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Graph Neural Networks (GNNs) have attracted increasing attentions for multimodal Emotion Recognition in Conversation (ERC) due to their good performance in contextual understanding. However, most existing GNN-based methods suffer from two challenges: 1) How to explore and propagate appropriate information in a conversational graph. Typical GNNs in ERC neglect to mine the emotion commonality and discrepancy in the local neighborhood, leading to learn similar embbedings for connected nodes. However, the embeddings of these connected nodes are supposed to be distinguishable as they belong to different speakers with different emotions. 2) Most existing works apply simple concatenation or co-occurrence prior for modality combination, failing to fully capture the emotional information of multiple modalities in relationship modeling. In this paper, we propose a multimodal Decoupled Distillation Graph Neural Network (D2GNN) to address the above challenges. Specifically, D2GNN decouples the input features into emotion-aware and emotion-agnostic ones on the emotion category-level, aiming to capture emotion commonality and implicit emotion information, respectively. Moreover, we design a new message passing mechanism to separately propagate emotion-aware and -agnostic knowledge between nodes according to speaker dependency in two GNN-based modules, exploring the correlations of utterances and alleviating the similarities of embeddings. Furthermore, a multimodal distillation unit is performed to obtain the distinguishable embeddings by aggregating unimodal decoupled features. Experimental results on two ERC benchmarks demonstrate the superiority of the proposed model. Code is available at https://github.com/gityider/D2GNN.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles. The reliability of the pseudo-label plays a key role in domain adaptation Re-ID, while the different camera styles bring great challenges for pseudo-label prediction. To this end, a novel method is proposed, which bridges the gap of different cameras and extracts more discriminative features from an image. Specifically, an intra-to-intermechanism is introduced, in which samples from their own cameras are first grouped and then aligned at the class level across different cameras followed by our logical relation inference (LRI). Thanks to these strategies, the logical relationship between simple classes and hard classes is justified, preventing sample loss caused by discarding the hard samples. Furthermore, we also present a multiview information interaction (MvII) module that takes features of different images from the same pedestrian as patch tokens, obtaining the global consistency of a pedestrian that contributes to the discriminative feature extraction. Unlike the existing clustering-based methods, our method employs a two-stage framework that generates reliable pseudo-labels from the views of the intracamera and intercamera, respectively, to differentiate the camera styles, subsequently increasing its robustness. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods. The source code has been released at https://github.com/lhf12278/LRIMV.

Most current domain adaptation methods for fault diagnosis focus on single target domain. However, test data often comes from multiple target domains, as machines work under different operating conditions, subsequently generating a more complex and extensive distribution of target data. Unfortunately, single target domain adaptation methods are not adaptive for multiple target domains adaptation (MTDA), which results in transfer performance degradation. To this end, a novel disentanglement learning with adaptive centroid alignment is proposed for MTDA. Specifically for disentanglement learning, two encoders and two classifiers are constructed independently for fault-related and domain-related feature extractions and classifications. Followed by the dual-adversarial strategy, only fault-related but domain-irrelevant features are extracted. Furthermore, to achieve the category alignment, we also propose an adaptive centroid alignment strategy, so that the feature centroids of the same fault category in different domains are enforced to be close to each other. Extensive experiments demonstrate the superiority of our proposed method compared with other popular approaches.

With the wide application of portable wearable devices, a variety of electronic energy storage devices, including micro-supercapacitors (MSCs), have attracted wide attention. Laser-induced graphene (LIG) is widely used as electrode material for MSCs because of its large porosity and specific surface area. To further improve the performance of MSCs, it is an effective way to increase the specific surface area and the number of internal active sites of laser-induced graphene electrode materials. In this paper, N-doped polyimide/polyvinyl alcohol (PVA) as precursor was used to achieve in situ doping of nitrogen atoms in laser-induced graphene by laser irradiation. Through the addition of N atoms, nitrogen-doped laser-induced three-dimensional porous graphene (N-LIG) exhibits large specific surface area, many active sites, and good wettability all of which are favorable conditions for enhancing the capacitive properties of laser-induced graphene. After assembly with PVA/H2SO4as gel electrolyte, the high surface capacitance of the MSC device with N-LIG as electrode material is 16.57 mF cm−2at the scanning rate of 5 mV s−1, which is much higher than the 2.89 mF cm−2of the MSC device with LIG as electrode material. In addition, MSC devices with N-LIG as electrode materials have shown excellent cyclic stability and flexibility in practical tests, so they have a high application prospect in the field of flexible wearable microelectronics.

