Search results for: globular clusters

Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni, Fatma Elhadj Lakouas

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This research aims to present a surface water quality assessment of hydrochemical parameters in the Kebir Rhumel Basin, Algeria. The water quality index (WQI), Mann–Kendall (MK) test, and hierarchical cluster analysis (HCA) were used in oder to understand the spatio-temporal distribution of the surface water quality in the study area. Eleven hydrochemical parameters were measured monthly at eight stations from January 2016 to December 2020. The dominant cation in the surface water was found to be calcium, followed by sodium, and the dominant anion was sulfate, followed by chloride. In terms of WQI, a significant percentage of surface water samples at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khlifa (SK) exhibited poor water quality, with approximately 89.5%, 90.6%, 78.2%, and 62.7%, respectively, falling into this category. Mann–Kendall trend analysis revealed a significantly increasing trend in WQI values at stations Oued Boumerzoug (ON) and SK, indicating that the temporal variation of WQI in these stations is significant. Hierarchical clustering analysis classified the data into three clusters. The first cluster contained approximately 22% of the total number of months, the second cluster included about 30%, and the third cluster had the highest representation, approximately 48% of the total number of months. Within these clusters, certain stations exhibited higher WQI values. In the first cluster, stations GR and ON had the highest WQI values. In the second cluster, stations Oued Boumerzoug (OB) and SK showed the highest WQI values, while in the last cluster, stations AS, BH, El Milia (EM), and Hammam Grouz (HG) had the highest mean WQI values. Also, approximately 38%, 41%, and 38% of the total water samples in the first, second, and third clusters, respectively, were classified as having poor water quality. The findings of this study can serve as a scientific basis for decision-makers to formulate strategies for surface water quality restoration and management in the region.

Keywords: surface water, water quality index (WQI), Mann Kendall (MK) test, hierarchical cluster analysis (HCA), spatial-temporal distribution, Kebir Rhumel Basin

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Authors: Orit Baruth, Anat Cohen

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Online courses have become common in many learning programs and various learning environments, particularly in higher education. Social distancing forced in response to the COVID-19 pandemic has increased the demand for these courses. Yet, despite the frequency of use, online learning is not free of limitations and may not suit all learners. Hence, the growth of online learning alongside with learners' diversity raises the question: is online learning, as it currently offered, meets the needs of each learner? Fortunately, today's technology allows to produce tailored learning platforms, namely, personalization. Personality influences learner's satisfaction and therefore has a significant impact on learning effectiveness. A better understanding of personality can lead to a greater appreciation of learning needs, as well to assists educators ensure that an optimal learning environment is provided. In the context of online learning and personality, the research on learning design according to personality traits is lacking. This study explores the relations between personality traits (using the 'Big-five' model) and students' satisfaction with five techno-pedagogical learning solutions (TPLS): discussion groups, digital books, online assignments, surveys/polls, and media, in order to provide an online learning process to students' satisfaction. Satisfaction level and personality identification of 108 students who participated in a fully online learning course at a large, accredited university were measured. Cluster analysis methods (k-mean) were applied to identify learners’ clusters according to their personality traits. Correlation analysis was performed to examine the relations between the obtained clusters and satisfaction with the offered TPLS. Findings suggest that learners associated with the 'Neurotic' cluster showed low satisfaction with all TPLS compared to learners associated with the 'Non-neurotics' cluster. learners associated with the 'Consciences' cluster were satisfied with all TPLS except discussion groups, and those in the 'Open-Extroverts' cluster were satisfied with assignments and media. All clusters except 'Neurotic' were highly satisfied with the online course in general. According to the findings, dividing learners into four clusters based on personality traits may help define tailor learning paths for them, combining various TPLS to increase their satisfaction. As personality has a set of traits, several TPLS may be offered in each learning path. For the neurotics, however, an extended selection may suit more, or alternatively offering them the TPLS they less dislike. Study findings clearly indicate that personality plays a significant role in a learner's satisfaction level. Consequently, personality traits should be considered when designing personalized learning activities. The current research seeks to bridge the theoretical gap in this specific research area. Establishing the assumption that different personalities need different learning solutions may contribute towards a better design of online courses, leaving no learner behind, whether he\ she likes online learning or not, since different personalities need different learning solutions.

Keywords: online learning, personality traits, personalization, techno-pedagogical learning solutions

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Authors: Filiz Ersoz, Taner Ersoz, Tugrul Bayraktar

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Renewable energy is referred to as "clean energy" and common popular support for the use of renewable energy (RE) is to provide electricity with zero carbon dioxide emissions. This study provides useful insight into the European Union (EU) RE, especially, into electricity generation obtained from renewables, and their targets. The objective of this study is to identify groups of European countries, using multivariate statistical analysis and selected indicators. The hierarchical clustering method is used to decide the number of clusters for EU countries. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method and squared Euclidean distances. Hierarchical cluster analysis identified eight distinct clusters of European countries. Then, non-hierarchical clustering (k-means) method was applied. Discriminant analysis was used to determine the validity of the results with data normalized by Z score transformation. To explore the relationship between the selected indicators, correlation coefficients were computed. The results of the study reveal the current situation of RE in European Union Member States.

