Search results for: data analyses

Authors: Uzma Bashir, Jamaludin Md. Ali

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This study is concerned with the visualization of monotone data using a piece-wise C1 rational trigonometric interpolating scheme. Four positive shape parameters are incorporated in the structure of rational trigonometric spline. Conditions on two of these parameters are derived to attain the monotonicity of monotone data and other two are left-free. Figures are used widely to exhibit that the proposed scheme produces graphically smooth monotone curves.

Keywords: trigonometric splines, monotone data, shape preserving, C1 monotone interpolant

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Authors: Joshua Buli, David Pietrowski, Samuel Britton

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Processing SAR data usually requires constraints in extent in the Fourier domain as well as approximations and interpolations onto a planar surface to form an exploitable image. This results in a potential loss of data requires several interpolative techniques, and restricts visualization to two-dimensional plane imagery. The data can be interpolated into a ground plane projection, with or without terrain as a component, all to better view SAR data in an image domain comparable to what a human would view, to ease interpretation. An alternate but computationally heavy method to make use of more of the data is the basis of this research. Pre-processing of the SAR data is completed first (matched-filtering, motion compensation, etc.), the data is then range compressed, and lastly, the contribution from each pulse is determined for each specific point in space by searching the time history data for the reflectivity values for each pulse summed over the entire collection. This results in a per-3D-point reflectivity using the entire collection domain. New advances in GPU processing have finally allowed this rapid projection of acquired SAR data onto any desired reference surface (called backprojection). Mathematically, the computations are fast and easy to implement, despite limitations in SAR phase history data size and 3D-point cloud size. Backprojection processing algorithms are embarrassingly parallel since each 3D point in the scene has the same reflectivity calculation applied for all pulses, independent of all other 3D points and pulse data under consideration. Therefore, given the simplicity of the single backprojection calculation, the work can be spread across thousands of GPU threads allowing for accurate reflectivity representation of a scene. Furthermore, because reflectivity values are associated with individual three-dimensional points, a plane is no longer the sole permissible mapping base; a digital elevation model or even a cloud of points (collected from any sensor capable of measuring ground topography) can be used as a basis for the backprojection technique. This technique minimizes any interpolations and modifications of the raw data, maintaining maximum data integrity. This innovative processing will allow for SAR data to be rapidly brought into a common reference frame for immediate exploitation and data fusion with other three-dimensional data and representations.

Keywords: backprojection, data fusion, exploitation, three-dimensional, visualization

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Authors: Raúl Jiménez Vilches

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Native (L1) speakers can mark prosodically one part of an utterance and make it more relevant as opposed to the rest of the constituents. Conversely, non-native (L2) speakers encounter problems when it comes to marking prosodically information structure in English. In fact, the L2 speaker’s choice for the prosodic realization of focus is not so clear and often obscures the intended pragmatic meaning and the communicative value in general. This paper reports some of the findings obtained in an L2 prosodic training course for Spanish learners of English within the context of public speaking. More specifically, it analyses the effects of the course experiment in relation to the non-native production of the tonic syllable to mark focus and compares it with the public speeches delivered by native English speakers. The whole experimental training was executed throughout eighteen input sessions (1,440 minutes total time) and all the sessions took place in the classroom. In particular, the first part of the course provided explicit instruction on the recognition and production of the tonic syllable and how the tonic syllable is used to express focus. The non-native and native oral presentations were acoustically analyzed using Praat software for speech analysis (7,356 words in total). The investigation adopted mixed and embedded methodologies. Quantitative information is needed when measuring acoustically the phonetic realization of focus. Qualitative data such as questionnaires, interviews, and observations were also used to interpret the quantitative data. The embedded experiment design was implemented through the analysis of the public speeches before and after the intervention. Results indicate that, even after the L2 prosodic training course, Spanish learners of English still show some major inconsistencies in marking focus effectively. Although there was occasional improvement regarding the choice for location and word classes, Spanish learners were, in general, far from achieving similar results to the ones obtained by the English native speakers in the two types of focus. The prosodic realization of focus seems to be one of the hardest areas of the English prosodic system to be mastered by Spanish learners. A funded research project is in the process of moving the present classroom-based experiment to an online environment (mobile app) and determining whether there is a more effective focus usage through CAPT (Computer-Assisted Pronunciation) tools.

