Search results for: multivariate data analysis

Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Lin Li, P. G. R. N. I. Pussella

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When representing spatial data and their attributes on different types of maps, the scale plays a key role in the process of map generalization. The process is consisted with two main operators such as selection and omission. Once some data were selected, they would undergo of several geometrical changing processes such as elimination, simplification, smoothing, exaggeration, displacement, aggregation and size reduction. As a result of these operations at different levels of data, the geometry of the spatial features such as length, sinuosity, orientation, perimeter and area would be altered. This would be worst in the case of preparation of small scale maps, since the cartographer has not enough space to represent all the features on the map. What the GIS users do is when they wanted to analyze a set of spatial data; they retrieve a data set and does the analysis part without considering very important characteristics such as the scale, the purpose of the map and the degree of generalization. Further, the GIS users use and compare different maps with different degrees of generalization. Sometimes, GIS users are going beyond the scale of the source map using zoom in facility and violate the basic cartographic rule 'it is not suitable to create a larger scale map using a smaller scale map'. In the study, the effect of map generalization for GIS analysis would be discussed as the main objective. It was used three digital maps with different scales such as 1:10000, 1:50000 and 1:250000 which were prepared by the Survey Department of Sri Lanka, the National Mapping Agency of Sri Lanka. It was used common features which were on above three maps and an overlay analysis was done by repeating the data with different combinations. Road data, River data and Land use data sets were used for the study. A simple model, to find the best place for a wild life park, was used to identify the effects. The results show remarkable effects on different degrees of generalization processes. It can see that different locations with different geometries were received as the outputs from this analysis. The study suggests that there should be reasonable methods to overcome this effect. It can be recommended that, as a solution, it would be very reasonable to take all the data sets into a common scale and do the analysis part.

Keywords: generalization, GIS, scales, spatial analysis

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Veronica Kembabazi, Arnold Tigaiza, Juliet Aweko, Charles Opio, Michael Ediau, Elizabeth Ekirapa, Andrew Tusubira, Peter Waiswa

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Background: Digital payments have been branded as key in solving health payment challenges, evidence on their adoptability is still limited especially among Community Health Workers (CHWs), yet vital for ensuring sustainability. We therefore assessed the adoptability of digital payments for CHWs in Wakiso district, Uganda. Methods: In December 2022, we conducted a convergent parallel mixed-methods study among 150 randomly selected CHWs in Wakiso district. Supplementary qualitative data were collected from the Digital payment coordinators as Key Informants (KIs). We adopted the Technology Acceptance Model (TAM) framework to assess the adoptability of digital payments among CHWS. Factor analysis was performed to extract composite variables from the original constituting variables. Kaiser-Meyer-Olkin statistics were assessed for each construct to determine appropriateness for data reduction. Using logistic regression for multivariate analysis, we assessed the association between adoptability constructs and the CHW intention to use digital payments. Quantitative data was analyzed using STATA, while qualitative data was transcribed verbatim and analyzed using ATLAS.ti software. Results: Overall, 150 respondents were interviewed and nearly all participants (98.0%) had previously received payments through mobile money, a digital-payment method. The majority (52%) of CHWs said they intend to use digital payment modalities. Perceived risk had an 83% negative influence on the adoptability of digital payment modalities (OR= 0.167, p < 0.01), perceived trust had an almost three times positive influence on the adoptability of digital payment modalities (OR= 2.823, p < 0.01). Qualitative interviews showed that most CHWs reported good experiences in their use of digital health payment modalities except for delays associated with mobile money payments. Mobile money was reported to be easy to use, in addition to fostering financial responsibility compared to cash. Conclusion: CHWs in Wakiso district intend to use digital payment modalities, particularly mobile money/e-cash, and the perceived risk of the payment method and trust are key determinants of its adoptability. Synergized efforts by both payment providers and service operators to manage payment delays and identified risks among mobile money operators could attenuate perceived risk and build trust in digital payment modalities.