Over recent years, deep learning has significantly boosted scene text detection performance, and current segmentation-based scene text detectors can achieve compact bounding boxes for irregular texts. However, it is also challenging to tackle crowded or overlapping texts for these existing methods due to conglutination between adjacent text instances in segmentation results. To address these issues, we propose a more accurate scene text detector, Text Position-Aware Pixel Aggregation Network, termed TPPAN. Specifically, a Gaussian threshold representation is adaptively learned instead of a constant setting in Adaptively Text Kernel Thresholding (ATKT) module to obtain more accurate text kernels. Then Text Position-Aware Region Pixel Aggregation (TPAR-PA) module predicts the text regions in relative positions and generates more accurate text contours. Adequate experiments have demonstrated that the resulting detector has achieved state-of-the-art performance on multi-oriented and curved scene text benchmarks.

While Transformer has achieved remarkable performance in various high-level vision tasks, it is still challenging to exploit the full potential of Transformer in image restoration. The crux lies in the limited depth of applying Transformer in the typical encoder-decoder framework for image restoration, resulting from heavy self-attention computation load and inefficient communications across different depth (scales) of layers. In this paper, we present a deep and effective Transformer-based network for image restoration, termed as U2-Former, which is able to employ self-attention of Transformer as the core operation for feature learning to perform image restoration in a deep encoding and decoding space. Specifically, it leverages the nested U-shaped structure to facilitate the interactions across different layers with different scales of feature maps. Furthermore, we optimize the computational efficiency for the basic Transformer block by introducing a simple yet effective feature-filtering mechanism to compress the token representation. Apart from the typical supervision ways for image restoration, our U2-Former also performs multi-view contrastive learning, which constructs positive pairs in various aspects, to learn noise-sensitive but content-irrelevant features and further decouple the noise component from the background image. Extensive experiments on various image restoration tasks, including reflection removal, rain streak removal and dehazing respectively, demonstrate the effectiveness of the proposed U2-Former.

A bi-isotropic Huygens’ metasurface (BIHMS) is proposed for polarization-insensitive cross-polarization conversion and wavefront manipulation, which can transmit and twist a linearly polarized (LP) incident wave with arbitrary azimuth to its cross-polarized component, meanwhile, the phase shift of the transmitted wave can be manipulated within $0\pi $ – $2\pi $ . The BIHMS unit cell contains four patch-via-patch (PVP) structures that form a fourfold rotational symmetry; each PVP structure consists of two orthogonally placed patches connected by a via, only with two layers of metal. As a proof of concept, a polarization-insensitive cross-polarization converter (PICPC) and a polarization-insensitive phase gradient metasurface (PIPGM) are proposed based on the unit cell. The simulated and measured results show that the PICPC exhibits a −1 dB cross-polarization transmission bandwidth within 8.4–11.2 GHz (27.7%) and the corresponding polarization conversion ratio (PCR) is over 92% under transverse electric, 45° LP and transverse magnetic incident waves, and the PIPGM deflects the different LP incident waves to the same direction within 8.6–10.8 GHz (22.7%) with efficiency over 60%. The proposed broadband BIHMS exploits a new option for polarization-insensitive electromagnetic (EM) manipulation and can find application in various wireless systems.

Magnetic and electric dipoles have similar symmetry and are therefore expected to exhibit many common structures. However, despite frequent reports of various spin textures composed of magnetic dipoles, investigations on long-range ordered electric dipoles have been scarce, until recently. Here we discover spontaneous toroidal polar topology in an emerging ferroelectric liquid state with polarization helices, dubbed ‘helielectric nematic’. The interplay between liquid-crystal elasticity and polar interactions results in a continuous in-plane rotation of the polarization, producing a periodic toroidal assembly. In partial analogy with magnetism, the neighbouring toroidal domains are separated by circularly shaped domain walls. The local polarization switching enables unique shrinkage and expansion dynamics of the toroidal domains. The discovery provides opportunities for developing designable and switchable ferroelectric-liquid-matter opto-electronics.

