Search results for: heterogeneous massive data

Authors: Getalem E. Haylia

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The Ribb dam is one of the irrigation projects in the Upper Blue Nile basin, Ethiopia, to irrigate the Fogera plain. Reservoir sedimentation is a major problem because it reduces the useful reservoir capacity by the accumulation of sediments coming from the watersheds. Estimates of sediment yield are needed for studies of reservoir sedimentation and planning of soil and water conservation measures. The objective of this study was to simulate the Ribb dam catchment sediment yield using SWAT model and to estimate Ribb reservoir effective life according to trap efficiency methods. The Ribb dam catchment is found in North Western part of Ethiopia highlands, and it belongs to the upper Blue Nile and Lake Tana basins. Soil and Water Assessment Tool (SWAT) was selected to simulate flow and sediment yield in the Ribb dam catchment. The model sensitivity, calibration, and validation analysis at Ambo Bahir site were performed with Sequential Uncertainty Fitting (SUFI-2). The flow data at this site was obtained by transforming the Lower Ribb gauge station (2002-2013) flow data using Area Ratio Method. The sediment load was derived based on the sediment concentration yield curve of Ambo site. Stream flow results showed that the Nash-Sutcliffe efficiency coefficient (NSE) was 0.81 and the coefficient of determination (R²) was 0.86 in calibration period (2004-2010) and, 0.74 and 0.77 in validation period (2011-2013), respectively. Using the same periods, the NS and R² for the sediment load calibration were 0.85 and 0.79 and, for the validation, it became 0.83 and 0.78, respectively. The simulated average daily flow rate and sediment yield generated from Ribb dam watershed were 3.38 m³/s and 1772.96 tons/km²/yr, respectively. The effective life of Ribb reservoir was estimated using the developed empirical methods of the Brune (1953), Churchill (1948) and Brown (1958) methods and found to be 30, 38 and 29 years respectively. To conclude, massive sediment comes from the steep slope agricultural areas, and approximately 98-100% of this incoming annual sediment loads have been trapped by the Ribb reservoir. In Ribb catchment, as well as reservoir systematic and thorough consideration of technical, social, environmental, and catchment managements and practices should be made to lengthen the useful life of Ribb reservoir.

Keywords: catchment, reservoir effective life, reservoir sedimentation, Ribb, sediment yield, SWAT model

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Authors: Purabi Saikia

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The present study was carried out across 84 vegetated grids (>10% vegetation cover) along an urbanization gradient, ranging from the urban core to peri-urban and natural vegetation in and around Ranchi, Eastern India, aiming to examine the phytosociological attributes by belt transect (167 transects each of 0.5 ha) method. Overall, plant species richness was highest in natural vegetation (242 spp.), followed by peri-urban (198 spp.) and urban (182 spp.). Similarly, H’, CD, E, Dmg, Dmn, and ENS showed significant differences in the tree layer (H’: 0.45-3.36; CD: 0.04-1.00; E: 0.25-0.96; Dmg: 0.18-7.15; Dmn: 0.03-4.24, and ENS: 1-29) in the entire urbanization gradient. Various α-diversity indices of the adult trees (H’: 3.98, Dmg: 14.32, Dmn: 2.38, ENS: 54) were comparatively better in urban vegetation compared to peri-urban (H’: 2.49, Dmg: 10.37, Dmn: 0.81, ENS: 12) and natural vegetation (H’: 2.89, Dmg: 13.46, Dmn: 0.85, ENS: 18). Tree communities have shown better response and adaptability in urban vegetation than shrubs and herbs. The prevalence of rare (41%), very rare (29%), and exotic species (39%) in urban vegetation may be due to the intentional introduction of a number of fast-growing exotic tree species in different social forestry plantations that have created a diverse and heterogeneous habitat. Despite contagious distribution, the majority of trees (36.14%) have shown no regeneration in the entire urbanization gradient. Additionally, a quite high percentage of IUCN red-listed plant species (51% and 178 spp.), including endangered (01 sp.), vulnerable (03 spp.), near threatened (04 spp.), least concern (163 spp.), and data deficient (07 spp.), warrant immediate policy interventions. Overall, the study witnessed subsequent transformations in floristic composition and structure from urban to natural vegetation in Eastern India. The outcomes are crucial for fostering resilient ecosystems, biodiversity conservation, and sustainable development in the region that supports diverse plant communities.

