Search results for: hierarchical cluster analysis

Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

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Authors: Seung-Jun Yu, Young-Min Ko, Hyoung-Kyu Song

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Recently developed cooperative diversity scheme enables a terminal to get transmit diversity through the support of other terminals. However, most of the introduced cooperative schemes have a common fault of decreased transmission rate because the destination should receive the decodable compositions of symbols from the source and the relay. In order to achieve high data rate, we propose a cooperative scheme that employs hierarchical modulation. This scheme is free from the rate loss and allows seamless cooperative communication.

Keywords: cooperative communication, hierarchical modulation, high data rate, transmission scheme

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: Gysele Lima Ricci, Jose Miguel Rodriguez Anton

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Companies in the tourism sector need to achieve competitive advantages for their survival in the market. In this way, the models based on association, cooperation, complementarity, distribution, exchange and mutual assistance arise as a possibility of organizational development, taking as reference the concept of networks. Many companies seek to partner in local networks as clusters to act together and associate. The main objective of the present research is to identify the specificities of management and the practices of cooperation in the tourist destination of São Paulo - Brazil, and to propose a new management model with possible cluster of tourism. The empirical analysis was carried out in three phases. As a first phase, a research was made by the companies, associations and tourism organizations existing in São Paulo, analyzing the characteristics of their business. In the second phase, the management specificities and cooperation practice used in the tourist destination. And in the third phase, identifying the possible strengths and weaknesses that potential or potential tourist cluster could have, proposing the development of the management model of the same adapted to the needs of the companies, associations and organizations. As a main result, it has been identified that companies, associations and organizations could be looking for synergies with each other and collaborate through a Hiperred organizational structure, in which they share their knowledge, try to make the most of the collaboration and to benefit from three concepts: flexibility, learning and collaboration. Finally, it is concluded that, the proposed tourism cluster management model is viable for the development of tourism destinations because it makes it possible to strategically address agents which are responsible for public policies, as well as public and private companies and organizations in their strategies competitiveness and cooperation.

Keywords: cluster, management model, networks, tourism sector

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Authors: Zhidong Zhang, Amanda Resendez

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This study is to examine how teachers’ perspective on education technology use in their class influence their students’ achievement. The authors hypothesized that teachers’ perspective can directly or indirectly influence students’ learning, performance, and achievements. In this study, a questionnaire entitled, Teacher’s Perspective on Educational Technology, was delivered to 63 teachers and 1268 students’ mathematics and reading achievement records were collected. The questionnaire consists of four parts: a) demographic variables, b) attitudes on technology integration, c) outside factor affecting technology integration, and d) technology use in the classroom. Kruskal-Wallis and hierarchical regression analysis techniques were used to examine: 1) the relationship between the demographic variables and teachers’ perspectives on educational technology, and 2) how the demographic variables were causally related to students’ mathematics and reading achievements. The study found that teacher demographics were significantly related to the teachers’ perspective on educational technology with p < 0.05 and p < 0.01 separately. These teacher demographical variables included the school district, age, gender, the grade currently teach, teaching experience, and proficiency using new technology. Further, these variables significantly predicted students’ mathematics and reading achievements with p < 0.05 and p < 0.01 separately. The variations of R² are between 0.176 and 0.467. That means 46.7% of the variance of a given analysis can be explained by the model.

Keywords: teacher's perception of technology use, mathematics achievement, reading achievement, Kruskal-Wallis test, hierarchical regression analysis

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Amine Ganibardi, Cherif Arab Ali

