Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 1990. / Thesis Advisor(s): Johnson, Laura. Second Reader: Parry, Sam H. "September 1990." Description based on title screen as viewed on December 21, 2009. DTIC Identifier(s): Chemical Warfare Casualties, Chemical Warfare Agents, Mathematical Models. Author(s) subject terms: Chemical Casualties, Chemical Warfare, Regression, CHEMCAS. Includes bibliographical references (p. 36-37). Also available in print.

Uncertainties in chemical process performance behaviour continue to cause considerable concern to engineers and other stakeholders. The traditional deterministic uncertainty modelling methods lead to excess overdesign, which is expensive, and have also been shown to give limited insight into the behaviour of complex chemical engineering systems. The present work develops a new framework, termed “Stochastic Process Performance Modelling Framework (SPPMF)”, which combines traditional deterministic process simulation, response surface modelling techniques and advanced structural reliability analysis methods to facilitate efficient performance modelling and optimisation of chemical process systems under uncertainties. Cross application of structural reliability principles to chemical processes presents some challenges; however, means of addressing such issues are proposed and discussed in this thesis. For instance, to facilitate Process Reliability Analysis (PRA), stochastic constraints have been added to the conventional process optimisation formulation. Both first order reliability method and Monte Carlo simulation are then applied to gain a wide range of performance measures. In addition, to allow for automated response surface generation, an interface for linking process simulators and a new stochastic module has been developed; making it possible to obtain samples in the order of thousands, typically in minutes. A number of Structural Reliability Analysis (SRA) concepts have been re-defined to reflect the unique characteristics of chemical processes. For example, while SRA is mainly concerned with the effects of random forces and mechanical properties on structural performance, PRA is focused on random process conditions (e.g. changes in pH, reaction rates, etc) and their effects on both product quantity and quality. Finally, SPPMF has been successfully applied to model stochastic properties of a range of typical process systems. The results show that the new framework can be efficiently implemented in process engineering with significant benefits over the traditional methods. Limitations of SPPMF and directions for future work are also highlighted. This thesis contains commercially confidential information which should not be divulged to any third party without the written consent of the author.

The research reported in this thesis comprised a cross-age inquiry of learners' mental models for chemical bonding. Learners were chosen purposefully from three academic levels-senior secondary school (Year-13, age range 17-18 years old), undergraduate (age range 19-21 years), and postgraduate (comprising MSc and PhD; age range 22- 27 years). The principal research goal was to establish learners' preferred mental models for the concept of chemical bonding. Other research goals were to establish if and how learners made use of analogy to understand chemical bonding and to establish the prevalence of learners' alternative conceptions for chemical bonding. The research inquiry was conducted from within a constructivist paradigm; specifically the researcher ascribed to a social and contextual constructivist belief system.Based on a review of the science education literature a decision was made to classify mental models into four classes according to the typology of Norman (1983), namely, the target system, a conceptual model, the users' or learners' mental model and the scientists' conceptualisation. A conceptual theme for the inquiry was developed based on this typology resulting in the identification of target systems-metallic, ionic and covalent bonding. Subsequently, target models for each of the three target systems were identified, namely, the sea of electrons model and the band theory for metallic bonding; the electrostatic model, and the theoretical electrostatic model for ionic bonding; and the octet rule, the valence bond approach, the molecular orbital theory and the ligand field theory for covalent bonding. A conceptual model, consisting of a summary of the salient points of the target models, was developed by the researcher. Once validated by four of the instructors involved in the inquiry, this formed the