Search results for: count data

Authors: Osman Kök, İlhami̇ Tüzün, Yaşar Aluç

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This study was conducted to explore the effect of direct current (DC) applications on the growth of microalgae Chlorella vulgaris KKU71, isolated from highly saline freshwater. Experiments were implemented based upon the cross-combinations of both the intensity and duration of electric applications, generating a full factorial design of 10V, 20V, 30V, and 5s, 30s, 60s, respectively. Growth parameters of cultures were monitored on Optical Density (OD), Cell Count (CC), Chlorophyll-a, b (Chl-a, b), and Total Carotenoids (TCar). All DC-assisted treatments stimulated the growth and thus led to higher values of growth parameters such as OD, CC, Chl-a, and TCar. Monotonically increasing with the intensity and duration of DC applications, wet and dry biomass yields of the harvested algae reached their highest level at 30V-60s in all sets of treatments. In addition, this increase between DC applications was listed as C(control)<10V<20V<30V and C<5s<30s<60s. As a result, direct current applications increased the biomass.

Keywords: Chlorella Vulgaris, direct current, growth, biomass

Procedia PDF Downloads 138

Authors: Carla Silva

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Recent development of information and communication technology enables us to acquire, collect, analyse data in various fields of socioeconomic – technological systems. Along with the increase of economic globalization and the evolution of information technology, data mining has become an important approach for economic data analysis. As a result, there has been a critical need for automated approaches to effective and efficient usage of massive amount of educational data, in order to support institutions to a strategic planning and investment decision-making. In this article, we will address data from several different perspectives and define the applied data to sciences. Many believe that 'big data' will transform business, government, and other aspects of the economy. We discuss how new data may impact educational policy and educational research. Large scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe educational activity and educational impact. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in educational and furthermore in economics. Finally, we highlight a number of challenges and opportunities for future research.

Keywords: data mining, research analysis, investment decision-making, educational research

Procedia PDF Downloads 358

Authors: Digvijaysingh S. Bana, Kiran R. Trivedi

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This paper introduces various methods for the alpha wave to detect the difference between two states of brain. One healthy subject participated in the experiment. EEG was measured on the forehead above the eye (FP1 Position) with reference and ground electrode are on the ear clip. The data samples are obtained in the form of EEG raw data. The time duration of reading is of one minute. Various test are being performed on the alpha band EEG raw data.The readings are performed in different time duration of the entire day. The statistical analysis is being carried out on the EEG sample data in the form of various tests.

Keywords: electroencephalogram(EEG), biometrics, authentication, EEG raw data

Procedia PDF Downloads 464

Authors: Mudasir Ahmad Syed, Raashid Ahmd Wani, Mashooq Ahmad Dar, Uneeb Urwat, Riaz Ahmad Shah, Nazir Ahmad Ganai

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Salmonella enterica serovar Typhimurium (Salmonella Typhimurium) is a primary avian pathogen responsible for severe intestinal pathology in younger chickens and economic losses. However, the Salmonella Typhimurium is also able to cause infection in humans, described by typhoid fever and acute gastro-intestinal disease. A study was conducted at days to investigate pathological, histopathological, haemato-biochemical, immunological and expression kinetics of NRAMP (natural resistance associated macrophage protein) gene family (NRAMP1 and NRAMP2) in broiler chickens following experimental infection of Salmonella Typhimurium at 0,1,3,5,7,9,11,13 and 15 days respectively. Infection was developed in birds through oral route at 2×108 CFU/ml. Clinical symptoms appeared 4 days post infection (dpi) and after one-week birds showed progressive weakness, anorexia, diarrhea and lowering of head. On postmortem examination, liver showed congestion, hemorrhage and necrotic foci on surface, while as spleen, lungs and intestines revealed congestion and hemorrhages. Histopathological alterations were principally observed in liver in second week post infection. Changes in liver comprised of congestion, areas of necrosis, reticular endothelial hyperplasia in association with mononuclear cell and heterophilic infiltration. Hematological studies confirm a significant decrease (P<0.05) in RBC count, Hb concentration and PCV. White blood cell count showed significant increase throughout the experimental study. An increase in heterophils was found up to 7dpi and a decreased pattern was observed afterwards. Initial lymphopenia followed by lymphocytosis was found in infected chicks. Biochemical studies showed a significant increase in glucose, AST and ALT concentration and a significant decrease (P<0.05) in total protein and albumin level in the infected group. Immunological studies showed higher titers of IgG in infected group as compared to control group. The real time gene expression of NRAMPI and NRAMP2 genes increased significantly (P<0.05) in infected group as compared to controls. The peak expression of NRAMP1 gene was seen in liver, spleen and caecum of infected birds at 3dpi, 5dpi and 7dpi respectively, while as peak expression of NRAMP2 gene in liver, spleen and caecum of infected chicken was seen at 9dpi, 5dpi and 9dpi respectively. This study has role in diagnostics and prognostics in the poultry industry for the detection of salmonella infections at early stages of poultry development.

