Search results for: regression lines

Authors: Hirbaye Mokona, Abebaw Gebeyehu, Aemro Zerihun

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Introduction: Attention deficit hyperactivity disorder is serious public health problem affecting millions of children throughout the world. Method: A cross-sectional study conducted from May to June 2015 among children age 6 to 17 years living in rural area of Girja district. Multi-stage cluster sampling technique was used to select 1302 study participants. Disruptive Behavior Disorder rating scale was used to collect the data. Data were coded, entered and cleaned by Epi-Data version 3.1 and analyzed by SPSS version 20. Logistic regression analysis was used and Variables that have P-values less than 0.05 on multivariable logistic regression was considered as statistically significant. Results: Prevalence of Attention deficit hyperactivity disorder (ADHD) among children age 6 to 17 years was 7.3%. Being male [AOR=1.81, 95%CI: (1.13, 2.91)]; living with single parent [AOR=5.0, 95%CI: (2.35, 10.65)]; child birth order/rank [AOR=2.35, 95%CI: (1.30, 4.25)]; low family socio-economic status [AOR= 2.43, 95%CI: (1.29, 4.59)]; maternal alcohol/khat use during pregnancy [AOR=3.14, 95%CI: (1.37, 7.37)] and complication at delivery [AOR=3.56, 95%CI: (1.19, 10.64)] were more likely to develop Attention deficit hyperactivity disorder. Conclusion: In this study, the prevalence of Attention deficit hyperactivity disorder was similar with worldwide prevalence. Prevention and early management of its modifiable risk factors should be carryout alongside increasing community awareness.

Keywords: attention deficit hyperactivity disorder, ADHD, associated factors, children, prevalence

Procedia PDF Downloads 186

Authors: Jelena S. Kiurski, Vesna S. Kecić, Snežana M. Aksentijević

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The identification and evaluation of organic and inorganic pollutants were performed in a flexographic facility in Novi Sad, Serbia. Air samples were collected and analyzed in situ, during 4-hours working time at five sampling points by the mobile gas chromatograph and ozonometer at the printing of collagen casing. Experimental results showed that the concentrations of isopropyl alcohol, acetone, total volatile organic compounds and ozone varied during the sampling times. The highest average concentrations of 94.80 ppm and 102.57 ppm were achieved at 200 minutes from starting the production for isopropyl alcohol and total volatile organic compounds, respectively. The mutual dependences between target hazardous and microclimate parameters were confirmed using a multiple linear regression model with software package STATISTICA 10. Obtained multiple coefficients of determination in the case of ozone and acetone (0.507 and 0.589) with microclimate parameters indicated a moderate correlation between the observed variables. However, a strong positive correlation was obtained for isopropyl alcohol and total volatile organic compounds (0.760 and 0.852) with microclimate parameters. Higher values of parameter F than F_critical for all examined dependences indicated the existence of statistically significant difference between the concentration levels of target pollutants and microclimates parameters. Given that, the microclimate parameters significantly affect the emission of investigated gases and the application of eco-friendly materials in production process present a necessity.

Keywords: flexographic printing, indoor air, multiple regression analysis, pollution emission

Procedia PDF Downloads 195

Authors: Teja Joshi

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Laterality has shown to play a role in performance as well as injuries especially in unilateral sports disciplines. Uniquely, Artistic Gymnastics involves combination of unilateral, bilateral and complex multi-planer elements as well as gymnastics specific rotational preference. Therefore, this study was conducted to explore if any such preferences are associated with number of injuries in artistic gymnasts. To explore the association between lateral preferences, rotational preferences and injuries incidence in artistic gymnastics. Artistic gymnasts above 16 years of age, were invited to participate in an online survey. The survey included consent, lateral preference inventory, injury data collection according to anatomical locations and rotational preference for selected gymnastics elements performed on the floor exercise. SPSS version 24 was used to analyse Non-parametric data using Kruskal-Wallis (K- independent test) test. Multiple regression was performed to identify the predictor for injuries and their side in gymnasts. Total number of injuries per gymnast was associated with handedness (p value-0.049) and no significant association was noted for footdness (p value-0.207), eyedness (p value-0.491) and eardness (p value-0.798). Additionally, rotational preferences did not influence number of injuries (p value-0.521). In multiple regression, eyedness was identified as a predicting factor to determine the number of injuries. Rotational preferences were neither determined as a national strategy nor a product of lateral preference. Dominant hand had higher number of injuries in artistic gymnasts. Rotational preference is independent of laterality, number of injuries and nationality.

