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Influenza A virus (IAV) is a widespread pathogen that poses a significant threat to human health, causing pandemics with high mortality and pathogenicity. Given the emergence of increasingly drug-resistant strains of IAV, currently available antiviral drugs have been reported to be inadequate to meet clinical demands. Therefore, continuous exploration of safe, effective and broad-spectrum antiviral medications is urgently required. Here, we found that the small molecule compound J1 exhibited low toxicity both in vitro and in vivo. Moreover, J1 exhibits broad-spectrum antiviral activity against enveloped viruses, including IAV, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human coronavirus OC43 (HCoV-OC43), herpes simplex virus type 1 (HSV-1) and HSV-2. In this study, we explored the inhibitory effects and mechanism of action of J1 on IAV in vivo and in vitro. The results showed that J1 inhibited infection by IAV strains, including H1N1, H7N9, H5N1 and H3N2, as well as by oseltamivir-resistant strains. Mechanistic studies have shown that J1 blocks IAV infection mainly through specific interactions with the influenza virus hemagglutinin HA2 subunit, thereby blocking membrane fusion. BALB/c mice were used to establish a model of acute lung injury (ALI) induced by IAV. Treatment with J1 increased survival rates and reduced viral titers, lung index and lung inflammatory damage in virus-infected mice. In conclusion, J1 possesses significant anti-IAV effects in vitro and in vivo, providing insights into the development of broad-spectrum antivirals against future pandemics.

A machine learning (ML) interatomic potential was developed to predict the behavior of complex high-entropy alloys (HEAs) containing Al, Cr, Fe, Co, Ni, and Cu. This ML potential accurately reproduces DFT calculations, allowing for extensive molecular dynamics simulations. We analyzed the mechanical properties of face-centered cubic (FCC), body-centered cubic (BCC), and polycrystalline HEA phases at different element concentrations and temperatures. The ML potential accurately predicts key system parameters such as lattice constants and elastic moduli and describes the stress–strain material behavior. Strain analysis shows a complex phase transformation and dislocation formation in the FCC phase under tension. High Co and Ni concentrations improve the mechanical properties of the FCC phase. Polycrystalline structures form BCC-HCP mixtures, highlighting the dependence of their stability on the element concentration. Temperature-dependent simulations show a transition from the FCC phase to the amorphous phase at 1200 K. Interestingly, Cu-rich clusters play a crucial role in stabilizing the FCC phase at lower temperatures. This study demonstrates the efficiency of the ML potential for studying HEAs and unveils the complex relations between element composition and the resulting mechanical properties.

Smart shape-changing structures in aerospace applications are vulnerable to damage in harsh environments. Balancing high mechanical performance with self-repair capabilities poses a challenge due to inherent trade-offs between strength and flexibility. To address this challenge, an asymmetric bilayer-structured actuator was fabricated using commercially available continuous carbon fiber tows (CFs) as the passive layer and a dynamic cross-linked epoxy vitrimer as the active layer. The construction of the vitrimer-CF actuator involves a simple and scalable hot-pressing process, resulting in a tensile strength of 234 MPa and an interfacial bonding strength of 405 N·m–1. This actuator exhibits remarkable deformation capability (210°/7 s) and an efficient self-repair ability under various stimuli, including thermal (60–160 °C), light (0.4–1.0 W·cm–2), electric (2–4 V), and solvent (acetone). By adjustment of the orientation angle of CFs, complex left-handed and right-handed curling structures can be achieved. Leveraging the insights from photothermal/electrothermal actuation mechanisms, a quadruped crawling robot is developed capable of crawling 4 cm with a single light illumination. The actuator can lift objects 45 times its weight when subjected to light stimuli. Additionally, a flap actuator is constructed to achieve an angle change of 63° within 10 s under an electric stimulus, enabling remote control over the aircraft flight angle. These results demonstrate the potential of the vitrimer-CF actuator for advanced applications in intelligent aerospace structures.

The future power systems with renewable energy sources (RESs) and electric vehicles (EVs) are usually subject to strong uncertainties in system parameters, communication network, and disturbances, which may severely degrade the frequency control performance. This article proposes a performance enhancing load frequency control (LFC) scheme for future power systems with strong uncertainties based on the Kalman-filter (KF) control compensation method. First, the LFC of a multiarea interconnected power system (MAIPS) with RESs and EVs is conceptualized as a linear stochastic and discrete model. Then, KF is introduced to evaluate the unmeasurable states of the LFC, so as to design an additional KF-based state feedback control law. The KF-based control loop serves as compensation for the original controller of the LFC system. Next, an optimal compensation control gain of the KF-based control loop is derived based on a differential optimal calculation method to enhance the control performance of the LFC of MAIPS under strong uncertainties. Finally, simulation results are conducted on a typical three-area LFC systems that integrate RESs and EVs, which demonstrate that the proposed control strategy can provide better control performance and robustness than the original LFC strategy when the systems are subject to strong uncertainties.

Pipeline magnetic flux leakage (MFL) detection is an efficient and energy-saving nondestructive testing (NDT) method. However, under the high-speed detector, MFL signals become distorted with the influence of velocity effects, which adversely affects the accuracy of defect size inversion. The essential cause is the distorted signal multiplicity by velocity effects. In response to this issue, a high-precision defect size inversion method is proposed for the first time, which is called knowledge-guided contrastive fusion network (KCF-Net). First, MFL and eddy current (EC) mechanisms are analyzed, which are concluded that the sensitivity of EC signals to speed is much lower than that to defect sizes, so that EC and MFL abstract features are mined to improve the sensitivity of defect sizes. Moreover, MFL mechanism representations are mined to supervise neural networks to enhance the interpretability of the network. MFL and EC knowledge including abstract features and mechanism representations is fused to highlight the disparities between undistorted and distorted signals and enrich available information. Then, joint decision-making is proposed to eliminate the instability of fusion knowledge and enhance the universality and effectiveness of defect inversion. Finally, the experiments prove the effectiveness of KCF-Net. The length, width, and depth inversion MAEs of measured signals reach 2.2676, 1.6185, and 0.5664, respectively.