Keywords: focus, prosody, public speaking, Spanish learners of English

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Authors: Vladimira Vytlacilova

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Recycling of construction and demolition waste (C&DW) and their new reuse in structures is one of the solutions of environmental problems. Construction and demolition waste creates a major portion of total solid waste production in the world and most of it is used in landfills all the time. The paper deals with the situation of the recycling of the building and demolition waste in the Czech Republic during the recent years. The paper is dealing with questions of C&D waste recycling, it also characterizes construction and demolition waste in general, furthermore it analyses production of construction waste and subsequent production of recycled materials.

Keywords: Recycling, Construction and demolition waste, Recycled rubble, Waste management

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Authors: Jean Christian Ralaivao, Fabrice Razafindraibe, Hasina Rakotonirainy

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This document constitutes a resumption of work carried out in the field of complex data warehouses (DW) relating to the management and formalization of knowledge and metadata. It offers a methodological approach for integrating two concepts, knowledge and metadata, within the framework of a complex DW architecture. The objective of the work considers the use of the technique of knowledge representation by description logics and the extension of Common Warehouse Metamodel (CWM) specifications. This will lead to a fallout in terms of the performance of a complex DW. Three essential aspects of this work are expected, including the representation of knowledge in description logics and the declination of this knowledge into consistent UML diagrams while respecting or extending the CWM specifications and using XML as pivot. The field of application is large but will be adapted to systems with heteroge-neous, complex and unstructured content and moreover requiring a great (re)use of knowledge such as medical data warehouses.

Keywords: data warehouse, description logics, integration, knowledge, metadata

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Authors: Sanjivrao Katakam, Thanumoorthi I., Antony Gerald, Ratan Kulkarni, Shaju Nair

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With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors.

Keywords: energy analytics, energy management, operational data, business intelligence, optimization

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Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

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In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

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Authors: Iyad Ismail, Fathilatul Abdul Hamid

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This research was mainly investigated to recognize the extent of big data analysis by external auditors. This paper adopts grounded theory as a framework for conducting a series of semi-structured interviews with eighteen external auditors. The research findings comprised the availability extent of big data and big data analysis usage by the external auditors in Palestine, Gaza Strip. Considering the study's outcomes leads to a series of auditing procedures in order to improve the external auditing techniques, which leads to high-quality audit process. Also, this research is crucial for auditing firms by giving an insight into the mechanisms of auditing firms to identify the most important strategies that help in achieving competitive audit quality. These results are aims to instruct the auditing academic and professional institutions in developing techniques for external auditors in order to the big data analysis. This paper provides appropriate information for the decision-making process and a source of future information which affects technological auditing.

Keywords: big data analysis, external auditors, audit reliance, internal audit function

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Authors: Safwat Mohamed, Rasha Metawi, Hadeel Khalil, Hussein Kamal

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This paper discusses and reveals the manufacturing techniques of the leather scale armour of Tutankhamun. This armour was in critical condition and went under many conservation procedures as it suffered from some serious deterioration aspects including fragmentation. In addition, its original shape was lost, the leather scales were found scattered in the box and separated from the linen basis, and hence its outlines were blurred and incomprehensible. In view of this, the leather scale armour of Tutankhamun was desperate for urgent conservation and reconstruction interventions. Documentation measures were done before conservation. Several re-treatable conservation procedures were applied seeking for stabilizing the armour and reaching sustainable condition. The conservation treatments included many investigations and analyses that helped in revealing materials and techniques of making the armour. The leather scale armour of Tutankhamun consisted of leather scales attached to a linen support. This linen support consisted of several layers. Howard Carter assumed that the linen support consisted of 6 layers. The undertaken conservation treatments helped in revealing the actual number of layers of the linen support as well as in reaching the most sustainable condition. This paper views the importance of the conservation procedures, which were recently carried out on Tutankhamun’s leather scale armour, in identifying and revealing all materials and techniques used in its manufacturing. The collected data about manufacturing techniques were used in making a replica of the leather scale armour with the same methods and materials.

