On the Adaptability Range, Self-Selection, and Economic Nature of Biological Evolution

This article employs a combined approach of biology and economics to reveal that biological evolution has an economic nature, evolving towards improved energy efficiency. The orthodox Darwinian theory of evolution describes evolution as the random variation of organisms and their survival through natural selection. In fact, the natural environment itself is a constantly changing context, and the strategy to adapt to this change is to enhance behavioral capabilities, thereby expanding the range and dimensions of behavior. Therefore, the improvement of behavioral capabilities is an important aspect of evolution. The enhancement of behavioral capabilities expands the range of adaptation to the natural environment and increases the space for behavioral choices. Within this space of behavioral choices, some options are more effective and superior to others;thus, the ability to select is necessary to make the improved behavioral capabilities more beneficial to the organism itself. The birth and development of the brain serve the purpose of selection. By using the brain to make selections, at least the “better” behavior will be chosen between two alternatives. Once the better behavior yields better results, and the organism can associate these results with the corresponding behavior, it will persist in this behavior. The persistent repetition of a behavior over generations will form a habit. Habits passed down through generations constitute a new environment, causing the organism’s genes to activate or deactivate certain functions, ultimately leading to genetic changes that are beneficial to that habit. Since the brain’s selection represents the organism’s self-selection, it differs from random variation;it is also a rational selection, choosing behaviors that either obtain more energy or reduce energy consumption. Thus, this evolution possesses an economic nature.

A self region based real-valued negative selection algorithm

Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.

Unsupervised Feature Selection Using Structured Self-Representation

Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation( SSR) by simultaneously taking into account the selfrepresentation property and local geometrical structure of features. Concretely,according to the inherent selfrepresentation property of features,the most representative features can be selected. Mean while,to obtain more accurate results,we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore,an efficient algorithm is presented for optimizing the objective function. Finally,experiments on the synthetic dataset and six benchmark real-world datasets,including biomedical data,letter recognition digit data and face image data,demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms.

1.5μm Self-Aligned Spotsize Converter Integrated DFB Fabricated by Selective Area Grown MOVPE

High performance 1 57μm spotsize converter monolithically integrated DFB is fabricated by the technique of self aligned selective area growth.The upper optical confinement layer and the butt coupled tapered thickness waveguide are regrown simultaneously,which not only offeres the separated optimization of the active region and the integrated spotsize converter,but also reduces the difficulty of the butt joint selective regrowth.The threshold current is as low as 4 4mA.The output power at 49mA is 10 1mW.The side mode suppression ratio (SMSR) is 33 2dB.The vertical and horizontal far field divergence angles are as small as 9° and 15° respectively,the 1dB misalignment tolerance are 3 6μm and 3 4μm.

Path Selection in Disaster Response Management Based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.

Walking and Aerobic Capacity in Old Adults after Concentric and Eccentric Endurance Exercise at Self-Selected Intensities

Self-selected exercise intensity can be a useful exercise prescription tool for older adults;however, it is not known if it can elicit improvements in walking and aerobic capacity. In older adults, effects of concentric or eccentric endurance exercise at self-selected walking speed were examined on 1-mile indoor walk performance, predicted maximum oxygen uptake and physiological parameters. Twenty-four older adults (67 ± 4 years) completed 3 × 30 min treadmill walks per week for 12-weeks on level (LTW, n = 11, 0%) or downhill (DTW, n = 13, ﹣10%) treadmill gradient at a self- selected speed, which progressed every 4 weeks. Maximal oxygen uptake was predicted using a 1-mile walk at 4-week intervals with physiological responses recorded using a portable metabolic system. One-mile walking speed increased from baseline following 8- and 12-weeks (12 weeks: LTW: 13% ± 6%, DTW: 14% ± 9%,

Molecular mapping of a novel wheat powdery mildew resistance gene Ml92145E8-9 and its application in wheat breeding by marker-assisted selection

