Search results for: R data science

Authors: Ji Eun Shin, Dong Hoon Lim

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

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

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Authors: Jui Hsuan Hung, Jeng Yi Tzeng

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Engineering education in the higher education is always male dominated. Therefore, women learning in this environment is an important research topic for feminists, gender researchers and engineering education researchers, especially in the era of gender mainstreaming. The research topics are from the dialectical discussion of feminism and science development history, gender issues of science education, to the subject choice of female students. These researches enrich the field of gender study in engineering education but lack of describing the detailed images of women in engineering education, including their learning, obstacles, needs or feelings. Otherwise, in order to keep up with the industrial trends of emphasizing group collaboration, engineering education turns from traditional lecture to creative group inquiry pedagogy in recent years. Creative project based learning is one of the creative group inquiry pedagogy which the engineering education in higher education adopts often, and it is seen as a gender-inclusive pedagogy in engineering education. Therefore, in order to understand the real situation of women learning in engineering education, this study took place in a course (Introduction to Engineering) offered by the school of engineering of a university in Taiwan. This course is designed for freshman students to establish basic understanding engineering from four departments (Chemical Engineering, Power Mechanical Engineering, Materials Science, Industrial Engineering and Engineering Management). One section of this course is to build a Hydraulic Robot designed by the Department of Power Mechanical Engineering. 321 students in the school of engineering took this course and all had the reflection questionnaire. These students are divided into groups of 5 members to work on this project. The videos of process of discussion of five volunteered groups with different gender composition are analyzed, and six women of these five groups are interviewed. We are still on the process of coding and analyzing videos and the qualitative data, but several tentative findings have already emerged. (1) The activity models of groups of both genders are gender segregation, and not like women; men never be the ‘assistants’. (2) The culture of the group is developed by the major gender, but men always dominate the process of practice in all kinds of gender composition groups. (3) Project based learning is supposed to be a gender-inclusive learning model in creative engineering education, but communication obstacles between men and women make it less women friendly. (4) Gender identity, not professional identity, is adopted by these women while they interact with men in their groups. (5) Gender composition and project-based learning pedagogy are not the key factors for women learning in engineering education, but the gender conscience awareness is.

Keywords: engineering education, gender education, creative project based learning, women learning

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Authors: R. Clancy, M. Ahern, D. O’Sullivan, K. Bruton

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The manufacturing industry is currently undergoing a digital transformation as part of the mega-trend Industry 4.0. As part of this phase of the industrial revolution, traditional manufacturing processes are being combined with digital technologies to achieve smarter and more efficient production. To successfully digitally transform a manufacturing facility, the processes must first be digitized. This is the conversion of information from an analogue format to a digital format. The objective of this study was to explore the research area of digitizing manufacturing data as part of the worldwide paradigm, Industry 4.0. The formal methodology of a systematic mapping study was utilized to capture a representative sample of the research area and assess its current state. Specific research questions were defined to assess the key benefits and limitations associated with the digitization of manufacturing data. Research papers were classified according to the type of research and type of contribution to the research area. Upon analyzing 54 papers identified in this area, it was noted that 23 of the papers originated in Germany. This is an unsurprising finding as Industry 4.0 is originally a German strategy with supporting strong policy instruments being utilized in Germany to support its implementation. It was also found that the Fraunhofer Institute for Mechatronic Systems Design, in collaboration with the University of Paderborn in Germany, was the most frequent contributing Institution of the research papers with three papers published. The literature suggested future research directions and highlighted one specific gap in the area. There exists an unresolved gap between the data science experts and the manufacturing process experts in the industry. The data analytics expertise is not useful unless the manufacturing process information is utilized. A legitimate understanding of the data is crucial to perform accurate analytics and gain true, valuable insights into the manufacturing process. There lies a gap between the manufacturing operations and the information technology/data analytics departments within enterprises, which was borne out by the results of many of the case studies reviewed as part of this work. To test the concept of this gap existing, the researcher initiated an industrial case study in which they embedded themselves between the subject matter expert of the manufacturing process and the data scientist. Of the papers resulting from the systematic mapping study, 12 of the papers contributed a framework, another 12 of the papers were based on a case study, and 11 of the papers focused on theory. However, there were only three papers that contributed a methodology. This provides further evidence for the need for an industry-focused methodology for digitizing and analyzing manufacturing data, which will be developed in future research.

