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ABSTRACT As technological advances appear, it is desirable to integrate them into new engineering education teaching methods, aiming to enhance students' comprehension and engagement with complex subjects. Augmented reality (AR) emerges as a promising tool in this effort, offering students opportunities to visualize and conceptualize challenging topics that are otherwise too abstract or difficult to grasp. Within civil engineering curriculums, structural analysis, a junior‐level course forming the foundation of many other courses, poses challenges in visualization and understanding. This paper investigates the development of a mobile AR application intended to improve the conceptual understanding of structural analysis material. This application is designed to overlay schematic representations of structural components (i.e., beams, columns, frames, and trusses) onto images of iconic local campus buildings, allowing students to interactively explore exaggerated deflections and internal and external forces under various loading conditions. By contextualizing structural analysis calculations within familiar settings, the goal is to leverage a sense of relevance and place‐based attachments in students' learning. Furthermore, the paper examines the development process and usability of the AR application, providing insights into its implementation in educational settings. Experimental results, including comparisons with a control group, are analyzed to assess the efficacy of the AR application in improving students' understanding of structural analysis concepts. Furthermore, the paper examines the development process and usability of the AR application, providing insights into its implementation in educational settings. Perspectives from structural analysis faculty members are also discussed, shedding light on the potential benefits and challenges associated with integrating AR technology into engineering education. In addition, the study highlights the value of place‐based learning, wherein students engage with real‐world structures in their immediate environment, fostering deeper connections between theoretical concepts and practical applications. Overall, this research contributes to the growing body of literature on innovative teaching approaches in engineering education and highlights the potential of AR as a valuable tool for enhancing student learning experiences in structural analysis and related disciplines. 

Abstract The first reactions of thioimidates under radical‐mediated conditions are described along with the delineation of structural factors that impact radical reactivity and possible side reactions. Thioimidate‐containing copolymers with methylmethacrylate (MMA) are synthesized through radical‐mediated, chain‐growth polymerization. These materials serve as a synthetic branch point for facile conversion into amidines by treatment with a weak acid and an external amine. Our approach allows for more diverse amidine structures than have been previously reported in polymers. This chemistry also enables crosslinking to form novel hydrogels with finely tuned acid–base behavior. Subsequent examination of the acid–base properties revealed that these features are preserved across linear, soluble amidine polymers to cross‐linked amidine gel polymer architectures. 

This study investigates the facile hydride synthesis method guided by theoretical predictions to explore the K–T–Bi (T = Zn, Cd) phase spaces. Using an adaptive genetic algorithm (AGA) and density functional theory (DFT), candidate compositions are identified for experimental validation via a facile hydrides route, permitting experimental screening of K–Zn–Bi and “empty” K–Cd–Bi systems. The previously reported KZnBi and KZn₂Bi₂are synthesized alongside newly discovered KCdBi and KCd₂Bi₂. While the AGA and DFT predict the stability of these compounds, structural predictions align with the experiment only for KZnBi and KZn₂Bi₂. Single‐crystal X‐ray structure refinements confirm that KZnBi and KZn₂Bi₂adopt the hexagonal ZrBeSi‐ and tetragonal ThCr₂Si₂‐structure types, respectively. KCdBi has tetragonal PbClF‐structure type and KCd₂Bi₂belongs to the ThCr₂Si₂‐structure type. A trend based on the ratio of the metal ionic radii allows to rationalize variation in the structure types within theATBi family (A = Li–Cs), correctly identifying KCdBi as isostructural to NaZnBi. Thermal stability studied by high‐temperature powder X‐ray diffraction reveals that Zn‐containing compounds melt at higher temperatures (821 K for KZn₂Bi₂) than Cd‐containing KCd₂Bi₂(635 K). This study highlights the efficacy of combining rapid synthesis techniques with predictive modeling, though structural predictions show some limitations in accuracy. 

