This thesis presents why a new user-centred design process for eco-design would be beneficial and demonstrates how this could be done. The research produces a methodology for collecting and measuring behaviour information and a framework for assessing its impact. It explores the role of and effective introduction of information in the design process and finally concludes with the proposed design approach for reducing the environmental impact of products during their use. Utilising a range of qualitative and quantitative research methods, energy models, observational studies, laboratory design experiments, participation research, product prototyping and industrial consultation, a comprehensive picture of designing for energy-efficient user behaviour is formed. It illustrates how behaviour information can be recorded and quantified, assessing the division between a product's intrinsic, technology-based, energy efficiencies and those that are deemed user-related. Finally, in conclusion this information is then used in a new design approach which proposes a framework for the effective and time-efficient design of products, producing a prototype design which achieves an ongoing 43% energy saving in user related losses.

Barely Visible Impact Damage (BVID) can occur when laminated composite material is subject to impact, i.e. from runway debris or dropped tools, and may result in a significant reduction in the compressive strength of composite structures. A component containing BVID subjected to compression may fail via a number of mechanisms. However, it is assumed that the impact damage problems to be modelled will fail by delamination buckling leading to propagation of damage away from the original site. This precludes problems where the initial mechanism of failure is via kink banding or buckling of the full laminate. An analytical model is presented, for application to various composite structures, which predicts the level of compressive strain below which growth of BVID following local buckling of a delaminated sublaminate will not occur. The model is capable of predicting the critical through-thickness level for delamination, the stability of delamination growth, the sensitivity to experimental error in geometric measurements of the damage area and additionally establishes properties desirable for laminates optimised for damage tolerance. Problems treated with the model are split into two impact categories; ‘face’ (i.e. an out-of-plane skin impact) and ‘free edge’ (i.e. an in-plane stiffener edge impact) and two compressive loading regimes; ‘static’ and ‘fatigue’. Analytical results for static and fatigue compression of face impacted plates show an agreement of threshold strain to within 4% and 17% of experimental values respectively. In particular, for impacts to the skin under a stiffener subject to static loading the model is accurate to within 5%. An optimised laminate stacking sequence has shown an experimental increase of up to 29% in static strength can be achieved in comparison to a baseline configuration. Finally, compression testing has been undertaken on three coupons in order to validate an analysis of static free edge problems. Analytical results are, on average, within 10% of experimental results. An optimised laminate is theoretically predicted to increase static compression after free edge impact strength by at least 35%.

This paper investigates the application of tuned mass damper (TMD) systems to bridge pier systems for structural impact damage mitigation and thus reduce the risk of collapses. A bridge superstructure and substructures are designed in accordance with The American Association of State Highway and Transportation Officials (AASHTO) specifications. A variety of vessel collision forces are obtained from collision testing of a scaled reinforced concrete pier. The optimal parameters of TMD systems are then determined such that the drift and displacement of the bridge superstructure are minimized for various impact scenarios. The structural impact mitigation performance of the pier equipped with the proposed optimal TMD system is compared with five different TMD systems employing the benchmark TMD optimal parameters. The uncontrolled responses are used as a baseline. It was demonstrated from the extensive simulations that the control effectiveness of the proposed TMD system was 25% better than all of the existing TMD models in reducing structure responses.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Chemical Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography : leaves 115-118. / M.S.

One of the most significant challenges in the composite development field is to find a low-cost manufacturing route capable of producing large volumes of material. This thesis develops and characterises a potential avenue for addressing this, an induction furnace-based process. This process produced a composite of A357 matrix and 10% wt Dramix 3D 80/30 SL steel fibres. The method was evaluated by microstructural analysis and optimum casting parameters were approximated. The fibres were introduced to liquid A357 at 700°C and the composite was brought to a measured temperature of 650°C over not more than 120 seconds before being removed from the furnace and cooled. 10% wt was the ideal reinforcement ratio for this process. Characterising the tensile and compressive strength of the composite material, it reached a peak stress 130% higher than A357 produced under the same conditions, though the peak stresses were still 20% of the literature values for T6 tempered A357. This suggests the need for development of a temper which does not degrade the properties of the composite. 3-point bending tests and some tensile specimens also showed post-failure strength. Under dynamic loading, the composite showed a peak stress in excess of 100 MPa without reaching maximum compression under SHPB loading, and comparable performance to SiC-reinforced MMCs under ballistic testing. The linear decrease in work-hardening with increasing distance from the impact site shows shock and pressure-pulse dissipation properties, attributed to the difference in acoustic impedance between the matrix and the reinforcement.

