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A three-component annulation reaction and trifluoromethylation for the construction of 3-(trifluoromethyl)-4H-pyrans using β-CF3-1,3-enynes, BrCF2CO2Et, and sulfoxonium ylides as readily available substrates has been developed. This metal-free process involves two C–F bond cleavages of β-CF3-1,3-enynes and a CF3group generated in situfrom BrCF2CO2Et. This method is applicable to the late-stage modification of pharmaceutically active molecules.

Here, we report the preparation of lactones via Ni-catalyzed alkene hydroxylarylation and sequential intramolecular lactonization with O2as a green oxidant and oxygen source. The bulky 1,3-diketone ligand is crucial by enabling Ni-catalyzed hydroxylarylation of alkenes, providing numerous phthalide and furanone derivatives with high efficiency under mild conditions. The synthetic value of this methodology was further demonstrated by the efficient synthesis of typhaphthalide and a monoamine oxidase B inhibitor.

Tight sandstone reservoirs are significant sources of natural gas reserves. As traditional reserves become increasingly scarce and costly, optimizing the development of these reservoirs becomes crucial. This study introduces a novel two-phase gas–water flow model for single wells, incorporating both Darcy and non-Darcy flow equations. These equations are derived from mass conservation and momentum principles for both gas and water phases. Using data from a real tight gas well, our model, which includes stress-sensitive phases for gas and water, outperforms traditional Darcy flow models. Specifically, the average relative deviations in daily production rates were 0.1815% for gas and − 0.2677% for water, which are significantly smaller compared to traditional Darcy flow models. Further application of the non-Darcy flow model reveals strategies to enhance well performance. For example, mitigating liquid lock damage within a 2 m radius near the well could restore the permeability from 0.045 to 0.143 mD, thereby tripling the daily gas production. This non-Darcy flow model is easy to implement and shows significant potential in forecasting production yields in tight sandstone reservoirs, highlighting its importance in the petroleum and natural gas industry.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view–consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the $k$ -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via $\boldsymbol {u}$ nified and $\boldsymbol {d}$ iscrete $\boldsymbol {b}$ ipartite $\boldsymbol {g}$ raph $\boldsymbol {l}$ earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view–consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view–consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

Byline: Yong. Tang, Weiwei. Li, Yanhua. Chu, Quanhong. Han Haptic slippage is a frequently encountered and technically demanding complication in Yamane's surgery. The slipped haptic could be regained when bimanual [...]

Bergmann’s rule posits that an organism’s body size or mass increases with decreasing temperature. While generally established in homeotherms, Bergmann’s rule has been largely inconsistent in insects due to limited taxonomic and spatial coverage and variable sampling methods applied to the same taxonomic group. To rectify these shortfalls in tests of Bergmann’s rule in insects, we sampled ants, ichneumonid wasps, carabid beetles, and geometrid moths simultaneously from three locations (representing tropical, subtropical, and subalpine altitudinal gradients) across Yunnan, Southwest China, where temperature and productivity generally decline with latitude. We sought generalities in Bergmann’s rule in insects by investigating whether community-level altitudinal body size patterns within each location were dependent on geographic locations, microhabitats (forest canopy vs understory), focal taxa (insect families), and taxonomic scale (subfamilies within individual families). We found that altitudinal clines, when present, varied across geographic locations; carabids and geometrids showed positive clines in the tropical altitudinal gradient and negative clines in the subtropical and subalpine altitudinal gradients. Understory and canopy geometrids showed similar patterns while different subfamilies showed variable patterns. Carabid and geometrid body size patterns complied with the combined resource allocation model, which posits that the body size varies with resource availability along altitudinal and latitudinal ranges. Overall, our study revealed idiosyncratic altitudinal and latitudinal body size patterns for different insect taxa. Our study also illustrates the value of fully standardized, large-scale studies in revealing generalities (or the lack thereof) in Bergmann’s rule in insects and we suggest incorporating the natural history and biology of target groups to better explain patterns of body size variation.

An artificial intelligence system that assists prescription determination for orthokeratology lenses of varied brands have been developed and validated in real-world settings that involved clinicians with differing levels of experience.

Running exercise has been shown to alleviate depressive symptoms. However, the mechanism underlying the antidepressant effects of running exercise is not fully understood. The imbalance of M1/M2 microglia phenotype/polarization and concomitant dysregulation of neuroinflammation play crucial roles in the pathogenesis of depression. Running exercise increases circulating levels of adiponectin which is known to cross the blood‒brain barrier and suppress inflammatory responses. AdipoR1 is an adiponectin receptor that is involved in regulating microglial phenotypes and activation states. However, whether running exercise regulates hippocampal microglial phenotypes and neuroinflammation through adiponectin/AdipoR1 to exert its antidepressant effects remains unclear. In the current study, 4 weeks of running exercise significantly alleviated the depressive-like behaviors of chronic unpredictable stress (CUS)-exposed mice. Moreover, running exercise decreased the microglial numbers and altered microglial morphology in three subregions of the hippocampus to restore the M1/M2 balance; these effects were accompanied by regulation of pro-/anti-inflammatory cytokine production and secretion in CUS-exposed mice. These effects may involve elevation of peripheral tissue (adipose tissue and muscle) and plasma adiponectin levels, and hippocampal AdipoR1 levels as well as activation of the AMPK-NF-κB/STAT3 signaling pathway by running exercise. When an adeno-associated virus was used to knock down hippocampal AdipoR1, mice showed depressive-like behaviors and alterations in microglia and inflammatory factor expression in the hippocampus that were similar to those observed in CUS-exposed mice. Together, these results suggest that running exercise maintains the M1/M2 balance and inhibits neuroinflammation in the hippocampus of CUS-exposed mice. These effects might occur via adiponectin/AdipoR1-mediated activation of the AMPK-NF-κB/STAT3 signaling pathway.

