Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

Modeling,Simulation,and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Energy storage batteries can smooth the volatility of renewable energy sources.The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system(BESS).However,the current modeling of grid-connected BESS is overly simplistic,typically only considering state of charge(SOC)and power constraints.Detailed lithium(Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions.Additionally,there is a lack of real-time batteries risk assessment frameworks.To address these issues,in this study,we establish a thermal-electric-performance(TEP)coupling model based on a multitime scale BESS model,incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS.Additionally,considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors,we developed a battery pack operational riskmodel,which takes into account SOCand charge-discharge rate(Cr),using amodified failure rate to represent the BESS risk.By integrating detailed simulation of energy storage with predictive failure risk analysis,we obtained a detailed model for BESS risk analysis.This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times,day-level performance degradation,minutescale failure rate,and second-level BESS characteristics.It offers a critical tool for the study of BESS.Finally,the performance and risk of energy storage batteries under three scenarios—microgrid energy storage,wind power smoothing,and power grid failure response—are simulated,achieving a real-time state-dependent operational risk analysis of the BESS.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Analysis and Dynamics of Fractional Order Mathematical Model of COVID-19in Nigeria Using Atangana-Baleanu Operator

We propose a mathematical model of the coronavirus disease 2019(COVID-19)to investigate the transmission and control mechanism of the disease in the community of Nigeria.Using stability theory of differential equations,the qualitative behavior of model is studied.The pandemic indicator represented by basic reproductive number R0 is obtained from the largest eigenvalue of the next-generation matrix.Local as well as global asymptotic stability conditions for the disease-free and pandemic equilibrium are obtained which determines the conditions to stabilize the exponential spread of the disease.Further,we examined this model by using Atangana–Baleanu fractional derivative operator and existence criteria of solution for the operator is established.We consider the data of reported infection cases from April 1,2020,till April 30,2020,and parameterized the model.We have used one of the reliable and efficient method known as iterative Laplace transform to obtain numerical simulations.The impacts of various biological parameters on transmission dynamics of COVID-19 is examined.These results are based on different values of the fractional parameter and serve as a control parameter to identify the significant strategies for the control of the disease.In the end,the obtained results are demonstrated graphically to justify our theoretical findings.

Comprehensive Evaluation Model of Reservoir Operation Based on Improved Set Pair Analysis

A comprehensive evaluation model based on improved set pair analysis is established. Considering the complexity in decision-making process, the model combines the certainties and uncertainties in the schemes, i.e., identical degree, different degree and opposite degree. The relations among different schemes are studied, and the traditional way of solving uncertainty problem is improved. By using the gray correlation to determine the difference degree, the problem of less evaluation indexes and inapparent linear relationship is solved. The difference between the evaluation parameters is smaller in both the fuzzy comprehensive evaluation model and fuzzy matter-element method, and the dipartite degree of the evaluation result is unobvious. However, the difference between each integrated connection degree is distinct in the improved set pair analysis. Results show that the proposed method is feasible and it obtains better effects than the fuzzy comprehensive evaluation method and fuzzy matter-element method.

Dynamic Modeling of Heterogeneous Spatial Analysis for Energy and Natural Resources in Grid Integration Environment

With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in China.In these fields,from the model based on topology data,through simple feature data model to rule-based data model,the basic spatial analysis algorithms have been developed

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues.In this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels.In this approach,the overall population was separated into five cohorts.Furthermore,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity analysis.We included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of solutions.Several numerical simulations for various fractional orders and randomization intensities are illustrated.

Intraoperative thermostatic nursing and failure mode and effects analysis enhance gastrectomies’ care quality

BACKGROUND Utilizing failure mode and effects analysis(FMEA)in operating room nursing provides valuable insights for the care of patients undergoing radical gastric cancer surgery.AIM To evaluate the impact of FMEA on the risk of adverse events and nursing-care quality in patients undergoing radical surgery.METHODS Among 230 patients receiving radical cancer surgery between May 2019 and May 2024,115 were assigned to a control group that received standard intraoperative thermoregulation,while the observation group benefited from FMEA-modeled operating room care.Clinical indicators,stress responses,postoperative gastroin-testinal function recovery,nursing quality,and the incidence of adverse events were compared between the two groups.RESULTS Significant differences were observed in bed and hospital stay durations between the groups(P<0.05).There were no significant differences in intraoperative blood loss or postoperative body temperature(P>0.05).Stress scores improved in both groups post-nursing(P<0.05),with the observation group showing lower stress scores than the control group(P<0.05).Gastrointestinal function recovery and nursing quality scores also differed significantly(P<0.05).Additionally,the incidence of adverse events such as stress injuries and surgical infections varied notably between the groups(P<0.05).CONCLUSION Incorporating FMEA into operating room nursing significantly enhances patient care by improving safety,expediting recovery,and reducing healthcare-associated risks.

