(De)Valuing Multimodality: Exploring One Teacher-Writer’s Uneven Development in a Multimodal Composition Course

This paper examines the learning experiences and identity development of one ELA pre-service teacher (Elise) in a multimodal composition course. The authors rely on single-case study methods to understand Elise’s multimodal compositions and reflections across the semester. This inquiry asks: a) In what ways does a multimodal literacy course influence PSTs\u27 views of and positions on multimodal literacy instruction? b) What influence does a course focused on multimodal literacy/composing have on the identity development of ELA/writing teachers? c) What prior experiences and understandings facilitate or prevent PSTs uptake of multimodal concepts? Findings detail 1) how Elise at once valued and devalued multimodal composition, often in subtle ways and 2) how prior discourses and learning experiences -- such as a reliance on “learning styles” theory -- both supported and interfered with her learning of multimodal concepts. The authors conclude with recommendations for ELA teacher educators

ARcode: HPC Application Recognition Through Image-encoded Monitoring Data

Knowing HPC applications of jobs and analyzing their performance behavior play important roles in system management and optimizations. The existing approaches detect and identify HPC applications through machine learning models. However, these approaches rely heavily on the manually extracted features from resource utilization data to achieve high prediction accuracy. In this study, we propose an innovative application recognition method, ARcode, which encodes job monitoring data into images and leverages the automatic feature learning capability of convolutional neural networks to detect and identify applications. Our extensive evaluations based on the dataset collected from a large-scale production HPC system show that ARcode outperforms the state-of-the-art methodology by up to 18.87% in terms of accuracy at high confidence thresholds. For some specific applications (BerkeleyGW and e3sm), ARcode outperforms by over 20% at a confidence threshold of 0.8

An Analysis of the Efficacy of Climate Challenge

The temperature in our atmosphere is steadily rising; therefore, we need a method of communicating climate risk that educates and motivates people faster than the rising sea level. Our presentation analyzes the communicative effectiveness of Climate Challenge, a game created for this purpose. We will also analyze and utilize prior research of climate change games and serious games. We gathered participants for our research using convenience and snowball sampling. We conducted a pre-test survey and post-test interview, along with a screencast-recorded playthrough of Climate Challenge. After the research session we used grounded theory and inductive thematic analysis to categorize and find trends in the data. Our analysis suggests that Climate Challenge relies on text to relay its message on climate change, which falls under the category of narratological teaching. Such an approach “deals with the structures and function of narrative storylines/backgrounds” (Ouariachi, T., Olvera-Lobo, M. D., & Gutiérrez-Pérez, J., 2017). This could make an effective climate change game, but too much text can lead the player to become bored with the game before it impacts the player. This will lead into a discussion of narratological and ludological methods of teaching through games. We will discuss the results of our study and suggest ways that researchers can continue exploring the possibilities of risk communication games

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of small Unmanned Aerial Systems (sUAS) continue to multiply. Therefore, solutions to managing sUAS in highly congested airspace must be explored. In this study, a Fuzzy Logic based approach was used to help mitigate the risk of collisions between aircraft using separation assurance and collision avoidance techniques. The system was evaluated for its effectiveness to mitigate the risk of mid-air collisions between aircraft. This system utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The avoidance logic was verified using formal methods and shown to select the correct action in all instances. Additionally, the Fuzzy Logic Controllers were shown to always turn the vehicles in the correct direction. Numerical testing demonstrated that the avoidance system was able to prevent a mid-air collision between two sUAS in all tested cases. Simulations were also performed in a three-dimensional environment with a heterogenous fleet of sUAS performing a variety of realistic missions. Simulations showed that the system was 99.98 effective at preventing mid-air collisions when separation assurance was disabled (unmitigated case) and 100 effective when enabled (mitigated case)

First-principles study of field emission from carbon nanotubes and graphene nanoribbons

A real-space, real-time implementation of time-dependent density functional theory is used to study electron field emission from nanostructures. Carbon nanotubes and graphene nanoribbons are used as model systems. The calculations show that carbon nanotubes with iron adsorbates have spin-polarized emission currents. Graphene nanoribbons are shown to be good field emitters with spatial variation of the emission current influenced by the presence of passivating hydroge

First-principles study of field emission from carbon nanotubes and graphene nanoribbons

A real-space, real-time implementation of time-dependent density functional theory is used to study electron field emission from nanostructures. Carbon nanotubes and graphene nanoribbons are used as model systems. The calculations show that carbon nanotubes with iron adsorbates have spin-polarized emission currents. Graphene nanoribbons are shown to be good field emitters with spatial variation of the emission current influenced by the presence of passivating hydroge

Soft-Switching GaN-Based Isolated Power Conversion System for Small Satellites with Wide Input Voltage Range

As we pursue the advancement of small satellites for space missions with more capabilities, there is a significant need for cutting-edge, modularly configurable, high density power converters. This article proposes a fixed switching frequency, high efficiency, compact isolated converter for sensitive loads such as radar, communication systems, or other instruments on small satellites

Analyzing Resource Utilization in an HPC System: A Case Study of NERSC Perlmutter

Resource demands of HPC applications vary significantly. However, it is common for HPC systems to primarily assign resources on a per-node basis to prevent interference from co-located workloads. This gap between the coarse-grained resource allocation and the varying resource demands can lead to HPC resources being not fully utilized. In this study, we analyze the resource usage and application behavior of NERSC's Perlmutter, a state-of-the-art open-science HPC system with both CPU-only and GPU-accelerated nodes. Our one-month usage analysis reveals that CPUs are commonly not fully utilized, especially for GPU-enabled jobs. Also, around 64% of both CPU and GPU-enabled jobs used 50% or less of the available host memory capacity. Additionally, about 50% of GPU-enabled jobs used up to 25% of the GPU memory, and the memory capacity was not fully utilized in some ways for all jobs. While our study comes early in Perlmutter's lifetime thus policies and application workload may change, it provides valuable insights on performance characterization, application behavior, and motivates systems with more fine-grain resource allocation