Keywords: share of electricity generation, k-means clustering, discriminant, CO2 emission

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Authors: Juland D. Salayo

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With numerous innovations introduced in the Philippine educational system, the country’s struggle of materializing its national goal of transforming lives ends with great loss. Many agree that the failure to emerge the integral values of the program, framework and the implementers impedes realization. Employing a descriptive-correlational method, it aimed to determine the value clusters of the Grade 9 teachers as assessed by themselves and by the students, the significant difference of the assessed values and the significant difference on the values based on their profile. Respondents were composed of sixty-nine (69) teachers and three hundred forty (340) students using simple random sampling. Through a survey-questionnaire, the study revealed that the teachers have high regards on their self-reliance, honesty and trustworthiness, obedience, politeness and respect and self-discipline and spirituality. In contrast, they have ranked the following values fairly: justice and fairness, courage, responsibility and punctuality and nationalism and patriotism. Having assessed by the students, they have highly regarded their teachers’ self-reliance, responsibility and punctuality, obedience, politeness and respect and fair play and sportsmanship. On the other hand, the student-respondents made a low assessment on the level of the teachers’ justice and fairness, nationalism and patriotism, honesty and trustworthiness and excellence. Using t-test, it showed that there is a significant difference between the assessments of the respondents. Finally, among the demographic profiles, only civil status and age rejected the hypothesis. The following were recommended: provide educators value-enhancement trainings and conferences, organize value-oriented organizations and activities, and make intensive value-campaigns heightening the low-assessed values. Thus, a Revised Values Education Program (RVEP) was made to further meet the objectives of the program, address the needs of its clienteles, and responding to the demands of both education and society towards excellence in service, social and economic revolution, and constructive national goals which are based from integral values.

Keywords: values, value clusters, values education program, values education, teachers' assessed values

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Authors: Arpan Paul, Joy Sen

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Livability is a holistic concept whose factors include many complex characteristics and levels of interrelationships among them. It has been considered as people’s need for public amenities and is recognized as a major element to create social welfare. The concept and principles of livability are essential for recognizing the significance of community well-being. The attributes and dimensions of livability are also important aspects to measure the overall quality of environment. Livability potential is mainly considered as the capacity to develop into the overall well-being of an urban area in future. The intent of the present study is to identify the prime factors to evaluate livability potential within a metropolis. For ground level case study, the paper has selected Kolkata Metropolitan Area (KMA) as it has wide physical, social, and economic variations within it. The initial part of the study deals with detailed literature review on livability and its significance of evaluating its potential within a metropolis. The next segment is dedicated for identifying the primary factors which would evaluate livability potential within a metropolis. In pursuit of identifying primary factors, which have a direct impact on urban livability, this study delineates the metropolitan area into various clusters, having their distinct livability potential. As a final outcome of the study, variations of livability potential of those selected clusters are highlighted to explain the complexity of the metropolitan development.

Keywords: Kolkata Metropolitan Area (KMA), livability potential, metropolis, wellbeing

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Authors: Minji Seo

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Fashion stakeholders are becoming increasingly aware of technological innovation in manufacturing. In 2020, the COVID-19 pandemic caused transformations in working patterns, such as working remotely rather thancommuting. To enable smooth remote working, 3D fashion design software is being adoptedas the latest trend in design and production. The majority of fashion designers, however, are still resistantto this change. Previous studies on 3D fashion design software solely highlighted the beneficial and detrimental factors of adopting design innovations. They lacked research on the relationship between resistance factors and the adoption of innovation. These studies also fell short of exploringthe perspectives of users of these innovations. This paper aims to investigate the key drivers and barriers of employing 3D fashion design software as wellas to explore the challenges faced by designers.It also toucheson the governmental support for digital manufacturing in Seoul, South Korea, and London, the United Kingdom. By conceptualising local support, this study aims to provide a new path for industrial clusters to optimise digital apparel manufacturing. The study uses a mixture of quantitative and qualitative approaches. Initially, it reflects a survey of 350 samples, fashion designers, on innovation resistance factors of 3D fashion design software and the effectiveness of local support. In-depth interviews with 30 participants provide a better understanding of designers’ aspects of the benefits and obstacles of employing 3D fashion design software. The key findings of this research are the main barriers to employing 3D fashion design software in fashion production. The cultural characteristics and interviews resultsare used to interpret the survey results. The findings of quantitative data examine the main resistance factors to adopting design innovations. The dominant obstacles are: the cost of software and its complexity; lack of customers’ interest in innovation; lack of qualified personnel, and lack of knowledge. The main difference between Seoul and London is the attitudes towards government support. Compared to the UK’s fashion designers, South Korean designers emphasise that government support is highly relevant to employing 3D fashion design software. The top-down and bottom-up policy implementation approach distinguishes the perception of government support. Compared to top-down policy approaches in South Korea, British fashion designers based on employing bottom-up approaches are reluctant to receive government support. The findings of this research will contribute to generating solutions for local government and the optimisation of use of 3D fashion design software in fashion industrial clusters.