Keywords: floristic communities, urbanization gradients, exotic species, regeneration

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 83

Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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Authors: Sami Kabir, Sadek Hossain Khoka

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Open Government Data (OGD) refers to all data produced by government which are accessible in reusable way by common people with access to Internet and at free of cost. In line with “Digital Bangladesh” vision of Bangladesh government, the concept of open data has been gaining momentum in the country. Opening all government data in digital and customizable format from single platform can enhance e-governance which will make government more transparent to the people. This paper presents a well-in-progress case study on OGD portal by Bangladesh Government in order to link decentralized data. The initiative is intended to facilitate e-service towards citizens through this one-stop web portal. The paper further discusses ways of collecting data in digital format from relevant agencies with a view to making it publicly available through this single point of access. Further, possible layout of this web portal is presented.

Keywords: e-governance, one-stop web portal, open government data, reusable data, web of data

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Authors: Sandra Young

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The biodiversity literature is vast and heterogeneous. In today’s data age, numbers of data integration and standardisation initiatives aim to facilitate simultaneous access to all the literature across biodiversity domains for research and forecasting purposes. Ontologies are being used increasingly to organise this information, but the rationalisation intrinsic to ontologies can hit obstacles when faced with the intrinsic fluidity and inconsistency found in the domains comprising biodiversity. Essentially the problem is a conceptual one: biological taxonomies are formed on the basis of specific, physical specimens yet nomenclatural rules are used to provide labels to describe these physical objects. These labels are ambiguous representations of the physical specimen. An example of this is with the genus Melpomene, the scientific nomenclatural representation of a genus of ferns, but also for a genus of spiders. The physical specimens for each of these are vastly different, but they have been assigned the same nomenclatural reference. While there is much research into the conceptual stability of the taxonomic concept versus the nomenclature used, to the best of our knowledge as yet no research has looked empirically at the literature to see the conceptual plurality or singularity of the use of these species’ names, the linguistic representation of a physical entity. Language itself uses words as symbols to represent real world concepts, whether physical entities or otherwise, and as such lexicography has a well-founded history in the conceptual mapping of words in context for dictionary making. This makes it an ideal candidate to explore this problem. The lexicographic approach uses corpus-based analysis to look at word use in context, with a specific focus on collocated word frequencies (the frequencies of words used in specific grammatical and collocational contexts). It allows for inconsistencies and contradictions in the source data and in fact includes these in the word characterisation so that 100% of the available evidence is counted. Corpus analysis is indeed suggested as one of the ways to identify concepts for ontology building, because of its ability to look empirically at data and show patterns in language usage, which can indicate conceptual ideas which go beyond words themselves. In this sense it could potentially be used to identify if the hierarchical structures present within the empirical body of literature match those which have been identified in ontologies created to represent them. The first stages of this research have revealed a hierarchical structure that becomes apparent in the biodiversity literature when annotating scientific species’ names, common names and more general names as classes, which will be the focus of this paper. The next step in the research is focusing on a larger corpus in which specific words can be analysed and then compared with existing ontological structures looking at the same material, to evaluate the methods by means of an alternative perspective. This research aims to provide evidence as to the validity of the current methods in knowledge representation for biological entities, and also shed light on the way that scientific nomenclature is used within the literature.

Keywords: ontology, biodiversity, lexicography, knowledge representation, corpus linguistics

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Authors: C. B. Abhilash, Kavi Mahesh

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Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: Ana M. Antolín, Sandra Contreras, Francesc Medina, Didier Tichit

