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ABSTRACT: Ethyl cellulose (EC) is a biocompatible, renewable, and recyclable material with diverse sources, making it an attractive candidate for industrial applications. Electrospinning has gained significant attention for the production of EC fibers. However, conventional electrospinning methods face challenges such as bead formation, low yield, and the absence of porous internal structures, limiting both the functional performance and scalability. This study presents an optimized approach for producing EC fibers by using a gravity-driven ultrahigh-speed electrospinning (GUHS-ES) system. This system leverages gravity to reshape the Taylor cone morphology during electrospinning, enhancing stability and dramatically increasing throughput. As flow rates increase, the Taylor cone contracts inward, while the tip structure expands and stabilizes, reaching maximum size at ultrahigh flow rates (100−150 mL/h). This unique Taylor cone structure enables a fiber production rate of 24.5 g/h, hundreds of times greater than conventional electrospinning techniques. Another advantage of the GUHS-ES system is its ability to achieve both high diameter uniformity and adjustable porosity. At ultrahigh flow rates, the pore sizes of the EC fibers reached 321 nm. The highly porous structure of EC fibers exhibited an absorption capacity of 56.6 to 110.7 times their weight, exceeding most previously reported oil-absorbing materials and demonstrating high efficacy for rapid waste oil absorption. This green, efficient technology represents a promising advancement for the large-scale production and application of natural polymer fibers with broad implications for sustainable industrial processes. 

Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TABMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TABMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TABMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TABMWP. To mitigate this, we further propose a novel approach, PROMPTPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples. 

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.