Keywords: digital apparel production, industrial clusters, innovation resistance, 3D fashion design software, manufacturing, innovation, technology, digital manufacturing, innovative fashion design process

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Authors: Ismatilla Mardanov

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There are three types of countries, the first of which is willing to attract foreign direct investment (FDI) in enormous amounts and do whatever it takes to make this happen. Therefore, FDI pours into such countries. In the second cluster of countries, even if the country is suffering tremendously from the shortage of investments, the governments are hesitant to attract investments because they are at the hands of local oligarchs/cartels. Therefore, FDI inflows are moderate to low in such countries. The third type is countries whose companies prefer investing in the most efficient locations globally and are hesitant to invest in the homeland. Sorting countries into such clusters, the present study examines the essential institutions and economic factors that make these countries different. Past literature has discussed various determinants of FDI in all kinds of countries. However, it did not classify countries based on government motivation, institutional setup, and economic factors. A specific approach to each target country is vital for corporate foreign direct investment risk analysis and decisions. The research questions are 1. What specific institutional and economic factors paint the pictures of the three clusters; 2. What specific institutional and economic factors are determinants of FDI; 3. Which of the determinants are endogenous and exogenous variables? 4. How can institutions and economic and political variables impact corporate investment decisions Hypothesis 1: In the first type, country institutions and economic factors will be favorable for FDI. Hypothesis 2: In the second type, even if country economic factors favor FDI, institutions will not. Hypothesis 3: In the third type, even if country institutions favorFDI, economic factors will not favor domestic investments. Therefore, FDI outflows occur in large amounts. Methods: Data come from open sources of the World Bank, the Fraser Institute, the Heritage Foundation, and other reliable sources. The dependent variable is FDI inflows. The independent variables are institutions (economic and political freedom indices) and economic factors (natural, material, and labor resources, government consumption, infrastructure, minimum wage, education, unemployment, tax rates, consumer price index, inflation, and others), the endogeneity or exogeneity of which are tested in the instrumental variable estimation. Political rights and civil liberties are used as instrumental variables. Results indicate that in the first type, both country institutions and economic factors, specifically labor and logistics/infrastructure/energy intensity, are favorable for potential investors. In the second category of countries, the risk of loss of assets is very high due to governmentshijacked by local oligarchs/cartels/special interest groups. In the third category of countries, the local economic factors are unfavorable for domestic investment even if the institutions are well acceptable. Cluster analysis and instrumental variable estimation were used to reveal cause-effect patterns in each of the clusters.

Keywords: foreign direct investment, economy, institutions, instrumental variable 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: Laith F. Gulli, Nicole M. Mallory

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The undertaking to develop a psychometric instrument is monumental. Understanding the relationship between variables and events is important in structural and exploratory design of psychometric instruments. Considering this, we describe a method used to group, pair and combine multiple Philosophical Assumption statements that assisted in development of a 13 item psychometric screening instrument. We abbreviated our Philosophical Assumptions (PA)s and added parameters, which were then condensed and mathematically modeled in a specific process. This model produced clusters of combinatorics which was utilized in design and development for 1) information retrieval and categorization 2) item development and 3) estimation of interactions among variables and likelihood of events. The psychometric screening instrument measured Knowledge, Assessment (education) and Beliefs (KAB) of New Addictions Research (NAR), which we called KABNAR. We obtained an overall internal consistency for the seven Likert belief items as measured by Cronbach’s α of .81 in the final study of 40 Clinicians, calculated by SPSS 14.0.1 for Windows. We constructed the instrument to begin with demographic items (degree/addictions certifications) for identification of target populations that practiced within Outpatient Substance Abuse Counseling (OSAC) settings. We then devised education items, beliefs items (seven items) and a modifiable “barrier from learning” item that consisted of six “choose any” choices. We also conceptualized a close relationship between identifying various degrees and certifications held by Outpatient Substance Abuse Therapists (OSAT) (the demographics domain) and all aspects of their education related to EB-NAR (past and present education and desired future training). We placed a descriptive (PA)1tx in both demographic and education domains to trace relationships of therapist education within these two domains. The two perceptions domains B1/b1 and B2/b2 represented different but interrelated perceptions from the therapist perspective. The belief items measured therapist perceptions concerning EB-NAR and therapist perceptions using EB-NAR during the beginning of outpatient addictions counseling. The (PA)s were written in simple words and descriptively accurate and concise. We then devised a list of parameters and appropriately matched them to each PA and devised descriptive parametric (PA)s in a domain categorized information grid. Descriptive parametric (PA)s were reduced to simple mathematical symbols. This made it easy to utilize parametric (PA)s into algorithms, combinatorics and clusters to develop larger information grids. By using matching combinatorics we took paired demographic and education domains with a subscript of 1 and matched them to the column with each B domain with subscript 1. Our algorithmic matching formed larger information grids with organized clusters in columns and rows. We repeated the process using different demographic, education and belief domains and devised multiple information grids with different parametric clusters and geometric arrays. We found benefit combining clusters by different geometric arrays, which enabled us to trace parametric variables and concepts. We were able to understand potential differences between dependent and independent variables and trace relationships of maximum likelihoods.

