We present a systematic framework for the reverse fractal design of Lindenmayer (L-) systems from target box-counting dimensions. By inverting the self-similarity formula D=lnnln(1/r) to r=n−1/D, we analytically determine L-system parameters (copy count n and scaling factor r) that generate fractals with prescribed dimensions. Our key innovation is a topology template library encoding eight spatial arrangements that separate dimensional constraints from geometric structure, enabling systematic design space exploration. For a single target dimension D=1.5, the framework generates 21 topologically distinct L-systems (spanning binary, ternary, quaternary, and pentagonal patterns), all achieving machine-precision accuracy (error <10−15) with sub-millisecond efficiency. Comprehensive validation through 25 unit tests and standard fractals (Koch snowflake and Sierpinski triangle) confirms mathematical correctness. This template-based approach provides unprecedented flexibility: users control complexity through n selection, visual density through r, and esthetics through topology choice. Applications span fractal art generation, natural structure simulation, procedural content generation, and texture synthesis.

The structural characteristics of biomolecules are a major focus in the field of structural biology. Molecular visualization plays a crucial role in displaying structural information in an intuitive manner, aiding in the understanding of molecular properties. This paper provides a comprehensive overview of core concepts, key techniques, and tools in molecular visualization. Additionally, it presents the latest research findings to uncover emerging trends and highlights the challenges and potential directions for the development of the field.

Abstract In the ancient Chinese collection “Liezi · Tangwen”, there is a story about an argument between two children comparing the distance of the sun in the morning and at noon. One child argued based on the sun’s size, while the other arguedbased on the temperature. Confucius was unable to determine who was right, and today’s students who read the story from Chinese elementary school textbooks face the same dilemma. This paper aims to bridge the gap between Chinese literature and physics by providing scientific facts to the story. It will examine the impact of revolution and rotation on the difference in distance between morning and noon, and then combine them to determine which is closer. The findings show that the result is more complex than expected.

The world is facing one of the greatest public health threats in modern history. Various techniques based on contact tracing have been developed to support non-pharmaceutical interventions. The growing evidence shows that app-based contact tracing can reduce the spread of COVID-19 if a certain proportion of the population uses the apps. However, the risk of privacy breaches that comes with such apps has long been a public concern which may hinder the uptake of the apps. In this paper, the authors attempt to find a solution to complete the spatiotemporal intersection computation without exposing the infected patient location and the user location to one another. The authors implement the solution in the WeChat applet to aid the local health center. This study conducts experiments for six scenarios to justify the applicability of the applet. Experiment results indicate that the applet is a promising non-pharmaceutical tool for curbing the spread of COVID-19.

We have developed a software tool called “GWGPM” that generates wallpaper group patterns for molecular visualization. Assuming a 2D image file has already been created using other chemical or biological software, it can be fed into the software tool and generate all 17 wallpaper group patterns. The output can be exported as image files or files in other formats. While the software was developed for scientific purposes, it can also be used for entertainment since all operations in GWGPM are based on general 2D images.

云计算、大数据、物联网及人工智能等技术的快速发展在给人们生活带来便利的同时,也造成隐私泄露和信息滥用等问题,因此在不泄露行程轨迹的情况下对行程轨迹求交问题具有重要的现实意义.提出两种多维行程轨迹数据集隐私集合求交方案,并进行了性能分析实验验证.实验结果表明,基于Rivest-Shamir-Adleman(RSA)公钥密码体制的隐私集合求交方法具有较高的运算效率,而基于Ben-Or-Goldwasser-Wigderson(BGW)秘密共享的隐私集合求交方法支持更复杂的运算,从而可实现近似求交.由此提出结合两方法特点、取长补短的综合方案.

One driving application for multi-robots is source seeking, especially in the hazardous environment. It consists of two essential subtasks: source location and path search. Nature inspired meta-heuristic is preferable in addressing the source location subtask which is an inversion problem, while the Astar algorithm and its variants are widely used for the path search subtask. In this paper, we present a multi-robot team simulator as a container which contains both algorithms as components. The simulator takes the constraints into consideration, including the size and the speed bound of each robot, the obstacle and collision avoidance. We provide a python implementation and example problems for research and test purposes. The well-structured code with object-oriented design can be conveniently upgraded by adding new excellent nature inspired metaheuristics, or extended to other source seeking problems in various field applications. The python code can be downloaded from the website: https://github.com/buctlab/source-seeking-multi-robot-team-simulator.