Keywords: digital health payments, community health workers, adoptability, technology acceptance model

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Sidi Yang, Haiyi Zhang

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Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields.

Keywords: text mining, Twitter, topic model, sentiment analysis

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Authors: Ho Yeon Park, Kyoung-Jae Kim

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The purpose of this study is to investigate whether friendship in social media can be an important factor in recommender system through social scientific analysis of friendship in popular social media such as Facebook and Twitter. For this purpose, this study analyzes data on friendship in real social media using component analysis and clique analysis among sub-group analysis in social network analysis. In this study, we propose an algorithm to reflect the results of sub-group analysis on the recommender system. The key to this algorithm is to ensure that recommendations from users in friendships are more likely to be reflected in recommendations from users. As a result of this study, outcomes of various subgroup analyzes were derived, and it was confirmed that the results were different from the results of the existing recommender system. Therefore, it is considered that the results of the subgroup analysis affect the recommendation performance of the system. Future research will attempt to generalize the results of the research through further analysis of various social data.

Keywords: sub-group analysis, social media, social network analysis, recommender systems

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Authors: Seonjae Lee, Sungjoo Lee

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Excavation is necessary to remain competitive in the current business environment. The company survived the rapidly changing industry conditions by adapting new business strategy and reducing technology challenges. Traditionally, the two methods are conducted excavations for new businesses. The first method is, qualitative analysis of expert opinion, which is gathered through opportunities and secondly, new technologies are discovered through quantitative data analysis of method patents. The second method increases time and cost. Patent data is restricted for use and the purpose of discovering business opportunities. This study presents the company's characteristics (sector, size, etc.), of new business opportunities in customized form by reviewing the value chain perspective and to contributing to creating new business opportunities in the proposed model. It utilizes the trademark database of the Korean Intellectual Property Office (KIPO) and proprietary company information database of the Korea Enterprise Data (KED). This data is key to discovering new business opportunities with analysis of competitors and advanced business trademarks (Module 1) and trading analysis of competitors found in the KED (Module 2).

Keywords: value chain, trademark, trading analysis, new business opportunity

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Authors: Austin P. Hopkins, R. Daren Harmel, Jim A Ippolito, P. J. A. Kleinman, D. Sahoo

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Field-scale runoff and water quality data are critical to understanding the fate and transport of nutrients applied to agricultural lands and minimizing their off-site transport because it is at that scale that agricultural management decisions are typically made based on hydrologic, soil, and land use factors. However, regional influences such as precipitation, temperature, and prevailing cropping systems and land use patterns also impact nutrient runoff. In the present study, the recently-updated MANAGE (Measured Annual Nutrient loads from Agricultural Environments) database was used to conduct an ecoregion-level analysis of nitrogen and phosphorus runoff from agricultural lands in the North America. Specifically, annual N and P runoff loads for cropland and grasslands in North American Level II EPA ecoregions were presented, and the impact of factors such as land use, tillage, and fertilizer timing and placement on N and P runoff were analyzed. Specifically we compiled annual N and P runoff load data (i.e., dissolved, particulate, and total N and P, kg/ha/yr) for each Level 2 EPA ecoregion and for various agricultural management practices (i.e., land use, tillage, fertilizer timing, fertilizer placement) within each ecoregion to showcase the analyses possible with the data in MANAGE. Potential differences in N and P runoff loads were evaluated between and within ecoregions with statistical and graphical approaches. Non-parametric analyses, mainly Mann-Whitney tests were conducted on median values weighted by the site years of data utilizing R because the data were not normally distributed, and we used Dunn tests and box and whisker plots to visually and statistically evaluate significant differences. Out of the 50 total North American Ecoregions, 11 were found that had significant data and site years to be utilized in the analysis. When examining ecoregions alone, it was observed that ER 9.2 temperate prairies had a significantly higher total N at 11.7 kg/ha/yr than ER 9.4 South Central Semi Arid Prairies with a total N of 2.4. When examining total P it was observed that ER 8.5 Mississippi Alluvial and Southeast USA Coastal Plains had a higher load at 3.0 kg/ha/yr than ER 8.2 Southeastern USA Plains with a load of 0.25 kg/ha/yr. Tillage and Land Use had severe impacts on nutrient loads. In ER 9.2 Temperate Prairies, conventional tillage had a total N load of 36.0 kg/ha/yr while conservation tillage had a total N load of 4.8 kg/ha/yr. In all relevant ecoregions, when corn was the predominant land use, total N levels significantly increased compared to grassland or other grains. In ER 8.4 Ozark-Ouachita, Corn had a total N of 22.1 kg/ha/yr while grazed grassland had a total N of 2.9 kg/ha/yr. There are further intricacies of the interactions that agricultural management practices have on one another combined with ecological conditions and their impacts on the continental aquatic nutrient loads that still need to be explored. This research provides a stepping stone to further understanding of land and resource stewardship and best management practices.

