Socio-behavioral Response of Survivors to Campus Active Shooter Events

This research uses available secondary data from two incidents on college campuses to analyze survivor behavior in response to campus-based active shooter events. The study employs a qualitative inductive design using grounded theory methodology within a multiple case study strategy. Themes in survivor behavior develop across the cases. The study results in the development of a data-grounded Active Shooter Behavioral Response Model (ASBRM), which traces the behavioral response of survivors from incident recognition to implementation and reassessment of protective behaviors. The model details environmental cues, social cues, and social interaction leading to information gathering activities that result in protective behavior implementation and reassessment. The model shows similar characteristics to models developed to explain behavior in other event types. The theoretical assessment of the ASBRM shows the application of the emergent norm theory of collective behavior with consideration for ecological factors that affect the operation of the model. The study advances four findings related to survivor behavior in campus active shooting events. (1) Survivor response is social rather than asocial and includes helping behavior between survivors consistent with research findings in other disaster event types. (2) Survivors process environmental cues, social cues, and engage in social interaction to define the situation, gather information, and implement and reassess protective behavior choices within a framework that maintains and extends social and organizational roles. (3) Survivors implement protective behaviors that include taking cover on the floor, running to evacuate, running to shelter, hiding, using available resources to barricade themselves, locking door, turning off lights, and barricading doors. (4) Survivors show group level interaction for confirmation of environmental cues and processing of additional incident cues that lead to: (1) implementation of protective actions and (2) the division of tasks for information gathering and implementation and (3) reassessment of protective behaviors.Political Scienc

AUTOMOTIVE DESIGN-TO LIFE-CYCLE CRITERIA FOR LOWERING WARRANTY COSTS AND IMPROVING OWNERSHIP EXPERIENCE THROUGH THE USE OF A NEW "BINARY DECISION MODEL" AND APPLICATION OF A "WARRANTY INDEX"

Financial challenges facing the automotive sector require identification of new opportunities for quality improvement. A new Design-To Life-Cycle-Cost strategy is introduced that applies a unique "Binary Decision Logic Model" that classifies corrective action opportunity into Life-Cycle categories. The intended result is to lower a manufacture's warranty costs and improve ownership experience. This is done by setting Design-To goals in a Life-Cycle way for Reliability and Serviceability. The sample space for data to drive this change of process is found in an existing warranty system with data elements consisting of failure occurrence, failure symptom, mileage, part cost, and labor cost. One can investigate new factors, such as the "Warranty Index," that parses the corrective action in favor of lowering part costs or labor costs found in a typical service event. The data considers opportunities over mileage and time domains to improve vehicle quality over the Life-Cycle

Modes of production, metabolism and resilience: toward a framework for the analysis of complex social-ecological systems

The field of environmental sociology has undergone drastic change in recent decades, in context of a broader reconfiguration of the terrain of sociological theory and practice. Systems-based approaches to the study of human society, located at the interface between the natural and social sciences have since yielded to a fragmentary body of theory and practice. Subsequent developments such as the emergence of actor network theory, linguistic constructivism and epistemic relativism, have sought not only to question the status of scientific discourse as immutable authority, but also the legitimacies of positivism and macro-theoretical modeling as tenable research programs. This thesis suggests that much of this critique is misdirected, informed as it is by false dichotomies of theory and method which empahsise the separatism of the social, and the difficulty of normative analysis. Over the past twenty years, sociologists have begun to re-engage with systemic theory, albeit with a plethora of new anti-reductionist informants rooted in epistemologies of emergentism, complexity and critical realism. Parallel developments in Marxian ecological thought and human ecology offer further conceptual complementarities and points of dialogue, with which to develop new methodologies for the study of human collectives as ‗social-ecological systems‘. The objectives of this work are thus twofold; (1) to advance an alternative basis for theory and practice in environmental sociology, drawing upon the informants of complexity theory, resilience-based human ecology, and Marx‘s concepts of mode of production and metabolic rift; (2) to contribute to this largely theoretical body of knowledge, by operationalising the preceding informants within a specific case study; that of communal farming, or the 'rundale system‘, in nineteenth century Ireland. The ecological dynamics of the rundale system are thus explored through the imposition of a range of quantitative, archival and comparative methods, as an exercise in the explanatory capacities of the investigative framework developed throughout this work. This methodology rejects existing explanatory models which emphasise the role of 'prime movers‘ in the generation of differential ecological outcomes, toward an account which emphasises both macro-structural complexity, and the augmentation of adaptive capacity from below

GPU Accelerated Simulation of Transport Systems

Computer modelling and simulation of road networks are a vital tool used to evaluate, design and manage road network infrastructure. Road network simulations are however computationally expensive, with simulation runtime imposing limits on the scale and quantity of simulations performed within a reasonable time frame. This thesis examines the appropriateness of many-core processing architectures (such as GPUs) for the acceleration of microscopic and macroscopic road network simulation, and the potential impact on the choice of modelling approach. Fine-grained agent-based microscopic simulations of individual vehicles are parallelised using GPUs, achieving high performance through a novel graph-based communication strategy for data-parallel simulations. A minimal benchmark model and scalable road network are defined and used experimentally to evaluate performance compared to Aimsun, a commercial simulation tool for multi-core processors. Performance improvements of up to 67x are demonstrated for large scale simulations. High-level macroscopic simulations model network flow rather than individual vehicles. Although less computationally demanding than microscopic models, simulation runtimes can still be significant, often due to the calculation of many shortest paths. A novel Many-Source Shortest Path (MSSP) algorithm is proposed to concurrently find multiple shortest paths through sparse transport networks using GPUs. This is embedded within a commercial multi-core CPU macroscopic simulation tool, SATURN, and the performance evaluated on large-scale real-world road networks, demonstrating assignment performance improvements of up to 8.6x when comparing multi-processor GPU and CPU implementations. Finally, the impact of the performance improvements to both modelling techniques are evaluated using a common benchmark model and the relative improvements demonstrated by the benchmarking of each approach using different transport networks. These results suggest that GPUs will allow modellers to shift towards using finer-grained simulations for a broader range of modelling tasks

Second Conference on Artificial Intelligence for Space Applications

The proceedings of the conference are presented. This second conference on Artificial Intelligence for Space Applications brings together a diversity of scientific and engineering work and is intended to provide an opportunity for those who employ AI methods in space applications to identify common goals and to discuss issues of general interest in the AI community

Reflections on the Fukushima Daiichi Nuclear Accident: Toward Social-Scientific Literacy and Engineering Resilience

Nuclear Engineering; Environmental Science and Engineering; Social Sciences, genera

Reports to the President

A compilation of annual reports for the 1986-1987 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans