Beautiful and breezy cities on small tropical islands, it turns out, may not be exempt from the same local climate change effects and urban heat island effects seen in large continental cities such as Los Angeles or Mexico City. A surprising, recent discovery indicates that this is the case for San Juan, Puerto Rico, a relatively affluent coastal tropical city of about two million inhabitants that is spreading rapidly into the once‐rural areas around it. A recent climatological analysis of the surface temperature of the city has revealed that the local temperature has been increasing over the neighboring vegetated areas at a rate of 0.06°C per year for the past 30 years. This is a trend that may be comparable to climate changes induced by global warming.

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In this paper, a new algorithm for texture classification based on logical operators is presented. Operators constructed from logical building blocks are convolved with texture images. An optimal set of six operators are selected based on their texture discrimination ability. The responses are then converted to standard deviation matrices computed over a sliding window. Zonal sampling features are computed from these matrices. A feature selection process is applied and the new set of features are used for texture classification. Classification of several natural and synthetic texture images are presented demonstrating the excellent performance of the logical operator method. The computational superiority and classification accuracy of the algorithm is demonstrated by comparison with other popular methods. Experiments with different classifiers and feature normalization are also presented. The Euclidean distance classifier is found to perform best with this algorithm. The algorithm involves only convolutions and simple arithmetic in the various stages which allows faster implementations. The algorithm is applicable to different types of classification problems which is demonstrated by segmentation of remote sensing images, compressed and reconstructed images and industrial images.

This autoethnography examines the experiences of an assistant professor of elementary social studies methods at a predominantly White institution (PWI). Drawing on the Latina/o Critical Race Theory (LatCrit) methodology of testimonio, the assistant professor in this study, who self-identifies as Chicano, intentionally situates Latinx immigration counter-narratives as oppositional stories to the master narrative of "who belongs." Using a Critical Race Theory (CRT) framework for analysis, this paper argues that counter-narratives serve as necessary correctives for reconceptualizing racist, essentialist, and nativist master narratives. This paper shows how social studies education courses in teacher preparation programs (TEPs) can serve as sites for counter-hegemonic resistance to the master narratives that racialize and criminalize recent Latinx immigrants.