Polyphenols in twenty cultivars of blue honeysuckle (Lonicera caerulea L.): Profiling, antioxidant capacity, and α-amylase inhibitory activity.

The polyphenols extracted from 20 blue honeysuckle cultivars were comprehensively characterized and quantified by HPLC-DAD and HPLC-ESI-QTOF-MS2 analyses and evaluated for antioxidant capacity (ABTS, DPPH, FRAP) and α-amylase inhibitory activity. The 17 anthocyanins and 59 non-anthocyanin phenolics were characterized. Among them, cyanidin-3-glucoside, quercetin-3-galactoside, myricetin-3-galactoside, and 3-caffeoylquinic acid were the major polyphenols. These polyphenols not only contributed to the antioxidant capacity, but were also good α-amylase inhibitors. 'Lanjingling' showed the strongest antioxidant capacity evaluated by FRAP, while 'CBS-2' and '14-13-1' showed the strongest antioxidant capacity evaluated by ABTS and DPPH. All the twenty cultivars showed α-amylase inhibitory activity, and the IC50 values ranged from 0.12 ± 0.01 to 0.69 ± 0.02 mg/mL. 'Lanjingling' showed the most potent α-amylase inhibitory activity. Additionally, principal component analysis indicated that Lonicera. caerulea subsp. emkuyedao bred in Japan differed markedly in phenolics and bioactivity compared to the other four subspecies bred in China and Russia.

Counterion-Dependent Material Properties of Phosphorylated Nanocellulose.

The material properties of cellulose nanofibers (CNFs) are governed by the surface chemical structure of the fibers. The chemical structure-property relationships for monovalent carboxylated CNFs are well understood. Here, we report the basic sheet properties of divalent phosphorylated CNFs with different phosphorus contents and counterion types. All examined sheet properties, including conditioned and wet tensile properties, electrical resistivities, and fire-retardant properties of the CNF sheets, were greatly enhanced by the counterion exchange from the initial sodium ions to calcium or aluminum ions. The phosphorus content had significant influences only on the conditioned tensile and fire-retardant properties. In comparison to CNF sheets with monovalent carboxy groups, the CNF sheets with divalent phosphate groups were superior in terms of their wet tensile properties and fire-retardant properties. Our research shows that the combination of the divalent phosphate introduction and counterion exchange provides a successful strategy for the practical application of CNF sheets as antistatic materials and flexible substrates for electronic devices.

Event-Triggered Communication Network With Limited-Bandwidth Constraint for Multi-Agent Reinforcement Learning.

Communicating agents with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer from restrictions on limited bandwidth communication. If the bandwidth is fully occupied, some agents are not able to send messages promptly to others, causing decision delay and impairing cooperative effects. Recent related work has started to address the problem but still fails in maximally reducing the consumption of communication resources. In this article, we propose an event-triggered communication network (ETCNet) to enhance communication efficiency in multi-agent systems by communicating only when necessary. For different task requirements, two paradigms of the ETCNet framework, event-triggered sending network (ETSNet) and event-triggered receiving network (ETRNet), are proposed for learning efficient sending and receiving protocols, respectively. Leveraging the information theory, the limited bandwidth is translated to the penalty threshold of an event-triggered strategy, which determines whether an agent at each step participates in communication or not. Then, the design of the event-triggered strategy is formulated as a constrained Markov decision problem and reinforcement learning finds the feasible and optimal communication protocol that satisfies the limited bandwidth constraint. Experiments on typical multi-agent tasks demonstrate that ETCNet outperforms other methods in reducing bandwidth occupancy and still preserves the cooperative performance of multi-agent systems at the most.

Empirical Policy Optimization for n-Player Markov Games.

In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with the environment. In multiplayer Markov games (MGs), however, the interaction is nonstationary due to the behaviors of other players, so the agent has no fixed optimization objective. The challenge becomes finding equilibrium policies for all players. In this research, we treat the evolution of player policies as a dynamical process and propose a novel learning scheme for Nash equilibrium. The core is to evolve one's policy according to not just its current in-game performance, but an aggregation of its performance over history. We show that for a variety of MGs, players in our learning scheme will provably converge to a point that is an approximation to Nash equilibrium. Combined with neural networks, we develop an empirical policy optimization algorithm, which is implemented in a reinforcement-learning framework and runs in a distributed way, with each player optimizing its policy based on own observations. We use two numerical examples to validate the convergence property on small-scale MGs, and a pong example to show the potential on large games.

Distribution and Quantification of Diverse Functional Groups on Phosphorylated Nanocellulose Surfaces.

