Dynamic Fitness Research Articles

Gimme What I Need! Examining Firefighters Response to Servant Leadership from a Dynamic Fit Lens

How servant leadership influences health and performance on a daily basis? We draw on the person-environment fit and the Job Demand-Resources (JD-R) perspectives to examine follower’s health and performance variations related to the congruence between servant leadership needed and received. Based on a daily within-person examination, among firefighters, results indicated that emotional exhaustion is lower on days when received servant leadership fit followers’ needs. We also found that emotional exhaustion mediated the within-person effects of servant leadership needed and received on followers’ daily task performance. Implications to theory and practice are discussed.

Stakeholder Orientation and Organizational Adaptation

This paper examines the conditions under which stakeholder orientation facilitates (or hinders) firms’ efforts to adapt to environmental disruptions, particularly technological shocks. To do so, we apply evolutionary economics to develop a theoretical framework on how variation, selection, and retention processes might differ in firms with a high vs. a low level of stakeholder orientation and how these differences can positively or negatively influence their adaptive capacity. We subsequently apply this reasoning to develop theory regarding four boundary conditions of the effectiveness of stakeholder orientation as an adaptation mechanism: two of these conditions are related to characteristics of the technological disruptions, and two are organizational characteristics. The proposed model offers a first application of evolutionary thinking to explain the dynamic fit between stakeholder orientation and organizational adaptive responses, under conditions of environmental (technological) disruption. A research agenda for future scholars in stakeholder theory, organizational evolution, and adaptation to radical change concludes.

Coronavirus disease 2019 epidemic prediction in Shanghai under the “dynamic zero-COVID policy” using time-dependent SEAIQR model

It’s urgently needed to assess the COVID-19 epidemic under the “dynamic zero-COVID policy” in China, which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control. Here, we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-removed (SEAIQR) model with stage-specific interventions based on recent Shanghai epidemic data, considering a large number of asymptomatic infectious, the changing parameters, and control procedures. The data collected from March 1st, 2022 to April 15th, 2022 were used to fit the model, and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting. We then calculated the effective regeneration number (Rt) and analyzed the sensitivity of different measures scenarios. Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai, and Pudong is the district with the most positive cases. The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th, 2022, with 1963 and 28,502 cases, respectively, and zero community transmission may be achieved in early to mid-May. The prediction errors for newly confirmed cases were considered to be reasonable, and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th. The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477 ∼ 47,749 and 402,254 ∼ 730,176, respectively. At the beginning of the outbreak, Rt was 6.69. Since the implementation of comprehensive control, Rt showed a gradual downward trend, dropping to below 1.0 on April 15th, 2022. With the early implementation of control measures and the improvement of quarantine rate, recovery rate, and immunity threshold, the peak number of infections will continue to decrease, whereas the earlier the control is implemented, the earlier the turning point of the epidemic will arrive. The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well, which can provide a reference for decision making of the “dynamic zero-COVID policy”.

A simple data-driven method to optimize the penalty strengths of penalized models and its application to non-parametric smoothing

ABSTRACT Information of interest can often only be extracted from data by model fitting. When the functional form of such a model cannot be deduced from first principles, one has to make a choice between different possible models. A common approach in such cases is to minimize the information loss in the model by trying to reduce the number of fit variables (or the model flexibility, respectively) as much as possible while still yielding an acceptable fit to the data. Model selection via the Akaike information criterion (AIC) provides such an implementation of Occam’s razor. We argue that the same principles can be applied to optimize the penalty strength of a penalized maximum-likelihood model. However, while in typical applications AIC is used to choose from a finite, discrete set of maximum-likelihood models, the penalty optimization requires to select out of a continuum of candidate models and these models violate the maximum-likelihood condition. We derive a generalized information criterion AICp that encompasses this case. It naturally involves the concept of effective free parameters, which is very flexible and can be applied to any model, be it linear or non-linear, parametric or non-parametric, and with or without constraint equations on the parameters. We show that the generalized AICp allows an optimization of any penalty strength without the need of separate Monte Carlo simulations. As an example application, we discuss the optimization of the smoothing in non-parametric models, which has many applications in astrophysics, like in dynamical modelling, spectral fitting, or gravitational lensing.