Keywords: biochemistry, histopathology, NRAMP, poultry, real time expression, Salmonella Typhimurium

Procedia PDF Downloads 332

Authors: Katarina Ott, Velibor Mačkić, Mihaela Bronić, Branko Stanić

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The determinants of national budget transparency have been widely discussed in the literature, while research on determinants of local budget transparency are scarce and empirically inconclusive, particularly in the new, fiscally centralised, EU member states. To fill the gap, we combine two strands of the literature: that concerned with public administration and public finance, shedding light on the economic and financial determinants of local budget transparency, and that on the political economy of transparency (principal agent theory), covering the relationships among politicians and between politicians and voters. Our main hypothesis states that variables describing residents’ capacity have a greater impact on local budget transparency than variables indicating the institutional capacity of local government units (LGUs). Additional subhypotheses test the impact of each variable analysed on local budget transparency. We address the determinants of local budget transparency in Croatia, measured by the number of key local budget documents published on the LGUs’ websites. By using a data set of 128 cities and 428 municipalities over the 2015-2017 period and by applying panel data analysis based on Poisson and negative binomial distribution, we test our main hypothesis and sub-hypotheses empirically. We measure different characteristics of institutional and residents’ capacity for each LGU. Age, education and ideology of the mayor/municipality head, political competition indicators, number of employees, current budget revenues and direct debt per capita have been used as a measure of the institutional capacity of LGU. Residents’ capacity in each LGU has been measured through the numbers of citizens and their average age as well as by average income per capita. The most important determinant of local budget transparency is average residents' income per capita at both city and municipality level. The results are in line with most previous research results in fiscally decentralised countries. In the context of a fiscally centralised country with numerous small LGUs, most of whom have low administrative and fiscal capacity, this has a theoretical rationale in the legitimacy and principal-agent theory (opportunistic motives of the incumbent). The result is robust and significant, but because of the various other results that change between city and municipality levels (e.g. ideology and political competition), there is a need for further research (both on identifying other determinates and/or methods of analysis). Since in Croatia the fiscal capacity of a LGU depends heavily on the income of its residents, units with higher per capita incomes in many cases have also higher budget revenues allowing them to engage more employees and resources. In addition, residents’ incomes might be also positively associated with local budget transparency because of higher citizen demand for such transparency. Residents with higher incomes expect more public services and have more access to and experience in using the Internet, and will thus typically demand more budget information on the LGUs’ websites.

