Search results for: biological data mining

Authors: P. J. Kagosi, M. Mndolwa, E. Japhate

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The study was conducted to communities adjacent MNR, Lushoto district, Tanzania. The MNR is one of the nine nature reserves in the Eastern Arc Mountains of Tanzania with an area of 8,700ha with high biological diversity. However, biodiversity in MNR have been threatened by increasing human activities for livelihood in 1970s. The AF systems in the study area was practised since 1980s however, no study was conducted on AF impacts. This paper presents the influence of AF on livelihood of communities adjacent MNR and biodiversity conservation. Qualitative and quantitative data were collected using socio-economic survey and botanical surveys. Data were analysed using Statistical Packages for Social Sciences and content analysis. The study found that in 1970s free livestock grazing caused considerable surface runoff, soil erosion and reduction of crop production. Since 1980s, the study area received various interventions based on the land conservations and improved livelihood through practising AF systems. It was further found that the AF farming improved crop productivity, reduced soil erosion, increased firewood (80.2%) and other forest products availability and AF encouraged community members practicing indoor livestock keeping.The dominant agroforestry tree found in the study area is grevillea reported by 74.1% of respondents planting an average of 40 trees. The study found that the AF reduced pressure to MNR as forest products and fodders were obtained from community's farms in turn, currently water flow from MNR has been increased. Thus AF products support livelihood needs and conserve biodiversity. The study recommends continuity education on new AF technology packages.

Keywords: impact of agroforestry, biodiversity management, communities’ livelihoods, Magamba nature reserve

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Authors: Amal Mohammed Alqahatni

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This paper is a non-empirical analysis of existing literature on digital leadership competency, data-driven organizations, and dealing with AI technology (big data). This paper will provide insights into the importance of developing the leader’s analytical skills and style to be more effective for high-quality decision-making in a data-driven organization and achieve creativity during the organization's transformation to be digitalized. Despite the enormous potential that big data has, there are not enough experts in the field. Many organizations faced an issue with leadership style, which was considered an obstacle to organizational improvement. It investigates the obstacles to leadership style in this context and the challenges leaders face in coaching and development. The leader's lack of analytical skill with AI technology, such as big data tools, was noticed, as was the lack of understanding of the value of that data, resulting in poor communication with others, especially in meetings when the decision should be made. By acknowledging the different dynamics of work competency and organizational structure and culture, organizations can make the necessary adjustments to best support their leaders. This paper reviews prior research studies and applies what is known to assist with current obstacles. This paper addresses how analytical leadership will assist in overcoming challenges in a data-driven organization's work environment.

Keywords: digital leadership, big data, leadership style, digital leadership challenge

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Authors: Chaitanya Varma, Arpan Mehar

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The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

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Authors: Jialin Ma, Jia Meng