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Introduction: High levels of nitrates (> 50 ppm NO3-) in drinking water are potentially risky to human health. In the recent years, the trend of nitrate concentration in groundwater is rising in the EU and other countries. Conventional catalytic nitrate reduction processes into N2 and H2O lead to some toxic intermediates and by-products, such as NO2-, NH4+, and NOx gases. Alternatively, photocatalytic nitrate removal using solar irradiation and heterogeneous catalysts is a very promising and ecofriendly technique. It has been scarcely performed and more research on highly efficient catalysts is still needed. In this work, different nanocatalysts supported on Aeroxide Titania P25 (P25) have been prepared varying: 0.5-4 % wt. Ag); Pt (2, 4 % wt.); Pt precursor (H2PtCl6/K2PtCl6); and impregnation order of both metals. Pt was chosen in order to increase the selectivity to N2 and decrease that to NO2-. Catalysts were characterized by nitrogen physisorption, X-Ray diffraction, UV-visible spectroscopy, TEM and X Ray-Photoelectron Spectroscopy. The aim was to determine the influence of the composition and the preparation method of the catalysts on the conversion and selectivity in the nitrate reduction, as well as going through an overall and better understanding of the process. Nanocatalysts synthesis: For the mono and bimetallic catalysts preparation, wise-drop wetness impregnation of the precursors (AgNO3, H2PtCl6, K2PtCl6) followed by a reduction step (NaBH4) was used to obtain the metal colloids. Results and conclusions: Denitration experiments were performed in a 350 mL PTFE batch reactor under inert standard operational conditions, ultraviolet irradiations (λ=254 nm (UV-C); λ=365 nm (UV-A)), and presence/absence of hydrogen gas as a reducing agent, contrary to most studies using oxalic or formic acid. Samples were analyzed by Ionic Chromatography. Blank experiments using respectively P25 (dark conditions), hydrogen only and UV irradiations without hydrogen demonstrated a clear influence of the presence of hydrogen on nitrate reduction. Also, they demonstrated that UV irradiation increased the selectivity to N2. Interestingly, the best activity was obtained under ultraviolet lamps, especially at a closer wavelength to visible light irradiation (λ = 365 nm) and H2. 2% Ag/P25 leaded to the highest NO3- conversion among the monometallic catalysts. However, nitrite quantities have to be diminished. On the other hand, practically no nitrate conversion was observed with the monometallics based on Pt/P25. Therefore, the amount of 2% Ag was chosen for the bimetallic catalysts. Regarding the bimetallic catalysts, it is observed that the metal impregnation order, amount and Pt precursor highly affects the results. Higher selectivity to the desirable N2 gas is obtained when Pt was firstly added, especially with K2PtCl6 as Pt precursor. This suggests that when Pt is secondly added, it covers the Ag particles, which are the most active in this reaction. It could be concluded that Ag allows the nitrate reduction step to nitrite, and Pt the nitrite reduction step toward the desirable N2 gas.

Keywords: heterogeneous catalysis, hydrogenation, nanocatalyst, nitrate removal, photocatalysis

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Authors: Dina Ahmad Alkhodary

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This study aimed to investigate the practices of Data Mining on the telecommunication companies in Jordan, from the viewpoint of the respondents. In order to achieve the goal of the study, and test the validity of hypotheses, the researcher has designed a questionnaire to collect data from managers and staff members from main department in the researched companies. The results shows improvements stages of the telecommunications companies towered Data Mining.

Keywords: data, mining, development, business

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Authors: Julio Silva, Lia Hasenclever, Gilson G. Silva Jr.

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The aim of this paper is to check possible convergence in health measures (aged-standard rate of morbidity and mortality) for communicable diseases between developed and developing countries, conditional to productive structures features. Understanding the interrelations between health patterns and economic development is particularly important in the context of low- and middle-income countries, where economic development comes along with deep social inequality. Developing countries with less diversified productive structures (measured through complexity index) but high heterogeneous inter-sectorial labor productivity (using as a proxy inter-sectorial coefficient of variation of labor productivity) has on average low health levels in communicable diseases compared to developed countries with high diversified productive structures and low labor market heterogeneity. Structural heterogeneity and productive diversification may have influence on health levels even considering per capita income. We set up a panel data for 139 countries from 1995 to 2015, joining several data about the countries, as economic development, health, and health system coverage, environmental and socioeconomic aspects. This information was obtained from World Bank, International Labour Organization, Atlas of Economic Complexity, United Nation (Development Report) and Institute for Health Metrics and Evaluation Database. Econometric panel models evidence shows that the level of communicable diseases has a positive relationship with structural heterogeneity, even considering other factors as per capita income. On the other hand, the recent process of convergence in terms of communicable diseases have been motivated for other reasons not directly related to productive structure, as health system coverage and environmental aspects. These evidences suggest a joint dynamics between the unequal distribution of communicable diseases and countries' productive structure aspects. These set of evidence are quite important to public policy as meet the health aims in Millennium Development Goals. It also highlights the importance of the process of structural change as fundamental to shift the levels of health in terms of communicable diseases and can contribute to the debate between the relation of economic development and health patterns changes.