Initiatives to electrify urban transport promote the purchase and usage of Electric Vehicles (EVs) and have great potential to mitigate the pressing challenges of climate change, energy scarcity and local air quality. Transportation electrification is a huge innovation and could directly and indirectly impact and/or be impacted by several urban sub-systems. This project develops an agent-based integrated framework for investigating how the EV market expands in the context of urban evolution at the micro scale, and assessing the potential impacts of the market expansion on the environment, power grid system and transport facilities, considering the interactions and dynamics found there. The framework may be useful for stakeholders, such as governments, as an aid to decision making. The integrated framework, SelfSim-EV, is updated from a Land Use and Transport (L-T) model, SelfSim, by incorporating several EV-related modules, including an EV market model, an activity-based travel demand model, a transport facility development model and a social network model. In order to somewhat present the behavioural rules of some key agents in SelfSim-EV, two questionnaire surveys on individual EV travel and purchase behaviours were delivered to members of the general public in Beijing, and semi-structured interviews with EV stakeholders were also carried out. The collected data was analysed using discrete choice models and Geographic Information System (GIS). SelfSim-EV was fully tested within two test cases in China, Baoding (a medium-sized city) and Beijing (the capital of China): first, parameter Sensitivity Analyses (SAs) were carried out to test SelfSim-EV within the test case of Baoding from both global and local perspectives, investigating the relationships between the 127 model parameters and 78 outputs of interest; Then SelfSim-EV was further tested within the case study of Beijing, involving in model initialisation, calibration, validation and prediction. Specifically, the SA results were used to calibrate SelfSim-EV in Beijing from 2011 to 2014 by matching various observed and simulated data types at both city- and district-levels, and the calibrated SelfSim-EV model was further validated against historical data in 2015. Then the future of EVs in Beijing was explored within a Reference Scenario (RefSc) from 2016 to 2020. Due to the model uncertainty in future events, several "what-if" scenarios were set up with the SelfSim-EV Beijing model to explore how three typical types of driving factors, namely policy, technology and infrastructure, may influence the EV market expansion at both aggregate and disaggregate levels. The results indicate that policies tend to be more influential than technologies and infrastructures in terms of EV penetration rates. RefSc eventually shows some improvement in total emissions, however, boosting sales of EVs (particularly PHEVs) in the wrong way could have negative impacts. Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system in RefSc, and it does not increase linearly as the EV sales rise. Slow charging posts appear to be necessary, whereas fast charging facilities seem to contribute slightly to the EV market expansion and thus may be not necessary at the current stage.

Exposure to whole body vibration has been identified as a risk factor for the development of low back problems and exposure to frequent vertical impacts has been identified as a risk factor for both acute and chronic back injury. While there have been many prior studies into human response to both vibration and impacts, these studies have only examined them in isolation from one another, i.e. only sinusoidal vibration or impacts with no concurrent vibration. This does not reflect the environments in which occupational exposures take place and limits our ability to generalize these findings to the real world. The first obstacle in examining the interaction between vibration and impacts is the lack of any quantitative definition of what constitutes an impact within a vibrating environment. To take the first steps toward creating this quantitative definition of an impact, we examined acceleration, posture, and erector spinae electromyography (EMG) data from farm vehicle operators as they completed routine farm tasks. We created several novel impact detection methods based upon our current understanding of human muscle response to impacts that analyze acceleration data and return the locations in the data at which an impact is believed to have occurred. These novel impact detection methods are the Thump, Womp, and Wiggle Methods. We compared their relative successes in predicting a substantial change in EMG activity immediately following an identified impact to that of a method that randomly selected points in the data, as determined by a novel locally-normalized muscle response evaluation method. We then created a series of generalized linear mixed models that included posture and subject-specific data to compare how the odds ratios between the quartiles of each predictor align with we would expect these predictors to affect the likelihood of an impact to trigger a muscle response. We found that none of our novel impact detection methods predicted muscle response at an appreciably different rate than the random method. However, when posture and subject-specific predictors are introduced into generalized linear mixed models, we see statistical significance in how increases in the chest and lumbar angles affect the likelihood of a muscle response in impacts identified by the Thump Method (p<0.001). We also see statistical significance in how increases in the magnitude of the impact metric in impacts identified by the Wiggle Method increase the likelihood of an observed muscle response being observed (p<0.05). We believe that the Thump and Wiggle Methods of impact identification described within this thesis together provide a foundation for the development of an ideal impact identification method for future studies into impacts within vibrating environments.