Severe capacity/voltage fading still poses substantial obstacles in the commercial applications of Li-rich layered oxides, which stems from the aggregation of Li2MnO3-like domains and unstable surface structure. Here, we report highly stabilized Co-free Li1.2Ni0.2Mn0.6O2with uniformly dispersed Li2MnO3-like domains and a protective rock-salt structure shell by reducing the oxygen partial pressure during high-temperature calcination. Experimental characterizations and DFT calculations reveal that the uniformly dispersed and small-sized Li2MnO3-like domains suppress the peroxidation of lattice oxygen, enabling highly reversible oxygen redox and excellent structural stability. Moreover, the induced rock-salt structure shell significantly restrains lattice oxygen release, TM dissolution, and interfacial side reactions, thereby improving the interfacial stability and facilitating Li+diffusion. Consequently, the obtained Li1.2Ni0.2Mn0.6O2which was calcinated under an oxygen partial pressure of 0.1% (LNMO-0.1) delivers a high reversible capacity of 276.5 mAh g–1at 0.1 C with superior cycling performance (a capacity retention rate of 85.4% after 300 cycles with a small voltage fading rate of 0.76 mV cycle–1) and excellent thermal stability. This work links the synthesis conditions with the domain structure and electrochemical performance of Li-rich cathode materials, providing some insights for designing high-performance Li-rich cathodes.

Superoscillation is an extraordinary phenomenon capable of generating small-size focusing spots, which can be used for super-resolution focusing and imaging. Herein, we elaborate on a reconfigurable near-field focusing reflector based on the superoscillation principle, where the focal position and operation frequencies are modulated by the dynamic phase-compensation mechanism of an engineered metamirror. A varactor diode is embedded into each resonant meta-atom, enabling to achieve nearly 340° phase coverage. To validate the proposed reflector, the fabricated metamirror platform is experimentally measured in an anechoic chamber. The results show that the near-field focusing maintains subdiffraction property when the focal distances and frequencies are flexibly driven by the metamirror. Such a focusing reflector holds great integration potential to imaging devices for its tunability, high resolution, and nonnarrowband features.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Conventional multiview clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multiview clustering methods. Clustering on such incomplete multiview data is referred to as incomplete multiview clustering (IMC). In view of the promising application prospects, the research of IMC has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of IMC. Importantly, we provide some frameworks to unify the corresponding IMC methods and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the IMC field are offered for researchers. The related codes are released at https://github.com/DarrenZZhang/Survey_IMC.

Multichannel wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) has attracted a lot of interest for its ability of moving target detection and trajectory reconstruction. However, the parameter estimation of moving target is easily affected by clutter and noise, which deteriorates the performance of the following geolocation. In this article, a novel approach for trajectory reconstruction of moving target based on road line is proposed. First, after performing multichannel calibration, a novel Doppler centroid estimation and correction method is proposed. This method is not dependent on attitude information of platform nor does it require scene uniformity as a prior condition, which has high robustness in complex environments. Then, the weighted adaptive matched filtering (WAMF) and the modified Gaussian mixture probability hypothesis density (GMPHD) are performed for velocity estimation and tracking. Finally, the error items affecting geolocation accuracy were analyzed in detail. Also, a novel two-step geolocation method based on road lines is proposed to correct geolocation errors, which can be applied in complex road networks. The proposed method only needs 1-D parameter searching, which has high efficiency. The real-measured WasSAR data processing results demonstrated the effectiveness and practicability of the proposed algorithm.

The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes plays a vital role in reducing the entropy and boosting the joint rate-distortion performance. However, existing deep learning based entropy models generally assume the latent codes are statistically independent or depend on some side information or local context, which fails to take the global similarity within the context into account and thus hinders the accurate entropy estimation. To address this issue, we propose a special nonlocal operation for context modeling by employing the global similarity within the context. Specifically, due to the constraint of context, nonlocal operation is incalculable in context modeling. We exploit the relationship between the code maps produced by deep neural networks and introduce the proxy similarity functions as a workaround. Then, we combine the local and the global context via a nonlocal attention block and employ it in masked convolutional networks for entropy modeling. Taking the consideration that the width of the transforms is essential in training low distortion models, we finally produce a U-net block in the transforms to increase the width with manageable memory consumption and time complexity. Experiments on Kodak and Tecnick datasets demonstrate the priority of the proposed context-based nonlocal attention block in entropy modeling and the U-net block in low distortion situations. On the whole, our model performs favorably against the existing image compression standards and recent deep image compression models.