A composite solid electrolyte comprising a Cu−Al bimetallic metal-organic framework (CAB), lithium salt (LiTFSI) and polyethylene oxide (PEO) was fabricated through molecular grafting to enhance the ionic conductivity of the PEO-based electrolytes. Experimental and molecular dynamics simulation results indicated that the electrolyte with 10 wt.% CAB (PL-CAB-10%) exhibits high ionic conductivity (8.42×10−4S/cm at 60 °C), high Li+transference number (0.46), wide electrochemical window (4.91 V), good thermal stability, and outstanding mechanical properties. Furthermore, PL-CAB-10% exhibits excellent cycle stability in both Li−Li symmetric battery and Li/PL-CAB- 10%/LiFePO4asymmetric battery setups. These enhanced performances are primarily attributable to the introduction of the versatile CAB. The abundant metal sites in CAB can react with TFSI−and PEO through Lewis acid–base interactions, promoting LiTFSI dissociation and improving ionic conductivity. Additionally, regular pores in CAB provide uniformly distributed sites for cation plating during cycling.

Forest structural complexity can mediate the light and water distribution within forest canopies, and has a direct impact on forest biodiversity and carbon storage capability. It is believed that increases in forest structural complexity can enhance tree species diversity and forest productivity, but inconsistent relationships among them have been reported. Here, we quantified forest structural complexity in three aspects (i.e., horizontal, vertical, and internal structural complexity) from unmanned aerial vehicle light detection and ranging data, and investigated their correlations with tree species diversity and forest productivity by incorporating field measurements in three forest biomes with large latitude gradients in China. Our results show that internal structural complexity had a stronger correlation (correlation coefficient = 0.85) with tree species richness than horizontal structural complexity (correlation coefficient = -0.16) and vertical structural complexity (correlation coefficient = 0.61), and it was the only forest structural complexity attribute having significant correlations with both tree species richness and tree species evenness. A strong scale effect was observed in the correlations among forest structural complexity, tree species diversity, and forest productivity. Moreover, forest internal structural complexity had a tight positive coordinated contribution with tree species diversity to forest productivity through structure equation model analysis, while horizontal and vertical structural complexity attributes have insignificant or weaker coordinated effects than internal structural complexity, which indicated that the neglect of forest internal structural complexity might partially lead to the current inconsistent observations among forest structural complexity, tree species diversity, and forest productivity. The results of this study can provide a new angle to understand the observed inconsistent correlations among forest structural complexity, tree species diversity, and forest productivity.

Aiming at the transient instability and overcurrent issues of the virtual synchronous generator (VSG) under severe grid voltage sag, the accurate and simultaneous control for the phase angle and current of VSG is hard to be achieved without using the fault information. And the requirement of the grid code for the reactive current should be also considered. To address the issues, this paper proposes a non-fault information based dual-loop adaptive feedback control to take transient angle stability, current limitation and the demand of the reactive current of VSG into account. First the large-signal model of VSG with a dual-loop control is built. To design the feedback coefficients, the feasible coefficient region under different fault degrees and cases is analyzed subsequently. It provides reference for the curve fitting, which is further applied in the self-adaptive regulation of the feedback coefficients. Thereby, a dual-loop adaptive feedback control is realized based on an additional reactive power feedback loop. With the proposed control scheme, all of the three control objectives can be achieved without the fault information, since the feedback coefficients are within the feasible coefficient region by the self-adaptive regulation. Finally, the effectiveness and robustness of the proposed control scheme for both VSG and a paralleled system of VSG and grid-following (GFL) converter are validated by the simulation results and the experimental results.

Background In recent years, the number of patients with myocardial ischaemia is rising year by year in China.Objectives The study uses network pharmacology to predict targets and pathways of action of Apocynum venetum L. (AVL) for the treatment of myocardial ischemia (MI).Materials and Methods The essential active compounds of AVL were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The objective of selecting the target genes was achieved through the utilization of GeneCards and Swiss Target Prediction resources. The pool of MI-related targets was obtained from DisGeNET, Online Mendelian Inheritance in Man and the Therapeutic Target Database. The protein-protein interaction network was meticulously constructed using the STRING algorithm. The gene ontology (GO) analysis of AVL-MI targets was performed at Metascape. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted. Furthermore, molecular docking analysis was conducted to study the interactions of AVL components with estrogen receptor 1 (ESR1), heat shock protein 90 alpha (HSP90A), protein kinase B (Akt1), tyrosine kinase Src (SRC) and phosphatidylinositol 3-kinase (PI3K). Finally, cellular experiments were performed to validate the mechanism of action of AVL.Results The molecular target analysis revealed that AVL has the potential to interact with a total of 147 biomolecules. This comprises ESR1, HSP90A, epidermal growth factor receptor, protein kinase B alpha (Akt1), SRC and PI3K. As indicated by GO and KEGG analyses, AVL’s inhibitory potential against MI may be due to its action on the PI3K-Akt signalling pathway. Furthermore, docking studies conducted to assess the interactions between AVL components and their targets (ESR1, HSP90A, Akt1, SRC and PI3K) confirmed that they have robust binding affinities. Cell experiments also showed that AVL treatment significantly reduced oxidative stress indexes (p< 0.05). Additionally, the expression levels of Akt1 and PI3K proteins were significantly higher in the AVL group compared to the MI group (p< 0.05).Conclusion AVL therapy exhibits the potential to alleviate the symptoms of MI by mitigating oxidative stress and by orchestrating the expression of proteins such as PI3K and Akt1.