Heat curing is an essential step for industrial-scale fabrication of polyamide nanofiltration membranes, which can improve the membrane stability and promote the membrane element encapsulation feasibility, but thermal stresses cannot be avoided during this process. In this study, oven and microwave were used to cure nanofiltration membranes, and the results showed that compared with oven heat curing, the flux dispersion of nanofiltration membranes cured using microwave heat curing was reduced from 0.318 to 0.093, and its Na2SO4rejection dispersion was reduced from 0.063 to 0.005, providing a tremendous improvement in the uniformity of nanofiltration membranes. The effect of the thermal stress distribution on the uniformity of nanofiltration membranes during heat curing was elucidated by numerical simulations. The fast microwave heating speed and uniform temperature distribution reduced the defects of the PA layer due to uneven thermal stress distribution. In conclusion, this study demonstrated the effect of thermal stress on membrane uniformity in different thermal curing modes, which provides good prospects for the application of microwaves in the field of nanofiltration membranes.

O-Acylhydroxylamine has been widely employed as an electrophilic amination reagent in transition-metal-catalyzed C–N coupling reactions, but its use as an electrophilic oxygen source has not been disclosed. Here, we report a Pd-catalyzed 1,2-oxyarylation of alkenes with O-acylhydroxylamines as an oxidant and an oxygen source for the first time. With simple amide as the monodentate directing group, this method features a broad substrate scope, good functional group tolerance, and mild conditions.

In response to the current diversity of vegetables and the limitations of storage and preservation techniques caused by their propensity for spoilage and deterioration during transportation due to vigorous tissue metabolic activity, microbial activity, and moisture loss, this study focuses on the impact of High Voltage Electrostatic Field (HVEF) on the storage quality and metabolism of Chinese kale. Experimental data reveal that HVEF significantly inhibits respiration and reduces peak respiration during the storage of Chinese kale. It also effectively maintains storage quality attributes such as color, moisture, and chlorophyll content, thereby extending storage time. Further metabolomic studies show significant differences in the metabolite spectra between the HVEF-treated group and the control group, involving metabolites of vitamins, flavonoids, and alkaloids. Key metabolites such as valine, leucine, vitamin B6, ergonovine, thiamine, and β-lactose are significantly upregulated. In addition, enrichment pathway analysis indicates that HVEF significantly regulates the central carbon metabolism as well as the metabolism of alanine, aspartic acid, and glutamic acid. This study confirms the effect of HVEF in inhibiting the vitality of Chinese kale at the metabolic level. In conclusion, high-voltage electrostatic field treatment is considered a strategy that can effectively extend the storage period of Chinese kale and improve storage quality. This research provides a theoretical basis for the development of new vegetable storage preservation technologies.

Etrasimod is an investigational, once‐daily, oral, selective sphingosine 1‐phosphate receptor 1,4,5 modulator in development for immune‐mediated inflammatory diseases (IMIDs). Here, we report the human safety, pharmacokinetics, and pharmacodynamics of etrasimod obtained from both a single ascending dose (SAD; 0.1‐5 mg) study and a multiple ascending dose (MAD; 0.35‐3 mg once daily) study. Overall, 99 healthy volunteers (SAD n = 40, MAD n = 59) completed the 2 studies. Evaluated single and multiple doses were well tolerated up to 3 mg without severe adverse events (AEs). Gastrointestinal disorders were the most common etrasimod‐related AEs. Over the evaluated single‐ and multiple‐dose ranges, dose‐proportional and marginally greater‐than‐dose‐proportional etrasimod plasma exposure were observed, respectively. At steady state, etrasimod oral clearance and half‐life mean values ranged from 1.0 to 1.2 L/h and 29.7 to 36.4 hours, respectively. Dose‐dependent total peripheral lymphocyte reductions occurred following etrasimod single and multiple dosing. Etrasimod multiple dosing resulted in reductions from baseline in total lymphocyte counts ranging from 41.1% to 68.8% after 21 days. Lymphocyte counts returned to normal range within 7 days following treatment discontinuation. Heart rate lowering from pretreatment baseline on etrasimod dosing was typically mild, with mean reductions seen after the first dose of up to 19.5 bpm (5 mg dose). The favorable safety, pharmacokinetic, and pharmacodynamic properties of etrasimod in humans supported its further development and warranted its investigation for treatment of IMIDs.

An efficient synthetic method for constructing 2,3- and 2,4-disubstituted pyrimidio[1,2-b]indazole skeletons through I2-DMSO-mediated and substrate-controlled regioselective [4 + 2] cyclization is reported. The reaction conditions are mild, its operation is simple, and the substrate scope is wide. More than 60 pyrimidio[1,2-b]indazole derivatives have been synthesized, providing a new methodology for constructing related molecules and potentially enriching bioactive-molecule libraries.

In the development of China’s Shengli deep heavy oil reservoirs, steam injection is the primary method. However, the effectiveness and efficiency of this method decline as the number of injection rounds increases. This study investigates the impact of different huff and puff rounds on the recovery rate of porous media by conducting hot water huff and puff tests on rock samples and using nuclear magnetic resonance (NMR) experiments to quantitatively determine the residual oil distribution at different injection rounds. A novel relationship has also been discovered to link oil saturation with the NMR conversion factor (C), which differs significantly from the traditional model, where Cis considered a constant. Additionally, the research reveals migration patterns during the heavy oil huff and puff process. The results show that the maximum potential oil recovery achievable through huff and puff is only 45.4%, significantly lower than the oil recovery of 52.69% achieved through displacement methods. When the oil saturation exceeds 63.8%, the Cvalue decreases exponentially. Under saturated oil conditions, the oil primarily exists as droplets larger than 10 μm. During each huff and puff process, oil is redistributed. As the number of huff and puff rounds increases, residual oil recovery is prioritized at the injection end. The local maximum oil saturation shifts progressively toward the far end. This research sheds light on the distribution patterns of residual oil in pore structures, providing support for the selection and assessment of enhanced recovery rate technologies.