Keywords: economic development, inequality, population health, structural change

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Authors: Ivanna Shubina

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Background/Objectives and Goals: Recent interest in positive psychology is reflected in a plenty of studies conducted on its basic constructs (e.g. self-esteem and happiness) in interrelation with personality features, social rules, business and technology development. The purpose of this study is to investigate the mediating role of self-esteem, exploring the relationships between self-esteem and happiness, self-esteem and locus of control (LOC). It hypothesizes that self-esteem may be interpreted as a predictor of happiness and mediator in the locus of control establishment. A plenty of various empirical studies results have been analyzed in order to collect data for this theoretical study, and some of the analysed results can be considered as arguable or incoherent. However, the majority of results indicate a strong relationship between three considered concepts: self-esteem, happiness, the locus of control. Methods: In particular, this study addresses the following broad research questions: i) Is self-esteem just an index of global happiness? ii) May happiness be possible or realizable without a healthy self-confidence and self-acceptance? iii) To what extent does self-esteem influence on the level of happiness? iv) Is high self-esteem a sufficient condition for happiness? v) Is self-esteem is a strong predictor of internal locus of control maintenance? vi) Is high self-esteem related to internal LOC, while low self-esteem to external LOC? In order to find the answers for listed questions, 60 reliable sources have been analyzed, results of what are discussed more detailed below. Expected Results/Conclusion/Contribution:It is recognized that the relationship between self-esteem, happiness, locus of control is complex: internal LOC is contributing to happiness, but it is not directly related to it; self-esteem is a powerful and important psychological factor in mental health and well-being; the feelings of being worthy and empowered are associated with significant achievements and high self-esteem; strong and appropriate self-esteem (when the discrepancy between “ideal” and “real” self is balanced) is correlated with more internal LOC (when the individual tends to believe that personal achievements depend on possessed features, vigor, and persistence). Despite the special attention paid to happiness, the locus of control and self-esteem, independently, theoretical and empirical equivocations within each literature foreclose many obvious predictions about the nature of their empirical distinction. In terms of theoretical framework, no model has achieved consensus as an ultimate theoretical background for any of the mentioned constructs. To be able to clarify the relationship between self-esteem, happiness, and locus of control more interdisciplinary studies have to take place in order to get data on heterogeneous samples, provided from various countries, cultures, and social groups.

Keywords: happiness, locus of control, self-esteem, mediation

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Chahrazed Salhi, Ayoub Zeroual, Yasmina Hamitouche

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Soil water erosion poses a significant threat to the watersheds in Algeria today. The degradation of storage capacity in large dams over the past two decades, primarily due to erosion, necessitates a comprehensive understanding of the factors that contribute to soil erosion. The Isser watershed, located in the Northwestern region of Algeria, faces additional challenges such as recurrent droughts and the presence of delicate marl and clay outcrops, which amplify its susceptibility to water erosion. This study aims to employ advanced techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS), in conjunction with the Canonical Correlation Analysis (CCA) method and Soil Water Assessment Tool (SWAT) model, to predict specific erosion patterns and analyze the key factors influencing erosion in the Isser basin. To accomplish this, an array of data sources including rainfall, climatic, hydrometric, land use, soil, digital elevation, and satellite data were utilized. The application of the SWAT model to the Isser basin yielded an average annual soil loss of approximately 16 t/ha/year. Particularly high erosion rates, exceeding 12 T/ha/year, were observed in the central and southern parts of the basin, encompassing 41% of the total basin area. Through Canonical Correlation Analysis, it was determined that vegetation cover and topography exerted the most substantial influence on erosion. Consequently, the study identified significant and spatially heterogeneous erosion throughout the study area. The impact of land topography on soil loss was found to be directly proportional, while vegetation cover exhibited an inverse proportional relationship. Modeling specific erosion for the Ladrat dam sub-basin estimated a rate of around 39 T/ha/year, thus accounting for the recorded capacity loss of 17.80% compared to the bathymetric survey conducted in 2019. The findings of this research provide valuable decision-support tools for soil conservation managers, empowering them to make informed decisions regarding soil conservation measures.

Keywords: Isser watershed, RS, CCA, SWAT, vegetation cover, topography

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Authors: Leyli Hashemi Rafsanjani, Hoda Mortazavi Alavi, Amirhossein Habibzadeh

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At present, the atmospheric and environmental factors impose massive damages to buildings. Thus, to reduce these damages, researchers pay more attention on qualitative and quantitative characteristic of buildings materials. Condensation is one of the problems in Contemporary Sustecture Design. It could cause serious damages to the frontage, interior and structural elements of buildings. As a result, taking preventative steps to avoid condensation from occurring in buildings will help prevent avoidable and costly problems in the future. Hence, the aim of this paper is to answer the question: “Does the use of advanced materials cause the reduction of condensation formed on the walls?" In response to those flaws, this paper considered similar articles and selected 20 buildings randomly from contemporary architecture of developing countries which have been built in recent decade from 2002 to 2012, to find out the mutual relation between the usage of advanced materials and level of condensation damages. This consideration shows that by using advanced materials, we will have fewer damages.