Powdery mildew, caused by Blumeria graminis f. sp. tritici(Bgt), is one of the most devastating diseases of common wheat(Triticum aestivum L.). The wheat line 92145 E8-9 is immune to Bgt isolate E09. Genetic analysis reveals that the powdery mildew resistance in 92145 E8-9 is controlled by a single dominant gene, temporarily designated Ml92145 E8-9. Bulkedsegregant analysis(BSA) with simple sequence repeat(SSR) markers indicates that Ml92145 E8-9 is located on chromosome 2 AL. According to the reactions of 92145 E8-9,VPM1(Pm4 b carrier), and Lankao 906(PmLK906 carrier) to 14 Bgt isolates, the resistance spectrum of 92145 E8-9 differs from those of Pm4 b and PmLK906, both of which were previously localized to 2 AL. To test the allelism among Ml92145 E8-9, Pm4 b and PmLK906, two F2 populations of 92145 E8-9 × VPM1(Pm4 b) and 92145 E8-9 × Lankao 906(PmLK906) were developed in this study. Screening of 784 F2 progeny of 92145 E8-9 × VPM1 and 973 F2 progeny of 92145 E8-9 × Lankao 906 for Bgt isolate E09 identified 37 and 19 susceptible plants, respectively. These findings indicated that Ml92145 E8-9 is non-allelic to either Pm4 b or PmLK906. Thus, Ml92145 E8-9 is likely to be a new powdery mildew resistance gene on2 AL. New polymorphic markers were developed based on the collinearity of genomic regions of Ml92145 E8-9 with the reference sequences of the International Wheat Genome Sequencing Consortium(IWGSC). Ml92145 E8-9 was mapped to a 3.6 c M interval flanked by molecular markers Xsdauk13 and Xsdauk682. This study also developed five powdery mildew-resistant wheat lines(SDAU3561, SDAU3562, SDAU4173, SDAU4174, and SDAU4175)using flanking marker-aided selection. The markers closely linked to Ml92145 E8-9 would be useful in marker-assisted selection for wheat powdery mildew resistance breeding.

Grain selection and growth orientation of prior-β phase for Ti-6-4 during additive manufacturing:insights from a modeling perspective

The microstructure of Ti-6-4 components produced by additive manufacturing suffers from the coarse and elongated prior-β grain,which leads to a decrease of the tensile behavior and the occurrence of anisotropy.To understand and control the grain evolution,a multiscale simulation is applied to investigate the relationship between the grain selection,growth orientation,and the molten pool morphology with the different deposition layer numbers and processing parameters.The accuracy of the simulation is validated by experiments in both qualitative and quantitative ways.Results show that when the grain with unfavorable orientation loses the competitive growth with its neighbors,there will be a great chance that the blocked grain is eliminated in the following layer-and-layer deposition,which leads to the increase of the grain width.The size of the molten pool increases remarkably as the layer number increases,which lays a heavy burden on the stability of the molten pool.The analytical relationship between the molten pool morphology and the grain growth orientation is also deduced.The flat molten pool causes the grains with the <001> direction close to the building direction to have greater survival potential.Besides,decreasing the line power energy shows little effect on the stability of the molten pool and the grain growth orientation,especially when the deposited layer number is large.The revealing mechanisms will help in understanding and further controlling the grain evolution.

基于阴性选择原则的Non-self探测器生成算法

基于免疫系统异己检测原理,深入进行了计算机免疫系统探测器生成算法的研究.首先,简要介绍了阴性选择算法,总结了相关的探测器生成算法;然后,基于阴性选择原则提出了两种探测器生成算法,即位变异算法(BMGDGA)和余数生长算法(AGDGA).文中对两种算法在多种不同的数据集上进行了全面的验证和实验,并与穷尽式探测器生成算法进行了全面系统的比较.结果表明,两种探测器生成算法在综合性能上均优于穷尽式探测器生成算法.

