information update, and linear stretching is also done in order to distinguish water from other land cover types. Then for the first temporal image acquired in 2000, lake contours are extracted via the RSF model with initialization of user-defined rectangles. Afterwards, using the lake contours extracted the previous temporal image as the initialized values, lake contours are updated for the current temporal image by means of the RSF model. Meanwhile, the changed and unchanged lakes are also detected. The results show that great changes have taken place in two lakes, i.e. Dalong Lake and Panan Lake, and RSF can actually extract and effectively update lake contours using multi-temporal satellite image.

Keywords: level set model, multi-temporal image, lake contour extraction, contour update

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Authors: Zaida Perez-Bassart, Berta Polanco-Estibalez, Maria Jose Fabra, Amparo Lopez-Rubio, Antonio Martinez-Abad

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Mushroom production has enormously increased in recent years, not only as food products but also for applications in pharmaceuticals, nutraceuticals, and cosmetics. Consequently, interest in its chemical composition, nutritional value, and therapeutic properties has also increased. Fungi are rich in bioactive compounds such as polysaccharides, polyphenols, glycopeptides, and ergosterol, of great medicinal value, but within polysaccharides, β-glucans are the most prominent molecules. They are formed by D-glucose monomers, linked by β-glucosidic bonds β-(1,3) with side chains linked by β-(1,6) bonds. The number and position of the β-(1,6) branches strongly influence the arrangement of the tertiary structure, which, together with the molecular weight, determine the different attributed bioactivities (immunostimulating, anticancer, antimicrobial, prebiotic, etc.) and physico-chemical properties (solubility, bioaccessibility, viscosity or emulsifying). On the other hand, there is a growing interest in the study of fungi as an alternative source of chitin obtained from the by-products of the fungal industry. In this work, a cascade extraction process using aqueous neutral and alkaline treatments was carried out for Grifola frondosa and Lentinula edodes, and the compositional analysis and functional properties of each fraction were characterized. Interestingly, the first fraction obtained by using aqueous treatment at room temperature was the richest in polysaccharides, proteins, and polyphenols, thus obtaining a greater antioxidant capacity than in the other fractions. In contrast, the fractions obtained by alkaline treatments showed a higher degree of β-glucans purification compared to aqueous extractions but a lower extraction yield. Results revealed the different structural recalcitrance of β-glucans, preferentially linked to proteins or chitin depending on the fungus type, which had a direct impact on the functionalities and bioactivities of each fraction.

Keywords: fungi, mushroom, β-glucans, chitin

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Authors: U. P. L. Wijayarathne, P. W. Vidanage, H. K. D. Jayampath, K. W. P. M. Kothalawala

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Feasibility of utilizing Empty Bunch (EB) fibre, a solid waste of palm oil extraction process, as an adsorbent is analysed in this study. Empty bunch fibre is generated after the extraction of retained oil in the sterilized and threshed empty fruit bunches. Besides the numerous characteristics of EB fibre, which enable its utilization as a fuel, a bio-composite material, or mulch, EB fibre also shows exceptional characteristics of a good adsorbent. Fixed bed adsorption method is used to study the adsorptivity of EB fibre using a continuous adsorption column with Methyl-blue (1.13ppm) as the feed. Adsorptivity is assumed to be solely dependent on the bed porosity keeping other parameters (feed flow rate, bed height, bed diameter, and operating temperature) constant. Bed porosity is changed by means of compact ratio and the variation of the feed concentration is analysed using a photometric method. Break through curves are plotted at different porosity levels and optimum bed porosity is identified for a given feed stream. Feasibility of using the EB fibre as an inexpensive and an abundant adsorbent in wastewater treatment facilities, where the effluent colour reduction is adamant, is also discussed.

