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Abstract Chiral recognition is crucial for applications in chiral purity assessment and biomedical fields. However, achieving chiral recognition through visible room temperature phosphorescence remains challenging. Here, two chiral molecules, designated as host and guest are synthesized, which possess similar structural configurations. A viable strategy involving a chiral configuration‐dependent energy transfer process to enable selective phosphorescence expression is proposed, thereby enabling chiral recognition in a host‐guest doping system. The chiral and structural similarity between host and guest facilitates efficient Dexter energy transfer due to the reduced spatial distance between the molecules. This mechanism significantly enhances the intensity of red phosphorescence from the guest molecule, characterized by an emission peak at 612 nm and a prolonged lifetime of 32.7 ms. This work elucidates the mechanism of chiral‐dependent energy transfer, demonstrating its potential for selectively expressing phosphorescence in chiral recognition.

Abstract Creating circularly polarized organic afterglow system with elevated triplet energy levels, suppressed non-radiative transitions, and effective chirality, which are three critical prerequisites for achieving blue circularly polarized afterglow, has posed a formidable challenge. Herein, a straightforward approach is unveiled to attain blue circularly polarized afterglow materials by covalently self-confining isolated chiral chromophore within polymer matrix. The formation of robust hydrogen bonds within the polymer matrix confers a distinctly isolated and stabilized molecular state of chiral chromophores, endowing a blue emission band at 414 nm, lifetime of 3.0 s, and luminescent dissymmetry factor of ~ 10−2. Utilizing the synergistic afterglow and chirality energy transfer, full-color circularly polarized afterglow systems are endowed by doping colorful fluorescent molecules into designed blue polymers, empowering versatile applications. This work paves the way for the streamlined design of blue circularly polarized afterglow materials, expanding the horizons of circularly polarized afterglow materials into various domains.

In the task of emotion inference, a common issue is the lack of common sense knowledge, particularly in the context of dialogue, where traditional research has failed to effectively extract structural features, resulting in lower accuracy in emotion inference. To address this, this paper proposes a dialogue emotion inference model based on Common Sense Enhancement and Graph Model (CEICG). This model integrates external common sense with graph model techniques by dynamically constructing nodes and defining diverse edge relations to simulate the evolution of dialogue, thereby effectively capturing the structural and semantic features of the conversation. The model employs two methods to incorporate external common sense into the graph model, overcoming the limitations of previous models in understanding complex dialogue structures and the absence of external knowledge. This strategy of integrating external common sense significantly enhances the model's emotion inference capabilities, improving the understanding of emotions in dialogue. Experimental results demonstrate that the CEICG model outperforms six existing baseline models in emotion inference tasks across three datasets.

Abstract Deep learning has achieved tremendous success in various applications owing to its robust feature representations of complex high-dimensional nonlinear data. Financial time-series prediction is no exception. Hence, the volatility trend prediction in financial time series (FTS) has been an active topic for several decades. Inspired by generative adversarial networks (GAN), which have been studied extensively in image processing and have achieved excellent results, we present the ordinal regression GAN for financial volatility trends (ORGAN-FVT) method for the end-to-end multi-classification task of FTS. An improved generative model based on convolutional long short-term memory (ConvLSTM) and multilayer perceptron (MLP) is proposed to capture temporal features effectively and mine the data distribution of volatility trends (short, neutral, and long) from given FTS data. Meanwhile, ordinal regression is leveraged for the discriminator to improve the multi-classification performance, making the model more practical. Finally, we empirically compare ORGAN-FVT with several state-of-the-art approaches on three real-world stock datasets: MICROSOFT(MSFT), Tesla(TSLA), and The People’s Insurance Company of China(PAICC). ORGAN-FVT demonstrated significantly better AUC and F1 scores, at most 20.81% higher than its competitors.

The development of organic afterglow materials with sufficient color purity and high quantum efficiency for hyperafterglow is a great challenging. Here, the authors demonstrate an efficient strategy to achieve hyperafterglow emission through sensitizing and stabilizing isolated fluorescence chromophores by integrating multiresonance fluorescence chromophores into afterglow host in a singlecomponent copolymer.

Image fusion is the generation of an informative image that contains complementary information from the original sensor images, such as texture details and attentional targets. Existing methods have designed a variety of feature extraction algorithms and fusion strategies to achieve image fusion. However, these methods ignore the extraction of common features in the original multi-source images. The point of view proposed in this paper is that image fusion is to retain, as much as possible, the useful shared features and complementary differential features of the original multi-source images. Shared and differential learning methods for infrared and visible light image fusion are proposed. An encoder with shared weights is used to extract shared common features contained in infrared and visible light images, and the other two encoder blocks are used to extract differential features of infrared images and visible light images, respectively. Effective learning of shared and differential features is achieved through weight sharing and loss functions. Then, the fusion of shared features and differential features is achieved via a weighted fusion strategy based on an entropy-weighted attention mechanism. The experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with the-state-of-the-art methods, the significant advantage of the proposed method is that it retains the structural information of the original image and has better fusion accuracy and visual perception effect.