Keywords: sports injury, rotational preference, gymnastics, handedness

Procedia PDF Downloads 117

Authors: Sudip Mondal, Biswanath Mondal, Sudit S. Mukhopadhyay, Apurba Dey

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Bioactive materials improve devices for a long lifespan but have mechanical limitations. Mechanical characterization is one of the very important characteristics to evaluate the life span and functionality of the scaffold material. After implantation of scaffold material the primary stage rejection of scaffold occurs due to non biocompatible effect of host body system. The second major problems occur due to the effect of mechanical failure. The mechanical and biocompatibility failure of the scaffold materials can be overcome by the prior evaluation of the scaffold materials. In this study chemically treated Labeo rohita scale is used for synthesizing hydroxyapatite (HAp) biomaterial. Thermo-gravimetric and differential thermal analysis (TG-DTA) is carried out to ensure thermal stability. The chemical composition and bond structures of wet ball-milled calcined HAp powder is characterized by Fourier Transform Infrared spectroscopy (FTIR), X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy (TEM), Energy Dispersive X-ray (EDX) analysis. Fish scale derived apatite materials consists of nano-sized particles with Ca/P ratio of 1.71. The biocompatibility through cytotoxicity evaluation and MTT assay are carried out in MG63 osteoblast cell lines. In the cell attachment study, the cells are tightly attached with HAp scaffolds developed in the laboratory. The result clearly suggests that HAp material synthesized in this study do not have any cytotoxic effect, as well as it has a natural binding affinity for mammalian cell lines. The synthesized HAp powder further successfully used to develop porous scaffold material with suitable mechanical property of ~0.8GPa compressive stress, ~1.10 GPa a hardness and ~ 30-35% porosity which is acceptable for implantation in trauma region for animal model. The histological analysis also supports the bio-affinity of processed HAp biomaterials in Wistar rat model for investigating the contact reaction and stability at the artificial or natural prosthesis interface for biomedical function. This study suggests the natural sourced fish scale-derived HAp material could be used as a suitable alternative biomaterial for tissue engineering application in near future.

Keywords: biomaterials, hydroxyapatite, scaffold, mechanical property, tissue engineering

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Authors: Yi-Ling Lin, Lu-Ming Tseng

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Customer sexual harassment is recognized as a serious problem in the personal selling industry. At a personal level, customer sexual harassment could have very negative impacts on the salespeople's physical and mental health. At the organizational level, customer sexual harassment is destructive in terms of organizational reputation. Therefore, this research takes Taiwan's life insurance salesperson as the research sample and explores the impacts of customer power and perceived behavioral control on the life insurance salespeople's whistleblowing intentions to report quid pro quo and hostile work environment types of customer sexual harassment. This study then investigates how personal factors (such as gender difference) may relate to the intentions. Questionnaires are often used as a data collection instrument in studies on workplace sexual harassment. This study collects data through questionnaire surveys, and the research sample of this research is the full-time life insurance salespeople in Taiwan. The hypotheses are examined by using PLS regression approach. The main results show that the types of customer sexual harassment, customer power, and gender are related to the whistleblowing intentions. To our best knowledge, this is the first empirical study to test the relationships among customer reward power, customer coercive power, perceived behavioral control, and the salespeople's whistleblowing intentions toward customer sexual harassment. The findings may provide some implications for the researchers and official authorities.

Keywords: customer sexual harassment, life insurance salespeople, perceived behavioral control, PLS regression

Procedia PDF Downloads 127

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: B. Mack, K. Tampe-Mai, E. Diesch

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Findings of an ongoing discrete choice study of future transport mode choice will be presented. Many urban centers face the triple challenge of having to cope with ever increasing traffic congestion, environmental pollution, and greenhouse gas emission brought about by private car use. In principle, private car use may be diminished by extending public transport systems like bus lines, trams, tubes, and trains. However, there are limits to increasing the (perceived) spatial and temporal flexibility and reducing peak-time crowding of classical public transport systems. An emerging new type of system, publicly or privately operated on-demand shuttle bus services, seem suitable to ameliorate the situation. A fleet of on-demand shuttle busses operates without fixed stops and schedules. It may be deployed efficiently in that each bus picks up passengers whose itineraries may be combined into an optimized route. Crowding may be minimized by limiting the number of seats and the inter-seat distance for each bus. The study is conducted as a discrete choice experiment. The choice between private car, public transport, and shuttle service is registered as a function of several push and pull factors (financial costs, travel time, walking distances, mobility tax/congestion charge, and waiting time/parking space search time). After the completion of the discrete choice items, the study participant is asked to rate the three modes of transport with regard to the pull factors of comfort, safety, privacy, and opportunity to engage in activities like reading or surfing the internet. These ratings are entered as additional predictors into the discrete choice experiment regression model. The study is conducted in the region of Stuttgart in southern Germany. N=1000 participants are being recruited. Participants are between 18 and 69 years of age, hold a driver’s license, and live in the city or the surrounding region of Stuttgart. In the discrete choice experiment, participants are asked to assume they lived within the Stuttgart region, but outside of the city, and were planning the journey from their apartment to their place of work, training, or education during the peak traffic time in the morning. Then, for each item of the discrete choice experiment, they are asked to choose between the transport modes of private car, public transport, and on-demand shuttle in the light of particular values of the push and pull factors studied. The study will provide valuable information on the potential of switching from private car use to the use of on-demand shuttles, but also on the less desirable potential of switching from public transport to on-demand shuttle services. Furthermore, information will be provided on the modulation of these switching potentials by pull and push factors.