The diagnosis of disease biomarkers is crucial for the identification, monitoring, and prognostic assessment of malignant disease. However, biological samples with autofluorescence, complex components, and heterogeneity pose major challenges to reliable biosensing. Here, we report the self-assembly of natural proteins and the triplet–triplet annihilation upconversion (TTA-UC) pair to form upconverted protein clusters (∼8.2 ± 1.1 nm), which were further assembled into photon upconversion supramolecular assemblies (PUSA). This PUSA exhibited unique features, including a small size (∼44.1 ± 4.1 nm), oxygen tolerance, superior biocompatibility, and easy storage via lyophilization, all of which are long sought after for photon upconversion materials. Further, we have revealed that the steric hindrance of the annihilator suppresses the stacking of the annihilator in PUSA, which is vital for maintaining the water dispersibility and enhancing the upconversion performance of PUSA. In conjunction with sarcosine oxidase, this near infrared (NIR)-excitable PUSA nanoprobe could perform background-free biosensing of urinary sarcosine, which is a common biomarker for prostatic carcinoma (PCa). More importantly, this nanoprobe not only allows for qualitative identification of urinary samples from PCa patients by the unaided eye under NIR-light-emitting diode (LED) illumination but also quantifies the concentration of urinary sarcosine. These remarkable findings have propelled photon upconversion materials to a new evolutionary stage and expedited the progress of upconversion biosensing in clinical diagnostics.

We propose a M-QAM chaotic optical communication system under amplitude and phase encryption. In the legitimate transmitter (Alice), we first design an electro-optic nonlinear delay chaotic source which can provide chaotic optical intensity and phase carriers under ∼15 GHz bandwidth. The M-QAM plaintext signal is masked by both chaotic optical intensity and phase carriers for ensuring the safety of intensity and phase information of M-QAM signal. In the legitimate receiver (Bob), the chaotic encrypted M-QAM signal after chromatic dispersion compensation by dispersion compensation fiber is decrypted by open-loop chaotic synchronization operation. Subsequently, the decrypted signal is processed by offline digital signal processing (DSP) module for impairments equalization. The proposed scheme is numerically demonstrated in an 84 Gbit/s 8QAM chaotic optical communication system over 1100 km standard single-mode fiber (SSMF), and a 112 Gbit/s 16QAM chaotic optical communication system over 700 km SSMF. We believe that the proposed scheme represents a promising strategy for enhancing the security of high-speed and long-haul optical communication system.

Oxygen-mediated triplet-triplet annihilation upconversion (TTA-UC) quenching limits the application of such organic upconversion materials. Here, we report that the photooxidation of organic amines is an effective and versatile strategy to suppress oxygen-mediated upconversion quenching in both organic solvents and aqueous solutions. The strategy is based on the dual role of organic amines in photooxidation, i.e., as singlet oxygen scavengers and electron donors. Under photoexcitation, the photosensitizer sensitizes oxygen to produce singlet oxygen for the oxidation of alkylamine, reducing the oxygen concentration. However, photoinduced electron transfer among photosensitizers, organic amines, and oxygen leads to the production of superoxide anions that suppress TTA-UC. To observe oxygen-tolerating TTA-UC, we find that alkyl secondary amines can balance the production of singlet oxygen and superoxide anions. We then utilize polyethyleneimine (PEI) to synthesize amphiphilic polymers to encapsulate TTA-UC pairs for the formation of water-dispersible, ultrasmall, and multicolor-emitting TTA-UC nanoparticles.

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.

Objectives To investigate the mechanism of Cistanche deserticolaMa (CDA) in the treatment of myocardial ischemia-reperfusion (I/R) injury by network pharmacology and cell experiments.Materials and Methods The main active components of CDA were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). I/R-related targets were identified from DisGeNET, OMIMD, and TTD; the I/R protein–protein interaction (PPI) network was constructed using the STRING input. The targets of CDA that inhibit I/R injury in Matescape and Microshengxin were subjected to Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Cell viability, levels of lactate dehydrogenase (LDH), superoxide dismutase (SOD), and malondialdehyde (MDA), and protein expression of phosphatidylinositol 3-kinase (PI3K) and serine/threonine kinase 1 (Akt1) were determined.Results A total of 236 targets were identified, with PI3K, Akt, epithelial growth factor receptor (EGFR), and another kinase being the major targets, and according to GO and KEGG analysis, CDA was most likely to inhibit I/R through the PI3K-Akt pathway. The optimal concentration of 10% medicated serum of CDA was determined to be the most effective concentration. The levels of LDH and MDA were significantly decreased in the CDA and BEZ23 groups, but the levels of SOD were significantly increased, thereby alleviating cell damage. In addition, the expression of PI3K, Akt, and p-AKT proteins was significantly reduced in the CDA group.Conclusion CDA alleviates I/R injury through antioxidation and inhibition of the PI3K/Akt signaling pathway.

Objectives To evaluate the Spectrum-Effect relationships between the High-Performance Liquid Chromatography (HPLC) Fingerprints of fractions with different polarities and the anti-inflammatory and antioxidant effects of A. trifoliatusand to identify its pharmacodynamic material basis.Materials and Methods Chemical Fingerprints of A. trifoliatusfractions with different polarities (all fractions and ethyl acetate, n-butanol and water fractions) were obtained by HPLC, and Principal Component Analysis (PCA) was performed. The foot swelling and prostaglandin E2 (PGE2) content in mice induced by carrageenan were used as anti-inflammatory markers, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods were used as antioxidant indicators. Moreover, the Spectrum-Effect relationship between common peaks and efficacy value was conducted by Grey Relational Analysis (GRA) and Partial Least Squares analysis (PLS).Results PCA identified three principal components, and all fractions of A. trifoliatushad good anti-inflammatory and antioxidant effects. Based on GRA and PLS analysis, peaks 1 and 2 (neochlorogenic acid), 4 and 5 (chlorogenic acid), 8, 9, 10, 11 (isochlorogenic acid B), and 12 were involved in anti-inflammatory efficacy, and peaks 1 and 2 (neochlorogenic acid), 3, 4, and 5 (chlorogenic acid), and 6, 7, 9, 10, and 13 (isochlorogenic acid A) were involved in antioxidant efficacy.Conclusion This study on the Spectrum-Effect relationship of different polar fractions of A. trifoliatusprovides data to support the discovery of the pharmacodynamic material basis of the anti-inflammatory and antioxidant properties of A. trifoliatus.

