Quantile regression is a powerful tool for detecting exposure-outcome associations given covariates across different parts of the outcome's distribution, but has two major limitations when the aim is to infer the effect of an exposure. Firstly, the exposure coefficient estimator may not converge to a meaningful quantity when the model is misspecified, and secondly, variable selection methods may induce bias and excess uncertainty, rendering inferences biased and overly optimistic. In this paper, we address these issues via partially linear quantile regression models which parametrize the conditional association of interest, but do not restrict the association with other covariates in the model. We propose consistent estimators for the unknown model parameter by mapping it onto a nonparametric main effect estimand that captures the (conditional) association of interest even when the quantile model is misspecified. This estimand is estimated using the efficient influence function under the nonparametric model, allowing for the incorporation of data-adaptive procedures such as variable selection and machine learning. Our approach provides a flexible and reliable method for detecting associations that is robust to model misspecification and excess uncertainty induced by variable selection methods. The proposal is illustrated using simulation studies and data on annual health care costs associated with excess body weight.
We obtain several sharp spectral bounds, approximations, and exact values for the isoperimetric number and related edge-expansion parameters of graphs. Our results focus on graph powers and on families of graphs with rich algebraic or geometric structure, including distance-regular graphs and graphs arising from finite geometries, among others. Our proofs use techniques from spectral graph theory, linear optimization, finite geometry, and probability, yielding new machinery for analysing edge-expansion phenomena in highly structured graphs.
We demonstrate that the one-dimensional shallow water moment equations satisfy an auxiliary entropy conservation law, where the entropy function corresponds to the total energy. Additionally, we show that the classical Newtonian slip friction and Manning friction terms are entropy dissipative with respect to the developed entropy variables. The results from the continuous entropy analysis are used to construct an entropy stable and well-balanced nodal discontinuous Galerkin spectral element method for the spatial approximation. Key to ensure the entropy stability of the scheme is the derivation of entropy conservative numerical fluxes that satisfy a discrete version of the entropy flux compatibility condition. Finally, numerical examples demonstrate the performance of the scheme and validate the theoretical results.
Nanosecond pulsed plasmas show strong potential towards H-2 production via CH4 reforming, owing to their highly non-equilibrium nature. In this work, we investigate how the pulse repetition pattern, i.e. comparing single pulse operation vs. high-frequency pulse packages, correlates to the plasma properties and reforming process performance in atmospheric pressure pure CH4 using a combination of plasma diagnostics and gas chromatography. We show that the electrical properties (voltage, current, pulse energy), as well as gas temperature, electron density and plasma volume are significantly altered after the first pulse cycle in the pulse package as a result of the pronounced "memory effect" active at very short inter-pulse periods, during which the gas properties in the discharge gap (temperature, pressure, and composition) do not have enough time to return to the original conditions. These changes lead to a product selectivity shift towards highly unsaturated hydrocarbons, specifically C2H2, and energy cost reduction (from 480 to 400 kJ.mol(- 1)). In contrast, the overall CH4 conversion remains unaffected (similar to 21 %) as complete dissociation in the plasma region is already achieved after the first pulse cycle. These results demonstrate that tailoring the pulse repetition pattern provides an effective strategy to manipulate plasma properties and, thus, optimise CH4 conversion, energy efficiency, and control product selectivity in nanosecond pulsed CH4 plasmas.
Increasing the yield of recombinant proteins can save time and materials and, consequently, any strategy that seems promising for achieving this objective is worth investigating. Lately, it has been proposed that modifications of upstream sequences from pET vectors would enhance the transcription efficiency. To validate this approach, we expressed a pool of constructs in modified pET vectors and compared the yields with those obtained using unmodified pET-derived vectors. Constructs were selected according to their different structural properties and protein yields. In our experimental setting, the results were generally deceiving. However, improvements were observed when additional elements of the construct were optimized concurrently. In particular, repositioning the His-tag from the C-terminus to the N-terminus positively affected the final protein yield. Taken together, these findings indicate that the modified vectors developed in this study represent one of several factors to consider during the initial screening aimed at identifying optimal expression conditions for a new construct, rather than a broadly applicable solution for enhancing the yield of any protein construct.