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This paper addresses the data cleaning issue as a part of web usage data preprocessing within the scope of Web Usage Mining. Weblog data recorded by web servers within log files reflect usage activity, i.e., End-users’ clicks and underlying user-agents’ hits. As Web Usage Mining is interested in End-users’ behavior, user-agents’ hits are referred to as noise to be cleaned-off before mining. Filtering hits from clicks is not trivial for two reasons, i.e., a server records requests interlaced in sequential order regardless of their source or type, website resources may be set up as requestable interchangeably by end-users and user-agents. The current methods are content-centric based on filtering heuristics of relevant/irrelevant items in terms of some cleaning attributes, i.e., website’s resources filetype extensions, website’s resources pointed by hyperlinks/URIs, http methods, user-agents, etc. These methods need exhaustive extra-weblog data and prior knowledge on the relevant and/or irrelevant items to be assumed as clicks or hits within the filtering heuristics. Such methods are not appropriate for dynamic/responsive Web for three reasons, i.e., resources may be set up to as clickable by end-users regardless of their type, website’s resources are indexed by frame names without filetype extensions, web contents are generated and cancelled differently from an end-user to another. In order to overcome these constraints, a clustering-based cleaning method centered on the logging structure is proposed. This method focuses on the statistical properties of the logging structure at the requested and referring resources attributes levels. It is insensitive to logging content and does not need extra-weblog data. The used statistical property takes on the structure of the generated logging feature by webpage requests in terms of clicks and hits. Since a webpage consists of its single URI and several components, these feature results in a single click to multiple hits ratio in terms of the requested and referring resources. Thus, the clustering-based method is meant to identify two clusters based on the application of the appropriate distance to the frequency matrix of the requested and referring resources levels. As the ratio clicks to hits is single to multiple, the clicks’ cluster is the smallest one in requests number. Hierarchical Agglomerative Clustering based on a pairwise distance (Gower) and average linkage has been applied to four logfiles of dynamic/responsive websites whose click to hits ratio range from 1/2 to 1/15. The optimal clustering set on the basis of average linkage and maximum inter-cluster inertia results always in two clusters. The evaluation of the smallest cluster referred to as clicks cluster under the terms of confusion matrix indicators results in 97% of true positive rate. The content-centric cleaning methods, i.e., conventional and advanced cleaning, resulted in a lower rate 91%. Thus, the proposed clustering-based cleaning outperforms the content-centric methods within dynamic and responsive web design without the need of any extra-weblog. Such an improvement in cleaning quality is likely to refine dependent analysis.

Keywords: clustering approach, data cleaning, data preprocessing, weblog data, web usage data

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Authors: Fangjing Zhou, Wen Chen, Brian J. Hall, Yu Wang, Carl Latkin, Li Ling, Joseph D. Tucker

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Background: Economic and social reforms designed to open China to the world has been successful, but also appear to have rapidly laid the foundation for the reemergence of STIs since 1980s. Changes in sexual behaviors, relationships, and norms among Chinese contributed to the STIs epidemic. As the massive population moved during the last 30 years, early coital debut, multiple sexual partnerships, and unprotected sex have increased within the general population. Our objectives were to assess associations between residences location, sexual risk behaviors and sexually transmitted infections (STIs) among adults living in Guangzhou, China. Methods: Stratified cluster sampling followed a two-step process was used to select populations aged 18-59 years in Guangzhou, China. Spatial methods including Geographic Information Systems (GIS) were utilized to identify 1400 coordinates with latitude and longitude. Face-to-face household interviews were conducted to collect self-report data on sexual risk behaviors and diagnosed STIs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of sexual risk behaviors and STIs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results: In this study, 1215 of 1400 households attempted surveys, with 368 refusals, resulting in a sample of 751 completed surveys. The prevalence of self-reported sexual risk behaviors was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STIs was 7.06%. Anal intercourse clustered in an area located along the border within the rural-urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou less than one year (p=0.007) overlapped this cluster. Excess cases for sex without a condom (p=0.031) overlapped the cluster for college students (p<0.001). Conclusions: Short-term migrants and college students reported greater sexual risk behaviors. Programs to increase safer sex within these communities to reduce the risk of STIs are warranted in Guangzhou. Spatial analysis identified geographical clusters of sexual risk behaviors, which is critical for optimizing surveillance and targeting control measures for these locations in the future.

