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Sugar beet has strong drought resistance and requires a large amount of nitrogen (N) during growth. Can deficit irrigation combined with low N supply of sugar beet improve water productivity (WP) and N use efficiency (NUE)? In this field study in 2020–2021, two irrigation regimes (W1, field capacity (FC) of the 0–60 cm soil layer was not lower than 70% from 45 days after emergence (DAE) to harvest period; W2, FC of the 0–60 cm soil layer was not lower than 50% in 45–90 DAE, but was consistent with that of W1 after 90 DAE) and three N application rates (0 (N0), 150 (N1), and 225 (N2) kg N ha−1) were designed. Then, their impacts on taproot yield (TY), water consumption (WC) and N uptake (NU) were explored. The results showed that there was no significant difference in TY between W2 and W1 treatments under the N1 and N2 levels. Under the N1 and N2 levels, the daily water consumption (DWC, 0.121 and 0.125 mm ℃d−1) and daily N uptake (DNU, 53.6 and 71.6 g N ℃d−1) of W2 treatment were significantly lower than the DWC (0.161 and 0.164 mm ℃d−1) and DNU (60.6 and 86.5 g N ℃d−1) of W1 treatment in 0–90 DAE. The W2N1 treatment had similar WP and higher NUE compared with the W1N2 treatment. In addition, the fertilizer-15N recovery rate in W2N1 treatment was 8.8% higher than that in W1N2 treatment, the fertilizer-15N loss rate was 6.6% lower than that in W1N2 treatment, but there was no difference in the fertilizer-15N residue between the two. This study clarified that implementing W2 deficit irrigation (50% FC at the canopy rapid growth stage (45–90 DAE)) and N1 nitrogen application rate (33% nitrogen reduction based on 225 kg N ha−1) could not reduce TY but improve WP and NUE.

To obtain higher accuracy of PV prediction to enhance PV power generation technology. This paper proposes a spatio-temporal prediction method based on a deep learning neural network model. Firstly, spatio-temporal correlation analysis is performed for 17 PV sites. Secondly, we compare CNN-LSTM with a single CNN or LSTM model trained on the same dataset. From the evaluation indexes such as loss map, regression map, RMSE, and MAE, the CNN-LSTM model that considers the strong correlation of spatio-temporal correlation among the 17 sites has better performance. The results show that our method has higher prediction accuracy.

Optical report of neurotransmitter release allows visualisation of excitatory synaptic transmission. Sensitive genetically-encoded fluorescent glutamate reporters operating with a range of affinities and emission wavelengths are available. However, without targeting to synapses, the specificity of the fluorescent signal is uncertain, compared to sensors directed at vesicles or other synaptic markers. We fused the state-of-the-art reporter iGluSnFR to glutamate receptor auxiliary proteins in order to target it to postsynaptic sites. Chimeras of Stargazin and gamma-8 that we named SnFR-γ2 and SnFR-γ8, were enriched at synapses, retained function and reported spontaneous glutamate release in rat hippocampal cells, with apparently diffraction-limited spatial precision. In autaptic mouse neurons cultured on astrocytic microislands, evoked neurotransmitter release could be quantitatively detected at tens of synapses in a field of view whilst evoked currents were recorded simultaneously. These experiments revealed a specific postsynaptic deficit from Stargazin overexpression, resulting in synapses with normal neurotransmitter release but without postsynaptic responses. This defect was reverted by delaying overexpression. By working at different calcium concentrations, we determined that SnFR-γ2 is a linear reporter of the global quantal parameters and short-term synaptic plasticity, whereas iGluSnFR is not. On average, half of iGluSnFR regions of interest (ROIs) showing evoked fluorescence changes had intense rundown, whereas less than 5% of SnFR-γ2 ROIs did. We provide an open-source analysis suite for extracting quantal parameters including release probability from fluorescence time series of individual and grouped synaptic responses. Taken together, postsynaptic targeting improves several properties of iGluSnFR and further demonstrates the importance of subcellular targeting for optogenetic actuators and reporters.