Keywords: psychometric, parametric, domains, grids, therapists

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Authors: Zeki Kara, Kevser Yazar

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Table grapes (Vitis vinifera L.) are an important crop worldwide. Postharvest problems like berry shattering, decay and stem dehydration are some of the important factors that limit the marketing of table grapes. Edible coatings are an alternative for increasing shelf-life of fruits, protecting fruits from humidity and oxygen effects, thus retarding their deterioration. This study aimed to compare different grape seed oil applications (GSO, 0.5 g L^-1, 1 g L^-1, 2 g L^-1) and SO₂ generating pads effects (SO₂-1, SO₂-2). Treated grapes with GSO and generating pads were packaged into polyethylene trays and stored at 0 ± 1°C and 85-95% moisture. Effects of the applications were investigated by some quality and sensory evaluations with intervals of 15 days. SO₂ applications were determined the most effective treatments for minimizing weight loss and changes in TA, pH, color and appearance value. Grape seed oil applications were determined as a good alternative for grape preservation, improving weight losses and °Brix, TA, the color values and sensory analysis. Commercially, ‘Alphonse Lavallée’ clusters were stored for 75 days and ‘Antep Karası’ clusters for 60 days. The data obtained from GSO indicated that it had a similar quality result to SO₂ for up to 40 days storage.

Keywords: postharvest, quality, sensory analyses, Vitis vinifera L.

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Authors: R. Yulianingsih, S. Gohtani

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Characteristics of pregelatinized waxy rice starch (PWR) gelatinized at different temperatures (65, 75, and 85 °C, abbreviated as PWR 65, 75 and 85 respectively) and their emulsion-stabilizing properties at different starch concentrations (3, 5, 7, and 9%) were studied. The yield stress and consistency index value of PWR solution increased with an increase in starch concentration. The pseudoplasticity of PWR 65 solution increased and that for both PWR 75 and 85 solution decreased with an increase in starch concentration. Small angle X-ray scattering (SAXS) profiles analyzed by Kratky Plot indicated that PWR 65 is natively unfolded particles while PWR 75 and 85 are the globular particles. The characteristics of emulsions stabilized with PWR were influenced by the temperature of gelatinization process and starch concentration. Elevated concentration of starch decreased the value of yield stress and increased the consistency index. PWR 65 produce stable emulsion to creaming at starch concentrations more than 5%, while PWR 85 is able to produce stable emulsion to both creaming and coalescence of droplets.

Keywords: emulsion, gelatinization temperature, rheology, small-angle X-ray scattering, waxy rice starch

Procedia PDF Downloads 157

Authors: Neha Gupta, Sonika Verma, Seema Puri, Nikhil Tandon, Narendra K. Arora

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The concurrent increasing prevalence of underweight and overweight/obesity among children with changing lifestyle and the rapid transitioning society has necessitated the need for a unifying/multi-level approach to understand the determinants of the problem. The present community-based cross-sectional research study was conducted to assess the associations between lifestyle behavior and food environment of the child at household, neighborhood, and school with the BMI of children (6-12 year old) (n=612) residing in three rural clusters of Palwal district, Haryana. The study used innovative and robust methods for assessing the lifestyle and various components of food environment in the study. The three rural clusters selected for the study were located at three different locations according to their access to highways in the SOMAARTH surveillance site. These clusters were significantly different from each other in terms of their socio-demographic and socio-economic profile, living conditions, environmental hygiene, health seeking behavior and retail density. Despite of being different, the quality of living conditions and environmental hygiene was poor across three clusters. The children had higher intakes of dietary energy and sugars; one-fifth share of the energy being derived from unhealthy foods, engagement in high levels of physical activity and significantly different food environment at home, neighborhood and school level. However, despite having a high energy intake, 22.5% of the recruited children were thin/severe thin, and 3% were overweight/obese as per their BMI-for-age categories. The analysis was done using multi-variate logistic regression at three-tier hierarchy including individual, household and community level. The factors significantly explained the variability in governing the risk of getting thin/severe thin among children in rural area (p-value: 0.0001; Adjusted R2: 0.156) included age (>10years) (OR: 2.1; 95% CI: 1.0-4.4), the interaction between minority category and poor SES of the household (OR: 4.4; 95% CI: 1.6-12.1), availability of sweets (OR: 0.9; 95% CI: 0.8-0.99) and cereals (OR: 0.9; 95% CI: 0.8-1.0) in the household and poor street condition (proxy indicator of the hygiene and cleanliness in the neighborhood) (OR: 0.3; 95% CI: 0.1-1.1). The homogeneity of other factors at neighborhood and school level food environment diluted the heterogeneity in the lifestyles and home environment of the recruited children and their households. However, it is evident that when various individual factors interplay at multiple levels amplifies the risk of undernutrition in a rural community. Conclusion: These rural areas in Haryana are undergoing developmental, economic and societal transition. In correspondence, no improvements in the nutritional status of children have happened. Easy access to the unhealthy foods has become a paradox.

