Search for: All records

Abstract This study presents an experimentally validated demonstration of an inverse-optimized binary phase-only gallium oxide diffractive optical element (DOE). This DOE transforms an incident Gaussian beam into a square flat-top beam at the working plane. The design methodology for this binary phase-only DOE beam shaper is founded on an efficient process that integrates the modified Gerchberg-Saxton algorithm and the adjoint method. Experimental characterization of the fabricated device on a single crystal$$(\overline{2} \; 01)$$gallium oxide substrate is conducted at a wavelength of 532 nm, confirming its ability to transform an incident Gaussian beam into a focused square flat-top beam. Such a device holds significant promise for various high-power laser applications, notably in laser welding and similar domains. Furthermore, because of the ultrawide bandgap of gallium oxide, DOEs operating at shorter wavelengths in the UV are also possible based on this technique. 

Biodegradable polymers offer a promising solution to the growing issue of global microplastic pollution. To effectively replace conventional plastics, it is essential to develop strategies for tuning the properties of biodegradable polymers without relying on additives. Biaxial stretching promotes anisotropic crystallization in polymer domains, thereby altering the mechanical performance of polymer blends. In this study, we employed a design of experiment (DoE) approach to investigate the effects of biaxial stretching at three drawing temperatures (Tds) and draw ratios (λs) on a biodegradable blend of poly(lactic acid) (PLA) and poly(butylene adipate-co-terephthalate) (PBAT), aiming to optimize both the strength and ductility. The DoE analysis revealed that the composition, the λ, the interaction between the λ and composition, and the interaction between the Td and composition significantly affect the elongation at break (εBreak). For the stress at break (σBreak), the most influential factors were the interaction between the λ and PLA concentration; a three-way interaction among the λ, PLA, and Td; the Td; the λ; and finally the PLA concentration alone. The optimal εBreak and σBreak were achieved at a λ = 5 × 5 and Td = 110 °C, with a composition of 10% PLA and 90% PBAT. The stretched samples exhibited higher crystallinity compared to the pressed samples across all compositions. This work demonstrates that in addition to the composition, the processing parameters, such as the λ and Td, critically influence the mechanical properties, enabling performance enhancements without the need for compatibilizers or toxic additives. 

Abstract Spatial voting models are widely used in political science to analyze legislators’ preferences and voting behavior. Traditional models assume that legislators’ ideal points are static across different types of votes. This article extends the Bayesian spatial voting model to incorporate hierarchical Bayesian methods, allowing for the identification of covariates that explain differences in legislators’ ideal points across voting domains. We apply this model to procedural and final passage votes in the U.S. House of Representatives from the 93rd through 113th Congresses. Our findings indicate that legislators in the minority party and those representing moderate constituencies are more likely to exhibit different ideal points between procedural and final passage votes. This research advances the methodology of ideal point estimation by simultaneously scaling ideal points and explaining variation in these points, providing a more nuanced understanding of legislative voting behavior. 

Community detection plays a central role in uncovering meso scale structures in networks. However, existing methods often suffer from disconnected or weakly connected clusters, undermining interpretability and robustness. Well-Connected Clusters (WCC) and Connectivity Modifier (CM) algorithms are post-processing techniques that improve the accuracy of many clustering methods. However, they are computationally prohibitive on massive graphs. In this work, we present optimized parallel implementations of WCC and CM using the HPE Chapel programming language. First, we design fast and efficient parallel algorithms that leverage Chapel’s parallel constructs to achieve substantial performance improvements and scalability on modern multicore architectures. Second, we integrate this software into Arkouda/Arachne, an open-source, high-performance framework for large-scale graph analytics. Our implementations uniquely enable well-connected community detection on massive graphs with more than 2 billion edges, providing a practical solution for connectivity-preserving clustering at web scale. For example, our implementations of WCC and CM enable community detection of the over 2-billion edge Open-Alex dataset in minutes using 128 cores, a result infeasible to compute previously.