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The Denali Ice Cores were collected from the summit of Begguya (Mt. Hunter), Denali National Park, Alaska in the summer of 2013. Sampling permits were granted by Denali National Park for the drilling and removal of the ice cores. Here, we use the Cameca SX100 at the University of Maine to examine tephra particles recovered from the ice cores. 

In 2013, two parallel ice cores (commonly referred to as the Denali Ice Cores) were drilled to bedrock on the summit plateau of Begguya, Alaska (62.93 N 151.083 W, 3912 m asl; also known as Mount Hunter). A robust chronology has been developed using a combination of techniques including annual layer counting, sulfate peaks (volcanics), radiocarbon dating and the 1963 atmospheric nuclear weapons testing horizon. Here, we employed tephrochronology practices to isolate and document the presence of the Lena Ash Layer and White River Ash east (WRAe) volcanic eruptions within the ice. We separated tephra from the meltwater and analyzed them using SEM-EDS and EPMA methodologies. The data are not immediately conclusive, and work is still ongoing to understand the findings. 

The Denali Ice Cores were collected from the summit of Begguya (Mt. Hunter), Denali National Park, Alaska in the summer of 2013. Sampling permits were granted by Denali National Park for the drilling and removal of the ice cores. Here, we use the Tescan II at the University of Maine to examine tephra particles recovered from the ice cores. 

Abstract This article presents a novel approach for generating metamaterial designs by leveraging texture information learned from stochastic microstructure samples with exceptional mechanical properties. This eXplainable Artificial Intelligence (XAI)-based approach reduces the reliance on brainstorming and trial-and-error in inspiration-driven design practices. The key research question is whether the texture information extracted from stochastic microstructure samples can be used to design metamaterials with periodic structural patterns that surpass the original stochastic microstructures in mechanical properties. The proposed approach employs a pretrained supervised neural network and applies the Activation Maximization Texture Synthesis (AMTS) method to extract representative textures from high-performance stochastic microstructure samples. These textures serve as building blocks for creating novel periodic metamaterial designs. Using three benchmark cases of stochastic microstructure-inspired periodic metamaterial design, we compare the proposed approach with an earlier XAI design approach based on Gradient-weighted Regression Activation Mapping (Grad-RAM). Unlike the proposed approach, Grad-RAM extracts local microstructure patches directly from the original sample images rather than synthesizing representative textures to generate novel periodic metamaterial designs. Both XAI-based design approaches are evaluated based on the mechanical properties of the resulting designs. The relative merits of both approaches in terms of design performance and the need for human intervention are discussed. 

Abstract BackgroundGraduate‐level education is gaining attention in engineering education scholarship. While “socialization” is a key term in doctoral literature, little is known about how socialization occurs over time. One common assumption asserts that socialization increases over time, encompassing factors such as belongingness, research ability, and advisor relationship as students acclimate to the norms and values of their advisors, departments, universities, and disciplines. We investigate engineering doctoral student socialization trends: students likely to complete their degrees and those who have questioned whether to persist in their programs. Understanding these trends is essential, as many students consider leaving their programs. Purpose/HypothesisThis paper aims to understand how socialization processes occur over several years in engineering students who questioned leaving their PhD programs. Design/MethodWe present longitudinal survey data collected from two cohorts (N_A = 113 andN_B = 355) of engineering doctoral students at R1 universities in the United States. Data were collected over 2 years through SMS surveys with participants receiving text messages three times per week. We analyzed data using descriptive and time series analysis methods. ResultsBoth cohorts showed lower levels of belongingness over time, reported declining advisor relationships, and experienced higher levels of stress. Students later in their programs also reported deteriorating overall social relationships. These findings contradict canonical socialization theory, which expects socialization to naturally improve over time. ConclusionWhile many assume socialization occurs passively and students acculturate into their department and research team over time, our results show students who question whether to persist are de‐socializing from graduate school.