Search results for: Data cleaning

Authors: Annur Suhadi, Haris B. Harahap, Zaim Arrosyidi, Epan, Darmapala

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Oilfields under waterflood often face the problem of plugging injectors either by internal filtration or external filter cake built up inside pore throats. The content of suspended solids shall be reduced to required level of filtration since corrective action of plugging is costly expensive. The performance of nutshell filters, where filtration takes place, is good using pecan and walnut shells. Candlenut shells were used instead of pecan and walnut shells since they were abundant in Indonesia, Malaysia, and East Africa. Physical and chemical properties of walnut, pecan, and candlenut shells were tested and the results were compared. Testing, using full-scale nutshell filters, was conducted to determine the oil content, turbidity, and suspended solid removal, which was based on designed flux rate. The performance of candlenut shells, which were deeply bedded in nutshell filters for filtration process, was monitored. Cleaned water outgoing nutshell filters had total suspended solids of 17 ppm, while oil content could be reduced to 15.1 ppm. Turbidity, using candlenut shells, was below the specification for injection water, which was less than 10 Nephelometric Turbidity Unit (NTU). Turbidity of water, outgoing nutshell filter, was ranged from 1.7-5.0 NTU at various dates of operation. Walnut, pecan, and candlenut shells had moisture content of 8.98 wt%, 10.95 wt%, and 9.95 wt%, respectively. The porosity of walnut, pecan, and candlenut shells was significantly affected by moisture content. Candlenut shells had property of toluene solubility of 7.68 wt%, which was much higher than walnut shells, reflecting more crude oil adsorption. The hardness of candlenut shells was 2.5-3 Mohs, which was close to walnut shells’ hardness. It was advantage to guarantee the cleaning filter cake by fluidization process during backwashing.

Keywords: Candlenut shells, walnut shells, pecan shells, nutshell filter, filtration.

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Authors: Teewin Plangsrinont, Wasawat Nakkiew

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In this study, Computational Fluid Dynamics (CFD) was utilized to simulate and predict the path of water from water soot blower through an ambient flow field in 300-megawatt tangentially burned pulverized coal boiler that utilizes a water soot blower as a cleaning device. To predict the position of the impact of water on the opposite side of the water soot blower under identical conditions, the nozzle size and water flow rate were fixed in this investigation. The simulation findings demonstrated a high degree of accuracy in predicting the direction of water flow to the boiler's water wall tube, which was validated by comparison to experimental data. Results show maximum deviation value of the water jet trajectory is 10.2%.

Keywords: Computational fluid dynamics, tangentially fired boiler, thermal power plant, water soot blower.

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of big data technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centres or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through VADER and RoBERTa model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and Term Frequency – Inverse Document Frequency (TFIDF) Vectorization and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide if the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: Counter vectorization, Convolutional Neural Network, Crawler, data technology, Long Short-Term Memory, LSTM, Web Scraping, sentiment analysis.

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Authors: C. Lanzerstorfer

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Liquid sprays of water are frequently used in air pollution control for gas cooling purposes and for gas cleaning. Twin-fluid nozzles with internal mixing are often used for these purposes because of the small size of the drops produced. In these nozzles the liquid is dispersed by compressed air or another pressurized gas. In high efficiency scrubbers for particle separation, several nozzles are operated in parallel because of the size of the cross section. In such scrubbers, the scrubbing water has to be re-circulated. Precipitation of some solid material can occur in the liquid circuit, caused by chemical reactions. When such precipitations are detached from the place of formation, they can partly or totally block the liquid flow to a nozzle. Due to the resulting unbalanced supply of the nozzles with water and gas, the efficiency of separation decreases. Thus, the nozzles have to be cleaned if a certain fraction of blockages is reached. The aim of this study was to provide a tool for continuously monitoring the status of the nozzles of a scrubber based on the available operation data (water flow, air flow, water pressure and air pressure). The difference between the air pressure and the water pressure is not well suited for this purpose, because the difference is quite small and therefore very exact calibration of the pressure measurement would be required. Therefore, an equation for the reference air flow of a nozzle at the actual water flow and operation pressure was derived. This flow can be compared with the actual air flow for assessment of the status of the nozzles.

Keywords: Twin-fluid nozzles, operation data, condition monitoring, flow equation.

