Numerical study of plasmas start-up by electron cyclotron waves in NCST spherical tokamak and CN-H1 stellarator

According to the physics of tokamak start-up,this study constructs a zero-dimensional(0D)model applicable to electron cyclotron(EC)wave assisted start-up in NCST spherical torus(spherical tokamak)and CN-H1 stellarators.Using the constructed 0D model,the results obtained in this study under the same conditions are compared and validated against reference results for pure hydrogen plasma start-up in tokamak.The results are in good agreement,especially regarding electron temperature,ion temperature and plasma current.In the presence of finite Ohmic electric field in the spherical tokamak,a study on the EC wave assisted start-up of the NCST plasma at frequency of 28 GHz is conducted.The impact of the vertical magnetic field B_(v)on EC wave assisted start-up,the relationship between EC wave injection power P_(inj),Ohmic electric field E,and initial hydrogen atom density n_(H0)are explored separately.It is found that under conditions of Ohmic electric field lower than ITER(~0.3 V m^(-1)),EC wave can expand the operational space to achieve better plasma parameters.Simulating the process of28 GHz EC wave start-up in the CN-H1 stellarator plasma,the plasma current in the zerodimensional model is replaced with the current in the poloidal coil of the stellarator.Plasma startup can be successfully achieved at injection powers in the hundreds of kilowatts range,resulting in electron densities on the order of 10^(17)-10^(18)m^(-3).

A related degree-based frequent pattern mining algorithm for railway fault data

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

Numerical Simulation of a Two-Phase Flow with Low Permeability anda Start-Up Pressure Gradient

A new numerical model for low-permeability reservoirs is developed.The model incorporates the nonlinear characteristics of oil-water two-phase flows while taking into account the initiation pressure gradient.Related numerical solutions are obtained using a finite difference method.The correctness of the method is demonstrated using a two-dimensional inhomogeneous low permeability example.Then,the differences in the cumulative oil and water production are investigated for different starting water saturations.It is shown that when the initial water saturation grows,the water content of the block continues to rise and the cumulative oil production gradually decreases.

Backward Support Computation Method for Positive and Negative Frequent Itemset Mining

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.

Dynamic evaluation of a scaled-down heat pipe-cooled system during start-up/shut-down processes using a hardware-in-the-loop test approach

Micro-mobile heat pipe-cooled nuclear power plants are promising candidates for distributed energy resource power genera-tors and can be flexibly deployed in remote places to meet increasing electric power demands.However,previous steady-state simulations and experiments have deviated significantly from actual micronuclear system operations.Hence,a transient analysis is required for performance optimization and safety assessment.In this study,a hardware-in-the-loop(HIL)approach was used to investigate the dynamic behavior of scaled-down heat pipe-cooled systems.The real-time features of the HIL architecture were interpreted and validated,and an optimal time step of 500 ms was selected for the thermal transient.The power transient was modeled using point kinetic equations,and a scaled-down thermal prototype was set up to avoid mod-eling unpredictable heat transfer behaviors and feeding temperature samples into the main program running on a desktop PC.A series of dynamic test results showed significant power and temperature oscillations during the transient process,owing to the inconsistency of the rapid nuclear reaction rate and large thermal inertia.The proposed HIL approach is stable and effective for further studying of the dynamic characteristics and control optimization of solid-state small nuclear-powered systems at an early prototyping stage.

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several datasets.The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system,intrusion detection plays a significant role.The network security system detects the malicious actions in the network and also conforms the availability,integrity and confidentiality of data informa-tion resources.Intrusion identification system can easily detect the false positive alerts.If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks.Many research works have been done.The issues in the existing algo-rithms are more memory space and need more time to execute the transactions of records.This paper proposes a novel framework of network security Intrusion Detection System(IDS)using Modified Frequent Pattern(MFP-Tree)via K-means algorithm.The accuracy rate of Modified Frequent Pattern Tree(MFPT)-K means method infinding the various attacks are Normal 94.89%,for DoS based attack 98.34%,for User to Root(U2R)attacks got 96.73%,Remote to Local(R2L)got 95.89%and Probe attack got 92.67%and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

Uncharted Territory: Frequent Relapsing, Steroid Sensitive Secondary Minimal Change Nephrotic Syndrome Cause by Solid Tumor of the Gastro-Esophageal Junction —(Case Presentation and Review of the Literature)

We reported a biopsy proved case of minimal change nephrotic syndrome in a 72-year-old patient. The minimal change nephrotic syndrome has been steroid sensitive, but the patient had 7 relapses over a span of 5 years. Each time the dose of steroid is tapered, a relapse of the nephrotic syndrome occurred. Eventually, the patient was complaining of dysphagia and difficulty swallowing. Hospital work-up with barium swallow, endoscopy, and CT of the chest, abdomen and pelvis, revealed a focal stenotic lesion with mild to moderate esophageal dysmotility 7/15/2022. A diagnosis of an ulcerating lesion with biopsy confirmed a neuro-endocrine carcinoma of the gastro-esophageal junction was entertained. The CT of the chest/abdomen/pelvis, 7/19/2022, has shown, an esophageal mass of 5.1 × 5.6 × 7 cm of the gastro-esophageal junction with ulceration. No evidence of spread beyond the esophagus and stomach. The histology revealed a poorly differentiated neuroendocrine tumor of the gastro-esophageal junction. The patient underwent several rounds of chemotherapy, radiation, and surgery culminating in tumor control. His nephrotic syndrome was resolved after the tumor has been controlled by surgery and chemotherapy.

