Search results for: cluster of processors (COPs)

Authors: Yu-Ting Tsai, Chiun-Chieh Hsu, Yu-Chun Chu

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In recent years, due to the development of wireless network technology, many researchers have devoted to the study of wireless sensor networks. The applications of wireless sensor network mainly use the sensor nodes to collect the required information, and send the information back to the users. Since the sensed area is difficult to reach, there are many restrictions on the design of the sensor nodes, where the most important restriction is the limited energy of sensor nodes. Because of the limited energy, researchers proposed a number of ways to reduce energy consumption and balance the load of sensor nodes in order to increase the network lifetime. In this paper, we proposed the Energy-Balanced Clustering method with Auxiliary Members on Wireless Sensor Networks（EBCAM）based on the cluster routing. The main purpose is to balance the energy consumption on the sensed area and average the distribution of dead nodes in order to avoid excessive energy consumption because of the increasing in transmission distance. In addition, we use the residual energy and average energy consumption of the nodes within the cluster to choose the cluster heads, use the multi hop transmission method to deliver the data, and dynamically adjust the transmission radius according to the load conditions. Finally, we use the auxiliary cluster members to change the delivering path according to the residual energy of the cluster head in order to its load. Finally, we compare the proposed method with the related algorithms via simulated experiments and then analyze the results. It reveals that the proposed method outperforms other algorithms in the numbers of used rounds and the average energy consumption.

Keywords: auxiliary nodes, cluster, load balance, routing algorithm, wireless sensor network

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Authors: Vivek Anand, Rainer Naegele

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Industry 4.0 is a global mega-trend revolutionizing the world of advanced manufacturing, but also bringing up challenges for SMEs. In response, many regional, as well as digital Industry 4.0 Platforms, have been set up to boost the competencies of established enterprises as well as SMEs. The concept of 'Clusters' is a policy tool that aims to be a starting point to establish sustainable and self-supporting structures in industries of a region by identifying competencies and supporting cluster actors with services that match their growth needs. This paper is motivated by the idea that Clusters have the potential to enable firms, particularly SMEs, to accelerate the innovation process and transition to digital technologies. In this research, the efficacy of Industry 4.0 platforms as Cluster ecosystems is evaluated, especially for SMEs. Focusing on the Baden Wurttemberg region in Germany, an action research method is employed to study how SMEs leverage other actors on Industry 4.0 Platforms to further their Industry 4.0 journeys. The aim is to evaluate how such Industry 4.0 platforms stimulate innovation, cooperation and competitiveness. Additionally, the barriers to these platforms fulfilling their promise to serve as capacity building cluster ecosystems for SMEs in a region will also be identified. The findings will be helpful for academicians and policymakers alike, who can leverage a ‘cluster policy’ to enable Industry 4.0 ecosystems in their regions. Furthermore, relevant management and policy implications stem from the analysis. This will also be of interest to the various players in a cluster ecosystem - like SMEs and service providers - who benefit from the cooperation and competition. The paper will improve the understanding of how a dialogue orientation, a bottom-up approach and active integration of all involved cluster actors enhance the potential of Industry 4.0 Platforms. A strong collaborative culture is a key driver of digital transformation and technology adoption across sectors, value chains and supply chains; and will position Industry 4.0 Platforms at the forefront of the industrial renaissance. Motivated by this argument and based on the results of the qualitative research, a roadmap will be proposed to position Industry 4.0 Platforms as effective clusters ecosystems to support Industry 4.0 adoption in a region.

Keywords: cluster policy, digital transformation, industry 4.0, innovation clusters, innovation policy, SMEs and startups

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Authors: Şehri Çınar, Aydın Akın

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This study was conducted Razakı grape variety (Vitis vinifera L.) and its vine which was aged 19 was grown on 5 BB rootstock in a vegetation period of 2014 in Afyon province in Turkey. In this research, it was investigated whether the applications of Control (C), 1/3 Cluster Tip Reduction (1/3 CTR), Shoot Tip Reduction (STR), 1/3 CTR + STR, Boric Acid (BA), 1/3 CTR + BA, STR + BA, 1/3 CTR + STR + BA on yield and yield components of Razakı grape variety. The results were obtained as the highest fresh grape yield (7.74 kg/vine) with C application, as the highest cluster weight (244.62 g) with STR application, as the highest 100 berry weight (504.08 g) with C application, as the highest maturity index (36.89) with BA application, as the highest must yield (695.00 ml) with BA and (695.00 ml) with 1/3 CTR + STR + BA applications, as the highest intensity of L* color (46.93) with STR and (46.10) with 1/3 CTR + STR + BA applications, as the highest intensity of a* color (-5.37) with 1/3 CTR + STR and (-5.01) with STR, as the highest intensity of b* color (12.59) with STR application. The shoot tip reduction to increase cluster weight and boric acid application to increase maturity index of Razakı grape variety can be recommended.