Keywords: water quality, ecoregions, nitrogen, phosphorus, agriculture, best management practices, land use

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Authors: Cheng-Cao Sun, Shu-Jun Li, De-Jia Li

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Long non-coding RNA (lncRNA) X inactivate-specific transcript (XIST) has been verified as an oncogenic gene in several human malignant tumors, and its dysregulation was closed associated with tumor initiation, development and progression. Nevertheless, whether the aberrant expression of XIST in human nasopharyngeal carcinoma (NPC) is corrected with malignancy, metastasis or prognosis has not been elaborated. Here, we discovered that XIST was up-regulated in NPC tissues and higher expression of XIST contributed to a markedly poorer survival time. In addition, multivariate analysis demonstrated XIST was an independent risk factor for prognosis. XIST over-expression enhanced, while XIST silencing hampered the cell growth in NPC. Additionally, mechanistic analysis revealed that XIST up-regulated the expression of miR-34a-5p targeted gene E2F3 through acting as a competitive ‘sponge’ of miR-34a-5p. Taking all into account, we concluded that XIST functioned as an oncogene in NPC through up-regulating E2F3 in part through ‘spongeing’ miR-34a-5p.

Keywords: X inactivate-specific transcript; hsa-miRNA-34a-5p, miR-34a-5p; E2F3, nasopharyngeal carcinoma, tumorigenesis

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Authors: Sathiya Susuman Appunni

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Background: This article reviews the self-reported pregnancies among teenage girls currently attending secondary school in South Africa. The study aim is to examine the demographic and socio-economic factors associated with self-reported pregnancies among teenage girls currently attending secondary school in the study area. Data and Methods: Secondary data drawn from the General Household Surveys 2016 and Community Survey 2016 as well as 10 % sample data from the 2011 South African census were used. Bivariate, and Multivariate analyses were carried in order to meet the aims of the study. Results: The independent variable identified was the number of economically active people in the household, which indicated 3.3% in 2011 and 3.6% in 2016 for the household with no economically active member. Among the provinces, Limpopo has been leading by 5.2% of self-reported pregnancies among the girls currently attending secondary school in South Africa. Conclusion: It is recommended that the needs to be special health policies and strategies in place to address this epidemic and such policies need to be targeted to the different needs of teenagers in the different demarcations of the country.

Keywords: pregnancy prevalence, demographic, household, teenage girls, socio-economic

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Christina Zourna, Ioanna Papavassiliou-Alexiou

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The purpose of this multi-phased action research PhD study in Greece was to investigate if and how Drama in Education (DiE) – used as an innovative group counselling method – may have positive effects on secondary education students’career self-efficacy and career decision-making skills development. Using both quantitative and qualitative research tools, high quality data were gathered at various stages of the research and were analysed through multivariate methods and open-source computer aided data analysis software such as R Studio, QualCoder, and SPSS packages. After a five-month-long educational intervention based on DiE method, it was found that 9th, 10th, and 11th gradersameliorated their self-efficacy and learned the process of making an informed career decision – through targeted information gathering about themselves and possible study paths – thus, developing career problem-solving and career management skills. Gender differences were non statistically important, while differences in grades showed a minor influence on some of the measured factorssuch as general career indecisiveness and self-evaluation. Students in the 11th grade scored significantly higher than younger students in the career self-efficacy scale and have stronger faith in their abilities e.g., choosing general over vocational school and major study orientation. The study has shown that DiE can be effective in group career guidance, especially concerning the pillars of self-awareness, self-efficacy, and career decision-making processes.

