Search results for: community based rehabilitation

Authors: Abhinav Mane, Kedar Karvande, Shubham Patel, Abhayraj Lodha

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Gravity filters are one of the most commonly used, economically viable and moderately efficient water purification systems. Their efficiency is mainly based on the type of filter media installed and its location within the filter mass. Several researchers provide valuable input in decision of the type of filter media. However, the choice is mainly restricted to the chemical combinations of different substances. This makes it very much dependent on the factory made filter media, and no cheap alternatives could be found and used. This paper presents the use of disinfectants and filter medias either available naturally or could be prepared using natural resources in conventional mechanism of gravity filter. A small scale laboratory investigation was made with variation in filter media thickness and its location from the top surface of the filter. A rigid steel frame based custom fabricated test setup was used to facilitate placement of filter media at different height within the filter mass. Finely grinded sun dried Neem (Azadirachta indica) extracts and porous burnt clay pads were used as two distinct filter media and placed in isolation as well as in combination with each other. Ground water available in Marathwada region of Maharashtra, India which mainly consists of harmful materials like Arsenic, Chlorides, Iron, Magnesium and Manganese, etc. was treated in the filters fabricated in the present study. The evaluation was made mainly in terms of the input/output water quality assessment through laboratory tests. The present paper should give a cheap and eco-friendly solution to prepare gravity filter at the merit of household skills and availability.

Keywords: fliter media, gravity filters, natural disinfectants, porous clay pads

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Authors: Belgherbi Aicha, Bessaid Abdelhafid

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In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. The first and fundamental step in all these studies is the semi-automatic liver and spleen segmentation that is still an open problem. In this paper, a semi-automatic liver and spleen segmentation method by the mathematical morphology based on watershed algorithm has been proposed. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological to extract the liver and spleen. The second step consists to improve the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce the over-segmentation problem by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. The aim of this work is to develop a method for semi-automatic segmentation liver and spleen based on watershed algorithm, improve the accuracy and the robustness of the liver and spleen segmentation and evaluate a new semi-automatic approach with the manual for liver segmentation. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work. The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts. Liver segmentation has achieved the sensitivity and specificity; sens Liver=96% and specif Liver=99% respectively. Spleen segmentation achieves similar, promising results sens Spleen=95% and specif Spleen=99%.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: Yonas Shuke Kitawa

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Background: Although malaria incidence has substantially fallen sharply over the past few years, the rate of decline varies by district, time, and malaria type. Despite this turn-down, malaria remains a major public health threat in various districts of Ethiopia. Consequently, the present study is aimed at developing a predictive model that helps to identify the spatio-temporal variation in malaria risk by multiple plasmodium species. Methods: We propose a multivariate spatio-temporal Bayesian model to obtain a more coherent picture of the temporally varying spatial variation in disease risk. The spatial autocorrelation in such a data set is typically modeled by a set of random effects that assign a conditional autoregressive prior distribution. However, the autocorrelation considered in such cases depends on a binary neighborhood matrix specified through the border-sharing rule. Over here, we propose a graph-based optimization algorithm for estimating the neighborhood matrix that merely represents the spatial correlation by exploring the areal units as the vertices of a graph and the neighbor relations as the series of edges. Furthermore, we used aggregated malaria count in southern Ethiopia from August 2013 to May 2019. Results: We recognized that precipitation, temperature, and humidity are positively associated with the malaria threat in the area. On the other hand, enhanced vegetation index, nighttime light (NTL), and distance from coastal areas are negatively associated. Moreover, nonlinear relationships were observed between malaria incidence and precipitation, temperature, and NTL. Additionally, lagged effects of temperature and humidity have a significant effect on malaria risk by either species. More elevated risk of P. falciparum was observed following the rainy season, and unstable transmission of P. vivax was observed in the area. Finally, P. vivax risks are less sensitive to environmental factors than those of P. falciparum. Conclusion: The improved inference was gained by employing the proposed approach in comparison to the commonly used border-sharing rule. Additionally, different covariates are identified, including delayed effects, and elevated risks of either of the cases were observed in districts found in the central and western regions. As malaria transmission operates in a spatially continuous manner, a spatially continuous model should be employed when it is computationally feasible.