scientists' conceptualisation for the target ++ / models.Learners' mental models were elicited by the use of a three phase semi-structured interview protocol for each of the three target systems based on the translation interface developed by Johnson and Gott (1996). The protocol consisted of showing participants samples of common substances and asking them to describe the bonding in these materials. In addition, participants were shown Interviews About Events (IAE), focus cards which depicted events involving chemical bonding or contained depicted models of bonding for the three target systems. Transcriptions of audio-tapes combined with diagrams produced by the participants formed the data corpus for the inquiry. Learners' mental models were compiled into inventories for each of the target systems. Examination of inventories enabled identification of commonality of views which were validated by four instructors-two instructors from the teaching institutions involved in the inquiry, and two instructors independent of the inquiry.The research reported in this thesis revealed that learners across all three academic levels preferred simple or realist mental models for chemical bonding, such as the sea of electrons model and the octet rule. Learners frequently used concepts from other more sophisticated models to aid their explanations when their preferred mental models were found to be inadequate. Senior level learners were more critical of mental models, particularly depicted models provided on IAE focus cards. Furthermore, senior level learners were able to describe their mental models in greater detail than their younger counterparts. However, the inquiry found considerable commonality across all three levels of learner, suggesting mental models are relatively stable.Learners' use of analogy was classified according to Dagher's (1995a) typology, namely, simple, narrative, peripheral and compound. Learners' use of ++ / analogy for the understanding of chemical bonding was found to be idiosyncratic. When they struggled to explain aspects of their mental models for chemical bonding, learners made extensive use of simple analogy, that typically involved the mapping of a single attribute between the target and source domains. There did not appear to be any correlation between academic ability or academic level and use of analogy. However, learners made greater use of compound analogy for the target systems of metallic and ionic bonding, mostly as a result of the use of analogical models during instruction.This inquiry revealed prevalent alternative conceptions for chemical bonding across all three academic levels of learner. This is a somewhat surprising result considering that the mental models preferred by learners were typically simple, realist models they had encountered during instruction. Learners' alternative conceptions often concerned simple conceptions such as ionic size, the presence of charged species in non- polar molecular compounds, and misunderstandings about the strength of bonding in metals and ionic substances. The inquiry also revealed widespread confusion about intermolecular and intramolecular bonding, and the nature of lattices structures for ionic and metallic substances.The inquiry resulted in a number of recommendations. It is proposed that it may be more beneficial to teach less content at the introductory level, that is, delivering a curriculum that is more appropriate for non-specialist chemistry majors. Hence, one recommendation is for instructors to examine the intended curriculum carefully and be more critical regarding the value of inclusion of some course content. A second recommendation is that sophisticated models of chemical bonding are better taught only at advanced stages of the degree program, and that teaching from a contructivist view of ++ / learning may be beneficial. The third recommendation relates to the fact that learners spontaneously generated analogies to aid their explanations and conceptual understanding, consequently, learners may benefit from greater use of analogy during instruction.

