Essays on climate change adaptation and biotechnologies in U.S. agriculture

This dissertation examines climate change adaptation and biotechnologies in United States (US) agriculture. The first essay seeks a better understanding of the long-term and short-term implications of climate change for corn yields. Bayesian dynamic regressions are estimated for non-irrigated counties during 1960-2011 and used to forecast over 2012-2031. Yields are forecasted to generally increase 10-40\% over current averages by 2031, with the Corn Belt and Great Lakes experiencing the greatest growth. The long-run relationship between climate damages and Hicks-neutral technical change is then estimated. Standard damage functions are generalized to include extreme temperatures and precipitation, while controlling for soil productivity. Results indicate significant connections between climate damages and technical change and suggest adaptation possibilities beyond 2031. The second essay examines consumer demand for genetically modified potatoes. The US potato industry is working to lower acrylamide content, a probable human carcinogen forming naturally in potatoes and processed potato products cooked at high temperatures. Using random nth price auctions, we test combined effects of food labels and information on willingness-to-pay (WTP) for conventional potatoes and potato products using biotechnology to reduce acrylamide levels. Each subject receives a randomly-assigned information treatment that consists of one or two perspectives, e.g., an industry, scientific, and/or “environmental group” perspective. Results show for the first time that US consumers are willing to pay a premium for food safety obtained using biotechnology for two popular foods in the American diet. The third essay expands on previous agriculture-climate links by investigating the role of environmental inputs and climate on cropland use and allocation. A discrete-continuous model of crop-tillage combinations and acreage allocation is estimated using field-level data. In the first step, a multinomial logit model is used to estimate farmers’ choices of crops and tillage. In the second step, linear regressions quantify the impacts of climate, economic factors, management, and soil characteristics on crop acreage. There are significant climate impacts on optimal input use. No-till practices may be an effective adaptation strategy to intense heat and precipitation in the short run. In the long run, farmers may adjust crops and acreage, depending on relative output prices and soil characteristics

Macroeconomic models of consumer demand for consumer packaged goods in Asia

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 90-92).CPGCo, a global manufacturer of consumer packaged goods, has had tremendous difficulty in producing accurate forecasts for its products in developing markets. The problem was especially apparent during the global economic crisis in 2008, which caused demand for its products to become highly volatile. Its troubles have been aggravated by its long forecasting horizon, as it has not been able to adjust quickly enough to rapid market shifts due to fluctuations in various macroeconomic indicators. As a result, CPGCo faces heavy stockouts and excess inventories. This thesis explores the suitability of using macroeconomic indicators to forecast consumer demand for three developing countries in Asia as well as three separate product segments. A total of 27 macroeconomic models are constructed using stepwise multiple linear regression analysis employing three separate dependent variables: the firm's monthly wholesale shipment volume, retail market share by volume, and retail sales. The world oil price and country-specific exchange rates, stock indexes, interest rates, consumer price indexes, and consumer confidence indicators are used as independent variables. With our models, we are capable of producing extremely accurate forecasts for a small sample set with errors at or below 7.2%. Our findings also indicate that the consumer price index has the most influence on consumer demand, appearing in 81% of our models; thus, we recommend that CPGCo tracks the consumer price index of each country to complement its current forecasting processes.by Jonathan Mau and Bryan P. McFadden.M.Eng.in Logistic

Capacitance matrix technique for avoiding spurious eigenmodes in the solution of hydrodynamic stability problems by Chebyshev collocation method

We present a simple technique for avoiding physically spurious eigenmodes that often occur in the solution of hydrodynamic stability problems by the Chebyshev collocation method. The method is demonstrated on the solution of the Orr-Sommerfeld equation for plane Poiseuille flow. Following the standard approach, the original fourth order differential equation is factorised into two second-order equations using a vorticity-type auxiliary variable with unknown boundary values which are then eliminated by a capacitance matrix approach. However the elimination is constrained by the conservation of the structure of matrix eigenvalue problem, it can be done in two basically different ways. A straightforward application of the method results in a couple of physically spurious eigenvalues which are either huge or close to zero depending on the way the vorticity boundary conditions are eliminated. The zero eigenvalues can be shifted to any prescribed value and thus removed by a slight modification of the second approach.Comment: 10 pages, 1 figure, minor revision, to appear in J. Comp. Phy

Evaluation of the quality and impact of online learning through the SAFE EUROPE webinars