Keywords: budget transparency, count data, Croatia, local government, political economy

Procedia PDF Downloads 184

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

Procedia PDF Downloads 95

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

Procedia PDF Downloads 438

Authors: T. Angeline Priyanka, R. Ganesan

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Recent trends in microelectronics technology have gradually changed the strategies used in very large scale integration (VLSI) circuits. Complementary Metal Oxide Semiconductor (CMOS) technology has been the industry standard for implementing VLSI device for the past two decades, but due to scale-down issues of ultra-low dimension achievement is not achieved so far. Hence it paved a way for Quantum Cellular Automata (QCA). It is only one of the many alternative technologies proposed as a replacement solution to the fundamental limit problem that CMOS technology will impose in the years to come. In this brief, presented a new adder that possesses high speed of operation occupying less area is proposed. This adder is designed especially for error tolerant application. Hence in the proposed adder, the overall area (cell count) and simulation time are reduced by 88 and 73 percent respectively. Various results of the proposed adder are shown and described.

Keywords: quantum cellular automata, carry look ahead adder, ripple carry adder, lossy application, majority gate, crossover

Procedia PDF Downloads 556

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

Procedia PDF Downloads 162

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

Procedia PDF Downloads 355

Authors: Muazu A.

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Haphazard and inappropriate plant spacing and poor soilfertility management practice are among the major factorsconstraining onion production in Gashua, Bade Locale Government Yobe State.Field experiments were conducted in 2023 dry season ar Federal University, Gashua university farm assess the influence of intra-row spacing (2.5, 5, 7.5, 10 and 12.5 cm) and nitrogen fertilizerrate (0, 41, 82 and 123kg Nha-1) growth, bulb yield and quality of onion. The experiment was laid out in a randomized complete block design (RCBD) with three replications. The main effects of nitrogen rate and intra-row spacing influenced only the plant height stand count significantly obtained from 7.5cm and 82kg Nha-1 intra-row spacing and nitrogen fertilizer respectively. The highest yield was obtained from the application of 82kg Nha-1 and plant spacing of 5.0cm and 7.5cm respectively.

Keywords: onion, intra-row spacing, nitrogen fertilizer, yield

Procedia PDF Downloads 28

Authors: Rania Samir Zaki

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Because of their inhibitory effects, selected probiotic Lactobacilli may be used as antimicrobial against some hazardous microorganisms responsible for spoilage of fresh minced beef (cattle) minced meat and camel minced meat. Lactic acid bacteria were isolated from camel meat. These included 10 isolates; 1 Lactobacillus fermenti, 4 Lactobacillus plantarum, 4 Lactobacillus pulgaricus, 3 Lactobacillus acidophilus and 1 Lactobacillus brevis. The most efficient inhibitory organism was Lactobacillus plantarum which can be used as a propiotic with antibacterial activity. All microbiological analyses were made at the time 0, first day and the second day at altered ranges of temperature [4±2 ⁰C (chilling temperature), 25±2 ⁰C, and 38±2 ⁰C]. Results showed a significant decrease of pH 6.2 to 5.1 within variant types of meat, in addition to reduction of Total Bacterial Count, Enterococci, Bacillus cereus and Escherichia coli together with the stability of Coliforms and absence of Staphylococcus aureus.

Keywords: antibacterial, camel meat, inhibition, probiotics

Procedia PDF Downloads 299

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

Procedia PDF Downloads 162

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

Procedia PDF Downloads 497

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

Procedia PDF Downloads 493

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

Procedia PDF Downloads 479

Authors: Luís Miguel Cabeça Marques, Alberto Manuel Martinho Vale, José Pedro Miragaia Trancoso Vaz, Ana Sofia Baptista Fernandes, Rui Alexandre de Barros Coito, Tiago Miguel Prates da Costa

