Search results for: λ-levelwise statistical cluster points

Authors: Neha Bhardwaj

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In this paper, we consider positive linear operators and study the Voronovskaya type result of the operator then obtain an error estimate in terms of the higher order modulus of continuity of the function being approximated and its A-statistical convergence. Also, we compute the corresponding rate of A-statistical convergence for the linear positive operators.

Keywords: Poisson distribution, Voronovskaya, modulus of continuity, a-statistical convergence

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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: Chau-Kuang Chen, Juanita Buford, Colette Davis, Raisha Allen, John Hughes, James Tyus, Dexter Samuels

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During the post-Civil War era, the city of Nashville, Tennessee, had the highest mortality rate in the country. The elevated death and disease among ex-slaves were attributable to the unavailability of healthcare. To address the paucity of healthcare services, the College, an institution with the mission of educating minority professionals and serving the under served population, was established in 1876. This study was designed to assess if the College has accomplished its mission of serving under served communities and contributed to the elimination of health disparities in the United States. The study objective was to quantify the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities, which, in turn, was significantly associated with a health professional shortage score partly designated by the U.S. Department of Health and Human Services. Various statistical methods were used to analyze the alumni data in years 1975 – 2013. K-means cluster analysis was utilized to identify individual medical and dental graduates into the cluster groups of the practice communities (Disadvantaged or Non-disadvantaged Communities). Discriminant analysis was implemented to verify the classification accuracy of cluster analysis. The independent t test was performed to detect the significant mean differences for clustering and criterion variables between Disadvantaged and Non-disadvantaged Communities, which confirms the “content” validity of cluster analysis model. Chi-square test was used to assess if the proportion of cluster groups (Disadvantaged vs Non-disadvantaged Communities) were consistent with that of practicing specialties (primary care vs. non-primary care). Finally, the partial least squares (PLS) path model was constructed to explore the “construct” validity of analytics model by providing the magnitude effects of socioeconomic status and adverse health outcome on primary care professionals serving disadvantaged community. The social ecological theory along with statistical models mentioned was used to establish the relationship between medical and dental graduates (primary care professionals serving disadvantaged communities) and their social environments (socioeconomic status, adverse health outcome, health professional shortage score). Based on social ecological framework, it was hypothesized that the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities could be quantified. Also, primary care professionals serving disadvantaged communities related to a health professional shortage score can be measured. Adverse health outcome (adult obesity rate, age-adjusted premature mortality rate, and percent of people diagnosed with diabetes) could be affected by the latent variable, namely socioeconomic status (unemployment rate, poverty rate, percent of children who were in free lunch programs, and percent of uninsured adults). The study results indicated that approximately 83% (3,192/3,864) of the College’s medical and dental graduates from 1975 to 2013 were practicing in disadvantaged communities. In addition, the PLS path modeling demonstrated that primary care professionals serving disadvantaged community was significantly associated with socioeconomic status and adverse health outcome (p < .001). In summary, the majority of medical and dental graduates from the College provide primary care services to disadvantaged communities with low socioeconomic status and high adverse health outcomes, which demonstrate that the College has fulfilled its mission.

Keywords: disadvantaged community, K-means cluster analysis, PLS path modeling, primary care

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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: Lukanda Kalobo

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In South Africa, the High School Mathematics Curriculum provides teachers with specific aims and skills to be developed which involves the understanding about the measures of central tendency. The exploration begins with the definitions of statistical literacy, measurement of central tendency and a discussion on why statistical literacy is essential today. It furthermore discusses the statistical literacy basics involved in understanding the concepts of measures of central tendency. The statistical literacy test on the measures of central tendency, was used to collect data which was administered to 78 first year students direct from high schools. The results indicated that students seemed to have forgotten about the statistical literacy in understanding the concepts of measure of central tendency after completing their high school study. The authors present inferences regarding the alignment between statistical literacy and the understanding of the concepts about the measures of central tendency, leading to the conclusion that there is a need to provide in-service and pre-service training.