Keywords: cluster analysis, migrant, sexual risk behaviors, spatial distribution

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Authors: Graham Hancox, Saydia Razak, Sue Hignett, Jo Barnes, Jyri Silmari, Florian Kading

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In the event of a Chemical, Biological, Radiological and Nuclear (CBRN) incident rapidly gaining, situational awareness is of paramount importance and advanced technologies have an important role to play in improving detection, identification, monitoring (DIM) and patient tracking. Understanding how these advanced technologies can fit into current response systems is essential to ensure they are optimally designed, usable and meet end-users’ needs. For this reason, Human Factors (Ergonomics) methods have been used within an EU Horizon 2020 project (TOXI-Triage) to firstly describe (map) the hierarchical structure in a CBRN response with adapted Accident Map (AcciMap) methodology. Secondly, Hierarchical Task Analysis (HTA) has been used to describe and review the sequence of steps (sub-tasks) in a CBRN scenario response as a task system. HTA methodology was then used to map one advanced technology, ‘Tag and Trace’, which tags an element (people, sample and equipment) with a Near Field Communication (NFC) chip in the Hot Zone to allow tracing of (monitoring), for example casualty progress through the response. This HTA mapping of the Tag and Trace system showed how the provider envisaged the technology being used, allowing for review and fit with the current CBRN response systems. These methodologies have been found to be very effective in promoting and supporting a dialogue between end-users and technology providers. The Human Factors methods have given clear diagrammatic (visual) representations of how providers see their technology being used and how end users would actually use it in the field; allowing for a more user centered approach to the design process. For CBRN events usability is critical as sub-optimum design of technology could add to a responders’ workload in what is already a chaotic, ambiguous and safety critical environment.

Keywords: AcciMap, CBRN, ergonomics, hierarchical task analysis, human factors

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Authors: Jo-Ann Müller

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The integration-responsiveness (IR) framework presents four distinct internationalization strategies which differ depending on the extent of pressure the company faces for local responsiveness and global integration. This study applies the framework to standards by examining differences in the relative importance of three types of standards depending on the role the subsidiary plays within the corporate group. Hypotheses are tested empirically in a two-stage procedure. First, the subsidiaries are grouped performing cluster analysis. In the second step, the relationship between cluster affiliation and subsidiary strategy is tested using multinomial Probit estimation. While the level of local responsiveness of a firm relates to the relative importance of national and international formal standards, the degree of vertical integration is associated with the application of internal company.

Keywords: FDI, firm-level data, standards, subsidiary strategy

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Authors: Alex Rodriguez, Adam Guerrero

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Purpose: This study is designed to examine the institutions that comprise the Online Consortium of Independent Colleges and Universities (OCICU) to understand better the types of higher education institutions that comprise their membership. The literature on this topic is extensive in analyzing the current economic environment around higher education, which is largely considered to be negative for independent, tuition-driven institutions, and is forcing colleges and universities to reexamine how the college-attending population defines value and how institutions can best utilize their existing resources (and those of other institutions) to meet that value expectation. The results from this analysis are intended to give OCICU the ability to target their current customer base better, based on their most notable differences, and other institutions to see how to best approach consolidation within higher education. Design/Methodology: This study utilized k-means cluster analysis in order to explore the possibility that different segments exist within the seventy-one colleges and universities that have comprised OCICU. It analyzed fifty different variables, whose selection was based on the previous literature, collected by the Integrated Postsecondary Education Data System (IPEDS), whose data is self-reported by individual institutions. Findings: OCICU member institutions are partitioned into two clusters: "access institutions" and "conventional institutions” based largely on the student profile they target. Value: The methodology of the study is relatively unique as there are not many studies within the field of higher education marketing that have employed cluster analysis, and this type of analysis has never been conducted on OCICU members, specifically, or that of any higher education consolidated offering. OCICU can use the findings of this study to obtain a better grasp as to the specific needs of the two market segments OCICU currently serves and develop measurable marketing programs around how those segments are defined that communicate the value sought by current and potential OCICU members or those of similar institutions. Other consolidation efforts within higher education can also employ the same methodology to determine their own market segments.