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Genomic features, the genome-based coordinates, are commonly used for the representation of biological features such as genes, RNA transcripts and transcription factor binding sites. For the analysis of RNA-related genomic features, such as RNA modification sites, a common task is to correlate these features with transcript components (5'UTR, CDS, 3'UTR) to explore their distribution characteristics in terms of transcriptomic coordinates, e.g., to examine whether a specific type of biological feature is enriched near transcription start sites. Existing approaches for performing these tasks involve the manipulation of a gene database, conversion from genome-based coordinate to transcript-based coordinate, and visualization methods that are capable of showing RNA transcript components and distribution of the features. These steps are complicated and time consuming, and this is especially true for researchers who are not familiar with relevant tools. To overcome this obstacle, we develop a dedicated web app TARF, which represents web toolkit for annotating RNA-related genomic features. TARF web tool intends to provide a web-based way to easily annotate and visualize RNA-related genomic features. Once a user has uploaded the features with BED format and specified a built-in transcript database or uploaded a customized gene database with GTF format, the tool could fulfill its three main functions. First, it adds annotation on gene and RNA transcript components. For every features provided by the user, the overlapping with RNA transcript components are identified, and the information is combined in one table which is available for copy and download. Summary statistics about ambiguous belongings are also carried out. Second, the tool provides a convenient visualization method of the features on single gene/transcript level. For the selected gene, the tool shows the features with gene model on genome-based view, and also maps the features to transcript-based coordinate and show the distribution against one single spliced RNA transcript. Third, a global transcriptomic view of the genomic features is generated utilizing the Guitar R/Bioconductor package. The distribution of features on RNA transcripts are normalized with respect to RNA transcript landmarks and the enrichment of the features on different RNA transcript components is demonstrated. We tested the newly developed TARF toolkit with 3 different types of genomics features related to chromatin H3K4me3, RNA N6-methyladenosine (m6A) and RNA 5-methylcytosine (m5C), which are obtained from ChIP-Seq, MeRIP-Seq and RNA BS-Seq data, respectively. TARF successfully revealed their respective distribution characteristics, i.e. H3K4me3, m6A and m5C are enriched near transcription starting sites, stop codons and 5’UTRs, respectively. Overall, TARF is a useful web toolkit for annotation and visualization of RNA-related genomic features, and should help simplify the analysis of various RNA-related genomic features, especially those related RNA modifications.

Keywords: RNA-related genomic features, annotation, visualization, web server

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Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

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Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing

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Authors: Ade Dwi Utami

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Pedagogic competence is the basic competence of teachers to perform their tasks as educators. The ability to assess has become one of the demands in teachers pedagogic competence. Teachers ability to assess is related to curriculum instructions and applications. This research is aimed at obtaining data concerning teachers ability to assess that comprises of understanding assessment, determining assessment type, tools and procedure, conducting assessment process, and using assessment result information. It uses mixed method of explanatory technique in which qualitative data is used to verify the quantitative data obtained through a survey. The technique of quantitative data collection is by test whereas the qualitative data collection is by observation, interview and documentation. Then, the analyzed data is processed through a proportion study technique to be categorized into high, medium and low. The result of the research shows that teachers ability to assess can be grouped into 3 namely, 2% of high, 4% of medium and 94% of low. The data shows that teachers ability to assess is still relatively low. Teachers are lack of knowledge and comprehension in assessment application. The statement is verified by the qualitative data showing that teachers did not state which aspect was assessed in learning, record children’s behavior, and use the data result as a consideration to design a program. Teachers have assessment documents yet they only serve as means of completing teachers administration for the certification program. Thus, assessment documents were not used with the basis of acquired knowledge. The condition should become a consideration of the education institution of educators and the government to improve teachers pedagogic competence, including the ability to assess.

Keywords: assessment, early childhood education, pedagogic competence, teachers

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Authors: Vishwesh Kulkarni, Nikhil Bellarykar

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Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets.

Keywords: synthetic gene network, network identification, optimization, nonlinear modeling

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Authors: Akbarova Gulnozakhon

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Relevance. Physical exercises can prolong the function of the growth zones of long tubular bones, delay the fusion of the epiphyses and diaphyses of bones and, thus, increase the growth of the body. At the same time, intensive strength exercises can accelerate the process of ossification of bone growth zones and slow down their growth in length. The influence of physical exercises on the process of biological maturation is noted. Gymnastics, which requires intense speed and strength loads, delays puberty. On the other hand, it is indicated that the relatively slow puberty of gymnasts is associated with the selection of girls with a special somatotype in this sport. It was found that the later onset of menstruation in female athletes does not have a negative effect on the maturation process and fertility (the ability to procreate). Observations are made about the normalizing influence of sports on the puberty of girls. The purpose of the study. Our goal is to study physical activity of varying intensity on the formation of secondary sexual characteristics and hormonal status of girls in adolescence. Each biological process peculiar to a given organism is not in a stationary state, but fluctuates with a certain frequency. According to the duration, there are, for example, circadian cycles, and infradian cycles, a typical example of which is the menstrual cycle. Materials and methods, results. Violations of menstrual function in athletes were detected by applying a questionnaire survey that contains several paragraphs and sub-paragraphs where passport data, anthropometric indicators, taking into account anthropometric indices, information about the menstrual cycle are indicated. Of 135 female athletes aged 1-3 to 16 years engaged in various sports - gymnasts, menstrual function disorders were noted in 86.7% (primary or secondary amenorrhea, irregular MC), in swimming-in 57.1%. The general condition also changes during the menstrual cycle. In a large percentage of cases, athletes indicate an increase in irritability in the premenstrual (45%) and menstrual (36%) phases. During these phases, girls note an increase in fatigue of 46.5% and 58% (respectively). In girls, secondary sexual characteristics continue to form during puberty and the clearest indicator of the onset of puberty is the age of the onset of the first menstruation - menarche. Conclusions. 1. Physical exercise has a positive effect on all major systems of the body and thus promotes health.2. Along with a beneficial effect on human health, physical exercise, if the requirements of sports are not observed, can be harmful.

