Properties of collective flow and pion production in intermediate-energy heavy-ion collisions with a relativistic quantum molecular dynamics model

The relativistic mean-field approach was implemented in the Lanzhou quantum molecular dynamics transport model(LQMD.RMF). Using the LQMD.RMF, the properties of collective flow and pion production were investigated systematically for nuclear reactions with various isospin asymmetries. The directed and elliptic flows of the LQMD.RMF are able to describe the experimental data of STAR Collaboration. The directed flow difference between free neutrons and protons was associated with the stiffness of the symmetry energy, that is, a softer symmetry energy led to a larger flow difference. For various collision energies, the ratio between the π^(-) and π^(+) yields increased with a decrease in the slope parameter of the symmetry energy. When the collision energy was 270 MeV/nucleon, the single ratio of the pion transverse momentum spectra also increased with decreasing slope parameter of the symmetry energy in both nearly symmetric and neutron-rich systems.However, it is difficult to constrain the stiffness of the symmetry energy with the double ratio because of the lack of threshold energy correction on the pion production.

FMHADP:Design of an Efficient Pre-Forensic Layer for Mitigating Hybrid Attacks via Deep Learning Pattern Analysis

Network attack detection and mitigation require packet collection,pre-processing,feature analysis,classification,and post-processing.Models for these tasks sometimes become complex or inefficient when applied to real-time data samples.To mitigate hybrid assaults,this study designs an efficient forensic layer employing deep learning pattern analysis and multidomain feature extraction.In this paper,we provide a novel multidomain feature extraction method using Fourier,Z,Laplace,Discrete Cosine Transform(DCT),1D Haar Wavelet,Gabor,and Convolutional Operations.Evolutionary method dragon fly optimisation reduces feature dimensionality and improves feature selection accuracy.The selected features are fed into VGGNet and GoogLeNet models using binary cascaded neural networks to analyse network traffic patterns,detect anomalies,and warn network administrators.The suggested model tackles the inadequacies of existing approaches to hybrid threats,which are growing more common and challenge conventional security measures.Our model integrates multidomain feature extraction,deep learning pattern analysis,and the forensic layer to improve intrusion detection and prevention systems.In diverse attack scenarios,our technique has 3.5% higher accuracy,4.3% higher precision,8.5% higher recall,and 2.9% lower delay than previous models.

Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm

Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the pattern classification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the oversegmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle(UAV). We put forward an approach to evaluate the segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.

Theoretical Reflection on Meaning Production of Media Discourse in Intercultural Communications

Media discourse in the context of intercultural communications is an important channel that countries and cultures use to communicate. It is also a process of meaning interpretation and knowledge production, which exerts a great impact on the establishment of the world's cultural order. This paper discusses media discourse in intercultural communications theoretically from the perspective of knowledge production, media dialogue and meaning construction. It is suggested that an effective ideographic mechanism be developed and improved, and the essential meaning of Chinese culture be initiatively exported and integrated into a knowledge system of cognition and understanding about the world to promote the understanding and exchange between China and other countries and to help create an equal and reasonable world cultural order.

Mean Shift跟踪算法创新实验项目设计

视频跟踪算法是计算机视觉实践课程中比较受关注的实验项目。针对突变情况下传统Mean Shift跟踪算法无法实时准确跟踪的问题,设计了基于模板更新和线性预估的Mean Shift跟踪算法创新实验项目。在模板更新策略下,引入背景模板,通过将原目标模板和背景模板与设定的阈值进行比较来对干扰因素进行判定,当干扰因素判定目标受到遮挡时,引入线性预估方程进行目标位置预测,有效解决目标在遮挡情况下跟踪丢失的问题。通过对测试视频的跟踪效果和性能进行对比分析,验证了算法在突变情况下相较于传统算法具有更好的抗干扰能力。以算法创新设计为核心,通过开放性创新实验项目的选题、设计、答辩、反馈的闭环实验过程,有效提高了学生算法创新设计能力。

For more and purer hydrogen-the progress and challenges in water gas shift reaction

The water gas shift(WGS) reaction is a standard reaction that is widely used in industrial hydrogen production and removal of carbon monoxide. The improved catalytic performance of WGS reaction also contributes to ammonia synthesis and other reactions. Advanced catalysts have been developed for both high and low-temperature reactions and are widely used in industry. In recent years, supported metal nanoparticle catalysts have been researched due to their high metal utilization. Low-temperature catalysts have shown promising results, including high selectivity, high shift rates, and higher activity potential. Additionally, significant progress has been made in removing trace CO through the redox reaction in electrolytic cell. This paper reviews the development of WGS reaction catalysts, including the reaction mechanism, catalyst design, and innovative research methods. The catalyst plays a crucial role in the WGS reaction, and this paper provides an instant of catalyst design under different conditions. The progress of catalysts is closely related to the development of advanced characterization techniques.Furthermore, modifying the catalyst surface to enhance activity and significantly increase reaction kinetics is a current research direction. This review goals to stimulate a better understanding of catalyst design, performance optimization, and driving mechanisms, leading to further progress in this field.

