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Effect of Die Bearing Geometry on Extrudability of High-Strength AA6082 Alloy with Cu
No full textThis study investigated the impact of die bearing geometry on the surface cracking behavior, of a high strength AA6xxx alloy. Experimental and numerical methods were employed, along with differential scanning calorimetry tests to determine the material’s solidus temperature. Four different die geometries were employed in both the extrusion trial and the simulation. Extrusion trials were conducted for each die geometry over a range of extrusion speeds with the resulting surface defects being examined using SEM. The findings indicate that die bearing geometry significantly affects surface morphology and crack occurrence. Choked dies enabled crack-free extrusion at higher speeds, particularly a 12 mm choked bearing with a 1° angle, outperforming a 25 mm flat bearing and zero-bearing die. The 35 mm choked bearing achieved crack-free extrusion even at maximum extrusion speed, yielding smoother surfaces than the other dies. Numerical simulations demonstrated the differences in stress states using different die bearing geometries, showing that the choked bearings alter the stress state at the die corner to cause a transition from high tensile stress to lower tensile or compressive stress. The extrusion limit diagrams for different die bearings were also constructed based on the extrusion trial data to provide guidance for choosing appropriate extrusion parameters for future studies. This study adds a valuable contribution to the existing literature by shedding light on the role of die bearing geometry in controlling surface morphology and surface crack formation, providing important insights that can be used to optimize the extrusion process
Open-IRIS: Low Cost, Fully Open Source In-situ Infrared Inspection of Silicon
No full textShort-Wave Infrared (SWIR) imaging has become a powerful and well-known technique over the last two decades for silicon inspection and imaging. Open-IRIS is a low-cost, fully open-source system for in-situ InfraRed Inspection of Silicon devices (IRIS). It is designed to
lower the cost barrier for academic and research users requiring high-precision IR imaging of silicon microelectronics. This thesis details the design and implementation of the Open-IRIS platform, including its optomechanical components, motion control system, electrical system and software architecture. Its design is highly modular and low cost, making it an invaluable and extensible tool for many future applications, including microarchitectural security research, chip failure analysis, and biological imaging. Key design challenges, such as achieving high mechanical and optical resolution on a budget are addressed. Computational microscopy techniques, including Fourier Ptychographic Microscopy (FPM), are evaluated to improve resolution. The system’s imaging resolution on a standard resolution target is evaluated, as well as its motion repeatability and accuracy.
Results show that Open-IRIS achieves 5.34 μm optical resolution with a 5x objective, and 3.47 μm resolution with a 20x objective. Mechanically, it has 6.5 μm repeatability, and 35.5 μm accuracy, all on a total budget of less then US$1000 - a fraction of the cost of comparable commercial systems. The complete design is fully open-source, enabling broader access to advanced chip inspection
techniques, and serves as an excellent starting point for future expansion into advanced security research like laser fault injection.S.B
Accelerated small angle neutron scattering algorithms for polymeric materials
No full textSmall-angle neutron scattering (SANS) is an extremely powerful technique for characterizing a wide variety of soft, biological, magnetic, and quantum materials, but it is often throughput-limited. This work proposes an algorithm to accelerate small angle neutron scattering (SANS) experiments by estimating the minimum number of counts to perform parameter estimation and model differentiation tasks to a specified level of certainty. Three classes of model polymer materials were examined and analyzed, and time slices of SANS data were used to model a reduced number of counts. The scattering data with reduced numbers of counts were fitted to SANS model functions to perform parameter estimation and model differentiation tasks. For parameter estimation, estimators accurate to within 5–10% of the full count estimator can be produced with only 1–50% of the full counts depending upon the sample and parameter of interest. In order to project parameter uncertainties at lower number of counts prior to the completion of experiments, it is crucial to have a robust error quantification method that reflects the true uncertainty associated with each parameter. Uncertainties from Monte Carlo (MC) bootstrapping are shown to in general overestimate the error from fitting many experimental replicates. For most parameter estimation techniques, the weighted least squares estimator is unbiased; however, certain models yield biased estimators. To differentiate between models, both the Akaike information criterion (AIC) and Bayesian information criterion (BIC) can be used, and with either criterion, reduced numbers of counts can still identify the best model for our samples from a group of related candidate models for each material. The proposed algorithm can help SANS users optimize valuable beamtime and accelerate the use of SANS for structural characterization of libraries of materials while obtaining reasonable parameter estimation and model differentiation when scattering models are available
