Relating Weight Constraint and Aggregate Programs: Semantics and Representation

Weight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely the stable model semantics for weight constraint programs and the answer set semantics based on conditional satisfaction for aggregate programs. Both classes of programs are instances of logic programs with constraints, and in particular, the answer set semantics for aggregate programs can be applied to weight constraint programs. We show that the two semantics are closely related. First, we show that for a broad class of weight constraint programs, called strongly satisfiable programs, the two semantics coincide. When they disagree, a stable model admitted by the stable model semantics may be circularly justified. We show that the gap between the two semantics can be closed by transforming a weight constraint program to a strongly satisfiable one, so that no circular models may be generated under the current implementation of the stable model semantics. We further demonstrate the close relationship between the two semantics by formulating a transformation from weight constraint programs to logic programs with nested expressions which preserves the answer set semantics. Our study on the semantics leads to an investigation of a methodological issue, namely the possibility of compact representation of aggregate programs by weight constraint programs. We show that almost all standard aggregates can be encoded by weight constraints compactly. This makes it possible to compute the answer sets of aggregate programs using the ASP solvers for weight constraint programs. This approach is compared experimentally with the ones where aggregates are handled more explicitly, which show that the weight constraint encoding of aggregates enables a competitive approach to answer set computation for aggregate programs.Comment: To appear in Theory and Practice of Logic Programming (TPLP), 2011. 30 page

Essays in Leadership Communication

This work centers around leadership communication: how our (dis)information-rich and uncertain global environment has posed challenges to and offered opportunities for this key leadership behavior, and how leaders engage in difficult communications with their stakeholders. I focus on leader-stakeholder two-way dynamics to investigate leader communication in critical moments when they deliver undesirable information to their stakeholders and respond to tough questions from their stakeholders. Essay I reviews research on leader communication and discusses those challenges and opportunities. Essay II uses 107 million Twitter posts to examine stakeholder responses to political leaders’ COVID-19 communications and illustrates the evolving leader-stakeholder relationship throughout different phases of the global pandemic. Essay III explores organizational leaders’ response strategies when facing difficult questions from stakeholders in high-stakes corporate environments. In conclusion, I aim to highlight leaders’ indispensable responsibilities to communicate effectively, benevolently, and responsibly, enhancing the field’s current understanding of crisis leadership, followership, and strategic leadership

ReaxFF-lg: Correction of the ReaxFF Reactive Force Field for London Dispersion, with Applications to the Equations of State for Energetic Materials

The practical levels of density functional theory (DFT) for solids (LDA, PBE, PW91, B3LYP) are well-known not to account adequately for the London dispersion (van der Waals attraction) so important in molecular solids, leading to equilibrium volumes for molecular crystals ∼10-15% too high. The ReaxFF reactive force field is based on fitting such DFT calculations and suffers from the same problem. In the paper we extend ReaxFF by adding a London dispersion term with a form such that it has low gradients (lg) at valence distances leaving the already optimized valence interactions intact but behaves as 1/R^6 for large distances. We derive here these lg corrections to ReaxFF based on the experimental crystal structure data for graphite, polyethylene (PE), carbon dioxide, and nitrogen and for energetic materials: hexahydro-1,3,5-trinitro- 1,3,5-s-triazine (RDX), pentaerythritol tetranitrate (PETN), 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), and nitromethane (NM). After this dispersion correction the average error of predicted equilibrium volumes decreases from 18.5 to 4.2% for the above systems. We find that the calculated crystal structures and equation of state with ReaxFF-lg are in good agreement with experimental results. In particular, we examined the phase transition between α-RDX and γ-RDX, finding that ReaxFF-lg leads to excellent agreement for both the pressure and volume of this transition occurring at ∼4.8 GPa and ∼2.18 g/cm^3 density from ReaxFF-lg vs 3.9 GPa and ∼2.21 g/cm^3 from experiment. We expect ReaxFF-lg to improve the descriptions of the phase diagrams for other energetic materials

Development of Enhanced Emission Factor Through the Identification of an Optimal Combination of Input Variables Using Artificial Neural Network

A great deal of attention is being paid worldwide to particulate matter (PM), which is now considered a significant component of air pollution. Specifically, in this thesis, road dust is a primary source of PM that is having a significant impact on human health and air quality. For example, impaired visibility due to road dust can cause more vehicle accidents. Hence, in order to efficiently develop PM control strategies, it is critical to improve the estimation of PM concentration levels generating from paved and unpaved roads. Since 1979, the U.S. Environmental Protection Agency (EPA) has developed emission factor equations to quantify the magnitude of PM for paved and unpaved roads based on multiple linear regression (MLR) models. However, the MLR models are not suitable for PM data that exhibit the characteristics of complexity and non-linearity, thereby limiting the predictive accuracy of MLR to estimate PM. The objective of this thesis is to present a method to improve the quality of the existing EPA emission factor equations for paved and unpaved roads by employing an artificial neural network (ANN). The proposed method consists of the following steps: data processing for outliers, data normalization, data classification, ANN model training to determine the weights of emission factors identified, and method validation through additional data testing. This thesis included a case study using the data retrieved from the database used by the EPA to generate their emission factor equations for paved and unpaved roads. The proposed method was evaluated by demonstrating its improved performance as shown in the coefficient of determination (R2) and the root mean square error (RMSE) values compared to the values obtained with the existing EPA emission equations. The empirical findings of the case study verified that the proposed method using the ANN model is capable of improving the quality of the EPA emission equations, resulting in higher R 2 and lower RMSE values for both paved and unpaved roads. The expected significance of this thesis is that the proposed method improves the ability to develop more reliable emission factors for predictable PM levels that can help agencies establish enhanced PM control strategies. In addition, the method may have application in other fields that require a selection process to identify an optimal combination of input variables

Mechanism and Kinetics for the Initial Steps of Pyrolysis and Combustion of 1,6-Dicyclopropane-2,4-hexyne from ReaxFF Reactive Dynamics

We report the kinetic analysis and mechanism for the initial steps of pyrolysis and combustion of a new fuel material, 1,6-dicyclopropane-2,4-hexyne, that has enormous heats of pyrolysis and combustion, making it a potential high-energy fuel or fuel additive. These studies employ the ReaxFF force field for reactive dynamics (RD) simulations of both pyrolysis and combustion processes for both unimolecular and multimolecular systems. We find that both pyrolysis and combustion initiate from unimolecular reactions, with entropy-driven reactions being most important in both processes. Pyrolysis initiates with extrusion of an ethylene molecule from the fuel molecule and is followed quickly by isomerization of the fuel molecule, which induces additional radicals that accelerate the pyrolysis process. In the combustion process, we find three distinct mechanisms for the O2 attack on the fuel molecule: (1) attack on the cyclopropane, ring expanding to form the cyclic peroxide which then decomposes; (2) attack onto the central single bond of the diyne which then fissions to form two C_5H_5O radicals; (3) attack on the alkyne-cyclopropane moiety to form a seven-membered ring peroxide which then decomposes. Each of these unimolecular combustion processes releases energy that induces additional radicals to accelerate the combustion process. Here oxygen has major effects both as the radical acceptor and as the radical producer. We extract both the effective activation energy and the effective pre-exponential factor by kinetic analysis of pyrolysis and combustion from these ReaxFF simulations. The low value of the derived effective activation energy (26.18 kcal/mol for pyrolysis and 16.40 kcal/mol for combustion) reveals the high activity of this fuel molecule