NASA is planning to launch robotic landers to the Moon as part of the Artemis lunar program. We have proposed sending a greenhouse housed in a 1U CubeSat as part of one of these robotic missions. A major issue with these small landers is the limited power resources that do not allow for a narrow temperature range that we had on previous spaceflight missions with plants. Thus, the goal of this project was to extend this temperature range, allowing for greater flexibility in terms of hardware development for growing plants on the Moon. Our working hypothesis was that a mixture of ecotypes of Arabidopsis thaliana from colder and warmer climates would allow us to have successful growth of seedlings. However, our results did not support this hypothesis as a single genotype, Columbia (Col-0), had the best seed germination, growth, and development at the widest temperature range (11–25 °C). Based on results to date, we plan on using the Columbia ecotype, which will allow engineers greater flexibility in designing a thermal system. We plan to establish the parameters of growing plants in the lunar environment, and this goal is important for using plants in a bioregenerative life support system needed for human exploration on the Moon.
A series of CuO/CeO2 catalysts were successfully synthesized via solution combustion method (SCS) using different fuels and tested for CO oxidation. The catalysts were characterized by energy-dispersive X-ray analysis (EDXA), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), N2 adsorption-desorption isotherms and H2 temperature-programmed reduction (H2-TPR). It was found that the used fuels strongly affected the characterization and the low-temperature reduction behavior of CuO/CeO2 catalysts. The CuO/CeO2-urea catalyst exhibited higher catalytic activity toward CO oxidation (t50=120∘C, t100=159∘C) than the 5 other synthesized catalysts. In addition, the CuO/CeO2-urea catalyst displayed high stability for CO oxidation during five cycles and water resistance. The enhanced catalytic CO oxidation of the synthesized samples can be attributed by a combination of factors, such as smaller crystallite size, higher specific surface area, larger amount of amorphous copper(II) oxide, more mesoporous and uniform spherical-like structure. These findings are worth considering in order to continue the study of the CuO/CeO2 catalyst with low-temperature CO oxidation.
Disadvantages in the use of polylactic acid (PLA) as a base material for Tissue Engineering applications include the low osteoconductivity of this biomaterial, its acidic degradation and the deficient cellular adhesion on its surface. In order to counteract these drawbacks, calcium carbonate (CaCO3) and β-tricalcium phosphate (Ca3(PO4)2, β-TCP) were proposed in this work as additives of PLA-based support structures. Composite scaffolds (PLA:CaCO3: β-TCP 95:2.5:2.5) manufactured by fused deposition modeling (FDM) were tested under enzymatic degradation using proteinase K enzymes to assess the modification of their properties in comparison with neat PLA scaffolds. The samples were characterized before and after the degradation test by optical microscopy, scanning electron microscopy, compression testing and thermogravimetric and calorimetric analysis. According to the results, the combination of the PLA matrix with the proposed additives increases the degradation rate of the 3D printed scaffolds, which is an advantage for the application of the composite scaffold in the field of Tissue Engineering. The higher degradation rate of the composite scaffolds could be explained by the release of the additive particles and the statistically higher microporosity of these samples compared to the neat PLA ones.
Exact formulae relating parameters in conditional and reduced generalized linear models are introduced where the reduced model omits a continuous mediator from the conditional model. For certain link functions including logit, the natural direct effect and the natural indirect effect of the counterfactual method are smaller in magnitude than, respectively, the direct effect used by the difference method and the indirect effect by the product method. Contrary to what is implicitly assumed in Jiang and VanderWeele  for logit link, the total effect of the counterfactual method and the total effect used for the difference method are generally not the same. They are equal to each other only under special situations. For accelerated failure time models the difference method and the product method are equivalent regardless of censoring or not, a result stated in VanderWeele  in the absence of censorship but proved in a misleading manner. For proportional hazards models, maximum likelihood analysis indicates that these two methods can be equivalent in the absence of censorship. In the case of logit link, one can focus on the treatment effect on the marginalized odds instead of the odds of the marginalized event so that the product method would be equivalent to the difference method. Similarly, for the proportional hazards model, one can focus on the treatment effect on the marginalized hazards instead of the hazards for the reduced model.