Aiming at the single-phase grounding fault location problem of neutral point ungrounded distribution network system with distributed generators, a single-ended impedance method fault location method based on zero-sequence current is proposed. Firstly, the circuit voltage equation is written based on the line impedance parameters and the collected bus voltage, line bus current, end loads current and the distributed generator outlet current data, as well as the fault current series circuit voltage equation constructed by the zero-sequence component of the post-fault loads current, the zero-sequence component of the capacitor current to the ground and the zero-sequence component of the current at the head of the line. Secondly, the fault distance is given as a variable for the fault location equation of each line segment in turn, and finally the fault distance is obtained by solving the equation. In the process of fault location calculation, the problem of line-to-ground capacitor current compensation is considered, and the node voltage is used to calculate the capacitor current, the node voltage between the fault location and the bus end is calculated by the electrical quantity at the bus end, and the node voltage between the fault location and the end is calculated by the forward pushback generation method. An improved IEEE 34-node model with distributed power source and loads is constructed by PSCAD/EMTDC and the fault location simulation is verified, and the results show that the fault location of the distribution network with distributed generators and loads can be accurately located by the method proposed in this paper.

Recent advance in high-accuracy sensors has made point cloud become the main data format to characterize the three-dimensional world. Since the sensor can only scan and capture the 3D data within a limited field of view, an alignment algorithm is needed to generate the complete 3D scene. Point cloud registration is the solution for alignment problem that aims to estimate the transformation matrix between two frames of different point cloud sets. In this paper, we propose a neural network called OLFF-Net to achieve robust registration of 3D point clouds based on overlapped local feature fusion, which focuses on extracting rotational-invariant local features while providing enough information to achieve accurate alignment. Extensive experiments on representative datasets indicate that the framework can largely outperform competing methods with an average improvement of 16.82% in the metrics over the compared methods. More importantly, it shows significant generalization capability and can be widely applied to point cloud data with multiple complex structures.

The finite-difference method is widely used in seismic wave numerical simulation, imaging, and waveform inversion. In the finite-difference method, the finite difference operator is used to replace the differential operator approximately, which can be obtained by truncating the spatial convolution series. The properties of the truncated window function, such as the main and side lobes of the window function's amplitude response, determine the accuracy of finite-difference, which subsequently affects the seismic imaging and inversion results significantly. Although numerical dispersion is inevitable in this process, it can be suppressed more effectively by using higher precision finite-difference operators. In this paper, we use the krill herd algorithm, in contrast with the standard PSO and CDPSO (a variant of PSO), to optimize the finite-difference operator. Numerical simulation results verify that the krill herd algorithm has good performance in improving the precision of the differential operator.

Contact tracing is a monitoring process including contact identification, listing, and follow-up, which is a key to slowing down pandemics of infectious diseases, such as COVID-19. In this study, we use the scientific collaboration network technique to explore the evolving history and scientific collaboration patterns of contact tracing. It is observed that the number of articles on the subject remained at a low level before 2020, probably because the practical significance of the contact tracing model was not widely accepted by the academic community. The COVID-19 pandemic has brought an unprecedented research boom to contact tracing, as evidenced by the explosion of the literature after 2020. Tuberculosis, HIV, and other sexually transmitted diseases were common types of diseases studied in contact tracing before 2020. In contrast, research on contact tracing regarding COVID-19 occupies a significantly large proportion after 2000. It is also found from the collaboration networks that academic teams in the field tend to conduct independent research, rather than cross-team collaboration, which is not conducive to knowledge dissemination and information flow.

Condition monitoring and fault diagnosis of diesel engines are of great significance for safety production and maintenance cost control. The digital twin method based on data-driven and physical model fusion has attracted more and more attention. However, the existing methods lack deeper integration and optimization facing complex physical systems. Most of the algorithms based on deep learning transform the data into the substitution of the physical model. The lack of interpretability of the deep learning diagnosis model limits its practical application. The attention mechanism is gradually developed to access interpretability. In this study, a digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis is proposed with considering its signal characteristics of strong angle domain correlation and transient non-stationary, in which a new soft threshold filter is designed to draw more attention to multi decentralized local fault information dynamically in real time. Based on this attention mechanism, the distribution of fault information in the original signal can be better visualized to help explain the fault mechanism. The valve failure experiment on a diesel engine test rig is conducted, of which the results show that the proposed adaptive sparse attention mechanism model has better training efficiency and clearer interpretability on the premise of maintaining performance.

