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Chiral materials are crucial components in the natural world, and the exploration of chiral materials hold significant importance in advancing areas such as chiral sensing, bio-marking. The integration of Bound States in the Continuum (BICs) in the design of chiral metasurfaces has emerged as a focal point in optical research, offering widespread research value and practical applications. The chiral dielectric metasurface proposed in this paper is formed by a combination of silicon nanorod, annular cylinder, and silicon dioxide substrate. Through selective detaching, we simultaneously break the structural mirror symmetry and in-plane inversion symmetry, thereby eliciting quasi-BICs and intrinsic chirality. This design achieves dual-band chiral responses, yielding a maximum circular dichroism (CD) of 0.979 and a Q-factor of 1517.5. By adjusting the structural parameters, the tunability of the structure is achieved. The study of the sensing performance of the metasurface resulted in a maximum sensitivity (S) of 270.6 nm/RIU and a maximum Figure of Merit (FOM) of 587.5. Additionally, By comparing the sensing performance with achiral structure, we verified the enhancement of sensing performance through chiral effects. The proposed chiral metasurface advances the practical application of chirality in fields such as biosensing.

Recently, text style transfer has become a very hot research topic in the field of natural language processing. However, the conventional text style transfer is unidirectional, and it is not possible to obtain a model with multidirectional transformations through training once. To address this limitation, we propose a new task called multidirectional text style transfer. It aims to use a single model to transfer the underlying style of text among multiple style attributes and keep its main content unchanged. In this paper, we propose Unified Generative Adversarial Networks (UGAN), a practical approach that combines target vector and generative adversarial techniques to perform multidirectional text style transfer. Our model allows simultaneous training of multi-attribute data on a single network. Such unified structure makes our model more efficient and flexible than existing approaches. We demonstrate the superiority of our approach on three benchmark datasets. Experimental results show that our method not only outperforms other baselines, but also reduces training time by an average of 13%.

With the growing concerns on the energy crisis and the strong support of policies, the development and construction of the multi-energy infrastructure are booming in the world. The post-evaluation for the multi-energy infrastructure investment projects can discover problems, giving advice for improvement, and guide the future project process management, which plays a significant role in achieving closed-loop and lean management. In this article, a comprehensive overview on the post-evaluation indicators of overall multi-energy infrastructure construction processes is illustrated. Then, the post-evaluation indicators on economic benefits are surveyed from the financial and national economic perspectives. Furthermore, the indicators to evaluate the impact on society and environment induced by the multi-energy infrastructure are thoroughly analyzed and investigated. Also, the post-evaluation indicators on the sustainability of the multi-energy infrastructure investment projects are summarized. Lastly, some challenges with great importance in the future research are presented. This review contributes to finding out the problems existing in the multi-energy infrastructure projects and analyzing the causes of the problems, which helps to guide the enterprise's future investment direction, decision-making, and realize an accurate investment.

As the integration of wind power into electrical energy network increasing, accurate forecast of wind speed becomes highly important in the case of large-scale wind power connected into the grid. In order to improve the accuracy of wind speed forecast and the generalization ability of the model, Extreme Gradient Boosting (XGBoost) as an improvement from gradient boosting decision tree (GBDT) is trained and deployed in the cheaper central processing unit (CPU) devices instead of graphics processing unit (GPU) devices, thus, a wind speed forecast model based on Extreme Gradient Boosting is proposed in this paper. Firstly, the historical data is taken as a part of the input vectors for the model. Moreover, considering the monthly change of wind speed characteristics, the dataset of wind power is divided into four parts by month so that the models are constructed in different complexity by month. Finally, compared with back propagation neural networks (BPNN) and linear regression (LR) models, the experimental results show that the improved XGBoost model can promote the forecast accuracy effectively.

In the point-based method, the aliasing phenomenon in the hologram plane is usually considered as a harmful phenomenon because it will degrade the reconstructed image quality. In this paper, the advantage of the aliasing phenomenon is employed to improve the reconstructed image quality of the phase-only hologram. Through analysis of the principle of this phenomenon, we proposed a method to improve the reconstructed image quality of the phase-only hologram. By controlling the strength of the aliasing, the amplitude distribution in the hologram plane can be turned into a nearly uniform distribution. It is helpful to generate an optimized phase-only hologram, which results in higher quality of the reconstructed image because the effect of lost information caused by abandon the amplitude part can be diminished. Moreover, a holographic see-through display system is designed to demonstrate the effectiveness of the proposed method.