Keywords: girls health, anthropometric, physical development, reproductive health

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Authors: Byung Wook Yang, Jong Dae Park, Wang Soo Shin, Ji-Hyeon Song, Seo-Yun Choi, Boo-Yong Lee, Young Tae Hahm

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Korean ginseng (Panax ginseng C. A. Meyer) is frequently taken orally as a traditional herbal medicine with ginsenosides as the main pharmacological component in Asian countries, and its use is increasing worldwide. Recently, the increase in global temperature has been reported to cause various kinds of biological disorders induced by heat stress in human. The standardized black ginseng extract (SBGE; KGR-BG1) was developed in our biological screening experiment on the thermo-regulation, whose chemical characteristics were evaluated as ginsenoside Rg1, Rb1, Rg3(S), as well as Re, Rf, Rg2(S), Rh1(S), Rh2(S), and Rg5+Rk1. Heat stress responses such as body weight, food intake, water consumption have been measured when treated with Standardized Black Ginseng Extract (SBGE) in the animal experiment and also, biomarkers. SBGE treated group has been found to inhibit a decrease in body weight, a decrease in food intake and an increase in the water consumption when compared with non-treated group against environmental heat stress. These results suggest that SBGE might have a protective effect against environmental heat stress. And also, the several factors of stress response on the immune system need to be done for further studies and its evaluation is in progress.

Keywords: ginseng, ginsenoside, standardization, heat stress, immunomodulatory effect

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Authors: Y. N. Phang, E. F. Loh

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Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling over-dispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling over-dispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling over-dispersed medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling over-dispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian, and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling over-dispersed medical count data when ZIP and ZINB are inadequate.

Keywords: zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit

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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: Włodzimierz Lewandowski, Renata Świsłocka, Aleksandra Golonko, Grzegorz Świderski, Monika Kalinowska

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Caffeic acid (3,4-dihydroxycinnamic) is distributed in a free form or as ester conjugates in many fruits, vegetables and seasonings including plants used for medical purpose. Caffeic acid is present in propolis – a substance with exceptional healing properties used in natural medicine since ancient times. The antioxidant, antibacterial, antiinflammatory and anticarcinogenic properties of caffeic acid are widely described in the literature. The biological activity of chemical compounds can be modified by the synthesis of their derivatives or metal complexes. The structure of the compounds determines their biological properties. This work is a continuation of the broader topic concerning the investigation of the correlation between the electronic charge distribution and biological (anticancer and antioxidant) activity of the chosen phenolic acids and their metal complexes. In the framework of this study the synthesis of new metal complexes of sodium, magnesium, aluminium, iron (III) ruthenium (III) and osmium (III) with caffeic acid was performed. The spectroscopic properties of these compounds were studied by means of FT-IR, FT-Raman, UV-Vis, ¹H and ¹³C NMR. The quantum-chemical calculations (at B3LYP/LAN L2DZ level) of caffeic acid and selected complexes were done. Moreover the antioxidant properties of synthesized complexes were studied in relation to selected stable radicals (method of reduction of DPPH and method of reduction of ABTS). On the basis of the differences in the number, intensity and locations of the bands from the IR, Raman, UV/Vis and NMR spectra of caffeic acid and its metal complexes the effect of metal cations on the electronic system of ligand was discussed. The geometry, theoretical spectra and electronic charge distribution were calculated by the use of Gaussian 09 programme. The geometric aromaticity indices (Aj – normalized function of the variance in bond lengths; BAC - bond alternation coefficient; HOMA – harmonic oscillator model of aromaticity and I₆ – Bird’s index) were calculated and the changes in the aromaticity of caffeic acid and its complexes was discussed. This work was financially supported by National Science Centre, Poland, under the research project number 2014/13/B/NZ7/02-352.

