Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

基于小波集成一维卷积神经网络的抗噪声聚变电源故障诊断方法研究

数据驱动的电源故障诊断方法高度依赖于电源传感器的信号数据质量,托卡马克聚变装置中的电源系统往往在复杂电磁场耦合的环境下运行,导致其采集到的具有物理特征的信号常与大量无法解耦的噪声混合。为了抑制噪声对最终诊断结果的影响,提出了一种利用抗噪声小波增强一维卷积神经网络的多分支降噪网络(Hierarchy Branch Denoising Convolutional Neural Network,HBD-CNN),以完成噪声干扰下的电源系统故障诊断任务。具体而言,本研究将离散小波变换(Discrete Wavelet Transform,DWT)的信号分解功能植入CNN的网络层中,结合对噪声更加鲁棒的指数线性激活单元(Exponentially Linear Unit,ELU),对传统1D-CNN网络结构进行深度优化。此外,根据先验知识构建起的数据多层级结构,搭配网络中分层级的分类模块,提高了HBDCNN的泛化能力。最后,基于仿真电源数据集开展了对本模型架构的初步验证,当信噪比为10 dB时,对电源变换器的故障诊断准确率可达98.31%;当信噪比为2 dB时,准确率仍能保持92%以上。实验结果表明,HBDCNN在噪声工况下具有良好的故障诊断性能。

消除小鼠肝脏冰冻切片自发荧光4种封闭方法的比较

目的通过对小鼠肝脏冰冻切片进行不同的封闭处理,探寻消除小鼠肝脏自发荧光的最佳方法,以降低对免疫荧光阳性信号的干扰,提高免疫荧光结果的准确性。方法(1)经脾静脉注射肝脏荷瘤裸小鼠:将Hepa1-6-GFP细胞通过脾脏注射方法使其在雄性无胸腺BALB/c裸小鼠肝脏定植,并将肝脏进行冰冻连续切片,对切片分别进行AB液(内源性生物素封闭液)、blocking、AB液+blocking、丙酮+AB液+blocking封闭处理,然后检测小鼠肝脏冰冻切片自发荧光。(2)C57BL/6小鼠:对C57BL/6小鼠肝脏进行冰冻连续切片,对切片分别进行上述4种封闭处理,用F4/80标记肝巨噬细胞(Kupffer细胞),然后检测小鼠肝脏冰冻切片自发荧光。结果肝脏组织冰冻切片免疫荧光染色时,4种封闭处理都可以减弱肝脏切片的自发荧光,但丙酮+AB液+blocking封闭处理效果最佳。结论丙酮+AB液+blocking联合应用对小鼠肝脏组织冰冻切片自发荧光的消除效果最好。

基于垂直磁场压缩的EAST等离子体初步研究

基于等离子体参数随大半径/小半径变化的规律,分析了电流平顶段垂直磁场与相关等离子体参数的关系。垂直磁场的非线性部分用于分析非感应电流驱动效应,包括自举电流效应。此外,对于给定的等离子体电流,推导了平顶段垂直磁场与线平均密度之间的关系,并进一步研究压缩后的等离子体。基于EAST第41195次放电的数据分析表明,垂直磁场强度的增大可以使得等离子体温度、密度、βp和自举电流份额获得提升,为等离子体参数高参数,特别是βp提供了一种可能性参考。

Knowledge Fusion and Synchronization over Ubiquitous Ontology Mapping

With the rapid development and popularization of web services, the available information types and structure are becoming more and more complex and challenging. Actually web services involve the need for dynamic integration and transparent knowledge integration, in light of the urgent information changing track. Under this situation, the traditional search engine and information integration cannot finish this challenge, thereby bringing the opportunity for knowledge fusion and synchronization. This paper proposes a multi-matching strategy ontology mapping method for web information, i.e., ubiquitous ontology mapping method (U-Mapping), which can be viewed as a base collection of information on multiple ontologies made to appear anytime and everywhere. This approach is usually built independently by different information providers, avoiding the grammatical and semantic conflict. Finally, the ontology case information can be utilized under the consolidation of the U-Mapping, concerning language technology and machine learning methods.

Uncertainty Analysis for Software Service Evolution in the Heterogeneous Cloud Environment

To solve the problem of resource heterogeneity and the dynamic structure, loose coupling of integrated applications has brought a lot of benefits in clouds environment. Thus, the development of highly robust service-oriented applications has many challenges, especially for the autonomy of service resources over the system components to the end-user portal. In this paper, a proposed method for the business users can satisfy the service availability changes in the early warning and application for service relationship adjustment. Then, the designed mechanism can deal with exception not available for service in a real-time development application for a business user. Based on the heterogeneous model of service-oriented applications, an availability process with lifecycle analysis is proposed to ensure that service resources are available to integrate components at different levels.

Subject Oriented Autonomic Cloud Data Center Networks Model

This paper investigates autonomic cloud data center networks, which is the solution with the increasingly complex computing environment, in terms of the management and cost issues to meet users’ growing demand. The virtualized cloud networking is to provide a plethora of rich online applications, including self-configuration, self-healing, self-optimization and self-protection. In addition, we draw on the intelligent subject and multi-agent system, concerning system model, strategy, autonomic cloud computing, involving independent computing system development and implementation. Then, combining the architecture with the autonomous unit, we propose the MCDN (Model of Autonomic Cloud Data Center Networks). This model can define intelligent state, elaborate the composition structure, and complete life cycle. Finally, our proposed public infrastructure can be provided with the autonomous unit in the supported interaction model.