Dynamic Prediction Method of Production Logistics Bottleneck Based on Bottleneck Index

In modern manufacturing pattern, there are many uncertain factors in the modern manufacturing process, such as changes of product attribute, changes of manufacturing resources＇ state, and so on, which cause production logistics bottleneck frequently shift, and make decisions of production planning and control based on formed bottleneck deviated from practical production process. Considering these factors, present researches mainly apply afterwards control to optimize production process to passively adapt to bottleneck changes If the direction of bottleneck shifting can be accurately forecasted, the transition from afterwards control of chasing bottleneck to beforehand control can be realized. Therefore, aiming at the phenomenon of production logistics bottleneck shifting under uncertain manufacturing circumstances, this paper starts off with dynamic property of capability and requirement and then builds the concepts of bottleneck degree and bottleneck index to describe dynamic bottleneck characteristic of production unit; taken production capability, production load and quality assurance capability into consideration, mathematical model of bottleneck index is established to measure bottleneck degree accurately, consequently, quantitative research on mechanism of production logistics shifting is achieved. Based on bottleneck index, the prediction model of production logistics bottleneck is founded to predict dynamic change of bottleneck accurately. Finally, an example of forecasting and monitoring the production logistics bottleneck in one manufacturing shop is given to testify the validation and practicability of the prediction method.

Spatial and temporal relationships between precipitation and ANPP of four types of grasslands in northern China

Precipitation is considered to be the primary resource limiting terrestrial biological activity in water-limited regions. Its overriding effect on the production of grassland is complex. In this paper, field data of 48 sites （including temperate meadow steppe, temperate steppe, temperate desert steppe and alpine meadow） were gathered from 31 published papers and monographs to analyze the relationship between above-ground net primary productivity （ANPP） and precipitation by the method of regression analysis. The results indicated that there was a great difference between spatial pattern and temporal pattern by which precipitation influenced grassland ANPP. Mean annual precipitation （MAP） was the main factor determining spatial distribution of grassland ANPP （r^2 = 0.61, P 〈 0.01）; while temporally, no significant relationship was found between the variance of AN PP and inter-annual precipitation for the four types of grassland. However, after dividing annual preeipitation into monthly value and taking time lag effect into account, the study found significant relationships between ANPP and precipitation. For the temperate meadow steppe, the key variable determining inter-annual change of ANPP was last August-May precipitation （r^2 = 0.47, P = 0.01）; for the temperate steppe, the key variable was July precipitation （r^2 = 0.36, P = 0.02）; for the temperate desert steppe, the key variable was April-June precipitation （r^2 = 0.51, P 〈 0.01）; for the alpine meadow, the key variable was last September-May precipitation （r^2 = 0.29, P 〈 0.05）. In comparison with analogous research, the study demonstrated that the key factor determining inter-annual changes of grassland ANPP was the cumulative precipitation in certain periods of that year or the previous year.

Stability of Nonlinear Feedback Shift Registers with Periodic Input

The stability of Non-Linear Feedback Shift Registers(NFSRs)plays an important role in the cryptographic security.Due to the complexity of nonlinear systems and the lack of efficient algebraic tools,the theorems related to the stability of NFSRs are still not well-developed.In this paper,we view the NFSR with periodic inputs as a Boolean control network.Based on the mathematical tool of semi-tensor product(STP),the Boolean network can be mapped into an algebraic form.Through these basic theories,we analyze the state space of non-autonomous NFSRs,and discuss the stability of an NFSR with periodic inputs of limited length or unlimited length.The simulation results are provided to prove the efficiency of the model.Based on these works,we can provide a method to analyze the stability of the NFSR with periodic input,including limited length and unlimited length.By this,we can efficiently reduce the computational complexity,and its efficiency is demonstrated by applying the theorem in simulations dealing with the stability of a non-autonomous NFSR.

