High performance is always the research objective in developing triboelectric nanogenerators(TENGs) for future versatile applications.In this study, flexible triboelectric membranes were prepared based on polyimide(PI) membranes doped with barium titanate(BTO) nanoparticles and multi-walled carbon nanotubes(MWCNTs).The piezoelectric BTO nanoparticles were incorporated to boost the electric outputs by the synergistic effect of piezoelectricity and triboelectricity and MWCNTs were incorporated to provide a microcapacitor structure for enhancing the performance of TENGs.When the mass fraction of the BTO nanoparticle was 10% and the mass fraction of the MWCNT was 0.1%, the corresponding TENG achieved optimum electric outputs(an open-circuit voltage of around 65 V, a short-circuit current of about 20.0 μA and a transferred charge of about 25.0 nC), much higher than those of the TENG with a single PI membrane.The TENG is potentially used to supply energy for commercial light-emitting diodes and as self-powered sensors to monitor human physical training conditions.This research provides a guideline for developing TENGs with high performance, which is crucial for their long-term use.

The flexible conductive nanofiber membrane is widely used in the field of wearable electronics.High tensile properties of electrospun nanofiber membranes are essential for their successful commercial application.With cellulose nanocrystal(CNC) as the reinforcement, the flexible conductive polyacrylonitrile(PAN)/CNC@carbon nanotube(CNT) nanofiber membrane is electrospun from the PAN solution containing suspended CNC and impregnated with the CNT solution.The structure and properties of nanofiber membranes are studied.The results show that with the increase of the PAN mass fraction, the viscosity of the electrospinning solution increases, leading to an increase in the nanofiber diameter.When the mass fraction of PAN is 12%, PAN/CNC nanofiber membranes at different CNC mass fractions are successfully prepared.The structure and properties of PAN/CNC nanofiber membranes are affected by the addition of CNC.As the CNC mass fraction increases, the nanofibers become thicker, the nanofiber diameter distribution widens, and the tensile strength first increases and then decreases.When the mass ratio of PAN to CNC is 4 ∶1, the tensile strength of the PAN/CNC nanofiber membrane is the highest, and it is higher than that of the PAN nanofiber membrane.After impregnating the PAN/CNC nanofiber membrane with CNTs, the tensile strength of the nanofiber membrane increases to 3.12 MPa and the surface resistivity is 64 Ω/cm2.The flexible conductive nanofiber membranes would be used in energy storage and sensing fields, and the study might provide a strong base for their future development.

The thermotropic liquid crystal polyester(TLCP) fiber is an increasingly important strategic high-performance fiber.In this paper, the TLCP was prepared by two-step melt polymerization using 4-hydroxybenzoic acid(HBA) and 6-hydroxy-2-naphthoic acid(HNA) as comonomers at a molar ratio of 7∶3.The structure of TLCP was confirmed by the Fourier transform infrared(FTIR) spectrometer and nuclear magnetic resonance(NMR) spectrometer.The thermal and rheological properties of TLCP before and after heat treatment were analyzed systematically by the differential scanning calorimeter(DSC), dynamic mechanical analyzer(DMA) and high-temperature rotational rheometer.The results revealed that the melting temperature, glass transition temperature and melt viscosity of the TLCP increased significantly after heat treatment.It indicates that the crystallization of the TLCP is perfect, and solid-phase condensation occurs during heat treatment, which increases its molecular mass.In conclusion, heat treatment at a temperature below but close to the melting temperature can effectively regulate the structure and properties of the TLCP, and the results of this study can provide a reference for the high strengthening of TLCP fibers.

Aiming to solve the problem of large discharge and severe pollution of reactive dyeing wastewater for wool fabrics, peroxodisulfate(SPS) was used for the degradation and recycling of dyeing wastewater containing reactive dye Lanasol Red CE.The process of degrading the reactive dye was determined by using the dye residual rate as the evaluation index.The feasibility of reactive dyeing of wool fabrics using recycled dyeing wastewater was confirmed by measuring the dye uptake, exhaustion and fixation rates, as well as color parameters and fastness of the dyed fabrics.The results showed that the appropriate conditions for degrading Lanasol Red CE were 0.2 g/L SPS, an initial p H value of 3 and 100 ℃ for 30 min.Under these conditions, the dye degradation rate was as high as 93.14%.When the recycled dyeing wastewater was used for dyeing of wool fabrics, the exhaustion rate of Lanasol Red CE exceeded 99%, and the fixation rate was higher than that achieved by the conventional dyeing process.Under the same dyeing conditions, the recycled-dyed fabrics appeared darker.When the number of cycles was fewer than five, the effect on color fastness was not obvious.Although the color fastness to rubbing and washing of the fabrics dyed in the 10th cycle decreased by half a grade and 1 grade, respectively, compared to that of the fabrics dyed with the conventional dyeing process, they still met the production requirements.