Keywords: conceptual understanding, mean, median, mode, statistical literacy

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Authors: Daniel C. Bonzo

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Practical considerations lead to the use of unit of analysis within subjects, e.g., bleeding episodes or treatment-related adverse events, in rare disease settings. This is coupled with data augmentation techniques such as extrapolation to enlarge the subject base. In general, one can think about extrapolation of data as extending information and conclusions from one estimand to another estimand. This approach induces hierarchichal clustered data with varying cluster sizes. Extrapolation of clinical trial data is being accepted increasingly by regulatory agencies as a means of generating data in diverse situations during drug development process. Under certain circumstances, data can be extrapolated to a different population, a different but related indication, and different but similar product. We consider here the problem of estimation (point and interval) using a mixed-models approach under an extrapolation. It is proposed that estimators (point and interval) be constructed using weighting schemes for the clusters, e.g., equally weighted and with weights proportional to cluster size. Simulated data generated under varying scenarios are then used to evaluate the performance of this approach. In conclusion, the evaluation result showed that the approach is a useful means for improving statistical inference in rare disease settings and thus aids not only signal detection but risk-benefit evaluation as well.

Keywords: clustered data, estimand, extrapolation, mixed model

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Authors: Kanshio Richard Tyokyaa, Jagadish Singh

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In this paper, we examined the location and stability of Out-Of-Plane Equilibrium points in the elliptic restricted three-body problem of an infinitesimal body when both primaries are taken as oblate spheroids with oblateness up to zonal harmonic J₄. The positions of the Equilibrium points L₆,₇ and their stability depend on the oblateness of the primaries and the eccentricity of their orbits. We explored the problem numerically to show the effects of parameters involved in the position and stability of the Out-Of-Plane Equilibrium points for the systems: HD188753 and Gliese 667. It is found that their positions are affected by the oblateness of the primaries, eccentricity and the semi-major axis of the orbits, but its stability behavior remains unchanged and is unstable.

Keywords: out-of-plane, equilibrium points, stability, elliptic restricted three-body problem, oblateness, zonal harmonic

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Authors: Boonyarat Chaleephay

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The purpose of this research paper was to describe the relationship of acupressure points on the anterior surface of the upper limb in accordance with Applied Thai Traditional Massage (ATTM) and the deep structures located at those acupressure points. There were 2 population groups; normal subjects and cadaver specimens. Eighteen males with age ranging from 20-40 years old and seventeen females with ages ranging from 30-97 years old were studies. This study was able to obtain a fundamental knowledge concerning acupressure point and the deep structures that related to those acupressure points. It might be used as the basic knowledge for clinically applying and planning treatment as well as teaching in ATTM.

Keywords: acupressure point (AP), applie Thai traditional medicine (ATTM), paresthesia, numbness

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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: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

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Authors: O. Boussoufi, K. Lamrini Uahabi, M. Atounti

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The main purpose of this paper is to calculate the fractal dimension of some Julia Sets and Mandelbrot Set in the Misiurewicz Points. Using Matlab to generate the Julia Sets images that match the Misiurewicz points and using a Fractal software, we were able to find different measures that characterize those fractals in textures and other features. We are actually focusing on fractal dimension and the error calculated by the software. When executing the given equation of regression or the log-log slope of image a Box Counting method is applied to the entire image, and chosen settings are available in a FracLAc Program. Finally, a comparison is done for each image corresponding to the area (boundary) where Misiurewicz Point is located.

Keywords: box counting, FracLac, fractal dimension, Julia Sets, Mandelbrot Set, Misiurewicz Points

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Authors: Wan-Ting Shen, Ching-Liang Hsieh, Yi-Wen Lin

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Electroacupuncture (EA) has been reported effective for many kinds of pain and is a common treatment for acute or chronic pain. However, to date, there are limited studies examining the effect of acupuncture dosage. In our experiment, after injecting mice with Complete Freund’s Adjuvant (CFA) to induce inflammatory pain, two groups of mice were administered two different 15 min EA treatments at 2Hz. The first group received EA at a single acupuncture point (ST36, Zusanli) in both legs (two points), whereas the second group received two acupuncture points in both legs (four points) and the analgesic effect was compared. It was found that double points (ST36, Zusanli and SP6, Sanyinjiao) were significantly superior to single points (ST36, Zusanli) when evaluated using the electronic von Frey Test (mechanic) and Hargreaves’ Test (thermal). Through this study, it is expected more novel physiological mechanisms of acupuncture analgesia will be discovered.

