Gas transport in partially-saturated sand packs

Understanding gas transport in porous media and its mechanism has broad applications in various research areas, such as carbon sequestration in deep saline aquifers and gas explorations in reservoir rocks. Gas transport is mainly controlled by pore space geometrical and morphological characteristics. In this study, we apply a physically-based model developed using concepts from percolation theory (PT) and the effective-medium approximation (EMA) to better understand diffusion and permeability of gas in packings of angular and rounded sand grains as well as glass beads. Two average sizes of grain i.e., 0.3 and 0.5 mm were used to pack sands in a column of 6 cm height and 4.9 cm diameter so that the total porosity of all packs was near 0.4. Water content, gas-filled porosity (also known as gas content), gas diffusion, and gas permeability were measured at different capillary pressures. The X-ray computed tomography method and the 3DMA-Rock software package were applied to determine the average pore coordination number z. Results showed that both saturation-dependent diffusion and permeability of gas showed almost linear behavior at higher gas-filled porosities, while deviated substantially from linear scaling at lower gas saturations. Comparing the theory with the diffusion and permeability experiments showed that the determined value of z ranged between 2.8 and 5.3, not greatly different from X-ray computed tomography results. The obtained results clearly indicate that the effect of the pore-throat size distribution on gas diffusion and permeability was minimal in these sand and glass bead packs

Monotonicity and Continuity of the Critical Capital Stock in the Dechert-Nishimura Model

We show that the critical capital stock of the Dechert-Nishimura (1983) model is a decreasing and continuous function of the discount factor. We also show that the critical capital stock merges with a nonzero steady state as the discount factor decreases to a certain boundary value, and that the critical capital stock converges to the minimum sustainable capital stock as the discount factor increases to another boundary value.Dechert-Nishimura model, Nonconvexity, Optimal growth, critical capital stock

An IPW-based Unbiased Ranking Metric in Two-sided Markets

In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data

Time-course transcriptome analysis of human cellular reprogramming from multiple cell types reveals the drastic change occurs between the mid phase and the late phase

BackgroundHuman induced pluripotent stem cells (hiPSCs) have been attempted for clinical application with diverse iPSCs sources derived from various cell types. This proposes that there would be a shared reprogramming route regardless of different starting cell types. However, the insights of reprogramming process are mostly restricted to only fibroblasts of both human and mouse. To understand molecular mechanisms of cellular reprogramming, the investigation of the conserved reprogramming routes from various cell types is needed. Particularly, the maturation, belonging to the mid phase of reprogramming, was reported as the main roadblock of reprogramming from human dermal fibroblasts to hiPSCs. Therefore, we investigated first whether the shared reprogramming routes exists across various human cell types and second whether the maturation is also a major blockage of reprogramming in various cell types.ResultsWe selected 3615 genes with dynamic expressions during reprogramming from five human starting cell types by using time-course microarray dataset. Then, we analyzed transcriptomic variances, which were clustered into 3 distinct transcriptomic phases (early, mid and late phase); and greatest difference lied in the late phase. Moreover, functional annotation of gene clusters classified by gene expression patterns showed the mesenchymal-epithelial transition from day 0 to 3, transient upregulation of epidermis related genes from day 7 to 15, and upregulation of pluripotent genes from day 20, which were partially similar to the reprogramming process of mouse embryonic fibroblasts. We lastly illustrated variations of transcription factor activity at each time point of the reprogramming process and a major differential transition of transcriptome in between day 15 to 20 regardless of cell types. Therefore, the results implied that the maturation would be a major roadblock across multiple cell types in the human reprogramming process.ConclusionsHuman cellular reprogramming process could be traced into three different phases across various cell types. As the late phase exhibited the greatest dissimilarity, the maturation step could be suggested as the common major roadblock during human cellular reprogramming. To understand further molecular mechanisms of the maturation would enhance reprogramming efficiency by overcoming the roadblock during hiPSCs generation

Constructing Machine-learned Interatomic Potentials for Covalent Bonding Materials and MD Analyses of Dislocation and Surface

As machine learning potentials for molecular dynamics (MD) simulations, Spectral Neighbor Analysis Potential (SNAP) and quadratic SNAP (qSNAP) were constructed for silicon (Si) and silicon carbide (SiC). The reproducibility of the basic material properties about perfect crystal, free surface and dislocation cores in Si and 3C-SiC was investigated. The coefficients of SNAP and qSNAP were optimized using liner regression to present energy and force obtained by DFT. In addition, hyperparameters (cutoff length and weights for optimization, here) were determined using genetic algorithm to reproduce elastic moduli obtained by DFT. Lattice constant and elastic moduli of Si crystal by MD using our SNAP or qSNAP agree well with the values of DFT, and they have higher accuracy than those by any empirical potential. Additionally, melting point and specific heat at constant pressure were calculated by MD correctly. Especially in qSNAP of Si, the surface energy of {100} and {111} planes and the reconstructed {100} surface structure were almost reproduced. For 3C-SiC, SNAP reproduces lattice constant and elastic moduli of DFT. Furthermore, edge dislocation cores were generated successfully. However, the potentials we constructed have insufficient reproducibility in the plastic region, so it is necessary to continue development