Gibson Energy (Canada)

This study analyses the ways in which Caitlin, a high school English 1 teacher in the Southeastern US, recalled her mentor teacher (MT), Travis Mann, at different points in her developmental trajectory. (All names of people and places are pseudonyms.) Our focus for this inquiry is on Caitlin’s mediated memory of Travis. These memories, we argue, were subject to mediated activity in schools that changed how she recalled their relationship. To study Caitlin’s mediated memory of Travis, we inquired into the following research questions: (1) What memories did Caitlin report of her relationship with Travis and his influence on her teaching over the course of the study? (2) What mediators appear to account for the ways in which she remembered her relationship with Travis over time and incorporated those memories into her quest for equilibrium?

There is limited evidence identifying best practices to promote competency of accurate assignment of the Emergency Severity Index (ESI) acuity level to patients who present to the emergency department (ED) triage. Triage-trained nurses do not receive competency training in an ESI triage tool. A retrospective chart review of 150 patients was completed to evaluate mistriage rates before and after triage-trained nurses completed an ESI competency assessment. The retrospective chart review showed no statistically significant difference in mistriage from pre- to postintervention ( P = .8535). Implementation of an ESI annual competency assessment aligns well with an emerging theme in the literature that ED nurses should be provided with ongoing education that reinforces knowledge and implementation of ESI triaging.

Data-based individualization (DBI) is a framework that allows educators to make timely and informed decisions about student progress in academics or behavior. In this article, we focus on the DBI framework as applied to math intervention within a tiered support model for students experiencing math difficulty. We review how DBI starts with an instructional platform paired with progress monitoring. Then, we discuss decision making within a DBI framework and highlight how diagnostic data informs instructional adaptations. Finally, we emphasize the on-going use of DBI to determine the best set of instructional practices in math for each student.

Abstract Founded in 1993, the Yellowstone to Yukon (Y2Y) vision was one of the earliest large‐landscape conservation visions. Despite growing recognition of large‐landscape conservation strategies, there have been few tests to date of conservation gains achieved through such approaches. We tested for conservation gains in the Y2Y region of North America following initiation of the Y2Y conservation vision in 1993 using a counterfactual spatiotemporal comparison and tracking change in five different conservation metrics. First, we enumerated the area of land within Y2Y in designated protected areas. We then compared the rate of change of protected area growth before‐ and after‐initiation of Y2Y in 1993 and to two adjacent counterfactual regions. Protected areas in the Y2Y grew by 7.8%, increasing by 107,289 km2, exceeding the Aichi target of 17% of the area under protection by 2018. More importantly, the rate of protected area growth increased 90% following initiation of the Y2Y large‐landscape conservation vision in 1993, whereas protected area growth declined in adjacent regions, or remained constant throughout North America. Sustained growth in protected areas and private land conservation was complemented by expansion of endangered grizzly bears in the U.S. portion of Y2Y, the greatest global expansion from zero to at least 117 wildlife road‐crossing structures and growing mainstreaming coverage of the Y2Y vision. Our counterfactual comparison provides valuable evidence that large‐landscape conservation strategies such as Y2Y can enhance protected area growth and other conservation metrics. We conclude that large‐landscape conservation strategies may be a useful model for achieving global large‐landscape conservation and biodiversity conservation targets.

In this chapter, we discuss the ARM-based computer's memory. So far, we've used memory to hold our Assembly instructions; now we will look in detail at how to define data in memory, then how to load memory into registers for processing, and, finally, how to write the results back to memory.

In this chapter, we return to mathematics. We've covered addition, subtraction, and a collection of bit operations on our 32-bit registers. Now we will cover multiplication and division. The ARM processor has a surplus of multiply instructions, then a dearth of division operations.

In Chapter 4 , "Controlling Program Flow," we learned how to branch code conditionally based on flags in the CPSR. In this chapter, we will look at how this can be generalized to all instructions.

The Assembly code we’ve been developing produces compact code compared to high-level languages due to not needing a runtime and each instruction only taking 32 bits. However, in the early days of the ARM processor, there were a lot of complaints that this was too large. People used ARMs in small embeddable devices with very limited RAM and needed more compact programs. Others created systems with a 16-bit memory bus that allowed 64K of memory—tiny by today’s standards and took two memory cycles to load each 32-bit instruction slowing down the processor.