Keywords: career decision-making skills, career self-efficacy, CDDQ scale, CDMSE-SF scale, drama in education method

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Authors: Ubaidillah Zuhdi, Shunsuke Mori, Kazuhisa Kamegai

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The purpose of this study is to forecast the influences of Information and Communication Technology (ICT) on the structural changes of Japanese economies based on Leontief Input-Output (IO) coefficients. This study establishes a statistical analysis to predict the future interrelationships among industries. We employ the Constrained Multivariate Regression (CMR) model to analyze the historical changes of input-output coefficients. Statistical significance of the model is then tested by Likelihood Ratio Test (LRT). In our model, ICT is represented by two explanatory variables, i.e. computers (including main parts and accessories) and telecommunications equipment. A previous study, which analyzed the influences of these variables on the structural changes of Japanese industrial sectors from 1985-2005, concluded that these variables had significant influences on the changes in the business circumstances of Japanese commerce, business services and office supplies, and personal services sectors. The projected future Japanese economic structure based on the above forecast generates the differentiated direct and indirect outcomes of ICT penetration.

Keywords: forecast, ICT, industrial structural changes, statistical analysis

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Authors: Takahiro Nishigaki, Takashi Onoda

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In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.

Keywords: independent topic analysis, topic extraction, topic naming, web search engine

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Authors: Tri Wijayanti Septiarini, Agus Maman Abadi, Muhammad Rifki Taufik

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The exchange rate of IDR/USD can be the indicator to analysis Indonesian economy. The exchange rate as a important factor because it has big effect in Indonesian economy overall. So, it needs the analysis data of exchange rate. There is decomposition data of exchange rate of IDR/USD to be frequency and time. It can help the government to monitor the Indonesian economy. This method is very effective to identify the case, have high accurate result and have simple structure. In this paper, data of exchange rate that used is weekly data from December 17, 2010 until November 11, 2014.

Keywords: the exchange rate, fuzzy mamdani, discrete wavelet transforms, fuzzy wavelet

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Authors: Yuyun Wabula, B. J. Dewancker

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In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Keywords: geolocation, Twitter, distribution analysis, human mobility

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Authors: Kebede Mengistu, Kassahun Alemu, Bikes Destaw

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Introduction: Malnutrition continues to be a major public health problem in developing countries. It is the most important risk factor for the burden of diseases. It causes about 300, 000 deaths per year and responsible for more than half of all deaths in children. In Ethiopia, child malnutrition rate is one of the most serious public health problem and the highest in the world. High malnutrition rates in the country pose a significant obstacle to achieving better child health outcomes. Objective: To assess prevalence of malnutrition and associated factors among children aged 6-59 months at Hidabu Abote district, North shewa, Oromia. Methods: A community based cross sectional study was conducted on 820 children aged 6-59 months from September 8-23, 2012 at Hidabu Abote district. Multistage sampling method was used to select households. Children were selected from each kebeles by simple random sampling. Anthropometric measurements and structured questioners were used. Data was processed using EPi-info soft ware and exported to SPSS for analysis. Then after, sex, age, months, height, and weight transferred with HHs number to ENA for SMART 2007software to convert nutritional data into Z-scores of the indices; H/A, W/H and W/A. Bivariate and multivariate logistic regressions were used to identify associated factors of malnutrition. Results: The analysis this study revealed that, 47.6%, 30.9% and 16.7% of children were stunted, underweight and wasted, respectively. The main associated factors of stunting were found to be child age, family monthly income, children were received butter as pre-lacteal feeding and family planning. Underweight was associated with number of children HHs and children were received butter as per-lacteal feeding but un treatment of water in HHs only associated with wasting. Conclusion and recommendation: From the findings of this study, it is concluded that malnutrition is still an important problem among children aged 6-59 months. Therefore, especial attention should be given on intervention of malnutrition.

