Hydrological variations in central China over the past millennium and their links to the Tropic Pacific and North Atlantic Oceans

Variations of precipitation, aka the Meiyu rain, in East Asian summer monsoon (EASM) domain during the last millennium could help enlighten the hydrological response to future global warming. Here we present a precisely dated and highly resolved stalagmite δ18O record from the Yongxing Cave, central China. Our new record, combined with a previously published one from the same cave, indicates that the Meiyu rain has changed dramatically in association with the global temperature change. In particular, our record shows that the Meiyu rain has been weakened during the Medieval Climate Anomaly (MCA), but intensified during the Little Ice Age (LIA). During the Current Warm Period (CWP), our record indicates a similar weakening of the Meiyu rain. Furthermore, during the MCA and CWP, our records show that the atmospheric precipitation is similarly wet in northern China and similarly dry in central China, but relatively wet during the CWP in southern China. This spatial discrepancy indicates a complicated localized response of the regional precipitation to the anthropogenic forcing. The weakened (intensified) Meiyu rain during the MCA (LIA) matches well with the warm (cold) phases of Northern Hemisphere surface air temperature. This Meiyu rain pattern also corresponds well with the climatic conditions over the Tropical Indo-Pacific warm pool. On the other hand, our record shows a strong association with the North Atlantic climate as well. The reduced (increased) Meiyu rain correlates well with positive (negative) phases of North Atlantic Oscillation. In addition, our record links well with the strong (weak) Atlantic meridional overturning circulation during the MCA (LIA) period. All above-mentioned localized correspondences and remote teleconnections on decadal to centennial timescales indicate that the Meiyu rain is coupled closely with oceanic processes in the Tropical Pacific and North Atlantic Oceans during the MCA and LIA

Understanding Three Hydration-Dependent Transitions of Zwitterionic Carboxybetaine Hydrogel by Molecular Dynamics Simulations

In this work, molecular dynamics simulations were performed to study a carboxybetaine methacrylate (CBMA) hydrogel under various swelling states. The water content in this study ranged from 28% to 91% of the total weight of the hydrogel. Three transitions of the CBMA hydrogel were observed as the water content increased. The first transition occurs when the water content increases from 33% to 37%. The observed kink in the self-diffusion coefficient of water indicates that the hydration of the polymer network of the hydrogel is saturated; the further added water is in a less confined state. The second transition was found to be related to the physical cross-links of the polymer network. As the water content rises to above 62%, the lifetime of the physical cross-links decreases significantly. This abrupt change in the lifetime indicates that the transition represents the equilibrium swelling state of the hydrogel. Finally, the third transition was observed when the water content goes above 81%. The significant increases in the bond and angle energies of the polymer network indicate that the hydrogel reaches its upper limit swelling state at this transition. These results are comparable to previously published experimental studies of similar zwitterionic hydrogels

Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data