航班延误特征可视分析方法

为分析航班延误发生规律,提出一种数学模型联合多视图协同的可视分析方法。对SEIR传染病传播模型进行调整,建立航班延误传播模型分析延误的传播特征,在此基础上,运用频繁子图挖掘算法提取延误频繁模式;设计基于中点分割的地图网络图、矩阵热力图和时序图,分析延误的时空分布特征;设计VA-FDC系统用于方法验证。实验结果表明,VA-FDC能够有效分析航班延误时空分布特征,依据航班延误传播模型准确描述延误传播特征,为相关部门有效措施的制定提供借鉴。

生物启发式的模式挖掘方法综述

频繁项集挖掘、关联规则挖掘和高效用项集挖掘是模式挖掘中相互关联且不断发展的三个领域。近年来,由于传统算法无法应对爆炸式增长的数据环境,启发式算法已成为模式挖掘方法中的研究热点。为揭示模式挖掘领域的研究与发展现状,从粒子群、遗传、蚁群、蜂群等多个生物启发式算法角度对近年的频繁模式和高效用项集方法研究成果进行全面的分析与总结。对不同生物启发的模式挖掘方法,从使用策略、对比算法、数据集、优缺点进行概述,对相同数据集的实验结果及性能指标进行详细的对比分析。针对目前生物启发式模式挖掘方法中的不足,提出了下一步的研究方向,包括动态数据流、多目标进化、模糊计算和复杂数据类型的研究。

一种基于关联程度的高效用数量比频繁模式挖掘算法

高效用频繁模式挖掘算法运用数据项的重要度信息,能够从数据中挖掘出更重要的频繁模式,而高效用数量比频繁模式挖掘算法可以进一步研究频繁模式中数据项的数量比例关系,是目前数据挖掘领域中的研究课题。从提高算法性能和实用性的角度出发对高效用数量比频繁模式挖掘算法进行优化,提出了一种基于关联程度的高效用数量比频繁模式挖掘算法RHUQI-Miner。RHUQI-Miner首先提出关联程度的概念,依据关联程度构建项目关联程度结构,并给出关联剪枝优化策略,寻找关联程度更高的项目集合,减少冗余和无效的频繁模式;随后运用修正模式长度策略,修正挖掘过程中项集的效用信息,使算法可根据实际数据情况控制输出频繁模式的长度,进一步提升算法的性能,提高算法的实用性。通过对RHUQI-Miner在动车组PHM系统车载故障数据集上的实验结果进行分析,表明该算法能够有效减少挖掘过程中的时间以及内存消耗,可以得出该算法适用于铁路实际数据和业务的有效结论。

基于频繁模式挖掘算法的中医问诊策略研究

目的研究中医问诊策略,实现快速捕捉患者的关键病情信息,推进中医问诊客观化的发展。方法采用基于关联分析中频繁模式挖掘算法的症状提问模型,并使用交叉合并的方法建立中医单系统症状提问与多系统综合症状提问的中医症状问诊策略,达到通过最短的时间、最高的效率来获取到患者关键病情信息。结果实现了从单系统问诊到五系统综合问诊的突破,通过单系统与五系统两种症状提问模式实现了高效获取患者病情信息的过程,且对比传统量表提问方式,系统减少了65%的提问次数就可获取到患者92%的症状信息,大大提高了对患者症状信息获取的效率。结论在两种不同的症状提问模式下,打破了中医基于量表来询问患者的传统问诊模式,缩短了对患者症状获取的时间,简化了问诊流程,减少了由于经验不足或人为主观造成的差异,能够用于中医临床辅助诊断中。

基于知识的目标关系分析挖掘技术

在战场目标价值分析和打击目标排序分析过程中,为了构建敌方作战目标体系,需要分析战场目标间的关联关系。轨迹和部署数据中隐藏大量信息,提出了一种从轨迹和部署数据中挖掘出感兴趣的目标关系类型信息的方法,所提方法对轨迹部署数据进行时空聚类,从聚类结果提取目标;对聚类目标使用频繁项挖掘算法分析挖掘满足一定支持度的有关联关系的目标,再根据构建的关系类型知识库或关系规则,分析目标间的具体关系类型。所提方法能对积累的目标历史轨迹部署数据分析挖掘出目标间的关联关系,挖掘出目标潜在的关系类型可为后续构建目标体系提供关系数据。