Keywords: razakı, 1/3 cluster tip reduction, shoot tip reduction, boric acid, yield and yield components

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

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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: I Dewa Gede Arya Putra

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Principal Component Analysis (PCA) is a mathematical procedure that uses orthogonal transformation techniques to change a set of data with components that may be related become components that are not related to each other. This can have an impact on clustering wind speed characteristics in Indonesia. This study uses data daily wind speed observations of the Site Meteorological Station network for 30 years. Multicollinearity tests were also performed on all of these data before doing clustering with PCA. The results show that the four main components have a total diversity of above 80% which will be used for clusters. Division of clusters using Ward's method obtained 3 types of clusters. Cluster 1 covers the central part of Sumatra Island, northern Kalimantan, northern Sulawesi, and northern Maluku with the climatological pattern of wind speed that does not have an annual cycle and a weak speed throughout the year with a low-speed ranging from 0 to 1,5 m/s². Cluster 2 covers the northern part of Sumatra Island, South Sulawesi, Bali, northern Papua with the climatological pattern conditions of wind speed that have annual cycle variations with low speeds ranging from 1 to 3 m/s². Cluster 3 covers the eastern part of Java Island, the Southeast Nusa Islands, and the southern Maluku Islands with the climatological pattern of wind speed conditions that have annual cycle variations with high speeds ranging from 1 to 4.5 m/s².

Keywords: PCA, cluster, Ward's method, wind speed

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Authors: Henry Ataburo, Dominic Essuman, Emmanuel Kwabena Anin

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Recent trends of catastrophic events, such as the Covid-19 pandemic, the Suez Canal blockage, the Russia-Ukraine conflict, the Israel-Hamas conflict, and the climate change crisis, continue to devastate supply chains and the broader society. Prior authors have advocated for a simultaneous pursuit of resilience and sustainability as crucial for navigating these challenges. Nevertheless, the relationship between resilience and sustainability is a rather complex one: resilience and sustainability are considered unrelated, substitutes, or complements. Scholars also suggest that different firms prioritize resilience and sustainability differently for varied strategic reasons. However, we know little about whether, how, and when these choices produce different typologies of firms to explain differences in financial and market performance outcomes. This research draws inferences from the systems configuration approach to organizational fit to contend that a taxonomy of firms may emerge based on how firms configure resilience and environmental sustainability. The study further examines the effects of these taxonomies on financial and market performance in differing transportation disruption conditions. Resilience is operationalized as a firm’s ability to adjust current operations, structure, knowledge, and resources in response to disruptions, whereas environmental sustainability is operationalized as the extent to which a firm deploys resources judiciously and keeps the ecological impact of its operations to the barest minimum. Using primary data from 199 firms in Ghana and cluster analysis as an analytical tool, the study identifies four clusters of firms based on how they prioritize resilience and sustainability: Cluster 1 - "strong, moderate resilience, high sustainability firms," Cluster 2 - "sigh resilience, high sustainability firms," Cluster 3 - "high resilience, strong, moderate sustainability firms," and Cluster 4 - "weak, moderate resilience, strong, moderate sustainability firms". In addition, ANOVA and regression analysis revealed the following findings: Only clusters 1 and 2 were significantly associated with both market and financial performance. Under high transportation disruption conditions, cluster 1 firms excel better in market performance, whereas cluster 2 firms excel better in financial performance. Conversely, under low transportation disruption conditions, cluster 1 firms excel better in financial performance, whereas cluster 2 firms excel better in market performance. The study provides theoretical and empirical evidence of how resilience and environmental sustainability can be configured to achieve specific performance objectives under different disruption conditions.

