Design of Experiments and Sample Surveys Commons^™

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

The Influence Of Framing And Recent Experience On Farmer Choices In Experimental Games Depicting Risk-Reducing Agricultural Technologies, Ana Maria Ospina Tobar

Electronic Theses and Dissertations

Climate change is a major threat to food security, particularly in low and middle-income countries that are highly dependent on staple crops for subsistence. The vulnerability of staple crops, like maize, in the face of climate change, is increasing due to the increasing frequency of droughts. This thesis aims to evaluate two mechanisms through which farmers may be more willing to adopt new technologies that increase their resilience to climate change: First, I evaluate the effectiveness of a new virtual maize farming game as a learning tool to teach farmers about the outcomes they could obtain under different weather events …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

All Dissertations

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …