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ABSTRACTCu(im)2-derived Cu@N–C(600) catalyst has been efficiently utilized for the preparation of N-tosyl(H)−2-substituted indoles through one-pot domino Sonogashira coupling-cyclization reactions between N-tosyl(acetyl)−2-iodo anilines and aryl acetylenes. The catalytic system was heterogeneous and showed excellent functional group compatibility and could be performed well, affording the corresponding coupling products in good to excellent yields.

Due to protection of extracellular polymeric substances, the therapeutic efficiency of conventional antimicrobial agents is often impeded by their poor infiltration and accumulation in biofilm. Herein, one type of surface charge adaptable nitric oxide (NO) nanogenerator was developed for biofilm permeation, retention and eradication. This nanogenerator (PDG@Au–NO/PBAM) is composed of a core-shell structure: thermo-sensitive NO donor conjugated AuNPs on cationic poly(dopamine-co-glucosamine) nanoparticle (PDG@Au–NO) served as core, and anionic phenylboronic acid-acryloylmorpholine (PBAM) copolymer was employed as a shell. The NO nanogenerator featured long circulation and good biocompatibility. Once the nanogenerator reached acidic biofilm, its surface charge would be switched to positive after shell dissociation and cationic core exposure, which was conducive for the nanogenerator to infiltrate and accumulate in the depth of biofilm. In addition, the nanogenerator could sustainably generate NO to disturb the integrity of biofilm at physiological temperature, then generate hyperthermia and explosive NO release upon NIR irradiation to efficiently eradicate drug-resistant bacteria biofilm. Such rational design offers a promising approach for developing nanosystems against biofilm-associated infections.

Abstract Electromagnetic wave simulation is of pivotal importance in the design and implementation of photonic nano-structures. In this study, we developed a lattice Boltzmann model with a single extended force term (LBM-SEF) to simulate the propagation of electromagnetic waves in dispersive media. By reconstructing the solution of the macroscopic Maxwell equations using the lattice Boltzmann equation, the final form only involves an equilibrium term and a non-equilibrium force term. The two terms are evaluated using the macroscopic electromagnetic variables and the dispersive effect, respectively. The LBM-SEF scheme is capable of directly tracking the evolution of macroscopic electromagnetic variables, leading to lower virtual memory requirement and facilitating the implementation of physical boundary conditions. The mathematical consistency of the LBM-SEF with the Maxwell equations was validated by using the Champman-Enskog expansion; while three practical models were used to benchmark the numerical accuracy, stability, and flexibility of the proposed method.

Antibiotics cannot be effectively removed by traditional wastewater treatment processes, and have become widespread pollutants in various environments. In this study, a Z-type heterojunction photo-catalyst Pg-C3N4 (PCN)/Nitrogen doped biochar (N-Biochar)/BiVO4 (NCBN) for the degradation of norfloxacin (NOR) was prepared by the hydrothermal method. The specific surface area of the NCBN (42.88 m2/g) was further improved compared to BiVO4 (4.528 m2/g). The photo-catalytic performance of the catalyst was investigated, and the N-Biochar acted as a charge transfer channel to promote carrier separation and form Z-type heterojunctions. Moreover, the NCBN exhibited excellent performance (92.5%) in removing NOR, which maintained 70% degradation after four cycles. The main active substance of the NCBN was •O2−, and the possible degradation pathways are provided. This work will provide a theoretical basis for the construction of heterojunction photo-catalysts.

AimTo explore the effects of brain–computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.BackgroundThe prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients’ ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients’ overall quality of life is the focus and difficulty of clinical rehabilitation work.MethodsPatients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain–computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).ResultsA total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.ConclusionBrain–computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.ImpactThis study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.Clinical review registrationhttps://www.chictr.org.cn/, identifier ChiCTR2300070382.

The quality of crude oil varies significantly according to its geographical origin. The efficient identification of the source region of crude oil is pivotal for petroleum trade and processing. However, current methods, such as mass spectrometry and fluorescence spectroscopy, suffer problems such as complex sample preparation and a long characterization time, which restrict their efficiency. In this work, by combining terahertz time-domain spectroscopy (THz-TDS) and a machine learning analysis of the spectra, an efficient workflow for the accurate and fast identification of crude oil was established. Based on THz-TDS of 83 crude oil samples obtained from six countries, a machine learning protocol involving the dimension reduction of spectra and classification was developed to identify the geological origins of crude oil, with an overall accuracy of 96.33%. This work demonstrates that THz spectra combined with a modern numerical scheme analysis can be readily employed to categorize crude oil products efficiently.

Circular dichroism (CD) of chiral structures has wide applications in ultra-sensitive biosensing, wavefront manipulation, and other fields. However, 3D chiral structures require a complex fabrication process, and 2D non-intrinsic chiral structures are difficult to achieve narrow-band perfect CD. Here we proposed a tilted cylindrical structure of a terahertz intrinsic chiral metasurface. Through energy band calculation, topological analysis, multipole decomposition and other research methods, we have realized the separation and merging of topological charges, the degeneracy of modes with opposite polarizations, the free selection of mode eigenstates, and the perfect CD with ultra-high quality (Q) factor. In the study of narrowband chirality, the Q-factor reaches 6822 and the CD reaches 0.99. In addition, both bound states in the continuum (BICs) of the structure exhibit CD at the same time, with Q-factors of 862 and 2673 and CD of –0.98 and 0.98 for the two BICs, respectively. Our work provides new solutions for the study of terahertz narrowband chirality and spin-selective asymmetric transport.

Astragalus membranaceus (A. membranaceus) is a homologous plant with high medicinal and edible value. Therefore, the extraction methods of Astragalus polysaccharide (APS) have attracted the attention of many research groups, but the yield of the active components is still not high. The aim of this study was to extract APS by a semi-bionic extraction method, optimize the extraction process, and evaluate the anti-aging activities of APS in vivo. The results showed that the APS yield was 18.23% when extracted by the semi-bionic extraction method. Anti-aging evaluation in rats showed that APS extracted by this method significantly decreased the malondialdehyde (MDA) content and increased superoxide dismutase (SOD) activity to cope with D-galactose-induced aging. Serum metabolomic analysis indicated that a total of 48 potential biomarkers showed significant differences, mainly involving 5 metabolic pathways. These altered metabolic pathways were mainly related to energy metabolism, amino acid metabolism, and lipid metabolism. These results indicated that the semi-bionic extraction method can effectively improve the yield of APS, and the extracted APS exhibited anti-aging activity in rats. Our study provided a novel and effective method to extract APS and indicated that APS can be used as functional food and natural medicine to delay aging and prevent its complications.

Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.

Abstract The two‐dimensional (2D) in‐plane (lateral) heterostructures have attracted increasing interest for potential applications in the atomically thin electronics and optoelectronics. While most studies focus on semiconductor‐semiconductor lateral heterostructures with highly similar lattice structures between the constituent components, the synthesis of metal‐semiconductor lateral heterostructures is much less explored and usually more challenging due to more distinct lattice structures or chemical properties. Herein, a vapor phase epitaxy growth method of high‐quality metal‐semiconductor lateral heterostructures between tetragonal CoSe and hexagonal WSe2 is reported. The 2D CoSe can selectively nucleate at the edge of pre‐grown WSe2 nanosheets to form CoSe‐WSe2 metal‐semiconductor lateral heterostructures. Optical microscopy (OM), scanning electron microscopy (SEM), and atomic force microscopy (AFM) studies reveal distinct contrast across the heterostructure interface. High‐resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED) studies further confirm the microstructure modulation across the heterostructure interface. The field‐effect transistors (FETs) of CoSe‐WSe2 lateral heterostructures show satisfactory Ohmic contacts and considerably better FET performance over those with deposited Cr/Au contacts, suggesting the in‐plane metal‐semiconductor junctions may function as improved contacts for the atomically thin electronics.

The present study combined the attention network test and event-related potential approaches to investigate the neurocognitive expression of resource reduction on attention function as a result of long-term high-altitude exposure in immigrants of Tibet. When compared with low-altitude residents, the study found that high-altitude exposure decreased executive-control behavioral performance but enhanced the alerting response. Correspondingly, changes in the target N2 and P3 amplitudes indicated a decrease in conflict inhibition underlying the executive-control network. Instead, the study noted that high-altitude exposure induced additional attentional resources to the alerting stage from the aspect of a change in the cue/target N1 and P1 amplitudes, which may be derived from a reduced self-referencing function. Taken together, the current findings provided experimental evidence for the tight relationship between reduced general cognitive inhibition to the hypersensitivity of the altering attention network to external stimuli mainly observed in immigrants to Tibet.

Alluvial fans are an important land resource with agricultural potential in Qinghai–Tibet Plateau. The spatiotemporal variation in land use/cover is an important indicator to understand the value of alluvial fans and protect and make scientific use of such fans. In this study, the spatiotemporal characteristics of land use/cover are determined by analysing the land use/cover changes of alluvial fans in the Lhasa River Basin (LRB) at different times, counties/districts, altitudes, and gradients. Results show that the area of cultivated land and the artificial land provided by alluvial fans for LRB has continuously increased. In 2000, 2010, and 2020, 17.72%, 21.84%, and 24.17% of cultivated land and 7.89%, 7.51%, and 25.24% of artificial land in LRB were provided by alluvial fans, respectively. At all altitudes and slopes, cultivated land and artificial land are increasing but the increasing part is basically due to the massive loss of grassland. The spatiotemporal changes in all land use/cover types of alluvial fans were dominated by human activities, although they were also influenced by natural factors to some degree.

Abstract Thickness‐dependent chemical and physical properties have gained tremendous interest since the emergence of two‐dimensional (2D) materials. Despite attractive prospects, the thickness‐controlled synthesis of ultrathin nanosheets remains an outstanding challenge. Here, a chemical vapor deposition (CVD) route is reported to controllably synthesize high‐quality PtSe2 nanosheets with tunable thickness and explore their thickness‐dependent electronic and magnetotransport properties. Raman spectroscopic studies demonstrate all Eg, A1g, A2u, and Eu modes are red shift in thicker nanosheets. Electrical measurements demonstrate the 1.7 nm thick nanosheet is a semiconductor with room temperature field‐effect mobility of 66 cm2 V−1 s−1 and on/off ratio of 106. The 2.3–3.8 nm thick nanosheets show slightly gated modulation with high field‐effect mobility up to 324 cm2 V−1 s−1 at room‐temperature. When the thickness is over 3.8 nm, the nanosheets show metallic behavior with conductivity and breakdown current density up to 6.8 × 105 S m–1 and 6.9 × 107 A cm−2, respectively. Interestingly, magnetoresistance (MR) studies reveal magnetic orders exist in this intrinsically non‐magnetic material system, as manifested by the thickness‐dependent Kondo effect, where both metal to insulator transition and negative MR appear upon cooling. Together, these studies suggest that PtSe2 is an intriguing system for both developing novel functional electronics and conducting fundamental 2D magnetism study.

Strain engineering is a promising method to manipulate properties of two-dimensional (2D) materials but slippage between material and substrate makes strain transfer inefficient. Here the authors overcome slipping effects by encapsulating a 2D material in a polymer substrate.

This paper mainly studies the problem of image-text matching in order to make image and text better match. Existing cross-media retrieval methods only make use of the information of image and part of text, that is, matching the whole image with the whole sentence, or matching some image areas with some words. In order to better reveal the potential connection between image and text semantics, this paper proposes a fusion of two levels of similarity across media images-text retrieval method, constructed the cross-media two-level network to explore the better matching between images and texts, it contains two subnets for dealing with global features and local characteristics. Specifically, in this method, the image is divided into the whole picture and some image area, the text is divided into the whole sentences and words, to study respectively, to explore the full potential alignment of images and text, and then use a two-level alignment framework is used to promote each other, fusion of two kinds of similarity can learn to complete representation of cross-media retrieval. Through the experimental evaluation on Flickr30K and MS-COCO datasets, the results show that the method in this paper can make the semantic matching of image and text more accurate, and is superior to the international popular cross-media retrieval method in various evaluation indexes.

A good document summary should summarize the core content of the text. Research on automatic text summarization attempts to solve this problem. The encoder-decoder model is widely used in text summarization research. Soft attention is used to obtain the required contextual semantic information during decoding. However, due to the lack of access to the key features, the generated summary deviates from the core content. In this paper, we proposed an encoder-decoder model based on a double attention pointer network (DAPT). In DAPT, the self-attention mechanism collects key information from the encoder, the soft attention and the pointer network generate more coherent core content, and the fusion of both generates accurate and coherent summaries. In addition, the improved coverage mechanism is used to address the repetition problem and improve the quality of the generated summaries. Simultaneously, scheduled sampling and reinforcement learning (RL) are combined to generate new training methods to optimize the model. Experiments on the CNN/Daily Mail dataset and the LCSTS dataset show that our model performs as well as many state-of-the-art models. The experimental analysis shows that our model achieves higher summarization performance and reduces the occurrence of repetition.

A novel lattice Boltzmann method (LBM) with a pseudo-equilibrium potential is proposed for electromagnetic wave propagation in one-dimensional (1D) plasma photonic crystals. The final form of the LBM incorporates the dispersive effect of plasma media with a pseudo-equilibrium potential in the equilibrium distribution functions. The consistency between the proposed lattice Boltzmann scheme and Maxwell’s equations was rigorously proven based on the Chapman–Enskog expansion technique. Based on the proposed LBM scheme, we investigated the effects of the thickness and relative dielectric constant of a defect layer on the EM wave propagation and defect modes of 1D plasma photonic crystals. We have illustrated that several defect modes can be tuned to appear within the photonic bandgaps. Both the frequency and number of the defect modes could be tuned by changing the relative dielectric constant and thickness of the defect modes. These strategies would assist in the design of narrowband filters.

Community detection in the network has become an invaluable tool to explore and reveal the internal organization of nodes. In particular, the target community detection focuses on discovering the “local” links within and connecting to the target community related to user's preference, which refers to a limited number of nodes in the whole network. A few works in the literature discuss the target community detection. In this paper, we propose a target community detection with user's preference and attribute subspace. Our method utilizes not only network structure but also node attributes within a certain subspace to quantify both internal consistency and external separability, which is able to capture a user preferred target community. First, the similarity between nodes is calculated via both attributes and structures, and the center node set of the target community can be obtained by extending the sample node given by the user with its neighbors. Second, an attribute subspace calculation method with entropy weights is established based on the center node set, and the attribute subspace of the target community can thus be deduced. Finally, the target community quality, which is the combination of internal connectivity and external separability, is defined, based on which the target community with a user's preference can be detected. The experimental results on both synthetic network and real-world network datasets demonstrated the efficiency and effectiveness of the proposed algorithm.