Keywords: resilience, environmental sustainability, developing economy, transportation disruption

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Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

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his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

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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: 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: Collen Kawiya

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In an effort of reducing post-harvest losses and improving the supply of quality fish products in Malawi, the fish solar tent dryers have been designed in the southern part of Lake Malawi for processing small fish species under the project of Cultivate Africa’s Future (CultiAF). This study was done to promote the adoption of the fish solar tent dryers by the many small scale fish processors in Malawi through the assessment of the economic viability of these dryers. With the use of the project’s baseline survey data, a business model for a constructed ‘ready for use’ solar tent dryer was developed where investment appraisal techniques were calculated in addition with the sensitivity analysis. The study also conducted a risk analysis through the use of the Monte Carlo simulation technique and a probabilistic net present value was found. The investment appraisal results showed that the net present value was US$8,756.85, the internal rate of return was 62% higher than the 16.32% cost of capital and the payback period was 1.64 years. The sensitivity analysis results showed that only two input variables influenced the fish solar dryer investment’s net present value. These are the dried fish selling prices that were correlating positively with the net present value and the fresh fish buying prices that were negatively correlating with the net present value. Risk analysis results showed that the chances that fish processors will make a loss from this type of investment are 17.56%. It was also observed that there exist only a 0.20 probability of experiencing a negative net present value from this type of investment. Lastly, the study found that the net present value of the fish solar tent dryer’s investment is still robust in spite of any changes in the levels of investors risk preferences. With these results, it is concluded that the fish solar tent dryers in Malawi are an economically viable investment because they are able to improve the returns in the fish processing activity. As such, fish processors need to adopt them by investing their money to construct and use them.

Keywords: investment appraisal, risk analysis, sensitivity analysis, solar tent drying

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Authors: Faith Eweluegim Enahoro-Ofagbe, Alika Eke Joseph

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Information on genetic variation within a population is crucial for utilizing heterozygosity for breeding programs that aim to improve crop species. The study was conducted to ascertain the genotypic similarities among twelve sugarcane (Saccharum officinarum) varieties to group them for purposes of hybridizations for cane yield improvement. The experiment was conducted at the University of Benin, Faculty of Agriculture Teaching and Research Farm, Benin City. Twelve sugarcane varieties obtained from National Cereals Research Institute, Badeggi, Niger State, Nigeria, were planted in three replications in a randomized complete block design. Each variety was planted on a five-row plot of 5.0 m in length. Data were collected on 12 agronomic traits, including; the number of millable cane, cane girth, internode length, number of male and female flowers (fuss), days to flag leaf, days to flowering, brix%, cane yield, and others. There were significant differences, according to the findings among the twelve genotypes for the number of days to flag leaf, number of male and female flowers (fuss), and cane yield. The relationship between the twelve sugarcane varieties was expressed using hierarchical cluster analysis. The twelve genotypes were grouped into three major clusters based on hierarchical classification. Cluster I had five genotypes, cluster II had four, and cluster III had three. Cluster III was dominated by varieties characterized by higher cane yield, number of leaves, internode length, brix%, number of millable stalks, stalk/stool, cane girth, and cane length. Cluster II contained genotypes with early maturity characteristics, such as early flowering, early flag leaf development, growth rate, and the number of female and male flowers (fuss). The maximum inter-cluster distance between clusters III and I indicated higher genetic diversity between the two groups. Hybridization between the two groups could result in transgressive recombinants for agronomically important traits.

Keywords: sugarcane, Saccharum officinarum, genotype, cluster analysis, principal components analysis

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Authors: Olusola Joseph Adesina, Sunmaila Oyetunji Raimi, Olufemi Akinloye Bolaji, Abiodun Ezekiel Adesina

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The quality of education and the standard of a nation cannot rise above the quality of the teacher (NPE, 2004). Efforts at improving the falling standard of education in the country call for the need-based assessment of the primary tier of education in Nigeria. It was revealed that the teachers’ standard of performance and pupils’ achievement was below average. Teachers’ cluster meeting intervention was therefore recommended as a step towards enhancing the teachers’ professional competency, efficient and effective proactive and interactive lesson presentation. The study thus determined the impact of the intervention on teachers’ professional performance (lesson note preparation and teaching performance) in Oyo State, Nigeria. The main and interaction effects of the gender of the teachers as moderator variable were also determined. Three null hypotheses guided the study. Pre-test, posttest control group quazi experimental design was adopted for the study. Three hundred intact classes from three hundred different schools were randomly selected into treatment and control groups. Two response instruments-Classroom Lesson Note Preparation Checklist (CLNPC; r = 0.89) Cluster Lesson Observation Checklist (CLOC; r = 0.86) were used for data collection. Mean, Standard deviation and Analysis of Covariance (ANCOVA) were used to analyse the collected data. The results showed that the teachers’ cluster meeting have significant impact on teachers’ lesson note preparation (F(1,295) = 31.607; p < 0.05; η2 = .097) and teaching performance (F(1,295) = 20.849; p < 0.05; η2 = .066) in the core subjects of primary schools in Oyo State, Nigeria. The study therefore recommended among others that teachers’ cluster meeting should be sustained for teachers’ professional development in the State.