Precise fault detection is essential to enhance the safety and reliability of unmanned aerial vehicle (UAV) system. With poor generalization capability, model-based approaches are greatly restricted by excessive dependence on aircrafts’ dynamics. In this study, a novel data-driven method, spatial-temporal graph attention Transformer network (ST-GATrans), is proposed to implement intelligent fault detection of UAVs through joint spatial-temporal learning on multivariate flight data. In the designed architecture, an enhanced graph attention network (EGAT) with graph multiheaded self-attention (GMSA) is first designed to excavate deep spatial connections inherent in the high-dimensional multivariate flight data. After establishing the spatial association among different status variables, one transformer encoder combining multiheaded self-attention (MSA) and convolutional token fusion (CTF) is developed to mine more comprehensive temporal features, whereby MSA enables to model long time dependencies and the CTF operation can capture indispensable local details. By modeling the spatial-temporal connections, the future values of multivariate flight data can be predicted precisely. The occurrence of faults will be detected by judging if the residual between predicted value and ground truth exceeds the predefined threshold. To eliminate the negative impact of noise or large fluctuations in flight data, a bidirectional adaptive exponential weighted moving average (Bi-AEWMA) method is proposed to smooth the residual sequence and determine the fault threshold. Effectiveness of the proposed methodology is verified on the collected real flight data. In different fault sceneries, the accuracy (ACC), true positive rate (TPR), and area under curve (AUC) of our approach can reach above 96%, 97%, and 0.98, respectively. The comparative experimental results demonstrate that our approach can obtain better fault detection performance.

The closure of the Paleo-Asian Ocean was a significant geological event in northern Pangea during the Carboniferous-Permian. It had a significant effect on climate, biota, and environmental conditions of the Late Paleozoic Ice Age, and resulted in the development of vast energy resources. This paper reports on the first discovery of marine red algal fossils in the Junggar Basin and its linkage to hydrocarbon generation. Red algae occur mainly in the Fengcheng Formation (ca.300 Ma) and provide direct fossil evidence for closure of the Paleo-Asian Ocean. The red algal fossils contain well-preserved reproductive organs, such as cystocarps and carpospores. High concentrations of C27steranes (C27regular steranes/sum of C27–C29regular steranes×100=14.30%–21.30%) and the marine biomarker 24-n-propylcholesterane (C30diasterane [βα20S]/sum of C27–C30diasteranes [βα20S]×100=1.15%–1.85%) were detected in the red-algae-bearing hydrocarbon source rocks. Thermal experiments that simulate hydrocarbon generation show that the oil generation potential of the red-algae-bearing source rocks is 363.71 mg g−1total organic C. This result, combined with oil-source rock correlations, indicates these rocks contributed to the formation of oil and gas resources, particularly in the marginal areas of the depression. The lake basin inherited the water and biological conditions of the Paleo-Asian Ocean during a marine regression, which was an important factor in the development of these high-quality hydrocarbon source rocks in an alkaline saline lake. The results advance our understanding of the evolution of the Paleo-Asian Ocean, interactions between the ocean and a lake during the deposition of terrestrial hydrocarbon source rocks, and whether red algae can effectively generate hydrocarbons.

A covalent organic framework (COF) named TpPa-1 was co-modified by chlorine grafting and ZnIn2S4integration for concurrent photocatalytic H2O2generation and wastewater purification under visible light. The introduction of electron-withdrawing chlorine groups on TpPa-1 decreases the density of adjacent electron clouds, which promotes the charge carrier separation and the intimate combination with ZnIn2S4by Cl–Zn/Cl–In bonds, providing an appreciably enhanced photocatalytic activity. Specifically, in pure water, the optimal ZnIn2S4/TpPa-Cl2sample can achieve a H2O2yield of 766 μmol L−1, which is 1.5, 2, 7.8 and 5 times those of ZnIn2S4/TpPa-1, TpPa-Cl2, ZnIn2S4and TpPa-1, respectively. In the tetracycline solution, 95% of tetracycline was degraded and, concomitantly, H2O2with a yield of 846 μmol L−1was generated. It is found that H2O2generation undergoes a two-step O2reduction with ·O2−as the intermediate, while ·O2−, holes and ·OH are the active species in tetracycline degradation. Likewise, other common pollutants like o-nitrophenol, acid golden yellow and acid red in wastewater could also be effectively degraded along with the appreciable production of H2O2, which realizes the so-called wastewater valorization. This study could advance COF-based photocatalysts and provide a novel insight into wastewater treatment.

In this study, a novel algorithm for high spatial resolution leaf area index (LAI) retrieval, specifically tailored for mountain forests, has been developed. As an essential climate variable, LAI has been incorporated into many ecohydrological process simulation models; however, the majority of the algorithms are developed on the assumption of flat terrain. Previous studies have proved that neglecting the influence of topography may introduce significant biases and uncertainties into LAI estimates particularly in rugged areas. As an important species in the mountain area, forests occupy a large land area worldwide; nevertheless, it is still challenging to obtain high-quality LAIs from satellite images due to their complex canopy structures. In spite of numerous attempts having been made to address such issues with topographic correction (TC) or mountain canopy reflectance models, few algorithms were actually available for LAI estimation of mountain forests. Here, we try to employ the geometric optical and mutual shadowing and scattering from the arbitrarily inclined-leaves model coupled with the topography (GOSAILT) model to retrieve forest LAI over complex terrain. GOSAILT is a combined model that incorporates the radiative transfer model (RTM) into the geometrical optical model (GOM) on the slope surface. It is capable of characterizing the bidirectional reflectance of both discrete and continuous canopies. The validations against computer-simulated LAIs reveal root-mean square errors (RMSEs) being 1.7160 and 0.6260, corresponding to terrain-ignored scenario and terrain-considered scenario, respectively. Besides, the validation against in situ LAIs demonstrated that the RMSE is 0.9262 over flat terrain and 0.6402 over sloped terrain. This evidence underscores the robust performance of the newly developed algorithm.

