Search results for: analysis of scientific data

Authors: J. R. Wang, H. C. Chang, A. L. Ho, J. H. Yang, S. W. Chen, C. Shih

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The purpose of the study is to analyze the load rejection transient of ABWR by using TRACE, PARCS, and SNAP codes. This study has some steps. First, using TRACE, PARCS, and SNAP codes establish the model of ABWR. Second, the key parameters are identified to refine the TRACE/PARCS/SNAP model further in the frame of a steady state analysis. Third, the TRACE/PARCS/SNAP model is used to perform the load rejection transient analysis. Finally, the FSAR data are used to compare with the analysis results. The results of TRACE/PARCS are consistent with the FSAR data for the important parameters. It indicates that the TRACE/PARCS/SNAP model of ABWR has a good accuracy in the load rejection transient.

Keywords: ABWR, TRACE, PARCS, SNAP.

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Authors: Kanso Hassan, Elhore Ali, Soule-dupuy Chantal, Tazi Said

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Information Retrieval has the objective of studying models and the realization of systems allowing a user to find the relevant documents adapted to his need of information. The information search is a problem which remains difficult because the difficulty in the representing and to treat the natural languages such as polysemia. Intentional Structures promise to be a new paradigm to extend the existing documents structures and to enhance the different phases of documents process such as creation, editing, search and retrieval. The intention recognition of the author-s of texts can reduce the largeness of this problem. In this article, we present intentions recognition system is based on a semi-automatic method of extraction the intentional information starting from a corpus of text. This system is also able to update the ontology of intentions for the enrichment of the knowledge base containing all possible intentions of a domain. This approach uses the construction of a semi-formal ontology which considered as the conceptualization of the intentional information contained in a text. An experiments on scientific publications in the field of computer science was considered to validate this approach.

Keywords: Information research, text analyzes, intentionalstructure, segmentation, ontology, natural language processing.

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Authors: Giovanni Luca Masala

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Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: Mohsen Mohammadi, Toshio Fujimi

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High population and irregular urban development in Kabul city, Afghanistan's capital, are among factors that increase its vulnerability to earthquake disasters (on top of its location in a high seismic region); this can lead to widespread economic loss and casualties. This study aims to evaluate earthquake risks in Kabul's 13th district based on scientific data. The research data, which include hazard curves of Kabul, vulnerability curves, and a questionnaire survey through sampling in district 13, have been incorporated to develop risk curves. To estimate potential casualties, we used a set of M parameters in a model developed by Coburn and Spence. The results indicate that in the worst case scenario, more than 90% of district 13, which comprises mostly residential buildings, is exposed to high risk; this may lead to nearly 1000 million USD economic loss and 120 thousand casualties (equal to 25.88% of the 13th district's population) for a nighttime earthquake. To reduce risks, we present the reconstruction of the most vulnerable buildings, which are primarily adobe and masonry buildings. A comparison of risk reduction between reconstructing adobe and masonry buildings indicates that rebuilding adobe buildings would be more effective.

Keywords: Earthquake risk evaluation, Kabul, mitigation, vulnerability.

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Authors: I. Severin, M. Caramihai, R. El Abdi, M. Poulain, A. Avadanii

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In order to assess optical fiber reliability in different environmental and stress conditions series of testing are performed simulating overlapping of chemical and mechanical controlled varying factors. Each series of testing may be compared using statistical processing: i.e. Weibull plots. Due to the numerous data to treat, a software application has appeared useful to interpret selected series of experiments in function of envisaged factors. The current paper presents a software application used in the storage, modelling and interpretation of experimental data gathered from optical fibre testing. The present paper strictly deals with the software part of the project (regarding the modelling, storage and processing of user supplied data).

Keywords: Optical fibres, computer aided analysis, data models, data processing, graphical user interfaces.

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Authors: Florin Gorunescu

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Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.

Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.

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Authors: Mustafa Elfituri, Jonathan Cook

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Recently, graph-based computations have become more important in large-scale scientific computing as they can provide a methodology to model many types of relations between independent objects. They are being actively used in fields as varied as biology, social networks, cybersecurity, and computer networks. At the same time, graph problems have some properties such as irregularity and poor locality that make their performance different than regular applications performance. Therefore, parallelizing graph algorithms is a hard and challenging task. Initial evidence is that standard computer architectures do not perform very well on graph algorithms. Little is known exactly what causes this. The Graph500 benchmark is a representative application for parallel graph-based computations, which have highly irregular data access and are driven more by traversing connected data than by computation. In this paper, we present results from analyzing the performance of various example implementations of Graph500, including a shared memory (OpenMP) version, a distributed (MPI) version, and a hybrid version. We measured and analyzed all the factors that affect its performance in order to identify possible changes that would improve its performance. Results are discussed in relation to what factors contribute to performance degradation.