基于选择性自校正卷积U-Net的肺部X射线图像肺实质分割

针对U-Net分割算法无法提取多尺度特征、易受到伪影和噪声干扰而导致在肺部X射线图像中肺实质分割不精确的问题,提出一种基于选择性自校正卷积的U-Net改进算法。改进后的U-Net算法将普通卷积模块替换为选择性自校正卷积模块,该模块采用多分支结构提取多尺度特征信息,使用Sigmoid函数和Softmax函数对多尺度特征信息进行选择性校正,使校正后的特征信息聚焦于肺实质区域,输出特征更加具有针对性。实验表明,该方法对骰子系数、交并比、F_(1)评分结果以及对肺实质分割结果都有一定程度的提升。

基于MTCN-Informer的铁矿球团工艺预测模型

成品球团流量的预测是生产过程的关键,它决定着整个生产的效率和产量。铁矿球团链箅机—回转窑是生产铁矿石制备高品质铁合金的重要工艺过程之一,具有大时滞、参数庞杂、耦合关系复杂等特点,且成品球团流量波动剧烈,使球团流量难以预测。为此,该文使用移动平均滤波器来平滑波动的数据,互信息法对庞杂的参数做特征选择,再利用基于自注意力机制的Informer球团流量预测模型,其降低传统自注意力机制的时间复杂度,提高了模型训练效率。同时,针对Informer模型的概率稀疏自注意力机制难以把握长时间序列波动的问题,通过TCN时间卷积网络来提取长时间序列的扩展信息依赖,同时结合Informer编码解码网络来处理上下文的信息,从而完成球团流量的精确预测。通过对工厂实际数据进行实验分析可知,与循环神经网络这类传统的深度学习模型相比,所提集成模型在预测精度、稳定性方面均为最优。

Biologically inspired self-organizing networks

Information networks are becoming more and more complex to accommodate a continuously increasing amount of traffic and networked devices,as well as having to cope with a growing diversity of operating environments and applications. Therefore,it is foreseeable that future information networks will frequently face unexpected problems,some of which could lead to the complete collapse of a network. To tackle this problem,recent attempts have been made to design novel network architectures which achieve a high level of scalability,adaptability,and robustness by taking inspiration from self-organizing biological systems. The objective of this paper is to discuss biologically inspired networking technologies.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Self - Crosslinking Behavior of Self - Emulsion Water - Based Polyure-thane Coating Agents

The relation between structures and properties of polyurethane are investigated by modern physical and chemical methods.The results obtained are as follows:the effects of the content of self-crosslinking agent on the properties of polyurethane,i.e.,dispersion stability,dynamical viscoelasticity and mechanical properties are discussed.It is found that the optimum molar ratio of epichlorohydrin and diethylenetriamine is 1:2.A mois-

Culture Self- Consciousness in Transmission of Chinese Philosophy from the Perspective of English Globalization -- Taking the Belt and Road Initiative for Example

The Belt and Road Initiative contains the aspiration of Chinese nation to pursue ideological freedom and desire of strengthening international exchanges and cooperation. This paper takes the Belt and Road Initiative for example,emphasizing the interpretation of the inheritance and development of Chinese culture so as to study the importance of Culture Self-Consciousness in Chinese Philosophy,in the context of English globalization. That is to say,this paper stresses the spirits and the stand of nation 's demonstration on pursuing peace,cooperation and sustainable development in the civilization continuity from traditional China to the contemporary China,with practical philosophic view.

基于FSN-MCCN-SA-BiLSTM的短期风速预测

为了提升短期风速预测的准确性,提出一种新型的短期风速预测方法。该方法以历史风速和气象数据作为输入,首先利用特征选择网络量化输入序列中每个时间步不同特征的重要程度,其次采用多尺度因果卷积网络捕捉其局部时序特征,然后引入自注意力融合不同卷积层的特征,得到能反映风速多尺度变化特性的高维特征序列,最后利用双向长短期记忆网络提取高维特征序列的长期时序特征并得到风速预测结果。实验结果表明,该方法能考虑不同输入特征对于风速的动态影响,同时充分提取风速序列的局部与长期时序特征,其进行提前1 h的风速预测时,所得归一化均方根误差与平均绝对误差分别为11.92%和8.11%,相关系数和决定系数分别为0.9735和0.9477,可有效提高短期风速预测的准确性。

