Dense suspensions are a prototype of fluid that can dynamically enhance its viscosity to resist strong forcing. Recent work established that the key to a large viscosity increase, often in excess of an order of magnitude, is the ability to switch from lubricated, frictionless particle interactions at low stress to a network of frictional contacts at higher stress. However, to isolate network features responsible for the large viscosity has been difficult, given the lack of an appropriate physics inspired network measure. Here we apply rigidity theory to simulations of dense suspensions in two dimensions and identify from the frictional contact network the subset of mechanically rigid clusters at each strain step. We find that rigid clusters emerge at large shear stress well before the onset of jamming and that the continual break-up and reconfiguration of system spanning rigid clusters is responsible for the flow states of the highest viscosity. By showing how viscosity is correlated with the rigidity of the underlying network of contact forces our results provide new insight beyond mean-field models and uncover a new contribution to dissipation in dense suspensions

In this study, four types of soybean products with different processing methods (soybean meal 1 and soybean meal 2, extruded soybean meal, fermented soybean meal and extruded soybean) were used to examine the effect of fermentation and extrusion on molecular structures of protein and carbohydrate. Extrusion and fermentation significantly decreased (P < 0.05) the values of related protein spectral intensities (height and area of amide and secondary structure) and the biggest reduction was found in extruded soybean compared to soybean meal 1 and soybean meal 2. Compared with extruded soybean meal, the area ratio of amide I to amide II and the height ratio of α-helix to β sheet in extruded soybean were significantly reduced (P < 0.05), and there was no difference in these spectral values between extruded and fermented soybean. Extrusion and fermentation significantly decreased (P < 0.05) the values of carbohydrate spectral intensities, including structural carbohydrate (STCHO) and cellulosic compounds (CELC) and total carbohydrate (CHO), compared to soybean meal 2. The ratio of α-helix to β-sheet was positively related to the DM of soybean degradability in the rumen (P < 0.05, r = 0.590), so was A-CELC to A-STCHO (P < 0.05, r = 0.747). A positive relationship was found between CP degradability in the rumen and the area ratios of amide I and amide II, CELC to CHO, and STCHO to CHO. Spectral intensity of CHO area was negatively associated with neutral detergent fibre (NDF) and acid detergent fibre (ADF) degradability in the rumen. The study indicated that extrusion and fermentation could alter the molecular structure of protein and carbohydrate and the degradation characteristics of soybean products in the rumen.

The experiment was aimed to predict the relationship between rumen degradation parameters and chemical composition, especially acid detergent lignin, and molecular structure profiles of lignin (the relative content ratio of syringyl and guaiacyl unit) of twenty-two herbaceous and leguminous forage, which were commonly used as roughage for dairy cows in the northeast of China. Analyses of the spectra of forage materials' high boiling solvent lignin samples showed that common features and specific vibrations to each unique lignin were found in the spectrum. In this paper, the spectra of materials high boiling solvent lignin demonstrated absorption at the band around 1332 cm–1 (syringyl) and 1258 cm–1 (guaiacyl). The spectra indicated that the lignin of the plant materials used in this study are H-G-S type. A broad range was found among the chemical composition for each feedstuff. The degradation kinetics characteristics among feedstuffs also had relatively large range of variation. The relative content ratio of syringyl (S) and guaiacyl (G) unit of leguminous samples positively correlated with some rumen degradation characteristics. However, there was no correlation between relative content ratio of S and G unit and degradation parameters among the herbaceous materials (p > 0.05). According to analyses, content acid detergent lignin (ADL) and relative content ratio of S and G could be the predictors of the degradation parameters expect SCP among forage. In conclusion, the relative content ratio of S and G might be potentially used to predict some of the degradation parameters (Kd CP, EDCP, SNDF) of leguminous feedstuffs.

Idioms are an important language phenomenon in Chinese, but idiom translation is notoriously hard. Current machine translation models perform poorly on idiom translation, while idioms are sparse in many translation datasets. We present PETCI, a parallel English translation dataset of Chinese idioms, aiming to improve idiom translation by both human and machine. The dataset is built by leveraging human and machine effort. Baseline generation models show unsatisfactory abilities to improve translation, but structureaware classification models show good performance on distinguishing good translations. Furthermore, the size of PETCI can be easily increased without expertise. Overall, PETCI can be helpful to language learners and machine translation systems.