Keywords: condensation, advanced materials, contemporary sustecture, moisture

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Authors: Amal M. Alrayes

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Data and system quality play a central role in organizational success, and the quality of any existing information system has a major influence on the effectiveness of overall system performance.Given the importance of system and data quality to an organization, it is relevant to highlight their importance on organizational performance in the Kingdom of Bahrain. This research aims to discover whether system quality and data quality are related, and to study the impact of system and data quality on organizational success. A theoretical model based on previous research is used to show the relationship between data and system quality, and organizational impact. We hypothesize, first, that system quality is positively associated with organizational impact, secondly that system quality is positively associated with data quality, and finally that data quality is positively associated with organizational impact. A questionnaire was conducted among public and private organizations in the Kingdom of Bahrain. The results show that there is a strong association between data and system quality, that affects organizational success.

Keywords: data quality, performance, system quality, Kingdom of Bahrain

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Authors: L. O. Luengas, E. V. Morales, L. F. G. De Souza, I. S. Bott

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Duplex Stainless Steels are well known for its good mechanical properties, and corrosion resistance. However, when submitted to heating, these features can be lost since the good properties are strongly dependent on the austenite-ferrite phase ratio which has to be approximately 1:1 to keep the phase balance. In a welded joint, the transformation kinetics at the heat affected zone (HAZ) is a function of the cooling rates applied which in turn are dependent on the heat input. The HAZ is usually ferritized at these temperatures, and it has been argued that small variations of the chemical composition can play a role in the solid state transformation sequence of ferrite to austenite during cooling. The δ → γ transformation has been reported to be massive and diffusionless due to the fast cooling rate, but it is also considered a diffusion controlled transformation. The aim of this work is to evaluate the effect of different heat inputs on the HAZ of two duplex stainless steels UNS S32304 and S32750, obtained by physical simulation.

Keywords: duplex stainless steels, HAZ, microstructural characterization, physical simulation

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Authors: Krishna Chandra Paudel

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This paper explores the association between the perceived classroom environment and mathematics learning and test anxiety among secondary level students in Nepal. Categorizing the students in three dominant variables- gender, ethnicity and previous schooling, and selecting sample students with respect to higher mathematics anxiety from five heterogeneous classes, the research explores disparities in student's mathematics cognition and reveals nexus between classroom environment and mathematics learning and test anxiety. This research incorporates social learning theory and social development theory as interpretive tool for analyzing themes through qualitative data. Focussing on the interviews with highly mathematics learning anxious students, the study sheds light on how mathematics anxiety among the targeted students is interlinked with multiple factors. The research basically exposes the students’ lack of mathematical passion, their association with other students and participation in classroom learning, asymmetrical content and their lack of preparedness for the tests as caustic factors behind such anxieties. The study further reveals that students’ lack of foundational knowledge and complexity of mathematical content have jointly contributed to mathematics anxiety. Admitting learning as a reciprocal experience, the study points out that the students’ gender, ethnicity and disparities in previous schooling in the context of Nepal has very insignificant impact on students’ mathematics anxiety. It finally recommends that the students who get trapped into the vicious cycle of mathematics anxiety require positive and supportive classroom environment along with inspiring comments/compliments and symmetrical course contents.

Keywords: anxiety, asymmetry, cognition, habitus, pedagogy, preparedness

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Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

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Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.

Keywords: cloud computing, data mining, computing models, cloud services

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Authors: Amy Gooden, Meshandren Naidoo

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Genetic research and transfers of big data are not confined to a particular jurisdiction, but there is a lack of clarity regarding the legal requirements for importing and exporting such data. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

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Authors: Tejinder Singh

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Information system is the flow of data from different levels to different directions for decision making and data operations in information system (IS). Data can be violated by different manner like manual or technical errors, data tampering or loss of integrity. Security system called firewall of IS is effected by such type of violations. The flow of data among various levels of Information System is done by networking system. The flow of data on network is in form of packets or frames. To protect these packets from unauthorized access, virus attacks, and to maintain the integrity level, network security is an important factor. To protect the data to get pirated, various security techniques are used. This paper represents the various security techniques and signifies different harmful attacks with the help of detailed data analysis. This paper will be beneficial for the organizations to make the system more secure, effective, and beneficial for future decisions making.

