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Growing interconnect bandwidth demand in large datacenters requires energy-efficient optical transceivers that operate with four-level pulse amplitude modulation (PAM4) to enable high per-wavelength data rates. Further increases in bandwidth density is possible by leveraging wavelength-division multiplexing (WDM), which optical link architectures based on silicon photonic microring modulators (MRMs) and drop filters inherently enable. This paper presents high-speed PAM4 transmitter and receiver front-ends implemented in a 28nm CMOS process that are co-designed with these silicon photonic optical devices to enable energy-efficient operation. The transmitter utilizes an optical digital-to-analog converter (DAC) approach with two PAM2 AC-coupled pulsed-cascode high-swing voltage-mode output stages to drive the MRM MSB/LSB segments. A 3.42Vppd output swing is achieved when operating at 80Gb/s PAM4 with an energy efficiency of 3.66pJ/bit. The receiver front-end interfaces with a silicon-germanium avalanche photodiode (APD) and utilizes a low-bandwidth input transimpedance amplifier followed by continuous-time linear equalizer and variable-gain amplifier stages. Biasing the APD to realize a gain of 2 allows for -7dBm optical modulation amplitude (OMA) sensitivity at 56Gb/s PAM4 with a BER=10-4 and an energy efficiency of 1.61pJ/bit. Experimental verification of the full PAM4 transceiver at 50Gb/s operation shows -4.66dBm OMA sensitivity at a BER~4x10-4. 

Material scientists have made progress in controlling alloy performance through microstructure quantification. However, attempts at numerically modeling microstructures have failed due to the complex nature of the solidification process. In this research, we present the AlloyGAN deep learning model to generate microstructures for castable aluminum alloys. This innovative model demonstrates its capacity to simulate the evolution of aluminum alloy microstructures in response to variations in composition and cooling rates. Specifically, it is successful to simulate various effects on castable aluminum, including: (1) the influence of Si and other elements on microstructures, (2) the relationship between cooling rate and Secondary Dendritic Arm Spacing, and (3) the impact of P/Sr elements on microstructures. Our model delivers results that match the accuracy and robustness of traditional computational materials science methods, yet significantly reduces computation time. 

In the United States, national and state standardized assessments have become a metric for measuring student learning and high-quality learning environments. As the COVID- 19 pandemic offered a multitude of learning modalities (e.g., hybrid, socially distanced face-to-face instruction, virtual environment), it becomes critical to examine how this learning disruption influenced elementary mathematic performance. This study tested for differences in mathematics performance on fourth grade standardized tests before and during COVID-19 in a case study of a rural Ohio school district using the Measure of Academic Progress (MAP) mathematics test. A two-way ANOVA showed that fourth- grade MAP mathematics scores were statistically similar for the 2019 pre-COVID cohort (n = 31) and 2020 COVID-19 cohort (n = 82), and by gender group, between Fall 2019 and Fall 2020. Implications for rural students’ academic performance in virtual learning environments are discussed. 

The Delphi method has been adapted to inform item refinements in educational and psychological assessment development. An explanatory sequential mixed methods design using Delphi is a common approach to gain experts' insight into why items might have exhibited differential item functioning (DIF) for a sub-group, indicating potential item bias. Use of Delphi before quantitative field testing to screen for potential sources leading to item bias is lacking in the literature. An exploratory sequential design is illustrated as an additional approach using a Delphi technique in Phase I and Rasch DIF analyses in Phase II. We introduce the 2 × 2 Concordance Integration Typology as a systematic way to examine agreement and disagreement across the qualitative and quantitative findings using a concordance joint display table. A worked example from the development of the Problem-Solving Measures Grades 6–8 Computer Adaptive Tests supported using an exploratory sequential design to inform item refinement. The 2 × 2 Concordance Integration Typology (a) crystallized instances where additional refinements were potentially needed and (b) provided for evaluating the distribution of bias across the set of items as a whole. Implications are discussed for advancing data integration techniques and using mixed methods to improve instrument development. 

Existing literature has established that interpersonal and academic validating experiences help provide college students with the necessary personal and scholastic skillsets to thrive in higher education (e.g., Coronella, 2018; Ekal et al., 2011). This intrinsic mixed methods case study explores the extent to which undergraduate students perceived academic and interpersonal validation within a science, technology, engineering, and mathematics (STEM) pipeline program (CMSP) can empower students and influence their attitudes towards their learning environment.