Seeded Ising Model and Distributed Biometric Template Storage and Matching

It is known that a variant of Ising model, called Seeded Ising Model, can be used to recover the information content of a biometric template from a fraction of information therein. The method consists in reconstructing the whole template, which is called the intruder template in this paper, using only a small portion of the given template, a partial template. This reconstruction method may pose a security threat to the integrity of a biometric identity management system. In this paper, based on the Seeded Ising Model, we present a systematic analysis of the possible security breach and its probability of accepting the intruder templates as genuine. Detailed statistical experiments on the intruder match rate are also conducted under various scenarios. In particular, we study (1) how best a template is divided into several small pieces called partial templates, each of which is to be stored in a separate silo; (2) how to do the matching by comparing partial templates in the locked-up silos, and letting only the results of these intra-silo comparisons be sent to the central tallying server for final scoring without requiring the whole templates in one location at any time.     

晚期HEMP作用下铁路牵引供电系统GIC算法研究

高空核爆电磁脉冲晚期效应（E₃）会引起地磁场剧烈变化并形成地面感应电场。感应电场等效为激励源与地面长距离导体和大地构成回路,产生地磁感应电流 （GIC）。GIC可引起牵引供电系统中变压器直流偏磁,从而严重威胁牵引供电系统的安全运行。本文基于平面波理论、分层大地电导率模型并结合牵引供电系统的电路模型,提出E₃作用下的牵引供电系统GIC算法,并以带回流线的直接供电方式的铁路牵引供电系统为例,首次计算了系统GIC情况。结果表明,该供电方式下牵引供电系统中的GIC远大于系统中变压器等设备的耐受值,为进一步研究E₃作用下牵引供电系统效应及我国铁路设备选型、灾害防治等提供支撑。     

减反膜对单晶硅太阳能电池性能的影响分析

在太阳能电池效率的评价中,电池材料、掺杂浓度、扩散长度等都是比较重要的参数,合理地改变相关参数可以优化太阳能电池的性能,提高电池效率。此外,在太阳能电池表面镀一层具有减反作用的光学薄膜（简称减反膜）也是提高电池效率的重要手段。以提高电池效率为目标,对单晶硅太阳能电池的掺杂浓度和扩散长度等微观参数进行计算优化,分析了掺杂浓度和扩散长度变化对电池效率的影响。并在此基础上分析了不同类型的减反膜对于电池效率的影响,给出了最佳减反膜材料及其膜系厚度,并且结合镀膜后电池量子效率的变化验证了其准确性。结果表明,在优化电池掺杂浓度和扩散长度的基础上,选择合适的减反膜,电池效率最高可达20.35%,相比于优化前提高了8.25%。     

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.     

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Gaussian network model (GNM) is an efficient method to investigate the structural dynamics of biomolecules. However, the application of GNM on RNAs is not as good as that on proteins, and there is still room to improve the model. In this study, two novel approaches, named the weighted GNM (wGNM) and the force-constant-decayed GNM (fcdGNM), were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs. In wGNM, the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides. In fcdGNM, all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs. The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules. The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures. Compared with the conventional GNM, the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85% and 6.76% for wGNM and fcdGNM, respectively. Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.     

Modeling and dynamics of double Hindmarsh-Rose neuron with memristor-based magnetic coupling and time delay

The firing of a neuron model is mainly affected by the following factors:the magnetic field, external forcing current, time delay, etc. In this paper, a new time-delayed electromagnetic field coupled dual Hindmarsh-Rose neuron network model is constructed. A magnetically controlled threshold memristor is improved to represent the self-connected and the coupled magnetic fields triggered by the dynamic change of neuronal membrane potential for the adjacent neurons. Numerical simulation confirms that the coupled magnetic field can activate resting neurons to generate rich firing patterns, such as spiking firings, bursting firings, and chaotic firings, and enable neurons to generate larger firing amplitudes. The study also found that the strength of magnetic coupling in the neural network also affects the number of peaks in the discharge of bursting firing. Based on the existing medical treatment background of mental illness, the effects of time lag in the coupling process against neuron firing are studied. The results confirm that the neurons can respond well to external stimuli and coupled magnetic field with appropriate time delay, and keep periodic firing under a wide range of external forcing current.     

Fission fragment mass yields of Th to Rf even-even nuclei

Fission properties of the actinide nuclei are deduced from theoretical analysis. We investigate potential energy surfaces and fission barriers and predict the fission fragment mass yields of actinide isotopes. The results are compared with experimental data where available. The calculations were performed in the macroscopic-microscopic approximation with the Lublin-Strasbourg Drop (LSD) for the macroscopic part, and the microscopic energy corrections were evaluated in the Yukawa-folded potential. The Fourier nuclear shape parametrization is used to describe the nuclear shape, including the non-axial degree of freedom. The fission fragment mass yields of the nuclei considered are evaluated within a 3D collective model using the Born-Oppenheimer approximation.     

A Study on Consumers’ Visual Image Evaluation of Wrist Wearables

This study aimed to investigate consumers’ visual image evaluation of wrist wearables based on Kansei engineering. A total of 8 representative samples were screened from 99 samples using the multidimensional scaling (MDS) method. Five groups of adjectives were identified to allow participants to express their visual impressions of wrist wearable devices through a questionnaire survey and factor analysis. The evaluation of eight samples using the five groups of adjectives was analyzed utilizing the triangle fuzzy theory. The results showed a relatively different evaluation of the eight samples in the groups of “fashionable and individual” and “rational and decent”, but little distinction in the groups of “practical and durable”, “modern and smart” and “convenient and multiple”. Furthermore, wrist wearables with a shape close to a traditional watch dial (round), with a bezel and mechanical buttons (moderate complexity) and asymmetric forms received a higher evaluation. The acceptance of square- and elliptical-shaped wrist wearables was relatively low. Among the square- and rectangular-shaped wrist wearables, the greater the curvature of the chamfer, the higher the acceptance. Apparent contrast between the color of the screen and the casing had good acceptance. The influence of display size on consumer evaluations was relatively small. Similar results were obtained in the evaluation of preferences and willingness to purchase. The results of this study objectively and effectively reflect consumers’ evaluation and potential demand for the visual images of wrist wearables and provide a reference for designers and industry professionals.     

Network-Based Driving Force of National Economic Development: A Social Capital Perspective

Network science has been widely applied in theoretical and empirical studies of global value chain (GVC), and many related articles have emerged, forming many more mature and complete analytical frameworks. Among them, the GVC accounting method based on complex network theory is different from the mainstream economics in both research angle and content. In this paper, we build up global industrial value chain network (GIVCN) models based on World Input–Output Database, introduce the theoretical framework of Social Capital, and define the network-based indicators with economic meanings. Second, we follow the econometric framework to analyze the hypothesis and test whether it is true. Finally, we study how the three types of capital constituted by these indicators interact with each other, and discuss their impact on the social capital (economic development level, i.e., GDP). The results prove that the structural capital (industrial status) has a positive impact on the social capital; the relational capital (industrial correlation) has a positive impact on both social capital and structural capital; the cognitive capital (industrial structure) has a small impact on the social capital, structural capital, and relational capital.