This study investigates the impact of urban form and landscaping type on the mid-afternoon microclimate in semi-arid Phoenix, Arizona. The goal is to find effective urban form and design strategies to ameliorate temperatures during the summer months. We simulated near-ground air temperatures for typical residential neighborhoods in Phoenix using the three-dimensional microclimate model ENVI-met. The model was validated using weather observations from the North Desert Village (NDV) landscape experiment, located on the Arizona State University's Polytechnic campus. The NDV is an ideal site to determine the model's input parameters, since it is a controlled environment recreating three prevailing residential landscape types in the Phoenix metropolitan area (mesic, oasis, and xeric).

After validation, we designed five neighborhoods with different urban forms that represent a realistic cross-section of typical residential neighborhoods in Phoenix. The scenarios follow the Local Climate Zone (LCZ) classification scheme after Stewart and Oke. We then combined the neighborhoods with three landscape designs and, using ENVI-met, simulated microclimate conditions for these neighborhoods for a typical summer day. Results were analyzed in terms of mid-afternoon air temperature distribution and variation, ventilation, surface temperatures, and shading. Findings show that advection is important for the distribution of within-design temperatures and that spatial differences in cooling are strongly related to solar radiation and local shading patterns. In mid-afternoon, dense urban forms can create local cool islands. Our approach suggests that the LCZ concept is useful for planning and design purposes.

Surveys have shown that several hundred billion weather forecasts are obtained by the United States public each year, and that weather news is one of the most consumed topics in the media. This indicates that the forecast provides information that is significant to the public, and that the public utilizes details associated with it to inform aspects of their life. Phoenix, Arizona is a dry, desert region that experiences a monsoon season and extreme heat. How then, does the weather forecast influence the way Phoenix residents make decisions? This paper aims to draw connections between the weather forecast, decision making, and people who live in a desert environment. To do this, a ten-minute survey was deployed through Amazon Mechanical Turk (MTurk) in which 379 respondents were targeted. The survey asks 45 multiple choice and ranking questions categorized into four sections: obtainment of the forecast, forecast variables of interest, informed decision making based on unique weather variables, and demographics. This research illuminates how residents in the Phoenix metropolitan area use the local weather forecast for decision-making on daily activities, and the main meteorological factors that drive those decisions.

Optimal foraging theory provides a suite of tools that model the best way that an animal will <br/>structure its searching and processing decisions in uncertain environments. It has been <br/>successful characterizing real patterns of animal decision making, thereby providing insights<br/>into why animals behave the way they do. However, it does not speak to how animals make<br/>decisions that tend to be adaptive. Using simulation studies, prior work has shown empirically<br/>that a simple decision-making heuristic tends to produce prey-choice behaviors that, on <br/>average, match the predicted behaviors of optimal foraging theory. That heuristic chooses<br/>to spend time processing an encountered prey item if that prey item's marginal rate of<br/>caloric gain (in calories per unit of processing time) is greater than the forager's<br/>current long-term rate of accumulated caloric gain (in calories per unit of total searching<br/>and processing time). Although this heuristic may seem intuitive, a rigorous mathematical<br/>argument for why it tends to produce the theorized optimal foraging theory behavior has<br/>not been developed. In this thesis, an analytical argument is given for why this<br/>simple decision-making heuristic is expected to realize the optimal performance<br/>predicted by optimal foraging theory. This theoretical guarantee not only provides support<br/>for why such a heuristic might be favored by natural selection, but it also provides<br/>support for why such a heuristic might a reliable tool for decision-making in autonomous<br/>engineered agents moving through theatres of uncertain rewards. Ultimately, this simple<br/>decision-making heuristic may provide a recipe for reinforcement learning in small robots<br/>with little computational capabilities.