Keywords: Consolidation, Colleges, Enrollment, Higher Education, Marketing, Strategy, Universities

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Hadush Kidane Meresa

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The hydrological extremes frequency analysis is the foundation for the hydraulic engineering design, flood protection, drought management and water resources management and planning to utilize the available water resource to meet the desired objectives of different organizations and sectors in a country. This spatial variation of the statistical characteristics of the extreme flood and drought events are key practice for regional flood and drought analysis and mitigation management. For different hydro-climate of the regions, where the data set is short, scarcity, poor quality and insufficient, the regionalization methods are applied to transfer at-site data to a region. This study aims in regional high and low flow frequency analysis for Poland River Basins. Due to high frequent occurring of hydrological extremes in the region and rapid water resources development in this basin have caused serious concerns over the flood and drought magnitude and frequencies of the river in Poland. The magnitude and frequency result of high and low flows in the basin is needed for flood and drought planning, management and protection at present and future. Hydrological homogeneous high and low flow regions are formed by the cluster analysis of site characteristics, using the hierarchical and C- mean clustering and PCA method. Statistical tests for regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In compliance with results of the tests, the region river basin has been divided into ten homogeneous regions. In this study, frequency analysis of high and low flows using AM for high flow and 7-day minimum low flow series is conducted using six statistical distributions. The use of L-moment and LL-moment method showed a homogeneous region over entire province with Generalized logistic (GLOG), Generalized extreme value (GEV), Pearson type III (P-III), Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions as the regional drought and flood frequency distributions. The 95% percentile and Flow duration curves of 1, 7, 10, 30 days have been plotted for 10 stations. However, the cluster analysis performed two regions in west and east of the province where L-moment and LL-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The spatial variation and regional frequency distribution of flood and drought characteristics for 10 best catchment from the whole region was selected and beside the main variable (streamflow: high and low) we used variables which are more related to physiographic and drainage characteristics for identify and delineate homogeneous pools and to derive best regression models for ungauged sites. Those are mean annual rainfall, seasonal flow, average slope, NDVI, aspect, flow length, flow direction, maximum soil moisture, elevation, and drainage order. The regional high-flow or low-flow relationship among one streamflow characteristics with (AM or 7-day mean annual low flows) some basin characteristics is developed using Generalized Linear Mixed Model (GLMM) and Generalized Least Square (GLS) regression model, providing a simple and effective method for estimation of flood and drought of desired return periods for ungauged catchments.

Keywords: flood , drought, frequency, magnitude, regionalization, stochastic, ungauged, Poland

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Authors: V. Paployan, K. Madoyan, A. Melikyan, H. Minassian

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Resonant two-photon ionization (TPI) is a valuable technique for the study of clusters due to its ultrahigh sensitivity. The comparison of the observed TPI spectra with results of calculations allows to deduce important information on the shape, rotational and vibrational temperatures of the clusters with high accuracy. In this communication we calculate the TPI cross-section for pump-probe scheme in Ag neutral cluster. The pump photon energy is chosen to be close to the surface plasmon (SP) energy of cluster in dielectric media. Since the interband transition energy in Ag exceeds the SP resonance energy, the main contribution into the TPI comes from the latter. The calculations are performed by separating the coordinates of electrons corresponding to the collective oscillations and the individual motion that allows to take into account the resonance contribution of excited SP oscillations. It is shown that the ionization cross section increases by two orders of magnitude if the energy of the pump photon matches the surface plasmon energy in the cluster.