Keywords: children, Hidabu Abote district, malnutrition, public health

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Authors: Tsai Yu-hsin, Chung Yi-Hsin

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The employment of an HSR system, resulting in elevation in the inter-city/-region accessibility, is likely to promote spatial interaction between places in the HSR and extended territory. The inter-city/-region travel via HSR could be, among others, affected by the land use, transportation, and location of the HSR station at both trip origin and destination ends. However, relatively few insights have been shed on these impacts and spatial patterns of the HSR travel. The research purposes, as phase one of a series of HSR related research, of this study are threefold: to analyze the general spatial patterns of HSR trips, such as the spatial distribution of trip origins and destinations; to analyze if specific land use, transportation characteristics, and trip characteristics affect HSR trips in terms of the use of HSR, the distribution of trip origins and destinations, and; to analyze the socio-economic characteristics of HSR travelers. With the Taiwan HSR starting operation in 2007, this study emphasizes on the intermediate-term impact of HSR, which is made possible with the population and housing census and industry and commercial census data and a station area intercept survey conducted in the summer 2014. The analysis will be conducted at the city, inter-city, and inter-region spatial levels, as necessary and required. The analysis tools include descriptive statistics and multivariate analysis with the assistance of SPSS, HLM and ArcGIS. The findings, on the one hand, can provide policy implications for associated land use, transportation plan and the site selection of HSR station. On the other hand, on the travel the findings are expected to provide insights that can help explain how land use and real estate values could be affected by HSR in following phases of this series of research.

Keywords: high speed rail, land use, travel, spatial pattern

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Authors: Ramu Rawat

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Background: This paper is an effort to analyze the role of education and occupation as critical determinants in using smokeless tobacco among male adults in India and its selected states. Methodology: Global Adult Tobacco Survey (GATS) India 2009-10 data have been used for this study. Bivariate and Multivariate (Cox proportion hazards model) analyses are carried out to measure the impact of education and occupation on use of smokeless tobacco among male adults in India. Results and Conclusion: The study evidently suggests that, majority of Indian male adults are using Khaini and Gutkha (local names for smokeless tobacco). The analysis also shows that education and occupation are two important critical predictors of use of smokeless tobacco. The males at younger age with no or little education from rural area use smokeless tobacco more than their counterpart group from urban areas. This distinction can be attributable to their lack of knowledge or ignorance about the consequences of tobacco consumption. Place of residence played significant role for declining use of smokeless tobacco along with the effect of society. Society awareness about harmful effects of smokeless tobacco with local level government may play important role for controlling the use of any type of tobacco or tobacco products in India.

Keywords: smokeless tobacco, male, Khaini, education, occupation

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Authors: Seyed Habib Shojaei, Reza Eivazi, Mir Sajad Shojaei, Alireza Akbari, Pooria Mazloom, Seyede Mitra Sadati, Mir Zeinalabedin Shojaei, Farnaz Farbakhsh

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To study the genetic diversity in rapeseeds and agronomic traits, an experiment was conducted using multivariate statistical methods at Agricultural Research Station of Kashmar in 2012-2013.In this experiment, ten genotypes of rapeseed in a Randomized Complete Block designs with three replications were evaluated. The following traits were studied: seed yield, number of days to the fifty percent of flowering, plant height, number of pods on main stem, length of the pod, seed yield per plant, number of seed in pod, harvest index, weight of 100 seeds, number of pods on lateral branch, number of lateral branches. In analyzing the variance, differences between cultivars were significant. The average comparative revealed that the most valuable variety was Licord regarding to the traits while the least valuable variety was Opera. In stepwise regression, harvest index, grain yield per plant and number of pods per lateral branches were entering to model. Correlation analysis showed that the grain yield with the number of pods per lateral branches and seed yield per plant have positive and significant correlation. In the factor analysis, the first five components explained more than 83% of the variance in the data. In the first factor, seed yield and the number of pods per lateral branches were of the highest importance. The traits, seed yield per plant, and pod per main stem were of a great significance in the second factor. Moreover, in the third factor, plant height and the number of lateral branches were more important. In the fourth factor, plant height and one hundred seeds weight were of the highest variance. Finally, days to fifty percent of flowering and one hundred seeds weight were more important in fifth factor.