Keywords: leather scales armours, conservation, manufacturing techniques, Tutankhamun, producing a replica

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Authors: Nejoud Alostad, Anup Bora, Prashant Dhote

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Kuwait Oil Company (KOC) has been producing from its major reservoirs that are well defined and highly productive and of superior reservoir quality. These reservoirs are maturing and priority is shifting towards difficult reservoir to meet future production requirements. This paper discusses the results of the detailed integrated study for one of the satellite complex field discovered in the early 1960s. Following acquisition of new 3D seismic data in 1998 and re-processing work in the year 2006, an integrated G&G study was undertaken to review Lower Cretaceous prospectivity of this reservoir. Nine wells have been drilled in the area, till date with only three wells showing hydrocarbons in two formations. The average oil density is around 300API (American Petroleum Institute), and average porosity and water saturation of the reservoir is about 23% and 26%, respectively. The area is dissected by a number of NW-SE trending faults. Structurally, the area consists of horsts and grabens bounded by these faults and hence compartmentalized. The Wara/Burgan formation consists of discrete, dirty sands with clean channel sand complexes. There is a dramatic change in Upper Wara distributary channel facies, and reservoir quality of Wara and Burgan section varies with change of facies over the area. So predicting reservoir facies and its quality out of sparse well data is a major challenge for delineating the prospective area. To characterize the reservoir of Wara/Burgan formation, an integrated workflow involving seismic, well, petro-physical, reservoir and production engineering data has been used. Porosity and water saturation models are prepared and analyzed to predict reservoir quality of Wara and Burgan 3rd sand upper reservoirs. Subsequently, boundary conditions are defined for reservoir and non-reservoir facies by integrating facies, porosity and water saturation. Based on the detailed analyses of volumetric parameters, potential volumes of stock-tank oil initially in place (STOIIP) and gas initially in place (GIIP) were documented after running several probablistic sensitivity analysis using Montecalro simulation method. Sensitivity analysis on probabilistic models of reservoir horizons, petro-physical properties, and oil-water contacts and their effect on reserve clearly shows some alteration in the reservoir geometry. All these parameters have significant effect on the oil in place. This study has helped to identify uncertainty and risks of this prospect particularly and company is planning to develop this area with drilling of new wells.

Keywords: original oil-in-place, sensitivity, uncertainty, sandstone, reservoir modeling, Monte-Carlo simulation

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Authors: Poramatdha Chutimant

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The objective of the research was to propose a model of teacher leadership in history instruction for utilization. Everett M. Rogers’ Diffusion of Innovations Theory is applied as theoretical framework. Qualitative method is to be used in the study, and the interview protocol used as an instrument to collect primary data from best practices who awarded by Office of National Education Commission (ONEC). Open-end questions will be used in interview protocol in order to gather the various data. Then, information according to international context of history instruction is the secondary data used to support in the summarizing process (Content Analysis). Dendrogram is a key to interpret and synthesize the primary data. Thus, secondary data comes as the supportive issue in explanation and elaboration. In-depth interview is to be used to collected information from seven experts in educational field. The focal point is to validate a draft model in term of future utilization finally.

Keywords: history study, nationalism, patriotism, responsible citizenship, teacher leadership

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Authors: Mohammad Anwar, Shah Waliullah

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This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models.

Keywords: Bayesian approach, common effect, fixed effect, random effect, Dynamic Random Effect Model

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Balkibekova Venera Serikpaevna

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Background: Autism Spectrum Disorder (ASD) poses significant challenges in both diagnosis and treatment. Existing therapeutic interventions often target specific symptoms, necessitating the exploration of alternative approaches. This study investigates the RITM (Rhythm Integration Tapping Music) developed by Balkibekova, aiming to create imitation, social engagement and a wide range of emotions through brain development. Methods: A randomized controlled trial was conducted with 100 participants diagnosed with ASD, aged 1 to 4 years. Participants were randomly assigned to either the RITM therapy group or a control group receiving standard care. The RITM therapy, rooted in tapping rhythm to music such as: marche on the drums, waltz on bells, lullaby on musical triangle, dancing on tambourine, polka on wooden spoons. Therapy sessions were conducted over a 3 year period, with assessments at baseline, midpoint, and post-intervention. Results: Preliminary analyses reveal promising outcomes in the RITM therapy group. Participants demonstrated significant improvements in social interactions, speech understanding, birth of speech, and adaptive behaviors compared to the control group. Careful examination of subgroup analyses provides insights into the differential effectiveness of the RITM approach across various ASD profiles. Conclusions: The findings suggest that RITM therapy, as developed by Balkibekova, holds promise as intervention for ASD. The integrative nature of the approach, addressing multiple domains simultaneously, may contribute to its efficacy. Further research is warranted to validate these preliminary results and explore the long-term impact of RITM therapy on individuals with ASD. This abstract presents a snapshot of the research, emphasizing the significance, methodology, key findings, and implications of the RITM therapy method for consideration in an autism conference.