Keywords: determinants of travel mode choice, on-demand shuttle services, private car use, public transport

Procedia PDF Downloads 182

Authors: Omar Dib, Rita Yaacoub, Luc Eveleigh, Nathalie Locquet, Hussein Dib, Ali Bassal, Christophe B. Y. Cordella

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The potential of front-face fluorescence coupled with chemometric techniques, namely parallel factor analysis (PARAFAC) and multiple linear regression (MLR) as a rapid analysis tool to characterize Lebanese virgin olive oils was investigated. Fluorescence fingerprints were acquired directly on 102 Lebanese virgin olive oil samples in the range of 280-540 nm in excitation and 280-700 nm in emission. A PARAFAC model with seven components was considered optimal with a residual of 99.64% and core consistency value of 78.65. The model revealed seven main fluorescence profiles in olive oil and was mainly associated with tocopherols, polyphenols, chlorophyllic compounds and oxidation/hydrolysis products. 23 MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for 12 predicted variables), thus satisfactory prediction performances. Acid values, peroxide values, and Delta K had the models with the highest predictions, with R values of 0.89, 0.84 and 0.81 respectively. Among fatty acids, linoleic and oleic acids were also highly predicted with R values of 0.8 and 0.76, respectively. Factors contributing to the model's construction were related to common fluorophores found in olive oil, mainly chlorophyll, polyphenols, and oxidation products. This study demonstrates the interest of front-face fluorescence as a promising tool for quality control of Lebanese virgin olive oils.

Keywords: front-face fluorescence, Lebanese virgin olive oils, multiple Linear regressions, PARAFAC analysis

Procedia PDF Downloads 451

Authors: Yvette Reisinger

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Relatively little is known about the Arab travel market, especially the outbound travel market from Arab countries in the Middle East. The Kuwaiti travel market is the smallest yet fastest growing in the Gulf Cooperation Council (GCC) region. The Kuwaiti travel market represents a great potential for the international tourism industry. Kuwaiti nationals have a very high spending power due to the Kuwaiti dinar being the highest-valued currency unit in the world. Although Europe, North America, and Asia/Pacific try to attract the Arab tourist market the number of Kuwaiti travellers attracted to these destinations is very low. The success in attracting the Kuwaiti travel market to Western countries must be guided by an analysis of the factors that affect its travel decisions. The objective of the study is to identify major factors that influence Kuwaiti nationals’ intentions to travel to Western countries. A model is developed and empirically tested on a sample of 343 Kuwaiti nationals. A series of regression analyses are run to determine the effects of different factors on Kuwaiti’s travel decisions. A Herman’s single factor test and Durbin-Watson test are used to assess the validity of the regression model. Analysis is controlled for socio-demographics. The results show that the Muslim friendly amenities and destination cognitive image exert significant effects on Kuwaiti nationals’ intentions to travel to Western countries. The study provides a better understanding of the factors that attract Kuwaiti tourists to Western countries. By knowing what encourages Kuwaitis to travel to Western countries marketers can plan and promote these countries accordingly. The study provides a foundation of future empirical research into the Kuwaiti/Arab travel market.

Keywords: Kuwaiti travel market, travel decisions, Western countries

Procedia PDF Downloads 190

Authors: A. Myrzabekova, D. Nurmukhamed, K. Nurumov, A. Zhulbarissova

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It is widely believed that stress can affect teachers’ intention to change the workplace. While existing research primarily focuses on the intrinsic sources of stress stemming from the school climate, the current attempt analyzes educational policies as one of the determinants of teacher’s intention to leave schools. In this respect, Kazakhstan presents a unique case since the country endorsed several educational policies which directly impacted teaching and administrative practices within schools. Using Teaching and Learning International Survey 2018 (TALIS) data with the country specific questionnaire, we construct a statistical measure of stress caused by the implementation of educational policies and test its impact on teacher’s intention to leave through the logistic regression. In addition, we control for sociodemographic, professional, and students related covariates while considering the intrinsic dimension of stress stemming from the school climate. Overall, our results suggest that stress caused by the educational policies has a statistically significant positive effect on teachers’ intentions to transfer between schools. Both policy makers and educational scholars could find these results beneficial. For the former careful planning and addressing the negative effects of the educational policies is critical for the sustainability of the educational process. For the latter, accounting for exogenous sources of stress can lead to a more complete understanding of why teachers decide to change their schools.