Keywords: anti-inflammation, dose effect, electroacupuncture, pain control

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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: Gaye Demi̇rtaş Adli

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Research: It was conducted to determine the factors affecting the fear of self-injection and self-pricking of fingers of diabetic individuals.The study was conducted as a cross-sectional, relation-seeking, and descriptive study. The study was conducted on 122 patients who had just started insulin therapy. Data were obtained through The Descriptive Patient Form, The Diabetic Self-Injection, and the Fear of Testing Questionnaire Form (D-FISQ). Descriptive statistical methods used in the evaluation of data are the Mann-Whitney U test, Kruskal-Wallis H test, and the Spearman correlation. The factors affecting the scale scores were evaluated with multiple linear regression analysis. The value of P<0.05 was considered statistically significant. Study group: 56.6% of the patients are male patients. Fear of self-injection (injection), fear of self-testing (test), and total fear (total) scores of women were found to be statistically higher than men (p<0.001). Age, gender, and pain experience were important variables that affected patients' fear of injections. With a one-unit increase in age, the injection fear score decreased by 0.13 points, and the mean injection fear score of women was 2.11 points higher than that of men. It was determined that the patient's age, gender, living with whom, and blood donation history were important variables affecting the fear of self-testing. It is seen that the fear test score decreases by 0.18 points with an increase in age by one unit, and the fear test scores of women compared to men are on average 3,358 points, the fear test scores of those living alone are 4,711 points compared to those living with family members, and the fear test scores of those who do not donate blood are 2,572 compared to those who donate blood score, it was determined that those with more pain experience were 3,156 points higher on average than those with less fear of injection. As a result, it was seen that the most important factors affecting the fear of insulin injection and finger punching in individuals with diabetes were age, gender, pain experience, living with whom, and blood donation history.

Keywords: diabetes, needle phobia, fear of injection, insulin injection

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Authors: Maryam Tijjani Kolo

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Lack of refuse dumping points has made the residents of Shagari low-cost well armed with some health and environmental related hazards. These studies investigate the effect of household refuse on the resident of Shagari low-cost. A well structured questionnaire was administered to elicit views of the respondent in the study area through adopting cluster sampling method. A total of 100 questionnaires were selected and divided into 50, each to both sections of the study area. Interview was conducted to each household head. Data obtained were analyzed using simple parentages to determine the major hazard in the area. Result showed that majority of the household are civil servant and traders, earning reasonable monthly income. 68% of the respondent has experienced the effect of living close to waste dumping areas, which include unpleasant smell and polluted smoke when refuse is burnt, which causes eye and respiratory induction, human injury from broken bottles or sharp objects as well as water, insect and air borne diseases. Hence, the need to urgently address these menace before it overwhelms the capacities of the community becomes paramount. Thus, the community should be given more enlightenment and refuse dumping sites should be created by the local government area.

Keywords: household, refuse, refuse dumping points, Shagari low-cost

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Authors: Svetlana Pushkar

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The Leadership in Energy and Environmental Design (LEED) green building rating system is recognized in Europe. LEED uses regional priority (RP) points that are adapted to different environmental conditions. However, the appropriateness of the RP points is still a controversial question. To clarify this issue, two different parts of Europe: northern Europe (Finland and Sweden) and southern Europe (Turkey and Spain) were considered. Similarities and differences in the performances of LEED 2009-new construction (LEED-NC 2009) in these four countries were analyzed. It was found that LEED-NC 2009 performances in northern and southern parts of Europe in terms of Sustainable Sites (SS), Water Efficiency (WE), Materials and Resources (MR), and Indoor Environmental Quality (EQ) were similar, whereas in Energy and Atmosphere (EA), their performances were different. WE and SS revealed high performances (70-100%); EA and EQ demonstrated intermediate performance (40-60%); and MR displayed low performance (20-40%). It should be recommended introducing the following new RP points: for Turkey - water-related points and for all four observed countries - green power-related points for improving the LEED adaptation in Europe.

Keywords: green building, Europe, LEED, leadership in energy and environmental design, regional priority points

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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: Shaher A. I. Shalfawi, Fredrik Hviding, Bjornar Kjellstadli