Keywords: teachers’ cluster meeting, teacher lesson note preparation, teaching performance, teachers’ gender, primary schools in Oyo state

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Authors: Taekyun Hur, Yoonyoung Kim, Junkyu Lim

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Previous works on leisure activities had been categorizing activities arbitrarily and subjectively while focusing on a single dimension (e.g. active-passive, individual-group). To overcome these problems, this study proposed a Korean leisure activities’ matrix model that considered multidimensional features of leisure activities, which was comprised of 3 main factors and 6 sub factors: (a) Active (physical, mental), (b) Relational (quantity, quality), (c) Expert (entry barrier, possibility of improving). We developed items for measuring the degree of each dimension for every leisure activity. Using the developed Leisure Activities Dimensions (LAD) questionnaire, we investigated the presented dimensions of a total of 78 leisure activities which had been enjoyed by most Koreans recently (e.g. watching movie, taking a walk, watching media). The study sample consisted of 1348 people (726 men, 658 women) ranging in age from teenagers to elderlies in their seventies. This study gathered 60 data for each leisure activity, a total of 4860 data, which were used for statistical analysis. First, this study compared 3-factor model (Activity, Relation, Expertise) fit with 6-factor model (physical activity, mental activity, relational quantity, relational quality, entry barrier, possibility of improving) fit by using confirmatory factor analysis. Based on several goodness-of-fit indicators, the 6-factor model for leisure activities was a better fit for the data. This result indicates that it is adequate to take account of enough dimensions of leisure activities (6-dimensions in our study) to specifically apprehend each leisure attributes. In addition, the 78 leisure activities were cluster-analyzed with the scores calculated based on the 6-factor model, which resulted in 8 leisure activity groups. Cluster 1 (e.g. group sports, group musical activity) and Cluster 5 (e.g. individual sports) had generally higher scores on all dimensions than others, but Cluster 5 had lower relational quantity than Cluster 1. In contrast, Cluster 3 (e.g. SNS, shopping) and Cluster 6 (e.g. playing a lottery, taking a nap) had low scores on a whole, though Cluster 3 showed medium levels of relational quantity and quality. Cluster 2 (e.g. machine operating, handwork/invention) required high expertise and mental activity, but low physical activity. Cluster 4 indicated high mental activity and relational quantity despite low expertise. Cluster 7 (e.g. tour, joining festival) required not only moderate degrees of physical activity and relation, but low expertise. Lastly, Cluster 8 (e.g. meditation, information searching) had the appearance of high mental activity. Even though clusters of our study had a few similarities with preexisting taxonomy of leisure activities, there was clear distinctiveness between them. Unlike the preexisting taxonomy that had been created subjectively, we assorted 78 leisure activities based on objective figures of 6-dimensions. We also could identify that some leisure activities, which used to belong to the same leisure group, were included in different clusters (e.g. filed ball sports, net sports) because of different features. In other words, the results can provide a different perspective on leisure activities research and be helpful for figuring out what various characteristics leisure participants have.

Keywords: leisure, dimensional model, activity, relationship, expertise

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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: 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: Tariq Abbas, Younus Muhammad, Sayyad Aun Muhammad

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Background : Crimean Congo hemorrhagic fever (CCHF) is a tick born viral zoonotic disease that has been notified from almost all regions of Pakistan. The aim of this study was to investigate spatial distribution of CCHF cases reported to National Institue of Health , Islamabad during year 2013. Methods : Spatial statistics tools were applied to detect extent spatial auto-correlation and clusters of the disease based on adjusted cumulative incidence per million population for each district. Results : The data analyses revealed a large multi-district cluster of high values in the uplands of Balochistan province near Afghanistan border. Conclusion : The cluster included following districts: Pishin; Qilla Abdullah; Qilla Saifullah; Quetta, Sibi; Zhob; and Ziarat. These districts may be given priority in CCHF surveillance, control programs, and further epidemiological research . The location of the cluster close to border of Afghanistan and Iran highlight importance of the findings for organizations dealing with disease at national, regional and global levels.

