Minimum Average Deviance Estimation for Sufficient Dimension Reduction

Sufficient dimension reduction reduces the dimensionality of data while preserving relevant regression information. In this article, we develop Minimum Average Deviance Estimation (MADE) methodology for sufficient dimension reduction. It extends the Minimum Average Variance Estimation (MAVE) approach of Xia et al. (2002) from continuous responses to exponential family distributions to include Binomial and Poisson responses. Local likelihood regression is used to learn the form of the regression function from the data. The main parameter of interest is a dimension reduction subspace which projects the covariates to a lower dimension while preserving their relationship with the outcome. To estimate this parameter within its natural space, we consider an iterative algorithm where one step utilizes a Stiefel manifold optimizer. We empirically evaluate the performance of three prediction methods, two that are intrinsic to local likelihood estimation and one that is based on the Nadaraya-Watson estimator. Initial results show that, as expected, MADE can outperform MAVE when there is a departure from the assumption of additive errors

Mobile Information Systems: An Empirical Analysis of the Determinants of Mobile Commerce Acceptance in Jordan

Although mobile commerce have been used and widely researched in developed nations, there is a low usage in the Arab world. Also, there is a limited empirical research on mobile commerce in Jordan despite the high penetration of mobile phone subscribers in 2009. Among the aims of this quantitative research is to empirically investigate the determinants of mobile commerce adoption in a collectivist culture such as Jordan where social norms are valued and individual actions are influenced greatly by important reference groups. The Technology Acceptance Model (TAM) is extended to include four factors (facilitating conditions, cost, personal innovativeness in IT (PIIT) and subjective norms). Furthermore, in order to understand subjective norms in collectivist culture; subjective norms were decomposed into different levels (personal and societal injunctive and descriptive norms). The research framework consists of twelve latent variables (seven exogenous and five endogenous). Using self-administered survey, 40 items with 7-point Likert scale is used to collect data. Out of the 500 samples, 448 responses (89.6 % response rate) were collected; eventually 401 responses were usable. Structural Equation Modeling is applied to analyze the data. The findings of this study revealed that facilitating conditions, cost, PIIT, attitude and perceived usefulness are significant determinants of behavioral intention in Jordan. In addition, subjective norms, facilitating conditions, cost and perceived ease of use are significant antecedents of attitude which in turn influencing behavioral intention. Moreover, the empirical evidence indicated that personal injunctive norm, personal descriptive norm and societal injunctive norm are indeed antecedents of subjective norms. It can be concluded that extended TAM successfully enriched the model and increased the exploratory power to 53% in explaining behavioral intention variance

Independent And Dependent Job Scheduling Algorithms Based On Weighting Model For Grid Environment

Grid system has been used to solve complex problems that need years to be executed. One of the issues in improving the performance of a grid system is how to schedule the submitted jobs in an efficient, fast and reliable way. In addition, job scheduling is considered as a NP-hard problem. Therefore, finding the optimal sequence to improve the total execution time and average waiting time will be very difficult and will consume a lot of computational resources. As a result, researchers have tried to improve job scheduling system using multiple algorithms. However, the previous algorithms are complicated and needed a lot of computational resources. Besides that, their works have not considered using job categorical variables in serving jobs as a dominant parameter. This thesis presents job weighting model using a Twostep clustering to assign the categorical and continuous variables of jobs into classes for both independent and dependent job scheduling. After that, ranking equation is used to arrange the generated classes from lightest to heaviest. Moreover, linear regression model with the generated ranking classes is employed into the proposed weighting model. The resulting model is then applied onto the independent and dependent job scheduling algorithms to verify the capability of proposed job scheduling model in a real environment. To validate the capability of the proposed weighting model which aims to improve independent job scheduling, a combination between job weight value and job ranking backfilling is considered. On the other hand, a job weight is combined with a graph of dependent jobs to improve dependent job scheduling. By simulation, independent job scheduling algorithm showed improvement in total execution time and average waiting time which is equal to 1.13 and 7.12 times, respectively. For the dependent algorithm, the results outperform the previous algorithms in total execution time and average waiting time, the improvement is 1.31 and 3.05 times, respectively. The results have demonstrated that the categorical and continuous variables of jobs can be used to improve the total execution time and average waiting time of job scheduling algorithms with less overhead in a real environment

Knowledge and attitude of primary care doctors towards management of postmenopausal symptoms