Keywords: resonance enhancement, silver clusters, surface plasmon, two-photon ionization

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Authors: H. E. Elzain, S. Y. Chung, V. Senapathi, Kye-Hun Park

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Groundwater is considered a significant source for drinking and irrigation purposes in Miryang city, and it is attributed to a limited number of a surface water reservoirs and high seasonal variations in precipitation. Population growth in addition to the expansion of agricultural land uses and industrial development may affect the quality and management of groundwater. This research utilized multidisciplinary approaches of geostatistics such as multivariate statistics, factor analysis, cluster analysis and kriging technique in order to identify the hydrogeochemical process and characterizing the control factors of the groundwater geochemistry distribution for developing risk maps, exploiting data obtained from chemical investigation of groundwater samples under the area of study. A total of 79 samples have been collected and analyzed using atomic absorption spectrometer (AAS) for major and trace elements. Chemical maps using 2-D spatial Geographic Information System (GIS) of groundwater provided a powerful tool for detecting the possible potential sites of groundwater that involve the threat of contamination. GIS computer based map exhibited that the higher rate of contamination observed in the central and southern area with relatively less extent in the northern and southwestern parts. It could be attributed to the effect of irrigation, residual saline water, municipal sewage and livestock wastes. At wells elevation over than 85m, the scatter diagram represents that the groundwater of the research area was mainly influenced by saline water and NO3. Level of pH measurement revealed low acidic condition due to dissolved atmospheric CO2 in the soil, while the saline water had a major impact on the higher values of TDS and EC. Based on the cluster analysis results, the groundwater has been categorized into three group includes the CaHCO3 type of the fresh water, NaHCO3 type slightly influenced by sea water and Ca-Cl, Na-Cl types which are heavily affected by saline water. The most predominant water type was CaHCO3 in the study area. Contamination sources and chemical characteristics were identified from factor analysis interrelationship and cluster analysis. The chemical elements that belong to factor 1 analysis were related to the effect of sea water while the elements of factor 2 associated with agricultural fertilizers. The degree level, distribution, and location of groundwater contamination have been generated by using Kriging methods. Thus, geostatistics model provided more accurate results for identifying the source of contamination and evaluating the groundwater quality. GIS was also a creative tool to visualize and analyze the issues affecting water quality in the Miryang city.

Keywords: groundwater characteristics, GIS chemical maps, factor analysis, cluster analysis, Kriging techniques

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Authors: Muhammad Nouman Qureshi, Muhammad Hanif

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Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper.

Keywords: auxiliary information, adaptive cluster sampling, clustered populations, Hansen-Hurwitz estimation

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Authors: Agnieszka Feliczak-Guzik, Paulina Szczyglewska, Izabela Nowak

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A catalyst-assisted oxidation reaction is one of the key reactions exploited by various industries. Their conductivity yields essential compounds and intermediates, such as alcohols, epoxides, aldehydes, ketones, and organic acids. Researchers are devoting more and more attention to developing active and selective materials that find application in many catalytic reactions, such as cyclohexene epoxidation. This reaction yields 1,2-epoxycyclohexane and 1,2-diols as the main products. These compounds are widely used as intermediates in the perfume industry and synthesizing drugs and lubricants. Hence, our research aimed to use hierarchical zeolites modified with transition metal ions, e.g., Nb, V, and Ta, in the epoxidation reaction of cyclohexene using microwaveheating. Hierarchical zeolites are materials with secondary porosity, mainly in the mesoporous range, compared to microporous zeolites. In the course of the research, materials based on two commercial zeolites, with Faujasite (FAU) and Zeolite Socony Mobil-5 (ZSM-5) structures, were synthesized and characterized by various techniques, such as X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and low-temperature nitrogen adsorption/desorption isotherms. The materials obtained were then used in a cyclohexene epoxidation reaction, which was carried out as follows: catalyst (0.02 g), cyclohexene (0.1 cm3), acetonitrile (5 cm3) and dihydrogen peroxide (0.085 cm3) were placed in a suitable glass reaction vessel with a magnetic stirrer inside in a microwave reactor. Reactions were carried out at 45° C for 6 h (samples were taken every 1 h). The reaction mixtures were filtered to separate the liquid products from the solid catalyst and then transferred to 1.5 cm3 vials for chromatographic analysis. The test techniques confirmed the acquisition of additional secondary porosity while preserving the structure of the commercial zeolite (XRD and low-temperature nitrogen adsorption/desorption isotherms). The results of the activity of the hierarchical catalyst modified with niobium in the cyclohexene epoxidation reaction indicate that the conversion of cyclohexene, after 6 h of running the process, is about 70%. As the main product of the reaction, 2-cyclohexanediol was obtained (selectivity > 80%). In addition to the mentioned product, adipic acid, cyclohexanol, cyclohex-2-en-1-one, and 1,2-epoxycyclohexane were also obtained. Furthermore, in a blank test, no cyclohexene conversion was obtained after 6 h of reaction. Acknowledgments The work was carried out within the project “Advanced biocomposites for tomorrow’s economy BIOG-NET,” funded by the Foundation for Polish Science from the European Regional Development Fund (POIR.04.04.00-00-1792/18-00.