Keywords: disease mapping, MSTCAR, graph-based optimization algorithm, P. falciparum, P. vivax, waiting matrix

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Authors: Yen-Mei Lee

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Mobile learning (m-learning) has been applied in the education field not only because it is an alternative to web-based learning but also it possesses the ‘anytime, anywhere’ learning features. However, most studies focus on the technology-related issue, such as usability and functionality instead of addressing m-learning from the motivational perspective. Accordingly, the main purpose of the current paper is to integrate critical factors from different motivational theories and related findings to have a better understand the catalysts of an individual’s learning motivation toward m-learning. The main research question for this study is stated as follows: based on different motivational perspectives, what factors of applying mobile devices as medium can facilitate people’s learning motivations? Self-Determination Theory (SDT), Uses and Gratification Theory (UGT), Malone and Lepper’s taxonomy of intrinsic motivation theory, and different types of motivation concepts were discussed in the current paper. In line with the review of relevant studies, three motivating factors with five essential elements are proposed. The first key factor is autonomy. Learning on one’s own path and applying personalized format are two critical elements involved in the factor of autonomy. The second key factor is to apply a build-in instant feedback system during m-learning. The third factor is creating an interaction system, including communication and collaboration spaces. These three factors can enhance people’s learning motivations when applying mobile devices as medium toward learning. To sum up, in the currently proposed paper, with different motivational perspectives to discuss the m-learning is different from previous studies which are simply focused on the technical or functional design. Supported by different motivation theories, researchers can clearly understand how the mobile devices influence people’s leaning motivation. Moreover, instructional designers and educators can base on the proposed factors to build up their unique and efficient m-learning environments.

Keywords: autonomy, learning motivation, mobile learning (m-learning), motivational perspective

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Authors: Fahad Hanash Alzahrani

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This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation.

Keywords: data warehouse, data mining, knowledge discovery in databases, on-line analytical processing

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Authors: F. Farzan Nasab, H. J. M. Geijselaers, I. Baran, A. De Boer

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A method is introduced for the coupled skin-rib optimization of a wing box where mass minimization is the objective and local buckling is the constraint. The structure is made of composite materials where continuity of plies in multiple adjacent panels (blending) has to be satisfied. Blending guarantees the manufacturability of the structure; however, it is a highly challenging constraint to treat and has been under debate in recent research in the same area. To fulfill design guidelines with respect to symmetry, balance, contiguity, disorientation and percentage rule of the layup, a reference for the stacking sequences (stacking sequence table or SST) is generated first. Then, an innovative fully gradient-based optimization approach in relation to a specific SST is introduced to obtain the optimum thickness distribution all over the structure while blending is fulfilled. The proposed optimization approach aims to turn the discrete optimization problem associated with the integer number of plies into a continuous one. As a result of a wing box deflection, a rib is subjected to load values which vary nonlinearly with the amount of deflection. The bending stiffness of a skin affects the wing box deflection and thus affects the load applied to a rib. This indicates the necessity of a coupled skin-rib optimization approach for a more realistic optimized design. The proposed method is examined with the optimization of the layup of a composite stiffened skin and rib of a wing torsion box subjected to in-plane normal and shear loads. Results show that the method can successfully prescribe a valid design with a significantly cheap computation cost.

Keywords: blending, buckling optimization, composite panels, wing torsion box

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Authors: Orhan Akınoğlu, Arif Çömek, Ersin Elmacı, Tuğba Gündoğdu

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The concept map is known to be a powerful tool to organize the ideas and concepts of an individuals’ mind. This tool is a kind of visual map that illustrates the relationships between the concepts of a certain subject. The effect of concept mapping on cognitive and affective qualities is one of the research topics among educational researchers for last decades. We educators want to utilize it both as an instructional tool or an assessment tool in classes. For that reason, this study aimed to determine the effect of concept mapping as a learning strategy in science classes on students’ academic achievement and attitude. The research employed a randomized pre-test post-test control group design. Data collected from 60 sixth grade students participated in the study from a randomly selected primary school in Turkey. Sixth-grade classes of the school were analyzed according to students’ academic achievement, science attitude, gender, mathematics, science courses grades, and their GPAs before the implementation. Two of the classes found to be equivalent (t=0,983, p>0,05) and one of them was defined as experimental and the other one control group randomly. During a 5-weeks period, the experimental group students (N=30) used the paper-based concept mapping method while the control group students (N=30) were taught with the traditional approach according to the science and technology education curriculum for light and sound subject. Both groups were taught by the same teacher who is experienced using concept mapping in science classes. Before the implementation, the teacher explained the theory of the concept maps and showed how to create paper-based concept mapping individually to the experimental group students for two hours. Then for two following hours she asked them to create some concept maps related to their former science subjects and gave them feedback by reviewing their concept maps to be sure that they can create during the implementation. The data were collected by science achievement test, science attitude scale and personal information form. Science achievement test and science attitude scale were implemented as pre-test and post-test while personal information form was implemented just as once. The reliability coefficient of the achievement test was KR20=0,76 and Cronbach’s Alpha of the attitude scale was 0,89. SPSS statistical software was used to analyze the data. According to the results, there was a statistically significant difference between the experimental and control group for academic achievement but not for attitude. The experimental group had significantly greater gains from academic achievement test than the control group (t=0,02, p<0,05). The findings showed that the paper-and-pencil concept mapping can be used as an effective method for students’ academic achievement in science classes. The results have implications for further researches.