基于接收信号强度预测的自适应垂直切换算法

城市部署的超密集异构无线网络中车辆终端的运动状态和业务类型差异巨大,针对不同运动状态和业务类型的终端并发接入导致的频繁切换和拥塞问题,提出一种基于接收信号强度预测的自适应垂直切换算法。在切换触发阶段使用长短时记忆神经网络预测接收信号强度,在信道链路质量恶化之前提前触发切换,在网络选择阶段综合考虑网络参数以及终端在不同运动状态和业务类型下的接入偏好,并选出综合效益值最高的网络作为切换目标网络。仿真结果表明,该算法能更好地适应终端运动状态和业务类型的变化,能降低不必要的切换次数和网络拥塞度。

肉鸽规模养殖多发病的诊断和防治

重点探讨了规模化肉鸽养殖过程中多发病的诊断和防治方法,并以鸽新城疫、鸽副伤寒、鸽毛滴虫病、鸽腺病毒病等为例,详细介绍了每种鸽病的病原体、症状、诊断和防治技术,为肉鸽养殖业的健康发展提供技术支撑。

基于关联规则的群组共享密钥协议研究

静态的密钥管理策略难以适应网络动态变化.针对这一问题提出一种基于关联规则的群组密钥管理方案,使用关联规则分析移动分布式网络中密钥之间的关系,通过路由中密钥使用频率挖掘群组密钥之间的频繁关系,根据可信度建立合理的密钥共享方案.在效率上,该方案适应网络动态变化,降低对先验知识的需求,发现频繁密钥集规模与路由延时之间的折中关系,网络实体可根据密钥之间的频繁关系动态调整群组密钥管理方式.在安全性上,节点根据密钥规则选择密钥策略,在频繁密钥集上实施共享密钥协议,提高了安全性.

基于频繁序列挖掘的出租车轨迹特性分析

为进一步厘清不同出租车路径选择行为的差异性,采用频繁序列挖掘方法提取了同一个OD对间的频繁路径,构建路径选择集,分别从静态和动态两个角度分析路径集的相似特性。以西安市出租车的轨迹数据为研究对象,通过栅格划分与路网匹配,获得了不同OD对之间的路径集合。重新定义了频繁路径,采用PrefixSpan演变算法,在得到频繁子序列的基础上引入动态阈值和频繁度指标挖掘频繁路径,提取了最短路径和其他路径,完成了3类有效路径集的构建,并分析了路径集的一般属性。其后,将路径上二维时间序列(轨迹)间的相似度表示为动态相似度,将一维有向序列(路段)间的相似度表示为静态相似度,基于改进的最长公共子序列和动态时间规整算法对3类路径进行了相似性分析。结果表明:频繁路径与最短路径的相似度较高,意味着大多数出租车仍然选择具有最低出行时间的路段,但不一定会选择最短路径;时间和距离仍是出行者选择路径时主要考虑的因素,但出行者并不完全追求时间最短或距离最短;试验得到的动态相似度计算结果显著高于静态相似度计算结果,说明路径上的二维时序相似度高于一维形状相似度;两种方法下频繁路径和最短路径的相似度均最高,最短路径和其他路径的相似度均最低,比较结果的一致性说明可以用动态轨迹的相似度来大致度量静态路径的相似度。文中的频繁路径挖掘算法具有一定的可靠性,可为城市交通管理者进行路径推荐、道路规划等提供支持。

基于滑动窗口含负项的高效用模式挖掘

针对传统高效用模式挖掘均未考虑项的效用值为负,以及对流数据处理的时效性问题,提出一种基于滑动窗口的高效用挖掘算法HUPN_SW。利用一种新定义的滑动窗口正负效用列表PNSWU-List,维护挖掘最近批次高效用模式集所需的所有信息,实现有效的逐批次挖掘,避免重复的数据库扫描,在不产生候选效用模式集的情况下,直接挖掘出高效用模式,使HUPN_SW有效适应于动态流数据。实验结果表明,HUPN_SW算法在运行时间和可扩展性方面有良好表现。

清热安神胶囊联合胺碘酮治疗痰火扰心型频发室早临床研究

目的观察清热安神胶囊联合胺碘酮治疗痰火扰心型频发室性早搏(premature ventricualr contraction,PVC)的临床疗效。方法将80例符合纳入排除标准的患者随机分成两组,对照组给予单用胺碘酮治疗,治疗组采用胺碘酮联合清热安神胶囊治疗,治疗1月后比较两组患者的检测指标和临床效果,房室大小、EF值、早搏类型、数量、形态、总心搏数、室性早搏数量等变化。结果治疗后治疗组患者心悸症状比例(16.25%vs 47.50%,P<0.05)、中医证候积分和室早负荷中位数(13.9%vs 16.4%,P<0.05)的改善程度明显优于对照组。结论联合使用胺碘酮和清热安神胶囊用于治疗PVC的临床效果明显优于单用胺碘酮治疗。