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Pervasive mechanical impact is growing requirement for advanced high-performance protective materials, while the electromagnetic interference (EMI) confers severe risk to human health and equipment operation. Bioinspired structural composites achieving outstanding safeguards against a single threat have been developed, whereas the synergistic implementation of impact/EMI coupling protection remains a challenge. This work proposes the concept of nacre-mimetic hierarchical composite duplicating the "brick-and-mortar" arrangement, which consists of freeze-drying constructed chitosan/MXene lamellar architecture skeleton embedded in a shear stiffening polyborosiloxane matrix. The resulting composite effectively attenuates the impact force of 85.9%-92.8% with extraordinary energy dissipation capacity, in the coordinative manner of strain-rate enhancement, structural densification, lamella dislocation and crack propagation. Attributed to the alternate laminated structure promoting the reflection loss of electromagnetic waves, it demonstrates an ultraefficient EMI shielding effectiveness of 47.2-71.8 dB within extremely low MXene loadings of 1.1-1.3 wt %. Furthermore, it serves favorably in impact monitoring and wireless alarm systems and accomplishes performance optimization through the combination of multiple biomimetic strategies. In conclusion, this function-integrated structural composite is shown to be a competitive candidate for sophisticated environments by resisting impact damage and EMI hazards.

Urban residential water uses entail energy consumption and associated carbon emissions. Reducing residential water uses thus can simultaneously save water and energy and help reduce carbon emissions. However, residential water uses are strongly affected by the choices of household appliances and fixtures, and water use behaviors. In this study, we first conducted a household water use survey in Shanghai, the largest city in China, to understand residential water use behaviors in different seasons. A two-stage stochastic optimization model is developed to optimize water and energy conservation decisions so as to minimize the expected total cost of water and water-related energy uses and maximize carbon emission reduction. Data collected through questionnaire surveys are used to parameterize the optimization model. Water and energy conservation choices are categorized into long- and short-term decisions. The results show that in Shanghai residential water uses has a strong impact on urban carbon emission reduction. Typical temperature range in different seasons strongly affects the effectiveness of short-term conservation actions. The results support a subsidy policy for water-saving appliances that can incentivize citizens in water-saving. Model results are useful for exploring the water-energy-carbon nexus of urban households considering seasonal factors

In this paper, a novel distributed model predictive control (DMPC) strategy based on voltage observer for multiple energy storage systems (ESs) is firstly proposed to achieve a tradeoff between voltage regulation and power sharing. In order to reduce the impact of communication delay on voltage observer, an improved DMPC consensus algorithm is proposed. This scheme improves the robustness of the system to the delay. Besides, a small-signal dynamic model of a DC microgrid with the improved DMPC controllers is established to analyze its dynamic performance. The time-domain simulation results are presented to verify that, under the constraints of the DC bus voltage, a flexible trade-off between voltage regulation and power distribution can be achieved. Besides, the communication delay problems can be reduced.

This paper aims to crack the individual EV charging scheduling problem considering the dynamic user behaviors and the electricity price. The uncertainty of the EV charging demand is described by several factors, including the driver’s experience, the charging preference and the charging locations for realistic scenarios. An aggregate anxiety concept is introduced to characterize both the driver’s anxiety on the EV’s range and uncertain events. A mathematical model is also provided to describe the anxiety quantitatively. The problem is formulated as a Markov Decision Process (MDP) with an unknown state transition function. The objective is to find the optimal sequential charging decisions that can balance the charging cost and driver’s anxiety. A model-free deep reinforcement learning (DRL) based approach is developed to learn the optimal charging control strategy by interacting with the dynamic environment. The continuous soft actor-critic (SAC) framework is applied to design the learning method, which contains a supervised learning (SL) stage and a reinforcement learning (RL) stage. Finally, simulation studies verify the effectiveness of the proposed approach under dynamic user behaviors at different charging locations.