Keywords: concept mapping, science education, constructivism, academic achievement, science attitude

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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: Emma Nemishalyan

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It is believed that learners’ mother tongue (L1 hereafter) has a huge impact on their second language acquisition (L2 hereafter). This hypothesis has been exposed to both positive and negative criticism. Based on research results of a wide range of learners’ corpora (Chinese, Japanese, Spanish among others) the hypothesis has either been proved or disproved. However, no such study has been conducted on the Armenian learners. The aim of this paper is to understand the implication of the hypothesis on the Armenian learners’ corpus in terms of the use of the passive voice. To this end, the method of Contrastive Interlanguage Analysis (hereafter CIA) has been used on native speakers’ corpus (Louvain Corpus of Native English Essays (LOCNESS)) and Armenian learners’ corpus which has been compiled by me in compliance with International Corpus of Learner English (ICLE) guidelines. CIA compares the interlanguage (the language produced by learners) with the one produced by native speakers. With the help of this method, it is possible not only to highlight the mistakes that learners make, but also to underline the under or overuses. The choice of the grammar issue (passive voice) is conditioned by the fact that typologically Armenian and English are drastically different as they belong to different branches. Moreover, the passive voice is considered to be one of the most problematic grammar topics to be acquired by learners of the English language. Based on this difference, we hypothesized that Armenian learners would either overuse or underuse some types of the passive voice. With the help of Lancsbox software, we have identified the frequency rates of passive voice usage in LOCNESS and Armenian learners’ corpus to understand whether the latter have the same usage pattern of the passive voice as the native speakers. Secondly, we have identified the types of the passive voice used by the Armenian leaners trying to track down the reasons in their mother tongue. The results of the study showed that Armenian learners underused the passive voices in contrast to native speakers. Furthermore, the hypothesis that learners’ L1 has an impact on learners’ L2 acquisition and production was proved.

Keywords: corpus linguistics, applied linguistics, second language acquisition, corpus compilation

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Authors: Yuri A. Kalvachev, Totka D. Todorova

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Zeolite mordenite has been investigated as a transition metal support for the preparation of efficient catalysts in the oxidation of volatile organic compounds (VOCs). The highly crystalline mordenite samples were treated with hydrofluoric acid and ammonium fluoride to get hierarchical material with secondary porosity. The obtained supports by this method have a high active surface area, good diffusion properties and prevent the extraction of metal components during catalytic reactions. The active metal phases platinum and copper were loaded by impregnation on both mordenite materials (parent and acid treated counterparts). Monometalic Pt and Cu, and bimetallic Pt/Cu catalysts were obtained. The metal phases were fine dispersed as nanoparticles on the functional porous materials. The catalysts synthesized in this way were investigated in the reaction of complete oxidation of propane and benzene. Platinum, copper and platinum/copper were loaded and there catalytic activity was investigated and compared. All samples are characterized by X-ray diffraction analysis, nitrogen adsorption, scanning electron microscopy (SEM), X-ray photoelectron measurements (XPS) and temperature programed reduction (TPR). The catalytic activity of the samples obtained is investigated in the reaction of complete oxidation of propane and benzene by using of Gas Chromatography (GC). The oxidation of three organic molecules was investigated—methane, propane and benzene. The activity of metal loaded mordenite catalysts for methane oxidation is almost the same for parent and treated mordenite as a support. For bigger molecules as propane and benzene, the activity of catalysts based on treated mordenite is higher than those based on parent zeolite.

Keywords: metal loaded catalysts, mordenite, VOCs oxidation, zeolites

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Authors: Mohamed Hrouda

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The Principal Component Analysis (PCA) was performed on a dataset comprising 41 biomarker concentrations from twenty-three core source rocks samples and seven oil samples from different location, with the objective of establishing the major sources of variance within the steranes, tricyclic terpanes, hopanes, and triaromatic steroid. This type of analysis can be used as an aid when deciding which molecular biomarker maturity, source facies or depositional environment parameters should be plotted, because the principal component loadings plots tend to extract the biomarker variables related to maturity, source facies or depositional environment controls. Facies characterization of the source rock samples separate the Silurian and Devonian source rock samples into three groups. Maturity evaluation of source rock samples based on biomarker and aromatic hydrocarbon distributions indicates that not all the samples are strongly affected by maturity, the Upper Devonian samples from wells located in the northern part of the basin are immature, whereas the other samples which have been selected from the Lower Silurian are mature and have reached the main stage of the oil window, the Lower Silurian source rock strata revealed a trend of increasing maturity towards the south and southwestern part of Ghadames Basin. Most of the facies-based parameters employed in this project using biomarker distributions clearly separate the oil samples into three groups. Group I contain oil samples from wells within Al-Wafa oil field Located in the south western part of the basin, Group II contains oil samples collected from Al-Hamada oil field complex in the south and the third group contains oil samples collected from oil fields located in the north

Keywords: Ghadamis basin, geochemistry, silurian, devonian

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Authors: Bo Wang

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The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: G. C. Almiranez, J. Mercado, L. U. Aumentado, J. M. Mahaguay, J. P. Cruz, M. L. Saballe