Keywords: rapeseed, variance analysis, regression, factor analysis

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Authors: Monika Rani, Claudio Marchesi, Stefania Federici, Laura E. Depero

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Degradation of the aquatic environment by plastic litter, especially microplastics (MPs), i.e., any water-insoluble solid plastic particle with the longest dimension in the range 1µm and 1000 µm (=1 mm) size, is an unfortunate indication of the advancement of the Anthropocene age on Earth. Microplastics formed due to natural weathering processes are termed as secondary microplastics, while when these are synthesized in industries, they are called primary microplastics. Their presence from the highest peaks to the deepest points in oceans explored and their resistance to biological and chemical decay has adversely affected the environment, especially marine life. Even though the presence of MPs in the marine environment is well-reported, a legitimate and authentic analytical technique to sample, analyze, and quantify the MPs is still under progress and testing stages. Among the characterization techniques, vibrational spectroscopic techniques are largely adopted in the field of polymers. And the ongoing miniaturization of these methods is on the way to revolutionize the plastic recycling industry. In this scenario, the capability and the feasibility of a miniaturized near-infrared (MicroNIR) spectroscopy combined with chemometrics tools for qualitative and quantitative analysis of urban plastic waste collected from a recycling plant and microplastic mixture fragmented in the lab were investigated. Based on the Resin Identification Code, 250 plastic samples were used for macroplastic analysis and to set up a library of polymers. Subsequently, MicroNIR spectra were analysed through the application of multivariate modelling. Principal Components Analysis (PCA) was used as an unsupervised tool to find trends within the data. After the exploratory PCA analysis, a supervised classification tool was applied in order to distinguish the different plastic classes, and a database containing the NIR spectra of polymers was made. For the microplastic analysis, the three most abundant polymers in the plastic litter, PE, PP, PS, were mechanically fragmented in the laboratory to micron size. The distinctive arrangement of blends of these three microplastics was prepared in line with a designed ternary composition plot. After the PCA exploratory analysis, a quantitative model Partial Least Squares Regression (PLSR) allowed to predict the percentage of microplastics in the mixtures. With a complete dataset of 63 compositions, PLS was calibrated with 42 data-points. The model was used to predict the composition of 21 unknown mixtures of the test set. The advantage of the consolidated NIR Chemometric approach lies in the quick evaluation of whether the sample is macro or micro, contaminated, coloured or not, and with no sample pre-treatment. The technique can be utilized with bigger example volumes and even considers an on-site evaluation and in this manner satisfies the need for a high-throughput strategy.

Keywords: chemometrics, microNIR, microplastics, urban plastic waste

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Authors: Aleme Mekuria, Samuel Mathewos

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Background: Teenage pregnancy has long been a worldwide social, economic and educational concern for the developed, developing and underdeveloped countries. Studies on adolescent sexuality and pregnancy are very limited in our country. Therefore, this study aims at assessing the prevalence of teenage pregnancy and its determinants among school adolescents of Arba Minch town. Methods: Institution- based, cross-sectional study was conducted from 20-30 March 2014. Systematic sampling technique was used to select a total of 578 students from four schools of the town. Data were collected by trained data collectors using a pre-tested, self-administered structured questionnaire. The analysis was made using the software SPSS version 20.0 statistical packages. Multivariate logistic regression was used to identify the predictors of teenage pregnancy. Results: The prevalence of teenage pregnancy among school adolescents of Arba Minch town was 7.7%. Being grade11(AOR=4.6;95%CI:1.4,9.3) and grade12 student (AOR=5.8;95% CI:1.3,14.4), not knowing the correct time to take emergency contraceptives(AOR=3.3;95%CI:1.4,7.4), substance use(AOR=3.1;95%CI:1.1,8.8), living with either of biological parents (AOR=3.3;95%CI:1.1,8.7) and poor parent-daughter interaction (AOR=3.1;95%CI:1.1,8.7) were found to be significant predictors of teenage pregnancy. Conclusion: This study revealed a high level of teenage pregnancy among school adolescents of Arba Minch town. A significant number of adolescent female school students were at risk of facing the challenges of teenage pregnancy in the study area. School-based reproductive health education and strong parent-daughter relationships should be strengthened.