Keywords: epoxidation, oxidation reactions, hierarchical zeolites, synthesis

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Authors: Maciej Szeląg, Stanisław Fic

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The impact of elevated temperatures on the construction materials manifests in change of their physical and mechanical characteristics. Stresses and thermal deformations that occur inside the volume of the material cause its progressive degradation as temperature increase. Finally, the reactions and transformations of multiphase structure of cementitious composite cause its complete destruction. A particularly dangerous phenomenon is the impact of thermal shock – a sudden high temperature load. The thermal shock leads to a high value of the temperature gradient between the outer surface and the interior of the element in a relatively short time. The result of mentioned above process is the formation of the cracks and scratches on the material’s surface and inside the material. The article describes the use of computer image analysis techniques to identify and assess the structure of the cluster cracks on the surfaces of modified cement pastes, caused by thermal shock. Four series of specimens were tested. Two Portland cements were used (CEM I 42.5R and CEM I 52,5R). In addition, two of the series contained metakaolinite as a replacement for 10% of the cement content. Samples in each series were made in combination of three w/b (water/binder) indicators of respectively 0.4; 0.5; 0.6. Surface cracks of the samples were created by a sudden temperature load at 200°C for 4 hours. Images of the cracked surfaces were obtained via scanning at 1200 DPI; digital processing and measurements were performed using ImageJ v. 1.46r software. In order to examine the cracked surface of the cement paste as a system of closed clusters – the dispersal systems theory was used to describe the structure of cement paste. Water is used as the dispersing phase, and the binder is used as the dispersed phase – which is the initial stage of cement paste structure creation. A cluster itself is considered to be the area on the specimen surface that is limited by cracks (created by sudden temperature loading) or by the edge of the sample. To describe the structure of cracks two stereological parameters were proposed: A ̅ – the cluster average area, L ̅ – the cluster average perimeter. The goal of this study was to compare the investigated stereological parameters with the mechanical properties of the tested specimens. Compressive and tensile strength testes were carried out according to EN standards. The method used in the study allowed the quantitative determination of defects occurring in the examined modified cement pastes surfaces. Based on the results, it was found that the nature of the cracks depends mainly on the physical parameters of the cement and the intermolecular interactions on the dispersal environment. Additionally, it was noted that the A ̅/L ̅ relation of created clusters can be described as one function for all tested samples. This fact testifies about the constant geometry of the thermal cracks regardless of the presence of metakaolinite, the type of cement and the w/b ratio.

Keywords: cement paste, cluster cracks, elevated temperature, image analysis, metakaolinite, stereological parameters

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Authors: Mohammad Rahim Rahnama, Lia Shaddel

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Today, along with the development of urbanism, its negative consequences on the health of citizens are emerging. Mental disorders are common in the big cities, while mental health enables individuals to become active citizens. Meanwhile, women have a larger share of mental problems. Depression and anxiety disorders have a higher prevalence rate among women and these disorders affect the health of future generations, too. Therefore, improving women’s mental health through the potentials offered by urban spaces are of paramount importance. The present study aims to first, evaluate the spatial autocorrelation of women’s mental health and walkable spaces and then present solutions, based on the findings, to improve the walkability index. To determine the spatial distribution of women’s mental health in Mashhad, Moran's I was used and 1000 questionnaire were handed out in various sub-districts of Mashhad. Moran's I was calculated to be 0.18 which indicates a cluster distribution pattern. The walkability index was calculated using the four variables pertaining to the length of walkable routes, mixed land use, retail floor area ratio, and household density. To determine spatial autocorrelation of mental health and the walkability index, bivariate Moran’s I was calculated. Moran's I was determined to be 0.37 which shows a direct spatial relationship between variables; 4 clusters in 9 sub-districts of Mashhad were created. In High-Low cluster, there was a negative spatial relationship and hence, to identify factors affecting walkability in urban spaces semi-structures interviews were conducted with 21 women in this cluster. The findings revealed that security is the major factor influencing women’s walking behavior in this cluster. In accordance with the findings, some suggestions are offered to improve the presence of women in this sub-district.