Keywords: analytics, digitization, industry 4.0, manufacturing

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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

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

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Authors: Rui Teixeira, Alan O’Connor, Maria Nogal

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The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events.

Keywords: extreme events, offshore structures, peak-over-threshold, significant wave data

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Authors: Mingbao Chen

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The blue economy is a new comprehensive marine economy integrated with resources, industries, and regions, and is an upgraded version of the marine economy. The blue economy attaches great importance to the coordinated development of the ecological environment and the economy, which is an emerging economic form advocated by all countries in the world. This paper constructs the model including four variables：natural capital, economic capital, intellectual capital, cultural capital. Theoretically, this paper deduces the function mechanism of variables on economic growth, and empirically calculates the driving force and influence of the blue economy on the national economy by using data of China's 2006-2015 year. The results show that natural capital and economic capital remain the main factors of blue growth in the blue economy. And with the development of economic society and technological progress, the role of intellectual capital and cultural capital is bigger and bigger. Therefore, promoting the development of marine science and technology and culture is the focus of the future blue economic development.

Keywords: blue growth, natural capital, intellectual capital, cultural capital

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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

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

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Authors: Sami Kabir, Sadek Hossain Khoka

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

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

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Authors: Xinyue Jiao, Yu-Ren Lin

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Argumentation is an essential aspect of scientific thinking which has been widely concerned in recent reform of science education. The purpose of the present studies was to explore the influences of two variables termed ‘the argumentation strategy’ and ‘the kind of science concept’ on student’s web-based argumentation. The first variable was divided into either monological (which refers to individual’s internal discourse and inner chain reasoning) or dialectical (which refers to dialogue interaction between/among people). The other one was also divided into either descriptive (i.e., macro-level concept, such as phenomenon can be observed and tested directly) or theoretical (i.e., micro-level concept which is abstract, and cannot be tested directly in nature). The present study applied the quasi-experimental design in which 138 7th grade students were invited and then assigned to either monological group (N=70) or dialectical group (N=68) randomly. An argumentation learning program called ‘the PWAL’ was developed to improve their scientific argumentation abilities, such as arguing from multiple perspectives and based on scientific evidence. There were two versions of PWAL created. For the individual version, students can propose argument only through knowledge recall and self-reflecting process. On the other hand, the students were allowed to construct arguments through peers’ communication in the collaborative version. The PWAL involved three descriptive science concept-based topics (unit 1, 3 and 5) and three theoretical concept-based topics (unit 2, 4 and 6). Three kinds of scaffoldings were embedded into the PWAL: a) argument template, which was used for constructing evidence-based argument; b) the model of the Toulmin’s TAP, which shows the structure and elements of a sound argument; c) the discussion block, which enabled the students to review what had been proposed during the argumentation. Both quantitative and qualitative data were collected and analyzed. An analytical framework for coding students’ arguments proposed in the PWAL was constructed. The results showed that the argumentation approach has a significant effect on argumentation only in theoretical topics (f(1, 136)=48.2, p < .001, η2=2.62). The post-hoc analysis showed the students in the collaborative group perform significantly better than the students in the individual group (mean difference=2.27). However, there is no significant difference between the two groups regarding their argumentation in descriptive topics. Secondly, the students made significant progress in the PWAL from the earlier descriptive or theoretical topic to the later one. The results enabled us to conclude that the PWAL was effective for students’ argumentation. And the students’ peers’ interaction was essential for students to argue scientifically especially for the theoretical topic. The follow-up qualitative analysis showed student tended to generate arguments through critical dialogue interactions in the theoretical topic which promoted them to use more critiques and to evaluate and co-construct each other’s arguments. More explanations regarding the students’ web-based argumentation and the suggestions for the development of web-based science learning were proposed in our discussions.