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This research project focused on the development of an application that aids the university administrators to establish an efficient and effective system in managing faculty attendance and discourage unnecessary absences. The Faculty Attendance Management System (FAMS) is a web based and mobile application which is proven to be efficient and effective in handling and recording data, generating updated reports and analytics needed in managing faculty attendance. The FAMS can facilitate not only a convenient and faster way of gathering and recording of data but it can also provide data analytics, immediate feedback system mechanism and analysis. The software database architecture uses MySQL for web based and SQLite for mobile applications. The system includes different modules that capture daily attendance of faculty members, generate faculty attendance reports and analytics, absences notification system for faculty members, chairperson and dean regarding absences, and immediate communication system concerning the absences incurred. Quantitative and qualitative evaluation showed that the system satisfactory meet the stakeholder’s requirements. The functionality, usability, reliability, performance, and security all turned out to be above average. System testing, integration testing and user acceptance testing had been conducted. Results showed that the system performed very satisfactory and functions as designed. Performance of the system is also affected by Internet infrastructure or connectivity of the university. The faculty analytics generated from the system may not only be used by Deans and Chairperson in their evaluation of faculty performance but as well as the individual faculty to increase awareness on their attendance in class. Hence, the system facilitates effective communication between system stakeholders through FAMS feedback mechanism and up to date posting of information.

Keywords: faculty attendance management system, MySQL, SQLite, FAMS, analytics

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Authors: Kraiem Khadija, Hamadi Kallali, Naceur Jedidi

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Constructed wetland systems are eco-technology recognized as environmentally friendly and emerging innovative solutions remediation as these systems are cost-effective and sustainable wastewater treatment systems. The performance of these biological system is affected by various factors such as plant, substrate, wastewater type, hydraulic loading rate, hydraulic retention time, water depth, and operation mood. The objective of this study was to to assess the alters of plant species on pollutants reduction and enrichment of anammox and nitrifing denitrifing bacteria in a modified vertical flow (VFCW) constructed wetland. This tests were carried out using three modified vertical constructed wetlands with a surface of 0.23 m² and depth 80 cm. It was a saturated vertical constructed wetland at the bottom. The saturation zone is maintained by the siphon structure at the outlet. The VFCW (₁) system was unplanted, VFCW (₂) planted with Typha angustofolia, and VFCW(₃) planted with Phragmites australis. The experimental units were fed with domestic wastewater and were operated by batch mode during 8 months at an average hydraulic loading rate around 20 cm day− 1. The operation cycle was two days feeding and five days rest. Results indicated that plants presence improved the removal efficiency; the removal rates of organic matter (85.1–90.9%; COD and 81.8–88.9%; BOD5), nitrogen (54.2–73%; NTK and 66–77%; NH4 -N) were higher by 10.7–30.1% compared to the unplanted vertical constructed wetland. On the other hand, the plant species had no significant effect on removal efficiency of COD, The removal of COD was similar in VFCW (₂) and VFCW (₃) (p > 0.05), attaining average removal efficiencies of 88.7% and 85.2%, respectively. Whereas it had a significant effect on NTK removal (p > 0.05), with an average removal rate of 72% versus 51% for VFCW (₂) and VFCW (₃), respectively. Among the three sets of vertical flow constructed wetlands, the VFCW(₂) removed the highest percent of total streptococcus, fecal streptococcus total coliforms, fecal coliforms, E. coli as 59, 62, 52, 63, and 58%, respectively. The presence and the plant species alters the community composition and abundance of the bacteria. The abundance of bacteria in the planted wetland was much higher than that in the unplanted one. VFCW(₃) had the highest relative abundance of nitrifying bacteria such as Nitrosospira (18%), Nitrosospira (12%), and Nitrobacter (8%). Whereas the vertical constructed wetland planted with typha had larger number of denitrifying species, with relative abundances of Aeromonas (13%), Paracoccus (11%), Thauera (7%), and Thiobacillus (6%). However, the abundance of nitrifying bacteria was very lower in this system than VFCW(₂). Interestingly, the presence of Thypha angustofolia species favored the enrichment of anammox bacteria compared to unplanted system and system planted with phragmites australis. The results showed that the middle layer had the most accumulation of anammox bacteria, which the anaerobic condition is better and the root system is moderate. Vegetation has several characteristics that make it an essential component of wetlands, but its exact effects are complex and debated.