Keywords: educational policies, Kazakhstani teachers, logistic regression factor analysis, sustainability education TALIS, teacher turnover intention, work stress

Procedia PDF Downloads 108

Authors: Bohsiu Wu

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Among serious crimes, child homicide is a rather rare event. However, the killing of children stirs up a special type of emotion in society that pales other criminal acts. This study examines the relevancy of three possible community-level explanations for child homicide: social deprivation, female empowerment, and social isolation. The social deprivation hypothesis posits that child homicide results from lack of resources in communities. The female empowerment hypothesis argues that a higher female status translates into a higher level of capability to prevent child homicide. Finally, the social isolation hypothesis regards child homicide as a result of lack of social connectivity. Child homicide data, aggregated by US postal ZIP codes in California from 1990 to 1999, were analyzed with a negative binomial regression. The results of the negative binomial analysis demonstrate that social deprivation is the most salient and consistent predictor among all other factors in explaining child homicide victimization at the ZIP-code level. Both social isolation and female labor force participation are weak predictors of child homicide victimization across communities. Further, results from the negative binomial regression show that it is the communities with a higher, not lower, degree of female labor force participation that are associated with a higher count of child homicide. It is possible that poor communities with a higher level of female employment have a lesser capacity to provide the necessary care and protection for the children. Policies aiming at reducing social deprivation and strengthening female empowerment possess the potential to reduce child homicide in the community.

Keywords: child homicide, deprivation, empowerment, isolation

Procedia PDF Downloads 193

Authors: Nicola Rubino

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This paper presents an analysis of a group of commodity exporting countries' nominal exchange rate movements in relationship to the US dollar. Using a series of Unrestricted Self-exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national CPI price differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute error measures on the basis of two-hundred and fifty-three months rolling window forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive non linear autoregressive model (AAR) and a simple linear Neural Network model (NNET). Our preliminary results confirm presence of some form of TAR non linearity in the majority of the countries analyzed, with a relatively higher goodness of fit, with respect to the linear AR(1) benchmark, in five countries out of sixteen considered. Although no model appears to statistically prevail over the other, our final out-of-sample forecast exercise shows that SETAR models tend to have quite poor relative forecasting performance, especially when compared to alternative non-linear specifications. Finally, by analyzing the implied half-lives of the > coefficients, our results confirms the presence, in the spirit of arbitrage band adjustment, of band convergence with an inner unit root behaviour in five of the sixteen countries analyzed.

Keywords: transition regression model, real exchange rate, nonlinearities, price differentials, PPP, commodity points

Procedia PDF Downloads 276

Authors: Etienne Provencal, David L. St-Pierre

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A facility exploiting only electronic gambling machines (EGMs) opened in 2007 in Quebec City, Canada under the name of Salons de Jeux du Québec (SdjQ). This facility is one of the first worldwide to rely on that business model. This paper models the performance of such EGMs. The interest from a managerial point of view is to identify the variables that can be controlled or influenced so that a comprehensive model can help improve the overall performance of the business. The EGM individual performance model contains eight different variables under study (Game Title, Progressive jackpot, Bonus Round, Minimum Coin-in, Maximum Coin-in, Denomination, Slant Top and Position). Using data from Quebec City’s SdjQ, a linear regression analysis explains 90.80% of the EGM performance. Moreover, results show a behavior slightly different than that of a casino. The addition of GameTitle as a factor to predict the EGM performance is one of the main contributions of this paper. The choice of the game (GameTitle) is very important. Games having better position do not have significantly better performance than games located elsewhere on the gaming floor. Progressive jackpots have a positive and significant effect on the individual performance of EGMs. The impact of BonusRound on the dependent variable is significant but negative. The effect of Denomination is significant but weakly negative. As expected, the Language of an EGMS does not impact its individual performance. This paper highlights some possible improvements by indicating which features are performing well. Recommendations are given to increase the performance of the EGMs performance.

Keywords: EGM, linear regression, model prediction, slot operations

Procedia PDF Downloads 253

Authors: Alba Barbara-i-Molinero, Rosalia Cascon Pereira, Ana Beatriz Hernandez Lara

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Our general motivation in undertaking this study is to propose alternative measures to lighten students experienced tensions during the transitions from high school to higher education based on the concept of professional identity strength. In order to do so, we measured the influence that three different factors external motivational conditionals, educational experience conditionals and personal motivation conditionals exerted over students’ professional identity strength and proposed the measures considering the obtained results. By using hierarchical regression analysis we addressed this issue, across disciplines and bachelor degrees, allowing us to gain also deeper insight into first-year university students PID. Our findings suggest that students’ from the different disciplines are influenced by personal motivational conditionals; while students from sciences are also influenced by external motivational conditionals. Based on the obtained results we propose three different alternative educational and recruitment strategies which aim to increase students’ professional identity strength and reduce the tensions generated during high school-university transitions. From this study theoretical contributions regarding the differences in the influence of these factors on students from different bachelor degrees arise; and practical implications for universities, derived from the proposed strategies.