In this article, we study the two-flavor Nambu and Jona-Lasinio (NJL) phase diagrams on the T–μ plane through three regularization methods. In one of these, we introduce an infrared three-momentum cutoff in addition to the usual ultraviolet regularization to the quark loop integrals and compare the obtained phase diagrams with those obtained from the NJL model with proper time regularization and Pauli–Villars regularization. We have found that the crossover appears as a band with a well-defined width in the T–μ plane. To determine the extension of the crossover zone, we propose a novel criterion, comparing it to another criterion that is commonly reported in the literature; we then obtain the phase diagrams for each criterion. We study the behavior of the phase diagrams under all these schemes, focusing on the influence of the regularization procedure on the crossover zone and the presence or absence of critical end points.
We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.
In this article, a numerical model for a Brusselator advection–reaction–diffusion (BARD) system by using an elegant numerical scheme is developed. The consistency and stability of the proposed scheme is demonstrated. Positivity preserving property of the proposed scheme is also verified. The designed scheme is compared with the two well-known existing classical schemes to validate the certain physical properties of the continuous system. A test problem is also furnished for simulations to support our claim. Prior to computations, the existence and uniqueness of solutions for more generic problems is investigated. In the underlying system, the nonlinearities depend not only on the desired solution but also on the advection term that reflects the pivotal importance of the study.
In this paper, a three-layered cement-based wave-absorbing board is designed and prepared by mixing wave-absorbing fillers such as nano-Si3N4, multi-layer nano graphene platelets (NGPs), nano-Ni, carbon fiber (CF) and carbon black (CB) into cement slurry. The effect of the amount of wave-absorbing fillers on the mechanical properties, resistivity and wave-absorbing reflectivity of cement slurry is studies. The microstructure of NGPs, nano-Si3N4 and the wave-absorbing board are characterized by TEM and SEM. Research shows that low content of NGPs and other wave-absorbing fillers can significantly reduce the resistivity of cement slurry and improve its mechanical strength, and dense massive crystals are precipitated in the cement hydration products. The reflectivity test reveals that in the frequency range of 2~18 GHz, the minimum reflectivity of the three-layered cement-based wave absorbing board reaches −18.8 dB, and the maximum bandwidth less than −10 dB reaches 15.3 GHz. This study can serve as reference for the preparation of new three-layered cement-based wave absorbing boards.
The development and application of computer-aided drug design/discovery (CADD) techniques (such as structured-base virtual screening, ligand-based virtual screening and neural networks approaches) are on the point of disintermediation in the pharmaceutical drug discovery processes. The application of these CADD methods are standing out positively as compared to other experimental approaches in the identification of hits. In order to venture into new chemical spaces, research groups are exploring natural products (NPs) for the search and identification of new hits and more efficient leads as well as the repurposing of approved NPs. The chemical space of NPs is continuously increasing as a result of millions of years of evolution of species and these data are mainly stored in the form of databases providing access to scientists around the world to conduct studies using them. Investigation of these NP databases with the help of CADD methodologies in combination with experimental validation techniques is essential to identify and propose new drug molecules. In this chapter, we highlight the importance of the chemical diversity of NPs as a source for potential drugs as well as some of the success stories of NP-derived candidates against important therapeutic targets. The focus is on studies that applied a healthy dose of the emerging CADD methodologies (structure-based, ligand-based and machine learning).
This paper discusses variable or covariate selection for high-dimensional quadratic Cox model. Although many variable selection methods have been developed for standard Cox model or high-dimensional standard Cox model, most of them cannot be directly applied since they cannot take into account the important and existing hierarchical model structure. For the problem, we present a penalized log partial likelihood-based approach and in particular, generalize the regularization algorithm under marginality principle (RAMP) proposed in Hao et al. (J Am Stat Assoc 2018;113:615–25) under the context of linear models. An extensive simulation study is conducted and suggests that the presented method works well in practical situations. It is then applied to an Alzheimer’s Disease study that motivated this investigation.