Keywords: wastawater, constructed wetland, anammox, removal

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Authors: Mohammad Hossein Panahi

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Modern social sciences developed in Western Europe, and from there, it disseminated to the rest of the world, including Muslim World. Within the hierarchical world social science system that emerged in the 19th and 20th centuries, the West occupied the center, and the Third and Muslim Worlds fell into its periphery. Many social scientists, especially sociologists, in the Third and Muslim World since the 1970s have criticized this worldwide unequal division of scientific labor and have called for the development of independent/indigenous social sciences relevant to their own social conditions. Based on the conceptual framework of the World Social Science System, this paper studied the encounter of Muslim social scientists/sociologists with the Western social sciences. Using inductive thematic content analysis as the method of research, the author analyzed 32 purposefully selected articles from among over 500 collected articles from the 1970s to 2018 and categorized the obtained themes. The findings revealed three main types of encounters: reception, indigenization, and Islamization. ‘Reception’ refers to the encounter of those Muslim social scientists who embrace the positivist approach and believe that Western social sciences are valid and applicable worldwide, including the Muslim World. ‘Indigenization’ refers to the approach of those Muslim social scientists who, along with many critical Third World social scientists, reject the universality of Western social sciences and call for the development of indigenous social sciences. ‘Islamization’ refers to the position of those religious Muslim social scientists who believe that Muslim nations should Islamize social sciences based on the Islamic value and knowledge systems, in order to attain viable social sciences and free themselves from Western domination. Discussing these encounters, their supporters and opponents, the paper concludes that despite various efforts, none of the two alternatives to the Western social sciences have been able to replace it so far.

Keywords: indigenization, Islamization, Muslim world, social sciences, world social science system

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Authors: Suhair Saleh, Ahmad Aljaberi

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Cationic lipid-mediated delivery of nucleic acids represents an exciting approach for developing therapeutically realistic gene medicines. Elucidation of the molecular and formulation requirements for efficient lipofection is a prerequisite to enhance the biological activity of such delivery systems. To this end, the in vitro lipofection activity of the ionizable asymmetric 1,2-dialkoylamidopropane-based derivatives bearing single primary amine group as the cationic head group was evaluated. The electrostatic interactions of these cationic lipids with plasmid DNA in physiologically relevant medium were investigated by means of gel electrophoresis retardation and Eth-Br quenching assays. The effect of the presence of the helper lipid on these interactions was evaluated. The physicochemical properties of these lipids in terms of bilayer fluidity and extent of ionization were investigated using fluorescence anisotropy and surface potential techniques, respectively. The results showed that only the active lipid, 1,2lmp[5], existed in a liquid crystalline state at physiological temperature. Moreover, the extent of ionization of this lipid in assemblies was significantly higher that it's saturated analogues. Inclusion of the helper lipid DOPE improved the encapsulation and association between 1,2lmp[5] and plasmid DNA, which was reflected by the significant boost of lipofection activity of the 1,2lmp[5]/DOPE formulation as compared to the lipid alone. In conclusion, membrane fluidity and sufficient protonation of ionizable cationic lipid are required for efficient association and encapsulation of plasmid DNA and promoting improved in vitro lipofection activity.

Keywords: cationic lipids, gene delivery, lipofection, membrane fluidity, helper lipids, surface potential

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Azin Shakibabarough, Mojtaba Valinejadshoubi, Ashutosh Bagchi

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Catastrophic bridge failure happens due to the lack of inspection, lack of design and extreme events like flooding, an earthquake. Bridge Management System (BMS) is utilized to diminish such an accident with proper design and frequent inspection. Visual inspection cannot detect any subsurface defects, so using Non-Destructive Evaluation (NDE) techniques remove these barriers as far as possible. Among all NDE techniques, Ground Penetrating Radar (GPR) has been proved as a highly effective device for detecting internal defects in a reinforced concrete bridge deck. GPR is used for detecting rebar location and rebar corrosion in the reinforced concrete deck. GPR profile is composed of hyperbola series in which sound hyperbola denotes sound rebar and blur hyperbola or signal attenuation shows corroded rebar. Interpretation of GPR images is implemented by numerical analysis or visualization. Researchers recently found that interpretation through visualization is more precise than interpretation through numerical analysis, but visualization is time-consuming and a highly subjective process. Automating the interpretation of GPR image through visualization can solve these problems. After interpretation of all scans of a bridge, condition assessment is conducted based on the generated corrosion map. However, this such a condition assessment is not objective and precise. Condition assessment based on structural integrity and strength parameters can make it more objective and precise. The main purpose of this study is to present an automated interpretation method of a reinforced concrete bridge deck through a visualization technique. In the end, the combined analysis of the structural condition in a bridge is implemented.