Keywords: argumentation, collaborative learning, scientific concepts, web-based learning

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Authors: C. B. Abhilash, Kavi Mahesh

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

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: T. Santiboon, S. Tongbu, P. S. Saihong

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This study focuses on investigating students' perceptions of their physics classroom learning environments of their individualizations and their interactions with the instructional practicum in teaching physics of the master science trainee educational students for improving and creating attitude skills’ sustainable development toward physics for upper secondary educational students in Thailand. Associations between these perceptions and students' attitudes toward physics were also determined. The learning environment perceptions were obtained using the 35-item Physics Laboratory Environment Inventory (PLEI) modified from the original Science Laboratory Environment Inventory. The 25-item Individualized Classroom Environment Questionnaire (ICEQ) was assessed those dimensions which distinguish individualized physics classrooms from convention on individualized open and inquiry-based education Teacher-student interactions were assessed with the 48-item Questionnaires on Teacher Interaction (QTI). Both these questionnaires have an Actual Form (assesses the class as it actually is) and a Preferred Form (asks the students what they would prefer their class to be like - the ideal situation). Students’ creating attitude skills’ sustainable development toward physics were assessed with the Test Of Physics-Related Attitude (TOPRA) modified from the original Test Of Science-Related Attitude (TOSRA) The questionnaires were administered in three phases with the Custer Random Sampling technique to a sample consisted of 989 students in 28 physics classes from 10 schools at the grade 10, 11, and 12 levels in the Secondary Educational Service Area 26 (Maha Sarakham Province) and Area 27 (Roi-Et). Statistically significant differences were found between the students' perceptions of actual-1, actual-2 and preferred environments of their physics laboratory and distinguish individualized classrooms, and teacher interpersonal behaviors with their improving and creating attitudes skills’ sustainable development to their physics classes also were found. Predictions of the monitoring and evaluation of master science trainee educational students of their practicum in teaching physics; students’ skills developments of their physics achievements’ sustainable for the set of actual and preferred environments as a whole and physics related attitudes also were correlated. The R2 values indicate that 58%, 67%, and 84% of the variances in students’ attitudes to their actuale-1, actual-2 and preferred for the PLEI; 42%,science trainee educational students of their practicum in teaching physics; students’ skill developments of their physics achievements’ sustainable for the set of actual and preferred environments as a whole and physics related attitudes also were correlated. The R2 values indicate that 58%, 67%, and 84% of the variances in students’ attitudes to their actuale-1, actual-2 and preferred for the PLEI; 42%, 63%, and 72% for the ICEQ, and 38%, 59%, and 68% for the QTI in physics environment classes were attributable to their perceptions of their actual and preferred physics environments and their developing creative science skills’ sustainable toward physics, consequently. Based on all the findings, suggestions for improving the physics laboratory and individualized classes and teacher interpersonal behaviors with students' perceptions are provided of their improving and creating attitude skills’ sustainable development by the master science trainee educational students ’ instructional administrations.

Keywords: promotion, instructional model, qualitative method, reflective thinking, trainee teacher student

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

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Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information of Earthquake Existed throughout history & Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, prediction, RBF neural network, earthquake