Keywords: professional identity, transitions, higher education, strategies

Procedia PDF Downloads 181

Authors: Seyedtaghi Mirmohammadi, J. Yazdani, S. M. Asadi, M. Rokni, A. Toosi

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There are many indoor air quality studies with an emphasis on indoor particulate matters (PM2.5) monitoring. Whereas, there is a lake of data about indoor PM2.5 concentrations in rural area schools (especially in classrooms), since preliminary children are assumed to be more defenseless to health hazards and spend a large part of their time in classrooms. The objective of this study was indoor PM2.5 concentration quality assessment. Fifteen preliminary schools by time-series sampling were selected to evaluate the indoor air quality in the rural district of Sari city, Iran. Data on indoor air climate parameters (temperature, relative humidity and wind speed) were measured by a hygrometer and thermometer. Particulate matters (PM2.5) were collected and assessed by Real Time Dust Monitor, (MicroDust Pro, Casella, UK). The mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3 in average. The multiple linear regression revealed that a correlation between PM2.5 concentration and relative humidity, distance from city center and classroom size. Classroom size yields reasonable negative relationship, the PM2.5 concentration was ranged from 65 to 540μg/m3 and statistically significant at 0.05 level and the relative humidity was ranged from 70 to 85% and dry bulb temperature ranged from 28 to 29°C were statistically significant at 0.035 and 0.05 level, respectively. A statistical predictive model was obtained from multiple regressions modeling for PM2.5 and indoor psychrometric parameters.

Keywords: particulate matters, classrooms, regression, concentration, humidity

Procedia PDF Downloads 309

Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

Procedia PDF Downloads 393

Authors: Mona Shawky, Khaled M. Elsheikh, Heba M. Darwish, Eman Abd El Elsamea

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Seams are the basic component in the structure of any apparel. The seam quality of the garment is a term that indicates both the aesthetic and functional performance of the garment. Seam slippage is one of the important properties that determine garment performance. Lightweight fabrics are preferred for their aesthetic properties. Since seam slippage is one of the most occurable faults for woven garments, in this study, a design of experiment of the following sewing parameters (three levels of needle size, three levels of stitch density, three levels of the seam allowance, two levels of sewing thread count, and two fabric types) was used to obtain the effect of the interaction between different sewing parameters on-seam slippage force. Two lightweight polyester woven fabrics with different constructions were used with lock stitch 301 to perform this study. Regression equations which can predict seam slippage force in both warp and weft directions were concluded. It was found that fabric type has a significant positive effect on seam slippage force in the warp direction, while it has a significant negative effect on seam slippage force on weft direction. Also, the interaction between needle size and stitch density has a significant positive effect on seam slippage force on warp direction, while the interaction between stitch density and seam allowance has a negative effect on seam slippage force in the weft direction.

Keywords: needle size, regression equation, seam allowance, seam slippage, stitch density

Procedia PDF Downloads 158

Authors: Lovro Štefan

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The main purpose of the present study was to investigate the associations between the overweight/obesity status and lifestyle factors. In this cross-sectional study, participants were 1950 urban secondary-school students (54.7% of female students) aged 17-18 years old. Dependent variable was body-mass index status derived from self-reported height and weight. The outcome was binarised, where participants with value <25 kg/m2 were collapsed into „normal“, while those ≥25 kg/m2 into „overweight/obesity“ category. Independent variables were gender, type of school, physical activity, sedentary behaviour, self-rated health, self-perceived socioeconomic status and psychological distress. The associations between the dependent and independent variables were analyzed by using multiple logistic regression analysis. In the univariate model, being overweight/obese was significantly associated with being a male student (OR 0.31; 95% CI 0.23 to 0.42), attending a vocational school (OR 1.87; 95% CI 1.42 to 2.48), not meeting the recommendations for moderate-to-vigorous physical activity (OR 0.44; 95% CI 0.22 to 0.88), more time spending in sedentary behaviour (OR 1.53; 95% CI 1.07 to 2.19), poor self-rated health (OR 0.35, 95% CI 0.20 to 0.56) and lower socioeconomic status (OR 0.63; 95% CI 0.48 to 0.84). In the multivariate model, the same associations occured between the dependent and independent variable. In both models, psychological distress was not associated with being overweight/obese. In conclusion, our findings suggest, that lifestyle factors are independently associated with body-mass index

Keywords: body mass index, secondary-school students, Croatia, physical activity, sedentary behaviour, logistic regression

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Authors: Azza T. Tahera, Eman M. Ahmeda, Nadia A. Khalila, Yassin M. Nissanb

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New 3-(pyridin-4-yl)-[1,2,4] triazolo [4,3-b] pyridazine derivatives 2a-i, 4a,b and 6a,b were designed, synthesized and evaluated as cytotoxic agents. All compounds were investigated for their in vitro cytotoxicity at a single dose 10-5M concentration towards 60 cancer cell lines according to USA NCI protocol. The preliminary screening results showed that the majority of tested compounds exhibited remarkable activity against SR (leukemia) cell panel. Molecular docking for all synthesized compounds was performed on the active site of c-Met kinase. The most active compounds, 2f and 4a were further evaluated at a seven dose level screening and their IC50 as a c-Met kinase inhibitors were determined in vitro.

