OPTIMIZATION OF AIRPORT TAXIING PLANNING DURING CONGESTED HOURS BASED ON IMMUNE CLONAL SELECTION ALGORITHM

In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.

Profiling of hepatocellular carcinoma neoantigens reveals immune microenvironment and clonal evolution related patterns

Objective: Neoantigens derived from tumor-specific genomic alterations have demonstrated great potential for immunotherapeutic interventions in cancers. However, the comprehensive profile of hepatocellular carcinoma(HCC) neoantigens and their complex interplay with immune microenvironment and tumor evolution have not been fully addressed.Methods: Here we integrated whole exome sequencing data, transcriptome sequencing data and clinical information of 72 primary HCC patients to characterize the HCC neoantigen profile, and systematically explored its interactions with tumor clonal evolution, driver mutations and immune microenvironments.Results: We observed that higher somatic mutation/neoantigen load was associated with better clinical outcomes and HCC patients could be further divided into two subgroups with distinct prognosis based on their neoantigen expression patterns. HCC subgroup with neoantigen expression probability high(NEP-H) showed more aggressive pathologic features including increased incidence of tumor thrombus(P=0.038), higher recurrence rate(P=0.029),more inclined to lack tumor capsule(P=0.026) and with more microsatellite instability sites(P=0.006). In addition,NEP-H subgroup was also characterized by higher chance to be involved in tumor clonal evolution [odds ratio(OR)=46.7, P<0.001]. Gene set enrichment analysis revealed that upregulation of MYC and its targets could suppress immune responses, leading to elevated neoantigen expression proportion in tumor cells. Furthermore, we discovered an immune escape mechanism that tumors could become more inconspicuous by evolving subclones with less immunogenicity. We observed that smaller clonal mutation clusters with higher immunogenicity in tumor were more likely to involve in clonal evolution. Based on identified neoantigen profiles, we also discovered series of neoantigenic hotspot genes, which could serve as potential actionable targets in future.Conclusions: Our results revealed the landscape of HCC neoantigens and discovered two clinically relevant subgroups with distinct neoantigen expression patterns, suggesting the neoantigen expression should be fully considered in future immunotherapeutic interventions.

Directional Filter for SAR Images Based on Nonsubsampled Contourlet Transform and Immune Clonal Selection

A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.

Feasibility Study of Parameter Identification Method Based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm

The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

Stator Winding Turn Faults Diagnosis for Induction Motor by Immune Memory Dynamic Clonal Strategy Algorithm

Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.

INFLUENCE OF IMMUNE STATUS OF THE IMMUNE DEFICIENT MICE ON THE METASTATIC PHENOTYPES OF THE HETEROGENEOUS CLONAL SUBLINES OF HUMAN LUNG GIANT CELL CARCINOMA

By using cell cloning technique, 4 sublines (A,C,D,E) were isolated from a cell line of human lung giant cell carcinoma (PLA-801). After subcutaneous inoculation in T-cell deficient BALB/c nude mice, the incidence of tumor growth and spontaneous metastasis were the highest in subline D, moderate in sublines A and E, and lowest in subline C. Tumor cells of subline C also showed similar low tumorigenicity in another T-cell deficient 615/ PB1 nude mice.However, in 615/PB1 beige nude mice with con-genitally combined immune-deficiency in both T and NK cell activity, tumor cells of the rarely metastatic subline C do produce significantly high frequency of tumor growth and spontaneous metastasis.Morphological studies (light microscope, electron microscope and immunohistochemistry) showed rich microfilaments and Vimentin positive in the cytoplasm of metastatic tumor cells. This may imply a possibility that tumor cells differentiate towards the direction favourable to spreading and metastasis.

Genome and clonal hematopoiesis stability contrasts with immune,cfDNA,mitochondrial,and telomere length changes during short duration spaceflight

Background The Inspiration4(I4)mission,the first all-civilian orbital flight mission,investigated the physiological effects of short-duration spaceflight through a multi-omic approach.Despite advances,there remains much to learn about human adaptation to spaceflight's unique challenges,including microgravity,immune system perturbations,and radiation exposure.Methods To provide a detailed genetics analysis of the mission,we collected dried blood spots pre-,during,and post-flight for DNA extraction.Telomere length was measured by quantitative PCR,while whole genome and cfDNA sequencing provided insight into genomic stability and immune adaptations.A robust bioinformatic pipeline was used for data analysis,including variant calling to assess mutational burden.Result Telomere elongation occurred during spaceflight and shortened after return to Earth.Cell-free DNA analysis revealed increased immune cell signatures post-flight.No significant clonal hematopoiesis of indeterminate potential(CHIP)or whole-genome instability was observed.The long-term gene expression changes across immune cells suggested cellular adaptations to the space environment persisting months post-flight.Conclusion Our findings provide valuable insights into the physiological consequences of short-duration spaceflight,with telomere dynamics and immune cell gene expression adapting to spaceflight and persisting after return to Earth.CHIP sequencing data will serve as a reference point for studying the early development of CHIP in astronauts,an understudied phenomenon as previous studies have focused on career astronauts.This study will serve as a reference point for future commercial and non-commercial spaceflight,low Earth orbit(LEO)missions,and deep-space exploration.

