Rapid urbanization of the planet is occurring at an unprecedented pace, primarily in arid and semi-arid hot climates [Golden, J.S., 2004. The built environment induced urban heat island effect in rapidly urbanizing arid regions – a sustainable urban engineering complexity. Environ. Sci. J. Integr. Environ. Res. 1 (4), 321–349]. This growth has manifested itself as a cause of various impacts including elevated urban temperatures in comparison to rural sites known as the Urban Heat Island (UHI) effect [Oke, T.R., 1982. The energetic basis of the urban heat island. Q. J. R. Meteor. Soc. 108, 1–24]. Related are the increased demands for electric power as a result of population growth and increased need for mechanical cooling due to the UHI. In the United States, the Environmental Protection Agency has developed a three-prong approach of (1) cool pavements, (2) urban forestry and (3) cool roofs to mitigate the UHI. Researchers undertook an examination of micro scale benefits of the utilization of photovoltaic panels to reduce the thermal impacts to surface temperatures of pavements in comparison to urban forestry. The results of the research indicate that photovoltaic panels provide a greater thermal reduction benefit during the diurnal cycle in comparison to urban forestry while also providing the additional benefits of supporting peak energy demand, conserving water resources and utilizing a renewable energy source.

Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

Water heaters that are manufactured for swimming pools come in several forms, most of which require an electrical input for a source of power. Passive-circulation systems, however, require no electrical power input because fluid circulation occurs as a result of thermal gradients. In solar-based systems, thermal gradients are developed by energy collected from sunlight. The combination of solar collection and passive circulation yields a system in which fluids, particularly water, are heated and circulated without need of assistance from external mechanical or electrical sources. The design of such a system was adapted from that of forced-circulation solar collector systems, as were the equations describing its thermodynamic properties. The design was developed based on such constraints as material corrosion resistance, overall system cost, and location-controlled size limitations. The thermodynamic description of the designed system was adjusted on the basis of the designed system’s physical aspects, such as the configuration and material of each component within the solar collector. Numerical analysis performed with the altered thermodynamic equations projected a total energy gain of 7.39 W between 9:00 and 10:00 A.M. and a total energy gain of 13.12 W between 4:00 and 5:00 P.M. The temperature of heated water exiting the collector system was projected to be 17.62°C in the morning and 25.56°C in the afternoon. The morning projection utilized an initial fluid temperature of 12°C and an ambient air temperature of 13°C, while the afternoon projection utilized an initial fluid temperature of 17°C and an ambient air temperature of 22°C. Field testing of the designed passive thermosyphon solar collector system was performed over a period of about one month with one temperature measurement taken at the collector outlet in the morning and another taken in the afternoon. For an ambient air temperature of 13°C, the linear regression developed from the morning dataset yielded an outlet water temperature of 20°C and that for the afternoon dataset yielded an outlet water temperature of 39°C for an ambient air temperature of 17°C. The percentage error between the projected and measured results was 13.51% for the morning period and 52.58% for the afternoon period. Numerical simulation and field data demonstrated that while the collector system operated successfully, its effects were limited to the volume of water immediately surrounding the outlet of the system; the rate of circulation within the system was too low for there to be a meaningful increase in the temperature of the water body at large. The stated results demonstrate that while the particular configuration of passive circulation solar collection technology developed in this instance is capable of transferring solar thermal energy to water without additional energy sources, significant modifications are necessary in order to improve the effectiveness of the technology. Such changes may come from improvements in material availability or alterations to the configuration of components of the collector system.