Keywords: transition, food environment, lifestyle, undernutrition, overnutrition

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Authors: Mustafa İlhan Akbaş, Özlem Garibay, Ivan Garibay

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The economic entities in innovation ecosystems form various industry clusters, in which they compete and cooperate to survive and grow. Within a successful and stable industry cluster, the entities acquire different roles that complement each other in the system. The universities and research centers have been accepted to have a critical role in these systems for the creation and development of innovations. However, the real effect of research institutions on regional economic growth is difficult to assess. In this paper, we present our approach for the identification of the impact of research activities on the regional entrepreneurship for a specific high-tech industry: optics and photonics. The optics and photonics has been defined as an enabling industry, which combines the high-tech photonics technology with the developing optics industry. The recent literature suggests that the growth of optics and photonics firms depends on three important factors: the embedded regional specializations in the labor market, the research and development infrastructure, and a dynamic small firm network capable of absorbing new technologies, products and processes. Therefore, the role of each factor and the dynamics among them must be understood to identify the requirements of the entrepreneurship activities in optics and photonics industry. There are three main contributions of our approach. The recent studies show that the innovation in optics and photonics industry is mostly located around metropolitan areas. There are also studies mentioning the importance of research center locations and universities in the regional development of optics and photonics industry. These studies are mostly limited with the number of patents received within a short period of time or some limited survey results. Therefore the first contribution of our approach is conducting a comprehensive analysis for the state and recent history of the photonics and optics research in the US. For this purpose, both the research centers specialized in optics and photonics and the related research groups in various departments of institutions (e.g. Electrical Engineering, Materials Science) are identified and a geographical study of their locations is presented. The second contribution of the paper is the analysis of regional entrepreneurship activities in optics and photonics in recent years. We use the membership data of the International Society for Optics and Photonics (SPIE) and the regional photonics clusters to identify the optics and photonics companies in the US. Then the profiles and activities of these companies are gathered by extracting and integrating the related data from the National Establishment Time Series (NETS) database, ES-202 database and the data sets from the regional photonics clusters. The number of start-ups, their employee numbers and sales are some examples of the extracted data for the industry. Our third contribution is the utilization of collected data to investigate the impact of research institutions on the regional optics and photonics industry growth and entrepreneurship. In this analysis, the regional and periodical conditions of the overall market are taken into consideration while discovering and quantifying the statistical correlations.

Keywords: entrepreneurship, industrial clusters, optics, photonics, emerging industries, research centers

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Eashwar V. Somasundaram, Raoul R. Wadhwa, Jacob G. Scott

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The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology.

Keywords: cancer biology, oncology, persistent homology, radiomics, topological data analysis, tumor imaging

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

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Authors: Zahra Emami, Mohammad Ebrahim Khamseh, Nahid Hashemi Madani, Iman Kermani

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The aim of the study was to map scientific research on adrenal gland diseases in the Middle East countries through the Web of Science database using scientometric analysis. Data were analyzed with Excel software; and HistCite was used for mapping of the scientific texts. In this study, from a total of 268 retrieved records, 1125 authors from 328 institutions published their texts in 138 journals. Among 17 Middle East countries, Turkey ranked first with 164 documents (61.19%), Israel ranked second with 47 documents (15.53%) and Iran came in the third place with 26 documents. Most of the publications (185 documents, 69.2%) were articles. Among the universities of the Middle East, Istanbul University had the highest science production rate (9.7%). The Journal of Clinical Endocrinology & Metabolism had the highest TGCS (243 citations). In the scientific mapping, 7 clusters were formed based on TLCS (Total Local Citation Score) & TGCS (Total Global Citation Score). considering the study results, establishment of scientific connections and collaboration with other countries and use of publications on adrenal gland diseases from high ranking universities can help in the development of this field and promote the medical practice in this regard. Moreover, investigation of the formed clusters in relation to Congenital Hyperplasia and puberty related disorders can be research priorities for investigators.

Keywords: mapping, scientific research, adrenal gland diseases, scientometric

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Authors: G. Tamilpavai, C. Vishnuppriya

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Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCSS) is one of the major challenges in various bioinformatics applications. The computation of the LCSS plays a vital role in biomedicine and also it is an essential task in DNA sequence analysis in genetics. It includes wide range of disease diagnosing steps. The objective of this proposed system is to find the longest common subsequence which presents in a normal and various disease affected human DNA sequence using Self Organizing Map (SOM) and LCSS. The human DNA sequence is collected from National Center for Biotechnology Information (NCBI) database. Initially, the human DNA sequence is separated as k-mer using k-mer separation rule. Mean and median values are calculated from each separated k-mer. These calculated values are fed as input to the Self Organizing Map for the purpose of clustering. Then obtained clusters are given to the Longest Common Sub Sequence (LCSS) algorithm for finding common subsequence which presents in every clusters. It returns nx(n-1)/2 subsequence for each cluster where n is number of k-mer in a specific cluster. Experimental outcomes of this proposed system produce the possible number of longest common subsequence of normal and disease affected DNA data. Thus the proposed system will be a good initiative aid for finding disease causing sequence. Finally, performance analysis is carried out for different DNA sequences. The obtained values show that the retrieval of LCSS is done in a shorter time than the existing system.