Keywords: Crimean Congo hemorrhagic fever, Pakistan, spatial autocorrelation, clusters , adjusted cumulative incidence

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Authors: E. Happ, Zs. Ivancsone Horvath

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With the development of the information technology, IT, sector, all industry of the world has a new path, dealing with digitalisation. Tourism is the most rapidly increasing industry in the world. Without digitalisation, tourism operators would not be competitive enough with foreign destinations or other experience-based service providers. Digitalisation is also necessary to enable organizations, which are interested in tourism to meet the growing expectations of consumers. With the help of digitalisation, tourism providers can also obtain information about tourists, changes in consumer behaviour, and the use of online services. The degree of digitalisation in tourism is different for different services. The research is based on a questionnaire survey conducted in 2018 in Hungary. The sample with more than 500 respondents was processed by the SPSS program, using a variety of analysis methods. The following two variables were observed from more aspects: frequency of travel and the importance of services related to online travel. With the help of these variables, a cluster analysis was performed among the participants. The sample can be divided into two groups using K-mean cluster analysis. Cluster ‘1’ is a positive group; they can be called the “most digital tourists.” They agree in most things, with low standard deviation, and for them, digitalisation is a starting point. To the members of Cluster ‘2’, digitalisation is important, too. The results show what is important (accommodation, information gathering) to them, but also what they are not interested in at all within the digital world (e.g., car rental or online sharing). Interestingly, there is no third negative cluster. This result (that there is no result) proves that tourism uses digitalisation, and the question is only the extent of the use of online tools and methods. With the help of the designed consumer groups, the characteristics of digital tourism segments can be identified. The help of different variables characterised these groups. One of them is the frequency of travel, where there is a significant correlation between travel frequency and cluster membership. The shift is clear towards Cluster ‘1’, which means, those who find services related to online travel more important, are more likely to travel as well. By learning more about digital tourists’ consumer behaviour, the results of this research can help the providers in what kind of marketing tools could be used to influence the consumer choices of the different consumer groups created using digital devices, furthermore how to conduct more detailed and effective marketing activities. The main finding of the research was that most of the people have digital tools which are important to be able to participate in e-tourism. Of these, mobile devices are increasingly preferred. That means the challenge for service providers is no longer the digital presence but having optimised application for different devices.

Keywords: cluster analysis, digital tourism, marketing tool, tourist behaviour

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Authors: Dejen Alemu, Murray E. Jennex, Temtim Assefa

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This paper is investigated to understand the design, the use, and the consequence of Knowledge Management System (KMS) for knowledge systems sharing and integration. A KMS for knowledge systems sharing and integration is designed to meet the challenges raised by knowledge management researchers and practitioners: the technical, the human, and social factors. Agricultural KMS involves various members coming from different Communities of Practice (CoPs) who possess their own knowledge of multiple practices which need to be combined in the system development. However, the current development of the technology ignored the indigenous knowledge of the local communities, which is the key success factor for agriculture. This research employed the multi-methodological approach to KMS research in action research perspective which consists of four strategies: theory building, experimentation, observation, and system development. Using the KMS development practice of Ethiopian agricultural transformation agency as a case study, this research employed an interpretive analysis using primary qualitative data acquired through in-depth semi-structured interviews and participant observations. The Orlikowski's structuration model of technology has been used to understand the design, the use, and the consequence of the KMS. As a result, the research identified three basic components for the architecture of the shared KMS, namely, the people, the resources, and the implementation subsystems. The KMS were developed using web 2.0 tools to promote knowledge sharing and integration among diverse groups of users in a distributed environment. The use of a shared KMS allows users to access diverse knowledge from a number of users in different groups of participants, enhances the exchange of different forms of knowledge and experience, and creates high interaction and collaboration among participants. The consequences of a shared KMS on the social system includes, the elimination of hierarchical structure, enhance participation, collaboration, and negotiation among users from different CoPs having common interest, knowledge and skill development, integration of diverse knowledge resources, and the requirement of policy and guideline. The research contributes methodologically for the application of system development action research for understanding a conceptual framework for KMS development and use. The research have also theoretical contribution in extending structuration model of technology for the incorporation of variety of knowledge and practical implications to provide management understanding in developing strategies for the potential of web 2.0 tools for sharing and integration of indigenous knowledge.