Chemical representations play a vital part in the teaching and learning of chemistry. The aim of this research was to investigate students’ understanding of chemical representations and to ascertain the influence of chemical representations on students’ developing mental models of chemical phenomena. Three primary threads flowing through the thesis are models, representations and learning. Each thread was found to play a vital part in students’ learning of chemical content, in their learning of the scientific process and in their learning about the process of learning itself. This research with students from Year 8 to first year university level comprised four studies that provide comparisons between ages, abilities, learning settings and teaching and learning approaches. Students’ modelling ability was observed to develop and improve through instruction and practice and usually coincided with an improvement in their understanding of chemical concepts. While students were observed to actively use models to make predictions and test ideas, some were not aware of the predictive nature of models when asked about it. From the research, five characteristics of scientific models have been identified: scientific models as multiple representations, scientific models as exact replicas, scientific models as explanatory tools, how scientific models are used, and the dynamic nature of scientific models. A theoretical framework relating the four types of models - teaching, scientific, mental and expressed - and a typology of models that highlights the significant attributes of models, support the research results. The data showed that students’ ability to describe the role of the scientific model in the process of science improved with their increasing age and maturity. / The relationship between the three levels of chemical representation of matter - the macroscopic level, the sub-microscopic level and the symbolic level - revealed some complexities concerning the representational and theoretical qualities and the reality of each level. The research data showed that generally most students had a good understanding of the macroscopic and symbolic levels of chemical representation of matter. However, students’ understanding of the sub-microscopic level varied, with some students being able to spontaneously envisage the sub- microscopic view while for others their understanding of the sub-microscopic level of chemical representation was lacking. To make sense of the sub-microscopic level, students’ appreciation of the accuracy and detail of any scientific model, or representation upon which their mental model is built, depended on them being able to distinguish reality from representation, distinguish reality from theory, know what a representation is, understand the role of a representation in the process of science, and understand the role of a theory in the process of science. In considering learning, the importance of an individual’s modelling ability was examined alongside the role of chemical representations and models in providing clear and concise explanations. Examining the links forged between the three levels of chemical representation of matter provided an insight into how students were learning and understanding chemical concepts. Throughout this research, aspects of students’ metacognition and intention were identified as being closely related to their development of mental models. / The research identified numerous factors that influenced learning, including internal factors such as students’ prior chemical and mathematical knowledge, their modelling ability and use of chemical representations, motivation, metacognitive ability and time management as well as external factors such as organisation, assessment, teaching resources, getting feedback and good explanations. The choice of learning strategies by students and instructors appeared to be influenced by those factors that influenced learning. Feedback to students, in the form of discussion with classmates, online quizzes and help from instructors on their understanding was observed to be significant in promoting the learning process. Many first year university non-major chemistry students had difficulties understanding chemical concepts due to a limited background knowledge in chemistry and mathematics. Accordingly, greater emphasis at the macroscopic level of representation of matter with contextual references is recommended. The research results confirmed the theoretical construct for learning chemistry - the rising iceberg - that suggests all chemistry teaching begins at the macroscopic level, with the sub-microscopic and symbolic levels being introduced as needed. More of the iceberg becomes visible as the students’ mental model and depth of understanding increases. In a variety of situations, the changing status of a concept was observed as students’ understanding in terms of the intelligibility, plausibility and fruitfulness of a concept developed. / The research data supported four aspects of learning - epistemological, ontological, social affective and metacognitive - as being significant in the students’ learning and the development of their mental models. Many university students, who are mature and are experienced learners, exhibited strong rnetacognitive awareness and an intentional approach to learning. It is proposed that the intentional and metacognitive learning approaches and strategies could be used to encourage students to be more responsible for their own learning.