Keywords: clustering, k-mers, longest common subsequence, SOM

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Authors: Hasan Basri Jumin

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The in vitro flowering of orange jessamine plantlets derived from protoplast was affected by the manipulation of plant growth regulators, sugar and light conditions. MT basal medium containing 5% sucrose and supplemented with 0.001 mg 1-1 indole-acetic-acid was found to be a suitable medium for development of globular somatic embryos derived from protoplasts to form heart-shaped somatic embryos with cotyledon-like structures. The highest percentage (85 %) of flowering was achieved with plantlet on half-strength MT basal medium containing 5% sucrose and 0.001 mg1-1 indole-acetic-acid in light. Exposure to darkness for more than 3 weeks followed by re-exposure to light reduced flowering. Flowering required a 10-day exposure to indole-acetic-acid. Photoperiod with 18 h and 79.4 µmol m-2 s-1 light intensity promoted in vitro flowering in high frequencies. The sucrose treatment affected the flower bud size distribution. Flower buds originating from plantlet derived from protoplasts developed into normal flowers.

Keywords: indole-acetc-acid, light-intensity, Murraya-paniculata, photoperiod, plantlet, Zeatin

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Authors: Yin-Yee Leong, Jack C. Yue, Hsin-Chung Wang

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Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

Keywords: mortality improvement, Lee–Carter model, spatial statistics, cluster detection

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Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

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With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: bipartite graph, one-mode projection, clustering, web proxy detection

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Authors: Placide Poba-Nzaou, Anicet Tchibozo, Malatsi Galani, Ali Etkkali, Erwin Halim

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Artificial intelligence (AI) is a general-purpose technology that is transforming many industries, working life and society by stimulating economic growth and innovation. Despite the huge potential of benefits to be generated, the adoption of AI varies from one organization to another, from one region to another, and from one industry to another, due in part to obstacles that can inhibit an organization or organizations located in a specific geographic region or operating in a specific industry from adopting AI technology. In this context, these obstacles and their implications for AI adoption from the perspective of configurational theory is important for at least three reasons: (1) understanding these obstacles is the first step in enabling policymakers and providers to make an informed decision in stimulating AI adoption (2) most studies have investigating obstacles or challenges of AI adoption in isolation with linear assumptions while configurational theory offers a holistic and multifaceted way of investigating the intricate interactions between perceived obstacles and barriers helping to assess their synergetic combination while holding assumptions of non-linearity leading to insights that would otherwise be out of the scope of studies investigating these obstacles in isolation. This study aims to pursue two objectives: (1) characterize organizations by uncovering the typical profiles of combinations of 15 internal and external obstacles that may prevent organizations from adopting AI technology, (2) assess the variation in terms of intensity of AI adoption associated with each configuration. We used data from a survey of AI adoption by organizations conducted throughout the EU27, Norway, Iceland and the UK (N=7549). Cluster analysis and discriminant analysis help uncover configurations of organizations based on the 15 obstacles, including eight external and seven internal. Second, we compared the clusters according to AI adoption intensity using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. The study uncovers three strongly separated clusters of organizations based on perceived obstacles to AI adoption. The clusters are labeled according to their magnitude of perceived obstacles to AI adoption: (1) Cluster I – High Level of perceived obstacles (N = 2449, 32.4%)(2) Cluster II – Low Level of perceived obstacles (N =1879, 24.9%) (3) Cluster III – Moderate Level of perceived obstacles (N =3221, 42.7%). The proposed taxonomy goes beyond the normative understanding of perceived obstacles to AI adoption and associated implications: it provides a well-structured and parsimonious lens that is useful for policymakers, AI technology providers, and researchers. Surprisingly, the ANOVAs revealed a “high level of perceived obstacles” cluster associated with a significantly high intensity of AI adoption.

Keywords: Artificial intelligence (AI), obstacles, adoption, taxonomy.

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Authors: Forrest Kaatz, Adhemar Bultheel

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We use an informational statistical mechanics approach to study the catalytic thermodynamics of platinum and palladium cuboctahedral nanoclusters. Nanoclusters and their adatoms are viewed as chemical graphs with a nearest neighbor adjacency matrix. We use the Morse potential to determine bond energies between cluster atoms in a coordination type calculation. We use adsorbate energies calculated from density functional theory (DFT) to study the adatom effects on the thermodynamic quantities, which are derived from a Hamiltonian. Oxygen radical and molecular adsorbates are studied on platinum clusters and hydrogen on palladium clusters. We calculate the entropy, free energy, and total energy as the coverage of adsorbates increases from bridge and hollow sites on the surface. Thermodynamic behavior versus adatom coverage is related to the structural distribution of adatoms on the nanocluster surfaces. The thermodynamic functions are characterized using a simple adsorption model, with linear trends as the coverage of adatoms increases. The data exhibits size effects for the measured thermodynamic properties with cluster diameters between 2 and 5 nm. Entropy and enthalpy calculations of Pt-O2 compare well with previous theoretical data for Pt(111)-O2, and our Pd-H results show similar trends as experimental measurements for Pd-H2 nanoclusters. Our methods are general and may be applied to wide variety of nanocluster adsorbate systems.