IntroductionThe SAFE EUROPE project, a European-funded project, addressed educational gaps of Therapeutic Radiographers/Radiation Therapists (TR/RTTs) by offering a series of free webinars. This study aimed to assess the quality of these webinars and their impact on professional practice.MethodsData collection involved two methods: an automated feedback form administered after each webinar, supplemented by a survey disseminated through social media. The collected data encompassed attendance statistics, participants’ professions and geographic locations, webinar quality assessment, the acquisition of new knowledge and skills, the application of this newfound knowledge in practice, and the likelihood of recommending these webinars. Descriptive statistics and thematic analysis were used to analyse the quantitative and qualitative data, respectively. Ethical approval for the study was obtained.Results11,286 individuals from 107 countries participated in 18 webinars. Despite 72.7% being radiographers, a diverse array of professionals attended the webinars, including medical physicists, oncologists, radiologists, and academics. Remarkably, 98.7% of respondents rated the webinar quality as either good or excellent. The average rating for the likelihood of recommending these webinars to colleagues was 8.96/10. A substantial proportion of respondents expressed agreement or strong agreement that the webinars enhanced their knowledge (85%) and skills (73%). Furthermore, 79% of participants indicated that the webinars motivated them to change practice, with 65% having already implemented these changes. The insights from open-ended questions corroborated these findings.ConclusionThe webinars effectively achieved the aim of the SAFE EUROPE project to enhance practice by increasing knowledge and skills. Participants overwhelmingly endorsed the quality of these webinars.Implications for practiceWebinars represent a cost-efficient training tool that reaches a global audience and various radiography/radiotherapy professions. The development of additional webinars is strongly recommended.<br/

Reporting radiographers’ interaction with Artificial Intelligence—How do different forms of AI feedback impact trust and decision switching?

Artificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services. Means of explainable AI, such as heatmaps have been proposed to increase AI transparency and trust by elucidating which parts of image the AI ‘focussed on’ when making its decision. The aim of this novel study was to quantify the impact of different forms of AI feedback on the expert clinicians’ trust. Whilst this study was conducted in the UK, it has potential international application and impact for AI interface design, either globally or in countries with similar cultural and/or economic status to the UK. A convolutional neural network was built for this study; trained, validated and tested on a publicly available dataset of MUsculoskeletal RAdiographs (MURA), with binary diagnoses and Gradient Class Activation Maps (GradCAM) as outputs. Reporting radiographers (n = 12) were recruited to this study from all four regions of the UK. Qualtrics was used to present each participant with a total of 18 complete examinations from the MURA test dataset (each examination contained more than one radiographic image). Participants were presented with the images first, images with heatmaps next and finally an AI binary diagnosis in a sequential order. Perception of trust in the AI systems was obtained following the presentation of each heatmap and binary feedback. The participants were asked to indicate whether they would change their mind (or decision switch) in response to the AI feedback. Participants disagreed with the AI heatmaps for the abnormal examinations 45.8% of the time and agreed with binary feedback on 86.7% of examinations (26/30 presentations).’Only two participants indicated that they would decision switch in response to all AI feedback (GradCAM and binary) (0.7%, n = 2) across all datasets. 22.2% (n = 32) of participants agreed with the localisation of pathology on the heatmap. The level of agreement with the GradCAM and binary diagnosis was found to be correlated with trust (GradCAM:—.515;—.584, significant large negative correlation at 0.01 level (p = &lt; .01 and—.309;—.369, significant medium negative correlation at .01 level (p = &lt; .01) for GradCAM and binary diagnosis respectively). This study shows that the extent of agreement with both AI binary diagnosis and heatmap is correlated with trust in AI for the participants in this study, where greater agreement with the form of AI feedback is associated with greater trust in AI, in particular in the heatmap form of AI feedback. Forms of explainable AI should be developed with cognisance of the need for precision and accuracy in localisation to promote appropriate trust in clinical end users

An evaluation of a checklist in Musculoskeletal (MSK) radiographic image interpretation when using Artificial Intelligence (AI)

Background: AI is being used increasingly in image interpretation tasks. There are challenges for its optimal use in reporting environments. Human reliance on technology and bias can cause decision errors. Trust issues exist amongst radiologists and radiographers in both over-reliance (automation bias) and reluctance in AI use for decision support. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. Method: A checklist, to be used in image interpretation with AI assistance, was developed. Participants interpreted 20 examinations with AI assistance and then re- interpreted the 20 examinations with AI and a checklist. The MSK images were presented to radiographers as patient examinations to replicate the image interpretation task in clinical practice. Image diagnosis and confidence levels on the diagnosis provided were collected following each interpretation. The participant perception of the use of the checklist was investigated via a questionnaire.Results: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. The impact of the use of a checklist in image interpretation with AI will be evaluated. Changes in accuracy and confidence will be investigated and results will be presented. Participant feedback will be analysed to determine perceptions and impact of the checklist also. Conclusion: A novel checklist has been developed to aid the interpretation of images when using AI. The checklist has been tested for its use in assisting radiographers in MSK image interpretation when using AI.<br/