No description available.

Lam Chun Pong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references. / Abstracts in English and Chinese. / Table of Contents --- p.i / Acknowledgements --- p.v / Abstracts --- p.vi / Abbreviations --- p.viii / Chapter CHAPTER 1. --- SYNTHESIS AND REACTIVITY STUDIES OF MODEL COMPLEXES FOR INTRADIOL DIOXYGENASES WITH BENZIMIDAZOLE- CONTAINING LIGAND / Chapter I.1 --- Introduction / Chapter I.1.1 --- General Background --- p.1 / Chapter I.1.2 --- A General Review on the Modeling Chemistry for Catechol Dioxygenases --- p.3 / Chapter I.1.3 --- Intradiol Dioxygenases --- p.3 / Chapter I.1.3.1 --- Early model studies for intradiol dioxygenases --- p.5 / Chapter I.1.3.2 --- Factors affecting enzymatic reactivity for intradiol dioxygenases --- p.6 / Chapter I.1.3.3 --- Other functional models for intradiol dioxygenases --- p.7 / Chapter I.1.3.4 --- Reactivity studies of model complexes --- p.8 / Chapter I.1.4 --- Extradiol Dioxygenases --- p.8 / Chapter I.1.4.1 --- Early model studies for extradiol dioxygenases --- p.11 / Chapter I.1.4.2 --- Iron(III) complexes with extradiol properties --- p.12 / Chapter I.1.5 --- Objective of This Work --- p.14 / Chapter I.2 --- Results and Discussion / Chapter I.2.1 --- Synthesis of the Ligand Ntb --- p.15 / Chapter I.2.2 --- Synthesis of the Model Complex [Fe(ntb)Cl2]Cl --- p.16 / Chapter I.2.3 --- Synthesis of Enzyme-Substrate Model Complexes --- p.16 / Chapter I.2.4 --- Oxygenation Reactivities of Enzyme-Substrate Model Complexes 2-4 --- p.18 / Chapter I.2.4.1 --- Oxygenation of [Fe(ntb)(dbc)](C104) (2) in DMF --- p.18 / Chapter I.2.4.2 --- Oxygenation of [Fe(ntb)(cat)](Cl04) (3) in DMF --- p.21 / Chapter I.2.4.3 --- Oxygenation of [Fe(ntb)(tcc)](ClO4) (4) in DMF --- p.23 / Chapter I.2.4.4 --- Comparison of the oxygenation reactivities of complexes2-4 --- p.25 / Chapter I.2.5 --- Identification of Oxidative Cleavage Products --- p.27 / Chapter I.2.5.1 --- Isolation of oxidative cleavage products of complex 2 --- p.27 / Chapter I.2.5.2 --- Identification of cleavage products --- p.27 / Chapter I.2.6 --- Physical Characterization of Complexes 1-4 --- p.29 / Chapter I.2.6.1 --- Melting-points --- p.29 / Chapter I.2.6.2 --- Cyclic Voltammograms --- p.30 / Chapter I.2.6.3 --- EPR spectra --- p.31 / Chapter I.2.7 --- Molecular Structures of Complexes 1-4 --- p.34 / Chapter I.2.7.1 --- Molecular structure of [Fe(ntb)Cl2]Cl-4H20 (1) --- p.34 / Chapter I.2.7.2 --- Molecular structure of [Fe(ntb)(dbc)](Cl04)-2Me0H-H20 (2) --- p.36 / Chapter I.2.7.3 --- Molecular structure of [Fe(ntb)(cat)](ClO4) H20 (3) --- p.38 / Chapter I.2.7.4 --- Molecular structure of [Fe(ntb)(tcc)](Cl04).Me2C(0).H20 (4) --- p.41 / Chapter I.2.7.5 --- Comparison of the molecular structures of complexes 1-4 --- p.43 / Chapter I.3 --- Experimentals for Chapter 1 --- p.45 / Chapter I.4 --- References for Chapter 1 --- p.49 / Chapter CHAPTER II --- iron(iii) complexes containing N202 and N3O type ligands as models for INTRADIOL DIOXYGENASES / Chapter II.1 --- Introduction / Chapter II.1.1 --- Brief Remarks on Model Studies of Intradiol Dioxygenases. --- p.53 / Chapter II.1.2 --- Objective of This Work --- p.53 / Chapter II.2 --- Results and Discussion / Chapter II.2.1 --- Synthesis of N202 and N30 Type Ligands --- p.55 / Chapter II.2.2 --- Synthesis of Model Complexes --- p.57 / Chapter II.2.2.1 --- Model complex with ligand L1H --- p.57 / Chapter II.2.2.2 --- Model complex with ligand L2H2 --- p.58 / Chapter II.2.3 --- Synthesis of Enzyme-Substrate Model Complexes --- p.59 / Chapter II.2.3.1 --- Synthesis of enzyme-substrate model complexes from 14.