Keywords: catalytic thermodynamics, palladium nanocluster absorbates, platinum nanocluster absorbates, statistical mechanics

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Authors: Janis E. Campbell

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Most of what is known about complementary, alternative or traditional medicine (CATM) in the United States today is known from either the National Health Interview Survey a cross-sectional survey with a few questions or from small cross-sectional or cohort studies with specific populations. The broad geographical distribution in CATM use or providers is not known. For this project, we used geospatial cluster analysis to determine if there were clusters of CATM provider by county in the US. In this analysis, we used the National Provider Index to determine the geographic distribution of providers in the US. Of the 215,769 CAMT providers 211,603 resided in the contiguous US: Acupuncturist (26,563); Art, Poetry, Music and Dance Therapist (2,752); Chiropractor (89,514); Doula/Midwife (3,535); Exercise (507); Homeopath (380); Massage Therapist (36,540); Mechanotherapist (1,888); Naprapath (146); Naturopath (4,782); Nutrition (42,036); Reflexologist (522); Religious (2,438). ESRI® spatial autocorrelation was used to determine if the geographic location of CATM providers were random or clustered. For global analysis, we used Getis-Ord General G and for Local Indicators of Spatial Associations with an Optimized Hot Spot Analysis using an alpha of 0.05. Overall, CATM providers were clustered with both low and high. With Chiropractors, focusing in the Midwest, religious providers having very small clusters in the central US, and other types of CAMT focused in the northwest and west coast, Colorado and New Mexico, the great lakes areas and Florida. We will discuss some of the implications of this study, including associations with health, economic, social, and political systems.

Keywords: complementary medicine, alternative medicine, geospatial, United States

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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: Faheema Khan

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In order to investigate the influence of genetic background on salt tolerance in Soybean (Glycine max) ten soybean genotypes released/notified in India were selected. (Pusa-20, Pusa-40, Pusa-37, Pusa-16, Pusa-24, Pusa-22, BRAGG, PK-416, PK-1042, and DS-9712). The 10-day-old seedlings were subjected to 0, 25, 50, 75, 100, 125, and 150 mM NaCl for 15 days. Plant growth, leaf osmotic adjustment, and RAPD analysis were studied. In comparison to control plants, the plant growth in all genotypes was decreased by salt stress, respectively. Salt stress decreased leaf osmotic potential in all genotypes however the maximum reduction was observed in genotype Pusa-24 followed by PK-416 and Pusa-20. The difference in osmotic adjustment between all the genotypes was correlated with the concentrations of ion examined such as Na+ and the leaf proline concentration. These results suggest that the genotypic variation for salt tolerance can be partially accounted for by plant physiological measures. The genetic polymorphisms between soybean genotypes differing in response to salt stress were characterized using 25 RAPD primers. These primers generated a total of 1640 amplification products, among which 1615 were found to be polymorphic. A very high degree of polymorphism (98.30%) was observed. UPGMA cluster analysis of genetic similarity indices grouped all the genotypes into two major clusters. Intra-clustering within the two clusters precisely grouped the 10 genotypes in sub-cluster as expected from their physiological findings. Our results show that RAPD technique is a sensitive, precise and efficient tool for genomic analysis in soybean genotypes.

Keywords: glycine max, NaCl, RAPD, proteomics

Procedia PDF Downloads 585

Authors: Gu Sihao

Abstract:

The coordinated development of "ecology-production-life" in cities has been highly concerned by the country, and the transformation development and sustainable development of resource-based cities have become a hot research topic at present. As an important part of China's resource-based cities, coal resource-based cities have the characteristics of large number and wide distribution. However, due to the adjustment of national energy structure and the gradual exhaustion of urban coal resources, the development vitality of coal resource-based cities is gradually reduced. In many studies, the deterioration of ecological environment in coal resource-based cities has become the main problem restricting their urban transformation and sustainable development due to the "emphasis on economy and neglect of ecology". Since the 18th National Congress of the Communist Party of China (CPC), the Central Government has been deepening territorial space planning and development. On the premise of optimizing territorial space development pattern, it has completed the demarcation of ecological protection red lines, carried out ecological zoning and ecosystem evaluation, which have become an important basis and scientific guarantee for ecological modernization and ecological civilization construction. Grasp the regional multiple ecosystem services is the precondition of the ecosystem management, and the relationship between the multiple ecosystem services study, ecosystem services cluster can identify the interactions between multiple ecosystem services, and on the basis of the characteristics of the clusters on regional ecological function zoning, to better Social-Ecological system management. Based on this cognition, this study optimizes the spatial function zoning of Gujiao, a coal resource-based city, in order to provide a new theoretical basis for its sustainable development. This study is based on the detailed analysis of characteristics and utilization of Gujiao city land space, using SOFM neural networks to identify local ecosystem service clusters, according to the cluster scope and function of ecological function zoning of space partition balance and coordination between different ecosystem services strength, establish a relationship between clusters and land use, and adjust the functions of territorial space within each zone. Then, according to the characteristics of coal resources city and national spatial function zoning characteristics, as the driving factors of land change, by cellular automata simulation program, such as simulation under different restoration strategy situation of urban future development trend, and provides relevant theories and technical methods for the "third-line" demarcations of Gujiao's territorial space planning, optimizes territorial space functions, and puts forward targeted strategies for the promotion of regional ecosystem services, providing theoretical support for the improvement of human well-being and sustainable development of resource-based cities.