Traveling salesman problem is a widely studied NP-hard problem in the field of combinatorial optimization. Many and various heuristics and approximation algorithms have been developed to address the problem. However, few studies were conducted on the multi-solution optimization for traveling salesman problem so far. In this article, we propose a circular Jaccard distance based multi-solution optimization (CJD-MSO) algorithm based on ant colony optimization to find multiple solutions for the traveling salesman problem. The CJD-MSO algorithm incorporates "distancing" niching technique with circular Jaccard distance metric which are both proposed in this paper for the first time. Experimental results verify that the proposed algorithm achieves good performance on both quality and diversity of the optimal solutions.

往复压缩机的故障诊断技术能够为工业生产提供有效保障,针对传统方法诊断准确率不高的问题,提出了一种基于振动信号时频图像灰度共生矩阵-方向梯度直方图(GLCM-HOG)特征融合的往复压缩机故障诊断方法.首先,采用小波变换的方法处理往复压缩机的振动信号,生成时频图像;其次,利用灰度共生矩阵(GLCM)和方向梯度直方图(HOG)的方法提取时频图像特征,融合构建GLCM-HOG特征;最后,将融合特征输入支持向量机(SVM)进行分类,以判别往复压缩机的运行状态.实验结果表明,所提方法对设备的状态识别准确率可以达到92.33％,能够实现往复压缩机的准确诊断.

As global scientific researches are witnessing rapid and high-quality development, cooperation has become the development trend in scientific research. This article models social networks, explains in detail two common propagation models, and gives the definition of the problem of maximizing influence in social networks. On the basis of which, the maximization of influence is introduced into the scientific collaboration network. It's mainly used to describe the cooperative contribution between scientific research scholars, the scientific research innovation ability and the academic leadership ability of the cooperative team. This paper proposes a method to find authors with greatest influence in scientific collaboration network, in order to mine the deep information within the scientific collaboration network.

针对深度卷积生成对抗网络(DCGAN)在小规模手写体汉字数据集下生成数据重复多样、分类效果较差的问题,提出结合传统数据增强方法的结合式生成方法X-DCGAN.该方法通过预增强模块给予神经网络部分更充足多样的训练数据,减少因网络过拟合与训练不充分而出现的样本重复率高、学习效果较差的状况.实验结果表明,本文方法生成的样本数据较单一方法在样本多样性方面显著提高,生成数据进行分类测试时获得的平均识别率较DCGAN方法提升了9.67％.X-DCGAN充分发挥了传统数据增强方法和生成式方法各自的优势,能够更加有效地解决小规模数据集的扩展与增强问题.

The last couple of decades have witnessed a steadily increasing applications of nature inspired optimization (NIO) in vast fields such as power engineering, environmental engineering, and civil engineering. Behavioral parameters deeply affect the optimization performance for a NIO algorithm. Meta-optimization is good choice for parameter optimization of NIOs, which uses an optimizer to optimize another optimizer. However, meta-optimization is a very time-consuming process. For this reason, we propose a multi-fidelity strategy based meta-optimization approach to speed up the parameter optimization. Four types of fidelity control functions determine the fidelity level in the course of meta-optimization. We test the proposed method in the meta-optimization systems with diverse meta-NIOs (cuckoo search, fruit fly optimizer, gray wolf optimizer, krill herd, and whale optimization algorithm), diverse optimized-NIOs (cuckoo search, differential evolution, particle swarm optimizer, squirrel search algorithm, and water wave optimizer), and diverse benchmark problems (Ackley-50, Eggholder-2, Michalewicz-5, Shubert-2, Sphere-50, and F1–20). We also apply it to a real-world engineering problem to estimate the source terms of gas emission. Experimental results indicate that multi-fidelity strategy can substantially speed up meta-optimization systems and hence has the potential to be generalized to various NIOs.

Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.

•The work focuses on elliptic curve pseudo-random scalar multiplication (ECPRSM), which accounts for a significant proportion of total scalar multiplication operations in almost all ECC schemes;•A group isomorphism between the pseudo-random number group and the elliptic curve point group is explicitly established;•A pseudo-random elliptic curve point generator (ECPM-LFG) is proposed;