The present study compares a single‐band chorus wave against a banded chorus wave observed by Van Allen Probes at adjacent times, and demonstrates that the single‐band chorus wave is associated with an anisotropic electron population over a broad energy range, while the banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction around Landau resonant energies. We further compare banded chorus waves with different spectral gap widths, and show that a wider spectral gap is associated with electron isotropization extending to higher energies with respect to the equatorial Landau resonant energy. We suggest that early generated chorus waves isotropize electrons via Landau resonant acceleration, and the waves that propagate to higher latitudes isotropize electrons at higher energies. The isotropization extending to higher energies leads to a larger spectral gap of new chorus waves after electrons bounce back to the equator. Naturally occurring chorus waves in the Earth's magnetosphere typically consist of two frequency bands. The present study aims to explain what controls the bandwidth of the chorus frequency gap that separates chorus waves into two bands. We first compare a single‐band chorus wave against a banded chorus wave observed by a Van Allen Probe satellite at adjacent times. The banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction due to Landau resonance, while this phenomenon is not clearly seen in association with the single‐band chorus wave. We further compare banded chorus waves with different gap widths. Satellite observations indicate that a wider frequency gap is associated with electron isotropization extending to higher energies. We suggest that Landau resonant acceleration extending to high latitudes isotropizes electron distribution at high energies, leading to new chorus waves with a large frequency gap. In contrast, Landau resonance that stops at a relatively lower latitude (due to waves being damped) leads to new chorus waves with a smaller frequency gap. Freshly injected anisotropic electron population without a PSD plateau generates single band chorus wavesBanded chorus waves are more common because electrons usually have already undergone isotropization at Landau resonant energies along the drift pathLandau acceleration extending to higher energies occurring at higher latitudes leads to more pronounced electron isotropization and larger chorus spectral gaps Freshly injected anisotropic electron population without a PSD plateau generates single band chorus waves Banded chorus waves are more common because electrons usually have already undergone isotropization at Landau resonant energies along the drift path Landau acceleration extending to higher energies occurring at higher latitudes leads to more pronounced electron isotropization and larger chorus spectral gaps

Neurotransmitters play essential roles in regulating neural circuit dynamics both in the central nervous system as well as at the peripheral, including the gastrointestinal tract1–3. Their real-time monitoring will offer critical information for understanding neural function and diagnosing disease1–3. However, bioelectronic tools to monitor the dynamics of neurotransmitters in vivo, especially in the enteric nervous systems, are underdeveloped. This is mainly owing to the limited availability of biosensing tools that are capable of examining soft, complex and actively moving organs. Here we introduce a tissue-mimicking, stretchable, neurochemical biological interface termed NeuroString, which is prepared by laser patterning of a metal-complexed polyimide into an interconnected graphene/nanoparticle network embedded in an elastomer. NeuroString sensors allow chronic in vivo real-time, multichannel and multiplexed monoamine sensing in the brain of behaving mouse, as well as measuring serotonin dynamics in the gut without undesired stimulations and perturbing peristaltic movements. The described elastic and conformable biosensing interface has broad potential for studying the impact of neurotransmitters on gut microbes, brain–gut communication and may ultimately be extended to biomolecular sensing in other soft organs across the body.

Background Lung cancer is the most common cancer in the world and has become one of the malignancies with the highest incidence and mortality; more than half of patients die within one year of being diagnosed with lung cancer. In recent years, the concept of “patient-centered” service has gained popularity, and patients’ subjective feelings have gradually been used in clinical decision-making. Therefore, this study determined the application of visual patient report outcomes in the field of lung cancer, in order to provide reference for specific clinical practice.Methods Using the Web of Science core collection as the main analysis content, Citespace and VOSviewer were used to conduct this scientometric study.Results A total of 499 literatures that met the inclusion criteria were retrieved. The most prolific institution was The University of Texas MD Anderson Cancer Center, and the United States dominates this field.Conclusion The measurement of patient-reported outcomes is considered the gold standard for effectively evaluating patients' perceptions of clinical interventions or diseases. It is recommended that patient-reported outcomes be integrated into routine care for cancer patients in order to enhance communication between patients and healthcare providers.Patient or public contribution Not applicable. All data in this paper are obtained from the web of science database.