Guided-mode resonance (GMR) bandpass filters have many important applications. The tolerance of fabrication errors that easily cause the transmission wavelength to shift has been well studied for one-dimensional (1D) anisotropic GMR gratings. However, the tolerance of two-dimensional (2D) GMR gratings, especially for different design architectures, has rarely been explored, which prevents the achievement of a high-tolerance unpolarized design. Here, GMR filters with common 2D zero-contrast gratings (ZCGs) were first investigated to reveal their differences from 1D gratings in fabrication tolerance. We demonstrated that 2D ZCGs are highly sensitive to errors in the grating linewidth against the case of 1D gratings, and the linewidth orthogonal to a certain polarization direction has much more influence than that parallel to the polarization. By analyzing the electromagnetic fields, we found that there was an obvious field enhancement inside the gratings, which could have a strong effect on the modes in the waveguide layer through the field overlap. Therefore, we proposed the introduction of an etch-stop (ES) layer between the gratings and the waveguide-layer, which can effectively suppress the interaction between the gratings and modal evanescent fields, resulting in 4-fold increased tolerance to the errors in the grating linewidth. Finally, the proposed etch-stop ZCGs (ES-ZCGs) GMR filters were experimentally fabricated to verify the error robustness.

Phytoremediation is currently an active field of research focusing chiefly on identifying and characterizing novel and high chelation action super-accumulators. In the last few years, molecular tools have been widely exploited to understand better metal absorption, translocation, cation, and tolerance mechanisms in plants. Recently more advanced CRISPR-Cas9 genome engineering technology is also employed to enhance detoxification efficiency. Further, advances in molecular science will trigger the understanding of adaptive phytoremediation ability plant production in current global warming conditions. The enhanced abilities of nucleases for genome modification can improve plant repair capabilities by modifying the genome, thereby achieving a sustainable ecosystem. The purpose of this manuscript focuses on biotechnology's fundamental principles and application to promote climate-resistant metal plants, especially the CRISPR-Cas9 genome editing system for enhancing the phytoremediation of harmful contamination and pollutants.

This paper presents a first attempt to explore the symplectic superposition method for analytic buckling solutions of non-Levy-type moderately thick rectangular plates, which were hard to tackle by classical semi-inverse methods. In contrast with the conventional Lagrangian-system-based expression that is solved in the Euclidean space, this study describes the issue in the Hamiltonian system for treatment in the symplectic space. The follow-up solution procedure involves the conversion of an original problem into subproblems with symplectic solutions and superposition for final analytic buckling solutions, where the constitution of symplectic eigenvalue problem and symplectic eigen expansion are crucial. Comprehensive buckling load and mode shape results for representative plates are tabulated or plotted, which are all well validated by satisfactory agreement with other solution methods. With the present solutions, parametric analysis has been investigated. Due to the advantages on strictness and accuracy, the developed method provides a solid approach for exploring new analytic solutions of similar problems.

Drawing on 1,965 cases of corruption by rural grassroots cadre from 1993 to 2017, this article examines the evolving patterns of and intrinsic reasons for corruption as well as its changing characteristics over time, by focusing on the following indicators: the number of newly increased corruption cases, the frequency of corruption activities, the average amount of cash value involved in the cases, the annual total cash value involved in the cases, and the sectors where corruption took place. This article ends with several recommendations on corruption prevention, including further measures on legislation, ideological education, supervising mechanisms, and investigation and punishment.

The conventional distributed secondary controller, consisting of a voltage controller and a power controller, is based on the parallel framework by exchanging two state variables and requires a voltage observer to regulate the voltage. In this article, a cascaded control framework is proposed to achieve the voltage regulation and power sharing, in which only the power state variable is exchanged without the voltage observer. The secondary power controller is designed to adjust the reference value of the secondary voltage controller. And the output of the voltage controller is used to modify the input of the droop controller. Then a resilient cascaded controller is further proposed to enhance the controller resilience against time delays and disturbances. An extra information exchange block between the power controller and the voltage controller is introduced in the resilient cascaded controller. Moreover, the steady-state analysis is carried out to prove the effectiveness of proposed controllers. A small-signal model is also established to investigate the impact of controller parameter variation on the system dynamics. Finally, several case studies are conducted by simulations to demonstrate the performance of proposed controllers.