Keywords: Mashhad, spatial autocorrelation, women’s mental health, walkability index

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Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

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The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

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Authors: Damtew Samson Zerihun

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This paper primarily describes the concept of a hierarchical safety control (HSC) in discrete manufacturing to up-hold productivity with human intervention and machine failures using a systematic approach, through increasing the system availability and using additional knowledge on machines so as to improve the human machine collaboration (HMC). It also highlights the implemented PLC safety algorithm, in applying this generic concept to a concrete pro-duction line using a lab demonstrator called FATIE (Factory Automation Test and Integration Environment). Furthermore, the paper describes a model and provide a systematic representation of human-machine collabora-tion in discrete manufacturing and to this end, the Hierarchical Safety Control concept is proposed. This offers a ge-neric description of human-machine collaboration based on Finite State Machines (FSM) that can be applied to vari-ous discrete manufacturing lines instead of using ad-hoc solutions for each line. With its reusability, flexibility, and extendibility, the Hierarchical Safety Control scheme allows upholding productivity while maintaining safety with reduced engineering effort compared to existing solutions. The approach to the solution begins with a successful partitioning of different zones around the Integrated Manufacturing System (IMS), which are defined by operator tasks and the risk assessment, used to describe the location of the human operator and thus to identify the related po-tential hazards and trigger the corresponding safety functions to mitigate it. This includes selective reduced speed zones and stop zones, and in addition with the hierarchical safety control scheme and advanced safety functions such as safe standstill and safe reduced speed are used to achieve the main goals in improving the safe Human Ma-chine Collaboration and increasing the productivity. In a sample scenarios, It is shown that an increase of productivity in the order of 2.5% is already possible with a hi-erarchical safety control, which consequently under a given assumptions, a total sum of 213 € could be saved for each intervention, compared to a protective stop reaction. Thereby the loss is reduced by 22.8%, if occasional haz-ard can be refined in a hierarchical way. Furthermore, production downtime due to temporary unavailability of safety devices can be avoided with safety failover that can save millions per year. Moreover, the paper highlights the proof of the development, implementation and application of the concept on the lab demonstrator (FATIE), where it is realized on the new safety PLCs, Drive Units, HMI as well as Safety devices in addition to the main components of the IMS.

Keywords: discrete automation, hierarchical safety controller, human machine collaboration, programmable logical controller

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Authors: Chao Hu, Xinwen Liao, Qing-Hua Qin, Gang Wang

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The hierarchical carbon nanotube (CNT)/carbon fiber (CF)/high density polyethylene (HDPE) was fabricated via compound extrusion and injection molding, in which to author’s best knowledge CNT was employed as a nano-coatings on the surface of CF for the first time by spray coating technique. The CNT coatings relative to CF was set at 1 wt% and the CF content relative to the composites varied from 0 to 25 wt% to study the influence of CNT coatings and CF contents on the mechanical, thermal and morphological performance of this hierarchical composites. The results showed that with the rise of CF contents, the mechanical properties, including the tensile properties, flexural properties, and hardness of CNT/CF/HDPE composites, were effectively improved. Furthermore, the CNT-coated composites showed overall higher mechanical performance than the uncoated counterparts. It can be ascribed to the enhancement of interfacial bonding between the CF and HDPE via the incorporation of CNT, which was demonstrated by the scanning electron microscopy observation. Meanwhile, the differential scanning calorimetry data indicated that by the introduction of CNT and CF, the crystallization temperature and crystallinity of HDPE were affected while the melting temperature did not have an obvious alteration.