Simulation Modeling and Analysis of Operator-Machine Ratio

Based on a simulation model of a semiconductor manufacturer, operator-machine ratio （OMR） analysis is made using work study and time study. Through sensitivity analysis, it is found that labor utilization decreases with the increase of lot size. Meanwhile, it is able to identify that the OMR for this company should be improved from 1 ： 3 to 1 ： 5. An application result shows that the proposed model can effectively improve the OMR by 33%.

EMA与OMA模态参数辨识统一性方法

现代模态测试的对象正从系统部件转向整体结构,系统的动力学特征正变得越来越复杂,表现在结构组件众多导致系统模态密集化、局部化;在实际的工程结构中,甚至无法对大型结构施加有效的激励或激励的成本很高,使得EMA方法不能进行,只能使用OMA方法。在系统动力学特征越加复杂与参数辨识难度增加的同时,对参数辨识的要求越来越高;对算法的模态辨识能力要求越来越高;EMA与OMA都面临着整体频段的拟合困难、模型阶次与模态参数选择困难以及测点数的迅速增大带来的计算量剧增等困难;并且,EMA与OMA之间还存在着辨识方法不能统一的问题;简要回顾了VF算法原理,并进一步将VF算法拓展到OMA问题中,将频域内的EMA与OMA建立一个统一辨识流程,实现了整个频段上的整体拟合;简化了模态参数辨识流程,减少人工介入工以及由于频段划分不当造成的识别质量的下降。利用公开数据进一步验证了EMA问题中,VF算法在低信噪比和大规模曲线数目时的辨识效果,以及在OMA问题中的良好适用性。

OMA模态参数的优化及盲分析技术探讨

提出了运行状态模态分析(operational modal analysis,简称OMA)模态参数的优化和盲分析的方法。通过模态参数优化,在不丢失物理模态的前提下,所有分析方法的模态分析结果经过优化最终都可以得到统一。提出了在优化过程中清除虚假模态的机制,由此可以实现完全自动化的OMA模态分析,即在选定频带范围内,不经过任何人机交互,都可以得到数学意义上最优的OMA模态分析结果。通过上海卢浦大桥的OMA试验对该优化算法及自动化分析算法进行了验证。结果表明,所提出的优化方法及自动化分析方法是可行的。自动化模态分析的方法操作简捷,分析结果不包含虚假模态,在数学意义上理论曲线和实测曲线最为吻合。

Modal macro-strain identification from operational macro-strain shape under changing loading conditions

To develop modal macro-strain （ MMS ） identification techniques and improve their applicability in a continuous health monitoring system for civil infrastructures, the concept of operational macro-strain shape （OMSS） and the corresponding identification method are proposed under unknown ever-changing loading conditions, and the MMS is then obtained. The core of the proposed technique is mainly based on the specific property that the macro-strain transmissibility tends to be independent of external excitations at the poles of the system and converges to a unique value. The proposed method is verified using the experimental data from a three-span continuous beam excited by an impact hammer at different locations. The identified results are also compared with the commonly used methods, such as the peak- picking （PP） method, the stochastic subspace identification （SSI） method, and numerical results, in the case of unknown input forces. Results show that the proposed technique has unique merits in accuracy and robustness due to its combining multiple tests under changing loading conditions, which also reveal the promising application of the distributed strain sensing system in identifying MMS of operational structures, as well as in the structural health monitoring （SHM） field.

基于SA-OMA的锯床齿轮箱动态性能优化方法

为优化锯床刚性及动态力学性能,提高其工作效率与稳定性,提出一种锯床齿轮箱动态性能优化方法。采用将各传动轴系简化为质量单元的建模方法建立锯床齿轮箱数值模型,得到了齿轮箱的模态参数。对齿轮箱各设计变量进行灵敏度分析(SA),得到其对目标函数的灵敏程度。结合运行模态分析(OMA),获取齿轮箱在不同工况下的实际振动特性,进而优化锯床齿轮箱结构。对优化前后的锯床进行锯切实验和振动信号采集,实验结果表明,优化后的齿轮箱固有频率得到显著提高;在各工况下齿轮箱振幅均明显降低,锯床工作稳定性得到改善。本研究可为机床多体动力学建模与优化提供理论参考,也可为锯床高速稳定切削提供技术解决方案。