Keywords: information systems, data integrity, TCP/IP network, vulnerability, decision, data

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Authors: Tamas Jancso, Andrea Podor, Eva Nagyne Hajnal, Peter Udvardy, Gabor Nagy, Attila Varga, Meng Qingyan

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The paper deals with the conditions and circumstances of integration of remotely sensed data for rural environmental monitoring purposes. The main task is to make decisions during the integration process when we have data sources with different resolution, location, spectral channels, and dimension. In order to have exact knowledge about the integration and data fusion possibilities, it is necessary to know the properties (metadata) that characterize the data. The paper explains the joining of these data sources using their attribute data through a sample project. The resulted product will be used for rural environmental analysis.

Keywords: remote sensing, GIS, metadata, integration, environmental analysis

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Authors: Syed Muzyan Shahzad, Liu Jianxin, Asim Shahzad, Muhammad Sharjeel Raza, Sun Ya, Fanidi Meryem

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Electrical resistivity investigation was conducted in vicinity of Tarbela Ghazi, in order to study the subsurface layer with a view of determining the depth to the aquifer and thickness of groundwater potential zones. Vertical Electrical Sounding (VES) using Schlumberger array was carried out at 16 VES stations. Well logging data at four tube wells have been used to mark the super saturated zones with great discharge rate. The present paper shows a geoelectrical identification of the lithology and an estimate of the relationship between the resistivity and Dar Zarrouk parameters (transverse unit resistance and longitudinal unit conductance). The VES results revealed both homogeneous and heterogeneous nature of the subsurface strata. Aquifer is unconfined to confine in nature, and at few locations though perched aquifer has been identified, groundwater potential zones are developed in unconsolidated deposits layers and more than seven geo-electric layers are observed at some VES locations. Saturated zones thickness ranges from 5 m to 150 m, whereas at few area aquifer is beyond 150 m thick. The average anisotropy, transvers resistance and longitudinal conductance values are 0.86 %, 35750.9821 Ω.m², 0.729 Siemens, respectively. The transverse unit resistance values fluctuate all over the aquifer system, whereas below at particular depth high values are observed, that significantly associated with the high transmissivity zones. The groundwater quality in all analyzed samples is below permissible limit according to World Health Standard (WHO).

Keywords: aquifer, Dar Zarrouk parameters, geoelectric layers, Tarbela Ghazi

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Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin

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Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.

Keywords: big data, machine learning, ontology model, urban data model

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Authors: Abeba Nigussie Turi, Xiangming Samuel Li

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Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.

Keywords: startup data analytics, data-driven decision-making, data acquisition, data generation, digital entrepreneurship

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Authors: P. Karalis, E. Dotsika, A. Godelitsas, M. Tassi, D. Ignatiadou

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Glass artefacts originated from Northern Greece, dated between 1st and 6th AC, were analyzed for their oxygen isotopic and chemical compositions in order to identify their raw materials provenance. The chemical composition of these glasses is rather heterogeneous although they are all obtained with natron as flux, having both K₂O and MgO contents lower than 1.5 wt%. The majority of these samples have a homogeneous oxygen isotopic composition (𝛿18O= 16‰,), which is equal to or very close to the mean value of “Roman” glass (from about 15‰ to 16.0‰). The rest of the samples present heavily enriched 𝛿18O values that indicate that their raw materials differ from those normally used in Roman and Medieval glass production, and this matches with the possibility of the different origins of these materials. So, all these fragments are soda-lime-silica natron-glass produced from natron, possibly coming from more than one source.

Keywords: ancient glass, provenance of raw materials of ancient glass, roman glass, oxygen isotope analysis in glass

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Authors: Lur N. Dreier

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Spontaneous transformation in Ursus maritimus is generally considered to be seldom, albeit not, to this author's best knowledge, previously unpublished in the medical literature. However, no case series has to date described transformative spontaneous processes to filios hominum species. Norwegian public hospital system, is, however, especially the grounds of the specific climate in the Northern hemisphere, and because of a high suited to observe such transformations, both on income level. Hence, this paper describes, to our knowledge, the first case series of 25 patients undergoing treatment for spontaneous transformation in four Norwegian hospitals. The methodology was to include patients on a consecutive basis, identifying clinically and laboratory the typology in each of the four hospitals. The major findings were that the archetypes were heterogeneous, with coercive laboratory findings, with a high degree of redundancy of the process. This might potentially lead to many advances in the diagnostics.

Keywords: case series, transformation, hominum species, maritimus species

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