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Authors: Low Khong Teck, Hasan S. M. Al-Khaffaf, Abdullah Zawawi Talib, Tan Kian Lam

Abstract:

In this paper, an extended study is performed on the effect of different factors on the quality of vector data based on a previous study. In the noise factor, one kind of noise that appears in document images namely Gaussian noise is studied while the previous study involved only salt-and-pepper noise. High and low levels of noise are studied. For the noise cleaning methods, algorithms that were not covered in the previous study are used namely Median filters and its variants. For the vectorization factor, one of the best available commercial raster to vector software namely VPstudio is used to convert raster images into vector format. The performance of line detection will be judged based on objective performance evaluation method. The output of the performance evaluation is then analyzed statistically to highlight the factors that affect vector quality.

Keywords: Performance Evaluation, Vectorization, Median Filter, Gaussian Noise.

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Authors: Abu Bakar Munir, Siti Hajar Mohd Yasin, Firdaus Muhammad-Sukki

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This paper seeks to analyse the benefits of big data and more importantly the challenges it pose to the subject of privacy and data protection. First, the nature of big data will be briefly deliberated before presenting the potential of big data in the present days. Afterwards, the issue of privacy and data protection is highlighted before discussing the challenges of implementing this issue in big data. In conclusion, the paper will put forward the debate on the adequacy of the existing legal framework in protecting personal data in the era of big data.

Keywords: Big data, data protection, information, privacy.

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Authors: F. Lazzeri, I. Reiter

Abstract:

Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: Time-series, features engineering methods for forecasting, energy demand forecasting, Azure machine learning.

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Authors: Radul Shishkov, Petar Penchev

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The digital era has revolutionized architectural practices, with Building Information Modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research presents a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data — a collection of data points in space, typically produced by 3D scanners — into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historical preservation.

Keywords: Algorithmic modeling, Building Information Modeling, point cloud, reconstruction.

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: Analytics, Big Data in Education, Hadoop, Learning Analytics.

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Authors: Salima Bella, Assia Belbachir, Ghalem Belalem

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This paper deals with the problem of monitoring and cleaning dirty zones of oceans using unmanned vehicles. We present a centralized cooperative architecture for unmanned aerial vehicles (UAVs) to monitor ocean regions and clean dirty zones with the help of unmanned surface vehicles (USVs). Due to the rapid deployment of these unmanned vehicles, it is convenient to use them in oceanic regions where the water pollution zones are generally unknown. In order to optimize this process, our solution aims to detect and reduce the pollution level of the ocean zones while taking into account the problem of fault tolerance related to these vehicles.

Keywords: Centralized architecture, fault tolerance, UAV, USV.

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Authors: N. Parimala, P. Pahwa

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OLAP uses multidimensional structures, to provide access to data for analysis. Traditionally, OLAP operations are more focused on retrieving data from a single data mart. An exception is the drill across operator. This, however, is restricted to retrieving facts on common dimensions of the multiple data marts. Our concern is to define further operations while retrieving data from multiple data marts. Towards this, we have defined six operations which coalesce data marts. While doing so we consider the common as well as the non-common dimensions of the data marts.

Keywords: Data warehouse, Dimension, OLAP, Star Schema.

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Authors: Mohamad Azmi Haniffa, Fakhruldin Mohd Hashim

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Pipeline infrastructures normally represent high cost of investment and the pipeline must be free from risks that could cause environmental hazard and potential threats to personnel safety. Pipeline integrity such monitoring and management become very crucial to provide unimpeded transportation and avoiding unnecessary production deferment. Thus proper cleaning and inspection is the key to safe and reliable pipeline operation and plays an important role in pipeline integrity management program and has become a standard industry procedure. In view of this, understanding the motion (dynamic behavior), prediction and control of the PIG speed is important in executing pigging operation as it offers significant benefits, such as estimating PIG arrival time at receiving station, planning for suitable pigging operation, and improves efficiency of pigging tasks. The objective of this paper is to review recent developments in speed control system of pipeline PIGs. The review carried out would serve as an industrial application in a form of quick reference of recent developments in pipeline PIG speed control system, and further initiate others to add-in/update the list in the future leading to knowledge based data, and would attract active interest of others to share their view points.

Keywords: Pipeline Inspection Gauge (PIG), In Line Inspection Tools (ILI), PIG motion, PIG speed control system

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Authors: Hoda A. Abdel Hafez

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Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: Mining Big Data, Big Data, Machine learning, Data Streams, Telecommunication.