Keywords: RITM therapy, tapping rhythm, autism, mirror neurons, bright emotions, social interactions, communications

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Authors: Adelina Castelo

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The increasing number of learners of Portuguese as Foreign Language in China justifies the need to define the phonetic profile of these learners and to design didactic materials that are adjusted to their specific problems in pronunciation. Different aspects of this topic have been studied, but the production of oral vowels still needs to be investigated. This study aims: (i) to identify the problems the Chinese learners of Portuguese experience in the pronunciation of oral vowels; (ii) to discuss the didactic implications drawn from those problems. The participants were eight native speakers of Mandarin Chinese that had been learning Portuguese in College for almost a year. They named pictured objects and their oral productions were recorded and phonetically transcribed. The selection of the objects to name took into account some linguistic variables (e.g. stress pattern, syllable structure, presence of the Portuguese oral vowels in different word positions according to stress location). The results are analysed in two ways: the impact of linguistic variables on the success rate in the vowels' production; the replacement strategies used in the non-target productions. Both analyses show that the Chinese learners of Portuguese (i) have significantly more difficulties with the mid vowels as well as the high central vowel and (ii) do not master the vowel height feature. These findings contribute to define the phonetic profile of these learners in terms of oral vowel production. Besides, they have important didactic implications for the pronunciation teaching to these specific learners. Those implications are discussed and exemplified.

Keywords: Chinese learners, learners’ phonetic profile, linguistic variables, Portuguese as foreign language, production data, pronunciation teaching, oral vowels

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Authors: Phumelele Kubheka, Pius Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model.

Keywords: big data, latent Dirichlet allocation, latent semantic indexing, telco, topic modeling, twitter

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Authors: Hamid Mustafa, Huson J. Heather, Kim Eiusoo, McClure Matt, Khalid Javed, Talat Nasser Pasha, Afzal Ali1, Adeela Ajmal, Tad Sonstegard

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Genetic differences associated with speciation, breed formation or local adaptation can help to preserve and effective utilization of animals in selection programs. Analyses of population structure and breed diversity have provided insight into the origin and evolution of cattle. In this study, we used a high-density panel of SNP markers to examine population structure and diversity among ten Pakistani indigenous cattle breeds. In total, 25 individuals from three cattle populations, including Achi (n=08), Bhagnari (n=04) and Cholistani (n=13) were genotyped for 777, 962 single nucleotide polymorphism (SNP) markers. Population structure was examined using the linkage model in the program STRUCTURE. After characterizing SNP polymorphism in the different populations, we performed a detailed analysis of genetic structure at both the individual and population levels. The whole-genome SNP panel identified several levels of population substructure in the set of examined cattle breeds. We further searched for spatial patterns of genetic diversity among these breeds under the recently developed spatial principal component analysis framework. Overall, such high throughput genotyping data confirmed a clear partitioning of the cattle genetic diversity into distinct breeds. The resulting complex historical origins associated with both natural and artificial selection have led to the differentiation of numerous different cattle breeds displaying a broad phenotypic variety over a short period of time.

Keywords: Pakistan, cattle, genetic diversity, population structure

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Peter M. Maurer Maurer

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DGL is a context free grammar-based tool for generating random data. Many types of simulator input data require some computation to be placed in the proper format. For example, it might be necessary to generate ordered triples in which the third element is the sum of the first two elements, or it might be necessary to generate random numbers in some sorted order. Although DGL is universal in computational power, generating these types of data is extremely difficult. To overcome this problem, we have enhanced DGL to include features that permit direct computation within the structure of a context free grammar. The features have been implemented as special types of productions, preserving the context free flavor of DGL specifications.