Dynamic fitness landscape-based adaptive mutation strategy selection mechanism for differential evolution

Differential evolution (DE) is the most efficient evolutionary algorithm widely used to solve continuous or discrete numerical optimization problems. However, the performance of DE highly depends on the choice of mutation strategy. In addition, for a given optimization problem, a different mutation strategy configuration of DE may be more effective than a single mutation strategy in searching for the optimal solution. Based on these observations, a dynamic fitness landscape-based adaptive mutation strategy selection differential evolution (DFLDE) is proposed in this paper. In DFLDE, the optimal mutation strategy selection is based on each optimization problem's dynamic fitness landscape characteristics during the evolutionary process. The CEC2017 benchmark function set is used to evaluate the performance of the proposed DFLDE algorithm. The experimental results indicate that the DFLDE is superior to the other five well-known DE variants in searching for the optimal value, convergence speed, and robustness.

Dynamic fit index cutoffs for one-factor models.

Assessing whether a multiple-item scale can be represented with a one-factor model is a frequent interest in behavioral research. Often, this is done in a factor analysis framework with approximate fit indices like RMSEA, CFI, or SRMR. These fit indices are continuous measures, so values indicating acceptable fit are up to interpretation. Cutoffs suggested by Hu and Bentler (1999) are a common guideline used in empirical research. However, these cutoffs were derived with intent to detect omitted cross-loadings or omitted factor covariances in multifactor models. These types of misspecifications cannot exist in one-factor models, so the appropriateness of using these guidelines in one-factor models is uncertain. This paper uses a simulation study to address whether traditional fit index cutoffs are sensitive to the types of misspecifications common in one-factor models. The results showed that traditional cutoffs have very poor sensitivity to misspecification in one-factor models and that the traditional cutoffs generalize poorly to one-factor contexts. As an alternative, we investigate the accuracy and stability of the recently introduced dynamic fit cutoff approach for creating fit index cutoffs for one-factor models. Simulation results indicated excellent performance of dynamic fit index cutoffs to classify correct or misspecified one-factor models and that dynamic fit index cutoffs are a promising approach for more accurate assessment of model fit in one-factor contexts.

Air microfluidics-enabled soft robotic transtibial prosthesis socket liner toward dynamic management of residual limb contact pressure and volume fluctuation.

Residual limb volume fluctuation and the resulting contact pressures are some of the key factors leading to skin ulcerations, suboptimal prosthetic functioning, pain, and diminishing quality of life of transtibial amputees. Self-management of socket fit is complicated by peripheral neuropathy, reducing the perception of pressure and pain in the residual limb. We introduce a novel proof-of-concept for a transtibial prosthetic socket liner with the potential to dynamically adjust the fit between the limb and socket. The core of the technology is a small air microfluidic chip (10 cm3 and 10 g) with 10 on-chip valves that enable sequential pressurizing of 10 actuators in custom sizes to match the pressures required by the residual limb's unique anatomy. The microfluidic chip largely reduced the number of electromechanical solenoid valves needed for sequential control of 10 actuators (2 instead of 10 valves), resulting in the reduction of the required power, size, mass, and cost of the control box toward an affordable and wearable prosthetic socket. Proof-of-concept testing demonstrated that the applied pressures can be varied in the desired sequence and to redistribute pressure. Future work will focus on integrating the system with biofidelic prosthetic sockets and residual limb models to investigate the ability to redistribute pressure away from pressure-sensitive regions (e.g., fibular head) to pressure tolerant areas. Overall, the dynamic prosthesis socket liner is very encouraging for creating a dynamic socket fit system that can be seamlessly integrated with existing socket fabrication methods for managing residual limb volume fluctuations and contact pressure.

The Efficiency of Hybrid Intelligent Models in Predicting Fiber-Reinforced Polymer Concrete Interfacial-Bond Strength

Fiber-reinforced polymer (FRP) has several benefits, in addition to excellent tensile strength and low self-weight, including corrosion resistance, high durability, and easy construction, making it among the most optimum options for concrete structure restoration. The bond behavior of the FRP-concrete (FRPC) interface, on the other hand, is extremely intricate, making the bond strength challenging to estimate. As a result, a robust modeling framework is necessary. In this paper, data-driven hybrid models are developed by combining state-of-the-art population-based algorithms (bald eagle search (BES), dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO), RUNge Kutta optimizer (RUN)) and artificial neural networks (ANN) named “BES-ANN”, “dFDB-MRFO -ANN”, and “RUN-ANN” to estimate the FRPC interfacial-bond strength accurately. The efficacy of these models in predicting bond strength is examined using an extensive database of 969 experimental samples. Compared to the BES-ANN and dFDB-MRFO models, the RUN-ANN model better estimates the interfacial-bond strength. In addition, the SHapley Additive Explanations (SHAP) approach is used to help interpret the best model and examine how the features influence the model’s outcome. Among the studied hybrid models, the RUN-ANN algorithm is the most accurate model with the highest coefficient of determination (R2 = 92%), least mean absolute error (0.078), and least coefficient of variation (18.6%). The RUN-ANN algorithm also outperformed mechanics-based models. Based on SHAP and sensitivity analysis method, the FRP bond length and width contribute more to the final prediction results.