High Voltage Direct Current (HVDC) can quickly vary its power to damp power system angle oscillation. To handle the uncertainties of the faults and the system model, it is necessary to estimate the real-time linearized model of the AC/DC Hybrid system. This leads to two problems: Firstly, after the fault, the HVDC will quickly evolve to a steady state and, therefore, result in an unobservable control matrix. Secondly, the HVDC power should not exceed its stable range limited by the post-fault system. To address the above issues, the main novelties are as follows: First, this paper proposes a novel system identification method to handle that case when there is no control input excitation. Second, this paper presents a control strategy that fully considers the real-time stability constraints of the HVDC. More specifically, this paper estimates the system matrix and the control matrix separately. Based on the linearized model of the AC/DC hybrid system, the control matrix is calculated using the sensitivities of the active powers of the generators concerning those of the HVDC. Then, the system matrix is estimated using measures of the power angle and frequency. Finally, by processing the local voltage and current measurements, the system-side Thevenin equivalent parameters on the HVDC connecting point are estimated in real time and the regulating bound of the HVDC is determined. According to the linearized model and the HVDC power bound, a model predictive control strategy is used to damp the oscillation. Simulation results reveal the effectiveness of the proposed strategy.

Defect engineering has recently received much attention as an effective approach for improving photocatalytic performances. Herein, UiO-66-NH2with missing ligand defects (DUiO-66-NH2) wrapped by ZnIn2S4was investigated for photocatalytic H2O2production under visible light. The defects in DUiO-66-NH2enable efficient charge separation and O2capture, which are the key factors in accelerating H2O2production. Specifically, the O2adsorption capacity over ZnIn2S4/DUiO-66-NH2was improved by a factor of 3.1 as compared to its counterpart without defects. As a result, in ambient air and pure water without any sacrificial agents, the H2O2yield of ZnIn2S4/DUiO-66-NH2was 340 μmol L−1, which was significantly higher than that of ZnIn2S4/UiO-66-NH2(150 μmol L−1). The main H2O2formation pathway over ZnIn2S4/DUiO-66-NH2is an indirect oxygen reduction reaction with ·O2−as the intermediate, and the defects in UiO-66-NH2could act as active sites to adsorb and activate O2to produce ·O2−. However, the H2O2generation over ZnIn2S4/UiO-66-NH2undergoes both indirect and direct oxygen reduction reactions. This work could provide new insights and inspire further research into defect engineering of MOFs and photocatalytic H2O2synthesis.

The vascular endothelial growth factor pathway plays a key role in the pathogenesis of gastric cancer. In the multicenter, double-blind phase 3 FRUTIGA trial, 703 patients with advanced gastric or gastroesophageal junction adenocarcinoma who progressed on fluorouracil- and platinum-containing chemotherapy were randomized (1:1) to receive fruquintinib (an inhibitor of vascular endothelial growth factor receptor-1/2/3; 4 mg orally, once daily) or placebo for 3 weeks, followed by 1 week off, plus paclitaxel (80 mg/m2intravenously on days 1/8/15 per cycle). The study results were positive as one of the dual primary endpoints, progression-free survival (PFS), was met (median PFS, 5.6 months in the fruquintinib arm versus 2.7 months in the placebo arm; hazard ratio 0.57; 95% confidence interval 0.48–0.68; P< 0.0001). The other dual primary endpoint, overall survival (OS), was not met (median OS, 9.6 months versus 8.4 months; hazard ratio 0.96, 95% confidence interval 0.81–1.13; P= 0.6064). The most common grade ≥3 adverse events were neutropenia, leukopenia and anemia. Fruquintinib plus paclitaxel as a second-line treatment significantly improved PFS, but not OS, in Chinese patients with advanced gastric or gastroesophageal junction adenocarcinoma and could potentially be another treatment option for these patients. ClinicalTrials.gov registration: NCT03223376.

Accurate and generalized collaborative prediction of multi-cluster renewable energy power generation is both an inevitable trend and urgent demand as the growth of multi-region interconnected power grids with wind and photovoltaic (PV) power. In this paper, a novel heterogeneous spatiotemporal graph convolution network (HSTGCN) is proposed for ultra-short-term multi-modal prediction oriented to wind-PV power, which sufficiently considers spatiotemporal correlations in tens of wind farms or PV stations of each neighboring region and effectively coordinates the heterogeneities of different power generation types in different regions. This approach first designs a dynamical heterogeneous graph structure including modes, nodes, and edges to give a unified framework evolving over time for different interdependencies in the multi-cluster wind and PV sites, and then develops a hierarchical spatiotemporal learning mechanism to enhance representation power for multi-cluster prior information from temporal and spatial dimensions, integrating 2-D CNN with different sizes of filters and GCN embedded a specially designed lightweight graph convolution attention module (GCAM). Experiments including 57 operating wind farms and PV stations from 4 regions distributed over a broad spatial scale demonstrate the generalization and interpretation of HSTGCN compared with other commonly considered benchmarks.