Keywords: Graph computation, Graph500 benchmark, parallel architectures, parallel programming, workload characterization.

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Authors: Smail Hadj Mahammed

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Translation is not merely a process of conveying the meaning from one particular language into another to overcome language barriers and ensure a good understanding; it is also a work of civilization and progress. Without the translation of Greek, Indian and Persian works, Arabic and Islamic Civilization would not have taken off, and without the translations of Arabic works into Latin, and then into European languages, the scientific and technological revolution of the modern world would not have taken place. In this context, the present paper seeks to investigate how the translation movement contributed to the Arabic and Islamic Civilizations during the Golden Age. The paper consists of three major parts: the first part provides a brief historical overview of the translation movement during the golden age, which witnessed two important eras: the Umayyad and Abbasid eras. The second part shows the main reasons why translation was a prominent cultural activity during the Golden Age and why it gained great interest from the Arabs. The last part highlights the constructive contribution of translation to the Arabic and Islamic Civilization during the period (661–1258). The results demonstrate that Arabic translation movement during the Golden Age had significantly assisted in enriching the Arabic and Islamic civilizations considering the major and important scientific works of old Greek, Indian and Persian civilizations which had been absorbed.

Keywords: Arabic and Islamic civilization, contribution, golden age, translation.

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Authors: Larbi Omar, Elberrichi Zakaria

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Many research works are carried out on the analysis of traces in a digital learning environment. These studies produce large volumes of usage tracks from the various actions performed by a user. However, to exploit these data, compare and improve performance, several issues are raised. To remedy this, several works deal with this problem seen recently. This research studied a series of questions about format and description of the data to be shared. Our goal is to share thoughts on these issues by presenting our experience in the analysis of trace-based log files, comparing several approaches used in automatic classification applied to e-learning platforms. Finally, the obtained results are discussed.

Keywords: Classification, , e-learning platform, log file, Trace.

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Authors: Suwardo, Madzlan B. Napiah, Ibrahim B. Kamaruddin

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Stage bus operated in the mixed traffic might always meet many problems about low quality and reliability of services. The low quality and reliability of bus service can make the system not attractive and directly reduce the interest of using bus service. This paper presents the result of field investigation and analysis of on-time performance and service regularity of stage bus in mixed traffic. Data for analysis was collected from the field by on-board observation along the Ipoh-Lumut corridor in Perak, Malaysia. From analysis and discussion, it can be concluded that on-time performance and service regularity varies depend on station, typical day, time period, operation characteristics of bus and characteristics of traffic. The on-time performance and service regularity of stage bus in mixed traffic can be derived by using data collected by onboard survey. It is clear that on-time performance and service regularity of the existing stage bus system was low.

Keywords: mixed traffic, on-time performance, service regularity, stage bus

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Authors: Víctor D. Rodríguez, Edith P. Estupiñán, Juan C. Martínez

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The evolution in mobile broadband technologies has allowed to increase the download rates in users considering the current services. The evaluation of technical parameters at the link level is of vital importance to validate the quality and veracity of the connection, thus avoiding large losses of data, time and productivity. Some of these failures may occur between the eNodeB (Evolved Node B) and the user equipment (UE), so the link between the end device and the base station can be observed. LTE (Long Term Evolution) is considered one of the IP-oriented mobile broadband technologies that work stably for data and VoIP (Voice Over IP) for those devices that have that feature. This research presents a technical analysis of the connection and channeling processes between UE and eNodeB with the TAC (Tracking Area Code) variables, and analysis of performance variables (Throughput, Signal to Interference and Noise Ratio (SINR)). Three measurement scenarios were proposed in the city of Bogotá using QualiPoc, where two operators were evaluated (Operator 1 and Operator 2). Once the data were obtained, an analysis of the variables was performed determining that the data obtained in transmission modes vary depending on the parameters BLER (Block Error Rate), performance and SNR (Signal-to-Noise Ratio). In the case of both operators, differences in transmission modes are detected and this is reflected in the quality of the signal. In addition, due to the fact that both operators work in different frequencies, it can be seen that Operator 1, despite having spectrum in Band 7 (2600 MHz), together with Operator 2, is reassigning to another frequency, a lower band, which is AWS (1700 MHz), but the difference in signal quality with respect to the establishment with data by the provider Operator 2 and the difference found in the transmission modes determined by the eNodeB in Operator 1 is remarkable.