Interventions to improve gait in Parkinson's disease: a systematic review of randomized controlled trials and network meta-analysis.

Disabling gait symptoms, especially freezing of gait (FoG), represents a milestone in the progression of Parkinson's disease (PD). This systematic review and network meta-analysis assessed and ranked interventions according to their effectiveness in treating gait symptoms in people with PD across four different groups of gait measures. A systematic search was carried out across PubMed, EMBASE, PubMed Central (PMC), and Cochrane Central Library from January 2000 to April 2021. All interventions, or combinations, were included. The primary outcome was changes in objective gait measures, before and after intervention. Outcome measures in the included studies were stratified into four different types of gait outcome measures; dynamic gait, fitness, balance, and freezing of gait. For the statistical analysis, five direct head-to-head comparisons of interventions, as well as indirect comparisons were performed. Corresponding forest plots ranking the interventions were generated. The search returned 6288 articles. From these, 148 articles could be included. Of the four different groups of measurement, three were consistent, meaning that there was agreement between direct and indirect evidence. The groups with consistent evidence were dynamic gait, fitness, and freezing of gait. For dynamic gait measures, treatments with the largest observed effect were Aquatic Therapy with dual task exercising (SMD 1.99 [-1.00; 4.98]) and strength and balance training (SMD 1.95 [-0.20; 4.11]). For measures of fitness, treatments with the largest observed effects were aquatic therapy (SMD 3.41 [2.11; 4.71] and high-frequency repetitive transcranial magnetic stimulation (SMD 2.51 [1.48; 3.55]). For FoG measures, none of the included interventions yielded significant results. Some interventions can ameliorate gait impairment in people with PD. No recommendations on a superior intervention can be made. None of the studied interventions proved to be efficacious in the treatment of FoG. PROSPERO (registration ID CRD42021264076).

Robust Optimization of Dynamic Fit Parameters for Injection Mechanism in Squeeze Casting Based on Six-sigma Frame and Bayesian Kriging Metamodel

Robust Optimization of Dynamic Fit Parameters for Injection Mechanism in Squeeze Casting Based on Six-sigma Frame and Bayesian Kriging Metamodel

Tractable Term Structure Models

We introduce a new framework that facilitates term structure modeling with both positive interest rates and flexible time series dynamics but that is also tractable, meaning amenable to quick and robust estimation. Using both simulations and U.S. historical data, we compare our approach with benchmark Gaussian and stochastic volatility models as well as a shadow rate model that enforces positive interest rates. Our approach, which remains arbitrarily close to arbitrage free, offers a more accurate characterization of bond Sharpe ratios because of a better fit of the volatility dynamics and a more efficient estimation of the return dynamics. Further, the shadow rate and stochastic volatility models exhibit important restrictions that are largely absent in our approach. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.4214 .

Dynamic RNA Fitness Landscapes of a Group I Ribozyme during Changes to the Experimental Environment.

Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl2). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.

Solving Optimization Problems of Metamaterial and Double T-Shape Antennas Using Advanced Meta-Heuristics Algorithms

This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types of antennas. The two types of antennas are metamaterial and double T-shape monopoles. The ADSCFGWO algorithm is based on an adaptive dynamic technique and two recently developed and powerful optimization techniques: a modified grey wolf optimization (GWO) based on fitness value and a sine cosine algorithm (SCA). The suggested approach utilizes the capabilities of both algorithms to balance better the exploration and exploitation responsibilities of the optimization process while achieving rapid convergence. First, a new feature selection approach is proposed to choose the most significant features from the metamaterial dataset using the suggested ADSCFGWO-based ensemble model for optimal performance. The ADSCFGWO algorithm also optimizes a bidirectional recurrent neural network (BRNN) to estimate the double T-shape monopole antenna characteristics. Several experiments were undertaken to demonstrate the superiority of the suggested algorithms by comparing their results to those of existing optimization algorithms, feature selectors, and regression models. In addition, a statistical analysis is offered to evaluate the algorithm’s effectiveness and stability. The achieved findings demonstrate the efficacy and superiority of the suggested method over numerous competing algorithms.

