The Effect of Shear and Alum Addition on Alkenyl Succinic Anhydride Retention

Alkenyl succinic anhydride is an effective sizing agent involved in alkaline papermaking. There are many factors in the alkaline papermaking system which effect the retention of the sizing agent. This study reviews factors which influence sizing and the effects of shear and alum addition on the retention of ASA. The Dynamic Drainage Jar was employed to subject four levels of shear on the system and Hercules size determinations were conducted as a measure of retention. Alum is necessary in the system to provide proper retention of the ASA. Retention of ASA with alum present was found to increase with increasing shear until a point of 1000 RPM was reached. After this point, retention decreased sharply

Communications

Various communications concerning previous topics covered in Performance Practice Revie

Low Impact Storm Water Management Projects at the University of New Hampshire

The University of New Hampshire has become increasingly concerned with storm water management on the Durham campus. Due to Federal regulations many regional municipalities are feeling pressure to enhance and increase management of storm water to reduce impacts to surface waters. The specific objective of this proposal is to demonstrate reductions in the discharge of storm water runoff from UNH-Durham campus properties. The construction and use of three Low Impact Development (LID) integrated management systems on UNH property will help UNH and the UNH Stormwater Center to champion innovative approaches in the state and region for reducing storm water runoff and improving the health of coastal watershed areas

Sample size and statistical significance of hazard regression parameters. An exploration by means of Monte Carlo simulation of four transition models based on Hungarian GGS data

In this paper, we explore the relation between sample sizes of female respondents aged 18-44 and the statistical significance of parameter estimates in four piecewise constant proportional hazard regression models by means of microsimulation. The underlying models for first marriage, first birth, second birth, and first divorce are estimated from Hungarian GGS data and interpreted and used as typical event-history models for the analysis of GGS data in general. The models are estimated from the full biographies as well as from three- and six-year inter-panel biographies of the simulated samples. The simulation results indicate that there is great sensibility of the parameters that reach statistical significance to the sample size precisely in the sample range of the GGS. This means that any reduction or increase in the sample size will notably affect the statistical analysis of the data. Marginal gains in terms of the number of significant parameters are especially high up to 3.000 respondents when applying rather modest thresholds of significance. For higher thresholds, marginal gains remain steep for sample sizes up to 5.000 respondents. When analyzing inter-panel histories, especially for a single three-year interval, the likelihood that parameter estimates are significant is very moderate. For 6-year inter-panel histories, we get better results, at least for a sample size of at least 3.000. When reducing the sample size to below 3.000, the number of significant results for inter-panel histories deteriorates rapidly.event history surveys, microsimulation, samples, simulation

Regularities and peculiarities of birth schedules in industrialized countries: an analysis of FFS data

Inter-individual diversity of women according to birth numbers (quantum) and birth spacing (tempo) are important for understanding of fertility regimes. Elsewhere, we have shown that diversity with respect to fertility quantum is increasing from older to younger cohorts. The present study looks at tempo dimension by decomposing the diversity of birth schedules. The data set contains pooled FFS data from 19 industrialized countries and covers 11124 women aged 40-44 at survey. The analyses include descriptive characteristics of birth schedules, their classification by cluster analysis, and the identification of some of the underlying factors by two types of regression analyses. The first of them is a multinomial logistic regression linking types of birth schedules with characteristics of women at the time of interview. The second includes event-history analyses examining the transition to second and third conception (leading to birth), where time since previous birth is combined with the current values of the covariates. Age at first birth is a major component of inter-individual differences in birth schedules and it largely determines their clustering. Distributions of second, third, and fourth births over time since the previous births are very similar to each other. The median length of birth intervals is 3-3.5 years and 75% of births occur within 5-6 years after the previous delivery. One cluster stands out of this regularity as it is characterized by long last birth interval of about 11 years. Age distributions of fertility for women from this cluster are bimodal and their shape points at unexpectedly “renewed” fertility careers. Additional births produced by the phenomenon compose about 6% of all births. Regression analyses show that the long last birth interval is associated with new partnerships. Some influence of contraceptive failure can not be excluded, too. More in-depth research is needed to learn about the dynamic factors of birth schedules and particularly about the relationship between entering new partnerships and childbearing.Europe, age distribution, birth spacing, fertility, fertility surveys

The Determinants of University Dropouts : A Sequential Decision Model with Selectivity Bias

In this paper, we study the determinants of university dropouts with a longitudinal data set on students' enrollments at the University of Montreal. With a bivariate probit model with selectivity bias, the variables explaining persistence and dropouts are related to the information gathered on the student about his or her interests and abilities. The average number of students in first-year compulsory courses is also a determining factor. These results support a simple version of Manski's model of schooling as an experimentation process and the models of sequential decisions of Altonji, Oosterbeek and Hartog et al. Also supported is the idea of person-environment fit, pioneered by Tinto. Cette étude examine les déterminants des abandons scolaires à l'aide de données longitudinales de l'Université de Montréal. Un modèle probit bivarié avec biais de sélection montre que les variables expliquant la persévérance et l'abandon sont reliées à l'information acquise par l'étudiant concernant ses intérêts et ses habiletés. Le nombre moyen d'étudiants dans les cours obligatoires en première année est également un facteur déterminant. Les résultats obtenus supportent une version simple du modèle d'expérimentation scolaire de Manski et des modèles de décisions séquentielles d'Altonji, d'Oosterbeek et d'Hartog et al., sans pour autant rejeter l'approche de l'adéquation entre l'environnement et la personne proposée par Tinto.university dropout, persistence, intervention, bivariate probit model, sample selection, abandon scolaire, persévérance, intervention, modèle probit bivarié, biais de sélection

Mapping Subsets of Scholarly Information

We illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its practitioners.Comment: 10 pages, 4 figures, presented at Arthur M. Sackler Colloquium on "Mapping Knowledge Domains", 9--11 May 2003, Beckman Center, Irvine, CA, proceedings to appear in PNA