Keywords: DGL, Enhanced Context Free Grammars, Programming Constructs, Random Data Generation

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: A. Chari, A. El Bouari, B. Orayech, A. Faik, J. M. Igartua

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The phosphate is a natural resource of great importance in Morocco. In order to exploit this wealth, synthesis and studies of new a material based phosphate, were carried out. The apatite structure present o lot of characteristics, One of the main characteristics is to allow large and various substitutions for both cations and anions. Beside their biological importance in hard tissue (bone and teeth), apatites have been extensively studied for their potential use as fluorescent lamp phosphors or laser host materials.The apatite have interesting possible application fields such as in medicine as materials of bone filling, coating of dental implants, agro chemicals as artificial fertilizers. The LiPb2Ca2(PO4)3 was synthesized by the solid-state method, its crystal structure was investigated by Rietveld analysis using XRPD data. This material crystallizes with a structure of lacunar apatite anion deficit. The LiPb2Ca2(PO4)3 is hexagonal apatite at room temperature, adopting the space group P63/m (ITA No. 176), Rietveld refinements showed that the site 4f is shared by three cations Ca, Pb and Li. While the 6h is occupied by the Pb and Li cations. The structure can be described as built up from the PO4 tetrahedra and the sixfold coordination cavities, which delimit hexagonal tunnels along the c-axis direction. These tunnels are linked by the cations occupying the 4 f sites. Raman and Infrared spectroscopy analyses were carried out. The observed frequencies were assigned and discussed on the basis of unit-cell group analysis and by comparison to other apatite-type materials.

Keywords: apatite, Lacunar, crystal structure, Rietveldmethod, LiPb2Ca2(PO4)3, Phase transition

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Authors: Butta Singh

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This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.

Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram

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Authors: Agustinus Bandur, Dyah Budiastuti

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With the rapid development of information and communication technology (ICT) in globalisation era, higher education (HE) internationalisation has become a worldwide phenomenon. However, even though various studies have been widely published in existing literature, the settings of these studies were taken places in developed countries. Accordingly, the major purpose of this article is to explore the current trends of higher education internationalisation programs with particular reference to identify the benefits and challenges confronted by participating staff and students. For these purposes, ethnographic qualitative study with the usage of NVivo 11 software was applied in coding, analyzing, and visualization of non-numeric data gathered from interviews, videos, web contents, social media, and relevant documents. Purposive sampling technique was applied in this study with a total of ten high-ranked accredited government and private universities in Indonesia. On the basis of thematic and cross-case analyses, this study indicates that while Australia has led other countries in dual-degree programs, partner universities from Japan and Korea have the most frequent collaboration on student exchange programs. Meanwhile, most visiting scholars who have collaborated with the universities in this study came from the US, the UK, Japan, Australia, Netherlands, and China. Other European countries such as Germany, French, and Norway have also conducted joint research with Indonesian universities involved in this study. This study suggests that further supports of government policy and grants are required to overcome the challenges as well as strategic leadership and management roles to achieve high impacts of such programs on higher education quality.

Keywords: higher education, internationalisation, challenges, Indonesia

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Authors: S. Turkan

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In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: Hyun-Woo Cho

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The real-time fault monitoring and diagnosis of large scale production processes is helpful and necessary in order to operate industrial process safely and efficiently producing good final product quality. Unusual and abnormal events of the process may have a serious impact on the process such as malfunctions or breakdowns. This work try to utilize process measurement data obtained in an on-line basis for the safe and some fault-free operation of industrial processes. To this end, this work evaluated the proposed intelligent process data monitoring framework based on a simulation process. The monitoring scheme extracts the fault pattern in the reduced space for the reliable data representation. Moreover, this work shows the results of using linear and nonlinear techniques for the monitoring purpose. It has shown that the nonlinear technique produced more reliable monitoring results and outperforms linear methods. The adoption of the qualitative monitoring model helps to reduce the sensitivity of the fault pattern to noise.

Keywords: process data, data mining, process operation, real-time monitoring

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Authors: Débora C. Mendes, Matthias Eckert, Cláudia S. Moço, Hélio Martins, Jean-Pierre P. Gonçalves, Miguel Oliveira, José P. Da Silva