The spouted bed has been used in the coating process of high-density nuclear fuel particle. The particle fluidization behaviors in pseudo-2D and 3D spouted beds were simulated and validated. The effects of four independent variables (cone angle, particle density, inlet gas velocity, and particle loading) on particle fluidization behaviors in the 3D spouted bed were investigated systematically. The cone angle effect on fluidization mechanism was studied quantitatively first time. A new fluidization quality index was proposed based on the particle entrainment principle, and an extreme value was obtained when the cone angle was 60°, considered to be the optimum value for the 3D conical spouted bed. It was indicated the gas–solid contact efficiency can be kept up if the gas velocity was proportional to ρp0.65and Np0.78when the particle density or loading was increased. These results will be useful for geometry and operation parameters design of the 3D conical spouted bed and helpful for developing the fluidization mechanism of high-density particles.

The anti-Stokes shift represents the capacity of photon upconversion to convert low-energy photons to high-energy photons. Although triplet exciton-mediated photon upconversion presents outstanding performance in solar energy harvesting, photoredox catalysis, stereoscopic 3D printing, and disease therapeutics, the interfacial multistep triplet exciton transfer leads to exciton energy loss to suppress the anti-Stokes shift. Here, we report near infrared-II (NIR-II) excitable triplet exciton-mediated photon upconversion using a hybrid photosensitizer consisting of lead sulfide quantum dots (PbS QDs) and new surface ligands of thiophene-substituted diketopyrrolopyrrole (Th-DPP). Under 1064 nm excitation, this photon upconversion revealed a record-corrected upconversion efficiency of 0.37% (normalized to 100%), with the anti-Stokes shift (1.07 eV) approaching the theoretical limit (1.17 eV). The observation of this unexpected result is due to our discovery of the presence of a weak interaction between the sulfur atom on Th-DPP and Pb2+on the PbS QDs surface, facilitating electronic coupling between PbS QDs and Th-DPP, such that the realization of triplet exciton transfer efficiency is close to 100% even when the energy gap is as small as 0.04 eV. With this premise, this photon upconversion as a photocatalyst enables the production of standing organic gel via photopolymerization under 1064 nm illumination, displaying NIR-II photon-driven photoredox catalysis. This research not only establishes the foundation for enhancing the performance of NIR-II excitable photonic upconversion but also promotes its development in photonics and photoredox catalysis.

The microstructure evolution and strengthening behavior of the ultra-high strength Ti–15Mo–2.7Nb– 3Al–0.2Si titanium alloy were studied utilizing XRD, OM, SEM, and TEM analyses. The results show that the dislocation-strengthening and precipitation-strengthening effects could mostly affect the yield strength of this alloy. The highest yield strength of 1518 MPa was obtained under a combined process of cold rolling + recrystallization + cold rolling + duplex aging. This trend is mainly due to the high density of remaining dislocations, as well as dense and thin secondary αphases in microstructures. A theoretical composite-strengthening model was built, and the prediction error is within 16.6%. Furthermore, it is found that increasing the content of the secondary αphase can continuously strengthen the intragrain zone. This feature causes the intergranular fracture to appear and gradually dominate the fracture surface.

Near-infrared light excitable triplet–triplet annihilation upconversion (NIR TTA-UC) materials have attracted interest in a variety of emerging applications such as photoredox catalysis, optogenetics, and stereoscopic 3D printing. Currently, the practical application of NIR TTA-UC materials requires substantial improvement in photostability. Here, we found that the new annihilator of π-expanded diketopyrrolopyrrole (π-DPP) cannot activate oxygen to generate superoxide anion via photoinduced electron transfer, and its electron-deficient characteristics prevent the singlet oxygen-mediated [2 + 2] cycloaddition reaction; thus, π-DPP exhibited superior resistance to photobleaching. In conjunction with the NIR photosensitizer PdTNP, the upconversion efficiency of π-DPP is as high as 8.9%, which is eight times of the previously reported PdPc/Furan-DPP. Importantly, after polystyrene film encapsulation, less than 10% photobleaching was observed for this PdTNP/π-DPP-based NIR TTA-UC material after four hours of intensive NIR light exposure. These findings provide a type of annihilator with extraordinary photostability, facilitating the development of NIR TTA-UC materials for practical photonics.

Predators may adapt to global warming via behavioural plasticity. However, empirical evidence showing such adaptations in terrestrial ecosystems is scarce. Here we report behavioural shifts that alter the web mesh size of two dominant predatory spider species in response to experimental warming in an alpine meadow field. Experimental large open-top chambers increased the mean annual air temperature by 0.6 °C, resulting in a decrease in the web mesh size of the large spider (−43.6%), and an increase in the web mesh size of the small spider (+79.8%). Structural equation models indicated that the changes in mesh size and web area were primarily the result of warming-induced changes in prey size spectra, which in turn were impacted by warming-induced changes in soil moisture and plant community. These results indicate that predators can adjust their behavioural responses to warming-induced changes in the physical setting and prey community.

