Safety and Health in the workplace has long been the priority work of the Hong Kong administration but the accidentstatistics in Hong Kong tell another story. No matter how sophisticated a safe system is designed, its ultimate success depends very much on the person who carries out the job. Safe behaviour has therefore become the contemporary study of safety and health at work.Since human behaviour is a multidimensional construct, its understanding requires a multiple theory approach. Inspired by this concept, this study explores a Multi-Dimensional Safe Behaviour Model in explaining Construction Workers' Safe Behaviour. The study examines its implication for management when safe behaviours are to be instilled. Nine psychological theories and models, identified under the perspective of the Intrapersonal, Interpersonal and Community Level are examined. A short list of 9 variables of "Social Norm", "Management Commitment", "Safety Knowledge", "Perceived Risk", "Safety Experience", "Self Efficacy"; "Perceived Consequence", "Chance" and Intention to Behave" was constructed. Three hypothetical constructs of "Social Support", "Attitude" and "Expectance" composed of observable indicators from the nine identified variables are also formed. The variables are then put together into a hypothesised Multi-Dimensional Safe Behaviour Model with the casual relationships between variables identified. A Structural Equation Modelling procedure shows that the hypothesised Multi-Dimensional Safe Behaviour Model fits the data reflecting the necessity of adopting a holistic approach in addressing behavioural issues. "Safety Attitude" is found to impose a positive effect on the worker's "Intention to Behave Safely" indirectly via a mediating factor of "Chance Locus". The study conveys practical implications to safety management and researchers. Research limitations and areas for further study are also discussed. Safety behavioural initiatives based on the model testing results in promotion workers' safety behaviours are also addressed.

Thesis (M.S.)--Rochester Institute of Technology, 1986. / Typescript. Includes bibliographical references (leaves 71-72).

Project and Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1991. / Vita. Abstract. Includes bibliographical references (leaves 98-102). Also available via the Internet.

<p> In a large-scale outdoor, airborne, hazardous materials (HAZMAT) incident, such as ruptured chlorine rail cars during a train derailment, the local Incident Commanders and HAZMAT emergency responders must obtain accurate information quickly to assess the situation and act promptly and appropriately. HAZMAT responders must have a clear understanding of key information and how to integrate it into timely and effective decisions for action planning. This study examined the use of HAZMAT plume modeling as a decision support tool during incident action planning in this type of extreme HAZMAT incident. The concept of Situation Awareness as presented by Endsley's dynamic situation awareness model contains three levels: perception, comprehension and projection. It was used to examine the actions of incident managers related to adequate data acquisition, current situational understanding, and accurate situation projection. Scientists and engineers have created software to simulate and predict HAZMAT plume behavior, the projected hazard impact areas, and the associated health effects. Incorporating the use of HAZMAT plume projection modeling into an Incident Action Plan may be a complex process. The present analysis employed a mixed qualitative and quantitative methodological approach and examined the use and limitations of a "HAZMAT Plume Modeling Cycle" process that can be integrated into the incident action planning cycle. HAZMAT response experts were interviewed using a computer-based simulation. One of the research conclusions indicated the "HAZMAT Plume Modeling Cycle" is a critical function so that an individual/team can be tasked with continually updating the hazard plume model with evolving data, promoting more accurate situation awareness.</p>

