Rider University

About: Rider University is a education organization based out in Lawrenceville, New Jersey, United States. It is known for research contribution in the topics: Dosimetry & Creativity. The organization has 881 authors who have published 1934 publications receiving 50752 citations.

Mother's Education and Working: Effect on the School Child

Abstract: (1972). Mother's Education and Working: Effect on the School Child. The Journal of Psychology: Vol. 82, No. 1, pp. 27-37.

The quenching effect in PRESAGE ® dosimetry of proton beams: is an empirical correction feasible?

Abstract: Chemical dosimeters, including PRESAGE® as used in optical CT, exhibit significant quenching effects in response to proton irradiation and this may limit their widespread uptake. This study performs careful measurements of the observed quenching of a recently developed variant of PRESAGE® in a 60 MeV proton beam and uses them to attempt an empirical correction of a simple superposition of two unmodulated beams.

Using Exploratory Bifactor Analysis to Understand the Latent Structure of Multidimensional Psychological Measures: An Example Featuring the WISC-V

Abstract: Exploratory bifactor analysis (EBFA) represents a methodological advancement for implementing a bifactor model in exploratory factor analysis (EFA). However, little is known about how to properly e...

Characteristic properties of order statistics based on random sample size from an exponential distribution

Abstract: Suppose X1, X2, Xm is a random sample of size m from a population with probability density function f(x), x > 0), and let X1, m< × 2, m <… < Xm, m be the corresponding order statistics. We assume m is an integer-valued random variable with P(m=k) =p (1-p)k-1, k= 1,2,… and 0 < p < 1. Two characterizations of the exponential distribution are given based on the distributional properties of Xl, m.

A Model‐Based Approach to Investigate Performance Improvements in Rule‐Based Expert Systems

Abstract: One of the major issues in the development of large, rule-based expert systems is related to improving their performance efficiency. One way to address this issue is by reducing the number of unsuccessful tries a system goes through before executing a rule to establish a goal or an intermediary fact. On the average, the number of unsuccessful tries can be reduced if the rules that are tried first are those that are expected to execute most frequently, and this can be established by extracting information on the probability distributions of the input parameters. In this paper, a rule base is modeled as a network and simulated to investigate potential performance improvements by changing the order used to test the rules. The model of the rule base is also used to investigate performance gains achieved by parameter factorization and premise clause reordering.