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Objective: This study aimed to investigate the influence of degree of polymerization of amylose on the digestibility characteristics of its ternary complexes. Methods: Ternary complexes of seedless breadfruit starch, prepared by separately mixing amyloses extracted from five cultivars (GJS, FSS, SMS, SS and CS) with amylopectin extracted from GJS, having the highest degree of polymerization among amylopectins from these cultviars, at a 1:1 ratio were prepared with lauric acid (LA) and β-lactoglobulin (β-LG) and characterized for supramolecular structure and digestion kinetics by high-performance size-exclusion chromatography (HPSEC), X-ray diffraction (XRD), small-angle X-ray scattering (SAXS), differential scanning calorimetry (DSC), atomic force microscopy (AFM), and Fourier transform infrared (FTIR) spectroscopy. All five complex samples displayed obvious characteristic peaks of ternary complexes, V-type crystalline structure and rodlike molecular configuration. As the degree of polymerization of amylose increased, the complexing index (CI) (from 68.81% to 83.55%), relative crystallinity (from 21.10% to 28.35%), short-range order (from 0.44 to 0.63), molecular mass (from 2.90 × 106 to 8.02 × 106 Da), molecular density index (from 3.50 to 23.07 g/(mol·nm3), gelation temperature (from 98.52 to 103.71 ℃), and gelation enthalpy (from 13.95 to 18.73 J/g), second-order kinetic rate constant (from 1.00 × 10-2 to 2.92 × 10-2 min-1) increased, while the semicrystalline lamellae thickness (from 3.20 to 4.82 nm), mass fractal dimension (from 1.28 to 1.81), characteristic length of crystallite unit (from 6.33 to 7.01 Å), radius of gyration (from 70.31 to 93.94 nm), and nanosurface “blocklet” size (from 7.05 to 15.03 nm) dropped, which led to a decrease of in vitro glycemic index (from 78.83 to 85.71). Statistical analysis showed that the degree of polymerization of amylose could remarkably affect the multiscale superstructure of the ternary complexes, which in turn could change its digestion kinetics. The results provide new theoretical guidance for the production of starch-based multicomponent foods.

Silica (SiO2) is one of the most promising inorganic nanofillers, and it has been widely used in the preparation of polymer nanocomposites due to its adjustable morphology, large specific surface area, and ease of functionalization. The surface modification, structural characteristics, and bonding mechanisms of SiO2 are crucial in enhancing the overall performance of polymer nanocomposites. This review focuses on summarizing the recent progress made regarding the preparation and structure characterization methods, unique properties, and rheological behavior of SiO2/polymer nanocomposites. In addition, the latest applications of SiO2/polymer nanocomposites in coatings, smart devices, biomedicine, and environment are further explored. A comprehensive summary of the main challenges, opportunities, and perspectives regarding SiO2/polymer nanocomposites is also presented.

Hybrid tabular-textual question answering (QA) is a crucial task in natural language processing that involves reasoning and locating answers from various information sources, primarily through numerical reasoning and span extraction. Cur-rent techniques in numerical reasoning often rely on autoregressive models to decode program sequences. However, these methods suffer from exposure bias and error propagation, which can significantly decrease the accuracy of program generation as the decoding process unfolds. To address these challenges, this paper proposes a novel multitasking hybrid tabular-textual question answering (MHTTQA) framework. Instead of generating operators and operands step by step, this framework can independently generate entire program tuples in parallel. This innovative approach solves the problem of error propagation and greatly improves the speed of program generation. The effectiveness of the method is demonstrated through experiments using the ConvFinQA and MultiHiertt datasets. Our proposed model outperforms the strong FinQANet baselines by 7% and 7.2% Exe/Prog Acc and the MT2Net baselines by 20.9% and 9.4% EM/F1. In addition, the program generation rate of our method far exceeds that of the baseline method. Additionally, our non-autoregressive program generation method exhibits greater resilience to an increasing number of numerical reasoning steps, further highlighting the advantages of our proposed framework in the field of hybrid tabular-textual QA. [ABSTRACT FROM AUTHOR]