SARS-CoV-2 receptor binding domain displayed on HBsAg virus–like particles elicits protective immunity in macaques
No full textAuthorized vaccines against SARS-CoV-2 remain less available in low- and middle-income countries due to insufficient supply, high costs, and storage requirements. Global immunity could still benefit from new vaccines using widely available, safe adjuvants, such as alum and protein subunits, suited to low-cost production in existing manufacturing facilities. Here, a clinical-stage vaccine candidate comprising a SARS-CoV-2 receptor binding domain–hepatitis B surface antigen virus–like particle elicited protective immunity in cynomolgus macaques. Titers of neutralizing antibodies (>104) induced by this candidate were above the range of protection for other licensed vaccines in nonhuman primates. Including CpG 1018 did not significantly improve the immunological responses. Vaccinated animals challenged with SARS-CoV-2 showed reduced median viral loads in bronchoalveolar lavage (~3.4 log10) and nasal mucosa (~2.9 log10) versus sham controls. These data support the potential benefit of this design for a low-cost modular vaccine platform for SARS-CoV-2 and other variants of concern or betacoronaviruses
A parametric approach to plot-based urban design: A climate-responsive algorithmic control for the generation of urban block
No full textIn modern urbanism, (re)production of urban land predominantly relies on large parcels through intensive capital investments. Such a mainstream significantly shapes the overall urban form, subsequently influencing the quality of life through the perceived characteristics of the form and program of the planned districts. Consequently, critical urban design theory increasingly prioritizes the plot as the fundamental unit of future urban development. While ‘plot-based urbanism’ presents a responsive approach to this issue, there remains a notable gap in systematic methodologies that can be universally applied across different contexts. In this paper, the authors propose an algorithmic framework that would be employed as a design control tool based on the associative logic of plot-based urban formation. The model framework comprises three steps: (1) plot layout generation, (2) building configuration, and (3) incremental formation of the block fabric. The applied model demonstrates the compositional variation and coherence within the urban block while concurrently optimizing the climatic performance of the emerging fabric
BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies
No full textCassandra Overney, Cassandra Moe, Alvin Chang, and Nabeel Gillani. 2025. BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies. Proc. ACM Hum.-Comput. Interact. 9, 2, Article CSCW040 (May 2025), 37 pages.Public school districts across the United States (US) play a pivotal role in shaping access to quality education
through their student assignment policies—most prominently, school attendance boundaries. Community
engagement processes for changing such policies, however, are often opaque, cumbersome, and highly
polarizing—hampering equitable access to quality schools in ways that can perpetuate disparities in achievement and future life outcomes. In this paper, we describe a collaboration with a large US public school district
serving nearly 150,000 students to design and evaluate a new sociotechnical system, “BoundarEase”, for
fostering more constructive community engagement around changing school attendance boundaries. Through
a formative study with 16 community members, we first identify several frictions in existing community
engagement processes during boundary planning, like individualistic over collective thinking; a failure to understand and empathize with different community members when considering policy impacts; and challenges
in accessing and understanding the impacts of boundary changes. We then use these frictions to inspire the
design and development of BoundarEase, a web platform that allows community members to explore and
offer feedback on potential boundaries based on their preferences. A user study with 12 community members
reveals that BoundarEase prompts reflection among community members on how policies might impact
families beyond their own, and increases transparency around the details of policy proposals. Our paper offers
education researchers insights into the challenges and opportunities involved in community engagement for
designing student assignment policies; human-computer interaction researchers a case study of how new
sociotechnical systems might help mitigate polarization in local policymaking; and school districts a practical
tool they might use to facilitate community engagement to foster more equitable student assignment policies