Keywords: adsorption, fixed bed, break through time, methylene blue, oil palm fibre

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Authors: Cristina S. Decano, Vitaliana U. Malamug, Melissa E. Agulto, Helen F. Gavino

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This study aimed to introduce new natural fiber to be used in the production of geotextile net for mitigation of soil erosion. Fiber extraction from the stalks was the main challenge faced during the processing of stalks to ropes. Thus, an investigation on the extraction procedures of corn (Zea mays L.) stalk under biological and chemical retting was undertaken. Results indicated significant differences among percent fiber yield as affected by the retting methods used with values of 15.07%, 12.97%, 11.60%, and 9.01%, for dew, water, chemical (1 day after harvest and15 days after harvest), respectively, with the corresponding average extracting duration of 70, 82, 89, and 94 minutes. Physical characterization of the developed corn stalk geotextile net resulted to average mass per unit area of 806.25 g/m² and 241% water absorbing capacity. The effect of corn stalk geotextile net in mitigating soil erosion was evaluated in a laboratory experiment for 30^o and 60^o inclinations with three treatments: bare soil (A₁), corn stalk geotextile net (A₂) and combined cornstalk geotextile net and vegetation cover (A₃). Results revealed that treatment A₂ and A₃ significantly decreased sediment yield and an increase in terms of soil loss reduction efficiency. The cost of corn stalk geotextile net is Php 62.41 per square meter.

Keywords: corn stalk, natural geotextile, retting, soil erosion

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Authors: Wided Bakari, Patrice Bellot, Omar Trigui, Mahmoud Neji

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This article constitutes an opening to think of the modeling and analysis of Arabic texts in the context of a question-answer system. It is a question of exceeding the traditional approaches focused on morphosyntactic approaches. Furthermore, we present a new approach that analyze a text in order to extract correct answers then transform it to logical predicates. In addition, we would like to represent different levels of information within a text to answer a question and choose an answer among several proposed. To do so, we transform both the question and the text into logical forms. Then, we try to recognize all entailment between them. The results of recognizing the entailment are a set of text sentences that can implicate the user’s question. Our work is now concentrated on an implementation step in order to develop a system of question-answering in Arabic using techniques to recognize textual implications. In this context, the extraction of text features (keywords, named entities, and relationships that link them) is actually considered the first step in our process of text modeling. The second one is the use of techniques of textual implication that relies on the notion of inference and logic representation to extract candidate answers. The last step is the extraction and selection of the desired answer.

Keywords: NLP, Arabic language, question-answering, recognition text entailment, logic forms

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Authors: Annamarie Rannou, Horace Bartilow

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Global concerns about xenophobic activity are on the rise across developed and developing countries. And yet, social science scholarship has almost exclusively examined xenophobia as a prejudice of advanced western nations. This research argues that the fields of study related to xenophobia must be re-conceptualized within a framework of ethnicity in order to level the playing field for cross-regional inquiry. This study develops a new concept of ethnic xenophobia and integrates existing explanations of anti-migrant expression into theories of ethnic threat. We argue specifically that political elites convert economic, political, and social threats at the national level into ethnic xenophobic activity in order to gain or maintain political advantage among their native selectorate. We expand on Stuart Kaufman’s theory of symbolic politics to underscore the methods of mobilization used against migrants and the power of elite discourse in moments of national crises. An original dataset is used to examine over 35,000 cases of ethnic xenophobic activity targeting refugees. Wordscores software is used to develop a unique measure of anti-migrant elite rhetoric which captures the symbolic discourse of elites in their mobilization of ethnic xenophobic activism. We use a Structural Equation Model (SEM) to test the causal pathways of the theory across seventy-two developed and developing countries from 1990 to 2016. A framework of Most Different Systems Design (MDSD) is also applied to two pairs of developed-developing country cases, including Kenya and the Netherlands and Lebanon and the United States. This study sheds tremendous light on an underrepresented area of comparative research in migration studies. It shows that the causal elements of anti-migrant activity are far more similar than existing research suggests which has major implications for policy makers, practitioners, and academics in fields of migration protection and advocacy. It speaks directly to the mobilization of myths surrounding refugees, in particular, and the nationalization of narratives of migration that may be neutralized by the development of deeper associational relationships between natives and migrants.

Keywords: refugees, ethnicity, symbolic politics, elites, migration, comparative politics

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Authors: Sohail Imran, Tariq Mahmood

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In complex healthcare diagnostic investigation process, medical practitioners have to focus on ways to standardize their processes to perform high quality care and optimize the time and costs. Process mining techniques can be applied to extract process related knowledge from data without considering causal and dynamic dependencies in business domain and processes. The application of process mining is effective in diagnostic investigation. It is very helpful where a treatment gives no dispositive evidence favoring it. In this paper, we applied process mining to discover important process flow of diagnostic investigation for hepatitis patients. This approach has some benefits which can enhance the quality and efficiency of diagnostic investigation processes.