基于自适应核带宽度Mean Shift算法的单木识别研究

为提高多树种森林中单木识别的精度,利用机载激光雷达点云数据作为研究对象,提出一种基于自适应核带宽度Mean Shift算法的单木识别方法。该方法先采用直方图分析法分离树冠点云和冠层下点云,再采用基于二维增量网格投影的区域生长法,估算单木冠幅有效半径,然后以单木冠幅有效半径作为自适应核带宽度,对树冠点云进行自适应Mean Shift聚类分析,得到树冠点簇,最后采用包络盒方法根据树冠点簇和树干点云的空间关系识别单木。试验结果表明,检测树与实际树的位置、树冠形态近似一致,单木召回率达到86.1%,准确率达到91.5%,高于2个对比试验的结果。研究证明设置的自适应核带宽度能够自动调整以反映局部树冠的实际大小,在多树种森林的单木识别中表现良好。

As Could We Assure Safety in Large-Scale Manufacturing of Nanoparticles for the Biomedical Use

Nanoparticles provide great advantages but also great risks. Risks associating with nanoparticles are the problem of all technologies, but they increase in many times in nanotechnologies. Adequate methods of outgoing production inspection are necessary to solve the problem of risks, and the inspection must be based on the safety standard. Existing safety standard results from a principle of “maximum permissible concentrations or MPC”. This principle is not applicable to nanoparticles, but a safety standard reflecting risks inherent in nanoparticles doesn’t exist. Essence of the risks is illustrated by the example from pharmacology, since its safety assurance is conceptually based on MPC and it has already come against this problem. Possible formula of safety standard for nanoparticles is reflected in many publications, but conventional inspection methods cannot provide its realization, and this gap is an obstacle to assumption of similar formulas. Therefore the development of nanoparticle industry as a whole (also development of the pharmacology in particular) is impossible without the creation of an adequate inspection method. There are suggested new inspection methods founded on the new physical principle and satisfying to the adequate safety standard for nanoparticles. These methods demonstrate that creation of the adequate safety standard and the outgoing production inspection in a large-scale manufacturing of nanoparticles are the solvable problems. However there is a great distance between the physical principle and its hardware realization, and a transition from the principle to the hardware demands great intellectual and material costs. Therefore it is desirable to call attention of the public at large to the necessity of urgent expansions of investigations associated with outgoing inspections in nanoparticles technologies. It is necessary also to attract attention, first, of representatives of state structures controlling approvals of the adequate safety standard to this problem, since it is impossible to compel producers providing the safety without the similar standard, and, second, of leaders of pharmacological industry, since their industry already entered into the nanotechnology era, and they have taken an interest in a forthcoming development of inspection methods.

Production of Meaning--A Critical Study of Narrative Ethics in Joyce Carol Oates＇s New Century Fiction

Narrative ethics shows multiple representing methods in Oates' s novels,including not only a variety of characters' narrative construction processes of self-narrative,other-narrative and social narrative but also the meaninggenerating process of the characters' interpretation and reorganization of the external and internal world.In so doing,the characters are able to obtain personal identity,the ethical communication and existence transcendence by the way of narrative.

Exploiting Virtual Elasticity of Production Systems to Respect OTD—Part 3: Basic Considerations for Modelling CPPS Characterized by Non-Ergodic Order Entry and Non-Deterministic Product-Mix for Fully Flexible Addressable Workstations

The recently experienced hype concerning the so-called “4th Industrial Revolution” of production systems has prompted several papers of various subtopics regarding Cyber-Phdysical Production Systems (CPPS). However, important aspects such as the modelling of CPPS to understand the theory regarding the performance of highly non-ergodic and non-deterministic flexible manufacturing systems in terms of Exit Rate (ER), Manufacturing Lead Time (MLT), and On-Time Delivery (OTD) have not yet been examined systematically and even less modeled analytically. To develop the topic, in this paper, the prerequisites for modelling such systems are defined in order to be able to derive an explicit and dedicated production mathematics-based understanding of CPPS and its dynamics: switching from explorative simulation to rational modelling of the manufacturing “physics” led to an own and specific manufacturing theory. The findings have led to enouncing, among others, the Theorem of Non-Ergodicity as well as the Batch Cycle Time Deviation Function giving important insights to model digital twin-based CPPS for complying with the mandatory OTD.