Keywords: triazolopyridazines, pyridazines, cytotoxic activity, cell panel

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Authors: Sandeep Thakur, Ismail Mondal, Phani Bhusan Ghosh, Papita Das, Tarun Kumar De

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Sundarbans Reserve forest of India has been undergoing major transformations in the recent past owing to population pressure and related changes. This has brought about major changes in the spatial landscape of the region especially in the western parts. This study attempts to assess the impacts of the Landcover changes on the mangrove habitats. Time series imageries of Landsat were used to analyze the Normalized Differential Vegetation Index (NDVI) patterns over the western parts of Indian Sundarbans forest in order to assess the heath of the mangroves in the region. The images were subjected to Land use Land cover (LULC) classification using sub-pixel classification techniques in ERDAS Imagine software and the changes were mapped. The spatial proliferation of aquaculture farms during the study period was also mapped. A multivariate regression analysis was carried out between the obtained NDVI values and the LULC classes. Similarly, the observed meteorological data sets (time series rainfall and minimum and maximum temperature) were also statistically correlated for regression. The study demonstrated the application of NDVI in assessing the environmental status of mangroves as the relationship between the changes in the environmental variables and the remote sensing based indices felicitate an efficient evaluation of environmental variables, which can be used in the coastal zone monitoring and development processes.

Keywords: aquaculture farms, LULC, Mangrove, NDVI

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Authors: Leonardo Parra, Daniel Martínez, Jorge Pizarro, Fernando Ortega, Manuel Chacón-Fuentes, Andrés Quiroz

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Control of Dalaca pallens or blackworms, one of the most important hypogeous pest in grassland in southern Chile, is based on the use of broad-spectrum insecticides such as organophosphates and pyrethroids. However, the rapid development of insecticide resistance in field populations of this insect and public concern over the environmental impact of these insecticides has resulted in the search for other control methods. Specifically, the use of endophyte fungi for controlling pest has emerged as an interesting and promising strategy. Endophytes from ryegrass (Lolium perenne), establish a biotrophic relationship with the host, defined as mutualistic symbiosis. The plant-fungi association produces alkaloids where peramine is the main toxic substance against Listronotus bonariensis, the most important epigean pest of ryegrass. Nevertheless, the effect of peramina on others pest insects, such as D. pallens, to our knowledge has not been studied, and also its possible metabolization in the body of the larvae. Therefore, we addressed the following research question: Do larvae of D. pallens store peramine after consumption of ryegrass endophyte infected (E+)? For this, specimens of blackworms were fed with ryegrass plant of seven experimental lines and one commercial cultivar endophyte free (E-) sown at the Instituto de Investigaciones Agropecuarias Carillanca (Vilcún, Chile). Once the feeding period was over, ten larvae of each treatment were examined. Individuals were dissected, and their gut was removed to exclude any influence of remaining material. The rest of the larva's body was dried at 60°C by 24-48 h and ground into a fine powder using a mortar. 25 mg of dry powder was transferred to a microcentrifuge tube and extracted in 1 mL of a mixture of methanol:water:formic acid. Then, the samples were centrifuged at 16,000 rpm for 3 min, and the supernatant was colected and injected in the liquid chromatography of high resolution (HPLC). The results confirmed the presence of peramine in the larva's body of D. pallens. The insects that fed the experimental lines LQE-2 and LQE-6 were those where peramine was present in high proportion (0.205 and 0.199 ppm, respectively); while LQE-7 and LQE-3 obtained the lowest concentrations of the alkaloid (0.047 and 0.053 ppm, respectively). Peramine was not detected in the insects when the control cultivar Jumbo (E-) was tested. These results evidenced the storage and metabolism of peramine during consumption of the larvae. However, the effect of this alkaloid present in 'future ryegrass cultivars' (LQE-2 and LQE-6) on the performance and survival of blackworms must be studied and confirmed experimentally.

Keywords: blackworms, HPLC, alkaloid, pest

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Authors: B. G. Abiona, A. A. Onaseso, T. D. Odetayo, J. Yila, O. E. Fapojuwo, K. G. Adeosun

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This study determines the perceived effects of work-family balance on employees’ job satisfaction among Extension Agents in the Agricultural Development Programme (ADP) in southwest Nigeria. A multistage sampling technique was used to select 256 respondents for the study. Data on personal characteristics, work-family balance domain, and job satisfaction were collected. The collected data were analysed using descriptive statistics, Chi-square, Pearson Product Moment Correlation (PPMC), multiple linear regression, and Student T-test. Results revealed that the mean age of the respondents was 40 years; the majority (59.3%) of the respondents were male, and slightly above half (51.6%) of the respondents had MSc as their highest academic qualification. Findings revealed that turnover intention (x ̅ = 3.20) and work-role conflict (x ̅ = 3.06) were the major perceived work-family balance domain in the studied areas. Further, the result showed that the respondents have a high (79%) level of job satisfaction. Multiple linear regression revealed that job involvement (ß=0.167, p<0.01) and work-role conflict (ß= -0.221, p<0.05) contributed significantly to employees’ level of job satisfaction. The results of the Student T-test revealed a significant difference in the perceived work-family balance domain (t = 0.43, p<0.05) between the two studied areas. The study concluded that work-role conflict among employees causes work-family imbalance and, therefore, negatively affects employees’ job satisfaction. The definition of job design among the respondents that will create a balance between work and family is highly recommended.