Participation of carbon capture power plants and demand response in power system dispatch is an important mean to achieve the carbon-neutral goal. In order to take into account uncertainty of wind power and low-carbon economic operation of the distribution system, a two-stage robust optimal dispatch model of the distribution system considering carbon capture and demand response is proposed in this paper. In the day-ahead stage, the unit commitment plan and the price demand response (PDR) schedule are established. In the intra-day stage, the carbon related cost is included in the optimization objective. The strategies for the generators and the incentive demand response (IDR) are optimized under the worst-case output of wind power based on the results of the day-ahead stage. Karush-Kuhn-Tucker (KKT) conditions and the column-and-constraint generation (C&CG) algorithm are used to solve the proposed two-stage model. A revised IEEE 33-bus distribution system is used to verify the feasibility and effectiveness of the proposed model.

A number of canisters need to be lightweight designed to store the spherical fuel elements (SFE) used in high-temperature gas-cooled reactors (HTGR). The main challenge for engineering is pursuing high-accuracy and high-efficiency optimization simultaneously. Accordingly, a hybrid surrogate model-based multi-objective optimization method with the numerical method for the lightweight and safe design of the SFE canister is proposed. To be specific, the drop analysis model of the SFE canister is firstly established where the finite element method—discrete element method (FEM–DEM) coupled method is integrated to simulate the interaction force between the SFE and canister. Through simulation, the design variables, optimization objectives, and constraints are identified. Then the hybrid radial basis function—response surface method (RBF–RSM) surrogate method is carried out to approximate and simplify the accurate numerical model. A non-dominated sorting genetic algorithm (NSGA-II) is used for resolving this multi-objective model. Optimal design is validated using comprehensive comparison, and the reduction of weight and maximum strain can be up to 2.46% and 44.65%, respectively. High-accuracy simulation with high-efficiency optimization is successfully demonstrated to perform the lightweight design on nuclear facilities.

Reliable transportation of fuel elements is vital for High Temperature Reactor (HTR) power plant. Therefore, the safety performance of the containment boundary in HTR-PM600 fresh fuel transport package under impact loading should be assessed. Specifically, factors such as the structural integrity, bolt safety, and sealing performance of the flange are considered. However, it is not convenient to assess the sealing performance using the helium leakage test without a prototypical experiment. Therefore, to accurately assess the safety performance in the design stage, we propose a detection technique combing with the finite element method to measure the leak channel in the flange for HTR-PM600 transport package. The results show that the boundary containment has no failure, and there is no complete leak channel in the flange of HTR-PM600 package. Hence, this package has a great safety performance under impact loading. This work also provides references for design of fresh fuel transport packages in pebble-bed reactors.

High-quality answers, usually given by experts, play an important role in community question answering (CQA) websites. Therefore, experts in these websites are defined as those who provide high-quality answers. We propose two semi-supervised learning models based on the graph convolution neural network (GCN) to identify them. Both models comprehensively extract features from the social behavior network, user profiles, and question and answer text. Specifically, we construct a social behavior network according to the co-answering relationship among answerers, which means every two answerers are connected if they answer the same questions. The difference between these two models is the methods of extracting text features. One model named GCN-Doc uses Doc2vec to get text vectors before training. The other model named GCN-Lstm with a long short term memory (LSTM) network extracts text features while training. Experiments using real-world data from Zhihu.com, one of the largest Chinese CQA websites, show that both GCN-Doc and GCN-Lstm can identify experts effectively comparing with baselines of PageRank and other GCN based neural network models. Besides, GCN-Lstm performs better than GCN-Doc.

With the increased penetration of the photovoltaic (PV) energy, the power system stability problem becomes an issue, as the output power of PV plants has unpredictable fluctuations. To maintain the power stability of the PV plants, battery energy storages (BESs) play an important role due to their fast and accurate response speed. However, it is challenging that the BES with multiple sub-modules responds well to the PV power fluctuations resulting from the various influence factors, such as defects, faults, and partial shading. Therefore, a bi-level control strategy is proposed in this paper, aiming at minimizing the operation cost of BES in maintaining power stability. The control strategy consists of the PV power fluctuations identification block and the mitigation block. In specific, the identification block can output the power fluctuation of a PV system by the PV power fluctuation identification technique. The technique is developed based on the characteristics of PV-string current under electrical faults and partial shading conditions. Meanwhile, the mitigation block can manage the multiple battery sub-modules with different regulation characteristics to meet the power fluctuations. At last, the promising results are obtained by MATLAB\Simulink with the coordinated operation of those two blocks, including the precise condition of the PV system and the optimal power output of each battery sub-module. Therefore, a comprehensive bi-level control strategy is developed to regulate the operation of battery sub-modules for PV-BES systems.