Despite the varied decision responses are revealed between adolescents with emotional stability and instability (ES and EI), the possible association underlying decision making and resting-state activity remains unknown. The study explored the potential relationship between the resting-state electroencephalogram network and decision responses when adolescents with different emotional stabilities participated in the ultimatum game (UG). A significant relationship between the resting-state activation of the two frequency bands (i.e., delta and alpha) and the UG acceptance rate. Furthermore, the ES adolescents mainly activated the frontal cortex in the delta band compared to the EI adolescents, whereas the EI adolescents exhibited stronger frontal-occipital linkages in the alpha band than the ES group. During decision making, the delta oscillation played a positive role in the cognitive process, while the alpha activity decreased. Finally, emotional stability could be predicted by a multivariable linear prediction model based on the resting-state metrics while adolescents made decisions in the UG task. The distinct resting-state electrophysiological evidence may deepen our knowledge of decision making in individuals with different emotional traits, which may further facilitate the prediction of individual decision behaviors.

Often in practical applications, such as coordinated motion of autonomous vehicles, multi-agent systems (MASs) utilize information obtained from sensors to accomplish complex tasks, asynchronously. In this work, we consider the problem of consensus where the agents obtain relative-state measurements, at their own sampling frequencies, and employ a distributed event-triggered protocol to dictate when to update control. For the designed event-triggered protocol, only local intermittent relative-state measurements are utilized, where they are obtained and evaluated only at pre-determined event-monitoring instants; these instants are governed by sampling periods whose bounds are explicitly pre-computed, individually, for each agent. Hence, the designed protocol is inherently asynchronous and avoid Zeno behaviour by construction. To cope with the continuous-time dynamics of the agents and discrete-time sensing and controller updates, the overall MAS is modelled using the hybrid system framework. A numerical example is provided to clearly demonstrate the effectiveness of the designed protocol.

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. Note to Practitioners—The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

This article investigates the relocalization of robots in indoor environments. To achieve this, indoor corners were classified into eight distinct categories, and a two-dimensional grid map was constructed using various sensors. Deep learning technology was employed to extract semantic information from corners, and the Bayesian method was used to build a semantic corner map incrementally. Additionally, the class attributes and positional relationships for each corner were explored, thereby facilitating the establishment of a dimension chain for semantic corners. Furthermore, a fast and efficient method was introduced for retrieving this dimension chain. During the relocalization process, the dimension chain of semantic corners was utilized for initial positioning, followed by the application of improved adaptive Monte Carlo localization (AMCL) algorithm for precise localization. Through comparative analysis using AMCL and several state-of-the-art methods, superior performance in both localization success rate and real-time implementation was demonstrated. Finally, extensive relocalization experiments were conducted to validate the effectiveness of the proposed method.

Recently, deep hashing-based cross-modal retrieval has attracted much attention of researchers, due to its advantages of fast retrieval efficiency and low storage overhead, etc. However, the existing deep hashing-based cross-modal retrieval methods typically 1) suffer from inadequately capturing the semantic relevance and coexistent information for cross-modal data, which may result in sub-optimal retrieval performance, 2) require a more comprehensive similarity measurement for cross-modal features to ensure high retrieval accuracy, 3) lack of scalability for lightweight deployment framework. To handle the issues mentioned above, we propose a CLIP-based knowledge distillation hashing (CKDH) for cross-modal retrieval, by referring the research trend of combining traditional methods and modern neural architecture to design lightweight networks based on large language models. Specifically, to effectively help capture the semantic relevance and coexistent information, CLIP is fine-tuned to extract visual features, while a graph attention network is used to enhance textual features extracted by bag-of-words model in the teacher model. Then, for better supervising the training of student model, a more comprehensive similarity measurement is introduced to represent distilled knowledge by jointly preserving the log-likelihood, intra and inter modality similarities. Finally, the student model extracts deep features by a lightweight networks, and generates the hash codes under the supervision of the similarity matrix produced by the teacher model. Experimental results on three widely used datasets demonstrate that CKDH can outperform some state-of-the-art methods, by delivering the best result consistently.

A chaotic optical communication scheme assisted by artificial intelligence (AI) method is proposed. Different from other traditional solutions, the chaos synchronization of the proposed scheme is accomplished via an AI-based optoelectronic oscillator (AI-OEO) model and a QPSK driving signal distorted by chromatic dispersion. Specifically, the distorted driving signal excites the chaotic AI-OEO model to generate the chaotic signal for encryption and decryption. One significant advantage is that the strict dependence of chaos synchronization on physical devices can be reduced by using deep learning technology to model the actual experimental chaotic system. The proposed scheme has been experimentally validated in a chaotic-encrypted 256 Gbit/s (32-GBaud) polarization-multiplexed 16QAM system over 1600 km standard single-mode fiber (SSMF). The results show that the BER performance after 1600 km transmission is lower than the 20% FEC threshold limit (BER = 2.4 × 10−2). Furthermore, we conducted a comprehensive security assessment of the system, comparing it with traditional chaotic encryption schemes and performing a detailed analysis of the key space and key sensitivity. The proposed scheme is compatible with the existing high-speed coherent optical fiber communication systems, and the realization of precise chaos synchronization only requires ensuring that the driving signal is decoded without errors at the receiver. We believe that it has the potential to become a candidate solution for bidirectional high-speed secure communication with low complexity and cost.