Background Neuropathic pain is a multifaceted and ubiquitous disease across the globe. Mood disorders, such as anxiety and depression, are frequently observed in patients suffering from neuropathic pain. Both neuropathic pain and comorbid mood disorders seriously impact quality of life. Accumulated evidence shows that activation of the NOD-like receptor protein 3 (NLRP3) inflammasome is involved in the neuroinflammatory pathogenesis of neuropathic pain, anxiety, and depression. However, the role of the NLRP3 inflammasome in the pathological process of anxiety and depression under the neuropathic pain state has not been fully described. Albiflorin, a monoterpene glycoside, may be a potential regulator of the NLRP3 inflammasome, but it is not clear whether albiflorin relates to NLRP3 inflammasome activation. Methods We used a systematic pharmacological method to confirm whether the activation of the NLRP3 inflammasome in the hippocampus was involved in the development of neuropathic pain associated with mood disorders and whether albiflorin could be an effective treatment for these symptoms. Results The NLRP3 inflammasome contributed to the neuropathic pain and comorbid anxiety and depression-like behaviors induced by chronic constriction injury of the sciatic nerve, and albiflorin may relieve these symptoms via inhibition of the NLRP3 inflammasome activity. Moreover, albiflorin enhanced the translocation of the nuclear factor erythroid 2-related factor 2 into the nucleus and suppressed nuclear factor-kappa B activity in the hippocampus. Conclusions Albiflorin, as a potential therapeutic agent, might greatly improve the overall symptoms of neuropathic pain.

Neuropathic pain remains one of the most common pain conditions worldwide. Accumulating evidence shows that activation of the NOD-like receptor protein 3 (NLRP3) inflammasome contributes to the pathogenesis of neuropathic pain, although the role of the NLRP3 inflammasome in neuropathic pain has not yet been fully elucidated. In animal models of neuropathic pain, paeoniflorin (PF) was shown to have analgesic, anti-inflammatory, and antidepressant effects. However, the role of the NLRP3 inflammasome in the analgesic properties of PF has not yet been studied. In this study, we aimed to confirm whether activation of the NLRP3 inflammasome in the spinal cord was involved in the development of neuropathic pain and whether PF could be an effective treatment for this type of pain. We found that activation of the NLRP3 inflammasome mediated the development of neuropathic pain following chronic constriction injury of the sciatic nerve and that PF attenuated neuropathic pain by inhibiting NLRP3 inflammasome activation. Moreover, PF enhanced the translocation of the transcription factor nuclear factor erythroid 2-related factor 2 into the nucleus and suppressed nuclear factor-kappa B activity in the spinal cord. These results suggest that PF may be a potential therapeutic agent for neuropathic pain.

For a distributed DC microgrid, the conventional voltage control highly relies on the accuracy of the voltage observer, and the observer will produce an estimation error under time delays. To address the problem brought by the voltage observer, this paper transforms the voltage control into an optimization problem and integrates it into the power sharing control based on the PI consensus algorithm. With the proposed optimal control, the DC bus voltage control and proportional power sharing of the energy storages can be realized in one controller, so that the controller structure is dramatically simplified and the control accuracy will not be affected by the time delay. In addition, to further improve the system stability to resist delay, scattering transformation is introduced in the distributed optimal controller. By constructing Lyapunov function, it has been proved that the stability of the controller under the time delay can be guaranteed. Simulations are conducted to verify the effectiveness of the proposed control strategy and the transformation with and without time delay.

The conventional voltage observer based distributed control can make a tradeoff between voltage restoration and reactive power sharing. However, it suffers from estimation error when there exist input disturbances, such as initial values and communication delays. To address this problem, this paper proposes a novel distributed secondary control based on PI consensus algorithm. A voltage controller and a frequency controller are designed separately to realize the restoration of voltage/frequency and also accurate reactive/active power allocation. Moreover, a detailed small-signal model with PI consensus algorithm is established to show the impact of the proposed control strategy on the system stability. Compared with the conventional distributed control, the proposed control scheme is simpler with higher control accuracy under input disturbances. Simulations are conducted to verify the effectiveness of the proposed control strategy in an isolated ac microgrid.