Keywords: antioxidant properties, caffeic acid, metal complexes, spectroscopic methods

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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: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

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Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic aspect-based sentiment analysis approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity

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Authors: Melissa Vang, Raheyma Khan, Haihua Chen

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The coronavirus disease 2019 (COVID‐19) pandemic has changed people's lives from all over the world. More people have turned to Twitter to engage online and discuss the COVID-19 vaccine. This study aims to present a text mining approach to identify people's attitudes towards the COVID-19 vaccine on Twitter. To achieve this purpose, we collected 54,268 COVID-19 vaccine tweets from September 01, 2020, to November 01, 2020, then the BERT model is used for the sentiment and topic analysis. The results show that people had more negative than positive attitudes about the vaccine, and countries with an increasing number of confirmed cases had a higher percentage of negative attitudes. Additionally, the topics discussed in positive and negative tweets are different. The tweet datasets can be helpful to information professionals to inform the public about vaccine-related informational resources. Our findings may have implications for understanding people's cognitions and feelings about the vaccine.

Keywords: BERT, COVID-19 vaccine, sentiment analysis, topic modeling

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Authors: Shruti Malviya, Chris Bowler

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Analysis of microbial eukaryotic diversity is fundamental to understanding ecosystems’ structure, biology, and ecology. Diatoms (Stramenopiles, Bacillariophyceae) are one of the most diverse and ecologically prominent groups of phytoplankton. This study was performed to enhance the understanding of global biodiversity patterns and structure of planktonic diatom communities across the world's oceans. We used the metabarcoding data set generated from the biological samples and associated environmental data collected during the Tara Oceans (2009-2013) global circumnavigation covering all major oceanic provinces. A total of ~18 million diatom V9-18S rDNA tags from 126 sampling stations, constituting 631 size-fractionated plankton communities were generated. Using ~250,000 unique diatom metabarcodes, the global diatom distribution and diversity across size classes, genus and ecological niches was assessed. Notably, our analysis revealed: (i) a new estimate of the total number of planktonic diatom species, (ii) a considerable unknown diversity and exceptionally high diversity in the open ocean, and (iii) complex diversity patterns across oceanic provinces. Also, co-occurrence of several ribotypes in locations separated by great geographic distances (equatorial stations) demonstrated a widespread but not ubiquitous distribution. This work provides a comprehensive perspective on diatom distribution and diversity in the world’s oceans and elaborates interconnections between associated theories and underlying drivers. It shows how meta-barcoding approaches can provide a framework to investigate environmental diversity at a global scale, which is deemed as an essential step in answering various ecological research questions. Consequently, this work also provides a reference point to explore how microbial communities will respond to environmental conditions.