Keywords: bridge condition assessment, ground penetrating radar, GPR, NDE techniques, visualization

Procedia PDF Downloads 136

Authors: Jovana M. Ćirković, Aleksandar M. Radojković, Sanja Z. Perać, Jelena N. Jovanović, Zorica M. Branković, Slobodan D. Milanović, Ivan Lj. Milenković, Jovan N. Dobrosavljević, Nemanja V. Simović, Vanja M. Tadić, Ana R. Žugić, Goran O. Branković

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The protection of forests is a national interest and of strategic importance in every country. The spongy moth (Lymantria dispar) is a damaging invasive pest that can weaken and destroy trees by defoliating them. Chemical pesticides commonly used to protect forests against spongy moths not only have a negative impact on terrestrial and aquatic organisms/ecosystems but also often fail to provide significant protection. Therefore, many eco-friendly alternatives have been considered. Within this research, a new biopesticide was developed based on the method of nanoencapsulation of plant extracts in a biopolymer matrix, which provides a slow release of the active components during a substantial time period. The antifeedant activity of plant extracts of common (Fraxinus excelsior L.), manna (F. ornus L.) ash tree, and the tree of heaven Ailanthus altissima (Mill.) was tested on the spongy moth (Lymantria dispar L, 1758) larvae. To test the antifeedant activity of these compounds, the choice and non-choice tests in laboratory conditions for different plant extract concentrations (0.01, 0.1, 0.5, and 1 % v/v) were carried out. In both cases, the best results showed formulations based on the tree of heaven and common ash for the concentration of 1%, with deterioration indices of 163 and 132, respectively. The main benefit of these formulations is their versatility, effectiveness, prolonged effect, and because they are completely environmentally acceptable. Therefore, they can be considered for suppression of the spongy moth in forest ecosystems.

Keywords: Ailanthus altissima (Mill.), Fraxinus excelsior L., encapsulation, Lymantria dispar

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: Rose Branch-Allen, John Jayachandran

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Background and purpose: Canada is a pluralistic, multicultural society with an ethno-cultural composition that has been shaped over time by immigrants and their descendants. Although Canada welcomes these immigrants, many will endure hardship and assimilation difficulties. Despite these life hurdles, surveys consistently disclose high life satisfaction for all Canadians. Most research studies on Life Satisfaction/ Subjective Wellbeing (SWB) have focused on one main determinant and a variety of social demographic variables to delineate the determinants of life satisfaction. However, very few research studies examine life satisfaction from a holistic approach. In addition, we need to understand the causal pathways leading to life satisfaction, and develop theories that explain why certain variables differentially influence the different components of SWB. The aim this study was to utilize a holistic approach to construct a causal model and identify major determinants of life satisfaction. Data and measures: This study utilized data from the General Social Survey, with a sample size of 19, 597. The exogenous concepts included age, gender, marital status, household size, socioeconomic status, ethnicity, location, immigration status, religiosity, and neighborhood. The intervening concepts included health, social contact, leisure, enjoyment, work-family balance, quality time, domestic labor, and sense of belonging. The endogenous concept life satisfaction was measured by multiple indicators (Cronbach’s alpha = .83). Analysis: Several multiple regression models were run sequentially to estimate path coefficients for the causal model. Results: Overall, above average satisfaction with life was reported for respondents with specific socio-economic, demographic and lifestyle characteristics. With regard to exogenous factors, respondents who were female, younger, married, from high socioeconomic status background, born in Canada, very religious, and demonstrated high level of neighborhood interaction had greater satisfaction with life. Similarly, intervening concepts suggested respondents had greater life satisfaction if they had better health, more social contact, less time on passive leisure activities and more time on active leisure activities, more time with family and friends, more enjoyment with volunteer activities, less time on domestic labor and a greater sense of belonging to the community. Conclusions and Implications: Our results suggest that a holistic approach is necessary for establishing determinants of life satisfaction, and that life satisfaction is not merely comprised of positive or negative affect rather understanding the causal process of life satisfaction. Even though, most of our findings are consistent with previous studies, a significant number of causal connections contradict some of the findings in literature today. We have provided possible explanation for these anomalies researchers encounter in studying life satisfaction and policy implications.

Keywords: causal model, holistic approach, life satisfaction, socio-demographic variables, subjective well-being

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Authors: Eder Amayuelas, Begoña Bazán, M. Karmele Urtiaga, Gotzone Barandika, María I. Arriortua

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Metal Organic Frameworks (MOFs) have attracted great interest in recent years, taking a lead role in the field of catalysis, drug delivery, sensors and absorption. In the past decade, promising results have been reported specifically in the field of adsorption, based on the topology and chemical features of this type of porous material. Thus, its application in industry and environment for the adsorption of pollutants is presented as a response to an increasingly important need. In this area, organic dyes are nowadays widely used in many industries including medicine, textile, leather, printing and plastics. The consequence of this fact is that dyes are present as emerging pollutants in soils and water where they remain for long periods of time due to their high stability, with a potential risk of toxicity in wildlife and in humans. On the other hand, the presence of iodine in soils, water and gas as a nuclear activity pollutant product or its extended use as a germicide is still a problem in many countries, which indicates the imperative need for its removal. In this context, this work presents the characterization as an adsorbent of the activated compound αMOP@Ei2-1 obtained from the already reported [Cu₂₄(m-BDC)₂₄(DMF)₂₀(H₂O)₄]•24DMF•40H₂O (MOP@Ei2-1), where m-BDC is the 1,3-benzenedicarboxylic ligand and DMF is N,N′-dimethylformamide. The structure of MOP@Ei2-1 consists of Cu24 clusters arranged in such a way that 12 paddle-wheels are connected through m-BDC ligands. The clusters exhibit an internal cavity where crystallization molecules of DMF and water are located. Adsorption of dyes and iodine as pollutant examples has been carried out, focusing attention on the kinetics of the rapid process.