Dynamic Load Balancing with Overlay-Based Reconfiguration for Wireless Sensor Networks

Wireless sensor networks are characterized by multihop wireless links and resource constrained nodes. In terms of data collection and forwarding scheduling, this paper investigates the load balancing in sensor nodes and wireless link based on the performance of wireless sensor networks. Leveraging the property of dissimilarity distribution, a method to quantitatively evaluate the benefits of load balancing is presented, in order to access the profitability. Then a novel Dynamic Load Balancing of Overlay-based WSN (DLBO) algorithm has been put forward. In particular, the tradeoff between transferring ratio and the load imbalance among nodes is discussed. The load balancing method in this paper outperforms others based on balancing factor, different nodes number and data scales of applications. The proposed model and analytical results can be effectively applied for reliability analysis for other wireless applications (e.g., persistent data delivery is involved).

Adaptive Cache Allocation with Prefetching Policy over End-to-End Data Processing

With the speed gap between storage system access and processor computing, end-to-end data processing has become a bottleneck to improve the total performance of computer systems over the Internet. Based on the analysis of data processing behavior, an adaptive cache organization scheme is proposed with fast address calculation. This scheme can make full use of the characteristics of stack space data access, adopt fast address calculation strategy, and reduce the hit time of stack access. Adaptively, the stack cache can be turned off from beginning to end, when a stack overflow occurs to avoid the effect of stack switching on processor performance. Also, through the instruction cache and the failure behavior for the data cache, a prefetching policy is developed, which is combined with the data capture of the failover queue state. Finally, the proposed method can maintain the order of instruction and data access, which facilitates the extraction of prefetching in the end-to-end data processing.

Enantioselective Construction of Polycyclic Chromanes through Organocatalytic Sequential Quintuple Reaction via One-Pot Step-Wise Procedure

An efficient and highly stereoselective synthetic method to access polycyclic chromanes has been achieved through organocatalyzed one-pot step-wise reactions involving 2-hydroxycinnamaldehydes,2-aminochalcones,and malononitrile as substrates.The reactions underwent a quintuple process by aza-Michael/Michael/Knoevenagel/oxa-Michael/aldol-type reaction in sequence to give products bearing 3 new generated rings and 5 chiral centers in moderate to quantitative yields with excellent stereoselectivities.

Fabrication and evaluation of high-performance 3D interpenetrated network structures SiC/Al composites with high-purity plate-like a-SiC framework

To reinforce compatibility with the thermophysical and mechanical properties of SiC/Al composites for electronic packaging to improve the stability and reduce fatigue failure of electronic integrated devices,a novel 3D SiC reinforced framework with interpenetrated plate-like a-SiC grains was synthesized.A small amount of doped a-SiC was seeded to induce the transformation of b-SiC to plate-like a-SiC at 2,300℃,forming a high-purity a-SiC strongly bonded framework.Vacuum/gas pressure infiltration of Al alloy was subsequently used to manufacture the 3D interpenetrated network structure SiC/Al(SiC3D/Al)composite.Characterization results showed that 15%(in mass)seeds provided the composite with the optimal comprehensive performance,including a low coefficient of thermal expansion(CTE)of 5.54×10^(-6)K^(-1),a high thermal conductivity(l)of 239.08 W·m^(-1)·K^(-1),the highest flexural strength of 326.84 MPa,and a low thermal deformation parameter(TDP)of 0.023.High-purity plate-like grains enhanced the purity of the framework promoting a significant improvement in l.As the seed content increased to 20%(in mass),both CTE and l reached optimal values of 5.22×10^(-6)K^(-1)and 243.14 W·m^(-1)·K^(-1),but the mechanical properties declined by 10.30%.The synergistic effect of the well-bonded interface and the high-purity 3D SiC framework balanced excellent mechanical properties and multiple thermal functions.

核电人工智能应用:现状、挑战和机遇

近年来,人工智能技术被广泛应用于核电领域,以促进核电厂通过实现自诊断、自寻优、自适应,最终达到提高生产效率、降低运行成本、提高运行安全性的目的。本文介绍了在核电领域经常使用的人工智能技术,总结了其在智慧矿山、智能设计、智能制造和智能运维4个核工业典型应用场景中的研究现状,最后,从数据样本、网络安全、深度学习的解释性3个方面分析了人工智能技术在核电领域应用的挑战和发展趋势。

Effect of CeO_(2) on carbon deposition resistance of Ni/CeO_(2) catalyst supported on SiC porous ceramic for ethanol steam reforming

Ni/CeO_(2) catalysts(nCeO_(2):n_(Ni)=0,1,4,7,10)supported on SiC porous ce ramics for ethanol steam reforming(ESR)were investigated with respect to hydrogen production performance and growth of carbon deposition.The oxygen released from CeO_(2) enables the oxidation of CH_(x) species to serve as carbon precursors,thus providing Ni/CeO_(2) catalysts with stronger resistance to carbon deposition compared with Ni catalysts.The Ni/CeO_(2) catalysts prepared by inverse microemulsion and impregnation methods exhibit regular semicircular spherical shape on SiC porous ceramics.Under 500℃for 25 h of ESR reaction,the ethanol conversion rate over Ni/CeO_(2) catalysts(n_(CeO_(2)):n_(Ni)=7)is sustained up to 100%and H_(2) selectivity is essentially kept at 74%.The by-product selectivity declines stepwise with increasing content of CeO_(2),which is attributed to the adsorption and oxidation of CO and of CH_(x) species as CH_4 precursor from CeO_(2).The scanning electron microscopy(SEM)and transform electron microscopy(TEM)results reveal that further loading of CeO_(2) on the surface of Ni catalysts can alleviate both migration and sintering of Ni particles.Furthermore,carbon deposition on Ni/CeO_(2) catalysts preferentially outgrow filamentous rather than amorphous carbon,with a tendency for the latter to be more deactivated.