Image segmentation is an important research of computer vision. Due to the effects of intensity inhomogeneity, target edge and background complex, it is still challenging to achieve effective segmentation of target adaptively. To solve these issues, an image segmentation method based on saliency and level set is proposed in this paper. First, adaptive initial contour of level set is got by wavelet-based feature probability evaluation (WFPE) model, the initial contour is closer to the target contour, which can reduce background interference and evolve faster. Second, in order to realize the best detection of intensity mutation and locate the target edge more accurately, an edge constraint energy term is introduced with multi-scale information obtained by wavelet transform. Finally, to improve segmentation adaptability and speed, the region information and edge constraint energy term are merged into the adaptive active contour model, the final evolution curve evolves in coarse scale, and then interpolates to get the final segmentation contour. Experimental results show that the proposed method achieves high efficiency in the following aspects: adaptability to images, speed of evolution, close to human visual perception.

A highly efficient algorithm for analysis of electromagnetic scattering by a large cluster of independently moving objects is proposed in this paper. A double octree structure is introduced, which consists of a main octree stationary to the cluster as a whole, and many sub-octrees moving with individual objects. Based on the new double octree structure, the computation of interactions between two elements is divided into four categories: the two elements are on the same object and are near; the two elements are on the same object but are far; the two elements are on different objects but the two objects are adjacent; the two elements are on different objects and the two objects are non-adjacent. For different interacting categories, different approaches are adopted to maximize the computing efficiency. The new algorithm enables possible fast simulations of scattering by hundreds of independently moving objects. Compared with the conventional Multilevel Fast Multipole Algorithm (MLFMA) and the recently developed Multi-Moving-Object (MMO)-MLFMA, the present scheme has substantial improvement in saving CPU time, providing a viable solution for engineering applications. Numerical validations for accuracy and efficiency are performed.

Patch matching is a significant stage in numerous computer vision tasks. This paper proposed a novel network structure, named DFR-net, appropriate to match patches. The proposed network uses a dense connectivity pattern at the resolution level, making the training efficient. This connectivity pattern has been shown improving the accuracy of patch matching. The DFR-net, with a single-tower architecture, focused on the relationship between (non-)corresponding patches, which improved the performance of the traditional Siamese network. The component of DFR-net, named RDCNet block, produces a smaller model size and is demonstrated suiting for patch matching. To ensure the experimental effectiveness, the DFR-net was trained on the public Brown patch dataset and the HPatches dataset.

Semantic internet of things is a solution to the inherent contradiction of IoT (Internet of Things), that is, the diversity of objects and the limitation of subjects. The most prominent feature of semantic IoT service is to use ontology for semantic annotation and support understanding, and to use “things” as service providers and consumers. In view of the differences and challenges in service description, service environment and service resources, this paper proposes a dynamic composition method of Semantic Internet of Things services based on QoS. By perceiving the situation of service requester and physical device and their impact on the quality of service information, the service description file without semantic conflict is generated, and then the service set is dynamically selected, combined and mapped to provide more accurate services for different service objects and different service environments.This paper designs a QoS semantic IoT service dynamic combination prototype system based on the dynamic combination method of semantic Internet of Things services. The overall design, sub-module design and database design of the prototype system are carried out in turn. The main functions of the system are programmed and realized by selecting the implementation tools and configuring the implementation platform. Finally, the accuracy and dynamic of the experimental results are analyzed and evaluated in detail through experimental tests.

Abstract The integration of natural gas in electricity network requires a more reliable operating plan for increasing uncertainties in the whole system. In this paper, a three-stage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks. Non-convex constrains are linearized in order to formulate the dual problem of optimal energy flow. Then, the proposed three-stage problem can be reformulated into a two-stage mixed integer linear program (MILP) and solved by Benders decomposition algorithm. Numerical studies on IEEE 30-bus power system with 7-node natural gas network and IEEE 118-bus power system with 14-node natural gas network validate the feasibility of the proposed model for improving resilience of integrated energy system. Energy storage facilities are also considered for the resiliency analysis.

Taking advantage of an extended Lugiato-Lefever equation with third-order dispersion, we numerically show that dark cavity solitons formed in normal dispersion of microresonators are capable of emitting dispersive waves in both normal and anomalous dispersion regions with two different methods, resembling the behavior of the commonly encountered bright cavity solitons. The generated dispersive waves can be accurately predicted by the dissipative radiation theory. In addition, we demonstrate the stability enhancement of Kerr frequency combs in the normal dispersion regime in case the dispersive wave is emitted by dark solitons in the presence of third-order dispersion.