An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune

In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.

Acquired aplastic anemia:Is bystander insult to autologous hematopoiesis driven by immune surveillance against malignant cells?

We previously reported a serendipitous finding from a patient with refractory severe aplastic anemia who had gotten an unexpected hematological response to treatment with gut-cleansing preparations(GCPs).This patient experienced three recurrences over the ensuing one year of intermittent GCP treatments,with each recurrence occurring 7-8 wk from a GCP.After his third recurrence,he was prescribed successive treatment with rifampicin,berberine,and monthly administered GCP for 4 mo,and he developed an erythroid proliferative neoplasma and an overwhelming enteropathy,and eventually died of septic shock.Laboratory investigations had validated the resolution of myelosuppression and the appearance of malignant clonal hematopoiesis.From the treatment process and laboratory investigations,it is reasonably inferred that the engagement of gut inflammation is critically required in sustaining the overall pathophysiology of acquired aplastic anemia probably by creating a chronic inflammatory state.Incorporation of rifampicin,berberine,and monthly GCP into cyclosporine can enhance the immunosuppressive effect.In a subgroup of acquired aplastic anemia patients whose pathogenesis is associated with genotoxic exposure,the suppressed normal hematopoiesis may result from the bystander insult that is mediated by the soluble inflammatory cytokines generated in response to the immunogenic products of damaged hematopoietic cells in the context of chronic inflammatory state and may offer a protective antineoplastic mechanism against malignant proliferation.

Multi-agent immune recognition of water mine model

It is necessary for mine countermeasure systems to recognise the model of a water mine before destroying because the destroying measures to be taken must be determined according to mine model. In this paper, an immune neural network (INN) along with water mine model recognition system based on multi-agent system is proposed. A modified clonal selection algorithm for constructing such an INN is presented based on clonal selection principle. The INN is a two-layer Boolean network whose number of outputs is adaptable according to the task and the affinity threshold. Adjusting the affinity threshold can easily control different recognition precision, and the affinity threshold also can control the capability of noise tolerance.

Parkinson’s Disease Recognition Using Artificial Immune System

This work deals the application of the artificial immune system to discriminate between healthy and people with Parkinson’s disease (PWP). As the symptoms of Parkinson’s disease (PD) occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Taking inspiration from natural immune systems, we try to grab useful properties such as automatic recognition, memorization and adaptation. The developed algorithms have as a base the algorithm of training bio inspired CLONCLAS. The results obtained are satisfactory and show a great reliability of the approach.

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

An immunity-based technique to detect network intrusions

This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest’s negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.

混杂免疫多目标优化算法及对动态经济环境调度问题优化

动态经济环境调度(DEED)问题是电力系统调度中一类含大规模约束的高维多目标优化问题,传统的进化算法易于陷入局部最优,使得所获的Pareto前沿分布性和收敛性差。为了充分挖掘免疫系统的克隆选择原理,提出一种混杂免疫多目标优化算法(HIMOA)。该算法以传统进化算法为基本框架,面对高维决策变量优化易于陷入局部最优的缺陷,改进外部存档更新机制以保存历代优秀的多样性个体,采用克隆、高斯突变策略强化局部开采能力,有效地迫使算法跳出停滞搜索状态。为应对大规模约束,提出逐步微调机组出力策略,提高进化群体的可行性。数值仿真实验以10机系统为测试算例,将HIMOA与著名的六种算法MODE、NSGA-Ⅱ、IMOEA/D-CH、ADEA、MOHDE_SAT、MONNDE进行比较分析,结果表明,HIMOA能为DEED问题的10机系统提供较好的Pareto解,所获的Pareto前沿收敛性和分布性优越于其他算法,各评价指标的箱型图表明HIMOA具有优越于其他算法的统计特征。

基于Huff模型的电动汽车充电站选址定容方法

对于用户充电选择行为随机性的欠考虑,导致充电站服务范围划分和容量配置不合理的问题,提出了一种基于Huff模型的电动汽车充电站选址定容方法。综合考虑充电站规模、充电价格、用户充电成本对用户充电选择行为的影响,利用Huff模型分析用户对不同充电站的选择概率,并基于用户的选择概率确定充电站的服务范围和充电需求;综合考虑用户充电可达性、规划区域总功率、电动汽车充电功率,以充电站年总成本最小为目标,建立充电站的选址定容模型,并采用免疫克隆选择-变邻域搜索混合算法求解模型。MATLAB仿真结果表明所提选址定容方法能合理地划分服务范围,提高充电站规划的经济性。