Keywords: BLER, LTE, Network, Qualipoc, SNR.

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: Anomaly detection, autoencoder, data centers, deep learning.

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Authors: Inas Hussein

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Communication in the modern world is increasingly becoming multimodal due to globalization and the digital space we live in which have remarkably affected how people communicate. Accordingly, Multimodal Discourse Analysis (MDA) is an emerging paradigm in discourse studies with the underlying assumption that other semiotic resources such as images, colours, scientific symbolism, gestures, actions, music and sound, etc. combine with language in order to  communicate meaning. One of the effective multimodal media that combines both verbal and non-verbal elements to create meaning is political cartoons. Furthermore, since political and social issues are mirrored in political cartoons, these are regarded as potential objects of discourse analysis since they not only reflect the thoughts of the public but they also have the power to influence them. The aim of this paper is to analyze some selected cartoons on the recognition of Jerusalem as Israel's capital by the American President, Donald Trump, adopting a multimodal approach. More specifically, the present research examines how the various semiotic tools and resources utilized by the cartoonists function in projecting the intended meaning. Ten political cartoons, among a surge of editorial cartoons highlighted by the Anti-Defamation League (ADL) - an international Jewish non-governmental organization based in the United States - as publications in different Arabic-language newspapers in Egypt, Saudi Arabia, UAE, Oman, Iran and UK, were purposively selected for semiotic analysis. These editorial cartoons, all published during 6^th–18^th December 2017, invariably suggest one theme: Jewish and Israeli domination of the United States. The data were analyzed using the framework of Visual Social Semiotics. In accordance with this methodological framework, the selected visual compositions were analyzed in terms of three aspects of meaning: representational, interactive and compositional. In analyzing the selected cartoons, an interpretative approach is being adopted. This approach prioritizes depth to breadth and enables insightful analyses of the chosen cartoons. The findings of the study reveal that semiotic resources are key elements of political cartoons due to the inherent political communication they convey. It is proved that adequate interpretation of the three aspects of meaning is a prerequisite for understanding the intended meaning of political cartoons. It is recommended that further research should be conducted to provide more insightful analyses of political cartoons from a multimodal perspective.

Keywords: Multimodal discourse analysis, multimodal text, political cartoons, visual modality.

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Authors: Claude Takenga, Rolf-Dietrich Berndt, Erling Si, Markus Diem, Guohui Qiao, Melanie Gau, Michael Brandstoetter, Martin Kampel, Michael Dworzak

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In patients with acute lymphoblastic leukaemia (ALL), treatment response is increasingly evaluated with minimal residual disease (MRD) analyses. Flow Cytometry (FCM) is a fast and sensitive method to detect MRD. However, the interpretation of these multi-parametric data requires intensive operator training and experience. This paper presents a pipeline-software, as a ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients with ALL. The new tool increases accuracy in assessment of FCM-MRD in samples which are difficult to analyse by conventional operator-based gating since computer-aided analysis potentially has a superior resolution due to utilization of the whole multi-parametric FCM-data space at once instead of step-wise, two-dimensional plot-based visualization. The system developed as a telemedical network reduces the work-load and lab-costs, staff-time needed for training, continuous quality control, operator-based data interpretation. It allows dissemination of automated FCM-MRD analysis to medical centres which have no established expertise for the benefit of an even larger community of diseased children worldwide. We established a telemedical network system for analysis and clinical follow-up and treatment monitoring of Leukaemia. The system is scalable and adapted to link several centres and laboratories worldwide.

Keywords: Data security, flow cytometry, leukaemia, telematics platform, telemedicine.

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: Document analysis, sentimental analysis, emotion detection, WEKA tool, NRC Lexicon.

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: Road accident, machine learning, support vector machines.

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Authors: Inga Lapiņa, Indra Borkus, Olga Stariņeca

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Creating shared value (CSV) is a newly introduced concept whose essence and expressions, relationship to Corporate social responsibility (CSR) and implications for the business and society is now at the core of management and social responsibility debates of the scientific world. The aim of the paper is to gain clearer understanding of the CSR and CSV concepts, their implementation and role in sustainable development of organizations in Latvia. In this paper the authors discuss and compare the two conceptsand, based on the results of Sustainability Index (SI) initiative and analysis of publically available company information, evaluate their implementation in Latvia and draw conclusions on the development trends and potential of these approaches in Latvian market.