SVRice: An Automated Rice Seed Vigor Classification System via Radicle Emergence Testing Using Image-Processing, Curve-Fitting, and Clustering Methods

Highlights SVRice package: a simple and effective procedure for automatic rice seed vigor classification was verified. The package consists of three important parts: image analysis, curve-fitting, and cluster analysis. Findings provide an effective rice seed vigor classification system that promises to reduce the production costs. Abstract. Rice seed vigor classification is important for seed storage management by seed producers and by farmers while planning their cultivation activities. Field emergence is a direct method of seed vigor testing but is laborious, time-consuming and subjective. This article presents the SVRice package, a simple, cost-efficient, and flexible procedure based on computer image analysis for high-throughput, automatic rice seed vigor classification. SVRice consists of four steps: dynamic imaging, image processing, curve fitting, and clustering. Seed vigor was classified based on radicle emergence indices, such as maximum radicle emergence (MaxRE), mean radicle emergence time (MRET), radicle emergence speed (t50), uniformity of radicle emergence (U7525), and area under the curve of the radicle emergence fitted curve (AUC). Parameters used to classify rice seed vigor, such as MRET, U7525, and t50, were strongly negatively correlated with the saturated salt accelerated aging (SSAA) test. A germination time of 90 h at 25°C was sufficient for effective classification based on SVRice, whereas the SSAA test took approximately 400 h to complete. The SVRice software algorithm was created to be especially suitable for assessment after six months under controlled atmosphere storage (at 15°C and 37% RH in a hermetic bag). The study showed that SVRice could unambiguously classify 40 indica rice samples with different varieties, production years, production sites, storage times, and storage conditions compared with the SSAA test. Findings provide an effective seed vigor classification system that promises to reduce the production costs and encourages competitiveness of seed producers. Keywords: Image analysis, Oryza sativa, Radicle emergence, Seed vigor, SVRice.

Energy consumption modelling analysis of prefabricated buildings based on KPCA-WL SSVM

The energy consumption of prefabricated buildings under multi-layer building materials system is affected by different weight factors. In order to improve the level of energy consumption prediction, a method of energy consumption prediction of prefabricated buildings under multi-layer building materials system based on KPCA - WL SSVM is proposed. Taking indoor ambient temperature, lighting conditions, utilization rate of electric facilities, etc. as the combined weight of assembled building energy consumption under multi-layer building materials system structure, the energy consumption simulation and energy con?sumption allocation model of assembled building under multi-layer building materials system structure controlled by multi-component energy consumption parameter support vector machine is established, and the support vector machine based on KPCA - WL SSVM and principal component analysis dynamic fitting method are adopted. The energy consumption parameters of prefabricated buildings under multi-layer building materials system structure are detected and estimated, and the energy consumption patterns, energy consumption distribu?tion with different characteristics and energy consumption prediction model parameters of prefabricated buildings under multi-layer building materials system structure are obtained. Then, the emission factor and power consumption factor prediction model of prefabricated buildings under multi-layer building materials system structure is established, and the dynamic prediction and evaluation of energy consumption of prefabricated buildings under multi-layer building materials system structure are realized. The test results show that the fitting degree of energy consumption prediction of prefabricated buildings under multi-layer building material system structure is high, the model optimization design of energy consumption of prefabricated buildings is realized, the prediction accuracy of building energy consumption is good, and the energy consumption can be effectively reduced.

An optimized weighted voting based ensemble model for DDoS attack detection and mitigation in SDN environment

In recent years, software defined networking (SDN) has risen to prominence as a cutting-edge and promising networking approach. SDN is more secure and immune to DDoS attacks than traditional networks due to the bifurcation of the control and data planes, a global perspective of the whole network, centralized network control, and many other features. SDN still seems to be vulnerable to new DDoS attacks that can target the Application plane, control plane, or data plane, as well as the SDN architectural communication channels. In this paper, we propose a novel optimized weighted voting ensemble model to detect DDoS attack in an SDN environment. The proposed ensemble employs six base classifiers (two SVMs, two Random forests, and two Gradient Boosted Machines) that are differentiated by hyperparameter values. The optimal set of weights are identified by a novel hybrid metaheuristic optimization algorithm (BHO). Our approach eliminates false negatives by employing a novel dynamic fitness function. For training the ensemble framework we used CIC-DDoS2019 dataset. To examine the proposed model’s performance, a testbed is developed using mininet and POX controller and loaded with multiple datasets. Our model outperforms all the recent approaches, achieving a low variance and high classification accuracies of 99.4163% and 99.3591% on CIC-DDoS2019 and CAIDA-2007 datasets respectively.