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The detection and localization of hidden radioactive sources are of significant importance in countering the illicit traffic of Special Nuclear Materials and other radioactive sources and materials. Radiation portal monitors are commonly used at airports, seaports, and international land borders for inspecting cargo and vehicles. However, these equipment can be expensive and are not available at all checkpoints. Consequently, the localization of SNM and other radioactive sources often relies on handheld equipment, which can be time-consuming. The current study presents the advantages of real-time analysis of gamma-ray count rate data from a mobile radiation detection system based on simulated data and field tests. The incorporation of profit functions and decision criteria to optimize the detection system's path significantly enhances the radiation field information and reduces survey time during cargo inspection. For source position estimation, a maximum likelihood estimation algorithm is employed, and confidence intervals are derived using the Fisher information. The study also explores the impact of uncertainties, baselines, and thresholds on the performance of the profit function. The proposed detection system, utilizing a plastic scintillator with silicon photomultiplier sensors, boasts several benefits, including cost-effectiveness, high geometric efficiency, compactness, and lightweight design. This versatility allows for seamless integration into any mobile platform, be it air, land, maritime, or hybrid, and it can also serve as a handheld device. Furthermore, integration of the detection system into drones, particularly multirotors, and its affordability enable the automation of source search and substantial reduction in survey time, particularly when deploying a fleet of drones. While the primary focus is on inspecting maritime container cargo, the methodologies explored in this research can be applied to the inspection of other infrastructures, such as nuclear facilities or vehicles.

Keywords: plastic scintillators, profit functions, path planning, gamma-ray detection, source localization, mobile radiation detection system, security scenario

Procedia PDF Downloads 115

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

Procedia PDF Downloads 125

Authors: Anna Paola Micheli, Carmelo Intrisano, Anna Maria Calce

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This research focused on the capability of health tourism to improve the economic and financial performance of healthcare companies. It is assumed that health tourism companies have better profitability and financial efficiency because they can also count on cross-border demand differently from no health tourism companies. A three-level gap analysis was conducted: the first concerns health tourism companies located in Italy and in the other EU28 states; in the second Italian and EU28, no health tourism companies were compared; the third level is about the Italian system with a comparison between health tourism and no health tourism companies. Findings highlighted that Italian healthcare companies have better profitability performance if compared to European ones, but they present weaknesses in the financial position given the illiquidity and excessive leverage. Furthermore, studying the Italian system, we found that health tourism companies are more profitable than no health tourism companies.

Keywords: financial performance, gap analysis, health tourism, profitability performance, value creation

Procedia PDF Downloads 227

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

Procedia PDF Downloads 307

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

Procedia PDF Downloads 120

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

Procedia PDF Downloads 299

Authors: H. M. Wilhelm, P. O. Fernandes, L. P. Dill, C. Steffens, K. G. Moscon, S. M. Peres, V. Bender, T. Marchesan, J. B. Ferreira Neto

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Deterioration of insulating oil is a natural process that occurs during transformers operation. However, this process can be accelerated by some factors, such as oxygen, high temperatures, metals and, moisture, which rapidly reduce oil insulating capacity and favor transformer faults. Parts of building materials of a transformer can be degraded and yield soluble compounds and insoluble particles that shorten the equipment life. Physicochemical tests, dissolved gas analysis (including propane, propylene and, butane), volatile and furanic compounds determination, besides quantitative and morphological analyses of particulate are proposed in this study in order to correlate transformers building materials degradation with insulating oil characteristics. The present investigation involves tests of medium temperature overheating simulation by means of an electric resistance wrapped with the following materials immersed in mineral insulating oil: test I) copper, tin, lead and, paper (heated at 350-400 °C for 8 h); test II) only copper (at 250 °C for 11 h); and test III) only paper (at 250 °C for 8 h and at 350 °C for 8 h). A different experiment is the simulation of electric arc involving copper, using an electric welding machine at two distinct energy sets (low and high). Analysis results showed that dielectric loss was higher in the sample of test I, higher neutralization index and higher values of hydrogen and hydrocarbons, including propane and butane, were also observed. Test III oil presented higher particle count, in addition, ferrographic analysis revealed contamination with fibers and carbonized paper. However, these particles had little influence on the oil physicochemical parameters (dielectric loss and neutralization index) and on the gas production, which was very low. Test II oil showed high levels of methane, ethane, and propylene, indicating the effect of metal on oil degradation. CO₂ and CO gases were formed in the highest concentration in test III, as expected. Regarding volatile compounds, in test I acetone, benzene and toluene were detected, which are oil oxidation products. Regarding test III, methanol was identified due to cellulose degradation, as expected. Electric arc simulation test showed the highest oil oxidation in presence of copper and at high temperature, since these samples had huge concentration of hydrogen, ethylene, and acetylene. Particle count was also very high, showing the highest release of copper in such conditions. When comparing high and low energy, the first presented more hydrogen, ethylene, and acetylene. This sample had more similar results to test I, pointing out that the generation of different particles can be the cause for faults such as electric arc. Ferrography showed more evident copper and exfoliation particles than in other samples. Therefore, in this study, by using different combined analytical techniques, it was possible to correlate insulating oil characteristics with possible contaminants, which can lead to transformers failure.

