Meteorologists working for the state government in Ceara, Northeast Brazil claim that the kinds of forecasts they can currently produce are not useful for subsistence farmers, who lack resources to act on forecast-based decisions. I argue that scientific predictions do have meaning and consequences in rural communities. Official forecasts inform policies that affect farmers; therefore, farmers hold government accountable for predictions, even if they do not directly influence the farmers' own decision-making.My investigation takes the discussion beyond notions of "usefulness" as I demonstrate that prediction is more than a projection of the future based on the past and the present. In prediction discourse, people create understandings of their place in the social world, including their relationship to government. While government discourse constructs farmers as "non-users" and removes its responsibility to them, traditional "rain prophets" motivate farmers with optimistically-framed predictions and encourage autonomy from government.Prediction is a meaning-making endeavor―not just of ecological and atmospheric processes, but of who people are and how they live. Drawing on linguistic theories of performance and performativity, I analyze strategic language use within a cultural models framework, taking into account the emotions and motivations associated with experiences of living in a particular environment (both natural and material), and how these are crucial to understanding the meanings of prediction. Through prediction, people test the limits of their knowledge, judgement and faith. My examination of the connections between cultural models of 'prediction' and 'lie' explains how traditional predictions motivate farmers and build solidarity in opposition to exclusionary systems of government and science.This research furthers our understanding of how locally marginalized groups engage with government and the knowledge systems it privileges. After tracing constructions of "rain prophet" and "scientist" in the media, I show how rain prophets both oppose themselves to and align themselves with media representations of science, as they establish their authority and challenge meteorologists' expertise. Meanwhile, meteorologists work to authenticate science as the only legitimate authority. Thus, in prediction performances, meteorologists and rain prophets position themselves within local and global discourses about science and traditional knowledge.

This study seeks to expand the range of climates and conditions in which free cooling from an economizer can replace air conditioning power consumption in residential applications. To explore this issue, we first discretize a simple building model in space and in time. We then solve the associated energy and mass balances for the estimated hourly heating and cooling loads and humidity conditions with respect to an annual climate profile. We propose a forecast-based algorithm to control the rate of outdoor airflow brought in by an economizer, in response to the upcoming cooling load to be experienced by the interior airspace. The algorithm takes advantage of a range of acceptable temperatures for thermal comfort by precooling the envelope overnight to delay the onset of cooling demand during the day. In order to consider the highest potential benefit from such an algorithm, we bypass the considerable problem of forecast accuracy by basing the inputs on the upcoming cooling load according to an initial simulation of the full year. On the whole, even with the forecast-based control, the results of the study have much in common with previous findings in the literature. Precooling works better to reduce cooling load in cases of higher thermal and moisture mass, but a humid climate severely restricts when free cooling is beneficial. For the example house considered here with the Austin climate and other assumptions, the effect of the proposed forecast-based economizer control was to greatly reduce the indoor air cooling load while greatly increasing the number of annual hours of unacceptably high indoor humidity. When we adjusted the forecast-based algorithm to avoid the excess humidity, the remaining reduction in cooling load was not significant. To investigate further how a forecast-based economizer could reduce cooling load in humid climates, the prinicipal task should be to extend the control algorithm to forecast and manage upcoming indoor humidity levels in the same fashion as was done in this study for indoor air temperature. / text

<p dir="ltr">Methodologies for optimizing UAVs' control for varied environmental conditions have become crucial in the recent development for UAV control sector, yet they are lacking. This research focuses on the dynamism of the Gazebo simulator and PX4 Autopilot flight controller, frequently referenced in academic sectors for their versatility in generating close-to-reality digital environments. This thesis proposed an integrated simulation system that ensures realistic wind and gust interactions in the digital world and efficient data extraction by employing an industrial standard control communication protocol called MAVLink with the also the industry standard ground control software QGroundControl, using real and historical weather information from NOAA database. This study also looks into the potential of reinforcement learning, namely the DDPG algorithm, in determining optimal UAV safety distance, trajectory prediction, and mission planning under wind disruption. The overall goal is to enhance UAV stability and safety in various wind-disturbed conditions. Mainly focusing on minimizing potential collision risks in areas such as streets, valleys, tunnels, or really anywhere has winds and obstacles. The ROS network further enhanced these components, streamlining UAV response analysis in simulated conditions. This research presents a machine-learning approach to UAV flight safety and efficiency in dynamic environments by synthesizing an integrated simulation system with reinforcement learning. And the results model has a high accuracy, reaching 91%, 92%, and 97% accuracy on average in prediction of maximum shifting displacement, and left/right shifting displacement, when testing with real wind parameters from KLAF airport. </p>