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Uncontrolled disposal of construction and demolition waste (C & DW) in embankments in the periphery of cities causes both environmental and social problems, namely erosion, deforestation, water contamination and human conflicts. One of the milestones of EU Horizon 2020 Programme is the management of waste as a resource. To achieve this purpose for C & DW, a detailed analysis of the properties of these materials should be done. In this work we report the physical, chemical and environmental properties of C & DW aggregates from 25 different origins. The results are compared with those of common natural aggregates used in construction. Assays were performed according to European Standards. Additional analysis of heavy metals and organic compounds such as polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs), were performed to evaluate their environmental impact. Finally, properties of concrete prepared with C & DW aggregates are also reported. Physical analyses of C & DW aggregates indicated lower quality properties than natural aggregates, particularly for concrete preparation and unbound layers of road pavements. Chemical properties showed that most samples (80%) meet the values required by European regulations for concrete and unbound layers of road pavements. Analyses of heavy metals Cd, Cr, Cu, Pb, Ni, Mo and Zn in the C&DW leachates showed levels below the limits established by the Council Decision of 19 December 2002. Identification and quantification of PCBs and PAHs indicated that few samples shows the presence of these compounds. The measured levels of PCBs and PAHs are also below the limits. Other compounds identified in the C&DW leachates include phthalates and diphenylmethanol. In conclusion, the characterized C&DW aggregates show lower quality properties than natural aggregates but most samples showed to be environmentally safe. A continuous monitoring of the presence of heavy metals and organic compounds should be made to trial safe C&DW aggregates. C&DW aggregates provide a good economic and environmental alternative to natural aggregates.

Keywords: concrete preparation, construction and demolition waste, heavy metals, organic pollutants

Procedia PDF Downloads 337

Authors: Claire Baudry, Jessica Pearson, Laura-Emilie Savage, George Tarbulsy

Abstract:

Over the last fifty years, maternal sensitivity and child development among vulnerable families have been a priority for researchers. For this reason, attachment-based interventions have been implemented and been shown to be effective in enhancing child development. Most of the time, child outcomes are measured shortly after the intervention. Objectives: The goal of the study was to investigate the effects of an attachment-based intervention on child development shortly after the intervention ended and one-year post-intervention. Methods: Over the seventy-two mother-child dyads referred by Child Protective Services in the province of Québec, Canada, forty-two were included in this study: 24 dyads who received 6 to 8 intervention sessions and 18 dyads who did not. Intervention and none intervention dyads were matched for the following variables: duration of child protective services, the reason for involvement with child protection, age, sex, and family status. Internalizing and externalizing behaviors were measured 3 and 12 months after the end of the intervention when the average age of children were respectively 45 and 54 months old. Findings: Independent-sample t-tests were conducted to compare scores between the two groups and the two data collection times. In general, on differences observed between the two groups three months after the intervention ended, just a few of them were still present nine months later. Conclusions: This first set of analyses suggests that the effects of attachment-based intervention observed three months following the intervention are not lasting for most of them. Those results inform us of the importance of considering the possibility to offer more attachment-based intervention sessions for those highly vulnerable families.

Keywords: attachment-based intervention, child behaviors, child protective services, highly vulnerable families

Procedia PDF Downloads 126

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

Procedia PDF Downloads 197

Authors: Nada Alqadri, Omaima Nasir

Abstract:

Natural compounds are an important source of potential inhibitors; they have a lot of pharma potential with less adverse effects. The effective antiviral activities of natural products have been proved in different studies. The outbreak of COVID-19 in Wuhan, Hubei, in December 2019, coronavirus has had a significant impact on people's health and lives. Based on previous studies, natural products can be introduced as preventive and therapeutic agents in the fight against COVID-19; considering that no food or supplement has been authorized to prevent COVID-19, individuals continue to search for and consume specific herbs, foods, and commercial supplements for this purpose. This study will be aimed to estimate the uses of herbal and natural products during the COVID-19 infection to determine their usage reasons and evaluate their potential side effects. An online cross-sectional survey of different participants will be conducted and will be a focus on respondents’ chronic disease histories, socio-dmographic characteristics, and frequency and trends of using these products. Descriptive and univariate analyses will be performed to determine prevalence and associations between various products used and respondents’ socio-demographic data. Relationships will be tested using Pearson’s chi-square test or an exact probability test. Our main findings will give evidence of beneficial uses of natural products and herbal medicine as prophylactic and will be a vigorous approach to stop or at least slow down COVID-19 infection and transmission. This will be of great interest of public health, and the results of our study will lend health officials better control on the current pandemic.

Keywords: COVID-19, herbs, natural products, saudi arabia

Procedia PDF Downloads 200