Keywords: communities of practice, indigenous knowledge, participation, structuration model of technology, Web 2.0 tools

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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: Trapti Sharma, Devesh Kumar Srivastava

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This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

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Authors: Khanita Tumphasuwan

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The Wualai community of Northern Thailand represents important intellectual and social capital and their silver handicraft products are desirable tourist souvenirs within Chiang Mai Province. This community has been in danger of losing this social and intellectual capital due to the application of an improper tool, the Scottish Enterprise model of clustering. This research aims to analyze and increase its competitive advantages for preventing the loss of social and intellectual capital. To improve the Wualai’s competitive advantage, analysis is undertaken using a Porterian cluster approach, including the diamond model, five forces model and cluster mapping. Research results suggest that utilizing the community’s Buddhist beliefs can foster collaboration between community members and is the only way to improve cluster effectiveness, increase competitive advantage, and in turn conserve the Wualai community.

Keywords: industry clustering, silver handicraft, competitive advantage, intellectual capital, social capital

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Authors: Akm Shahidullah

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This paper assessed the medicinal plants production and related entrepreneurial and management aspects with a focus to understand the present medicinal plants-based supply chain of Bangladesh. It delineated the overall supply chain and the extent of benefit that the plant-producingagrientrepreneursderive out of the existing system of the chain. The key objective was to put forward innovative supply chain strategiesthatcan leverage the benefit of the rural farmer-entrepreneur of medicinal plants. A field-based investigation was carried out in the Natore district of northwest Bangladesh, where a total of 225 farmers and households from eight villages were engaged in the production of medicinal plant species. The research had a survey with the agrientrepreneurs of two of those villages and focus group discussions at a union level to gather information about the price, buyers, seasonality, and overall supply infrastructure and trading mechanisms of the plant products. The research also gathered explanations on the overall supply chain system of the plants and plant-based processed products through key informant interviews with the local and regional selling agents, stockists, wholesalers, and secondary processors. The findings revealed that, in the existing supply chain system, the primary and wholesale secondary markets were mostly dominated by middlemen who cause market distortions and inflated prices due to a lack of coordination between the primary producers and secondary processors. The discoordination and inefficiencies in the supply chain system could be offset by the producer-processor relationship integration that could result in a multitude of benefits to both the parties in terms of price, quality, lead time, and overall control of the supply chain. Therefore, to ensure the growth of medicinal plants production, the industry users, secondary processors, and policy stakeholders should ensure that the primary producers get the fair share of the benefit; the producer-processor relationship integration in the supply chain offers to ensure that fairness with maximum producer surplus.

Keywords: medicinal-plants, agrientrepreneur, supply chain, relationship integration, Bangladesh

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Authors: Yaşar Önal, Aydın Akın

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This study was conducted Ismailoglu grape type (Vitis vinifera L.) and its vine which was aged 15 was grown on its own root in a vegetation period of 2013 in Nevşehir province in Turkey. In this research, it was investigated whether the applications of Control (C), 1/3 cluster tip reduction (1/3 CTR), shoot tip reduction (STR), 1/3 CTR + STR, TKI-HUMAS (TKI-HM) (Soil) (S), TKI-HM (Foliar) (F), TKI-HM (S + F), 1/3 CTR + TKI-HM (S), 1/3 CTR + TKI-HM (F), 1/3 CTR + TKI-HM (S+F), STR + TKI-HM (S), STR + TKI-HM (F), STR + TKI-HM (S + F), 1/3 CTR + STR+TKI-HM (S), 1/3 CTR + STR + TKI-HM (F), 1/3 CTR + STR + TKI-HM (S + F) on yield and yield components of Ismailoglu grape type. The results were obtained as the highest fresh grape yield (16.15 kg/vine) with TKI-HM (S), as the highest cluster weight (652.39 g) with 1/3 CTR + STR, as the highest 100 berry weight (419.07 g) with 1/3 CTR + STR + TKI-HM (F), as the highest maturity index (44.06) with 1/3 CTR, as the highest must yield (810.00 ml) with STR + TKI-HM (F), as the highest intensity of L* color (42.04) with TKI-HM (S + F), as the highest intensity of a* color (2.60) with 1/3 CTR + TKI-HM (S), as the highest intensity of b* color (7.16) with 1/3 CTR + TKI-HM (S) applications. To increase the fresh grape yield of Ismailoglu grape type can be recommended TKI-HM (S) application.