Music education has been shown to be related to a variety of positive outcomes, including student achievement in math. This study was conducted to explore the relationship between music education and student achievement in math. The framework for the study was Miendlarzweska and Trost's model of musical instrument training. A deidentified archival data set consisting of middle school students' (N = 116) total math scores on the Iowa Assessments was used to determine the impact of music education on students' math achievement, while controlling for students' sex and socioeconomic status. Changes in student achievement were measured by calculating math scores between the 2012-2013, 2013-2014, and 2014-2015 academic school years. The data were accessed from a private school system in the northeast United States. Results of a t test indicated that there were no differences in baseline scores between the group of students who received music education and the group of students who did not receive music education. Results of a regression model for 2013-2014 showed that music education was a significant predictor of math growth scores (p = .015). Results of a regression model for 2014-2015 indicated that only socioeconomic status was a significant predictor of math growth scores (p = .039). Implications for social change include improved stakeholder awareness of the value of music education for student achievement, which may motivate teachers to become advocates for music education and administrators to include music education in their curriculums. By increasing student access to music education, students may be helped to achieve to their fullest potential.

PART I The horse numbers of Utah had not been accurately counted since the early 1960's. It was estimated that horse and pony numbers were reaching heights as recorded before the age of tractors and automobiles. Horsemen in Utah were asking for financial assistance for public horse facilities from the Utah legislature. The first part of this study was to survey the horse population to determine an accurate count of Utah horses. PART II In order to be satisfied that the horse industry deserved the spending of public funds, it was necessary to know the economic contribution of the horse industry. Part II information came from a second survey of expenses and incomes of horse owners. This study was to determine the economic importance of the horse industry to the State' s economy. The entire study was to determine the horse population and the impact of the horse industry on the economy of Utah.

The purpose of this study was to estimate statewide and local economic impact resulting from snowmobiling activities in Utah to gain a better understanding of preferences and opinions of Utah snowmobilers. The results will provide valuable information for snowmobiling management. The survey instrument was designed to describe trip behavior, snowmobiling-related trip and annual expenditures, level of satisfaction with Utah snowmobiling opportunities, and demographics. A telephone survey was conducted with randomly selected households with registered snowmobiles during the period from April to June 2000. A 54.5% response rate yielded 373 usable completed questionnaires for data analysis. For economic impact analysis, an input-output model was applied using IMPLANTM software using the Utah Cross Industrial Matrix in 1999. From the survey, statewide trip and annual snowmobiling associated expenditures data were estimated. Then output, value added, employment, income, and tax impacts were estimated using IMPLAN. For trip characteristics, level of satisfaction, and demographics, analyses were made using descriptive statistics for quantitative data and content analysis for qualitative data. The results indicate that average household per trip and annual expenditures were $126 and $2,932, respectively. About $53 million of reported expenditures created $34 million in local output impact. Total output impact was largest in the Wasatch Front and Mountain Lands planning districts and smallest in the Southeast and Uintah Basin. These results also indicate that the most popular snowmobiling area, Hardware Ranch, Monte Cristo, and Logan Canyon area in the Bear River planning district, had not necessarily received a large economic gain. The survey participants were moderately satisfied with snowmobiling facilities and services in Utah. However, there were still needed improvements in facilities and services, especially parking space availability and trail grooming.