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Authors: Tambe Daniel Atem

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Background: Clinical Research is an organized research on human beings intended to provide adequate information on the drug use as a therapeutic agent on its safety and efficacy. The significance of the study is to educate the global health and life science graduates in Clinical Research in depth to perform better as it involves testing drugs on human beings. Objectives: to provide an overall understanding of the scientific approach to the evaluation of new and existing medical interventions and to apply ethical and regulatory principles appropriate to any individual research. Methodology: It is based on – Primary data analysis and Secondary data analysis. Primary data analysis: means the collection of data from journals, the internet, and other online sources. Secondary data analysis: a survey was conducted with a questionnaire to interview the Clinical Research Professionals to understand the need of training to perform clinical trials globally. The questionnaire consisted details of the professionals working with the expertise. It also included the areas of clinical research which needed intense training before entering into hardcore clinical research domain. Results: The Clinical Trials market worldwide worth over USD 26 billion and the industry has employed an estimated 2,10,000 people in the US and over 70,000 in the U.K, and they form one-third of the total research and development staff. There are more than 2,50,000 vacant positions globally with salary variations in the regions for a Clinical Research Coordinator. R&D cost on new drug development is estimated at US$ 70-85 billion. The cost of doing clinical trials for a new drug is US$ 200-250 million. Due to an increase trained Clinical Research Professionals India has emerged as a global hub for clinical research. The Global Clinical Trial outsourcing opportunity in India in the pharmaceutical industry increased to more than $2 billion in 2014 due to increased outsourcing from U.S and Europe to India. Conclusion: Assessment of training need is recommended for newer Clinical Research Professionals and trial sites, especially prior the conduct of larger confirmatory clinical trials.

Keywords: clinical research, clinical trials, clinical research professionals

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Authors: Dina Ahmad Alkhodary

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

Keywords: data, mining, development, business

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Authors: Kiara Lawrence, Robynne Hansmann, Clive Greentsone

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Geographic Information Systems (GIS) are used to store, analyze, visualize, capture and monitor geographic data. Built environment professionals as well as urban planners specifically, need to possess GIS skills to effectively and efficiently plan spaces. GIS application extends beyond the production of map artifacts and can be applied to relate to spatially referenced, real time data to support spatial visualization, analysis, community engagement, scenarios, and so forth. Though GIS has been used in the built environment for a few decades, its use in education has not been researched enough to draw conclusions on the trends in the last 20 years. The study looks to discover current and emerging trends of GIS in built environment education. A bibliometric review analysis methodology was carried out through exporting documents from Scopus and Web of Science using keywords around "Geographic information systems" OR "GIS" AND "built environment" OR “geography” OR "architecture" OR "quantity surveying" OR "construction" OR "urban planning" OR "town planning" AND “education” between the years 1994 to 2024. A total of 564 documents were identified and exported. The data was then analyzed using VosViewer software to generate network analysis and visualization maps on the co-occurrence of keywords, co-citation of documents and countries and co-author network analysis. By analyzing each aspect of the data, deeper insight of GIS within education can be understood. Preliminary results from Scopus indicate that GIS research focusing on built environment education seems to have peaked prior to 2014 with much focus on remote sensing, demography, land use, engineering education and so forth. This invaluable data can help in understanding and implementing GIS in built environment education in ways that are foundational and innovative to ensure that students are equipped with sufficient knowledge and skills to carry out tasks in their respective fields.

Keywords: architecture, built environment, construction, education, geography, geographic information systems, quantity surveying, town planning, urban planning

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Authors: L. Vujičić, Ž. Ivković, Ž. Boneta

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There is a widespread belief in everyday discourse that science subjects (physics, chemistry and biology) are, along with math, the most difficult school subjects in the education of an individual. This assumption is usually justified by the following facts: low GPA in these subjects, the number of pupils who fail these subjects is high in comparison to other subjects, and the number of pupils interested in continuing their studies in the fields with a focus on science subjects is lower compared to non-science-oriented fields. From that perspective, the project: “Could it be different? How do children explore it?” becomes extremely interesting because it is focused on young children and on the introduction of new methods, with aim of arousing interest in scientific literacy development in 10 kindergartens by applying the methodology of an action research, with an ethnographic approach. We define scientific literacy as a process of encouraging and nurturing the research and explorative spirit in children, as well as their natural potential and abilities that represent an object of scientific research: to learn about exploration by conducting exploration. Upon project completion, an evaluation questionnaire was created for the parents of the children who had participated in the project, as well as for those whose children had not been involved in the project. The purpose of the first questionnaire was to examine the level of satisfaction with the project implementation and its outcomes among those parents whose children had been involved in the project (N=142), while the aim of the second questionnaire was to find out how much the parents of the children not involved (N=154) in this activity were interested in this topic.