<p> Many households in developing countries rely on contaminated and untreated drinking water sources, contributing to gastrointestinal illness and other health risks. Even piped water quality is often unreliable because of poorly-maintained treatment or distribution systems. Household water treatment (HWT) systems aim to enable users to treat their water at the point of use, making it safe to drink. While some HWT options have been successful in improving health in developing countries, low adoption and sustained use outside pilot projects and epidemiological trials remains one of the current challenges with this approach. Furthermore, Quantitative Microbiological Risk Assessment models predict that the health benefits from water quality interventions drop significantly with even occasional consumption of contaminated water. Therefore, to be effective, HWT options need to achieve high user compliance rates and provide safe water reliably. </p><p> I begin my thesis with an interdisciplinary analysis of the field of water, health, and development, followed by a description of my research study site. Using an interdisciplinary research approach, grounded in the local context, I led the development of an ultraviolet (UV) water disinfection system for rural households. This included an iterative process of design and field tests to create a user-friendly system and laboratory research to improve the performance of the technology. I also collaborated with a non-profit organization based in Mexico in the design of an implementation program to support the adoption and consistent use of the UV system. </p><p> Then I present the design and application of a stepped-wedge cluster randomized trial in rural Mexico to evaluate compliance with the implementation program and field efficacy of the UV system. I developed a framework that disaggregates and measures the components of compliance from initial adoption of a safe water practice to exclusive consumption of safe water. I applied this framework to measure compliance across intervention and control groups and to test if additional program components that improve convenience to users can be a cost-effective approach to increase compliance. I present evidence that the implementation program significantly improved compliance with the habit of consuming safe water, when compared to the practice of purchasing water bottled in reusable 20 L containers in the control group. The additional program components proved to be a cost-effective strategy to increase compliance immediately post-intervention, but their impact degraded with time. By analyzing results across different compliance components, I find limitations of the current HWT approach. I present the rational for pilot testing strategies outside the current HWT paradigm, such as expanding a narrow focus on drinking water to making all domestic water safe to drink or switching from a product-based to a service delivery model. </p><p> As a second component of the randomized trial, I present a series of controlled comparisons to evaluate the field efficacy of the UV system using <i> E. coli</i> as a fecal contamination indicator in drinking water. I use an as-treated-analysis to isolate the impact of the system and contrast these results with an impact evaluation of the implementation program led by a research colleague. I also created a drinking water reliability framework to compare potential contamination impacts from different household water management practices and a logistic regression model to assess household risk factors for post-UV-treatment contamination. I show that treating water with the UV system and storing it in 20 L narrow-necked containers, allowed households to significantly improve their drinking water quality and gain access to a more reliable source of safe water. </p><p> In the final chapter I investigate the greenhouse gas (GHG) emissions associated with the use of HWT technologies in Mexico. I do that by carrying out a literature review of existing studies assessing energy use of water treatment technologies; using secondary data to perform a life cycle assessment (LCA) capturing the embedded CO₂ equivalent emissions of individual HWT products; and developing model to calculate a metric of GHG emissions per volume of water used representative of the HWT sector in Mexico. Filtration, ozone, and UV disinfection technologies resulted in similar LCA emissions, while reverse osmosis had emissions five times higher than the average of the rest. I also find GHG emissions of HWT to be 30 times lower than water bottled in 20 L reusable containers. In a context in which mortgage institutions have created green credit mechanisms, this result is useful for expanding financing options for HWT products, which are often more cost-effective than bottled water, but require a higher capital investment. (Abstract shortened by UMI.)</p>

<p> Building complex physics-based systems in a timely cost-effective manner, that perform well, meet diverse user needs, and have no bad emergent behaviors is a challenge. To meet these requirements the solution is to model the physics-based system before building it. Modeling and Simulation capabilities for these type systems have advanced continuously during the past 20 years thanks to progress in the application of high fidelity computational codes that are able to model the real physical performance of system components. The problem is that it is often too time consuming and costly to model complex systems, end-to-end, using these high fidelity computational models alone. Missing are good approaches to segment the modeling of complex systems performance and behaviors, keep the model chain coherent and only model what is necessary. Current research efforts have shown that using multi-fidelity and/or surrogate models might offer alternative methods of performing the modeling and simulations needed to design and develop physics-based systems more efficiently. This study demonstrates that it is possible reduce the number of high fidelity runs allowing the use of classical systems engineering analysis and tools that would not be possible if only high fidelity codes were employed. This study advances the systems engineering of physics-based systems by reducing the number of time consuming high fidelity models and simulations that must be used to design and develop the systems. The study produced a novel approach to the design and development of complex physics-based systems by using a mix of variable fidelity physics-based models and surrogate models. It shows that this combination of increasing fidelity models enables the computationally and cost efficient modeling and simulation of these complex systems and their components. The study presents an example of the methodology for the analysis and design of two physics-based systems: a Ground Penetrating Radar (GPR) and a Nuclear Electromagnetic Pulse Bounded Wave System.</p>