Keywords: Corporate Social Responsibility, Creating Shared Value, Sustainability Index.

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Authors: Jane O. Munonye

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Climate change has both negative and positive effects in agricultural production. For agriculture to be sustainable in adverse climate change condition, some natural measures are needed. The issue is to produce more food with available natural resources and reduce the contribution of agriculture to climate change. The study reviewed climate change and sustainable agriculture in southeast Nigeria. Data from the study were from secondary sources. Ten scientific papers were consulted and data for the review were collected from three. The objectives of the paper were as follows: to review the effect of climate change on one major arable crop in southeast Nigeria (yam; Dioscorea rotundata); evident of climate change impact and methods for sustainable agricultural production in adverse weather condition. Some climatic parameter as sunshine, relative humidity and rainfall have negative relationship with yam production and significant at 10% probability. Crop production was predicted to decline by 25% per hectare by 2060 while livestock production has increased the incidence of diseases and pathogens as the major effect to agriculture. Methods for sustainable agriculture and damage of natural resources by climate change were highlighted. Agriculture needs to be transformed as climate changes to enable the sector to be sustainable. There should be a policy in place to facilitate the integration of sustainability in Nigeria agriculture.

Keywords: Agriculture, climate change, sustainability, yam.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya, P. Ranjetha

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Data mining idea is mounting rapidly in admiration and also in their popularity. The foremost aspire of data mining method is to extract data from a huge data set into several forms that could be comprehended for additional use. The data mining is a technology that contains with rich potential resources which could be supportive for industries and businesses that pay attention to collect the necessary information of the data to discover their customer’s performances. For extracting data there are several methods are available such as Classification, Clustering, Association, Discovering, and Visualization… etc., which has its individual and diverse algorithms towards the effort to fit an appropriate model to the data. STATISTICA mostly deals with excessive groups of data that imposes vast rigorous computational constraints. These results trials challenge cause the emergence of powerful STATISTICA Data Mining technologies. In this survey an overview of the STATISTICA software is illustrated along with their significant features.

Keywords: Data Mining, STATISTICA Data Miner, Text Miner, Enterprise Server, Classification, Association, Clustering, Regression.

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Authors: Amna Javed, Hisashi Masuda, Youji Kohda

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This study examines the value analysis in Islamic and conventional banking services in Pakistan. Many scholars have focused on co-creation of values in services but mainly economic values not non-economic. As Islamic banking is based on Islamic principles that are more concerned with non-economic values (well-being, partnership, fairness, trust worthy, and justice) than economic values as money in terms of interest.  This study is important to know the providers point of view about the co-created values, because, it may be more sustainable and appropriate for today’s unpredictable socio-economic environment. Data were collected from 4 banks (2 Islamic and 2 conventional banks). Text mining technique is applied for data analysis, and values with 100% occurrences in Islamic banking are chosen. The results reflect that Islamic banking is more centric towards non-economic values than economic values and it promotes team work and partnership concept by applying Islamic spirit and trust worthiness concept.

Keywords: Economic values, Islamic banking, Non-economic values, Value system.

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Authors: Ilma Robo, Saimir Heta, Nedja Hysi, Vera Ostreni

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Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values ​​according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.

Keywords: Marginal gingivitis, cross-sectional, retrograde, prevalence.

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Authors: B.R. Potgieter, G. Venter

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Numerical design optimization is a powerful tool that can be used by engineers during any stage of the design process. There are many different applications for structural optimization. A specific application that will be discussed in the following paper is experimental data matching. Data obtained through tests on a physical structure will be matched with data from a numerical model of that same structure. The data of interest will be the dynamic characteristics of an antenna structure focusing on the mode shapes and modal frequencies. The structure used was a scaled and simplified model of the Karoo Array Telescope-7 (KAT-7) antenna structure. This kind of data matching is a complex and difficult task. This paper discusses how optimization can assist an engineer during the process of correlating a finite element model with vibration test data.