… --- p.59 / Chapter II.2.3.2 --- Attempted synthesis of enzyme-substrate model complexes starting from 15 --- p.61 / Chapter II.2.4 --- Reaction of Complex 16 with Dioxygen --- p.61 / Chapter II.2.4.1 --- Oxygenation of [Fe(L1)(dbc)] (16) in DMF --- p.65 / Chapter II.2.5 --- Identification of Oxidative Cleavage Products --- p.64 / Chapter II.2.5.1 --- Isolation of oxidative cleavage products of complex 16 --- p.64 / Chapter II.2.5.2 --- Identification of cleavage products --- p.65 / Chapter II.2.6 --- "Physical Characterization of L1H, L2H2, Complexes 14-18" --- p.66 / Chapter II.2.6.1 --- NMR spectra --- p.67 / Chapter II.2.6.2 --- Melting-points --- p.69 / Chapter II.2.6.3 --- Mass spectra --- p.69 / Chapter II.2.6.4 --- Cyclic voltammogram --- p.69 / Chapter II.2.6.4 --- EPR spectra --- p.70 / Chapter II.2.7 --- "Molecular Structures of Complexes 14,15 and 18" --- p.71 / Chapter II.2.7.1 --- Molecular structure of [Fe(L1)(MeOH)Cl][BPh4].MeOH (14) --- p.72 / Chapter II.2.7.2 --- Molecular structure of [Fe(L2)Cl].MeOH (15) --- p.75 / Chapter II.2.7.3 --- Molecular structure of [Et3 Nh]3［Fe(tcc)3］.H2O(18) --- p.78 / Chapter II.3 --- Experimentals for Chapter 2 --- p.80 / Chapter II.4 --- References for Chapter 2 --- p.87 / APPENDIX 1 General Procedures and Physical Measurements --- p.89 / "APPENDIX 2 Selected Crystallographic Data for Complexes 1-4, 15,16 and 18.…" --- p.90 / Table A-l.Selected crystallographic data for complexes 1-4 --- p.91 / "Table A-2.Selected crystallographic data for complexes 15, 16 and 18" --- p.92 / "APPENDIX 3 Other Physical Data for Ligand L1H L2H2, Complexes 2 and 16" --- p.93 / Figure A-l.1H NMR spectrum of ligand L1H --- p.94 / Figure A-2.13C NMR spectrum of ligand L1H --- p.94 / Figure A-3.1H NMR spectrum of ligand L2H2 --- p.95 / Figure A-4.13C NMR spectrum of ligand L2H2 --- p.95 / Figure A-5.GC spectrum of the oxidative cleavage products of complex 2 --- p.96 / Figure A-6.- A-l 1.Mass spectra of the oxidative cleavage products of Complex 2 --- p.96 / Figure A-12.GC spectrum of the oxidative cleavage products of complex 16 --- p.99 / Figure A-13.- A-23.Mass spectra of the oxidative cleavage products of Complex 16 --- p.99 / Figure A-24.GC spectrum of dbcH2 standard --- p.105 / Figure A-25.Mass spectrum of dbcH2 standard --- p.106 / Figure A-26.GC spectrum of dbcq standard --- p.106 / Figure A-27.Mass spectrum of dbcq standard --- p.107

no / Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.

The following thesis presents a computational framework that can capture inherently non-linear and emergent biocomplex phenomena. The main motivation behind the investigations undertaken was the absence of a suitable platform that can simulate, both the continuous features as well as the discrete, interaction-based dynamics of a given biological system, or in short, dynamic reciprocity. In order to determine the most powerful approach to achieve this, the efficacy of two modelling paradigms, transport phenomena as well as agent-based, was evaluated and eventually combined. Computational Fluid Dynamics (CFD) was utilised to investigate optimal boundary conditions, in terms of meeting cellular glucose consumption requirements and exposure to physiologically relevant shear fields, that would support mesenchymal stem cell growth in a 3-dimensional culture maintained in a commercially available bioreactor. In addition to validating the default bioreactor configuration and operational parameter ranges as suitable towards sustaining stem cell growth, the investigation underscored the effectiveness of CFD as a design tool. However, due to the homogeneity assumption, an untenable assumption for most biological systems, CFD often encounters difficulties in simulating the interaction-reliant evolution of cellular systems. Therefore, the efficacy of the agent-based approach was evaluated by simulating a morphogenetic event: development of in vitro osteogenic nodule. The novel model replicated most aspects observed in vitro, which included: spatial arrangement of relevant players inside the nodule, interaction-based development of the osteogenic nodules, and the dependence of nodule growth on its size. The model was subsequently applied to interrogate the various competing hypotheses on this process and identify the one that best captures transformation of osteoblasts into osteocytes, a subject of great conjecture. The results from this investigation annulled one of the competing hypotheses, which purported the slow-down in the rate of matrix deposition by certain osteoblasts, and also suggested the acquisition of polarity to be a non-random event. The agent-based model, however, due to being inherently computationally expensive, cannot be recommended to model bulk phenomena. Therefore, the two approaches were integrated to create a modelling platform that was utilised to capture dynamic reciprocity in a bioreactor. As a part of this investigation, an amended definition of dynamic reciprocity and its computational analogue, dynamic assimilation, were proposed. The multi-paradigm platform was validated by conducting melanoma chemotaxis under foetal bovine serum gradient. Due to its CFD and agent-based modalities, the platform can be employed as both a design optimisation as well as hypothesis testing tool.