On the accuracy of nonsurvey regional input-output multipliers: An empirical study

Correct specification of the simple location quotients in regionalizing the national direct requirements table is essential to the accuracy of regional input-output multipliers. The purpose of this research is to examine the relative accuracy of these multipliers when earnings, employment, number of establishments, and payroll data specify the simple location quotients.^ For each specification type, I derive a column of total output multipliers and a column of total income multipliers. These multipliers are based on the 1987 benchmark input-output accounts of the U.S. economy and 1988-1992 state of Florida data.^ Error sign tests, and Standardized Mean Absolute Deviation (SMAD) statistics indicate that the output multiplier estimates overestimate the output multipliers published by the Department of Commerce-Bureau of Economic Analysis (BEA) for the state of Florida. In contrast, the income multiplier estimates underestimate the BEA's income multipliers. For a given multiplier type, the Spearman-rank correlation analysis shows that the multiplier estimates and the BEA multipliers have statistically different rank ordering of row elements. The above tests also find no significant different differences, both in size and ranking distributions, among the vectors of multiplier estimates. ^

An aquatic risk assessment of ethion and bromacil inputs to the C-25 Canal and Indian River Lagoon

Pesticide monitoring in St. Lucie County by various local, state and federal agencies has indicated consistent residues of several pesticides, including ethion and bromacil. Although pesticides have long been known to pose a threat to non-target species and much background monitoring has been done, no pesticide aquatic risk assessment has been done in this geographical area. Several recognized United States Environmental Protection Agency (USEPA) methods of quantifying risk are employed here to include hazard quotients (HQ) and probabilistic modeling with sensitivity analysis. These methods are employed to characterize potential impacts to aquatic biota of the C-25 Canal and the Indian River Lagoon (in St. Lucie County, Florida) based on current agricultural pesticide use and drainage patterns. The model used in the analysis incorporates available physical-chemical property data, local hydrology, ecosystem information, and pesticide use practices. HQ's, probabilistic distributions, and field sample analyses resulted in high levels of concern (LOCs), which usually indicates a need for regulatory action, including restrictions on use, or cancellation. ^

Predictors of developmental gains in children enrolled in an early intervention setting

The purpose of this research was to determine whether initial developmental delay, site of intervention, frequency of intervention, age of the child, socio-economic status (SES), gender and ethnicity significantly predict developmental gains in a group of children enrolled in an early intervention setting. The records of 134 children enrolled in an inner-city program in Miami, Florida were reviewed for inclusion in this study. ^ Demographic variables, site placement and treatment frequencies were collected during a retrospective chart review. Level of delay was expressed using the developmental quotient and developmental gain was calculated using the mean gain on age equivalent scores or developmental tests. A multiple regression analysis was performed to determine which of the above variables significantly predicted developmental gains. Multivariate analysis compared developmental gains for all the developmental domains based on intervention site (center versus home-based) while controlling for developmental delay. ^ Children made greater developmental gains if they had higher developmental quotients and if they were younger at the time services were initiated. Frequency of intervention significantly improved developmental outcomes in children attending center-based programs. Children attending center-based programs also made significantly greater gains in gross motor skills compared to children attending home-based programs. ^ These findings emphasize the importance of early screening and referral of children with developmental delay and adjusting intervention for the child's developmental quotient. Children should receive intense treatment to maximize results. Decisions regarding program placement should be individualized according to the child's unique developmental pattern. Policy and program decisions affecting the curriculum of a child in early intervention need to reflect these multivariate considerations. ^

Algebraic theory of minimal nondeterministic finite automata with applications

Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA’s behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.

Algebraic Theory of Minimal Nondeterministic Finite Automata with Applications

Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA's behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.

On the accuracy of nonsurvey regional input-output multipliers : An empirical study

Correct specification of the simple location quotients in regionalizing the national direct requirements table is essential to the accuracy of regional input-output multipliers. The purpose of this research is to examine the relative accuracy of these multipliers when earnings, employment, number of establishments, and payroll data specify the simple location quotients. For each specification type, I derive a column of total output multipliers and a column of total income multipliers. These multipliers are based on the 1987 benchmark input-output accounts of the U.S. economy and 1988-1992 state of Florida data. Error sign tests, and Standardized Mean Absolute Deviation (SMAD) statistics indicate that the output multiplier estimates overestimate the output multipliers published by the Department of Commerce-Bureau of Economic Analysis (BEA) for the state of Florida. In contrast, the income multiplier estimates underestimate the BEA's income multipliers. For a given multiplier type, the Spearman-rank correlation analysis shows that the multiplier estimates and the BEA multipliers have statistically different rank ordering of row elements. The above tests also find no significant different differences, both in size and ranking distributions, among the vectors of multiplier estimates.