Voltage-gated calcium channels (VGCCs) mediate the entry of Ca2+ ions into cells in response to membrane depolarization and play fundamental roles in the nervous system, and the α1 subunits are the main subunits of Ca2+ channels. Caenorhabditis elegans possesses genes encoding α1 subunits; however, very few of these genes have been cloned in plant-parasitic nematodes (PPNs). Ditylenchus destructor is a PPN that has been proposed as a new model for studying the biology and control of PPNs. To understand the structure and function of the VGCCs of this PPN, we first cloned and identified three full-length cDNAs of VGCC α1 subunit genes in D. destructor with the defining structural and conserved features of Cav1 (L-type), Cav2 (non-L-type) and Cav3 (T-type). In situ hybridization assays demonstrated that the Cav1 VGCC α1 subunit gene (DdCα1D) was expressed within body wall muscles. The Cav2 VGCC α1 subunit (DdCα1A) was expressed in the oesophageal gland, vulva and vas deferens of the worm, and the Cav3 VGCC α1 subunit (DdCα1G) was localized to the oesophagus and median bulb. In addition, on the basis of the in vitro knockdown of L-, non-L- and T-type genes via RNAi, these genes were predicted to play a key role in the modulation of locomotion, feeding and reproduction. After the silencing of DdCα1G, the median bulb muscle of D. destructor was obviously contracted, and its feeding and reproduction abilities were significantly inhibited. This study provides insight into the structure and function of VGCC α1 subunits in D. destructor.

Hybrid energy storage system (HESS) consisting of battery and supercapacitor (SC) is an effective approach to alleviate voltage stability problems brought by the fluctuation of renewable resources in a dc microgrid. This paper proposes a novel distributed control scheme for multiple HESSs based on a leaderless consensus protocol to realize the power splitting between batteries and SCs and thus regulate the dc bus voltage. Without assigning leaders among the batteries and SCs, each SC or battery has equal priority in participating in voltage or state of charge (SOC) regulation using only neighboring information exchange instead of global communication. This distributed approach makes the system robust against any physical failure or communication fault only if the remaining communication network is connected. Consensus for the state variable associated with the battery SOC, the SC terminal voltage, and the dc average bus voltage are all achieved in this scheme, and thus the voltage quality and the energy storage lifetime can be improved. In addition, the small-signal stability of a four-HESS system is investigated, and the impact of the control parameters on the system is analyzed. Simulations and experimental studies are conducted to demonstrate the effectiveness of the proposed control scheme in an islanded dc microgrid.

A smart magnetorheological elastomer (MRE)-based triboelectric nanogenerator (TENG) with tunable electric–mechanical performance has been developed by assembling MREs, Cu foil and metal wires. The MRE polymer, composed of carbonyl iron and a shear stiffening elastomer matrix, shows a high response to a magnetic field, and the MR effect is 114.68% with a maximum storage modulus of 0.77 MPa. The TENG presents a positive triboelectric performance (PTP) with an increase in the loading pressure. It generates a maximum power of 55.07 μW with a voltage of 20.99 V and lights up 39 LEDs, which proves the high mechanical energy-harvesting properties. More interestingly, the TENG shows a negative triboelectric performance (NTP) under the excitation of a magnetic field owing to the high MR effect and saturated magnetization value of 147.51 emu g−1. Therefore, the overall triboelectric properties of the TENG can be precisely manipulated by alternating the external multi-fields. In addition, the intelligent TENG, with a fast response and high flexibility, functions as a portable self-powered sensor to monitor human motion, distinguish various contact materials, and perceive external magnetic fields. To this end, this work opens up a new avenue to develop a smart TENG device with tunable rheological and triboelectric properties and the self-powered wearable sensor could detect complex multi-field circumstances, which proves it has promising applications in smart energy generation systems and wearable multi-sensing electronics.