Keywords: adsorption, organic dyes, iodine, metal organic frameworks

Procedia PDF Downloads 258

Authors: Feng Yue, Fei Dai

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With the rapid development of urbanization process in China, its environmental protection pressure is severely tested. So, analyzing and optimizing the landscape pattern is an important measure to ease the pressure on the ecological environment. This paper takes Wuhan Urban Development Zone as the research object, and studies its landscape pattern evolution and quantitative optimization strategy. First, remote sensing image data from 1990 to 2015 were interpreted by using Erdas software. Next, the landscape pattern index of landscape level, class level, and patch level was studied based on Fragstats. Then five indicators of ecological environment based on National Environmental Protection Standard of China were selected to evaluate the impact of landscape pattern evolution on the ecological environment. Besides, the cost distance analysis of ArcGIS was applied to simulate wildlife migration thus indirectly measuring the improvement of ecological environment quality. The result shows that the area of land for construction increased 491%. But the bare land, sparse grassland, forest, farmland, water decreased 82%, 47%, 36%, 25% and 11% respectively. They were mainly converted into construction land. On landscape level, the change of landscape index all showed a downward trend. Number of patches (NP), Landscape shape index (LSI), Connection index (CONNECT), Shannon's diversity index (SHDI), Aggregation index (AI) separately decreased by 2778, 25.7, 0.042, 0.6, 29.2%, all of which indicated that the NP, the degree of aggregation and the landscape connectivity declined. On class level, the construction land and forest, CPLAND, TCA, AI and LSI ascended, but the Distribution Statistics Core Area (CORE_AM) decreased. As for farmland, water, sparse grassland, bare land, CPLAND, TCA and DIVISION, the Patch Density (PD) and LSI descended, yet the patch fragmentation and CORE_AM increased. On patch level, patch area, Patch perimeter, Shape index of water, farmland and bare land continued to decline. The three indexes of forest patches increased overall, sparse grassland decreased as a whole, and construction land increased. It is obvious that the urbanization greatly influenced the landscape evolution. Ecological diversity and landscape heterogeneity of ecological patches clearly dropped. The Habitat Quality Index continuously declined by 14%. Therefore, optimization strategy based on greenway network planning is raised for discussion. This paper contributes to the study of landscape pattern evolution in planning and design and to the research on spatial layout of urbanization.

Keywords: landscape pattern, optimization strategy, ArcGIS, Erdas, landscape metrics, landscape architecture

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Authors: Madison Meyer

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Dermatological conditions are one of the most viewed medical subjects on the social media platform TikTok, resulting in the rise of several prominent American board-certified dermatologists as influencers. Notably, dermatology is one of the least diverse specialties. This cross-sectional study aimed to assess individuals’ preferences related to race, gender, and sexual identity of doctors in terms of dermatology-related information on TikTok and which group posts more reliable information. This study qualitatively and quantitatively evaluated the racial, gender, and sexual diversity of the top 55 dermatologist influencers on TikTok based on their follower count. The DISCERN tool was used to determine the reliability of consumer health content based on a score ranging from 1-5. Among the top 55 dermatologist influencers, African American (54,241.60) and Latinx (6,696) groups had the lowest mean number of followers compared to Caucasian (1,046,298.50) and Asian (1,403,393.50) physicians. Latinx and African American dermatologists had the highest DISCERN scores of 2 and 1.9, respectively. None of the physicians identified as a different gender or as LGBTQIA+ in any racial category. There is a considerable lack of minority dermatologist influencers on TikTok, especially Latinx, African American, and LGBTQIA+ physicians. The lack of diversity in the dermatology specialty can lead to inequitable care and health outcomes for racial/ethnic, gender, and sexual minority patient populations. This study’s findings also suggest Latinx and African American dermatologists post more reliable content compared with their Caucasian and Asian counterparts.

Keywords: dermatology, social media, sexual and gender minorities, racial minorities, skin of color, tiktok