Keywords: documenting, early childhood education, evaluation questionnaire for parents, scientific literacy development

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Authors: Amal M. Alrayes

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

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

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Authors: Lola Rosalia Saavedra Guzman, Plamen Neytchev Netchev

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In general, agnosticism is perceived as an uncertainty between a well-structured (religious) belief (in some Christian or pagan deity) and its absolute and total absence, often causing the suspicion that an agnostic is an atheist, which is "reinsured" in case if their personal belief is wrong. All of this, along with the prevailing view among the naturalists that science has already demonstrated the inexistence of God, has compelled us to seek the foundation of agnosticism and faith in the contemporary formal human logic, advanced mathematics, and the natural sciences. Along the way, we will find that no natural science can demonstrate the existence of God, nor could it discard it for rational considerations, which show that there is something beyond. After all, it seems that the human intellect is insufficient to respond surely with yes or no to the existence of higher intelligences leaving unconditional faith as the only path to God for Christians and transcendent techniques, for pagan religious beliefs.

Keywords: agnosticism, formal logic, axioms and postulates, Gödel theorems, and logical faults

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Authors: M. Mohammed Raoof, A. Enkhtaivan, H. Aljuaid

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This study follows the design science research methodology to design and implement a smartphone application artifact. The developed artifact was evaluated through three phases. The System Usability Scale (SUS) metric was used for the evaluation. The designed artifact aims to spread awareness about reducing air pollution, decreasing lung cancer development, and checking the air quality status in Southern California Counties. Participants have been drawn for a pilot study to facilitate awareness of air pollution. The study found that smartphone applications have a beneficial effect on the study’s aims.

Keywords: air pollution, design science research, indoor air pollution, lung cancer, outdoor air pollution, smartphone application

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Authors: Hwayeon Song

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The long-term purpose of research and development (R&D) programs is to strengthen national competitiveness by developing new knowledge and technologies. Thus, it is important to determine a proper budget for government programs to maintain the vigor of R&D when the total funding is tight due to the national deficit. In this regard, a ripple effect analysis for the budgetary changes in R&D programs is necessary as well as an investigation of the current status. This study proposes a new approach using Artificial Neural Networks (ANN) for both tasks. It particularly focuses on R&D programs related to Construction and Transportation (C&T) technology in Korea. First, key factors in C&T technology are explored to draw impact indicators in three areas: economy, society, and science and technology (S&T). Simultaneously, ANN is employed to evaluate the relationship between data variables. From this process, four major components in R&D including research personnel, expenses, management, and equipment are assessed. Then the ripple effect analysis is performed to see the changes in the hypothetical future by modifying current data. Any research findings can offer an alternative strategy about R&D programs as well as a new analysis tool.

Keywords: Artificial Neural Networks, construction and transportation technology, Government Research and Development, Ripple Effect

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Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

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

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

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Authors: Amy Gooden, Meshandren Naidoo

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

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

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Authors: Tejinder Singh

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

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

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

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

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

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Authors: Shivam Dubey, Vinit Srivastava, Abhay Singh Thakur, Rahul Vaish

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The presence of organic dyes in water is a critical issue that poses a significant threat to the environment and human health. We have investigated the use of corona discharge as a potential method for degrading organic dyes in water. Methylene Blue dye was exposed to corona discharge, and its photo-absorbance was measured over time to determine the extent of degradation. The results depicted a decreased absorbance for the dye and the loss of the characteristic colour of methylene blue. The effects of various parameters, including current, voltage, gas phase, salinity, and electrode spacing, on the reaction rates, were investigated. The highest reaction rates were observed at the highest current and voltage (up to 10kV), lowest salinity, smallest electrode spacing, and an environment containing enhanced levels of oxygen. These findings have possible applications for science education curriculum. By investigating the use of corona discharge for destroying organic dyes, we can provide students with a practical application of scientific principles that they can apply to real-world problems. This research can demonstrate the importance of understanding the chemical and physical properties of organic dyes and the effects of corona discharge on their degradation and provide a holistic understanding of the applications of scientific research. Moreover, our study also emphasizes the importance of considering the various parameters that can affect reaction rates. By investigating the effects of current, voltage, matter phase, salinity, and electrode spacing, we can provide students with an opportunity to learn about the importance of experimental design and how to evade constraints that can limit meaningful results. In conclusion, this study has the potential to provide valuable insights into the use of corona discharge for destroying organic dyes in water and has significant implications for science education. By highlighting the practical applications of scientific principles, experimental design, and the importance of considering various parameters, this research can help students develop critical thinking skills and prepare them for future careers in science and engineering.