With the worldwide mobility electrification initiative to reduce air pollution and energy security, more and more for-hire vehicles are being replaced with electric ones. A key difference between gas for-hire vehicles and electric for-hire vehicles (EFHV) is their energy replenishment mechanisms, i.e., refueling or charging, which is reflected in two aspects: (i) much longer charging processes versus much shorter refueling processes and (ii) time-varying electricity prices versus time-invariant gasoline prices during a day. The complicated charging issues (e.g., long charging time and dynamic charging pricing) potentially reduce the daily operation time and profits of EFHVs, and also cause overcrowded charging stations during some off-peak charging pricing periods. Motivated by a set of findings obtained from a data-driven investigation and field studies, in this paper, we design a fairness-aware vehicle displacement system called FairMove to jointly optimize the overall profit efficiency and profit fairness of EFHV drivers by considering both the passenger travel demand and vehicle charging demand. We first formulate the EFHV displacement problem as a Markov decision problem, and then we present a fairness-aware multi-agent actor-critic approach to tackle this problem. More importantly, we implement and evaluate FairMove with real-world streaming data from the Chinese city Shenzhen, including GPS data and transaction data from over 20,100 EFHVs, coupled with the data of 123 charging stations, which constitute, to our knowledge, the largest EFHV network in the world. Extensive experimental results show that our fairness-aware FairMove effectively improves the profit efficiency and profit fairness of the EFHV fleet by 26.9% and 54.8%, respectively. It also improves the charging station utilization fairness by 38.4%.

Despite the significant progress made in coalbed methane (CBM) exploration and development in recent years, understanding of CBM enrichment mechanisms remains limited. This study aims to elucidate the CBM enrichment mechanism in the southern Qinshui Basin, China, by analyzing characteristics of global CBM basins and building a geological model of the study area. Field analyses are conducted to predict sweet spots of high CBM abundance and production potential. The findings reveal a high-yield model of CBM accumulation at relatively elevated structural positions within enriched areas. Compared to other global basins, low permeability poses the primary challenge for CBM development in China. Coal seam thickness shows minimal variation in southern Qinshui Basin, exerting negligible impact on CBM productivity. The shallow burial depth of coal seams in this region results in low stress, conferring high permeability conducive to high CBM yields. In situ stress conditions exert a primary control on the development of microfracture systems, which in turn govern reservoir permeability. This work provides new insights into CBM enrichment patterns in the southern Qinshui Basin. The proposed high-yield model enables better understanding of favorable conditions for CBM accumulation. Overall, this study represents a valuable contribution toward unlocking China’s CBM potential through improved geological characterization.

Accurate weld defect detection is a critical signal processing technique for intelligent magnetic flux leakage (MFL) detection system. However, the MFL signals between the defect and the weld are coupled with each other, and the features between the two are difficult to distinguish, resulting in unsatisfactory accuracy of weld defect detection. To address this issue, a novel weld defect detection method named contextual relation network (CRNet) is proposed to achieve high-accuracy weld defect detection. First, based on the strong coupling relation of MFL signals, a feature extraction network with sparse attention is proposed to extract high-quality features in a sparse manner. Second, a multiscale relation exploration module is proposed to adaptively aggregate multiscale features and fully mine the coupling relation between defect and weld, so that the contextual dependencies between them can be distinguished, and more valid semantic features can be acquired. Third, a multiview collaborative detection mechanism is proposed to realize complementary and coarse-to-fine decisions, so that more accurate detection results can be obtained. The CRNet can clarify the coupling relation and feature differences between defect and weld, guaranteeing independent feature extraction and high detection accuracy. Experiments show that CRNet can reach an average detection accuracy of 95.5%, which is more effective than advanced methods and has strong practicality.

This paper introduces a robust control strategy for grid-connected voltage source converters using Nonlinear Adaptive Control (NAC) enhanced by a perturbation observer (PO). This approach precisely regulates grid current and adapts to unknown dynamics, time-varying parameters, and disturbances impacting the system. By estimating and compensating for these perturbations, the strategy achieves adaptive feedback linearization, effectively managing the system’s inherent nonlinearity without requiring an exact model or extensive measurements. The NAC offers substantial improvements over traditional vector control and feedback linearization, particularly under conditions where grid voltage significantly deviates from normal levels. Simulation results confirm the superior performance of the proposed method.

Photovoltaic devices based on metal oxide and organic semiconductor heterostructure generally exhibit poor performance due to inefficient exciton dissociation at the metal oxide/organic interface. Here, we present an enhancement in the performance of photodiodes fabricated via zinc oxide (ZnO)/C60/copper phthalocyanine (CuPc) double heterojunction solution. This obtained photodiode demonstrates photodetection in the wavelength range of 400–850 nm with high photoresponsivity (R), specific detectivity ( ${D}^{\ast } $ ), and transparency. Remarkably high R of 9040, 64.0, and 2.0 A/W and ${D}^{\ast } $ of $1.32\times 10^{{15}}$ , $2.86\times 10^{{12}}$ , and $1.10\times 10^{{11}}$ Jones were achieved, for representative wavelengths of 405, 650, and 850 nm, respectively. The device exhibited a maximal average, transparency of 0.444 in the wavelength range of 400–1000 nm. Investigation into the effects of C60 layer thickness ( ${d}_{\text {C60}}$ ) revealed a strong dependence of both R and ${D}^{\ast } $ on ${d}_{\text {C60}}$ . For shorter wavelengths (405 and 450 nm), the maxima of ${R} \sim {d}_{\text {C60}}$ curves were observed around ${d}_{\text {C60}} =10$ nm, whereas for longer wavelengths (532, 650, and 850 nm), they appeared at around ${d}_{\text {C60}} =5$ nm. The maxima of ${D}^{\ast } \sim {d}_{\text {C60}}$ curves were consistently located at ${d}_{\text {C60}} =5$ nm for all measured wavelengths. Further investigations on the impact of CuPc layer thickness ( ${d}_{\text {CuPc}}$ ) indicated that the device achieved maximal R and ${D}^{\ast } $ at ${d}_{\text {CuPc}} =2$ nm for light of wavelength 405 and 450 nm, and at 40 nm for light of wavelength 650 and 780 nm. Specifically, for the light of wave-length 532 nm, R was maximal at ${d}_{\text {CuPc}} =40$ nm, while ${D}^{\ast } $ peaked at ${d}_{\text {CuPc}} =2$ nm. Moreover, optical simulations partially elucidated the physical origin behind these results. The obtained results provide an effective strategy to fabricate high-performance semitransparent photodiodes.