Keywords: work-life, conflict, job satisfaction, extension agent

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Authors: Syed Toqueer Akhter, Muhammad Awais

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Pakistan is a country which is gifted by naturally abundant resources these resources are a pioneer towards a prospect and developed country. Pakistan is the fourth largest exporter of the textile in the world and with the passage of time the competitiveness of these exports is subject to a decline. With a lot of International players in the textile world like China, Bangladesh, India, and Sri Lanka, Pakistan needs to put up a lot of effort to compete with these countries. This research paper would determine the impact of Foreign Direct Investment upon technological diffusion and that how significantly it may be affecting on export performance of the country. It would also demonstrate that with the increase in Foreign Direct Investment, technological diffusion, strong property rights, and using different policy tools, export competitiveness of the country could be improved. The research has been carried out using time series data from 1995 to 2013 and the results have been estimated by using competing Econometrics modes such as Robust regression and Generalized least squares so that to consolidate the impact of the Foreign Investments and Technological diffusion upon export competitiveness comprehensively. Distributed Lag model has also been used to encompass the lagged effect of policy tools variables used by the government. Model estimates entail that 'FDI' and 'Technological Diffusion' do have a significant impact on the competitiveness of the exports of Pakistan. It may also be inferred that competitiveness of Textile Sector requires integrated policy framework, primarily including the reduction in interest rates, providing subsides, and manufacturing of value added products.

Keywords: high technology export, robust regression, patents, technological diffusion, export competitiveness

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Authors: Vassilios Moussas, Dimos N. Pantazis, Panagioths Stratakis

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The aim of this paper is to present the optimization methodology developed in the frame of a Coastal Transport Information System. The system will be used for the effective design of coastal transportation lines and incorporates subsystems that implement models, tools and techniques that may support the design of improved networks. The role of the optimization and decision subsystem is to provide the user with better and optimal scenarios that will best fulfill any constrains, goals or requirements posed. The complexity of the problem and the large number of parameters and objectives involved led to the adoption of an evolutionary method (Genetic Algorithms). The problem model and the subsystem structure are presented in detail, and, its support for simulation is also discussed.

Keywords: coastal transport, modeling, optimization

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Authors: Mohd. A. M. Rahim, Yahaya Hassan, Mathumalar L. Fahrni

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Second line antiretroviral therapy (ART) regimen is used when patients fail their first line regimen. There are many factors such as non-adherence, drug resistance as well as virological and immunological failure that lead to second line highly active antiretroviral therapy (HAART) regimen treatment failure. This study was aimed at determining predictor factors to treatment failure with second line HAART and analyzing median survival time. An observational, retrospective study was conducted in Sungai Buloh Hospital (HSB) to assess current status of HIV patients treated with second line HAART regimen. Convenience sampling was used and 104 patients were included based on the study’s inclusion and exclusion criteria. Data was collected for six months i.e. from July until December 2013. Data was then analysed using SPSS version 18. Kaplan-Meier and Cox regression analyses were used to measure median survival times and predictor factors for treatment failure. The study population consisted mainly of male subjects, aged 30-45 years, who were heterosexual, and had HIV infection for less than 6 years. The most common second line HAART regimen given was lopinavir/ritonavir (LPV/r)-based combination. Kaplan-Meier analysis showed that patients on LPV/r demonstrated longer median survival times than patients on indinavir/ritonavir (IDV/r) based combination (p<0.001). The commonest reason for a treatment to fail with second line HAART was non-adherence. Based on Cox regression analysis, other predictor factors for treatment failure with second line HAART regimen were age and mode of HIV transmission.

Keywords: adherence, antiretroviral therapy, second line, treatment failure

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Authors: Elsie M. Nolte, Annie M. Joubert, Roy Lakier, Maryke Etsebeth, Jolene M. Helena, Marcel Verwey, Laurence Lafanechere, Anne E. Theron