Flexible self-supported laser-induced graphene (LIG) electrode devices were facilely fabricated through laser ablation technique by employing commercial polyimide film as the precursor material. Compared with the widely used traditional glassy carbon electrodes, the resulted LIG electrodes displayed abundant porous structure and surface defects. Notably, the one-step yielded LIG electrode devices were endowed with large electrochemically active surface area and accelerated electron transfer ability. Benefiting from its superior electrochemical property, these unmodified LIG electrodes exhibited remarkable enhanced electrochemical oxidation reactivity toward the food additive molecule Allura Red. Based on the augmented oxidation signal of Allura Red molecules on the LIG electrodes, a novel electrochemical sensor with high sensitivity for the detection of Allura Red was successfully developed. The sensor demonstrated a linear detection range spanning from 5 nM to 1 μM and exhibited a detection limit as low as 2.5 nM. Besides, the sensitivity was calculated to be 240.62 µA μM−1cm−2. More importantly, the sensor manifested outstanding stability, reproducibility, and practicality, further emphasizing its potential for real-world application.

Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.

In the presence of 20 mol % of tributylphosphine, tert-butyl carbonate substrate 3aundergoes smoothly an intramolecular formal [3 + 2] cycloaddition reaction at room temperature to give benzobicyclo[4.3.0] compounds in 99% yield with a 19/81 ratio of 2aand 2a′. The mechanism of the isomerization of the product 2ainto 2a′ has been investigated in detail. On the basis of this mechanism, two strategies, using 20 mol % of triphenylphosphine or 10 mol % of tributylphosphine in the presence of 20 mol % of Ti(OiPr)4, have been established for the selective construction of benzobicyclo[4.3.0] compounds. Under neutral conditions, the reactions of compounds 3a−gafford benzobicyclo[4.3.0] compounds 2a−gwith high selectivities in good to excellent yields. In addition, α-methyl α,β-unsaturated ester 3halso works well to give the corresponding product 2hwith one quaternary carbon center in 99% yield under neutral and room temperature conditions.

Herein, facile room-temperature self-assembly and high-temperature pyrolysis strategy was successively conducted for in situ synthesizing novel TiO2/TiN@N-C heterostructure by using typical sandwich-like precursors (MXene/ZIF-8). Zero-dimensional (0D) TiO2, TiN and N-doped carbon nanoparticles were in situ formed and randomly anchored on the two-dimensional (2D) N-doped carbon substrate surface, making TiO2/TiN@N-C exhibit unique 0D/2D heterostructure. Relative to the extensively studied ZIF-8-derived N-doped carbon nanoparticles, TiO2/TiN@N-C heterostructure displayed greatly boosted electrochemical active specific surface. Benefiting from the enhanced electrochemical property of TiO2/TiN@N-C heterostructure, remarkable signal enhancement effect was achieved in terms of the oxidation of multiple hazardous substances, including clozapine, sunset yellow and benomyl. As a result, a novel electrochemical platform was constructed, the linear detection range were 10–1000 nM, 2.5–1250 nM, 10–1000 nM while the detection limits were evaluated to be 3.5 nM, 1.2 nM, 4.5 nM for clozapine, sunset yellow and benomyl, respectively. Besides, the practicability of the newly developed electrochemical method was verified by assessing the content of clozapine, sunset yellow and benomyl in real food samples.

Anomaly detection on attributed networks has received an increasing amount of attention in recent years. Despite the success, most of the existing methods only focus on detecting the abnormal nodes while fail to detect the abnormal subgraphs. In this paper, we define a new problem of hybrid-order anomaly detection on attributed networks, which aims to detect both of the abnormal nodes and subgraphs. To this end, a new deep learning model called Hybrid-Order Graph Attention Network (HO-GAT) is developed, which is able to simultaneously detect the abnormal nodes and motif instances in an attributed network. In order to model the mutual influence between nodes and motif instances, the learning procedures of the node representation and the motif instance representation are integrated into a unified graph attention network with a novel hybrid-order self-attention mechanism. After learning the node representation and the motif instance representation, two decoders are respectively designed to reconstruct the attribute information of the nodes and motif instances, and the hybrid-order topological structure among nodes and motif instances. And finally, the reconstruction errors are utilized as the abnormal score of nodes and motif instances respectively. Extensive experiments conducted on real-world datasets have confirmed the effectiveness of the HO-GAT method.

Nowadays, the learning management system (LMS) has been widely used in different educational stages from primary to tertiary education for student administration, documentation, tracking, reporting, and delivery of educational courses, training programs, or learning and development programs. Towards effective learning outcome assessment, the exam script generation problem has attracted many attentions recently. But the research in this field is still in its early stage. Two essential issues have been ignored largely by existing solutions. First, given a course, it is unknown yet how to generate an quality exam script which concurrently has (i) the proper difficulty level, (ii) the coverage of essential knowledge points, (iii) the capability to distinguish academic performances between students, and (iv) the student scores in normal distribution. Second, while frequently encountered in practice, it is unknown so far how to generate a pair of high quality exam scripts which are equivalent in assessment (i.e., the student scores are comparable by taking either of them) but have significantly different sets of questions. To fill the gap, this paper proposes ExamGAN (Exam Script Generative Adversarial Network) to generate high quality exam scripts, and then extends ExamGAN to T-ExamGAN (Twin-ExamGAN) to generate a pair of high quality exam scripts. Based on extensive experiments on three benchmark datasets, it has verified the superiority of proposed solutions in various aspects against the state-of-the-art. Moreover, we have conducted a case study which demonstrated the effectiveness of proposed solution in the real teaching scenarios.

Digital simulation is very important for the safe and economic operation of power systems. Power system dominant instability mode (DIM) identification is an intractable problem in large-scale transient simulation analysis, because transient instability and short-term voltage instability are often intertwined. Deep learning is a promising way to achieve accurate DIM identification. By encoding time series as images, this paper proposes a robust and transferable DIM identification framework combining curve filtering rules and a dual-channel VGGNet (DCVGG) image recognition network. The curve filtering rules can effectively reduce the redundancy of massive time-domain features, the model parameters, and training time. A skillfully designed method is proposed to encode time series as images and improve the feature extraction capability of convolutional neural networks. The proposed DCVGG network is capable of processing rotor angle and voltage images simultaneously and has the potential to become a pre-trained model, making transfer learning between different power systems possible. Case studies conducted on 8-machine 36-bus system and Northeast China Power Grid demonstrate the proposed framework has better performance, scalability, robustness, and transferable ability than contrastive machine learning methods.