Keywords: 1/3 cluster tip reduction, shoot tip reduction, TKI-Humas application, yield and yield components

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Authors: Khin Aye Myint, Mohd Rafii Yusop, Mohd Yusoff Abd Samad, Shairul Izan Ramlee, Mohd Din Amiruddin, Zulkifli Yaakub

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The narrow base of genetic is the main obstacle of breeding and genetic improvement in oil palm industry. In order to broaden the genetic bases, the Malaysian Palm Oil Board has been extensively collected wild germplasm from its original area of 11 African countries which are Nigeria, Senegal, Gambia, Guinea, Sierra Leone, Ghana, Cameroon, Zaire, Angola, Madagascar, and Tanzania. The germplasm collections were established and maintained as a field gene bank in Malaysian Palm Oil Board (MPOB) Research Station in Kluang, Johor, Malaysia to conserve a wide range of oil palm genetic resources for genetic improvement of Malaysian oil palm industry. Therefore, assessing the performance and genetic diversity of the wild materials is very important for understanding the genetic structure of natural oil palm population and to explore genetic resources. Principal component analysis (PCA) and Cluster analysis are very efficient multivariate tools in the evaluation of genetic variation of germplasm and have been applied in many crops. In this study, eight populations of MPOB-Senegal oil palm germplasm were studied to explore the genetic variation pattern using PCA and cluster analysis. A total of 20 yield and yield component traits were used to analyze PCA and Ward’s clustering using SAS 9.4 version software. The first four principal components which have eigenvalue >1 accounted for 93% of total variation with the value of 44%, 19%, 18% and 12% respectively for each principal component. PC1 showed highest positive correlation with fresh fruit bunch (0.315), bunch number (0.321), oil yield (0.317), kernel yield (0.326), total economic product (0.324), and total oil (0.324) while PC 2 has the largest positive association with oil to wet mesocarp (0.397) and oil to fruit (0.458). The oil palm population were grouped into four distinct clusters based on 20 evaluated traits, this imply that high genetic variation existed in among the germplasm. Cluster 1 contains two populations which are SEN 12 and SEN 10, while cluster 2 has only one population of SEN 3. Cluster 3 consists of three populations which are SEN 4, SEN 6, and SEN 7 while SEN 2 and SEN 5 were grouped in cluster 4. Cluster 4 showed the highest mean value of fresh fruit bunch, bunch number, oil yield, kernel yield, total economic product, and total oil and Cluster 1 was characterized by high oil to wet mesocarp, and oil to fruit. The desired traits that have the largest positive correlation on extracted PCs could be utilized for the improvement of oil palm breeding program. The populations from different clusters with the highest cluster means could be used for hybridization. The information from this study can be utilized for effective conservation and selection of the MPOB-Senegal oil palm germplasm for the future breeding program.

Keywords: cluster analysis, genetic variability, germplasm, oil palm, principal component analysis

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Authors: Zakiya Shireen, Sujin B. Babu

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Irreversible diffusion-limited cluster aggregation (DLCA) of binary sticky spheres was simulated by modifying the Brownian Cluster Dynamics (BCD). We randomly distribute N spheres in a 3D box of size L, the volume fraction is given by Φtot = (π/6)N/L³. We identify NA and NB number of spheres as species A and B in our system both having identical size. In these systems, both A and B particles undergo Brownian motion. Irreversible bond formation happens only between intra-species particles and inter-species interact only through hard-core repulsions. As we perform simulation using BCD we start to observe binary gels. In our study, we have observed that species B always percolate (cluster size equal to L) as expected for the monomeric case and species A does not percolate below a critical ratio which is different for different volume fractions. We will also show that the accessible volume of the system increases when compared to the monomeric case, which means that species A is aggregating inside the cage created by B. We have also observed that for moderate Φtot the system undergoes a transition from flocculation region to percolation region indicated by the change in fractal dimension from 1.8 to 2.5. For smaller ratio of A, it stays in the flocculation regime even though B have already crossed over to the percolation regime. Thus, we observe two fractal dimension in the same system.