Elevated carbon dioxide (CO2) (eCO2) has been shown to affect the nitrous oxide (N2O) emission from terrestrial ecosystems by altering the interaction of plants, soils, and microorganisms. However, the impact of eCO2on the N2O emission from agricultural soils remains poorly understood. This meta-analysis summarizes the effect of eCO2on N2O emission in agricultural ecosystems and soil physiochemical and biological characteristics using 50 publications selected. The eCO2effect values, which equal to the percentage changes of N2O emission under eCO2, were calculated based on the natural logarithm of the response ratio to eCO2. We found that eCO2significantly increased N2O emission (by 44%), which varied depending on experimental conditions, agricultural practices, and soil properties. In addition, eCO2significantly increased soil water-filled pore space (by 6%), dissolved organic carbon content (by 11%), and nitrate nitrogen content (by 13%), but significantly reduced soil pH (by 1%). Moreover, eCO2significantly increased soil microbial biomass carbon (by 28%) and soil microbial biomass nitrogen (by 7%) contents. Additionally, eCO2significantly increased the abundances of ammonia-oxidizing bacteria (AOB) amoA(by 21%), nirK(by 15%), and nirS(by 15%), but did not affect the abundances of ammonia-oxidizing archaea (AOA) amoAand nosZ. Our findings indicate that eCO2substantially stimulates N2O emission in agroecosystems and highlight that optimization of nitrogen management and agronomic options might suppress this stimulation and aid in reducing greenhouse effect.

Fast-response artificial phototropic materials are a promising tool for solar energy utilisation, yet their preparation remains challenging. Herein, we report the so-called photothermal domino strategy for constructing fast-response artificial phototropic materials. In this strategy, photothermal generation, heat conduction and thermal actuation are sequentially optimised by a coordination effect. For the first time, lignin-based organic radicals boosted by this coordination effect are used to significantly enhance photothermal conversion. Interfacial coordination bonds between lignin and an elastomer matrix promote interfacial heat conduction. Light-stimulated thermal actuation is significantly improved by coordination-assisted mechanical training. The prepared biomimetic phototropic material exhibits excellent phototropic ability, with a 2.5 s light-tracking process, showing great application potential for efficient solar energy utilisation. This strategy shows great significance for fabricating high-performance intelligent phototropic materials using widely available, green raw materials.

Here we highlight 35 researchers approximately under the age of 35. Age, of course, is just a number—our target was emerging early-career academics. Contributors were recruited in a self-propagating “pay-it-forward” manner, with each invitee being suggested by a peer who had already contributed. The final collection is an inspiring look at the challenges the current generation of materials researchers are tackling.

Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches.

The emerging ferroelectric nematic (NF) liquid crystal is a novel 3D-ordered liquid exhibiting macroscopic electric polarization. The combination of the ultrahigh dielectric constant, strong nonlinear optical signal, and high sensitivity to the electric field makes NFmaterials promising for the development of advanced liquid crystal electroopic devices. Previously, all studies focused on the rod-shaped small molecules with limited length (l) range and dipole moment (μ) values. Here, through the precision synthesis, we extend the aromatic rod-shaped mesogen to oligomer/polymer (repeat unit up to 12 with monodisperse molecular-weight dispersion) and increase the μ value over 30 Debye (D). The NFphase has a widespread existence far beyond our expectation and could be observed in all the oligomer/polymer length range. Notably, the NFphase experiences a nontrivial evolution pathway with the traditional apolar nematic phase completely suppressed, i.e., the NFphase nucleates directly from the isotropic liquid phase. The discovery of thte ferroelectric packing of oligomer/polymer rods not only offers the concept of extending the NFstate to oligomers/polymers but also provides some previously overlooked insights in oxybenzoate-based liquid crystal polymer materials.