Deep learning (DL) is a useful tool for power system stability assessment (PSSA) and dominant instability mode (DIM) identification. However, when faced with operational variability, the performance of DL models degrades. This paper proposes a bidirectional active transfer learning (Bi-ATL) framework for more adaptive PSSA and DIM identification, where the DL model is easier to adapt to unlearned operating conditions with fewer newly labeled instances. At the instance level, forward active learning and backward active learning are integrated to progressively build a mixed instance set by actively including the most label-worthy instances of new operating conditions and actively eliminating the most useless original operating condition instances. Then at the model parameter level, the mixed instance set is utilized to fine-tune the original DL model to new operating conditions. The Bi-ATL framework synthesizes three-way information of the instances and model of the original operating condition, and a few labeled instances of new operating conditions for more efficient adaptation. Intensive case studies conducted on a benchmark power system (CEPRI 36-bus system) and a real-world large-scale power system (Northeast China Power System-2131 bus) validate the efficacy and efficiency of the Bi-ATL framework as well as the role of the three-way information.

With the expansion of power system simulation scale, intelligent stability analysis based on simulation data becomes more and more important. However, the changeable operating conditions and fault conditions will seriously affect the performance of the intelligent analysis model. This paper proposes an advanced unsupervised transfer learning (UTL) method combining dynamic graph attention network (DGAT) and data augmentation to identify the instability mode towards multiple known and unknown scenarios. For the pre-training stage, the DGAT model with data augmentation is used for instability mode identification based on the basic dataset. For the transfer learning stage, it starts when faced with new operating conditions or fault conditions different from the basic dataset. The UTL scheme takes the distribution differences among different categories of source domain and target domain as the loss to fine-tune the pre-trained model. Moreover, considering the difficulty of sample-labeling, the UTL scheme can make the pre-trained model adapt to them without additional labeled samples. Case studies are conducted on 8-machine 36-bus system and Northeast China Power Grid. The results verify the superiority of the DGAT model with data augmentation and confirm the UTL scheme can help the model adapt to multiple unknown scenarios.

This paper proposes a novel service-oriented integration paradigm for Internet of Video Things (referred to as Video as a Service), which orchestrates network slicing and utilizes multi-agent deep reinforcement learning to customize and maximize video content and infrastructure potential.

Background: Pathological myopia (PM) is closely associated with blinding ocular morbidities. Identifying biomarkers can provide clues on pathogeneses. This study aimed to identify metabolic biomarkers and underlying mechanisms in the vitreous humour (VH) of PM patients with complications. Methods: VH samples were collected from 39 PM patients with rhegmatogenous retinal detachment (RRD) (n= 23) or macular hole (MH)/myopic retinoschisis (MRS) (n= 16) and 23 controls (MH with axial length &lt; 26 mm) who underwent surgical treatment. VH metabolomic profiles were investigated using ultra-performance liquid chromatography‒mass spectrometry. The area under the receiver operating characteristic curve (AUC) was computed to identify potential biomarkers for PM diagnosis. Results: Bioinformatics analysis identified nineteen and four metabolites altered in positive and negative modes, respectively, and these metabolites were involved in tryptophan metabolism. Receiver operating characteristic analysis showed that seventeen metabolites (AUC > 0.6) in the positive mode and uric acid in the negative mode represent potential biomarkers for PM with complications (AUC = 0.894). Pairwise and pathway analyses among the RRD-PM, MH/MRS-PM and control groups showed that tryptophan metabolism and uric acid were closely correlated with PM. Altered metabolites and pathways in our study were characterized by increased oxidative stress and altered energy metabolism. These results contribute to a better understanding of myopia progression with or without related complications. Conclusions: Our study provides metabolomic signatures and related immunopathological features in the VH of PM patients, revealing new insight into the prevention and treatment of PM and related complications.

The development of sustainable vinyl polymers that can be chemically recycled or upcycled is highly required but very challenging. Herein, we report the efficient synthesis, degradation, and upcycling of a high-molecular-weight PMMA-based copolymer completely made of a C–C-bonded backbone enabled by the same catalytic system (SaBOX/copper) under mild conditions for the first time. The atom transfer radical copolymerization (ATRcP) of MMA with α-chloroacrylate is conducted at 30 °C by the catalyst, producing linear PMMA with C–Cl bonds, which are thermally stable below 200 °C with Tgof 126 °C, exhibiting the thermophysical properties of commodity PMMA. At a higher temperature (≥65 °C), the catalyst could stimulate degradation at the position of the C–Cl bond, and the polymer chain is cleaved to give telechelic PMMA blocks with functional terminals. Remarkably, these blocks could be employed to readily prepare their parent copolymer, linear block copolymer, or novel branched block copolymer in the presence of a catalyst, realizing the chemical upcycling of a PMMA-based copolymer. Above all, catalysis plays a decisive role in these achievements, and strong catalyst effects are observed in the synthesis of degradable polymers, the degradation reactions, the structure of degradation products, and thus their upcycling. The SaBOX/copper catalytic system shows significant superiority over the copper catalysts commonly used in the literature.