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Authors: Yen-Yiu Liu, Richard J. Haynes

Abstract:

The effects of irrigation with dairy factory wastewater on soil properties were investigated at two sites that had received irrigation for > 60 years. Two adjoining paired sites that had never received DFE were also sampled as well as another seven fields from a wider area around the factory. In comparison with paired sites that had not received effluent, long-term wastewater irrigation resulted in an increase in pH, EC, extractable P, exchangeable Na and K and ESP. These changes were related to the use of phosphoric acid, NaOH and KOH as cleaning agents in the factory. Soil organic C content was unaffected by DFE irrigation but the size (microbial biomass C and N) and activity (basal respiration) of the soil microbial community were increased. These increases were attributed to regular inputs of soluble C (e.g. lactose) present as milk residues in the wastewater. Principal component analysis (PCA) of the soils data from all 11sites confirmed that the main effects of DFE irrigation were an increase in exchangeable Na, extractable P and microbial biomass C, an accumulation of soluble salts and a liming effect. PCA analysis of soil bacterial community structure, using PCR-DGGE of 16S rDNA fragments, generally separated individual sites from one another but did not group them according to irrigation history. Thus, whilst the size and activity of the soil microbial community were increased, the structure and diversity of the bacterial community remained unaffected.

Keywords: Dairy factory, wastewater; effluent, irrigation, soil quality.

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Authors: Yen-Yiu Liu, Richard J. Haynes

Abstract:

The effects of irrigation with dairy factory wastewater on soil properties were investigated at two sites that had received irrigation for > 60 years. Two adjoining paired sites that had never received DFE were also sampled as well as another seven fields from a wider area around the factory. In comparison with paired sites that had not received effluent, long-term wastewater irrigation resulted in an increase in pH, EC, extractable P, exchangeable Na and K and ESP. These changes were related to the use of phosphoric acid, NaOH and KOH as cleaning agents in the factory. Soil organic C content was unaffected by DFE irrigation but the size (microbial biomass C and N) and activity (basal respiration) of the soil microbial community were increased. These increases were attributed to regular inputs of soluble C (e.g. lactose) present as milk residues in the wastewater. Principal component analysis (PCA) of the soils data from all 11sites confirmed that the main effects of DFE irrigation were an increase in exchangeable Na, extractable P and microbial biomass C, an accumulation of soluble salts and a liming effect. PCA analysis of soil bacterial community structure, using PCR-DGGE of 16S rDNA fragments, generally separated individual sites from one another but did not group them according to irrigation history. Thus, whilst the size and activity of the soil microbial community were increased, the structure and diversity of the bacterial community remained unaffected.

Keywords: Dairy factory, wastewater; effluent, irrigation, soil quality.

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

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Over the past era, there have been a lot of efforts and studies are carried out in growing proficient tools for performing various tasks in big data. Recently big data have gotten a lot of publicity for their good reasons. Due to the large and complex collection of datasets it is difficult to process on traditional data processing applications. This concern turns to be further mandatory for producing various tools in big data. Moreover, the main aim of big data analytics is to utilize the advanced analytic techniques besides very huge, different datasets which contain diverse sizes from terabytes to zettabytes and diverse types such as structured or unstructured and batch or streaming. Big data is useful for data sets where their size or type is away from the capability of traditional relational databases for capturing, managing and processing the data with low-latency. Thus the out coming challenges tend to the occurrence of powerful big data tools. In this survey, a various collection of big data tools are illustrated and also compared with the salient features.

Keywords: Big data, Big data analytics, Business analytics, Data analysis, Data visualization, Data discovery.

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Authors: Tetsuya Furukawa, Masahiro Kuzunishi

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Collected data must be organized to be utilized efficiently, and hierarchical classification of data is efficient approach to organize data. When data is classified to multiple categories or annotated with a set of labels, users request multi-labeled data by giving a set of labels. There are several interpretations of the data expressed by a set of labels. This paper discusses which data is expressed by a set of labels by introducing orders for sets of labels and shows that there are four types of orders, which are characterized by whether the labels of expressed data includes every label of the given set of labels within the range of the set. Desirable properties of the orders, data is also expressed by the higher set of labels and different sets of labels express different data, are discussed for the orders.

