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Despite significant interest in their functional properties, the mechanical behavior of high-entropy oxides (HEOs) is not well studied, particularly at elevated temperatures. Bulk (Co,Cu,Mg,Ni,Zn)O (transition metal (TM)-HEO) samples were deformed under compression at applied stresses and temperatures ranging from 5 to 31 MPa and 600 to 850 °C, respectively. All of the deformation conditions result in creep stress exponents of n < 3, indicating that TM-HEO exhibits superplastic deformation. A transition from structural to solution-precipitation-based superplasticity is observed during deformation above 650 °C. Additionally, TM-HEO exhibits shear-thickening behavior when deformed at stresses above 9 MPa. The formation and behavior of a Cu-rich tenorite secondary phase during deformation is identified as a key factor underpinning the deformation mechanisms. The microstructure and phase state of TM-HEO before deformation also influenced the behavior, with finer grain sizes and increasing concentrations of Cu-rich tenorite, resulting in the increased prevalence of solution-precipitation deformation. While complex, the results of this study indicate that TM-HEO deforms through known superplastic deformation mechanisms. Superplasticity is a highly efficient manufacturing method and could prove to be a valuable strategy for forming HEO ceramics into complex geometries. 

Abstract Modern automatic speech recognition (ASR) systems are capable of impressive performance recognizing clean speech but struggle in noisy, multi-talker environments, commonly referred to as the “cocktail party problem.” In contrast, many human listeners can solve this problem, suggesting the existence of a solution in the brain. Here we present a novel approach that uses a brain inspired sound segregation algorithm (BOSSA) as a preprocessing step for a state-of-the-art ASR system (Whisper). We evaluated BOSSA’s impact on ASR accuracy in a spatialized multi-talker scene with one target speaker and two competing maskers, varying the difficulty of the task by changing the target-to-masker ratio. We found that median word error rate improved by up to 54% when the target-to-masker ratio was low. Our results indicate that brain-inspired algorithms have the potential to considerably enhance ASR accuracy in challenging multi-talker scenarios without the need for retraining or fine-tuning existing state-of-the-art ASR systems. 

The reaction of dibenzonorcarynyliden(e/oid) with phencyclone was recently reported to give a congested spiropentane withendostereochemistry. Herein we report that, in sharp contrast, an analogous reaction using tetracyclone, instead of phencyclone, gives the highly crowded title spiropentane but withexostereochemistry as determined by X-ray crystallography. This new tetracyclone adduct (C₄₄H₃₀O) crystallizes upon slow evaporation from hexanes/ethyl acetate in the monoclinic crystal system andP2₁/n(No. 14) space group. It has one molecule in the asymmetric unit and four molecules per unit cell. DLPNO-CCSD(T)/def2-TZVP//B3LYP/def2-SVP calculations indicate that theendospiropentane diastereomers from phencyclone and tetracyclone are both more stable than the correspondingexoforms by 6.68 and 5.35 kcal mol⁻¹, respectively. As noted previously in the phencyclone system, favorable π-stacking interactions between the two flat biphenyl moieties in the product and transition state may lead to the preferential formation of theendodiastereomer. However, the ability of the phenyl rings in the 3,4-position of the tetracyclone component to rotate could introduce destabilizing steric interactions in the transition state that hinder formation of theendodiastereomer in favor of the less thermodynamically stableexoisomer. 

Paper presentation at the Utah Council of Teachers of Mathematics Conference 

Binary nanoparticle superlattices (BNSLs) comprised of polymer-grafted shaped nanoparticles enable the construction of new isotropic mesomaterial. 

High‐entropy oxides (HEOs) are being extensively studied for various functional applications, but there is limited research into the mechanical behavior of these materials, especially at elevated temperatures. Bulk (Co, Cu, Mg, Ni, Zn)O (transition metal (TM)‐HEO) samples are formed into dome shapes at 800 °C and 70 kPa. Deformation experiments and finite element analysis (FEA) reveal that TM‐HEO has a creep stress exponent ofn = 0.6, indicating that TM‐HEO deforms through superplastic deformation and exhibits shear‐thickening behavior. Comparisons of experimental strain rates to those calculated using existing superplasticity mechanism models signify that TM‐HEO deforms through grain boundary sliding accommodated by a solution‐precipitation mechanism from a secondary phase. A Cu‐rich tenorite phase, commonly observed in the grain boundaries of TM‐HEO, is proposed as the secondary phase facilitating deformation. It is important to highlight here that the superplastic deformation behavior in TM‐HEO is active under modest temperature and pressure conditions, as noted above. Low‐temperature superplastic deformation will provide a powerful method of manufacturing HEO ceramics into net shape parts, greatly expanding their potential applications. 

Abstract DNA–transcription factor (TF) interactions are essential for gene regulation. Fully characterizing TF recognition specificities and identifying their genomic binding targets are important to understand TF function and regulatory networks. Recently, high-throughput sequencing technology HT-SELEX (high-throughput systematic evolution of ligands by exponential enrichment) has been used to measure hundreds of TFs, providing massive datasets that comprise TF binding preferences. However, there is a need to develop comprehensive computational modeling to fully extract and characterize critical TF binding preferences and fail to distinguish genome-wide binding targets. In this study, we developed a global pairwise model called DCA-Scapes trained with experimental HT-SELEX data. Our approach uncovered high-resolution TF recognition specificity landscapes, enabled the prediction of in vivo binding sequences, and was validated with ChIP-seq (ChIP sequencing) data. In addition, the DCA-Scapes model was utilized to refine the locations of binding regions and accurately identify the binding sites within the ChIP-seq enriched peaks. Moreover, we extended our model to cover the entire human genome, uncovering potential TF target sites that exhibit tissue-specific TF recognition across various cellular environments. 

Although electro-optic (EO) nonlinearities are essential for many quantum and classical photonics applications, a major challenge is inefficient modulation in cryogenic environments. Guided by the connection between phase transitions and nonlinearity, we identify the quantum paraelectric perovskite SrTiO₃as a strong cryogenic EO [>500 picometers per volt (pm/V)] and piezo-electric material (>90 picocoulombs per newton) atT= 5 K, at frequencies to at least 1 megahertz. Furthermore, by tuning SrTiO₃toward quantum criticality, we more than double the EO and piezo-electric effects, demonstrating a linear Pockels coefficient above 1000 pm/V. Our results probe the link between quantum phase transitions, dielectric susceptibility, and nonlinearity, unlocking opportunities in cryogenic optical and mechanical systems and providing a framework for discovering new nonlinear materials.