Keywords: dye degradation, corona discharge, science education, hands-on learning, chemical education

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

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

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

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

Abstract:

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

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

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Authors: Abeba Nigussie Turi, Xiangming Samuel Li

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

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

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Authors: Dur-E- Shahwar

Abstract:

Much attention has been given to ‘bridging the gap’ between neuroscience and educational practice. In order to gain a better understanding of the nature of this gap and of possibilities to enable the linking process, we have taken a boundary perspective on these two fields and the brain-based learning approach, focusing on boundary-spanning actors, boundary objects, and boundary work. In 26 semi-structured interviews, neuroscientists and education professionals were asked about their perceptions in regard to the gap between science and practice and the role they play in creating, managing, and disrupting this boundary. Neuroscientists and education professionals often hold conflicting views and expectations of both brain-based learning and of each other. This leads us to argue that there are increased prospects for a neuro-scientifically informed learning practice if science and practice work together as equal stakeholders in developing and implementing neuroscience research.

Keywords: language learning, explore, educational practices, mentalist, practice

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Authors: Tarek Saadawi, Rosario Gennaro, Jonathan Akeley

Abstract:

There is rapidly growing demand for professionals to fill positions in Cybersecurity. This is recognized as a national priority both by government agencies and the private sector. Cybersecurity is a very wide technical area which encompasses all measures that can be taken in an electronic system to prevent criminal or unauthorized use of data and resources. This requires defending computers, servers, networks, and their users from any kind of malicious attacks. The need to address this challenge has been recognized globally but is particularly acute in the New York metropolitan area, home to some of the largest financial institutions in the world, which are prime targets of cyberattacks. In New York State alone, there are currently around 57,000 jobs in the Cybersecurity industry, with more than 23,000 unfilled positions. The Cybersecurity Program at City College is a collaboration between the Departments of Computer Science and Electrical Engineering. In Fall 2020, The City College of New York matriculated its first students in theCybersecurity Master of Science program. The program was designed to fill gaps in the previous offerings and evolved out ofan established partnership with Facebook on Cybersecurity Education. City College has designed a program where courses, curricula, syllabi, materials, labs, etc., are developed in cooperation and coordination with industry whenever possible, ensuring that students graduating from the program will have the necessary background to seamlessly segue into industry jobs. The Cybersecurity Program has created multiple pathways for prospective students to obtain the necessary prerequisites to apply in order to build a more diverse student population. The program can also be pursued on a part-time basis which makes it available to working professionals. Since City College’s Cybersecurity M.S. program was established to equip students with the advanced technical skills needed to thrive in a high-demand, rapidly-evolving field, it incorporates a range of pedagogical formats. From its outset, the Cybersecurity program has sought to provide both the theoretical foundations necessary for meaningful work in the field along with labs and applied learning projects aligned with skillsets required by industry. The efforts have involved collaboration with outside organizations and with visiting professors designing new courses on topics such as Adversarial AI, Data Privacy, Secure Cloud Computing, and blockchain. Although the program was initially designed with a single asynchronous course in the curriculum with the rest of the classes designed to be offered in-person, the advent of the COVID-19 pandemic necessitated a move to fullyonline learning. The shift to online learning has provided lessons for future development by providing examples of some inherent advantages to the medium in addition to its drawbacks. This talk will address the structure of the newly-implemented Cybersecurity Master’s Program and discuss the innovations, challenges, and possible future directions.

Keywords: cybersecurity, new york, city college, graduate degree, master of science

Procedia PDF Downloads 147