1,874 research outputs found

    Use of waist to hip ratio in the determination of the body composition in preschool children in Latvian population

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    According to the World Health Organization (WHO), in 2008, the waist to hip ratio (WHR) has been suggested superior to the body mass index (BMI) in predicting the cardiovascular disease risk in adults and adolescents. There have been studies about the WHR in preschool children in the populations of Pakistan, Chile and Mexico; and it is not the WHO which recommended it as a routine method in preschool children.The present study includes 85 children (41 girls and 44 boys), aged 5 to 7 years, without any chronic conditions. Body height, body weight, waist circumference, hip circumference, triceps skinfold, abdominal skinfold and subscapular skinfold were measured. The WHR, the BMI, the sum of three skinfolds and the percentage of body fat (%BF) were calculated.It was found that the WHR decreased with age in girls; there were no specific changes found in the WHR with age in boys. The present study found no correlation in boys or girls between the WHR and the BMI; the WHR and the sum of three skinfolds; the WHR and the percentage of BF. There was also no correlation between the Z-scores of the BMI and Z-scores of the WHR.Conclusions. The WHR is a questionable body composition marker in preschool children in the Latvian population and must be evaluated separately from other body composition markers

    Numerical investigation on fluid flow of gear lubrication

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    Several details of the mechanism of gear lubrication are still in doubt in spite of many decades of study of this subject. The focus of our work is the investigation of the mechanism by which oil temperature variations affect gear idle rattle, which requires an understanding of the distributions of lubricant and heat within a gearbox. This paper presents the findings of a study of lubricant flow in a simple model gearbox by means of CFD (Computational Fluid Dynamics) and its validation by a series of tests on a spur gear rig. The commercial CFD code Fluent is used to simulate the splash flow of lubricant, using the techniques of dynamic meshing and VOF (Volume of Fluid). Our model takes into account the effects on the distribution of gear lubricant of lubricant level and physical properties as well as rotational speed. The results demonstrate that the flow patterns are strongly influenced by all these variables. The predictions are validated by high-speed flow visualisations using high-resolution imaging in conjunction with a pulsed Cu-vapour laser light source and powerful white light source. The simulated fluid flows are in good qualitative agreement with the experimental visualisation

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    <p>Cell scratch test and Transwell were used to measure the migration abilities of HSVSMCs. NC = Negative control group, only control siRNA transfected; GAS5(-) = lncRNA-GAS5 knockdown group transfected with silence siRNA. <b>A:</b>Cell scratch test was used to measure the migration abilities of HSVSMCs. The results showed that the HSVSMCs have the best migration abilities in the first 24 hours. Values are mean±SE, N = 4. <b>B:</b> The migration abilities of HSVSMCs measured by Transwell. After transfected by lncRNA-GAS5 siRNA for 48 hours, the HSVSMCs were passage into the Transwell Inserts. Then 4 hours, 7 hours, 10 hours later, the migration HSVSMCs were photographed and counted, respectively. Knockdown of lncRNA-GAS5 expression promotes migration of HSVSMCs. Optical microscope images under 200x magnification. <b>C:</b> The migration abilities of HSVSMCs were reflected indirectly by the new migration cells counting with Transwell. Silencing of lncRNA-GAS5 expression increses migration ability of HSVSMCs. Values are mean±SE, N = 10; *, P<0.05.</p

    Egy 14. századi új Salamon: V. (Bölcs) Károly francia király

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    The result of in-hospital all mortality (P < 0.001; RR 3.23; 95% CI 2.28–4.57). (DOCX 54 kb

    Exploring the potential of blended learning to promote retention and achievement in higher education professional study programs

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    In this paper, we present a blended learning model designed for a university professional study program attended by full-time professional workers, i.e. in-service teachers studying in the field of School Administration. The model integrates four main instructional strategies at the program level: mentoring; participation in an online community of professional learning and practice; collaborative concept-mapping with an object-typed knowledge modeling software, and face-to-face seminars in a work setting. Based on interview and observation data collected during two successive small-scale experimentations of the model, we explored potential factors that could have had an impact on students’ academic retention and achievement. Four types of factors were identified: personal, professional, institutional and pedagogical. We found that pedagogical and professional factors, which are insufficiently considered in theoretical models of student retention, are of primary concern for students who work full-time as professionals. A blended learning model designed at the program level and strongly “situated” in the professional practice of the students is a promising avenue to adjust to their career constraints and aspirations and, thus, promoting their academic retention and achievement

    Illite Spatial Distribution Patterns Dictate Cr(VI) Sorption Macrocapacity and Macrokinetics

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    This work examines the largely unexplored role of illite spatial distribution patterns in dictating the sorption of Cr­(VI), a ubiquitously occurring contaminant. Flow-through experiments were carried out at 0.6, 3.0, and 15.0 m/day using columns packed with the same illite and quartz mass however with different spatial patterns and permeability contrasts. Column-scale sorption macrocapacity and macrorates were found to decrease with transport connectivity, a quantitative measure of heterogeneity characteristics. At 0.6 and 3.0 m/day, well-connected low permeability illite zones oriented in the flow-parallel direction lead to diffusion-controlled mass transport limitation for accessing sorption sites. This results in up to 1.4 order of magnitude lower macrocapacity and macrorates compared to those in minimally connected columns with well-mixed illite and quartz. At 15.0 m/day, effects of spatial heterogeneities are less significant (up to a factor of 2.8) owing to the close to chemical kinetics-controlled condition. Although the column-scale macrocapacity can reach full sorption capacity under low flow conditions, the macrorates are 10<sup>–1</sup> to 10<sup>–3</sup> of the microrates measured in well-mixed reactors. Insights gained here bridge gaps between laboratory observations and field applications and advance predictive understanding of reactive transport processes in the naturally heterogeneous subsurface

    The values of Macro-Precision, Macro-Recall, Macro-F1 and Micro-F1 under different number of features reduced by DF method.

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    <p>The values of Macro-Precision, Macro-Recall, Macro-F1 and Micro-F1 under different number of features reduced by DF method.</p

    Offspring development time of <i>Sclerodermus harmandi</i> from different culturing systems on two host species and stages: (3a) female offspring, (3b) male offspring.

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    <p>Same alphabets on columns indicate no significant differences (P<0.05). For abbreviations see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018563#pone-0018563-g001" target="_blank">Figure 1</a> (Mean±SE, <i>n</i> = 20–24 females in each treatment, ** P<0.01 and ns. P>0.05).</p

    Feeding preference of <i>Sclerodermus harmandi</i> from different culturing systems on two development stages of hosts: (1a) pupae vs. larvae of Ma, (1b) pupae vs. larvae of Sp, (1c) pupae of Ma vs. Sp, and (1d) larvae of Ma vs. Sp.

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    <p>Abbreviations used: Ma, <i>Monochamus alternatus</i>; Sp, <i>Saperda populnea</i>; Ma larvae, Ma pupae, larvae and pupae of <i>M</i>. <i>alternatus</i>, respectively; Sp larvae, Sp pupae, larvae and pupae of <i>S</i>. <i>populnea</i>, respectively (Mean±SE, <i>n</i> = 25 females in each treatment, ** P<0.01, * P<0.05 and ns. P>0.05).</p
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