AbstractBackgroundAccording to the current recommendations, women with post-menopausal symptoms should be managed. Knowledge and perception of primary care physicians towards management of postmenopausal symptoms are deficient.AimThe aim of the present study was to explore knowledge and attitude of primary care doctors towards management of postmenopausal symptoms.MethodsThis study is a cross-sectional survey that was conducted from October to December 2010 in the five health regions in Kuwait. Two centers were selected randomly from each health region. All physicians who were currently working in the selected centers were asked to participate in the study. Out of 209 physicians, 142 agreed to participate and completed a self-administered questionnaire.ResultsThe study revealed that 82.4% of physicians had moderate knowledge about treatment options for postmenopausal symptoms, 88.0% discussed postmenopausal symptoms with their patients, and 45.1% of them either described or referred their patients for hormonal replacement therapy (HRT). The correct answers regarding 10 statements related to the Women Health Initiative finding were ranging from 2.8% to 78.9% which indicated low level of knowledge. Regarding the effectiveness of hormonal replacement therapy in postmenopausal women, the majority of the physicians agreed correctly that HRT is effective in prevention of osteoporosis (87.3%), treatment of vasomotor symptoms (83.7%), and treatment of vulvo-vaginal symptoms (82.4%). There was a variation among physicians opinion about the effectiveness of certain treatment options for managing hot flushes in postmenopausal women.ConclusionThe results suggest that there is a lack of primary care physicians knowledge and confidence in recognizing signs and symptoms of menopause and in identifying and prescribing appropriate management

Spatial patterns and links between microbial community composition and function in cyanobacterial mats

We imaged reflectance and variable fluorescence in 25 cyanobacterial mats from four distant sites around the globe to assess, at different scales of resolution, spatial variabilities in the physiological parameters characterizing their photosynthetic capacity, including the absorptivity by chlorophyll a (Achl), maximum quantum yield of photosynthesis (Ymax), and light acclimation irradiance (Ik). Generally, these parameters significantly varied within individual mats on a sub-millimeter scale, with about 2-fold higher variability in the vertical than in the horizontal direction. The average vertical profiles of Ymax and Ik decreased with depth in the mat, while Achl exhibited a sub-surface maximum. The within-mat variability was comparable to, but often larger than, the between-sites variability, whereas the within-site variabilities (i.e., between samples from the same site) were generally lowest. When compared based on averaged values of their photosynthetic parameters, mats clustered according to their site of origin. Similar clustering was found when the community composition of the mats' cyanobacterial layers were compared by automated ribosomal intergenic spacer analysis (ARISA), indicating a significant link between the microbial community composition and function. Although this link is likely the result of community adaptation to the prevailing site-specific environmental conditions, our present data is insufficient to identify the main factors determining these patterns. Nevertheless, this study demonstrates that the spatial variability in the photosynthetic capacity and light acclimation of benthic phototrophic microbial communities is at least as large on a sub-millimeter scale as it is on a global scale, and suggests that this pattern of variability scaling is similar for the microbial community composition. © 2014 Al-Najjar, Ramette, Kühl, Hamza, Klatt and Polerecky

Hyperspectral imaging of the microscale distribution and dynamics of microphytobenthos in intertidal sediments

We describe a novel, field-deployable hyperspectral imaging system, called Hypersub, that allows noninvasive in situ mapping of the microphytobenthos (MPB) biomass distribution with a high spatial (sub-millimeter) and temporal (minutes) resolution over areas of 1 x 1 m. The biomass is derived from a log-transformed and near-infrared corrected reflectance hyperspectral index, which exhibits a linear relationship (R-2 > 0.97) with the chlorophyll a (Ch1 a) concentration in the euphotic zone of the sediment and depends on the sediment grain size. Deployments of the system revealed that due to factors such as sediment topography, bioturbation, and grazing, the distribution of MPB in intertidal sediments is remarkably heterogeneous, with Ch1 a concentrations varying laterally by up to 400% of the average value over a distance of 1 cm. Furthermore, due to tidal cycling and diel light variability, MPB concentrations in the top 1 mm of sediments are very dynamic, changing by 40-80% over a few hours due to vertical migration. We argue that the high-resolution hyperspectral imaging method overcomes the inadequate resolution of traditional methods based on sedimentary Ch1 a extraction, and thus helps improve our understanding of the processes that control benthic primary production in coastal sediments

A COMPUTERIZED FORECASTING SYSTEM FOR STUDENT ENROLLMENT IN KFUPM

ABSTRACT. Any higher education institute like a university should have a tentative future plan. The first step in establishing such a plan is to get the required information concerning the projected student enrollment for the next few years. Though an enrollment forecasting is a specific type of estimation - one that uses past information in some manner to arrive at an estimate of future enrollments. The basic assumptions are that some historical pattern exists, that this pattern can be identified and data can be collected to measure it, and that it reflects what is it to occur in the future. In this paper, we will show a systematic analysis of students data to identify the hidden historical pattern and its important parameters which is essential for the forecasting. A computer program will be developed based on that historical pattern. The computer program then can be used as a powerful simulation tool to project the future enrollment