Regulating Sommerfeld resonances for multi-state systems and higher partial waves
No full textLong-range attractive interactions between dark matter particles can significantly enhance their annihilation, particularly at low velocities. This “Sommerfeld enhancement” is typically computed by evaluating the deformation of the two-particle wavefunction due to the long-range potential, while ignoring the physics associated with the annihilation, and then scaling the appropriate annihilation matrix elements by factors that depend on the wavefunction in the limit where the particles approach zero relative separation. It has long been recognized that this approach is a valid approximation only in the limit where the annihilation rate is small, and breaks down in the regime where the enhanced annihilation rate approaches the unitarity bound, in which case ignoring the impact of the annihilation physics on the two-particle wavefunction cannot be justified and leads to apparent violations of unitarity. In the case where the physics relevant to annihilation occurs at a parametrically shorter distance scale (higher energy scale) compared with the long-range potential, we provide a simple prescription for correcting the Sommerfeld enhancement for the effects of the short-range physics, valid for all partial waves and for systems where multiple states are coupled by the long-range potential
A Domain-Specific Probabilistic Programming Language for Reasoning about Reasoning (Or: A Memo on memo)
No full textThe human ability to think about thinking ("theory of mind") is a fundamental object of study in many disciplines. In recent decades, researchers across these disciplines have converged on a rich computational paradigm for modeling theory of mind, grounded in recursive probabilistic reasoning. However, practitioners often find programming in this paradigm challenging: first, because thinking-about-thinking is confusing for programmers, and second, because models are slow to run. This paper presents memo, a new domain-specific probabilistic programming language that overcomes these challenges: first, by providing specialized syntax and semantics for theory of mind, and second, by taking a unique approach to inference that scales well on modern hardware via array programming. memo enables practitioners to write dramatically faster models with much less code, and has already been adopted by several research groups
Cleavable Strand‐Fusing Cross‐Linkers as Additives for Chemically Deconstructable Thermosets with Preserved Thermomechanical Properties
No full textPermanently cross-linked polymer networks—thermosets—are often difficult to chemically deconstruct. Theinstallation of cleavable bonds into the strands of thermosets using cleavable comonomers as additives can facilitatethermoset deconstruction without replacement of permanent cross-links, but such monomers can lead to reducedthermomechanical properties and require high loadings to function effectively, motivating the design of new and optimalcleavable additives. Here, we introduce “strand-fusing cross-linkers” (SFCs), which fuse two network strands via a four-way cleavable cross-link. SFCs enable deconstruction of model polydicyclopentadiene (pDCPD) thermosets with aslittle as one-fifth of the molar loading needed to achieve deconstruction using traditional cleavable comonomers. SFCsfunction under traditional oven curing as well as low-energy frontal ring-opening metathesis polymerization (FROMP)conditions and lead to improved thermomechanical properties, for example, glass transition temperatures, compared toprior cleavable comonomer designs. This work motivates the development of increasingly improved cleavable additives toenable thermoset deconstruction without compromising material performance
Unified and Generalizable Reinforcement Learning for Facility Location Problems on Graphs
No full textWWW ’25, Sydney, NSW, AustraliaFacility location problems on graphs are ubiquitous in the real world and hold significant importance, yet their resolution is often impeded by NP-hardness. MIP solvers can find the optimal solutions but fail to handle large instances, while algorithm efficiency has a higher priority in cases of emergency. Recently, machine learning methods have been proposed to tackle such classical problems with fast inference, but they are limited to the myopic constructive pattern and only consider simple cases in Euclidean space. This paper introduces a unified and generalizable approach to tackle facility location problems on weighted graphs with deep reinforcement learning, demonstrating a keen awareness of complex graph structures. Striking a harmonious balance between solution quality and running time, our method stands out with superior efficiency and steady performance. Our model trained on small graphs is highly scalable and consistently generates high-quality solutions, achieving a speedup of more than 2000 times to Gurobi on instances with 1000 nodes. The experiments on Shanghai road networks further demonstrate its practical value in solving real-world problems. The source codes are available at https://github.com/AryaGuo/PPO-swap