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Treatment regimens for breast- and lung cancers may include both radiation- and chemotherapy. Ideally, a pharmaceutical agent which selectively sensitizes cancer cells to gamma (γ)-radiation would allow administration of lower doses of each modality, yielding synergistic anti-cancer benefits and lower metastasis occurrence, in addition to decreasing the side-effect profiles. A range of 2-methoxyestradiol (2-ME) analogues, namely 2-ethyl-3-O-sulphamoyl-estra-1,3,5 (10) 15-tetraene-3-ol-17one (ESE-15-one), 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10),15-tetraen-17-ol (ESE-15-ol) and 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10)16-tetraene (ESE-16) were in silico-designed by our laboratory, with the aim of improving the parent compound’s bioavailability in vivo. The main effect of these compounds is the disruption of microtubule dynamics with a resultant mitotic accumulation and induction of programmed cell death in various cancer cell lines. This in vitro study aimed to determine the cellular responses involved in the radiation sensitization effects of these analogues at low doses in breast- and lung cancer cell lines. The oestrogen receptor positive MCF-7-, oestrogen receptor negative MDA-MB-231- and triple negative BT-20 breast cancer cell lines as well as the A549 lung cancer cell line were used. The minimal compound- and radiation doses able to induce apoptosis were determined using annexin-V and cell cycle progression markers. These doses (cell line dependent) were used to pre-sensitize the cancer cells 24 hours prior to 6 gray (Gy) radiation. Experiments were conducted on samples exposed to the individual- as well as the combination treatment conditions in order to determine whether the combination treatment yielded an additive cell death response. Morphological studies included light-, fluorescence- and transmission electron microscopy. Apoptosis induction was determined by flow cytometry employing annexin V, cell cycle analysis, B-cell lymphoma 2 (Bcl-2) signalling, as well as reactive oxygen species (ROS) production. Clonogenic studies were performed by allowing colony formation for 10 days post radiation. Deoxyribonucleic acid (DNA) damage was quantified via γ-H2AX foci and micronuclei quantification. Amplification of the p53 signalling pathway was determined by western blot. Results indicated that exposing breast- and lung cancer cells to nanomolar concentrations of these analogues 24 hours prior to γ-radiation induced more cell death than the compound- and radiation treatments alone. Hypercondensed chromatin, decreased cell density, a damaged cytoskeleton and an increase in apoptotic body formation were observed in cells exposed to the combination treatment condition. An increased number of cells present in the sub-G1 phase as well as increased annexin-V staining, elevation of ROS formation and decreased Bcl-2 signalling confirmed the additive effect of the combination treatment. In addition, colony formation decreased significantly. p53 signalling pathways were significantly amplified in cells exposed to the analogues 24 hours prior to radiation, as was the amount of DNA damage. In conclusion, our results indicated that pre-treatment of breast- and lung cancer cells with low doses of 2-ME analogues sensitized breast- and lung cancer cells to γ-radiation and induced apoptosis more so than the individual treatments alone. Future studies will focus on the effect of the combination treatment on non-malignant cellular counterparts.

Keywords: cancer, microtubule dynamics, radiation therapy, radiosensitization

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Authors: Keisuke Takahata, Hiroshi Suetsugu

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Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

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Authors: Daria N. Podvigina, Tatiana V. Chernigovskaya

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We have studied the temporal characteristics of bistable perception of the stimuli of two types: one involves alterations in a perceived depth and another one has an ambiguous content. We used the Necker lattice and lines of shadowed circles ambiguously perceived either as spheres or holes as stimuli of the first type. The Winson figure (the Eskimo/Indian picture) was a stimulus of the second type. We have analyzed how often the reversals occurred (reversal rate) and for how long each of the two interpretations, or percepts, was observed during one presentation (stability durations). For all three ambiguous images the reversal rate and the stability durations had similar values, which provide another evidence for a significant role of top-down processes in multistable perception.

Keywords: multistable perception, perceived depth, reversal rate, top-down processes

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Authors: Jyoti Srivastava, Sandhya S. Kaushik, Mallika Tewari, Hari S. Shukla

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Little information is available about the development of psychological well being over time among women who have been undergoing treatment for breast cancer. The aim of this study was to identify the trajectories of depression anxiety and stress among women with early-stage breast cancer. Of the 48 Indian women with newly diagnosed early-stage breast cancer recruited from surgical oncology unit, 39 completed an interview and were assessed for depression anxiety and stress (Depression Anxiety Stress Scale-DASS 21) before their first course of chemotherapy (baseline) and follow up interviews at 3, 6 and 9 months thereafter. Growth mixture modeling was used to identify distinct trajectories of Depression Anxiety and Stress symptoms. Logistic Regression analysis was used to evaluate the characteristics of women in distinct groups. Most women showed mild to moderate level of depression and anxiety (68%) while normal to mild level of stress (71%). But one in 11 women was chronically anxious (9%) and depressed (9%). Young age, having a partner, shorter education and receiving chemotherapy but not radiotherapy might characterize women whose psychological symptoms remain strong nine months after diagnosis. By looking beyond the mean, it was found that several socio-demographic and treatment factors characterized the women whose depression, anxiety and stress level remained severe even nine months after diagnosis. The results suggest that support provided to cancer patients should have a special focus on a relatively small group of patient most in need.

Keywords: psychological well being, growth mixture modeling, logistic regression analysis, socio-demographic factors

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

Procedia PDF Downloads 80