Many solution methods for linear discrete ill-posed problems with error-contaminated data (right-hand side) apply Tikhonov regularization to compute a meaningful approximate solution. This solution depends on a regularization parameter. It is well known that iterated Tikhonov regularization often determines an approximate solution of higher quality than (standard) Tikhonov regularization. We consider the situation when an estimate of the norm of the error in the data is known and would like to apply iterative Tikhonov regularization to determine an approximate solution that satisfies the discrepancy principle. This requires a suitable choice of a regularization parameter. The standard approach to determine this parameter is to compute solutions for several values of the regularization parameter and choose a computed approximate solution that satisfies the discrepancy principle. This paper discusses iterated Tikhonov regularization based on partial Golub-Kahan bidiagonalization and describes how the regularization parameter can be determined without computing several approximate solutions by using the connection between Golub-Kahan bidiagonalization and Gauss quadrature. This approach reduces the computational effort required to compute a desired solution.
Abstract Large language models (LLMs) are increasingly used by students and professionals in the architecture, engineering, and construction (AEC) sector for learning and information retrieval. However, their reliability in performing construction management (CM) knowledge tasks remains insufficiently characterized. This study introduces CMExamSet, a benchmark data set consisting of 689 multiple-choice questions compiled from four professional CM certification programs. The data set covers core CM domains, including project and program management, safety management, cost control, scheduling, contract administration, and related professional knowledge areas. Four contemporary LLMs were evaluated using a standardized zero-shot protocol with five repeated runs per question. Performance was assessed using accuracy, response consistency, subject-area analysis, and structured error annotation. Mean accuracy ranged from 79.2% to 90.0% across the four examinations, with high overall agreement observed in repeated runs. Performance varied systematically by domain: higher accuracy was observed in information retrieval-based domain tasks, such as engineering concepts, construction geomatics, and sustainability, whereas lower accuracy was observed in operationally oriented domains such as bidding and estimating, time and schedule management, and cost control. When errors occurred, conceptual misunderstandings were the most frequently observed error type across domains, indicating that incorrect interpretation or application of domain principles remained a common source of failure. Performance improvements in newer models were generally modest across subject areas. These findings provide empirical evidence on the capabilities and limitations of LLMs in CM knowledge assessment and underscore the importance of structured verification, domain grounding, and instructional guidance when integrating LLM tools into construction education and professional preparation.
We present lattice QCD calculations of the odd Mellin moments of pion valence-quark generalized parton distribution up to fifth order (x4) and for the skewness range [-0.33; 01 using operator product expansion of bilocal quark-bilinear operators. The calculations are performed on an ensemble with lattice spacing a = 0.04 fm and valence pion mass 300 MeV, employing boosted pion states with momenta up to 2.428 GeV and momentum transfers reaching 2.748 GeV2. We employ ratio-scheme renormalization and next-to-leading logarithmic resummed perturbative matching. At zero skewness, our results are consistent with previous lattice studies. By combining matrix elements at multiple values of skewness and momentum transfer, skewness-dependent moments are obtained through simultaneous polynomiality-constrained fits.
To holistically understand the biology of animals, we must unravel the complexities and specificities of host-microbe interactions across animal taxa. Birds represent enigmatic and scientifically compelling hosts in which to understand these interactions. Here, we present a brief summary of a series of conversations among avian microbiome researchers regarding methodological challenges facing the avian microbiome field, where most research to date has focused on bacterial communities of the gut. Collectively, we acknowledged a commonly shared but underreported issue facing the avian microbiome field: that of difficulty in obtaining high-quality and high-yield microbial DNA from avian fecal samples. We discuss some of the potential reasons underlying low DNA yields, such as inhibitory compounds and rapid DNA degradation, and provide recommendations for how researchers in the avian microbiome field might cope with these methodological challenges. Collective and dedicated efforts to address these challenges will be required for a robust understanding of host-microbe interactions in avian systems.
We use theory from identity economics, which synthesizes research characterizing how personal identity shapes decisions in domains such as education and career selection, to predict that the process by which people sort into accounting careers produces a population of accountants with a distinctive set of values. Specifically, we hypothesize that two kinds of personal values, called conservation values and self-enhancement values, are overrepresented among accountants because they are associated with the decision to work as an accountant. Using data from 38 countries in the European Social Survey, we find support for both hypotheses. Given this evidence that accountants' values prioritize stability over change and concern for self over concern for others, we further hypothesize that, motivated by these values, accountants will be relatively skeptical about contemporary targets of social reforms, including those pursued by prominent accounting organizations. We test this prediction using attitudes about climate change and tolerance for minorities and find support for it. Based on our findings, we derive recommendations for an effective design of social reforms in the accounting profession. Our findings are relevant for accounting elites tasked with leading the profession into a dynamic future and contribute to the new and growing literature on accounting's human capital.
