Reconciling the Revolution: Resolving Conflict and Rebuilding Community in the Wake of Civil War in South Carolina, 1775-1860.

This dissertation examines a successful example of reconciliation between former enemies after civil war: South Carolina’s reintegration of Loyalists. At the end of a bruising guerilla civil war, South Carolina’s legislature, with the full support of the citizens, passed legislation confiscating the property of many prominent Loyalists and banishing them from the state, and putting an official imprimatur on the extralegal expulsion of others. Yet within two years, the General Assembly pardoned and readmitted the majority of proscribed Loyalists, despite some anti-Loyalist rioting. This dissertation reincorporates Loyalists into the fabric of the new nation as subjects of discussion and historical actors, rather than absent people who posed no problem for the making of a new republic. For both South Carolina legislators and ordinary South Carolinians, discussions of honor, conduct, loyalty, Republicanism, and citizenship did not take place in a conceptual vacuum, but were shaped by discussions about and with former Loyalists who sought to take their places in this new experiment. South Carolinians’ understanding of citizenship and belonging was shaped by both their wartime experience with civil war and their decision to reincorporate many Loyalists into the body politic. What actually makes reconciliation? In South Carolina’s case, it was a combination of official government action through the main branch of government (the legislature), the influential and persuasive voices of a few individuals who could encourage public opinion, and the individual and neighborhood decisions of many hundreds of individuals who decided that reintegrating most, though not all, of the former Loyalists into their communities was more important than pursuing revenge and punishment. South Carolina’s move towards reconciliation worked because there was support from below. South Carolinians continued carefully to manage public discussions of Revolutionary conflict for generations in order to maintain peaceful relations, reintegrating Loyalist descendants into the elite while avoiding any recognition of their heritage. This dissertation depends on extensive legislative records, including Loyalist petitions, as well as executive documents, family letters, church records, and newspapers to recreate the broader context of Loyalist reintegration in the crucial decades after the Revolution.Ph.D.HistoryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57715/2/brannonr_1.pd

Coordinated Machine Learning and Decision Support for Situation Awareness

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator\u27s input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario

Human error mitigation initiative (HEMI) : summary report.

Despite continuing efforts to apply existing hazard analysis methods and comply with requirements, human errors persist across the nuclear weapons complex. Due to a number of factors, current retroactive and proactive methods to understand and minimize human error are highly subjective, inconsistent in numerous dimensions, and are cumbersome to characterize as thorough. An alternative and proposed method begins with leveraging historical data to understand what the systemic issues are and where resources need to be brought to bear proactively to minimize the risk of future occurrences. An illustrative analysis was performed using existing incident databases specific to Pantex weapons operations indicating systemic issues associated with operating procedures that undergo notably less development rigor relative to other task elements such as tooling and process flow. Future recommended steps to improve the objectivity, consistency, and thoroughness of hazard analysis and mitigation were delineated

Multi-unit operations considerations.

Several nuclear weapons programs have or are pursuing the implementation of multi-unit operations for tasks such as disassembly and inspection, and rebuild. A multi-unit operation is interpreted to mean the execution of nuclear explosive operating procedures in a single facility by two separate teams of technicians. The institution of a multi-unit operations program requires careful consideration of the tools, resources, and environment provided to the technicians carrying out the work. Therefore, a systematic approach is necessary to produce safe, secure, and reliable processes. In order to facilitate development of a more comprehensive multi-unit operations program, the current work details categorized issues that should be addressed prior to the implementation of multi-unit operations in a given weapons program. The issues have been organized into the following categories: local organizational conditions, work process flow/material handling/workplace configuration, ambient environmental conditions, documented safety analysis, and training

Coordinated machine learning and decision support for situation awareness.

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario

LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC.

This Lab-Directed Research and Development (LDRD) sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed path-planning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables 'one-click' layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimental study to determine where parallelization would be productive in Umbra-based force-on-force (FOF) simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication

Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds

As the development of new classes of antibiotics slows, bacterial resistance to existing antibiotics is becoming an increasing problem. A potential solution is to develop treatment strategies with an alternative mode of action. We consider one such strategy: anti-adhesion therapy. Whereas antibiotics act directly upon bacteria, either killing them or inhibiting their growth, anti-adhesion therapy impedes the binding of bacteria to host cells. This prevents bacteria from deploying their arsenal of virulence mechanisms, while simultaneously rendering them more susceptible to natural and artificial clearance. In this paper, we consider a particular form of anti-adhesion therapy, involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads, which competitively inhibit the binding of bacteria to host cells. We develop a mathematical model, formulated as a system of ordinary differential equations, to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat. Benchmarking our model against in vivo data from an ongoing experimental programme, we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies, with the aim of improving treatment outcome. The model consists of two physical compartments: the host cells and the exudate. It is found that, when effective in reducing the bacterial burden, inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate. Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however, when combined with regular or continuous debridement of the exudate, elimination is theoretically possible. Lastly, we present ways to improve therapeutic efficacy, as predicted by our mathematical model

Materials in particulate form for tissue engineering. 1 Basic concepts

For biomedical applications, materials small in size are growing in importance. In an era where ‘nano’ is the new trend, micro- and nano-materials are in the forefront of developments. Materials in the particulate form aim to designate systems with a reduced size, such as micro- and nanoparticles. These systems can be produced starting from a diversity of materials, of which polymers are the most used. Similarly, a multitude of methods are used to produce particulate systems, and both materials and methods are critically reviewed here. Among the varied applications that materials in the particulate form can have, drug delivery systems are probably the most prominent, as these have been in the forefront of interest for biomedical applications. The basic concepts pertaining to drug delivery are summarized, and the role of polymers as drug delivery systems conclude this review

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Review of Production System Models of Cognition and Example Demonstration

There have been significant efforts to develop cognitively plausible software architectures of human information processing in the last three decades. This report summarizes several architectures that continue to be developed. The specific type of cognitive models developed are known as production system architectures, which refers to the characterization of knowledge in terms of procedural (''how-to'' knowledge) condition-action relationships consisting of declarative (''what'' or factual) knowledge. To illustrate the ability for these models to instantiate human cognitive performance, a simulation using ACT-R (Adaptive Control of Thought - Rational) was implemented for a supervisory control task. Correlations between simulated and human learning of the task were measured and yielded correlations as high as 0.93