Phosphorylated cellulose nanofiber (CNF) is attracting attention as a newly emerged CNF with high functionality. However, many structural aspects of phosphorylated CNF remain unclear. In this study, we investigated the chemical structures and distribution of ionic functional groups on the phosphorylated CNF surfaces via liquid-state nuclear magnetic resonance measurements of colloidal dispersion. In addition to the monophosphate group, polyphosphate groups and cross-linked phosphate groups were introduced in the phosphorylated CNFs. The proportion of polyphosphate groups increased as the phosphorylation time increased, reaching ∼30% of all phosphate groups. Only a small amount of cross-linked phosphate groups existed in the phosphorylated CNF after a prolonged reaction time. Furthermore, phosphorylation of cellulose using urea and phosphoric acid was found to be regioselective at the C2 and C6 positions. There existed no significant difference between the surface degrees of substitution at the C2 and C6 positions of the phosphorylated CNFs.

Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM.

Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite-ground communication scenarios, a stacked long short-term memory network (Stacked-LSTM) separation method based on deep learning is proposed. First, the coding feature representation of the mixed signal is extracted. Then, the long sequence input is divided into smaller blocks through the Stacked-LSTM network with the attention mechanism of the SE module, and the deep feature mask of the source signal is trained to obtain the Hadamard product of the mask of each source and the coding feature of the mixed signal, which is the encoding feature representation of the source signal. Finally, characteristics of the source signal is decoded by 1-D convolution to to obtain the original waveform. The negative scale-invariant source-to-noise ratio (SISNR) is used as the loss function of network training, that is, the evaluation index of single-channel blind source separation performance. The results show that in the single-channel separation of spatially aliased signals, the Stacked-LSTM method improves SISNR by 10.09∼38.17 dB compared with the two classic separation algorithms of ICA and NMF and the three deep learning separation methods of TasNet, Conv-TasNet and Wave-U-Net. The Stacked-LSTM method has better separation accuracy and noise robustness.

Anisotropic Thermal Expansion of Transparent Cellulose Nanopapers.

We report the anisotropic thermal expansion of a transparent nanopaper structure comprising cellulose nanofibers (CNFs). The coefficient of thermal expansion (CTE) of the nanopaper in the out-of-plane direction was 44.6 ppm/°C in the temperature range of 25-100°C, which is approximately five times larger than its CTE in the in-plane direction in the same temperature range (8.3 ppm/°C). Such a strong anisotropy in thermal expansion is mainly attributable to the anisotropic CTE values of single CNFs in the fiber axis and cross-sectional directions. We observed anisotropic thermal expansion even in a bioplastic composite containing only 2.5% w/w CNFs.

Carboxylated nanocellulose/poly(ethylene oxide) composite films as solid-solid phase-change materials for thermal energy storage.

Composite films of poly(ethylene oxide) (PEO) and 0%-20% surface-carboxylated cellulose nanofibrils (CNFs) were prepared by mixing the aqueous CNF dispersion and aqueous PEO solution at various weight ratios followed by casting and drying. The 20% CNF/PEO composite film was transparent, whereas the 100% PEO film was translucent. The addition of CNFs to the PEO matrix resulted in decreases of the crystallinity and crystal size of spherical PEO. The Young's modulus and tensile strength of the 100% PEO film were 0.2 GPa and 6.1 MPa, respectively, and remarkably increased to 2.4 GPa and 86 MPa, respectively, with the addition of 20% CNF. The CNF/PEO composite films had clear melting and crystallization temperatures in the heating and cooling processes, respectively. Nevertheless, the coefficients of thermal expansion at temperatures above the melting point of PEO significantly decreased with the CNF addition. The CNF/PEO composite films are therefore promising solid-solid phase-change materials for energy storage with high film dimensional stability.

Different Conformations of Surface Cellulose Molecules in Native Cellulose Microfibrils Revealed by Layer-by-Layer Peeling.

Layer-by-layer peeling of surface molecules of native cellulose microfibrils was performed using a repeated sequential process of 2,2,6,6-tetramethylpiperidine-1-oxyl radical-mediated oxidation followed by hot alkali extraction. Both highly crystalline algal and tunicate celluloses and low-crystalline cotton and wood celluloses were investigated. Initially, the C6-hydroxy groups of the outermost surface molecules of each algal cellulose microfibril facing the exterior had the gauche-gauche (gg) conformation, whereas those facing the interior had the gauche-trans (gt) conformation. All the other C6-hydroxy groups of the cellulose molecules inside the microfibrils contributing to crystalline cellulose I had the trans-gauche (tg) conformation. After surface peeling, the originally second-layer molecules from the microfibril surface became the outermost surface molecules, and the original tg conformation changed to gg and gt conformations. The plant cellulose microfibrils likely had disordered structures for both the outermost surface and second-layer molecules, as demonstrated using the same layer-by-layer peeling technique.