PROTESTANT CHURCH WORKERS\u27 KNOWLEDGE OF CHILD ABUSE REPORTING AND REPORTING BEHAVIOR

Child abuse is a prevalent problem on many levels. Mandated reporting laws exist to promote earlier intervention. Studies have shown that mandated reporters are more likely to report if they receive effective training. Protestant church workers interact with many children and need to have enough knowledge to detect and report child abuse. This study utilized a quantitative online survey to answer the question: What are Protestant clergy and church workers’ knowledge about child abuse reporting? Child abuse reporting knowledge was measured in categories of victimization, detection, and reporting. A convenience sample was obtained from attendees at a church conference. Correlational analysis and ANOVA tests were used to detect any association between child abuse knowledge and suspicions, and child abuse knowledge and reporting. This study’s results showed no significant correlation between knowledge and reporting behavior. The results from this study may be useful for social workers, particularly those working in child welfare systems, who work with church workers. Child welfare social workers could address any concerns or knowledge gaps that church workers may have. They may also make changes to child abuse reporting trainings and department policies

Oscillatory combustion in rockets Semiannual report, 1 Jun. - 31 Nov. 1969

Droplet vaporization in region of critical point in flowing stream and stagnant gas at high pressures, and varying temperature

The complexity and distribution of computationally useful problems

The solutions of certain natural decision problems such as the halting problem and the boolean satisfiability problem contain large amounts of useful information about computation that is highly organized and readily available to efficient computational processes. Such problems are computationally useful. This dissertation investigates the complexity and distribution of these computationally useful problems. The main results of this dissertation are of the following three general types. (1) Useful problems contain highly organized information. (2) Very useful problems are so highly organized that they are unusually simple and hence rare. (3) Useful problems are, as a whole, not rare and thus are not necessarily simple;A result of type (1) is proven in Chapter 3. Bennett recently extended algorithmic information theory to include a notion of computational depth that appears to quantify the level of organization in binary strings and sequences. The main result of Chapter 3 states that every weakly useful sequence is strongly deep. (A sequence x is weakly useful if a non-negligible set of recursive problems are decidable within a fixed recursive time bound when given access to x.);Results of type (2) are presented in Chapters 4 and 5. These results say that the ≤[subscript]sp m P-complete problems for E = DTIME(2[superscript] linear) and the ≤[subscript]sp m p/poly-complete problems for ESPACE = DSPACE(2[superscript] linear) are unusually simple and hence rare. Complete problems are very useful because every problem in E or ESPACE is efficiently decidable when given access to one of these problems;Chapter 6 develops a result of type (3). This result says that the weakly ≤[subscript]sp m P-complete problems for E and ESPACE are not rare and hence are not necessarily simple. Weakly complete problems are useful because every problem in a non-negligible subset of E or ESPACE is efficiently decidable when given access to one of these problems;The above results (and others along the way) are obtained through a systematic investigation of the measure-theoretic structure of complexity classes

A Taxonomy of Automatic Differential Tools

Many of the current automatic differentiation (AD) tools have similar characteristics. Unfortunately, it is often the case that the similarities between these various AD tools can not be easily ascertained by reading the corresponding documentation. To clarify this situation, a taxonomy of AD tools is presented. The taxonomy places AD tools into the Elemental, Extensional, Integral, Operational, and Symbolic classes. This taxonomy is used to classify twenty-nine AD tools. Each tool is examined individually with respect to the mode of differentiation used and the degree of derivatives computed. A list detailing the availability of the surveyed AD tools is provided as an appendix

Kolmagorav Complexity, Complexity Cores, and the Distribution of Hardness

Problems that are complete for exponential space are provably intractable and known to be exceedingly complex in several technical respects. However, every problem decidable in exponential space is efficiently reducible to every complete problem, so each complete problem must have a highly organized structure. The authors have recently exploited this fact to prove that complete problems are, in two respects, unusually simple for problems in expontential space. Specifically, every complete problem must have unusually small complexity cores and unusually low space-bounded Kolmogorov complexity. It follows that the complete problems form a negligibly small subclass of the problems decidable in exponential space. This paper explains the main ideas of this work

Computational Depth and Reducibility

This paper investigates Bennett\u27s notions of strong and weak computational depth (also called logical depth) for infinite binary sequences. Roughly, an infinite binary sequence x is defined to be weakly useful if every element of a non-negligible set of decidable sequences is reducible to x in recursively bounded time. It is shown that every weakly useful sequence is strongly deep. This result (which generalizes Bennett\u27s observation that the halting problem is strongly deep) implies that every high Turing degree contains strongly deep sequences. It is also shown that, in the sense of Baire category, almost every infinite binary sequence is weakly deep, but not strongly deep

Tight lower bounds for certain parameterized NP-hard problems

Based on the framework of parameterized complexity theory, we derive tight lower bounds on the computational complexity for a number of well-known NP-hard problems. We start by proving a general result, namely that the parameterized weighted satisfiability problem on depth-t circuits cannot be solved in time no(k) poly(m), where n is the circuit input length, m is the circuit size, and k is the parameter, unless the (t − 1)-st level W [t − 1] of the W-hierarchy collapses to FPT. By refining this technique, we prove that a group of parameterized NP-hard problems, including weighted sat, dominating set, hitting set, set cover, and feature set, cannot be solved in time no(k) poly(m), where n is the size of the universal set from which the k elements are to be selected and m is the instance size, unless the first level W [1] of the W-hierarchy collapses to FPT. We also prove that another group of parameterized problems which includes weighted q-sat (for any fixed q ≥ 2), clique, and independent set, cannot be solved in time no(k) unless all search problems in the syntactic class SNP, introduced by Papadimitriou and Yannakakis, are solvable in subexponential time. Note that all these parameterized problems have trivial algorithms of running time either n k poly(m) or O(n k).