ABSTRACTIntroductionAstrocytes have previously been considered as cells supporting neuronal functions, but they are now recognized as active players in maintaining central nervous system (CNS) homeostasis. Astrocytes can communicate with other CNS cells, i.e. through the gliotransmitter ATP and P2X7 receptors (Rs).Areas coveredIn this review, we will discuss how the P2X7R initiates the release of gliotransmitters and proinflammatory cytokines/chemokines, thereby establishing a dialog between astrocytes and neurons and, in addition, causing neuroinflammation. In astrocytes, dysregulation of P2X7Rs has been associated with neurodegenerative illnesses such as Alzheimer’s disease (AD), as well as the consequences of cerebral ischemic injury and status epilepticus (SE).Expert opinionAlthough all CNS cells are possible sources of ATP release, the targets of this ATP are primarily at microglial cells. However, astrocytes also contain ATP-sensitive P2X7Rs and have in addition the peculiar property over microglia to continuously interact with neurons via not only inflammatory mediators but also gliotransmitters, such as adenosine 5’-triphosphate (ATP), glutamate, γ-amino butyric acid (GABA), and D-serine. Cellular damage arising during AD, cerebral ischemia, and SE via P2X7R activation is superimposed upon the original disease, and their prevention by blood–brain barrier permeable pharmacological antagonists is a valid therapeutic option.

A $k$k-core is the special cohesive subgraph where each vertex has at least $k$k degree. It is widely used in graph mining applications such as community detection, visualization, and clique discovery. Because dynamic graphs frequently evolve, obtaining their $k$k-cores via decomposition is inefficient. Instead, previous studies proposed various methods for updating $k$k-cores based on inserted (removed) edges. Unfortunately, the parallelism of existing approaches is limited due to their theoretical constraints. To further improve the parallelism of maintenance algorithms, we refine the $k$k-core maintenance theorem and propose two effective parallel methods to update $k$k-cores for insertion and removal cases. Experimental results show that our methods outperform the state-of-the-art algorithms on real-world graphs by one order of magnitude.

This paper investigates the synchronization stability of voltage source converter (VSC) connected to infinite bus system with frequency limiter of phase-locked loop (PLL). First, three different types of PLL frequency limiters are modelled based on piecewise-smooth dynamical system (PSDS). Then the effects of different frequency limiters and frequency limit values on the region of attraction (ROA) of the system are studied and the properties of the PSDSs are analyzed. It is found that PLL with anti-windup limiter with back calculation can achieve global stability with appropriate limit value, and the principle of setting limit value based on a specific ROA estimation is proposed. Based on ROA estimation with Lyapunov function, a method of setting frequency limit value which guarantees global stability is proposed. The validity of the proposed strategy is verified through MATLAB/Simulink simulation.

The SOX9 transcription factor ensures proper tissue development and homeostasis and has been implicated in promoting tumor progression. However, the role of SOX9 as a driver of lung adenocarcinoma (LUAD), or any cancer, remains unclear. Using CRISPR/Cas9 and Cre-LoxP gene knockout approaches in the KrasG12D-driven mouse LUAD model, we found that loss of Sox9significantly reduces lung tumor development, burden and progression, contributing to significantly longer overall survival. SOX9 consistently drove organoid growth in vitro, but SOX9-promoted tumor growth was significantly attenuated in immunocompromised mice compared to syngeneic mice. We demonstrate that SOX9 suppresses immune cell infiltration and functionally suppresses tumor associated CD8+T, natural killer and dendritic cells. These data were validated by flow cytometry, gene expression, RT-qPCR, and immunohistochemistry analyses in KrasG12D-driven murine LUAD, then confirmed by interrogating bulk and single-cell gene expression repertoires and immunohistochemistry in human LUAD. Notably, SOX9 significantly elevates collagen-related gene expression and substantially increases collagen fibers. We propose that SOX9 increases tumor stiffness and inhibits tumor-infiltrating dendritic cells, thereby suppressing CD8+T cell and NK cell infiltration and activity. Thus, SOX9 drives KrasG12D-driven lung tumor progression and inhibits anti-tumor immunity at least partly by modulating the tumor microenvironment.

Etrasimod (APD334) is an investigational, once‐daily, oral, selective sphingosine 1‐phosphate receptor 1,4,5 modulator (S1P1,4,5) in development for treatment of various immune‐mediated inflammatory disorders. The disposition and mass balance of a single 2‐mg [14C]etrasimod dose were evaluated in 8 healthy males. An in vitro study was also conducted to identify etrasimod's oxidative metabolizing enzymes. Peak concentrations of etrasimod and total radioactivity in plasma and whole blood were typically reached 4–7 hours postdose. Etrasimod constituted 49.3% of total radioactivity plasma exposure, with multiple minor/trace metabolites making up the remainder. Etrasimod was slowly cleared mainly via biotransformation, predominantly by oxidative metabolism, with unchanged etrasimod recovered in feces accounting for only 11.2% of the dose and none in urine. The mean apparent terminal half‐lives of etrasimod and total radioactivity in plasma were 37.8 and 89.0 hours, respectively. Mean cumulative recovery of radioactivity in excreta over 336 hours was 86.9% of the dose, mostly in feces. The prevalent metabolites eliminated in feces were M3 (hydroxy‐etrasimod) and M36 (oxy‐etrasimod sulfate), accounting for 22.1% and 18.9% of the dose, respectively. From in vitro reaction phenotyping, the predominant enzymes involved in the oxidation of etrasimod were CYP2C8, CYP2C9, and CYP3A4, with minor contributions from CYP2C19 and CYP2J2.