Keywords: carbon fibers, carbon nanotubes, extrusion, high density polyethylene

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Authors: Prittesh Patel, Ramar Krishnamurthy

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In the present study, nine strains of C. falcatum obtained from different places and cultivars were characterized for sporulation, growth rate, and 18S rRNA gene sequence. All isolates had characteristic fast-growing sparse and fleecy aerial mycelia on potato dextrose agar with sickle shape conidia (length x width: varied from 20.0 X 3.89 to 25.52 X 5.34 μm) and blackish to orange acervuli with setae (length x width: varied from 112.37X 2.78 to 167.66 X 6.73 μm). They could be divided into two groups on the base of morphology; P1, dense mycelia with concentric growth and P2, sparse mycelia with uneven growth. Genomic DNA isolation followed by PCR amplification with ITS1 and ITS4 primer produced ~550bp amplicons for all isolates. Phylogeny generated by 18S rRNA gene sequence confirmed the variation in isolates and mainly grouped into two clusters; cluster 1 contained CoC671 isolates (cfNAV and cfPAR) and Co86002 isolate (cfTIM). Other isolates cfMAD, cfKAM, and cfMAR were grouped into cluster 2. Remaining isolates did not fall into any cluster. Isolate cfGAN, collected from Co86032 was found highly diverse of all the nine isolates. In a nutshell, we found considerable genetic divergence and morphological variation within C. falcatum accessions collected from different areas of south Gujarat, India and these can be used for the breeding program.

Keywords: Colletotrichum falcatum, ITS, morphology, red rot, sugarcane

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: Mst. Tuhina-Khatun, Mohamed Hanafi Musa, Mohd Rafii Yosup, Wong Mui Yun, Md. Aktar-Uz-Zaman, Mahbod Sahebi

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Rice is a staple crop of economic importance of most Asian people, and blast is the major constraints for its higher yield. Heritability of plants traits helps plant breeders to make an appropriate selection and to assess the magnitude of genetic improvement through hybridization. Diversity of crop plants is necessary to manage the continuing genetic erosion and address the issues of genetic conservation for successfully meet the future food requirements. Therefore, an experiment was conducted to estimate heritability and to determine the diversity of 27 blast resistant upland rice genotypes based on 18 quantitative traits using randomized complete block design. Heritability value was found to vary from 38 to 93%. The lowest heritability belonged to the character total number of tillers/plant (38%). In contrast, number of filled grains/panicle, and yield/plant (g) was recorded for their highest heritability value viz. 93 and 91% correspondingly. Cluster analysis based on 18 traits grouped 27 rice genotypes into six clusters. Cluster I was the biggest, which comprised 17 genotypes, accounted for about 62.96% of total population. The multivariate analysis suggested that the genotype ‘Chokoto 14’ could be hybridized with ‘IR 5533-55-1-11’ and ‘IR 5533-PP 854-1’ for broadening the gene pool of blast resistant upland rice germplasms for yield and other favorable characters.

Keywords: blast resistant, diversity analysis, heritability, upland rice

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: Alexander Denzler, Marcel Wehrle, Andreas Meier

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A knowledge base stores facts and rules about the world that applications can use for the purpose of reasoning. By applying the concept of granular computing to a knowledge base, several advantages emerge. These can be harnessed by applications to improve their capabilities and performance. In this paper, the concept behind such a construct, called a granular knowledge cube, is defined, and its intended use as an instrument that manages to cope with different data types and detect knowledge domains is elaborated. Furthermore, the underlying architecture, consisting of the three layers of the storing, representing, and structuring of knowledge, is described. Finally, benefits as well as challenges of deploying it are listed alongside application types that could profit from having such an enhanced knowledge base.

Keywords: granular computing, granular knowledge, hierarchical structuring, knowledge bases

Procedia PDF Downloads 465