Keywords: process mining, healthcare, diagnostic investigation process, process flow

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Authors: B. Kabane, G. G. Redhi

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An ammonium based ionic liquid (methyltrioctylammonium chloride) [N_{8 8 8 1}] [Cl] was investigated as an extraction potential solvent for volatile organic solvents (in this regard, solutes), which includes alkenes, alkanes, ketones, alkynes, aromatic hydrocarbons, tetrahydrofuran (THF), alcohols, thiophene, water and acetonitrile based on the experimental activity coefficients at infinite THF measurements were conducted by the use of gas-liquid chromatography at four different temperatures (313.15 to 343.15) K. Experimental data of activity coefficients obtained across the examined temperatures were used in order to calculate the physicochemical properties at infinite dilution such as partial molar excess enthalpy, Gibbs free energy and entropy term. Capacity and selectivity data for selected petrochemical extraction problems (heptane/thiophene, heptane/benzene, cyclohaxane/cyclohexene, hexane/toluene, hexane/hexene) were computed from activity coefficients data and compared to the literature values with other ionic liquids. Evaluation of activity coefficients at infinite dilution expands the knowledge and provides a good understanding related to the interactions between the ionic liquid and the investigated compounds.

Keywords: separation, activity coefficients, methyltrioctylammonium chloride, ionic liquid, capacity

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Authors: Tamanna Goyal, Divya Bansal, Sanjeev Sofat

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In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results.

Keywords: association rules, clustering, similarity measure, statistical approaches

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Authors: Bernadett Henn, Katalin Tálos, Éva Kováss Széles

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During the last 50 years, the worldwide permeation of the nuclear techniques induces several new problems in the environmental and in the human life. Nowadays, due to the increasing of the risk of terrorism worldwide, the potential occurrence of terrorist attacks using also weapon of mass destruction containing radioactive or nuclear materials as e.g. dirty bombs, is a real threat. For instance, the uranium pellets are one of the potential nuclear materials which are suitable for making special weapons. The nuclear forensics mainly focuses on the determination of the origin of the confiscated or found nuclear and other radioactive materials, which could be used for making any radioactive dispersive device. One of the most important signatures in nuclear forensics to find the origin of the material is the determination of the rare earth element patterns (REE) in the seized or found radioactive or nuclear samples. The concentration and the normalized pattern of the REE can be used as an evidence of uranium origin. The REE are the fourteen Lanthanides in addition scandium and yttrium what are mostly found together and really low concentration in uranium pellets. The problems of the REE determination using ICP-MS technique are the uranium matrix (high concentration of uranium) and the interferences among Lanthanides. In this work, our aim was to develop an effective chemical sample preparation process using extraction chromatography for separation the uranium matrix and the rare earth elements from each other following some publications can be found in the literature and modified them. Secondly, our purpose was the optimization of the ICP-MS measuring process for REE concentration. During method development, in the first step, a REE model solution was used in two different types of extraction chromatographic resins (LN® and TRU®) and different acidic media for environmental testing the Lanthanides separation. Uranium matrix was added to the model solution and was proved in the same conditions. Methods were tested and validated using REE UOC (uranium ore concentrate) reference materials. Samples were analyzed by sector field mass spectrometer (ICP-SFMS).

Keywords: extraction chromatography, nuclear forensics, rare earth elements, uranium

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Authors: Oluwasanmi Teniola, Abraham Adeleke, Ademola Ibitoye, Moshood Shitu