Keywords: adolescent, Arba minch, risk factors, school, southern Ethiopia, teenage pregnancy

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Authors: Stephanie Chen, Mitja Echim, Christof Büskens

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To describe and propagate the behavior of a system mathematical models are formulated. Parameter identification is used to adapt the coefficients of the underlying laws of science. For complex systems this approach can be incomplete and hence imprecise and moreover too slow to be computed efficiently. Therefore, these models might be not applicable for the numerical optimization of real systems, since these techniques require numerous evaluations of the models. Moreover not all quantities necessary for the identification might be available and hence the system must be adapted manually. Therefore, an approach is described that generates models that overcome the before mentioned limitations by not focusing on physical laws, but on measured (sensor) data of real systems. The approach is more general since it generates models for every system detached from the scientific background. Additionally, this approach can be used in a more general sense, since it is able to automatically identify correlations in the data. The method can be classified as a multivariate data regression analysis. In contrast to many other data regression methods this variant is also able to identify correlations of products of variables and not only of single variables. This enables a far more precise and better representation of causal correlations. The basis and the explanation of this method come from an analytical background: the series expansion. Another advantage of this technique is the possibility of real-time adaptation of the generated models during operation. Herewith system changes due to aging, wear or perturbations from the environment can be taken into account, which is indispensable for realistic scenarios. Since these data driven models can be evaluated very efficiently and with high precision, they can be used in mathematical optimization algorithms that minimize a cost function, e.g. time, energy consumption, operational costs or a mixture of them, subject to additional constraints. The proposed method has successfully been tested in several complex applications and with strong industrial requirements. The generated models were able to simulate the given systems with an error in precision less than one percent. Moreover the automatic identification of the correlations was able to discover so far unknown relationships. To summarize the above mentioned approach is able to efficiently compute high precise and real-time-adaptive data-based models in different fields of industry. Combined with an effective mathematical optimization algorithm like WORHP (We Optimize Really Huge Problems) several complex systems can now be represented by a high precision model to be optimized within the user wishes. The proposed methods will be illustrated with different examples.

Keywords: adaptive modeling, automatic identification of correlations, data based modeling, optimization

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Authors: Ahmadreza Jabalameli, Mohammad Ebrahimpour Borujeni

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This study aims to evaluate the effectiveness of intensive short-term dynamic psychotherapy (ISTDP) on ambiguity tolerance, emotional intelligence and stress coping strategies in financial market traders. The methodology of this study was quasi-experimental, pre-test and post-test with control group. The statistical population of this study includes all students at Jabalameli Information Technology Academy in 2022. Among them, 30 people were selected by voluntary sampling through interviews, and were randomly divided into two experimental and control groups of 51 people. And the components were measured according to McLain Ambiguity Tolerance Questionnaire, Bar-On Emotional Intelligence and Lazarus Stress Coping Strategies. The data were obtained by SPSS software and were analyzed by using multivariate analysis of covariance. The results indicate that intensive short-term dynamic psychotherapy influences the emotional intelligence as well as the ambiguity tolerance of traders.