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy consumption without compromising upon makespan, resource utilization and Quality of Service (QoS) parameters. The novelty of the proposed dynamic approaches in comparison to the existing static approaches is that the proposed approach dynamically creates and dissolves virtual machines as per incoming and completed requests which is a dire need of present computing paradigms via attachment of time-frame with each virtual machine. The simulations have been performed on JAVA platform and dynamic energy utilized-best fit decreasing bin packing technique has produced better results in maximum runs.

Improved Dynamical Masses for Six Brown Dwarf Companions Using Hipparcos and Gaia EDR3

Abstract We present comprehensive orbital analyses and dynamical masses for the substellar companions Gl 229 B, Gl 758 B, HD 13724 B, HD 19467 B, HD 33632 Ab, and HD 72946 B. Our dynamical fits incorporate radial velocities, relative astrometry, and, most importantly, calibrated Hipparcos-Gaia EDR3 accelerations. For HD 33632 A and HD 72946 we perform three-body fits that account for their outer stellar companions. We present new relative astrometry of Gl 229 B with Keck/NIRC2, extending its observed baseline to 25 yr. We obtain a <1% mass measurement of 71.4 ± 0.6 M Jup for the first T dwarf Gl 229 B and a 1.2% mass measurement of its host star (0.579 ± 0.007 M ⊙) that agrees with the high-mass end of the M-dwarf mass–luminosity relation. We perform a homogeneous analysis of the host stars’ ages and use them, along with the companions’ measured masses and luminosities, to test substellar evolutionary models. Gl 229 B is the most discrepant, as models predict that an object this massive cannot cool to such a low luminosity within a Hubble time, implying that it may be an unresolved binary. The other companions are generally consistent with models, except for HD 13724 B, which has a host star activity age 3.8σ older than its substellar cooling age. Examining our results in context with other mass–age–luminosity benchmarks, we find no trend with spectral type but instead note that younger or lower-mass brown dwarfs are overluminous compared to models, while older or higher-mass brown dwarfs are underluminous. The presented mass measurements for some companions are so precise that the stellar host ages, not the masses, limit the analysis.

Collateral implications of carbon and metal pollution on carbon dioxide emission at land-water interface of the Ganga River.

Atmospheric CO2 source and sink is among the most debated issues that have puzzled climate change geochemist for decades. Here, we tested whether heavy metal pollutants in river sediments favor preservation of organic matter through shielding microbial degradation. We measured CO2 emission and extracellular enzyme activities at land-water interface (LWI) of 7 sites along a 285 km main stem of the Ganga River and 60 locations up- and downstream of two contrasting point sources discharging urban (Assi drain; Asdr) and industrial (Ramnagar drain; Rmdr) wastewaters to the river. We found the lowest CO2 flux at Rmdr mouth characterized by the highest concentrations of Cu, Cr, Zn, Pb, Ni, and Cd. The fluxes were relatively higher at locations up- and downstream Rmdr. Substrate induced respiration (SIR), protease, FDAase, and β-D-glucosidase all showed a similar trend, but phenol oxidase and alkaline phosphatase showed opposite trend at the main river stem and Asdr. Sites rich in terrestrially derived organic matter have high phenol oxidase activity with low CO2 emission. The CO2 emission in the main river stem showed curvilinear relationships with total heavy metals (∑THM; R2 = 0.68; p < 0.001) and TOC (R2 = 0.65; p < 0.001). The dynamic fit model of main stem data showed that the ∑THM above 337.4 µg g-1 were able to significantly decrease the activities of protease, FDAase, and β-D-glucosidase. The study has implications for understanding C-cycling in human-impacted river sediments where metal pollution shields microbial degradation consequently carbon and nutrient release and merits attention towards river management decisions.

Research on operation and fault diagnosis technology for EHV/UHV SF6 circuit breakers pre-insertion resistors

Pre-insertion resistors (PIR) are critical components for suppressing the closing inrush current and operating overvoltage during the closing process of the circuit breaker. And it is widely adopted in 800kV and above transmission lines and alternating current filters of converter substations. These special application environments lead to higher failure rates than other electrical components. However, in the daily operation and fault diagnosis of PIR, there is no systematic method to analyze whether there are defects in PIR and the cause of failure after the PIR fails. Based on two PIR failure in series and parallel structures, this paper proposes methods such as dynamic resistance fitting, acoustic and vibration signal detection, insulation performance test, and heat capacity test during operation. In the aspect of fault diagnosis, methods such as PIR splicing based on Iterative Closest Point , stress analysis, and electric field simulation are proposed. Finally, proposed methods can be adjusted to estimate the operating state of PIR through the disassembly inspection to avoid the GIS breakdown accident during the operation of the circuit breaker.