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To establish an economical and efficient process for the recovery of gold metal from refractory gold ore obtained from Esperando axis of Osun state Nigeria, the adsorption of gold (III) from aqua reqia leached solution of the ore using bagasse nanoparticles has been studied under various experimental variables using batch technique. The extraction percentage of gold (III) on the prepared bagasse nanoparticles was determined from its distribution coefficients as a function of solution pH, contact time, adsorbent, adsorbate concentrations, and temperature. The rate of adsorption of gold (III) on the prepared bagasse nanoparticles is dependent on pH, metal concentration, amount of adsorbate, stirring rate, and temperature. The adsorption data obtained fit into the Langmuir and Freundlich equations. Three different temperatures were used to determine the thermodynamic parameters of the adsorption of gold (III) on bagasse nanoparticles. The heat of adsorption was measured to be a positive value ΔHo = +51.23kJ/mol, which serves as an indication that the adsorption of gold (III) on bagasse nanoparticles is endothermic. Also, the negative value of ΔGo = -0.6205 kJ/mol at 318K shows the spontaneity of the process. As the temperature was increased, the value of ΔGo becomes more negative, indicating that an increase in temperature favors the adsorption process. With the application of optimal adsorption variables, the adsorption capacity of gold was 0.78 mg/g of the adsorbent, out of which 0.70 mg of gold was desorbed with 0.1 % thiourea solution.

Keywords: adsorption, bagasse, extraction, nanoparticles, recovery

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Authors: Stella O. Olubodun, George E. Eriyamremu

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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.

Keywords: availability, crude oil contamination, EDTA, maize, metals

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Zharama M. Llarena

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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.

Keywords: chlorogenic acid, coleus aromaticus, ionic liquid, rosmarinic acid

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Authors: Fatemeh Cheraghali, Saeedeh Shojaee-Aliabadi, Seyede Marzieh Hosseini, Leila Mirmoghtadaie

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In recent years, the field of natural antimicrobial and antioxidant compounds is one of the main research topics in the food industry. Application of agricultural residues is mainly cheap, and available resources are receiving increased attention. Walnut green husk is one of the agricultural residues that is considered as natural compounds with biological properties because of phenolic compounds. In this study, maltodextrin 10% was used for microencapsulation of walnut green husk extract. At first, the extract was examined to consider extraction yield, total phenolic compounds, and antioxidant activation. The results showed the extraction yield of 81.43%, total phenolic compounds of 3997 [mg GAE/100 g], antioxidant activity [DPPH] of 84.85% for walnut green husk extract. Antioxidant activity is about 75%-81% and by DPPH. At the next stage, microencapsulation was done by spry-drying method. The microencapsulation efficiency was 72%-79%. The results of SEM tests confirmed this microencapsulation process. In addition, microencapsulated and free extract was more effective on gram-positive bacteria’s rather than the gram-negative ones. According to the study, walnut green husk can be used as a cheap antioxidant and antimicrobial compounds due to sufficient value of phenolic compounds.

Keywords: biopolymer, microencapsulation, spray-drying, walnut green husk

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Authors: Igor Jerkovic

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Biodiversity of flora provides many different nectar sources for the bees. Unifloral honeys possess distinctive flavours, mainly derived from their nectar sources (characteristic volatile organic components (VOCs)). Specific or nonspecific VOCs (chemical markers) could be used for unifloral honey characterisation as addition to the melissopalynologycal analysis. The main honey volatiles belong, in general, to three principal categories: terpenes, norisoprenoids, and benzene derivatives. Some of these substances have been described as characteristics of the floral source, and other compounds, like several alcohols, branched aldehydes, and furan derivatives, may be related to the microbial purity of honey processing and storage conditions. Selection of the extraction method for the honey volatiles profiling should consider that heating of the honey produce different artefacts and therefore conventional methods of VOCs isolation (such as hydrodistillation) cannot be applied for the honey. Two-way approach for the isolation of the honey VOCs was applied using headspace solid-phase microextraction (HS-SPME) and ultrasonic solvent extraction (USE). The extracts were analysed by gas chromatography and mass spectrometry (GC-MS). HS-SPME (with the fibers of different polarity such as polydimethylsiloxane/ divinylbenzene (PDMS/DVB) or divinylbenzene/carboxene/ polydimethylsiloxane (DVB/CAR/PDMS)) enabled isolation of high volatile headspace VOCs of the honey samples. Among them, some characteristic or specific compounds can be found such as 3,4-dihydro-3-oxoedulan (in Centaurea cyanus L. honey) or 1H-indole, methyl anthranilate, and cis-jasmone (in Citrus unshiu Marc. honey). USE with different solvents (mainly dichloromethane or the mixture pentane : diethyl ether 1 : 2 v/v) enabled isolation of less volatile and semi-volatile VOCs of the honey samples. Characteristic compounds from C. unshiu honey extracts were caffeine, 1H-indole, 1,3-dihydro-2H-indol-2-one, methyl anthranilate, and phenylacetonitrile. Sometimes, the selection of solvent sequence was useful for more complete profiling such as sequence I: pentane → diethyl ether or sequence II: pentane → pentane/diethyl ether (1:2, v/v) → dichloromethane). The extracts with diethyl ether contained hydroquinone and 4-hydroxybenzoic acid as the major compounds, while (E)-4-(r-1’,t-2’,c-4’-trihydroxy-2’,6’,6’-trimethylcyclo-hexyl)but-3-en-2-one predominated in dichloromethane extracts of Allium ursinum L. honey. With this two-way approach, it was possible to obtain a more detailed insight into the honey volatile and semi-volatile compounds and to minimize the risks of compound discrimination due to their partial extraction that is of significant importance for the complete honey profiling and identification of the chemical biomarkers that can complement the pollen analysis.