Graphs are used in numerous applications to model real-world systems and phenomena. The ever increasing size of graphs makes them difficult to query and analyze. In this paper, we propose HcRPC, a Highly compact Reachability Preserving Graph compression algorithm with Corrections, which is capable of preserving the reachability relations between the nodes in original graph. The highly compressed representation of a given graph consists of a compressed graph and a set of corrections. The original graph is compressed on the basis of equivalence class obtained via the reachability relations between nodes in the original graph. In the compressed graph, each node corresponds to a set of nodes from the original graph with similar ancestors and descendants, and each edge represents linkage between the original nodes in any two node sets. The corrections portion specifies the set of corrections, including equivalent class-node corrections and node-node corrections. MinHash technique is utilized to speed up checking whether equivalence classes are structure-similar and the pair of equivalence classes with high similarity are thus merged to acquire a highly compressed graph. Besides, we develop an algorithm for preserving compressed graph with a set of corrections in response to changes to the original graph. We evaluate our algorithms on real-life graph data sets and the results indicate that graph data sets can be highly compressed while preserving the reachability relations between nodes.

Object detection is an important research direction in the field of computer vision. In recent years, object detection has made great advances in public datasets, and the algorithm performance is also consistently similar to human capabilities. Therefore, all improvements in this paper are based on two-stage object detection. The algorithm model in the paper has three main innovations: first, considering that the resolution of the feature map and the size of the receptive field are not very good. Therefore, in this paper, a deep dilated convolution network is added to the backbone network to replace the conventional residual module, forming our own backbone network structure-Deep_Dilated Convolution Network (D_dNet). The formation of the D_dNet greatly reduces the number of parameters of the model during the training phase, and improves the quality of feature extraction. In addition, to improve accuracy, the network structure of the second part becomes “thick”, which increases the number of computations and reduces the detection speed. Therefore, by compressing the pretrained feature map and adding an 81-class fully connected layer to replace the original two layers, a light-weight network is formed. The light-weight network has changed the tradition of an overly thick fully connected layer, which not only makes the network lighter, but also significantly increases the corresponding detection speed. Finally, given the good hierarchy and transfer of the deep convolution neural network, this paper adds transfer learning to the training model to further optimize the model. In this paper, transfer learning is implemented to realize the transfer of weights. The training data samples are increased in disguise, there by increasing the stability of the training model. The evaluation of the entire model relies on the MSCOCO and PASCAL VOC datasets. In addition, the accuracy of the model is improved by 1.3%-2.2%.

Floral organ development is fundamental to sexual reproduction in angiosperms. Many key floral regulators (most of which are transcription factors) have been identified and shown to modulate floral meristem determinacy and floral organ identity, but not much is known about the regulation of floral organ growth, which is a critical process by which organs to achieve appropriate morphologies and fulfill their functions. Spatial and temporal control of anisotropic cell expansion following initial cell proliferation is important for organ growth. Cortical microtubules are well known to have important roles in plant cell polar growth/expansion and have been reported to guide the growth and shape of sepals and petals. In this study, we identified two homolog proteins, QWRF1 and QWRF2, which are essential for floral organ growth and plant fertility. We found severely deformed morphologies and symmetries of various floral organs as well as a significant reduction in the seed setting rate in the qwrf1qwrf2 double mutant, although few flower development defects were seen in qwrf1 or qwrf2 single mutants. QWRF1 and QWRF2 display similar expression patterns and are both localized to microtubules in vitro and in vivo. Furthermore, we found altered cortical microtubule organization and arrangements in qwrf1qwrf2 cells, consistent with abnormal cell expansion in different floral organs, which eventually led to poor fertility. Our results suggest that QWRF1 and QWRF2 are likely microtubule-associated proteins with functional redundancy in fertility and floral organ development, which probably exert their effects via regulation of cortical microtubules and anisotropic cell expansion.

This article provides insights into the localized stress in a defect zone of the rolling bearings when the rolling elements pass through the defect. A dynamic finite element model of a rolling bearing with an artificial round defect in its outer raceway is solved numerically using the explicit dynamic finite element software package, ABAQUS. The maximum Mises stress and maximum contact pressure in the defect zone are obtained in the simulation. The effects of radial load, rotation speed, and initial defect size on the stress level are investigated. The results show that higher stresses are generated during the rolling balls passing through the defect. The radial load and defect size have significant effects on the stress level of defect, while low-level rotation speed has neglected effects.