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The main purpose of this study was to measure the effect of two different strength training methods on muscle strength, muscle mass, fat mass and endurance factors. Fourteen physical education students accepted to participate in this study. The participants were then randomly divided into three groups, traditional training group (TTG), cluster training group (CTG) and control group (CG). TTG consisted of 4 participants aged ( ± SD) (22.3 ± 1.5 years), body mass (79.2 ± 15.4 kg) and height (178.3 ± 11.9 cm). CTG consisted of 5 participants aged (22.2 ± 3.5 years), body mass (81.0 ± 24.0 kg) and height (180.2 ± 12.3 cm). CG consisted of 5 participants aged (22 ± 2.8 years), body mass (77 ± 19 kg) and height (174 ± 6.7 cm). The participants underwent a hypertrophy strength training program twice a week consisting of 4 sets of 10 reps at 70% of one-repetition maximum (1RM), using barbell squat and barbell bench press for 8 weeks. The CTG performed 2 x 5 reps using 10 s recovery in between repetitions and 50 s recovery between sets, while TTG performed 4 sets of 10 reps with 90 s recovery in between sets. Pre- and post-tests were administrated to assess body composition (weight, muscle mass, and fat mass), 1RM (bench press and barbell squat) and a laboratory endurance test (Bruce Protocol). Instruments used to collect the data were Tanita BC-601 scale (Tanita, Illinois, USA), Woodway treadmill (Woodway, Wisconsin, USA) and Vyntus CPX breath-to-breath system (Jaeger, Hoechberg, Germany). Analysis was conducted at all measured variables including time to peak VO2, peak VO2, heart rate (HR) at peak VO2, respiratory exchange ratio (RER) at peak VO2, and number of breaths per minute. The results indicate an increase in 1RM performance after 8 weeks of training. The change in 1RM squat was for the TTG = 30 ± 3.8 kg, CTG = 28.6 ± 8.3 kg and CG = 10.3 ± 13.8 kg. Similarly, the change in 1RM bench press was for the TTG = 9.8 ± 2.8 kg, CTG = 7.4 ± 3.4 kg and CG = 4.4 ± 3.4 kg. The within-group analysis from the oxygen consumption measured during the incremental exercise indicated that the TTG had only a statistical significant increase in their RER from 1.16 ± 0.04 to 1.23 ± 0.05 (P < 0.05). The CTG had a statistical significant improvement in their HR at peak VO2 from 186 ± 24 to 191 ± 12 Beats Per Minute (P < 0.05) and their RER at peak VO2 from 1.11 ± 0.06 to 1.18 ±0.05 (P < 0.05). Finally, the CG had only a statistical significant increase in their RER at peak VO2 from 1.11 ± 0.07 to 1.21 ± 0.05 (P < 0.05). The between-group analysis showed no statistical differences between all groups in all the measured variables from the oxygen consumption test during the incremental exercise including changes in muscle mass, fat mass, and weight (kg). The results indicate a similar effect of hypertrophy strength training irrespective of the methods of the training used on untrained subjects. Because there were no notable changes in body-composition measures, the results suggest that the improvements in performance observed in all groups is most probably due to neuro-muscular adaptation to training.

Keywords: hypertrophy strength training, cluster set, Bruce protocol, peak VO2

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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: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

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Authors: E. Happ, Zs. Ivancsone Horvath

Abstract:

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: Rajib Mia, Badam Singh Kushvah

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The present research was motivated by the recent discovery of the binary star systems. In this paper, we use the restricted three-body problem in the binary stellar systems, considering photogravitational effects of both the stars. The aim of this study is to investigate the motion of the infinitesimal mass in the vicinity of the Lagrangian points. The stability and periodic orbits of collinear points and the stability and trajectories of the triangular points are studied in stellar binary systems Kepler-34, Kepler-35, Kepler-413 and Kepler-16 systems. A detailed comparison is made among periodic orbits and trajectories.

Keywords: exoplanetary systems, lagrangian points, periodic orbit, restricted three body problem, stability

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Authors: Insaf Bellamine, Hamid Tairi

Abstract:

Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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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

Abstract:

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: Chih-Yi Ho, Tzu-Fang Chang, Chih-Chia Huang, Chia-Yen Lee

Abstract:

In dental clinics, the dentists use the periapical radiography image to assess the effectiveness of endodontic treatment of teeth with chronic apical periodontitis. Periapical radiography images are taken at different times to assess alveolar bone variation before and after the root canal treatment, and furthermore to judge whether the treatment was successful. Current clinical assessment of apical tissue recovery relies only on dentist personal experience. It is difficult to have the same standard and objective interpretations due to the dentist or radiologist personal background and knowledge. If periapical radiography images at the different time could be registered well, the endodontic treatment could be evaluated. In the image registration area, it is necessary to assign representative control points to the transformation model for good performances of registration results. However, detection of representative control points (feature points) on periapical radiography images is generally very difficult. Regardless of which traditional detection methods are practiced, sufficient feature points may not be detected due to the low-contrast characteristics of the x-ray image. Barnard detector is an algorithm for feature point detection based on grayscale value gradients, which can obtain sufficient feature points in the case of gray-scale contrast is not obvious. However, the Barnard detector would detect too many feature points, and they would be too clustered. This study uses the local extrema of clustering feature points and the suppression radius to overcome the problem, and compared different feature point detection methods. In the preliminary result, the feature points could be detected as representative control points by the proposed method.

Keywords: feature detection, Barnard detector, registration, periapical radiography image, endodontic treatment

Procedia PDF Downloads 418