Every season from September to March in Taiji, Japan, around 23,000 dolphins, and other small cetaceans are slaughtered or sold to dolphinariums in the name of a 400-year-old tradition. The word ‘tradition’ is often used to rationalize and justify the terrible acts of animal cruelty, as seen in many countries such as bullfighting in Spain, fox hunting in Britain, Thanksgiving in America, and drive hunting in Japan. However, just because something is deemed as a tradition, does not mean it should not be challenged and judged against the standards of morality. Whale and dolphin hunting has stopped becoming a proud cultural tradition of small-scale subsistence whaling and has become a business run on wholesale slaughter and the exploitation of another species. The disconnect between the past and present has led to an evil distortion of the past.
However, this event cannot simply be explained by blaming solely greed and selfishness for driving this long-lasting tradition. By analyzing poems by Misuzu Kaneko, early hunting methods, memorial services, and graves built in the past and comparing them to the current hunting methods, dolphin shows, and the Taiji Whale Museum, one can determine the variety of factors driving these actions and find the point in time when the intentions of these practices shifted. By having a better understanding of the past and the present, one can follow a once-proud tradition becoming a source to justify unethical and cruel behavior.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

With the increase in the severity of drought conditions in the Southwest region of the U.S. paired with rising temperatures, it is becoming increasingly important to look at the systems used to keep people cool in hot-arid cities like Tempe, Arizona. Outdoor misting systems are often deployed by businesses. These systems rely on the evaporative cooling effect of water. This study examines the relationship between misting droplet size, water usage, and thermal comfort using low-pressure misting systems, tested within hot and dry conditions representative of the arid U.S. southwest. A model misting system using three nozzle orifice sizes was set up in a controlled heat chamber environment (starting baseline conditions of 40°C air temperature and 15 % relative humidity). Droplet size was measured using water-reactive paper, while water use was determined based on weight-change measurements. These measurements were paired with temperature and humidity measurements observed in several locations around the chamber to allow for a spatial analysis. Thermal comfort is determined based on psychrometric changes (temperature and absolute humidity) within the room. On average, air temperatures decreased between 2 to 4°C depending on nozzle size and sensor location. The 0.4 mm nozzle had a decent spread across the heat chamber and balanced water usage and effectiveness well. Limitations within the study showed ventilation is important for an effective system, corroborating other studies findings and suggesting that adding air circulation could improve evaporation and comfort and thus effectiveness. Finally, visual cues, such as wetted surfaces, can signal businesses to change nozzle sizes and/or make additional modifications to the system area.

Enantiomers are pairs of non-superimposable mirror-image molecules. One molecule in the pair is the clockwise version (+) while the other is the counterclockwise version (-). Some pairs have divergent odor qualities, e.g. L-carvone (“spearmint”) vs. D-carvone (“caraway”), while other pairs do not. Existing theory about the origin of such differences is largely qualitative (Friedman and Miller, 1971; Bentley, 2006; Brookes et al., 2008). While quantitative models based on intrinsic molecular features predict some structure–odor relationships (Keller et al., 2017), they cannot identify, e.g. the more intense enantiomer in a pair; the mathematical operations underlying such features are invariant under symmetry (Shadmany et al., 2018). Only the olfactory receptor (OR) can break this symmetry because each molecule within an enantiomeric pair will have a different binding configuration with a receptor. However, features that predict odor divergence within a pair may be identifiable; for example, six-membered ring flexibility has been offered as a candidate (Brookes et al., 2008). To address this problem, we collected detection threshold data for >400 molecules (organized into enantiomeric pairs) from a variety of public data sources and academic literature. From each pair, we computed the within-pair divergence in odor detection threshold, as well as Mordred descriptors (molecular features derived from the structure of a molecule) and Morgan fingerprints (mathematical representations of molecule structure). While these molecular features are identical within-pair (due to symmetry), they remain distinct across pairs. The resulting structure+perception dataset was used to build a predictive model of odor detection threshold divergence. It predicted a modest fraction of variance in odor detection threshold divergence (r 2 ~ 0.3 in cross-validation). We speculate that most of the remaining variance could be explained by a better understanding of the ligand-receptor binding process.