Procedia PDF Downloads 65

Authors: Mojtaba Eshraghi Arani

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Ship financing is the vital element in the development of shipping industry because while the ship constitutes the owners’ main asset, she is considered a reliable security in the financiers’ viewpoint as well. However, it is most probable that a financier who has accepted a ship as security will face many creditors who are privileged and rank before him for collecting, out of the ship, the money that they are owed. In fact, according to the current rule of maritime law, which was established by “Convention Internationale pour l’Unification de Certaines Règles Relatives aux Privilèges et Hypothèques Maritimes, Brussels, 10 April 1926”, the mortgages, hypotheques, and other charges on vessels rank after several secured claims referred to as “maritime liens”. Such maritime liens are an exhaustive list of claims including but not limited to “expenses incurred in the common interest of the creditors to preserve the vessel or to procure its sale and the distribution of the proceeds of sale”, “tonnage dues, light or harbour dues, and other public taxes and charges of the same character”, “claims arising out of the contract of engagement of the master, crew and other persons hired on board”, “remuneration for assistance and salvage”, “the contribution of the vessel in general average”, “indemnities for collision or other damage caused to works forming part of harbours, docks, etc,” “indemnities for personal injury to passengers or crew or for loss of or damage to cargo”, “claims resulting form contracts entered into or acts done by the master”. The same rule survived with only some minor change in the categories of maritime liens in the substitute conventions 1967 and 1993. The status que in maritime law have always been considered as a major obstacle to the development of shipping market and has inevitably led to increase in the interest rates and other related costs of ship financing. It seems that the national and international policy makers have yet to change their mind being worried about the deviation from the old marine traditions. However, it is crystal clear that the continuation of status que will harm, to a great extent, the shipowners and, consequently, the international merchants as a whole. It is argued in this article that the raison d'être for many categories of maritime liens cease to exist anymore, in view of which, the international community has to recognize only a minimum category of maritime liens which are created in the common interests of all creditors; to this effect, only two category of “compensation due for the salvage of ship” and “extraordinary expenses indispensable for the preservation of the ship” can be declared as taking priority over the mortgagee rights, in anology with the Geneva Convention on the International Recognition of Rights in Aircrafts (1948). A qualitative method with the concept of interpretation of data collection has been used in this manuscript. The source of the data is the analysis of international conventions and domestic laws.

Keywords: ship finance, mortgage, maritime liens, brussels convenion, geneva convention 1948

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Authors: Muftah H. El-Naas, Ameera F. Mohammad, Mabruk I. Suleiman, Mohamed Al Musharfy, Ali H. Al-Marzouqi

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CO₂ capture and storage technologies play a significant role in contributing to the control of climate change through the reduction of carbon dioxide emissions into the atmosphere. The present study evaluates and optimizes CO₂ capture through a process, where carbon dioxide is passed into pH adjusted high salinity water and reacted with sodium chloride to form a precipitate of sodium bicarbonate. This process is based on a modified Solvay process with higher CO₂ capture efficiency, higher sodium removal, and higher pH level without the use of ammonia. The process was tested in a bubble column semi-batch reactor and was optimized using response surface methodology (RSM). CO₂ capture efficiency and sodium removal were optimized in terms of major operating parameters based on four levels and variables in Central Composite Design (CCD). The operating parameters were gas flow rate (0.5–1.5 L/min), reactor temperature (10 to 50 ^oC), buffer concentration (0.2-2.6%) and water salinity (25-197 g NaCl/L). The experimental data were fitted to a second-order polynomial using multiple regression and analyzed using analysis of variance (ANOVA). The optimum values of the selected variables were obtained using response optimizer. The optimum conditions were tested experimentally using desalination reject brine with salinity ranging from 65,000 to 75,000 mg/L. The CO₂ capture efficiency in 180 min was 99% and the maximum sodium removal was 35%. The experimental and predicted values were within 95% confidence interval, which demonstrates that the developed model can successfully predict the capture efficiency and sodium removal using the modified Solvay method.

Keywords: CO2 capture, water desalination, Response Surface Methodology, bubble column reactor

Procedia PDF Downloads 274

Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

Procedia PDF Downloads 122

Authors: Muhammad Arif Jalil, Somchat Sonasang, Preecha Yupapin

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This study hypothesizes that the universe is at the center of the universe among the white and black holes, which are the entangled pairs. The coupling between them is in terms of spacetime forming the universe and things. The birth of things is based on exchange energy between the white and black sides. That is, the transition from the white side to the black side is called wave-matter, where it has a speed faster than light with positive gravity. The transition from the black to the white side has a speed faster than light with negative gravity called a wave-particle. In the part where the speed is equal to light, the particle rest mass is formed. Things can appear to take shape here. Thus, the gravity is zero because it is the center. The gravitational force belongs to the Earth itself because it is in a position that is twisted towards the white hole. Therefore, it is negative. The coupling of black-white holes occurs directly on both sides. The mass is formed at the saturation and will create universes and other things. Therefore, it can be hundreds of thousands of universes on both sides of the B and white holes before reaching the saturation point of multi-universes. This work will use the DJV circuit that the research team made as an entangled or two-level system circuit that has been experimentally demonstrated. Therefore, this principle has the possibility for interpretation. This work explains the emergence of multiple universes and can be applied as a practical guideline for searching for universes in the future. Moreover, the results indicate that the DJV circuit can create the elementary particles according to Feynman's diagram with rest mass conditions, which will be discussed for fission and fusion applications.

Keywords: multi-universes, feynman diagram, fission, fusion

Procedia PDF Downloads 51

Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 115