Clonal Strategy Algorithm Based on the Immune Memory

Based on the clonal selection theory and immune memory mechanism in the natural immune system, a novel artificial immune system algorithm, Clonal Strategy Algorithm based on the Immune Memory （CSAIM）, is proposed in this paper. The algorithm realizes the evolution of antibody population and the evolution of memory unit at the same time, and by using clonal selection operator, the global optimal computation can be combined with the local searching. According to antibody-antibody （Ab-Ab） affinity and antibody-antigen （Ab-Ag） affinity, the algorithm can allot adaptively the scales of memory unit and antibody population. It is proved theoretically that CSAIM is convergent with probability 1. And with the computer simulations of eight benchmark functions and one instance of traveling salesman problem （TSP）, it is shown that CSAIM has strong abilities in having high convergence speed, enhancing the diversity of the population and avoiding the premature convergence to some extent.

自动化药房多个出药单据的调度优化

为了提高自动化药房的出药速率,实现多个出药单据药品出库时间最短。以某医院自动化药房出药系统结构为基础,建立多个出药任务单据的时间调度模型。针对此模型设计了改进的混合鲸鱼优化算法(H-WOA)。首先对多个出药单据进行整合分组,融入了遗传算法的交叉、变异,来提高种群探索的多样性;其次引入了免疫克隆算法的抗体与抗体的亲和度判定原理,来帮助构建最佳执行序列;最后结合鲸鱼算法的包围收缩和螺旋更新机制来保证种群最终收敛于最佳鲸鱼位置。实现结果表明,以3个出药单据为例,H-WOA优化算法相比原算法和未含优化算法效率分别提高了6.11%和18.11%。在4个、5个出药单据调度优化上,具有同等的优化效果。证明改进算法具有很好的寻优能力。

改进型免疫克隆布谷鸟算法求解软后坐火炮多参数辨识

软后坐火炮利用前冲动能抵消部分后坐能量,从而减小火炮发射过程中的冲击与振动。为进一步研究软后坐火炮发射过程中的动力学特性以及解决软后坐火炮发射过程动力学模型多参数辨识困难的问题,首先依据伯努利方程建立软后坐火炮发射过程的动力学模型,其次提出了一种改进型免疫克隆布谷鸟算法(improved immune clone cuckoo algorithm,IICCA)。该算法在更新过程中引入随机交叉与高频变异提高了算法的局部搜索能力,引入自适应算子来克服算法不易取得最佳概率值的问题,引入精英抗体提取算子来提高算法的计算效率。为进一步提高算法的收敛速度,引入动态疫苗接种策略,对疫苗接种后的抗体种群采用莱维飞行和巢寄生行为进行二次搜索。通过5个多峰测试函数对改进算法进行验证,计算结果表明该改进算法相比免疫克隆选择算法、免疫遗传算法、自适应粒子群算法和改进型布谷鸟算法均具有更高的计算精度和更快的收敛速度。最终,辨识结果与试验结果的曲线相似度高达97.02%,表明IICCA在解决软后坐火炮发射过程多参数辨识问题上的有效性和准确性。

人工免疫算法及其应用

阐述了人工免疫系统的基本概念,讨论了几种典型的算法,包括基于免疫系统基本机制的免疫算法、基于免疫特异性的否定选择算法、基于免疫系统克隆选择理论的克隆选择算法、基于接种疫苗及免疫多样性的免疫进化算法、AIS与神经网络混合智能算法和模糊免疫系统等;以年代为序简述了AIS发展历史,介绍了AIS在若干具有代表性的领域中的应用情况。最后通过对AIS的特性和存在问题的分析,展望了今后的研究重点和发展趋势。

一种基于免疫克隆的偏好多维QoS云资源调度优化算法

针对云计算环境中高效资源调度问题,首先从理论上对云资源调度进行了建模,对用户应用偏好和多维QoS中的用户效用进行了量化,给出了多维QoS优化的目标函数.结合具有快速多目标优化能力的免疫克隆算法,提出了一种云资源调度优化算法.根据应用偏好信息为抗体分配偏好优先级,据此进行抗体的免疫克隆操作,提高抗体免疫基因操作的预见性,改善了向最优解的高效收敛能力.实验结果分析表明,该算法能改善云资源调度系统的可用性、负载均衡离差、有效时间等方面的性能,满足了云计算环境的实际需求.