Keywords: coal resource-based city, territorial spatial planning, ecosystem service cluster, gmop model, geosos-FLUS model, functional zoning optimization and upgrading

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Authors: M. Helen

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Chemical graph serves as a convenient model for any real or abstract chemical system. Dendrimers are novel three dimensional hyper branched globular nanopolymeric architectures. Drug delivery scientists are especially enthusiastic about possible utility of dendrimers as drug delivery tool. Dendrimers like poly L lysine (PLL), poly-propylene imine (PPI) and poly-amidoamine (PAMAM), etc., are used as gene carrier in drug delivery system because of their chemical characteristics. These characteristics of chemical compounds are analysed using topological indices (invariants under graph isomorphism) such as Wiener index, Zagreb index, etc., Prof. V. R. Kulli motivated by the application of Zagreb indices in finding the total π energy and derived Gourava indices which is an improved version over Zagreb indices. In this paper, we study the structure of PLL-Dendrimer that has the following applications: reduction in toxicity, colon delivery, and topical delivery. Also, we determine first and second Gourava indices, first and second hyper Gourava indices, product and sum connectivity Gourava indices for PLL-Dendrimer. Gourava Indices have found applications in Quantitative Structure-Property Relationship (QSPR)/ Quantitative Structure-Activity Relationship (QSAR) studies.

Keywords: connectivity Gourava indices, dendrimer, Gourava indices, hyper GouravaG indices

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Authors: Hamsini Pulugurtha, V.S. Lakshmi Jagadmaba Paluri

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Distributed denial of service attack (DDoS) is one altogether the top-rated cyber threats presently. It runs down the victim server resources like a system of measurement and buffer size by obstructing the server to supply resources to legitimate shoppers. Throughout this text, we tend to tend to propose a mathematical model of DDoS attack; we discuss its relevancy to the choices like inter-arrival time or rate of arrival of the assault customers accessing the server. We tend to tend to further analyze the attack model in context to the exhausting system of measurement and buffer size of the victim server. The projected technique uses an associate in nursing unattended learning technique, self-organizing map, to make the clusters of identical choices. Lastly, the abstract applies mathematical correlation and so the standard likelihood distribution on the clusters and analyses their behaviors to look at a DDoS attack. These systems not exclusively interconnect very little devices exchanging personal data, but to boot essential infrastructures news standing of nuclear facilities. Although this interconnection brings many edges and blessings, it to boot creates new vulnerabilities and threats which might be conversant in mount attacks. In such sophisticated interconnected systems, the power to look at attacks as early as accomplishable is of paramount importance.

Keywords: application attack, bandwidth, buffer correlation, DDoS distribution flooding intrusion layer, normal prevention probability size

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Authors: Reza Talebi

Abstract:

Wheat (Triticum aestivum) is one of the most important food staples in Iran. Understanding genetic variability among the landrace wheat germplasm is important for breeding. Landraces endemic to Iran are a genetic resource that is distinct from other wheat germplasm. In this study, 60 Iranian landrace wheat accessions were characterized AFLP and ISSR markers. Twelve AFLP primer pairs detected 128 polymorphic bands among the sixty genotypes. The mean polymorphism rate based on AFLP data was 31%; however, a wide polymorphism range among primer pairs was observed (22–40%). Polymorphic information content (PIC value) calculated to assess the informativeness of each marker ranged from 0.28 to 0.4, with a mean of 0.37. According to AFLP molecular data, cluster analysis grouped the genotypes in five distinct clusters. .ISSR markers generated 68 bands (average of 6 bands per primer), which 31 were polymorphic (45%) across the 60 wheat genotypes. Polymorphism information content (PIC) value for ISSR markers was calculated in the range of 0.14 to 0.48 with an average of 0.33. Based on data achieved by ISSR-PCR, cluster analysis grouped the genotypes in three distinct clusters. Both AFLP and ISSR markers able to showed that high level of genetic diversity in Iranian landrace wheat accessions has maintained a relatively constant level of genetic diversity during last years.

Keywords: wheat, genetic diversity, AFLP, ISSR

Procedia PDF Downloads 451