Keywords: ISTDP, ambiguity tolerance, trading, emotional intelligence, stress

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Authors: Xinge Liao, Francesc Xavier Roige Ventura, Dolores Sanchez Aguilera

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With the development of the Internet, the study of tourism behavior has rapidly expanded from the traditional physical market to the online market. Data on the Internet is characterized by dynamic changes, and new data appear all the time. In recent years the generation of a large volume of data was characterized, such as forums, blogs, and other sources, which have expanded over time and space, together they constitute large-scale Internet data, known as Big Data. This data of technological origin that derives from the use of devices and the activity of multiple users is becoming a source of great importance for the study of geography and the behavior of tourists. The study will focus on cultural heritage tourist practices in the context of Big Data. The research will focus on exploring the characteristics and behavior of Chinese tourists in relation to the cultural heritage of Catalonia. Geographical information, target image, perceptions in user-generated content will be studied through data analysis from Weibo -the largest social networks of blogs in China. Through the analysis of the behavior of heritage tourists in the Big Data environment, this study will understand the practices (activities, motivations, perceptions) of cultural tourists and then understand the needs and preferences of tourists in order to better guide the sustainable development of tourism in heritage sites.

Keywords: Barcelona, Big Data, Catalonia, cultural heritage, Chinese tourism market, tourists’ behavior

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Authors: Antoine Vikkey Hinson, Menonli Adjobimey, Gemayel Ahmed Biokou, Rose Mikponhoue

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Introduction: Construction industry is a critical concern with regard to Health and Safety Service worldwide. World health Organization revealed that work-related disease and trauma were held responsible for the death of one million nine hundred thousand people in 2016. The aim of this study it was to determine the prevalence and factors associated with the occurrence of work accidents in a construction industry in Benin. Method: It was a descriptive cross-sectional and analytical study. Data analysis was performed with R software 4.1.1. In multivariate analysis, we performed a binary logistic regression. OR adjusted (ORa) association measures and their 95% confidence interval [CI95%] were presented for the explanatory variables used in the final model. The significance threshold for all tests selected was 5% (p < 0.05) Result: In this study, 472 workers were included, and, of these, 452 (95.7%) were men corresponding to a sex ratio of 22.6. The average age of the workers was 33 years ± 8.8 years. Workers were mostly laborers (84.7%), and had declared having inadequate personal protective equipment (50.6%, n=239). The prevalence of work accidents is 50.8%. Collision with a rolling stock (25.8%), cut (16.2%), and stumbling (16.2%) were the main types of work accidents on the construction site. Four factors were associated with contributing to work accidents. Fatigue or exhaustion (ORa : 1.53[1.03 ; 2.28]); The use of dangerous tools (ORa : 1.81 [1.22 ; 2.71]); The various laborers’ jobs (ORa : 4.78 [2.62 ; 9.21]); and seniority in the company ≥ 4 years (ORa : 2.00 [1.35 ; 2.96]). Conclusion: This study allowed us to identify the associated factors. It is imperative to implement a rigorous policy of occupational health and security mostly the continuing training for workers safe, the supply of appropriate work tools and protective

Keywords: prevalence, work accident, associated factors, construction, benin

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: big data, big data analytics, Hadoop, cloud

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Moslem Hossain, Mukesh Kumar, K. C. Das

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Background: Household headship is the crucial decision-maker as well as the economic provider of the household. In Indian society, household heads occupied by men from the pre-colonial period. This study attempt to examine the changes in household headship in India. Methods: The study used univariate and multivariate analysis to examine the trends and patterns of different characteristics of the household head using the various rounds of national family health survey data. Results: The female household head is gradually increasing; on the other hand, the male-dominant is decreasing over the four national family and health surveys. The mean age of the household head is higher in rural areas than urban India. Only ten percentage of Households are higher educated, and 83 percent of the male household head has a low standard of living. The mean family size of the household has a decreasing trend in both the urban and rural areas during the study period. Conclusions: The result indicates that women's autonomy is increasing and leading to inclusive growth, which introduced in the eleven five year plan, especially focuses on the woman and young people in the country.

Keywords: household head, national family health survey, mean age, mean family size

Procedia PDF Downloads 111