<p> The purpose of this qualitative collective case study was to examine the perceptions and lived experiences of senior leaders of three different small-to medium sized manufacturing companies, located in the state of Colorado, as related to success strategies and barriers to lean manufacturing implementation. Findings resulted in nine emergent themes: (a) lean manufacturing implementation should be implemented in a way specific to the context of the individual organization; (b) time, resources, and changes in customer demand present challenges in sustaining lean manufacturing implementation; (c) resistance to change is a barrier to lean manufacturing implementation; (d) small-tomedium sized companies use outside consultants and trainers for training staff on lean manufacturing implementation; (e) front line workers need to be trained to apply lean tools and concepts for successful lean manufacturing implementation; (f) front line workers have to own and believe in lean manufacturing for it to be successful; (g) senior leaders have varying definitions of what their role is in leading lean manufacturing implementation; (h) senior leaders have differing perspectives regarding the degree of leadership knowledge required for successful lean manufacturing implementation; and (i) senior leaders struggle to expand lean manufacturing implementation into support departments. Recommendations included (a) viewing lean as a philosophy for managing the business, (b) training and education for senior leaders, (c) defining senior leader roles in implementing lean manufacturing, and (d) strategies when using outside consultants in a company&rsquo;s lean efforts.</p>

<p> Growing concerns over the environmental and social impacts related to the production of clothing and textiles have created a need for more discussion about supply chain practices. Currently, there are no guidelines or strategies for addressing sustainable supply chains in the apparel industry that minimize environmental impacts and uphold socially responsible practices. This exploratory case study traces an apparel supply chain, with a focus on cotton, in South Africa that is under a development strategy to create a sustainable supply chain from farm to retail. In-depth interviews were conducted across the supply chain, including cotton farmers, cotton gins, a cotton yarn manufacturer, a textile mill, an apparel manufacturer, and a retailer. The purpose of the study was to gain an understanding of the business decisions at each node of the supply chain that influence the economic, social and environmental dimensions of sustainability by applying the triple top line model (McDonough &amp; Braungart, 2002). This study identifies strengths and weaknesses in sustainable practices that adhere to the triple top line model within each node of the supply chain. Results found economic factors were drivers for good social and environmental practices, with the latter being the smallest segment of the three. The study makes recommendations for improving practices at each node of the supply chain and recommendations for strategies for developing sustainable apparel supply chains.</p>

Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2007. / Source: Dissertation Abstracts International, Volume: 68-09, Section: A, page: 3956. Adviser: Carol V. Brown. Title from dissertation home page (viewed May 5, 2008).

The planning and scheduling of appropriate resources is essential in engineering design for delivering quality products on time, within cost and at acceptable risk. There is an inherent complexity in deciding what resources should perform which tasks taking into account their effectiveness towards completing the task, whilst adjusting to their availabilities. The right resources must be applied to the right tasks in the correct order. In this context, process modelling and simulation could aid in resource management decision making. However, most approaches define resources as elements needed to perform the activities without defining their characteristics, or use a single classification such as human designers. Other resources such as computational and testing resources, amongst others have been overlooked during process planning stages. In order to achieve this, literature and empirical investigations were conducted. Firstly, literature investigations focused on what elements have been considered design resources by current modelling approaches. Secondly, empirical studies characterised key design resources, which included designers, computational, testing and prototyping resources. The findings advocated for an approach that allows allocation flexibility to balance different resource instances within the process. In addition, capabilities to diagnose the impact of attaining specific performance to search for a preferred resource allocation were also required. Therefore, the thesis presents a new method to model different resource types with their attributes and studies the impact of using different instances of those resources by simulating the model and analysing the results. The method, which extends a task network model, Applied Signposting Model (ASM), with Bayesian Networks (BN), allows testing the influence of using different resources combinations on process performance. The model uses BN within each task to model different instances of resources that carries out the design activities (computational, designers and testing) along with its configurable attributes (time, risk, learning curve etc.), and tasks requirements. The model was embedded in an approach and was evaluated by applying it to two aerospace case studies. The results identified insights to improve process performance such as the best performing resource combinations, resource utilisation, resource sensitive activities, the impact of different variables, and the probability of reaching set performance targets by the different resource instances.