Effects of Small Group Education on Interdialytic Weight Gain, and Blood Pressures in Hemodialysis Patients

Background: One of the most common problems in patients undergoing hemodialysis is interdialytic weight gain due to high liquid intake. Many patients are not fully aware of the fluid restriction. Group educations, such as small-group education, are among powerful methods to enable patients correct their behaviors, and enhance their capabilities, knowledge, and awareness. Objectives: The purpose of this study was to determine the effect of small-group education on interdialytic weight gain, and blood pressures in patients undergoing hemodialysis. Patients and Methods: This is a quasi-experimental study. Data collected from 42 patients undergoing hemodialysis. Before education, the mean of interdialytic weight gain during one week, and blood pressure were recorded. Then small-group education performed in 4 sessions. One week, and one month after the education, the mentioned parameters were recorded again. Repeated measure analysis of variances was conducted and pair-wise comparison was done using the Bonferroni test. Descriptive statistics were calculated for demographic variables. Results: The mean, and standard deviation of interdialytic weight gain of participants was 3.64 ± 0.88 kg, before the education, and significantly decreased to 1.34 ± 0.61 kg, and 1.71 ± 0.72 kg one week, and one month after the education, respectively (P = 0.001). Also, the mean and standard deviation of participants' systolic blood pressure was 139.7 ± 16.45 mmHg before the education, and significantly decreased to 129.6 ± 12.16, and 129.5 ± 11.51 mmHg one week, and one month after the education, respectively (P = 0.001). But, the mean and standard deviation of diastolic blood pressure of the participants was 81.4 ± 6.07 mmHg before the education, and decreased to 79.7 ± 5.51 and 81.7 ± 5.27 mmHg one week, and one month after the education respectively. However, no statistically significant difference was observed between the diastolic blood pressure in the three phases (P = 0.061). Conclusions: Small-group education in patients undergoing hemodialysis leads to a decrease in interdialytic weight gain, and systolic blood pressure, but has no effect on diastolic blood pressure

Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

A sediment source fingerprinting method, including a Monte Carlo simulation framework, was used to quantify the contributions of terrestrial sources of fine- (< 63 μm) and coarse-grained (63–500 μm) sediments sampled from three categories of coastal sediment deposits in the Jagin catchment, south-east of Jask, Hormozgan province, southern Iran: coastal dunes (CD), terrestrial sand dunes or onshore sediments (TSD), and marine or offshore sediments (MD). Forty-nine geochemical properties were measured in the two size fractions and a three-stage statistical process consisting of a conservation test, the Kruskal–Wallis H test, and stepwise discriminant function analysis (DFA) was applied to select final composite fingerprints for terrestrial source discrimination. Based on the statistical tests, four final fingerprints comprising Be, Ni, K and Cu and seven final fingerprints consisting Cu, Th, Be, Al, La, Mg and Fe were selected for discriminating terrestrial sources of the coastal fine- and coarse grained sediments, respectively. Two geological spatial sources, including Quaternary (clay flat, high and low level fans and valley terraces) and Palaeocene age deposits, were identified as the main terrestrial sources of the fine-grained sediment sampled from the coastal deposits. A geological spatial source consisting of sandstone with siltstone, mudstone and minor conglomerate (Palaeocene age deposits) was identified as the main terrestrial source for coarse-grained sediment sampled from the coastal deposits

Using GLUE to pull apart the provenance of atmospheric dust

© 2018 Identifying the sources of aeolian dust is a crucial step in mitigating the associated hazards. We apply a Generalized Likelihood Uncertainty Estimation (GLUE) model to constrain the uncertainties associated with sediment fingerprinting of atmospheric dust in the Sistan region on the Iran-Afghanistan border, one of the world's dustiest places. 57 dust samples were collected from the rooftop of the Zabol Department of Environmental Protection during a summer dusty period from June to October 2014, in addition to 31 surface soil samples collected from potential sources nearby, including cultivated land (n = 8), uncultivated rangeland (n = 7), and two dry lakes: Hamoun Puzak (n = 10) and Hamoun Saberi (n = 6). Dust and soil samples were analyzed for 24 tracers including 16 geochemical elements and 8 water-soluble ions. Five optimum composite fingerprints (Fe, Sr, Mn, Cr and Pb) were selected for discriminating sources by a two-stage statistical process involving a Kruskal-Wallis test and stepwise discriminant function analysis (DFA). Uncertainty ranges for source contributions of dust determined by the GLUE model showed that the dry lake Hamoun Puzak is the dominant source for all dust samples from Zabol and cultivated land is a secondary source. We found marked spatial variance in the importance of regional dry lake beds as dust sources, and temporal persistence in dust emissions from Hamoun Puzak, despite very large areas of adjacent lake beds drying during the study period. Aeolian sediment fingerprinting studies can benefit considerably from the constraints provided by modelling frameworks, such as GLUE, for quantifying the uncertainty in dust provenance data

Mathematical models in inventory systems

SIGLEAvailable from British Library Document Supply Centre- DSC:D44367/83 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Capacity Expansion and Reliability Evaluation on the Networks Flows with Continuous Stochastic Functional Capacity