Keywords: Classification Hierarchies, Multi-labeled Data, Multiple Classificaiton, Orders of Sets of Labels

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Authors: Tanapoom Phuncharoen, Tawiwat Sriwongsa, Kanita Boonruang, Apichit Svang-ariyaskul

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The production of Dimethyl acetal, Isovaleradehyde and Pyridine were simulated using Aspen Plus simulation. Upgrading cleaning water from wine industrial production is the main objective of the project. The winery waste composes of Acetaldehyde, Methanol, Ethyl Acetate, 1-propanol, water, iso-amyl alcohol and iso-butyl alcohol. The project is separated into three parts; separation, reaction, and purification. Various processes were considered to maximize the profit along with obtaining high purity and recovery of each component with optimum heat duty. The results show a significant value of the product with purity more than 75% and recovery over 98%.

Keywords: Dimethyl acetal, Pyridine, wine, Aspen Plus, Isovaleradehyde, polymeric precursors.

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Authors: Kumaresh Selvakumar, Man Young Kim

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Wet scrubbers have found widespread use in cleaning contaminated gas streams because of their ability to remove particulates and based on the applications of scrubbing of marine engine exhaust gases by spraying sea-water. In order to examine the flow characteristics inside the scrubber, the model is designated with flow properties of hot air and water sprayer. The flow dynamics of evaporation of hot air by the injection of water droplets is the key factor considered in this paper. The flow behavior inside the scrubber was investigated from the previous works and to sum up the evaporation rate with respect to the concentration of water droplets are predicted to bring out the competent modelling. The numerical analysis using CFD facilitates in understanding the problem better and empathies the behavior of the model over its entire operating envelope.

Keywords: Concentration of water droplets, Evaporation rate, Scrubber, Water sprayer.

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Authors: Iman Zangeneh, Mostafa Moradi, Ali Mokhtarbaf

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The necessity of ever-increasing use of distributed data in computer networks is obvious for all. One technique that is performed on the distributed data for increasing of efficiency and reliablity is data rplication. In this paper, after introducing this technique and its advantages, we will examine some dynamic data replication. We will examine their characteristies for some overus scenario and the we will propose some suggestion for their improvement.

Keywords: data replication, data hiding, consistency, dynamicdata replication strategy

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Authors: Prariyada Theptat, Sumaeth Chavadej, John F. Scamehorn

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One of the most challenges for hard surface cleaning product is to get rid of soap scum, a filmy sticky layer in the bathroom. The deposits of soap scum can be removed by using a proper surfactant solution with chelating agent. Unfortunately, the conventional chelating agent, ethylenediamine tetraacetic acid (EDTA), has low biodegradability, which can be tolerance in water resources and harmful to aquatic animal and microorganism. In this study, two biodegradable chelating agents, ethylenediamine disuccinic acid (EDDS) and glutamic acid diacetic acid (GLDA) were introduced as a replacement of EDTA. The result shows that using GLDA with amphoteric surfactant gave the highest equilibrium solubility of soap scum.

Keywords: Biodegradable chelating agent, EDDS, GLDA, Soap scum.

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Authors: Aysin Sev, Meltem Ezel

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Sustainability, being the urgent issue of our time, is closely related with the innovations in technology. Nanotechnology (NT), although not a new science, can be regarded relatively a new science for buildings with brand new materials and applications. This paper tends to give a research review of current and near future applications of nanotechnology (NT) for achieving high-performance and healthy buildings for a sustainable future. In the introduction, the driving forces for the sustainability of construction industry are explained. Then, the term NT is defined, and significance of innovations in NT for a sustainable construction industry is revealed. After presenting the application areas of NT and nanomaterials for buildings with a number of cases, challenges in the adoption of this technology are put forward, and finally the impacts of nanoparticles and nanomaterials on human health and environment are discussed.

Keywords: Nanomaterial, self-healing concrete, self-cleaning sensor, nano sensor, steel, wood, aerogel, flexible solar panel.

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Authors: Jihyun Song, Kyeongjoo Kim, Minsoo Lee

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Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.

Keywords: Clustering, data mining, DBSCAN, k-means, k-medoids, sensor data.