Keywords: Finite Element Model (FEM), Karoo Array Telescope(KAT-7), modal frequencies, mode shapes, optimization, shape optimization, size optimization, vibration tests

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Authors: G. Lavanya Devi, Allam Appa Rao, A. Damodaram, GR Sridhar, G. Jaya Suma

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Cluster analysis divides data into groups that are meaningful, useful, or both. Analysis of biological data is creating a new generation of epidemiologic, prognostic, diagnostic and treatment modalities. Clustering of protein sequences is one of the current research topics in the field of computer science. Linear relation is valuable in rule discovery for a given data, such as if value X goes up 1, value Y will go down 3", etc. The classical linear regression models the linear relation of two sequences perfectly. However, if we need to cluster a large repository of protein sequences into groups where sequences have strong linear relationship with each other, it is prohibitively expensive to compare sequences one by one. In this paper, we propose a new technique named General Regression Model Technique Clustering Algorithm (GRMTCA) to benignly handle the problem of linear sequences clustering. GRMT gives a measure, GR*, to tell the degree of linearity of multiple sequences without having to compare each pair of them.

Keywords: Clustering, General Regression Model, Protein Sequences, Similarity Measure.

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Authors: Shruti Taksali

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This paper focuses on the analysis of Nanoptenna for extreme fast communication. The Nanoptenna is basically a nano antenna designed for communication at optical range of frequencies. Since, this range of frequencies includes the visible spectrum of the light, so there is a high possibility of the data transfer at high rates and extreme fast communication (XFC). The shape chosen for the analysis is a bow tie structure due to its various characteristics of electric field enhancement.

Keywords: Nanoptenna, communication, optical range, XFC.

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Authors: Tristan O’Neill, Trina Myers, Jarrod Trevathan

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The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for cross-domain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.

Keywords: Virtual research environment, Semantic Web, performance analysis, tropical data hub.

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Authors: Chen May Oh, Nadzrah Abu Bakar

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This case study investigates the areas of self-reflection through the written content of four university students’ blogs. The study was undertaken to explore the categories of self-reflection in relation to the use of blogs. Data collection methods included downloading students’ blog entries and recording individual interviews to further support the data. Data was analyzed using computer assisted qualitative data analysis software, Nvivo, to categories and code the data. The categories of self-reflection revealed in the findings showed that university students used blogs to reflect on (1) life in varsity, (2) emotions and feelings, (3) various relationships, (4) personal growth, (5) spirituality, (6) health conditions, (7) busyness with daily chores, (8) gifts for people and themselves and (9) personal interests. Overall, all four of the students had positive experiences and felt satisfied using blogs for self-reflection.

Keywords: Blogging, personal growth, self-reflection, university students.

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Authors: T. Watanabe

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Combustion analysis of suspended sodium droplet is performed by solving numerically the Navier-Stokes equations and the energy conservation equations. The combustion model consists of the pre-ignition and post-ignition models. The reaction rate for the pre-ignition model is based on the chemical kinetics, while that for the post-ignition model is based on the mass transfer rate of oxygen. The calculated droplet temperature is shown to be in good agreement with the existing experimental data. The temperature field in and around the droplet is obtained as well as the droplet shape variation, and the present numerical model is confirmed to be effective for the combustion analysis.

Keywords: Combustion, analysis, sodium, droplet.

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Authors: M. Kebisek, L. Spendla, M. Kopcek, T. Skulavik

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In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel.

Keywords: Communication, computer network, data collection, probe.

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Authors: Monica Dudian , Raluca Andreea Popa

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This article analyses the relationship between sovereign credit risk rating and gross domestic product for Central and Eastern European Countries for the period 1996 – 2010. In order to study the metioned relationship, we have used a numerical transformation of the risk qualification, thus: we marked 0 the lowest risk; then, we went on ascending, with a pace of 5, up to the score of 355 corresponding to the maximum risk. The used method of analysis is that of econometric modelling with EViews 7.0. programme. This software allows the analysis of data into a pannel type system, involving a mix of periods of time and series of data for different entities. The main conclusion of the work is the one confirming the negative relationship between the sovereign credit risk and the gross domestic product for the Central European and Eastern countries during the reviewed period.

Keywords: credit rating agencies, economic growth, gross domestic product, sovereign credit risk rating.

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Authors: E. Y. Igde, S. Aydogan, F. E. Boran, D. Akay

Abstract:

Patent data have an increasingly important role in economic growth, innovation, technical advantages and business strategies and even in countries competitions. Analyzing of patent data is crucial since patents cover large part of all technological information of the world. In this paper, we have used the linguistic summarization technique to prove the validity of the hypotheses related to patent data stated in the literature.

Keywords: Data mining, fuzzy sets, linguistic summarization, patent data.

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