Low-inertia power grids could suffer from large frequency excursions under even small power disturbances. When a voltage source converter-based high voltage direct current (VSC-HVDC) system is used to integrate a low-inertia grid into a main grid, an ancillary frequency support service can be provided to the low-inertia grid by the VSC-HVDC system. This paper proposes a perturbation observer-based fast frequency support controller (POFFS) of VSC-HVDC systems to improve the frequency stability of low-inertia power grids. According to the feedback linearization and the high gain observer technique, the perturbation observer of the inverter station is designed to estimate the comprehensive impact of multiple perturbations, including the power disturbances in low-inertia grids, the uncertainty of grid inertia, and unknown nonlinear dynamics. The estimate of the perturbations are further compensated by the feedback control loop to achieve robust frequency regulation. Compared with the conventional frequency controller, the proposed POFFS can provide better frequency support to low-inertia power grids, without requiring an accurate system model and parameters. Two test systems are used to verify the effectiveness of the proposed POFFS.

Recent power curtailment-based photovoltaic (PV) flexible power point tracking (FPPT) algorithms mainly adopted intricate curve fitting or sophisticated curve-scanning mechanisms to ensure the grid supportive functionalities under partial shading conditions (PSCs), showing the limitations of mathematical solidity or system dynamics improvement. Accordingly, a novel fast-speed partial-shading-tolerant FPPT (PST-FPPT) algorithm is proposed in this article. Regarding the proposed scheme, a modified explicit PV model is developed to express the key operation points with the assistance of several representative current-voltage samples from the initialization process, which is beneficial to computational burden reduction and irradiance sensors removal. Additionally, to guarantee the tracking speed to system dynamics, a set point estimation-based direct voltage regulation strategy is proposed in this article, eliminating the redundant searching in approaching the predefined power command. Simulation and experimental evaluations under various PSCs and operational circumstances validated the effectiveness of the proposed control.

Ficus hirtaVahl is a healthy food with both medicinal and culinary properties and with anti-inflammatory and anti-aging effects. There is currently no standardized or universally accepted research strategy for evaluating the quality of Ficus hirtaVahl granules (FHGs). Therefore, the development of a comprehensive quality evaluation method is crucial for the quality control of FHGs. In this study, we used n-hexane : trichloromethane : ethyl acetate : glacial acetic acid = 20 : 4 : 7 : 1 as the optimal developing agent for TLC to separate and identify 15 batches of FHGs from different origins. Using HPLC, a fingerprint with 7 common peaks was established, and peaks 6 and 7 were attributed to psoralen and bergapten, respectively. The content of the identified components was determined. Further quality evaluation of FHGs was performed using chemical pattern recognition, and the results showed that hierarchical cluster analysis (HCA) could cluster 15 batches of FHGs into 2 categories. Principal component analysis (PCA) showed that 2 principal components can show the similarities and differences between different batches of FHGs. Orthogonal partial least squares discrimination (OPLS-DA) showed that components 5, 6 (psoralen) and 7 (bergapten) are landmark components that cause differences in FHG quality from different regions. By integrating the analytical modes of TLC, HPLC fingerprint and chemical pattern recognition, a scientific basis is provided for the comprehensive control and evaluation of herbal medicine quality.

Cross-shelf transport is important due to its role in the transport of nutrients, larvae, sediments, and pollutants. The role of coastal trapped waves (CTWs) and their contribution to the cross-shelf transport is presently unknown. The impact of wind-driven CTWs on the structure of the cross-shelf currents and transport is investigated in the East China Sea (ECS) starting from theory. The cross-shelf currents are divided into four terms: the geostrophic balance (GB) term, the second-order wave (SOW) term, the bottom friction (BF) term and Ekman (EK) term, as well as three modes: the Kelvin wave (KW) mode, the first shelf wave (SW1) mode and the second shelf wave (SW2) mode. Comparison among these decompositions shows that (1) for the four terms, the effect of the GB and EK terms is continual, while that of the BF term is confined to 60u2013240 km offshore, and the contribution of the SOW term can be ignored; (2) for the three modes, the KW and SW1 modes are dominant in cross-shelf transport. The results show that the total cross-shelf transport travels onshore under idealized wind stress on the order of 10-1, and it increases along the cross-shelf direction and peaks about -0.73 Sv at the continental shelf margin. With the increase of linear bottom friction coefficient, the cross-shelf transport declines with distance with the slope becoming more uniform.

In cross-domain classification, the conventional methods typically focus on enhancing sample correlation to address domain shift. However, these methods often overlook a critical factor influencing classifier decisions, i.e., the discrimination between the nearest instances from different categories. To address this issue, this article proposes a novel joint category compactness and disturbance reduction (CCDR) method for cross-domain classification. CCDR aims to enhance the discriminative capability of transformed features by addressing two fundamental aspects, which are the reinforcement of intraclass compactness and the reduction of irrelevant feature interference, respectively. To enhance intraclass compactness, CCDR introduces two metrics, a locality metric and a cluster metric within categories. These metrics can facilitate a shift in the data distribution perspective from domain-centric to category-centric, thereby augmenting the tightness of intraclass data clusters. To reduce the influence of irrelevant information, CCDR proposes a nearest-class discriminant strategy. This strategy widens the margin between the nearest instances from different categories, thereby effectively minimizing classifier misjudgments. Additionally, CCDR adopts Mahalanobis distance metric instead of Euclidean distance matric to capture feature correlations more effectively. The experimental results on five datasets demonstrate that CCDR outperforms some state-of-the-art methods in terms of cross-domain classification accuracy. Additionally, t-test results reveal a significant difference between the experimental results of CCDR and those of most comparison methods.