Video-based anomalous human behavior detection is widely studied in many fields such as security, medical care, education, and energy. However, there are still some open problems in anomalous behavior detection, such as the large and complicated model is difficult to train, the accuracy of anomalous behavior detection is not high enough and the speed is not fast enough. A spatiotemporal representation learning model is proposed in this paper. Firstly, the spatial-temporal features of the video are extracted by the constructed multi-scale 3D convolutional neural network. Then the scene background is modeled by the high-dimensional mixed Gaussian model and used for anomaly detection. Finally, the accurate position of anomalous behavior in the video data is achieved by calculating the position of the last output feature, that is, the position of the receptive field. The proposed model does not require specific training. Moreover, the proposed method has the advantages of high versatility, fast calculation speed and high detection accuracy. We validated the proposed algorithm on two representative surveillance scene datasets, the Subway and the UCSDSped2. Results show that proposed algorithm has achieved the detection rate of 18 FPS under the condition of common computing resources, and meet the real-time requirements. Moreover, compared the similar methods, the proposed method has achieved the competitive results in both frame-level accuracy and pixel-level accuracy.

The random noise and anisotropic motion of atmospheric turbulence can cause different degradation patterns, which make images of space targets observed from ground-based stations severely disturbed. In recent years, benefit from the development of convolutional neural networks (CNNs), a large number of effective end-to-end methods were proposed to restore images. However, a single-frame method whose input is just a single image can hardly achieve a further improvement for the restoration image due to the diversified degradation patterns of space-target images. In this paper, we proposed a multi-branch network with a multi-frame input to restore space-target images. The multi-frame input contains space-target images which own different degradation patterns at different moments. In this way, we can fully use the complementary information between input frames. And in this network, two effective technologies are introduced: one is the full resolution convolution module which extracts features by using convolutional layers with different dilation rates to keep feature information complete; the other is the branch-attention module which is used to pass effective information between different branches of the network. Furthermore, we demonstrated the effectiveness of our method by comparing it with those state-of-the-art methods.

The characterization of pressure-sensitive paint (PSP) is affected by many physical and chemical factors, making it is difficult to analyze the relationship between characterization and influencing factors. An artificial neural network (ANN)-based method for predicting pressure sensitivity using paint thickness and roughness was proposed in this paper. The mean absolute percentage error (MAPE) for predicting pressure sensitivity is 6.5088%. The difference of paint thickness and roughness between sample and model surface may be a source of experimental error in PSP pressure measurement tests. The Stern-Volmer coefficients A and B are strongly linked. Pressure sensitivity is approximately equal to coefficient B, so coefficient A is predicted using pressure sensitivity based on the same ANN, and the MAPE of predicting A is 2.1315%. Then, we try to calculate the pressure by using the thickness and roughness on a model to predict pressure sensitivity and Stern-Volmer coefficient A. The PSP pressure measurement test was carried out at the China Aerodynamic Research and Development Center. Using the Stern-Volmer coefficient calculated by the in situ method, the method in this paper, and the sample calibration experiment, the root mean square errors (RMSE) of the pressure are 47.4431 Pa, 63.4736 Pa, and 73.0223 Pa, respectively.

Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse tensor decomposition model for infrared dim small target detection is proposed in this paper. In this model, the entropy of image block is used for fast matching of non-local similar blocks to construct a better sparse tensor for small targets. An adaptive strategy of low-rank sparse tensor decomposition is proposed for different background environments, which adaptively determines the weight coefficient to achieve effective separation of background and small targets in different background environments. Tensor robust principal component analysis (TRPCA) was applied to achieve low-rank sparse tensor decomposition to reconstruct small targets and their backgrounds separately. Sufficient experiments on the various types data sets show that the proposed method is competitive.

Atmospheric turbulence-degraded images in typical practical application scenarios are always disturbed by severe additive noise. Severe additive noise corrupts the prior assumptions of most baseline deconvolution methods. Existing methods either ignore the additive noise term during optimization or perform denoising and deblurring completely independently. However, their performances are not high because they do not conform to the prior that multiple degradation factors are tightly coupled. This paper proposes a Noise Suppression-based Restoration Network (NSRN) for turbulence-degraded images, in which the noise suppression module is designed to learn low-rank subspaces from turbulence-degraded images, the attention-based asymmetric U-NET module is designed for blurred-image deconvolution, and the Fine Deep Back-Projection (FDBP) module is used for multi-level feature fusion to reconstruct a sharp image. Furthermore, an improved curriculum learning strategy is proposed, which trains the network gradually to achieve superior performance through a local-to-global, easy-to-difficult learning method. Based on NSRN, we achieve state-of-the-art performance with PSNR of 30.1 dB and SSIM of 0.9 on the simulated dataset and better visual results on the real images.

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m) and least squares support vector machine (LS-SVM) (γ,σ) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.

In this paper, we present a linguistic interval 2-tuple representation model and new linguistic interval 2-tuple aggregation operators, i.e., linguistic interval 2-tuple power average (LI2TPA) operator, linguistic interval 2-tuple weighted power average (LI2TWPA) operator and linguistic interval 2-tuple power ordered weighted average (LI2TPOWA) operator. Some desired properties of the developed operators are also studied. Moreover, we use these aggregation operators to deal with multiple attribute group decision making problems under linguistic interval 2-tuple environment. In the situations where the weighting vector of the decision makers is known, we use the LI2TWPA operator to make multiple attribute group decision. In the situations where the weighting vector of the decision makers is unknown, we use the LI2TPOWA operator to deal with multiple attribute group decision making. A numerical example is provided to show the effectiveness of our method.