In many systems such as computer network, fuel distribution, and transportation system, it is necessary to change the capacity of some arcs in order to increase maximum flow value from source s to sink t, while the capacity change incurs minimum cost. In real-time networks, some factors cause loss of arc’s flow. For example, in some flow distribution systems, evaporation, erosion or sediment in pipes waste the flow. Here we define a real capacity, or the so-called functional capacity, which is the operational capacity of an arc. In other words, the functional capacity of an arc equals the possible maximum flow that may pass through the arc. Increasing the functional arcs capacities incurs some cost. There is a certain resource available to cover the costs. First, we construct a mathematical model to minimize the total cost of expanding the functional capacities to the required levels. Then, we consider the loss of flow on each arc as a stochastic variable and compute the system reliability

The Minimum Universal Cost Flow in an Infeasible Flow Network

In this paper the concept of the Minimum Universal Cost Flow (MUCF) for an infeasible flow network is introduced. A new mathematical model in which the objective function includes the total costs of changing arc capacities and sending flow is built and analyzed. A polynomial time algorithm is presented to find the MUCF

Comparison of Characteristic Property among Tehran Offensive and Non-Offensive Drivers Using Cloninger, s Treatment and Character Inventory

 Backgrounds and Aims: Characteristic property among Tehran offensive and non- offensive drivers using Cloninger, s treatment and Character Inventory questionnaire was studied. Materials and Methods: A cross- sectional study was carried out after coordinating with traffic police. 300 drivers: 150 offensive ( drivers with more than ten penalty points and 150 non- offensive drivers with less than ten penalty points in one year prior were selected randomly. Data was collected using Cloninger, s treatment and Character Inventory standard questionnaire by trained experts in three different routes of Tehran. Results: The average age was 20-34 years (82% male, 18% female). Based on Cloninger,s treatment and Character Inventory standard questionnaire, a significant differences was observed in novelty seeking, harm avoidance, reward dependence, self-directive ness, co- operation (p&lt;0.001) and persistence (p&lt;0.013) among offensive and non-offensive drivers. By one score increasing of novelty seeking the chance of to be offencer will be increased 4.5 times (OR= 4.520, p&lt;0.007), in harm avoidance will be half (OR= 0.504, p&lt;0.045) and in reward dependence will be one fourth (OR= 0.278, p&lt;0.033). Conclusion: Novelty seeking sub scale of Cloningers treatment and Character Inventory questionnaire was higher and harm avoidance and reward dependence was lower among offensive drivers. REFERENCES World report on road traffic injury prevention 2004. World Health Organization (WHO) and the World Bank report; 2010; Available from: http://www.who.int/world-health-day/2004/en.Haghshenas H, Hassani M, Jamshidi M, Azizi HR. Relationship between characteristic property and driving behaviour in Shiraz city. Hakim. 2008;11:47-54Rothengather T. Psychological aspects of road user behaviours, an international review. Applied psychology. 1997; 46(3): 223-34.Lingard H, Rowlinson S. Lingard H, Rowlinson S. The Wilful Traffic Offender Profile and its implications for education and training. PhD Research Summary, School of Psychology, University of  Exeter, 2000. USA and Canada: Taylor&amp; Francis group; 2005.Burns P C, Wilde G J S. Risk taking in male taxi drivers: relationship among personality, observational data and driver records. Personality and Individual Differences. 1995; 18(2):267-78.Parker D, Reason J T, Manstead A S R, Stradling S. Driving errors, driving violations and accident involvement. Ergonomics. 1995; 38(5): 1038-48. Sommer  M, Herle  M, Hausler  J, Risser  R, Schutzhofer  B, Chaloupka  C. Cognitive and personality determinants of fitness to drive. Transportation of Research Board (TRB) 2008; 11(5):362-75.Sujata M, Patil J T S, Trivellore E R, Raymond C B. The Role of Personality Characteristics in Young Adult Driving.Traffic Inj Prev. 2006; 7(4): 328–34. Jonah BA, Thiessen R, Au-Yeung E. Sensation seeking, risky driving and behavioral adaptation. Accident Analysis &amp; Prevention. 2001; 33(5): 679–84.Ulleberg P, Rundmo T. Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers. Saf. Sci. 2003; 41(5):427–43.Vassallo S, Smart D, Sanson A, Harrison W, Harris A, Cockfield S and et al. Risky driving among young Australian drivers: trends, precursors and correlates. Accid Anal Prev. 2007; 39(3): 444–58.Sigve O, Torbjørn R. The effects of personality and gender on risky driving behaviour and accident involvement. Saf. Sci. 2006; 44(7): 621-8. Machin M, Anthony S, Kim S. Relationships between young drivers' personality characteristics, risk perceptions, and driving behaviour. Accid Anal Prev. 2008; 40(2): 541-7.Dahlen E R, Martin R C, Ragan K, Kuhlman M M. Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accid Anal Prev. 2005; 37(2): 341-8.Galanter M. Personality and alcoholism: Issues, methods, and etiological processes, Characteristics of children of alcoholics: Putative risk factors. New York: Kluwer Academic/ Plenum; 2005