Voltage equalizers (VEs) are essential for partially shaded photovoltaic (PV) modules by equalizing the voltage of PV modules and preventing anti-paralleled diodes from bypassing the shaded PV modules, resulting in improved power yield under partial shading conditions (PSCs). Recently, different topologies for VEs have been discussed based on the distributed maximum power point tracking (DMPPT) principle at the module level. Considering that the power flow distribution, operation modes, and actual performance of these VEs show distinct differences, it becomes increasingly important for the performance quantification and comparative assessment of various topologies both theoretically and experimentally. Here, three typical differential-power-processing-based VEs are selected, including series-resonant-voltage-multiplier (SRVM), flyback-based PV-to-IP (Flyback-PV-IP), and flyback-based PV-to-Bus (Flyback-PV-Bus). Key performance indexes for VEs have been defined, including the processed power, power losses, and overall system efficiency. To quantify the performance of different topologies of VEs, an algorithm is developed in MATLAB with daily irradiation and temperature under various PSCs. Moreover, three experimental prototypes for the selected topologies have been built and main tests under different mismatching conditions have been conducted. With a systematic performance quantification and a fair comparison of typical VEs, this article will propose a systematic evaluation method for VE schemes. Meanwhile, the optimal VE topology with its control for typical PSC cases will be identified.

The Tian Shan is one of the world's largest intracontinental orogens and provides an excellent example for deciphering the intracontinental responses to the tectonics of plate boundaries. Despite its significance, the timing and driving mechanism of the Cenozoic mountain building of the Tian Shan in the context of the India–Eurasia collision remain controversial. In this study, Cenozoic stratigraphy of the Tiereke section along the western Kuqa Depression of the northern Tarim Basin on the south foreland of Eastern Tian Shan (east of 80°E) has been investigated. The results indicate that the Cenozoic deposition of the Tiereke region sequentially experienced a transgression from the Kumugeliemu Group to the Suweiyi Formation and a regression from the Suweiyi to the Kuqa Formations. Based on the contact relationships and conglomerate textures, three stages of high-energy alluvial deposition have been identified in the lower Kumugeliemu Group, upper Jidike and Kangcun–Kuqa Formations, respectively. These sedimentary events were interpreted to represent phases of Eastern Tian Shan mountain building at c. 54 Ma, c. 27 Ma and since c. 9.7 Ma according to previous magnetostratigraphic results, which were possibly related to the initial India–Eurasia collision, the collision between the India and Tarim lithospheric mantles, and the basinward propagation of deformation, respectively. Thematic collection: This article is part of the Mesozoic and Cenozoic tectonics, landscape and climate change collection available at: https://www.lyellcollection.org/topic/collections/mesozoic-and-cenozoic-tectonics-landscape-and-climate-change [ABSTRACT FROM AUTHOR]

The counts and phenotypes of circulating tumor cells (CTCs) in whole blood are useful for disease monitoring and prognostic assessment of cancer. However, phenotyping CTCs in the blood is difficult due to the presence of a large number of background blood cells, especially some blood cells with features similar to those of tumor cells. Herein, we presented a viscoelastic-sorting integrated deformability cytometer (VSDC) for high-throughput label-free sorting and high-precision mechanical phenotyping of tumor cells. A sorting chip for removing large background blood cells and a detection chip for detecting multiple cellular mechanical properties were integrated into our VSDC. Our VSDC has a sorting efficiency and a purity of over 95% and over 81% for tumor cells, respectively. Furthermore, multiple mechanical parameters were used to distinguish tumor cells from white blood cells using machine learning. An accuracy of over 97% for identifying tumor cells was successfully achieved with the highest identification accuracy of 99.4% for MCF-7 cells. It is envisioned that our VSDC will open up new avenues for high-throughput and label-free single-cell analysis in various biomedical applications.

Hybrid plasmonic nanostructures have gained enormous attention in a variety of optoelectronic devices due to their surface plasmon resonance properties. Self-assembled hybrid metal/quantum dot (QD) architectures offer a means of coupling the properties of plasmonics and QDs to photodetectors, thereby modifying their functionality. The arrangement and localization of hybrid nanostructures have an impact on exciton trapping and light harvesting. Here, we present a hybrid structure consisting of self-assembled gold nanospheres (Au NSs) embedded in a solid matrix of PbS QDs for mapping the interface structures and the motion of charge carriers. Grazing-incidence small-angle X-ray scattering is utilized to analyze the localization and spacing of the Au NSs within the hybrid structure. Furthermore, by correlating the morphology of the Au NSs in the hybrid structure with the corresponding differences observed in the performance of photodetectors, we are able to determine the impact of interface charge carrier dynamics in the coupling structure. From the perspective of architecture, our study provides insights into the performance improvement of optoelectronic devices.

A thermal simulation methodology derived from the proper orthogonal decomposition (POD) and the Galerkin projection (GP), hereafter referred to as PODTherm-GP, is evaluated in terms of its efficiency and accuracy in a multi-core CPU. The GP projects the heat transfer equation onto a mathematical space whose basis functions are generated from thermal data enabled by the POD learning algorithm. The thermal solution data are collected from FEniCS using the finite element method (FEM) accounting for appropriate parametric variations. The GP incorporates physical principles of heat transfer in the methodology to reach high accuracy and efficiency. The dynamic power map for the CPU in FEM thermal simulation is generated from gem5 and McPACT, together with the SPLASH-2 benchmarks as the simulation workload. It is shown that PODTherm-GP offers an accurate thermal prediction of the CPU with a resolution as fine as the FEM. It is also demonstrated that PODTherm-GP is capable of predicting the dynamic thermal profile of the chip with a good accuracy beyond the training conditions. Additionally, the approach offers a reduction in degrees of freedom by more than 5 orders of magnitude and a speedup of 4 orders, compared to the FEM.