Keywords: Ferrography, gas analysis, insulating mineral oil, particle contamination, transformer failures

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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

Procedia PDF Downloads 418

Authors: K. Senthil Nathan, P. Rajasekaran

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Fatigue is a feeling of extreme physical or mental tiredness. Almost everyone becomes fatigued at some time, but driver’s fatigue is a serious problem that leads to thousands of automobile crashes each year. Fatigue process is often a change from the alertness and vigor state to the tiredness and weakness state. It is not only accompanied by drowsiness but also has a negative impact on mood. There have been studies to detect and quantify fatigue from the measurement of physiology variables such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). This project involves a multimodal sensing of driver’s drowsiness. The first method is to count the eye blinking rate. In the second level, we authenticate the results of eye blink module with a grip sensor. The Flexiforce sensor is placed over the steering wheel. In the third level, the activities are sensed, the time elapsed from the driver’s last activity is counted here. The activities in the sense: Changing gear, applying brake, pressing sound horns, and turning the steering wheel. Absence of these activities is also an indicator of fatigue.

Keywords: eye blink sensor, Flexiforce sensor, EEG, EOG, EMG

Procedia PDF Downloads 483

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

Procedia PDF Downloads 328

Authors: Danielle Shackley, Yetunde Folajimi

Abstract:

As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Nehal Adel Khalil, Amel Foad Ketat, Fairouz Elsayed Mohamed Ali, Nahla Abdelmoneim Hamid, Hazem Farag Manaa

Abstract:

Background: Chronic myeloid leukemia (CML) represents 15% of adult leukemias. Imatinib Mesylate (IM) is the gold standard treatment for new cases of CML. Treatment with IM results in improvement of the majority of cases. However, about 25% of cases may develop resistance. Sensitive and specific early predictors of IM resistance in CML patients have not been established to date. Aim: To investigate the value of miR-451 in CML as an early predictor for IM resistance in Egyptian CML patients. Methods: The study employed Real time Polymerase Reaction (qPCR) technique to investigate the leucocytic expression of miR-451 in fifteen newly diagnosed CML patients (group I), fifteen IM responder CML patients (group II), fifteen IM resistant CML patients (group III) and fifteen healthy subjects of matched age and sex as a control group (group IV). The response to IM was defined as < 10% BCR-ABL transcript level after 3 months of therapy. The following parameters were assessed in subjects of all the studied groups: 1- Complete blood count (CBC). 2- Measurement of plasma level of miRNA 451 using real-time Polymerase Chain Reaction (qPCR). 3- Detection of BCR-ABL gene mutation in CML using qPCR. Results: The present study revealed that miR-451 was significantly down-regulated in leucocytes of newly diagnosed CML patients as compared to healthy subjects. IM responder CML patients showed an up-regulation of miR- 451 compared with IM resistant CML patients. Conclusion: According to the data from the present study, it can be concluded that leucocytic miR- 451 expression is a useful additional follow-up marker for the response to IM and a promising prognostic biomarker for CML.

Keywords: chronic myeloid leukemia, imatinib resistance, microRNA 451, Polymerase Chain Reaction

Procedia PDF Downloads 293