Keywords: honey chemical biomarkers, honey volatile compounds profiling, headspace solid-phase microextraction (HS-SPME), ultrasonic solvent extraction (USE)

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Authors: Masni Mat Yusoff, Michael H. Gordon, Keshavan Niranjan

Abstract:

The study reports on the effect of aqueous enzymatic extraction (AEE) parameters on the Moringa oleifera (MO) oil yield and the formation of emulsion at the end of the process. A mixture of protease and cellulase enzymes was used at 3:1 (w/w) ratio. The highest oil yield of 19% (g oil/g sample) was recovered with the use of a mixture of pH 6, 1:4 material/moisture ratio, and incubation temperature, time, and shaking speed of 50 ⁰C, 12.5 hr, and 300 stroke/min, respectively. The use of pH 6 and 8 resulted in grain emulsions, while solid-intact emulsion was observed at pH 4. Upon fixing certain parameters, higher oil yield was extracted with the use of lower material/moisture ratio and higher shaking speed. Longer incubation time of 24 hr resulted in significantly (p < 0.05) similar oil yield with that of 12.5 hr, and an incubation temperature of 50 ⁰C resulted in significantly (p < 0.05) higher oil yield than that of 60 ⁰C. In overall, each AEE parameter showed significant effects on both the MO oil yields and the emulsions formed. One of the major disadvantages of an AEE process is the formation of emulsions which require further de-emulsification step for higher oil recovery. Therefore, critical studies on the effect of each AEE parameter may assist in minimizing the amount of emulsions formed whilst extracting highest total MO oil yield possible.

Keywords: enzyme, emulsion, Moringa oleifera, oil yield

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Authors: Malith Premarathne, Shehan Bandara, Kaushalya G. Batawala, Thilini U. Ariyadasa

Abstract:

The rapid depletion of fossil fuel supplies and the emission of carbon dioxide by their continued combustion have paved the way for increased production of carbon-neutral biodiesel from naturally occurring oil sources. The high biomass growth rate and lipid production of microalgae make it a viable source for biodiesel production compared to conventional feedstock. In Sri Lanka, the production of biodiesel by employing indigenous microalgae species is at its emerging stage. This work was an attempt to compare the various pre-treatment methods before extracting lipids such as autoclaving, microwaving and sonication. A mixed culture of microalgae predominantly consisting of Chlorella sp. was obtained from Beire Lake which is an algae rich, organically polluted water body located in Colombo, Sri Lanka. After each pre-treatment method, a standard solvent extraction using Bligh and Dyer’s method was used to compare the total lipid content in percentage dry weight (% dwt). The fatty acid profiles of the oils extracted with each pretreatment method were analyzed using gas chromatography-mass spectrometry (GC-MS). The properties of the biodiesels were predicted by Biodiesel Analyzer© Version 1.1, in order to compare with ASTM 6751-08 biodiesel standard.