Recently, we have demonstrated a general molecular design for emerging ferroelectric nematic liquid crystals by introducing large dipole moment and local bulkiness into rod-shaped mesogenic molecules. The giant dielectricity and strong nonlinear optic properties in the ferroelectric nematic liquid crystal materials could bring the vast technological potential for flexible supercapacitors, electro-optic devices, and nonlinear optical devices. Here, we extend the polar liquid crystal material from small molecules to polymer systems by elaborately manipulating the interplay between the side-chain joint position and dipole–dipole interaction. For this purpose, we developed three series of side-chain liquid crystalline polymers. We clarify that the polymers with side-jointed mesogens exhibit a polar nematic liquid crystalline phase with strong polarity confirmed by second harmonic generation, while the polymers with end-jointed mesogens self-assemble into a smectic A phase with no polarity. The magnitude of dipole moment was also critical for producing the macroscopic polarity in these side-chain liquid crystalline polymers.

Nickel-rich layered oxides (NCM) are a promising contender material for the cathode electrode of high-energy lithium-ion batteries (LIBs) due to their large reversible capacity and high operating voltage. However, the poor surface/interfacial stability and the dissolution of transition metal ions hinder the commercial application of NCM. To create an artificial cathode electrolyte interphase (CEI) with LiF-rich inorganic phase on the NCM surface, a practical and efficient way of quenching the NCM powder from high temperature in 1,1,2,2-Tetrafluoroethyl 2,2,2-trifluoroethyl ether (HFE) was devised. With this artificial CEI film, the side reactions between NCM and electrolytes are inhibited, and the dissolution of TM ions is retarded. The quenched-NCM achieved fantastic cycling performance and suppressed voltage decay. Our research offers an efficient and worthy approach for improving the surface/interfacial stabilization of nickel-rich cathode materials for high-energy-density LIBs.

The present study uncovers the fine structures of magnetosonic waves by investigating the EFW waveforms measured by Van Allen Probes. We show that each harmonic of the magnetosonic wave may consist of a series of elementary rising‐tone emissions, implying a nonlinear mechanism for the wave generation. By investigating an elementary rising‐tone magnetosonic wave that spans a wide frequency range, we show that the frequency sweep rate is likely proportional to the wave frequency. We studied compound rising‐tone magnetosonic waves, and found that they typically consist of multiple harmonics in the source region, and may gradually become continuous in frequency as they propagate away from source. Both elementary and compound rising‐tone magnetosonic waves last for ∼1 min which is close to the bounce period of the ring proton distribution, but their relation is not fully understood. Naturally occurring magnetosonic waves are a kind of electromagnetic wave in the Earth's magnetized space. Their frequency spectra typically exhibit a harmonic structure. Sometimes they exhibit a rising‐tone feature, that is, the wave starts at a low pitch and gradually increases its frequency to a high pitch. Using the high‐resolution data measured by Van Allen Probe satellites, the present study uncovers that each harmonic of a magnetosonic wave may have a fine structure: they consist of a series of elementary rising‐tone emissions. The traditionally known rising‐tone magnetosonic waves consist of multiple harmonics, and we name them “compound rising‐tone magnetosonic waves”. The compound rising‐tone waves may gradually become continuous in spectrum as they propagate away from source. These findings give new insights to the generation of magnetosonic waves and their impact on space environment. Each harmonic of a magnetosonic wave may consist of a series of elementary rising‐tone emissionsThe frequency sweep rate of elementary magnetosonic waves is likely proportional to the frequencyCompound rising‐tone magnetosonic waves consist of harmonics at the source, and gradually develop a continuous spectrum during propagation Each harmonic of a magnetosonic wave may consist of a series of elementary rising‐tone emissions The frequency sweep rate of elementary magnetosonic waves is likely proportional to the frequency Compound rising‐tone magnetosonic waves consist of harmonics at the source, and gradually develop a continuous spectrum during propagation

Due to the powerful capability of the data representation, deep learning has achieved a remarkable performance in supervised hash function learning. However, most of the existing hashing methods focus on point-to-point matching that is too strict and unnecessary. In this article, we propose a novel deep supervised hashing method by relaxing the matching between each pair of instances to a point-to-angle way. Specifically, an inner product is introduced to asymmetrically measure the similarity and dissimilarity between the real-valued output and the binary code. Different from existing methods that strictly enforce each element in the real-valued output to be either +1 or −1, we only encourage the output to be close to its corresponding semantic-related binary code under the cross-angle. This asymmetric product not only projects both the real-valued output and the binary code into the same Hamming space but also relaxes the output with wider choices. To further exploit the semantic affinity, we propose a novel Hamming-distance-based triplet loss, efficiently making a ranking for the positive and negative pairs. An algorithm is then designed to alternatively achieve optimal deep features and binary codes. Experiments on four real-world data sets demonstrate the effectiveness and superiority of our approach to the state of the art.