Keywords: BCD, fractals, percolation, sticky spheres

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Authors: Maciej Szeląg

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The changing nature of environmental impacts, in which cement composites are operating, are causing in the structure of the material a number of phenomena, which result in volume deformation of the composite. These strains can cause composite cracking. Cracks are merging by propagation or intersect to form a characteristic structure of cracks known as the cluster cracks. This characteristic mesh of cracks is crucial to almost all building materials, which are working in service loads conditions. Particularly dangerous for a cement matrix is a sudden load of elevated temperature – the thermal shock. Resulting in a relatively short period of time a large value of a temperature gradient between the outer surface and the material’s interior can result in cracks formation on the surface and in the volume of the material. In the paper, in order to analyze the geometry of the cluster cracks of the cement pastes, the image analysis tools were used. Tested were 4 series of specimens made of two different Portland cement. In addition, two series include microsilica as a substitute for the 10% of the cement. Within each series, specimens were performed in three w/b indicators (water/binder): 0.4; 0.5; 0.6. The cluster cracks were created by sudden loading the samples by elevated temperature of 250°C. Images of the cracked surfaces were obtained via scanning at 2400 DPI. Digital processing and measurements were performed using ImageJ v. 1.46r software. To describe the structure of the cluster cracks three stereological parameters were proposed: the average cluster area - A ̅, the average length of cluster perimeter - L ̅, and the average opening width of a crack between clusters - I ̅. The aim of the study was to identify and evaluate the relationships between measured stereological parameters, and the compressive strength and the bulk density of the modified cement pastes. The tests of the mechanical and physical feature have been carried out in accordance with EN standards. The curves describing the relationships have been developed using the least squares method, and the quality of the curve fitting to the empirical data was evaluated using three diagnostic statistics: the coefficient of determination – R2, the standard error of estimation - Se, and the coefficient of random variation – W. The use of image analysis allowed for a quantitative description of the cluster cracks’ geometry. Based on the obtained results, it was found a strong correlation between the A ̅ and L ̅ – reflecting the fractal nature of the cluster cracks formation process. It was noted that the compressive strength and the bulk density of cement pastes decrease with an increase in the values of the stereological parameters. It was also found that the main factors, which impact on the cluster cracks’ geometry are the cement particles’ size and the general content of the binder in a volume of the material. The microsilica caused the reduction in the A ̅, L ̅ and I ̅ values compared to the values obtained by the classical cement paste’s samples, which is caused by the pozzolanic properties of the microsilica.

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

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Authors: Masood Ahmad, Ataul Aziz Ikram, Ishtiaq Wahid

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Wireless sensor network (WSN) is the interconnection of mobile wireless nodes with limited energy and memory. These networks can be deployed formany critical applications like military operations, rescue management, fire detection and so on. In flat routing structure, every node plays an equal role of sensor and router. The topology may change very frequently due to the mobile nature of nodes in WSNs. The topology maintenance may produce more overhead messages. To avoid topology maintenance overhead messages, an optimized cluster based mobile wireless sensor network using memetic algorithm is proposed in this paper. The nodes in this network are first divided into clusters. The cluster leaders then transmit data to that base station. The network is validated through extensive simulation study. The results show that the proposed technique has superior results compared to existing techniques.

Keywords: WSN, routing, cluster based, meme, memetic algorithm

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Authors: Peng Hou

Abstract:

Natural products produced from marine bacteria serve as an immense reservoir for anti-infective drugs and therapeutic agents. Nowadays, heterologous expression of gene clusters of interests has been widely adopted as an effective strategy for natural product discovery. Briefly, the heterologous expression flowchart would be: biosynthetic gene cluster identification, pathway construction and expression, and product detection. However, gene cluster capture using traditional Transformation-associated recombination (TAR) protocol is low-efficient (0.5% positive colony rate). To make things worse, most of these putative new natural products are only predicted by bioinformatics analysis such as antiSMASH, and their corresponding natural products biosynthetic pathways are either not expressed or expressed at very low levels under laboratory conditions. Those setbacks have inspired us to focus on seeking new technologies to efficiently edit and refractor of biosynthetic gene clusters. Recently, two cutting-edge techniques have attracted our attention - the CRISPR-Cas9 and Gibson Assembly. By now, we have tried to pretreat Brevibacillus laterosporus strain genomic DNA with CRISPR-Cas9 nucleases that specifically generated breaks near the gene cluster of interest. This trial resulted in an increase in the efficiency of gene cluster capture (9%). Moreover, using Gibson Assembly by adding/deleting certain operon and tailoring enzymes regardless of end compatibility, the silent construct (~80kb) has been successfully refactored into an active one, yielded a series of analogs expected. With the appearances of the novel molecular tools, we are confident to believe that development of a high throughput mature pipeline for DNA assembly, transformation, product isolation and identification would no longer be a daydream for marine natural product discovery.

Keywords: biosynthesis, CRISPR-Cas9, DNA assembly, refactor, TAR cloning

Procedia PDF Downloads 255