应用Open Meta-Analysis软件实现诊断准确性试验的Meta分析

Open Meta-Analysis软件是用于二分类数据、连续型数据以及诊断数据Meta分析的开放软件,该软件提供了四种模型来执行诊断准确性试验的Meta分析,即诊断随机效应模型、倒方差混合效应模型、双变量模型和分层综合受试者工作特征曲线法,其中前两者为单变量模型,只能执行单个指标合并,而后两者为双变量模型,能够对灵敏度和特异度之间的负相关性进行综合分析。本文以实例就Open Meta-Analysis软件实现诊断准确性试验Meta分析做相关简述。

基于OMA隧道爆破振动下砌体结构的安全评价

以成渝客运专线新红岩隧道上方二层砌体结构为例,采用OMA(运行模态分析)方法研究了砌体结构在隧道爆破引起的地面振动作用下的安全性。结果表明:采用电子雷管爆破相比非电雷管能够有效降低地面振速,峰值振速最多降低约67%;二层砌体结构前4阶固有频率位于9~25Hz,有限元模型修正后其固有频率与实验值的误差明显减小,使其更加符合实际;非线性动力分析表明砌体结构门、窗的角部、砌体与混凝土材料的交界处及底层主应力集中程度高,但砌体的主压应力和主拉应力均小于规范值,表明砌体在爆破振动作用下是安全的,而且电子雷管更适用于结构安全性要求高的隧道爆破;现场调研发现砌体楼房在隧道爆破振动下没有发生损伤,表明基于OMA方法进行隧道爆破振动下砌体结构的安全评价是可行的。

Resilience based importance measure analysis for SoS

In a system of systems(SoS),resilience is an important factor in maintaining the functionality,stability,and enhancing the operation effectiveness.From the perspective of resilience,this paper studies the importance of the SoS,and a resilience-based importance measure analysis is conducted to provide suggestions in the design and optimization of the structure of the SoS.In this paper,the components of the SoS are simplified as four kinds of network nodes:sensor,decision point,influencer,and target.In this networked SoS,the number of operation loops is used as the performance indicator,and an approximate algorithm,which is based on eigenvalue of the adjacency matrix,is proposed to calculate the number of operation loops.In order to understand the performance change of the SoS during the attack and defense process in the operations,an integral resilience model is proposed to depict the resilience of the SoS.From different perspectives of enhancing the resilience,different measures,parameters and the corresponding algorithms for the resilience importance of components are proposed.Finally,a case study on an SoS is conducted to verify the validity of the network modelling and the resiliencebased importance analysis method.

An Integrated Dynamic Model of Ocean Mining System and Fast Simulation of Its Longitudinal Reciprocating Motion

An integrated dynamic model of China＇s deep ocean mining system is developed and the fast simulation analysis of its longitudinal reciprocating motion operation processes is achieved. The seafloor tracked miner is built as a three-dimensional single-body model with six-degree-of-freedom. The track-terrain interaction is modeled by partitioning the track-terrain interface into a certain number of mesh elements with three mutually perpendicular forces, including the normal force, the longitudinal shear force and the lateral shear force, acting on the center point of each mesh element. The hydrodynamic force of the miner is considered and applied. By considering the operational safety and collection efficiency, two new mining paths for the miner on the seafloor are proposed, which can be simulated with the established single-body dynamic model of the miner. The pipeline subsystem is built as a three-dimensional multi-body discrete element model, which is divided into rigid elements linked by flexible connectors. The flexible connector without mass is represented by six spring-damper elements. The external hydrodynamic forces of the ocean current from the longitudinal and lateral directions are both considered and modeled based on the Morison formula and applied to the mass center of each corresponding discrete rigid element. The mining ship is simplified and represented by a general kinematic point, whose heave motion induced by the ocean waves and the longitudinal and lateral towing motions are considered and applied. By integrating the single-body dynamic model of the miner and the multi-body discrete element dynamic model of the pipeline, and defining the kinematic equations of the mining ship, the integrated dynamic model of the total deep ocean mining system is formed. The longitudinal reciprocating motion operation modes of the total mining system, which combine the active straight-line and turning motions of the miner and the ship, and the passive towed motions of the pipeline, are proposed and simulated with the developed 3D dynamic model. Some critical simulation results are obtained and analyzed, such as the motion trajectories of key subsystems, the velocities of the buoyancy modules and the interaction forces between subsystems, which in a way can provide important theoretical basis and useful technical reference for the practical deep ocean mining system analysis, operation and control.

A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

Output-only structural identification is developed by a refined Frequency Domain Decomposition（rFDD） approach, towards assessing current modal properties of heavy-damped buildings（in terms of identification challenge）, under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type Ⅱ bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames（with variable structural features） are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 ＂Far-Field＂（22 NS, 22 WE） earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.