基于mRMR-SOM的异步电机轴承故障诊断研究

针对异步电机轴承故障诊断问题,提出了一种融合最大相关最小冗余特征选择算法(mRMR)和自组织映射神经网络(SOM)的故障诊断方法,并将其应用于轴承故障诊断的不同阶段。首先,在实验室环境下搭建了异步电机故障诊断试验平台,在不同电机状态下分别采集振动、电流和电压信号,利用统计学方法获取了高维混合特征集;然后,以互信息为背景,利用mRMR根据特征与状态标签间的相关性和特征间的冗余性,筛选了具备强区分能力的特征,以避免计算冗余和后验诊断性能下降;最后,采用SOM对异步电机健康和轴承故障状态进行了分类识别,验证了SOM对异步电机轴承故障诊断的有效性,以及mRMR对故障诊断结果的影响。研究结果表明:基于mRMR-SOM的异步电机轴承故障诊断方法能够准确地区分健康和故障状态,测试集分类准确率达到89%;使用mRMR特征筛选能够将154维特征降低至17维,缩短23.5%的网络收敛时间,并将分类准确率由89%提升至98%;试验结果验证了基于mRMR-SOM的异步电机轴承故障诊断方法对于异步电机轴承故障诊断问题的有效性,且证实其具备良好的诊断效果。

The Pattern Growth of Carbon Nanotubes by Self-assembled Monolayers Techniques

The well controllable selective growth of carbon nanotubes (CNTs)on the desired area is an important issue for their future applications. In this study, a novel method for selective growth of CNTs was proposed by using the technology of self-assembly monolayers (SAMs) and the Fe-assisted CNTs growth. The Si wafers with the a : Si/Si3N4 layer patterns were first prepared by low pressure chemical vapor deposition (LPCVD)and lithography techniques to act as the substrates for selective deposition of SAMs. The selectivity of SAMs from APTMS solution (N-(2-aminoethyl)-3-aminopropyltrimethoxsilane) is based on its greater reactivity of head group on a-Si than Si3N4 films. The areas of pattern with SAMs will first chelate the Fe3+ ions by their diamine-terminated group. The Fe3+ ions were then consolidated to become Fe-hydroxides in sodium boron hydride solution to form the Fe-hydroxides pattern. Finally, the Fe-hydroxides pattern was pretreated in H plasma to become a well-distributed Fe nano-particles on the surface, and followed by CNTs deposition using Fe as catalyst in a microwave plasma-chemical vapor deposition (MP-CVD) system to become the CNTs pattern. The products in each processing step, including microstructures and lattice images of CNTs, were characterized by contact angle measurements, scanning electron microscopy (SEM), XPS, Auger spectroscopy, transmission electron microscopy (TEM) and high resolution TEM (HRTEM)deposition. The results show that the main process parameters include the surface activation process and its atmosphere, consolidation time and temperature, H plasma pretreatment. The function of each processing step will be discussed.

基于自编码器的PCA-SOM公共服务资源配置评价与选址优化决策方法

当前城市规划和公共服务资源配置存在分配不均和选址效率低下的问题。将公共服务资源的电力消耗数据与其资源数量、区域人口数量相结合,基于主成分分析(principal component analysis,PCA)评估各区域公共服务资源的配置状况,并以兰州市为案例,运用自组织映射(self-organizingmapping,SOM)算法进行教育资源的优化选址。研究发现,电力数据能有效指示资源配置的不足,并为优化分配提供精确的指导。尤其在兰州市,SOM算法的应用不仅提高了教育资源选址的效率,还促进了资源的公平分配。不仅为甘肃省提供了公共服务资源配置的科学决策依据,也为其他地区在相似领域的研究提供了参考。