Personal protective air filtration masks are becoming increasingly desirable, but most commercial air purifiers have poor biocidal capabilities. Here the authors fabricate metal–organic framework-based air filters with both high particulate matter removal efficiencies and photocatalytic bactericidal properties.

Maternal regulatory factors endow the oocyte with developmental competence in vivo, which might be absent in current in vitro maturation (IVM) systems, thereby compromising oocyte quality. In the present study, by employing RNA sequencing data analysis, we expect to identify potential contributing factors to support porcine oocyte maturation through binding to their receptors on the oolemma. Here, C-X-C motif chemokine ligand 12 (CXCL12), vascular endothelial growth factor A (VEGFA), and Wingless-type MMTV integration site family member 5A (WNT5A), termed CVW, are selected and confirmed to be important maternal cytokines for porcine oocyte maturation. Combined supplementation of CVW promotes the nuclear maturation percentage from 57.2% in controls to 75.9%. More importantly, these maternal cytokines improve the developmental potential of matured oocytes by parthenogenesis, fertilization, and cloning, as their blastocyst formation efficiencies and total cell numbers are increased. CVW supplementation also enlarges perivitelline space and promotes cumulus expansion, which results in a more complete transzonal projection retraction on the zona pellucida, and a reduced incidence of polyspermy in fertilized oocytes. Meanwhile, inhibiting the CVW receptor-mediated signaling pathways severely impairs oocyte meiotic resumption and cumulus expansion during IVM. We further determine that maturation improvement by CVW is achieved through activating the MAPK pathway in advance and inhibiting the canonical WNT pathway at the end of the IVM period. These findings provide a new combination of three cytokines to promote the porcine IVM process, which also holds potential to be used in human assisted reproduction technologies as well as in other species.

Volume-regulated anion channels (VRACs) are central to cell volume regulation. Recently identified as hetero-hexamers formed by LRRC8 proteins, their activation mechanism remains elusive. Here, we measured Förster resonance energy transfer (FRET) between fluorescent proteins fused to the C-termini of LRRC8 subunits. Inter-subunit FRET from LRRC8 complexes tracked VRAC activation. With patch-clamp fluorometry, we confirmed that the cytoplasmic domains rearrange during VRAC opening. With these FRET reporters, we determined VRAC activation, non-invasively, in live cells and their subcompartments. Reduced intracellular ionic strength did not directly activate VRACs, and VRACs were not activated on endomembranes. Instead, pharmacological manipulation of diacylglycerol (DAG), and protein kinase D (PKD) activity, activated or inhibited plasma membrane-localized VRACs. Finally, we resolved previous contradictory reports concerning VRAC activation, using FRET to detect robust activation by PMA that was absent during whole-cell patch clamp. Overall, non-invasive VRAC measurement by FRET is an essential tool for unraveling its activation mechanism.

Efficient electrocatalytic oxidation of furfural to maleic acid (MA) is highly attractive, but challenging. Designing robust and cost-effective catalysts is the key for application. Herein, a bimetallic oxide catalyst (MnCeOx) was constructed by pyrolyzing the corresponding metal--organic framework (MOF) precursor. It was found that the obtained MnCeOx exhibited superior catalytic performance for electro-oxidation of furfural to produce MA, and the MA yield could reach 67.4% at an applied potential of 1.7 V vs. Ag/AgCl. Systematic investigations revealed that the excellent performance of MnCeOx originated from its suitable ability of forming surface oxygen vacancies, which could enhance the furfural adsorption on the active interfacial region of MnCeOx, thereby leading to the high catalytic activity of MnCeOx. [ABSTRACT FROM AUTHOR]