Negative air ions(NAIs) in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants, as well as inhibiting the growth of microorganisms, bacteria and viruses.This study investigates the common ionizers with different module types, and the mechanism of NAIs for enhancing indoor air quality, as well as the positive and negative impacts on human health.The association between NAI concentrations and human health outcomes is examined, and alternative measures to balance beneficial and unavailing effects are investigated.While NAIs demonstrate efficacy in removing particulate pollutants, alleviating depression, enhancing cognitive function and even stimulating sympathetic activity, it is pertinent to acknowledge the presence of contradictory findings concerning their effects on cardiac autonomic function and respiratory physiology.To address this complexity, it is imperative to consider alternative measures that strike a balance between the beneficial and unavailing effects of NAIs.These measures can encompass a general assessment of the characteristics of particulate pollutants, a strategic selection of ionizer technologies, and adherence to the recommended optimal concentration thresholds of NAIs.

A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However, existing recommendations do not fully exploit user style preferences.Typically, users prefer particular styles such as casual and athletic styles, and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences, this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling, termed as PSGNet.Firstly, a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly, a personal style prediction module extracts user style preferences by analyzing historical data.Then, to address the limitations of single-modal representations and enhance fashion compatibility, both fashion images and text data are leveraged to extract multi-modal features.Finally, PSGNet integrates these components through Bayesian personalized ranking(BPR) to unify the personal style and fashion compatibility, where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.

Multifocal metalenses are of great concern in optical communications, optical imaging and micro-optics systems, but their design is extremely challenging.In recent years, deep learning methods have provided novel solutions to the design of optical planar devices.Here, an approach is proposed to explore the use of generative adversarial networks(GANs) to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network, where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method, the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses, while also highlighting the potential of deep learning in optical device design.

Visual entailment(VE) is a prototypical task in multimodal visual reasoning, where current methods frequently utilize large language models(LLMs) as the knowledge base to assist in answering questions. These methods heavily rely on the textual modality, which inherently cannot capture the full extent of information contained within images. We propose a context-aware visual entailment(CAVE) model, which introduces a novel aggregation module designed to extract high-level semantic features from images. This module integrates lower-level semantic image features into high-level visual tokens, formatting them similarly to text tokens so that they can serve as inputs for LLMs. The CAVE model compensates for the loss of image information and integrates it more effectively with textual comprehension. Additionally, the CAVE model incorporates a new input format and training methodology, which is rooted in instruction tuning and in-context learning techniques. The objective of this research is to maximize the inherent logical reasoning capabilities of LLMs. Experimental results on the E-SNLI-VE dataset show that the proposed CAVE model exhibits outstanding performance.

Action recognition, a fundamental task in the field of video understanding, has been extensively researched and applied.In contrast to an image, a video introduces an extra temporal dimension.However, many existing action recognition networks either perform simple temporal fusion through averaging or rely on pre-trained models from image recognition, resulting in limited temporal information extraction capabilities.This work proposes a highly efficient temporal decoding module that can be seamlessly integrated into any action recognition backbone network to enhance the focus on temporal relationships between video frames.Firstly, the decoder initializes a set of learnable queries, termed video-level action category prediction queries.Then, they are combined with the video frame features extracted by the backbone network after self-attention learning to extract video context information.Finally, these prediction queries with rich temporal features are used for category prediction.Experimental results on HMDB51, MSRDailyAct3D, Diving48 and Breakfast datasets show that using TokShift-Transformer and VideoMAE as encoders results in a significant improvement in Top-1 accuracy compared to the original models(TokShift-Transformer and VideoMAE), after introducing the proposed temporal decoder.The introduction of the temporal decoder results in an average performance increase exceeding 11% for TokShift-Transformer and nearly 5% for VideoMAE across the four datasets.Furthermore, the work explores the combination of the decoder with various action recognition networks, including Timesformer, as encoders.This results in an average accuracy improvement of more than 3.5% on the HMDB51 dataset.The code is available at https://github.com/huangturbo/TempDecoder.

To minimize the calculation errors in the sound absorption coefficient resulting from inaccurate measurements of flow resistivity, a simple method for determining the sound absorption coefficient of sound-absorbing materials is proposed.Firstly, the sound absorption coefficients of a fibrous sound-absorbing material are measured at two different frequencies using the impedance tube method.Secondly, utilizing the empirical formulas for the wavenumber and acoustic impedance in the fibrous material, the flow resistivity and porosity of the sound-absorbing materials are calculated using the MATLAB cycle program.Thirdly, based on the values obtained through reverse calculations, the sound absorption coefficient, the real and the imaginary parts of the acoustic impedance of the sound-absorbing material at different frequencies are theoretically computed.Finally, the accuracy of these theoretical calculations is verified through experiments.The experimental results indicate that the calculated values are basically consistent with the measured values, demonstrating the feasibility and reliability of this method.

Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study, the refractive power calculation was performed based on the initial corneal information collected using the Placido disc.A corneal point cloud model was established in polar coordinates, and an interpolation algorithm was proposed to fill missing points of the local bicubic B-spline by searching control points in the self-defined interpolation matrix.The grid interpolation of the point cloud information and the smooth imaging of the final topographic map were achieved by Delaunay triangulation and Gaussian kernel function smoothing.Experiment results show that the proposed interpolation algorithm has higher accuracy than previous algorithms.The mean absolute error between the measured diopter of the original detection and the reconstructed is less than 0.300 D, indicating that this algorithm is feasible.

This paper focuses on the problem of leader-following consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers, accompanied by a series of Lyapunov functions devised throughout the iterative process, which solves the leader-following consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.