Keywords: biodiesel, lipid extraction, microalgae, pre-treatment

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Authors: Abu Yousuf, Maksudur Rahman Khan, Ahasanul Karim, Amirul Islam, Minhaj Uddin Monir, Sharmin Sultana, Domenico Pirozzi

Abstract:

Traditional biodiesel feedstock like edible oils or plant oils, animal fats and cooking waste oil have been replaced by microbial oil in recent research of biodiesel synthesis. The well-known community of microbial oil producers includes microalgae, oleaginous yeast and seaweeds. Conventional transesterification of microbial oil to produce biodiesel is lethargic, energy consuming, cost-ineffective and environmentally unhealthy. This process follows several steps such as microbial biomass drying, cell disruption, oil extraction, solvent recovery, oil separation and transesterification. Therefore, direct transesterification of biodiesel synthesis has been studying for last few years. It combines all the steps in a single reactor and it eliminates the steps of biomass drying, oil extraction and separation from solvent. Apparently, it seems to be cost-effective and faster process but number of difficulties need to be solved to make it large scale applicable. The main challenges are microbial cell disruption in bulk volume and make faster the esterification reaction, because water contents of the medium sluggish the reaction rate. Several methods have been proposed but none of them is up to the level to implement in large scale. It is still a great challenge to extract maximum lipid from microbial cells (yeast, fungi, algae) investing minimum energy. Electroporation technique results a significant increase in cell conductivity and permeability caused due to the application of an external electric field. Electroporation is required to alter the size and structure of the cells to increase their porosity as well as to disrupt the microbial cell walls within few seconds to leak out the intracellular lipid to the solution. Therefore, incorporation of electroporation techniques contributed in direct transesterification of microbial lipids by increasing the efficiency of biodiesel production rate.

Keywords: biodiesel, electroporation, microbial lipids, transesterification

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Authors: Karishma Chester, Sarvesh K. Paliwal, Sayeed Ahmad

Abstract:

Inab-us-salab also known as Solanum nigrum L. (Family: Solanaceae), is an important Indian medicinal plant and have been used in various unani traditional formulations for hepato-protection. It has been reported to contain significant amount of steroidal glycosides such as solamargine and solasonine as well as their aglycone part solasodine. Being important pharmacologically active metabolites of several members of solanaceae, these markers have been attempted various times for their extraction and quantification but separately for glycoside and aglycone part because of their opposite polarity. Here, we propose for the first time its fractionation and fingerprinting of aglycone (solasodine) and glycosides (solamargine and solasonine) in leaves and berries of S. nigrum using solvent extraction and fractionation followed by HPTLC analysis. The fingerprinting was done using silica gel 60F254 HPTLC plates as stationary phase and chloroform: methanol: acetone: 0.5% ammonia (7: 2.5: 1: 0.4 v/v/v/v) as mobile phase at 400 nm, after derivatization with antimony tri chloride reagent for identification of steroidal glycoside. The statistical data obtained can further be validated and can be used routinely for quality control of various solanaceous drugs reported for these markers as well as traditional formulations containing those plants as an ingredient.

Keywords: solanum nigrum, solasodine, solamargine, solasonine, quantification

Procedia PDF Downloads 378

Authors: Kay Thinzar Phu, Lwin Lwin Oo

Abstract:

In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset.

Keywords: adaptive thresholding based on RGB color, blue color detection, feature extraction, yellow color detection

Procedia PDF Downloads 284

Authors: Zuoji Huang, Haiming Qian, Chunlin Wang, Jinyan Sun, Nan Xu

Abstract:

In this paper, the strategy to extract double road edge lines from acquired road stripe image was explored. The workflow is as follows: the road stripes are acquired by probabilistic boosting tree algorithm and morphological algorithm immediately, and road centerlines are detected by thinning algorithm, so the initial road edge lines can be acquired along the road centerlines. Then we refine the results with big variation of local curvature of centerlines. Specifically, the energy function of edge line is constructed by gradient feature and spectral information, and Dijkstra algorithm is used to optimize the initial road edge lines. The Snake model is constructed to solve the fracture problem of intersection, and the discrete dynamic programming algorithm is used to solve the model. After that, we could get the final road network. Experiment results show that the strategy proposed in this paper can be used to extract the continuous and smooth road edge lines from high-resolution remote sensing images with an accuracy of 88% in our study area.

Keywords: road edge lines extraction, energy function, intersection fracture, Snake model

Procedia PDF Downloads 325