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Authors: Hsuan-Ming S Hu, Jolanta A Watson, Bronwen W Cribb, Gregory S Watson

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Biomimicry has many potential benefits as many technologies found in nature are superior to their man-made counterparts. As technological device components approach the micro and nanoscale, surface properties such as surface adhesion and friction may need to be taken into account. Lowering surface adhesion by manipulating chemistry alone might no longer be sufficient for such components and thus physical manipulation may be required. Adhesion reduction is only one of the many surface functions displayed by micro/nano-structured cuticles of insects. Here, we present a mini review of our understanding of insect cuticle structures and the relationship between the structure dimensions and the corresponding functional mechanisms. It may be possible to introduce additional properties to material surfaces (indeed multi-functional properties) based on the design of natural surfaces.

Keywords: Biomimicry, micro/nanostructures, self-cleaning surfaces, superhydrophobicity

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Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.

Keywords: Big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review.

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Authors: Lexuan Zhong, Chang-Seo Lee, Fariborz Haghighat, Stuart Batterman, John C. Little

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Ultraviolet photocatalytic oxidation (UV-PCO) technology has been recommended as a green approach to health indoor environment when it is integrated into mechanical ventilation systems for inorganic and organic compounds removal as well as energy saving due to less outdoor air intakes. Although much research has been devoted to UV-PCO, limited information is available on the UV-PCO behavior tested by the mixtures in literature. This project investigated UV-PCO performance and by-product generation using a single and a mixture of acetone and MEK at 100 ppb each in a single-pass duct system in an effort to obtain knowledge associated with competitive photochemical reactions involved in. The experiments were performed at 20 % RH, 22 °C, and a gas flow rate of 128 m3/h (75 cfm). Results show that acetone and MEK mutually reduced each other’s PCO removal efficiency, particularly negative removal efficiency for acetone. These findings were different from previous observation of facilitatory effects on the adsorption of acetone and MEK on photocatalyst surfaces.

Keywords: By-products, inhibitory effect, mixture, photocatalytic oxidation.

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Authors: Younies Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analyzing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics.

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Authors: Petr Vcelak, Jana Kleckova

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Our Medicine-oriented research is based on a medical data set of real patients. It is a security problem to share patient private data with peoples other than clinician or hospital staff. We have to remove person identification information from medical data. The medical data without private data are available after a de-identification process for any research purposes. In this paper, we introduce an universal automatic rule-based de-identification application to do all this stuff on an heterogeneous medical data. A patient private identification is replaced by an unique identification number, even in burnedin annotation in pixel data. The identical identification is used for all patient medical data, so it keeps relationships in a data. Hospital can take an advantage of a research feedback based on results.

Keywords: DASTA, De-identification, DICOM, Health Level Seven, Medical data, OCR, Personal data

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Authors: Maritsa Kurashvili, George Adamia, Tamar Ananiashvili, Lia Amiranasvili, Tamar Varazi, Marina Pruidze, Marlen Gordeziani, Gia Khatisashvili

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The goal of presented work is the development phytoremediation method targeted to cleaning environment polluted with organochlorine pesticides, based on joint application of plants and microorganisms. For this aim the selection of plants and microorganisms with corresponding capabilities towards three organochlorine pesticides (Lindane, DDT and PCP) has been carried out.

The tolerance of plants to tested pesticides and induction degree of plant detoxification enzymes by these compounds have been used as main criteria for estimating the applicability of plants in proposed technology. Obtained results show that alfalfa, maize and soybean among tested six plant species have highest tolerance to pesticides.

As a result of screening, more than 30 strains from genera Pseudomonas have been selected. As a result of GC analysis of incubation area, 11 active cultures for investigated pesticides are carefully chosen.

Keywords: DDT, Lindane, organochlorine pesticides, PCP, phytoremediation.

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Authors: Masahiro Kuzunishi, Tetsuya Furukawa, Ke Lu

Abstract:

Classifying data hierarchically is an efficient approach to analyze data. Data is usually classified into multiple categories, or annotated with a set of labels. To analyze multi-labeled data, such data must be specified by giving a set of labels as a semantic range. There are some certain purposes to analyze data. This paper shows which multi-labeled data should be the target to be analyzed for those purposes, and discusses the role of a label against a set of labels by investigating the change when a label is added to the set of labels. These discussions give the methods for the advanced analysis of multi-labeled data, which are based on the role of a label against a semantic range.

Keywords: Classification Hierarchies, Data Analysis, Multilabeled Data, Orders of Sets of Labels

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