GS-221高速摄影测量仪测角信息的采集与处理

在GS-221高速摄影测量仪中,测角信息采集与处理速度的快慢是重要性能指标之一。本文讨论了如何在尽可能短的时间内对测角信息进行采集和处理的软件实现方法。     

Excitation spectra of one-dimensional spin-1/2 Fermi gas with an attraction

Using an exact Bethe ansatz solution, we rigorously study excitation spectra of the spin-1/2 Fermi gas (called Yang–Gaudin model) with an attractive interaction. Elementary excitations of this model involve particle-hole excitation, hole excitation and adding particles in the Fermi seas of pairs and unpaired fermions. The gapped magnon excitations in the spin sector show a ferromagnetic coupling to the Fermi sea of the single fermions. By numerically and analytically solving the Bethe ansatz equations and the thermodynamic Bethe ansatz equations of this model, we obtain excitation energies for various polarizations in the phase of the Fulde–Ferrell–Larkin–Ovchinnikov-like state. For a small momentum (long-wavelength limit) and in the strong interaction regime, we analytically obtained their linear dispersions with curvature corrections, effective masses as well as velocities in particle-hole excitations of pairs and unpaired fermions. Such a type of particle-hole excitations display a novel separation of collective motions of bosonic modes within paired and unpaired fermions. Finally, we also discuss magnon excitations in the spin sector and the application of Bragg spectroscopy for testing such separated charge excitation modes of pairs and single fermions.     

An Edge Server Placement Method Based on Reinforcement Learning

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.     

Impact of Extracellular Polymeric Substance in the Inactivation of Harmful Algae by Ag2O/g-C3N4 under Visible Light

Photocatalysis has attracted much attention as an emerging algae removal technology, but the inactivation performance is inevitably affected by the extracellular polymeric substance (EPS) produced by algae. In this study, a photocatalyst (Ag₂O/g-C₃N₄) with efficient algae inactivation is adopted to investigate the interactions with EPS, and the impact of EPS on photocatalytic algae removal is studied. The results show that EPS can adhere to the surface of Ag₂O/g-C₃N₄ by electrostatic force. The interaction with EPS decreases the surface zeta potential of the Ag₂O/g-C₃N₄ from 7.71 to −22.3 mV with the increase in EPS concentration, and the maximum ratio of particle size increases from 825 to 1281 nm. In addition, the interaction with EPS inhibits the release of Ag⁺ in Ag₂O/g-C₃N₄ by half, thus, the toxicity of metal ions will be alleviated. Meanwhile, EPS can also be degraded by Ag₂O/g-C₃N₄, indicating that EPS can work as a radical scavenger to protect the algae cells. Without the protection of EPS, 97.8% of algae cells are inactivated after 5 h photocatalysis. Therefore, more attention should be given to the interaction between EPS and photocatalyst to promote the design and application of the photocatalytic.     

Nanomaterials in Cerebrovascular Disease Diagnose and Treatment

Cerebrovascular diseases (CVDs) are among the most serious diseases with high mortality and disability rates. The prevalent diagnosis and treatment methods of CVDs include imaging and interventional therapy. With the development of nanotechnology, large numbers of nanomaterials have been applied to the diagnosis and treatment of CVDs, mainly including carbon nanotubes, quantum dots, fullerenes, and dendrimers. In this review, the applications of nanomaterials in the field of diagnosis and treatment of CVDs, mainly including drug target delivery, imaging, therapy, endovascular treatment, and angiogenesis, are summarized. The applications of nanomaterials in the field of CVD are almost in the laboratory, and more effort is needed for clinical translation. The aim of this review is to provide useful information for future research and equipment development.     

Facile Synthesis of Manganese Tellurite Nanoparticles for Magnetic Resonance Imaging-Guided Photothermal Therapy

In this study, manganese tellurite (MnTeO₃) nanoparticles are developed as theranostic agents for magnetic resonance imaging (MRI)-guided photothermal therapy of tumor. MnTeO₃ nanoparticles are synthesized via a simple one-step method. The as-synthesized MnTeO₃ nanoparticles with uniform size show good biocompatibility. In particular, MnTeO₃ nanoparticles exhibit a high photothermal conversion efficiency (η = 26.3%), which is higher than that of gold nanorods. Moreover, MnTeO₃ nanoparticles also have high MRI performance. The longitudinal relaxivity (r₁) value of MnTeO₃ nanoparticles is determined to be 8.08 ± 0.2 mm ⁻¹ s⁻¹, which is higher than that of clinically approved T₁-contrast agents Gd-DTPA (4.49 ± 0.1 mm ⁻¹ s⁻¹). The subsequent MnTeO₃ nanoparticles-mediated photothermal therapy displays a highly efficient ablation of tumor cells both in vitro and in vivo with negligible toxicity. It is demonstrated that MnTeO₃ nanoparticles can serve as promising theranostic agents with great potentials for MRI-guided photothermal therapy.     

Tantalum Nitride Nanosheets for Photoacoustic Imaging-Guided Photothermal Cancer Therapy

In this study, the synthesis of TaN nanosheets and their application in theranostic agents is reported. After coating polyethylene glycol (PEG) on the TaN nanosheets, the as-synthesized PEG-modified TaN nanosheets (TaN-PEG) show good stability and biocompatibility. Because of their high absorbance in the near-IR region, TaN-PEG can be utilized as photoacoustic imaging contrast agents for tumor imaging. Moreover, TaN-PEG has significant photothermal conversion performance, exhibiting effective laser-induced tumor ablation capability. The TaN-PEG possessing excellent photoacoustic contrast effect and photothermal properties thus have great promise in theranostic applications, especially imaging-guided cancer treatment.     

Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.     

基于分数阶傅里叶变换的模式测控一体化方法

提出了一种基于分数阶傅里叶变换的模式测控一体化方法。利用分数阶傅里叶变换光路对光纤模式耦合态进行空间调制和相位调制,以实现模式的有效分解。与双重傅里叶变换（F²）法以及空间和频谱成像（S²）法相比,采用的分数阶傅里叶变换法,通过改变分数阶参数,控制模式的空间分布以及模式间的叠加状态,更易于分解出高阶模式。基于分数阶傅里叶变换的模式测量方法可在更广泛空间,研究模式的空间和相位叠加以及模式分解,也可退化为F²法和S²法。     

How much information is needed to be the majority during the binary-state opinion formation?

In this paper, we try to propose a toy model, which follows the majority rule with the Fermi function, to uncover the role of the heterogeneous interaction between individuals in opinion formation. In order to do this, we define the impact factor I_Fi$I{F}_i$, says individual i$i$, as the exponential function of its connectivity _ki$k_i$ with the tunable parameter β$\beta$. β$\beta$ also shows the public information that can be collected by individuals in the system. We realize our model in scale-free networks with mean connectivity 〈k〉$〈k〉$. We find that much more public information (β>_β2$\beta >{\beta }_2$) and less public information (β<_β1$\beta <{\beta }_1$) cannot let either of the two opinions be the majority during the opinion formation. Furthermore, _β1${\beta }_1$ is a constant and equal to −0.76(±0.04)$-0.76(\pm 0.04)$, and _β2${\beta }_2$ decreases as a power-law function of the mean connectivity 〈k〉$〈k〉$ of the network. Our work can provide some perspectives and tools to understand the diversity of opinion in social networks.