RNA is emerging as a valuable target for the development of novel therapeutic agents. The rational design of RNA-targeting small molecules, however, has been hampered by the relative lack of methods for the analysis of small molecule–RNA interactions. Here, we present our efforts to develop such a platform using photoaffinity labeling. This technique, termed Photoaffinity Evaluation of RNA Ligation-Sequencing (PEARL-seq), enables the rapid identification of small molecule binding locations within their RNA targets and can provide information on ligand selectivity across multiple different RNAs. These data, when supplemented with small molecule SAR data and RNA probing data enable the construction of a computational model of the RNA–ligand structure, thereby enabling the rational design of novel RNA-targeted ligands.

Group convolution is widely used in many mobile networks to remove the filter’s redundancy from the channel extent. In order to further reduce the redundancy of group convolution, this article proposes a novel repeated group convolutional (RGC) kernel, which has $M$ primary groups, and each primary group includes $N$ tiny groups. In every primary group, the same convolutional kernel is repeated in all the tiny groups. The RGC filter is the first kernel to remove the redundancy from group extent. Based on RGC, a sparse RGC (SRGC) kernel is also introduced in this article, and its corresponding network is called SRGC neural networks (SRGC-Net). The SRGC kernel is the summation of RGC kernel and pointwise group convolutional (PGC) kernel. The number of PGC’s groups is $M$ . Accordingly, in each primary group, besides the center locations in all channels, the values of parameters located in other $N-1$ tiny groups are all zero. Therefore, SRGC can significantly reduce the parameters. Moreover, it can also effectively retrieve spatial and channel-difference features by utilizing RGC and PGC to preserve the richness of produced features. Comparative experiments were performed on the benchmark classification data sets. Compared with the traditional popular networks, SRGC-Nets can perform better with timely reducing the model size and computational complexity. Furthermore, it can also achieve better performances than other latest state-of-the-art mobile networks on most of the databases and effectively decrease the test and training runtime.

The scarcity and weak durability of metal, especially precious metal catalysts are big obstacles for their large-scale application in many reactions. The state-of-the-art of the catalytic science prefers such type of catalysts, which can replace metal-based catalysts to alleviate energy and environmental crises and exhibit catalytic performance comparable to or even exceeding these metal catalysts. Herein, we report that N-doped porous carbon (NKC) derived from cheap and abundant radish can be employed as versatile and efficient bifunctional catalysts in both the catalytic reduction of 4-nitrophenol (NRR) and oxidation of styrene (SOR). The series of NKC catalysts were prepared with a simple and facile one-pot strategy by coupling the N-doping, carbonization and KOH activation processes. These catalysts show hierarchical porosity, with the specific surface area, total pore volume and N-doping content ranging from 918.9–3062.7 m2g−1, 1.01–2.04 cm3g−1and 1.29–15.3 at%, respectively. Interestingly, our finding suggests that the catalytic performance is not directly related to these parameters but correlates positively with the content of graphitic N dopants, which is the dominant contributor for impelling both the NRR and SOR. Another intriguing finding is that for both reactions, the optimal catalyst was found to be the NKC-3-800 which possesses the highest graphitic N content of 3.13 at%. In addition, to gain insight into the catalytic behavior, analyses of kinetics and thermodynamics were performed, and the catalytic mechanisms were postulated. This work paves the way for the construction of biomass-derived N-doped carbon catalysts for bi- or even multi-functional applications in various organic reactions.