A Competitive Analysis on the Agricultural Products of China and Thailand in the U. S. Market

This paper selects China and Thailand as a reference object. Based on the U. S. market,it analyzes the export competitiveness of the agricultural products of China and Thailand by market share and growth rate,measures the agricultural products of China and Thailand export competition degree by export product similarity index and estimates the agricultural products of China and Thailand export competitiveness strength by shift share method. The results show that: At present,in the U. S. market,the competition of the agricultural products of China and Thailand is fierce,but export growth of China's agricultural products is faster and more competitive. Finally on how to further improve China's agricultural exports competitiveness in the U. S. market and promote China's agricultural export trade to the U. S. it puts forward the related countermeasures and suggestions.

Theoretical Study of the Nuclear Charge Distributions of Tin Isotopes

Nuclear binding energies, charge radii and the charge distributions of even-even tin （Sn） isotopes are calculated using relativistic mean field theory, and the theoretical results are found to be in accordance with the experimental data. The nuclear charge form factors for Sn isotopes are calculated using the phase-shift analysis method. It is shown that the minima of the charge form factors shift upward and inward with an increase in the neutron number of the Sn isotopes.

The Industrial Structure Efficiency of Gansu Province since the Start of Reform and Opening up

According to the time series data of relevant industries from 1978 to 2008 in Gansu Province,the industrial structure efficiency of Gansu Province is studied from different perspectives based on the overview of the analysis methods and by applying the analysis models including comparative labor productivity,deviation degree of industrial structure and shift-share analysis model.The results show that,in Gansu Province,the comparative labor productivity is low in the primary and tertiary industries,while that in the second industry is high;the deviation coefficient of industrial structure is big and the industrial structure and employment structure is extremely asymmetric;the proportion of sunrise industry is large and has great contribution to economic growth.However,the competiveness of the three main industries is relatively weak and need further improvement.

基于分块颜色直方图的MeanShift跟踪算法

针对基于MeanShift的跟踪算法对目标尺度变化适应能力差并且不能对目标的旋转进行跟踪的缺点,提出了一种基于分块颜色直方图的MeanShift跟踪算法。新算法引入目标旋转和缩放矩阵使得算法能够适应目标的旋转和尺度变化,分块的颜色直方图包含了目标的空间信息,提高了跟踪算法的鲁棒性和适应能力。实验结果表明新算法能够同时对目标的尺度和旋转变化进行稳定的跟踪,改善了原算法的适应能力。

结合mean-shift与MST的K-means聚类算法

针对初始点选择不当导致K-means陷入局部最小值问题,提出一种结合自适应mean-shift与最小生成树(MST)的K-means聚类算法。将数据对象投影到主成分分析(PCA)子空间,给出自适应mean-shift算法,并在PCA子空间内将数据向密度大的区域聚集,再利用MST与图连通分量算法,找出数据的类别数和类标签,据此计算原始空间的密度峰值,并将其作为K-means聚类的初始中心点。对K-means的目标函数、聚类精度和运行时间进行比较,结果表明,该算法在较短的运行时间内能给出较优的全局解。

改进的抗遮挡MeanShift目标跟踪算法

传统Mean Shift目标跟踪算法通过bin-bin颜色直方图表示目标特征,直方图中往往会混入背景颜色信息,造成跟踪不准确;同时由于Mean Shift算法具有局部最优性,当目标受到严重遮挡丢失后,不能对目标重新定位跟踪。为了解决上述问题,在颜色直方图和抗遮挡能力方面进行了改进。利用交叉bin颜色直方图代替传统的bin-bin颜色直方图表示目标特征,减少背景颜色的干扰,提高Mean Shift算法跟踪精度;当目标受到严重遮挡丢失后,通过一种尺度变化调整机制,在全局范围内搜索目标位置,提高Mean Shift算法抗遮挡能力。实验显示,改进后的算法不仅在背景干扰大时对目标的跟踪精度更高,而且当目标受到严重遮挡丢失后,也能够对目标重新定位跟踪。

血细胞图像分割的改进MEANSHIFT方法

根据血细胞图像的特点,采用一种基于改进的MEANSHIFT算法,对血细胞图像的G通道直方图和S分量直方图进行处理,自适应地得到白细胞核和其他区域的分割阈值,通过阈值分割将白细胞核从图像中分割出来,再通过后处理得到白细胞浆区域,从而实现白细胞的自适应阈值分割。在对MEANSHIFT算法进行改进后,阈值的获取自适应性更强,实验证明,这种自适应的阈值分割方法快速、有效,并且对于图像颜色变化、染色条件差异等鲁棒性强。