• Functionalized membrane with customized pattern is prepared by fast laser carving. • Selective permeable membrane complemented with vacuum filtration is used to develop intact interdigital thin film. • Macroscopic assembly based on size effect rationally guide the structural design and precise fabrication of flexible electrode with expected high performances. • Flexible high-performance on-chip micro-supercapacitors with planar arbitrary shape are fabricated. Graphene-based electrodes with rational structural design have shown extraordinary prospect for enhanced electrical double-layer capacitance of micro-supercapacitors (MSCs). Herein, a facile fabrication method for flexible planar MSCs based on hierarchical graphene was demonstrated by using a laser-treated membrane for electrode patterning, complemented with hierarchical electrode configuration taking full advantages of size-determined functional graphene. The in-plane interdigital shape of MSCs was defined through vacuum filtration with the assistance of the functionalized polypropylene (PP) membrane. The hierarchical graphene films were built by macroscopic assembly based on size effect of different lateral sized graphene sheets (rGO-LSL). The sample of MSCs based on rGO-LSL (MSCs-LSL) exhibited excellent volumetric capacitance of 6.7 F cm−3 and high energy density of 0.37 mWh cm−3. The MSCs-LSL presented superb flexibility and cycling stability with no capacitance deteroriated after 2000 cycles. This newly developed fabrication strategy is of good scalability and designability to manufacture flexible electrode for MSCs with customized shapes, while the construction of hierarchical graphene can enlighten the structural design of analogous two-dimensional materials for potential advanced electronics. A facile mask-free filtration approach is developed for the fabrication of patterned onchip micro-supercapacitors based on hierarchical graphene. The use of the functionalized membrane complemented with macroscopic assembly based on size effect provides the control over the planar configuration of micro-supercapacitors, and rationally guide the structural design of flexible electrode with expected high performances. [Display omitted] [ABSTRACT FROM AUTHOR]

ABSTRACTObjectivesParkinson’s disease (PD) is the second most common neurodegenerative disease. Chlorogenic acid (CGA) is a polyphenolic substance derived from various medicinal plants. Although CGA is reported to have potential anti-PD effect, the beneficial effect and the underlying mechanism remain unclear. In this study, we aimed to further investigate the protective effect and clarify the mechanism of action of CGA in Caenorhabditis elegans (C. elegans) models of PD.MethodsMeasurements of a-synuclein aggregation, movement disorders, and lipid, ROS and malondialdehyde (MDA) contents were observed in NL5901 nematodes. Determinations of dopamine (DA) neuron degeneration, food perception, and ROS content were performed in 6-OHDA-exposed BZ555 nematodes. The autophagy activation of CGA was monitored using DA2123 and BC12921 nematodes. Meanwhile, RNAi technology was employed to knockdown the autophagy-related genes and investigate whether the anti-PD effect of CGA was associated with autophagy induction in C. elegans.ResultsCGA significantly reduced α-synuclein aggregation, improved motor disorders, restored lipid content, and decreased ROS and MDA contents in NL5901 nematodes. Meanwhile, CGA inhibited DA neuron-degeneration and improved food-sensing behavior in 6-OHDA-exposed BZ555 nematodes. In addition, CGA increased the number of GFP::LGG-1 foci in DA2123 nematodes and degraded p62 protein in BC12921 nematodes. Meanwhile, CGA up-regulated the expression of autophagy-related genes in NL5901 nematodes. Moreover, the anti-PD effect of CGA was closely related to autophagy induction via increasing the expression of autophagy-related genes, including unc-51, bec-1, vps-34, and lgg-1.ConclusionsThe present study indicates that CGA exerts neuroprotective effect in C. elegansvia autophagy induction.

Different ligand functionalized UiO-66 (UiO-66-X, X = OH, (OH)2and NH2) were prepared and then modified by ZnIn2S4lamellas to form ZnIn2S4/UiO-66-X heterostructures for visible-light-driven H2O2evolution in pure water. The H2O2yields using ZnIn2S4/UiO-66-NH2, ZnIn2S4/UiO-66-(OH)2and ZnIn2S4/UiO-66-OH are 799, 733 and 165 μmol L−1, respectively, which are 9.5, 8.7 and 2.0 times that of ZnIn2S4/UiO-66. The high performance of ZnIn2S4/UiO-66-NH2and ZnIn2S4/UiO-66-(OH)2is ascribed to the benign visible-light response and Z-scheme heterostructures, and the H2O2evolution abides by indirect O2reduction with O2−as an intermediate species. Additionally, H2O2yields using ZnIn2S4/UiO-66-NH2on tap water and Xuanwu Lake (Nanjing, China) water can be comparable to that on the above-deionized water. This study sheds light on the great promise of functionalized MOFs and their applications on green (photo)catalytic energy conversion.

Effective DC fault identification is very important for a half-bridge modular multi-level converter (HB-MMC) based high voltage direct current (HVDC) grid. The performance of the traditional protection principles lacks reliability and sensitivity, and there is a further limitation under the current limiting strategies. To address the problem, a scheme coordinating the control and protection is proposed, which actively creates the known feature for fault identification and limits the fault current at the same time. In this paper, the characteristics of the input impedance phase under different faults are analyzed in various frequency bands. Based on the analysis of the input impedance phase, the converter switches the control mode and generates a characteristic signal at a particular frequency after fault detection. Then, the Prony algorithm is employed to acquire the phase information at the injection frequency. Finally, the faulty zone can be identified by the value of the input impedance phase. This active injection protection scheme is applicable to identify different faults, verified in PSCAD/EMTDC, and tested by hardware-in-the-loop simulation in RTDS.

Photonic integrated circuits have potentials to advance artificial neural networks by providing ultra-fast and high power efficient computation resources. In many of optical neural network (ONN) architectures, data are encoded in light amplitudes, using magnitude and phase to denote the absolute value and the sign. However, the phase insensitivity of optical components such as saturable absorbers, semiconductor optical amplifiers and photodetectors can lead to limitations in ONN designs, including nonlinear activation and inference results readout. In this work, we propose a complementary decomposition approach to overcome the phase insensitive problem. Based on that, we further present a optical ReLU function implementation. The proposed designs can scale to multi-layer neural networks by cascading linear and nonlinear units. These optical neural networks do not require repeated bidirectional digital-analog conversions and optical-electronic conversions between each layers as well as reference signals for homodyne detection. These features could provide a potential implementation of compact, scalable, high efficiency ONNs. We evaluate the concept with a machine learning task. The numerical results demonstrate that the proposed design can tackle the aforementioned phase insensitive problem.

With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.