Abstract Deep‐focus earthquakes at 350–660 km are presumably caused by olivine‐spinel phase transformation (PT). This cannot, however, explain the observed high seismic strain rate, which requires PT to complete within seconds, while metastable olivine does not transform for over a million years. Recent theory quantitatively describes how severe plastic deformations (SPD) can solve this dilemma but lacking experimental proof. Here, we introduce dynamic rotational diamond anvil cell with rough diamond anvils to impose SPD on San Carlos olivine. While olivine never transformed to spinel at room temperature, we obtained reversible olivine‐ringwoodite PT under SPD at 15–28 GPa within tens of seconds. The PT pressure reduces with increasing dislocation density, microstrain, plastic strain, and decreasing crystallite size. Results demonstrate a new strain‐induced PT mechanism compared to a pressure/temperature‐induced one. Combined with SPD during olivine subduction, this mechanism can accelerate olivine‐ringwoodite PT from millions of years to timescales relevant to earthquakes. 

Abstract Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine maxL. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the performance of GP and PP for predicting soybean seed yield, protein, and oil. We additionally conducted genome‐wide association studies (GWAS) to identify significant single‐nucleotide polymorphisms (SNPs) associated with the traits of interest. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at two time points during the growing season. A genomic best linear unbiased prediction (GBLUP) model was trained on 269 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. These models did not outperform the original GP and PP models. These results show the capability of using ML for in‐season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs. 

Abstract Texturing the battery electrode to create low‐tortuosity ordered structures can significantly improve the sluggish mass transport in thick electrodes (areal mass loading>20 mg/cm²) during the energy storage electrochemical reactions. In this work, we presented an efficient and effective method to regulate the electrode structure by creating aligned channels throughout the thickness of the electrode. The method combines acoustic manipulation of particles and nonsolvent induced phase inversion and is highly compatible with a wide range of materials used in various battery chemistries. The textured electrodes show better structural integrity compared to electrodes of similar mass loading made with acoustic patterning only and with conventional solution casting. Compared with electrodes made with phase inversion only, it exhibits lower tortuosity, enhanced ion transport/kinetics, better rate capability and cyclic stability. 

Abstract Zirconium carbide (ZrC), a high‐performance refractory ceramic, exhibits complex defect dynamics that critically influence its behavior in extreme environments. In this work, we employ density functional theory (DFT) simulations to determine the formation energies and migration barriers of four defect types—isolated carbon vacancies, divacancies, Frenkel pairs, and Schottky pairs—across various charge states. The calculated formation energies reveal that isolated carbon vacancies are the most energetically favorable (1.13 eV), followed by Frenkel pairs (3.29 eV), while divacancies (6.86 eV) and Schottky pairs (8.29 eV) require higher formation energies, indicating their lower intrinsic concentrations. Isolated carbon vacancies exhibit the highest migration barrier (4.11 eV) in ZrC, with a modest increase to 4.13 eV upon adding one electron to 64‐atom supercell and a decrease to 4.06 eV with two electrons/64‐atom supercell—reflecting charge redistribution that stabilizes the local environment and weakens nearby Zr–C bonds. In contrast, Frenkel and Schottky pairs show barrier increases with electron doping and decreases with holes (ranging from 3.26 to 3.44 eV and 3.37 to 3.73 eV, respectively), while divacancies display increases (carbon vacancies: 2.69 to 2.93 eV; zirconium vacancies: 3.60 to 3.69 eV) upon electron addition. These results reveal the defect‐specific impact of charge carriers on mobility in ZrC, offering key insights for optimizing its performance in extreme environments. 

Abstract Machine unlearning is a cutting‐edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine unlearning research is associated with inherent vulnerabilities and threats, posing significant challenges for researchers and practitioners. In this article, we provide the first comprehensive survey of security and privacy issues associated with machine unlearning by providing a systematic classification across different levels and criteria. Specifically, we begin by investigating unlearning‐based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning. Additionally, we explore existing countermeasures and mitigation strategies designed to protect models from malicious unlearning‐based attacks targeting both security and privacy. Further, we provide a detailed comparison between machine unlearning‐based security and privacy attacks and traditional malicious attacks. Finally, we discuss promising future research directions for security and privacy issues posed by machine unlearning, offering insights into potential solutions and advancements in this evolving field.