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Frontal polymerization (FP) is a promising alternative manufacturing method for thermoset-based fiber-reinforced polymer composites (FRP) in comparison with the traditional autoclave/oven-curing method, due to its rapid curing process, low energy consumption, and low cost. Optimizing the weight contents of initiators relative to the resin’s mass is needed to adjust the mechanical properties of FRPs in industrial applications. This study investigates the effect of varying the photo-initiator (PI) weight content on tensile properties and the frontal polymerization characteristics, including the front velocity, front temperature, and degree of cure, in the FP process of the epoxy resin. Specifically, a dual-initiator system, including PI and thermal-initiator (TI), is used to initiate the polymerization process by ultraviolent (UV) light. The weight content of the TI is fixed at 1 w%, and the relative PI concentration is varied from 0.2 w% to 0.5 wt%. Results show that increasing the PI amount from 0.2 wt% to 0.3 wt% significantly improves the front velocity and the degree of cure by about two times. Increasing the PI content from 0.3 wt% to 0.4 wt% results in 15% and 26% higher degree of cure and front velocity, respectively. Moreover, due to the different front velocity in the top and bottom regions of the specimen, the specimens with 0.4 wt% PI exhibited a curved shape. The specimen with 0.5 wt% PI is thermally degraded and foamed. By comparing tensile properties, it is found that increasing the PI concentration from 0.2 wt% to 0.3 wt% improves the tensile strength and Young’s modulus by 3.91% and 7%, respectively, while the tensile strength and the Young’s modulus of frontal polymerized specimens are on average 8% and 14% higher than traditionally oven-cured ones, respectively. 

Abstract The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers. 

Existing three-dimensional (3D) culture techniques are limited by trade-offs between throughput, capacity for high-resolution imaging in living state, and geometric control. Here, we introduce a modular microscale hanging drop culture where simple design elements allow high replicates for drug screening, direct on-chip real-time or high-resolution confocal microscopy, and geometric control in 3D. Thousands of spheroids can be formed on our microchip in a single step and without any selective pressure from specific matrices. Microchip cultures from human LN229 glioblastoma and patient-derived mouse xenograft cells retained genomic alterations of originating tumors based on mate pair sequencing. We measured response to drugs over time with real-time microscopy on-chip. Last, by engineering droplets to form predetermined geometric shapes, we were able to manipulate the geometry of cultured cell masses. These outcomes can enable broad applications in advancing personalized medicine for cancer and drug discovery, tissue engineering, and stem cell research. 

Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this paper, we suggest the method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition with orthogonality constrained optimization to learn the underlying association networks, with broad applications to both unsupervised and supervised learning tasks, such as biclustering with sparse singular value decomposition, sparse principal component analysis, sparse factor analysis, and spare vector autoregression analysis. Exploiting the framework of convexity-assisted nonconvex optimization, we derive nonasymptotic error bounds for the suggested procedure characterizing the theoretical advantages. The statistical guarantees are powered by an efficient SOFAR algorithm with convergence property. Both computational and theoretical advantages of our procedure are demonstrated with several simulations and real data examples. 

Mesoscale organization of convection is typically not represented in global circulation models, and hence its influence on the global circulation is not accounted for. The heating component of a parameterization that represents the dynamical and physical effects of circulations associated with organized convection, referred to as the multiscale coherent structure parameterization (MCSP), is implemented in the Energy Exascale Earth System Model version 1 (E3SMv1). Numerical simulations are conducted to assess its impact on the simulated climate. Besides E3SMv1 simulations, we performed high‐resolution (2 km) simulations using the Weather Research and Forecasting (WRF) Model to determine the temperature tendencies induced by mesoscale convective systems embedded in deep convection. We tuned the free parameters of the MCSP based on the WRF simulations. MCSP heating enhances Kevin wave spectra in E3SMv1, improves the representation of the Madden‐Julian Oscillation, and reduces precipitation biases over the tropical Pacific.