Keywords: diatoms, Tara Oceans, biodiversity, metabarcoding

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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: 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: 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: M. Chiliza, H. P. Mbukwane, P Masita, H. Rutto

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Acid mine drainage (AMD) one of the main pollutants of water in many countries that have mining activities. AMD results from the oxidation of pyrite and other metal sulfides. When these metals gets exposed to moisture and oxygen, leaching takes place releasing sulphate and Iron. Acid drainage is often noted by 'yellow boy,' an orange-yellow substance that occurs when the pH of acidic mine-influenced water raises above pH 3, so that the previously dissolved iron precipitates out. The possibility of using environmentally friendly silane and phosphate based coatings on pyrite to remediate acid mine drainage and prevention at source was investigated. The results showed that both coatings reduced chemical oxidation of pyrite based on Fe and sulphate release. Furthermore, it was found that silane based coating performs better when coating synthesis take place in a basic hydrolysis than in an acidic state.

Keywords: acid mine drainage, pyrite, silane, phosphate

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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: I. Pina, M. Painho

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Increased knowledge about the nature of place and the conditions under which space becomes place is a key factor for better urban planning and place-making. Although there is a broad consensus on the relevance of this knowledge, difficulties remain in relating the theoretical framework about place and urban management. Issues related to representation of places are among the greatest obstacles to overcome this gap. With this critical discussion, based on literature review, we intended to explore, in a common framework for geographical analysis, the potential of stories to spell out place meanings, bringing together qualitative text analysis and text mining in order to capture and represent the singularity contained in each person's life history, and the patterns of social processes that shape places. The development of this reasoning is based on the extensive geographical thought about place, and in the theoretical advances in the field of Geographic Information Science (GISc).

Keywords: discourse analysis, geographic information science place, place-making, stories

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Authors: Anisuzzaman Khan, A. J. K. Masud

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Bangladesh is the largest delta in the world predominantly comprising large network of rives and wetlands. Wetlands in Bangladesh are represented by inland freshwater, estuarine brakishwater and tidal salt-water coastal wetlands. Bangladesh possesses enormous area of wetlands including rivers and streams, freshwater lakes and marshes, haors, baors, beels, water storage reservoirs, fish ponds, flooded cultivated fields and estuarine systems with extensive mangrove swamps. The past, present, and future of Bangladesh, and its people’s livelihoods are intimately connected to its relationship with water and wetlands. More than 90% of the country’s total area consists of alluvial plains, crisscrossed by a complex network of rivers and their tributaries. Floodplains, beels (low-lying depressions in the floodplain), haors (deep depression) and baors (oxbow lakes) represent the inland freshwater wetlands. Over a third of Bangladesh could be termed as wetlands, considering rivers, estuaries, mangroves, floodplains, beels, baors and haors. The country’s wetland ecosystems also offer critical habitats for globally significant biological diversity. Of these the deeply flooded basins of north-east Bangladesh, known as haors, are a habitat of wide range of wild flora and fauna unique to Bangladesh. The haor basin lies within the districts of Sylhet, Sunamgonj, Netrokona, Kishoregonj, Habigonj, Moulvibazar, and Brahmanbaria in the Northeast region of Bangladesh comprises the floodplains of the Meghna tributaries and is characterized by the presence of numerous large, deeply flooded depressions, known as haors. It covers about around 8,568 km2 area of Bangladesh. The topography of the region is steep at around foothills in the north and slopes becoming mild and milder gradually at downstream towards south. Haor is a great reservoir of aquatic biological resources and acts as the ecological safety net to the nature as well as to the dwellers of the haor. But in reality, these areas are considered as wastelands and to make these wastelands into a productive one, a one sided plan has been implementing since long. The programme is popularly known as Flood Control, Drainage and Irrigation (FCDI) which is mainly devoted to increase the monoculture rice production. However, haor ecosystem is a multiple-resource base which demands an integrated sustainable development approach. The ongoing management approach is biased to only rice production through FCDI. Thus this primitive mode of action is diminishing other resources having more economic potential ever thought.

Keywords: freshwater wetlands, biological diversity, biological resources, conservation and sustainable development

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Authors: Abbas Raza Ali

Abstract:

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

Procedia PDF Downloads 175

Authors: Peter M. Maurer Maurer

Abstract:

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

Abstract:

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: Butta Singh

Abstract:

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: S. Turkan

Abstract:

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

Procedia PDF Downloads 324