All three samples of PSi m-n have good surface activity as the equilibrium surface tension reached 23.5 mN/m at the CAC. While the solution of PSi m-n cannot spread on the parafilm even though good surface activity. Dynamic surface tension results indicate that the wettability of Psi m-n is not just affected by equilibrium surface tension but also by diffusion rate. Contact angles of there PSi m-n at same concentration (4 g L−1) also have big difference. Balance between hydrophobicity and hydrophilicity, as well as the adsorption rate at surface (interface) are both important factor for a good wetting agent in pratical application. A series of meglumine-based polysiloxane surfactants, (PSi m-n) with different numbers of meglumine branches were prepared. The formed surfactants were characterized by FT-IR and NMR techniques. Physiochemical properties, such as surface activity, aggregation behavior, and wettability, were closely examined and used to differentiate the various samples. The findings demonstrate that PSi m-n surfactants possess high surface activity and can wet-out a parafilm surface at all but extremely low concentrations. Furthermore, PSi 6-4, the surfactant with the highest meglumine concentration, can form larger complex aggregates in aqueous solutions. [ABSTRACT FROM AUTHOR]

In many repeat diseases, such as Huntington’s disease (HD), ongoing repeat expansions in affected tissues contribute to disease onset, progression and severity. Inducing contractions of expanded repeats by exogenous agents is not yet possible. Traditional approaches would target proteins driving repeat mutations. Here we report a compound, naphthyridine-azaquinolone (NA), that specifically binds slipped-CAG DNA intermediates of expansion mutations, a previously unsuspected target. NA efficiently induces repeat contractions in HD patient cells as well as en masse contractions in medium spiny neurons of HD mouse striatum. Contractions are specific for the expanded allele, independently of DNA replication, require transcription across the coding CTG strand and arise by blocking repair of CAG slip-outs. NA-induced contractions depend on active expansions driven by MutSβ. NA injections in HD mouse striatum reduce mutant HTT protein aggregates, a biomarker of HD pathogenesis and severity. Repeat-structure-specific DNA ligands are a novel avenue to contract expanded repeats.

Transient self-destroyed micromotors that autonomously disappear in biological media at controlled rates upon completing their task, without leaving a toxic residue, are presented. The propulsion and degradation characteristics of the self-destroyed Mg/ZnO, Mg/Si, and Zn/Fe Janus micromotors and single-component Zn micromotors are described. The degradation of the Janus micromotors relies on the different corrosion rates of their core–shell components. Inductively coupled plasma optical emission spectrometry measurements are used to probe the time-dependent degradation of the different constituents of the micromotors. The toxicity of the transient micromotors is discussed toward their potential use in biomedical applications. This concept of transient micromotors offers considerable potential for diverse practical applications in the near future.

Studies reported an association between rheumatoid arthritis and Diabetes mellitus. We sought to assess the causal association between rheumatoid arthritis and Diabetes mellitus risk using a two-way two-sample Mendelian randomization (MR) analysis.

Precise and reproducible manipulation of synthetic and biological microscale objects in complex environments is essential for many practical biochip and microfluidic applications. Here, we present an attractive acoustic topographical manipulation (ATM) method to achieve efficient and reproducible manipulation of diverse microscale objects. This new guidance method relies on the acoustically induced localized microstreaming forces generated around microstructures, which are capable of trapping nearby microobjects and manipulating them along a determined trajectory based on local topographic features. This unique phenomenon is investigated by numerical simulations examining the local microstreaming in the presence of microscale boundaries under the standing acoustic wave. This method can be used to manipulate a single microobject around a complex structure as well as collectively manipulate multiple objects moving synchronously along complicated shapes. Furthermore, the ATM can serve for automated maze solving by autonomously manipulating microparticles with diverse geometries and densities, including live cells, through complex maze-like topographical features without external feedback, particle modification, or adjustment of operational parameters.

The low ionic conductivities of aprotic electrolytes hinder the development of extreme fast charging technologies and applications at low temperatures for lithium-ion batteries (LIBs). Herein, we present an electrolyte with LiFSI in acetone (DMK). In DMK electrolytes, the solvation number is three, and solvent-separated ion pairs (SSIPs) are the dominant structure, which is largely different from other linear aprotic electrolytes where salts primarily exist as contact ion pairs (CIPs). With incompact solvation structures due to the weak solvation ability of DMK with Li+, the ionic conductivity reaches 45 mS/cm at room temperature. The percentage of SSIPs increases as temperatures decrease in DMK electrolytes, which is totally different from the carbonate-based electrolytes but greatly beneficial to low-temperature ionic conductivity. With the appropriate addition of VC and FEC, DMK-based electrolytes still exhibit a superhigh ionic conductivity. Even at -40 °C, the ionic conductivity is greater than 10 mS/cm. With DMK-based electrolytes, LIBs with thick LiFePO4electrodes can be cycled at high rates and at low temperatures.