Impact of initial fluctuations and nuclear deformations in isobar collisions

Relativistic isobar^(96)_(44)Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zrcollisions have revealed intricate differences in their nuclear size and shape,inspiring unconventional studies of nuclear structure using relativistic heavy ion collisions.In this study,we investigate the relative differences in the mean multiplicityR_(<Nch>)and the secondR_(ε2)and third-order eccentricityR_(ε3)between isobar collisions using initial state Glauber models.It is found that initial fluctuations and nuclear deformations have negligible effects on R_(<Nch>)in most central collisions,while both are important for the R_(ε2)and R_(ε3),the degree of which is sensitive to the underlying nucleonic or sub-nucleonic degree of freedom.These features,compared to real data,may probe the particle production mechanism and the physics underlying nuclear structure.

Optimization of Gas Production from Hydrate-Bearing Sediments with Fluctuation Characteristics

As an important source of low-carbon,clean fossil energy,natural gas hydrate plays an important role in improving the global energy consumption structure.Developing the hydrate industry in the South China Sea is important to achieving‘carbon peak and carbon neutrality’goals as soon as possible.Deep-water areas subjected to the action of long-term stress and tectonic movement have developed complex and volatile terrains,and as such,the morphologies of hydrate-bearing sediments(HBSs)fluctuate correspondingly.The key to numerically simulating HBS morphologies is the establishment of the conceptual model,which represents the objective and real description of the actual geological body.However,current numerical simulation models have characterized HBSs into horizontal strata without considering the fluctuation characteristics.Simply representing the HBS as a horizontal element reduces simulation accuracy.Therefore,the commonly used horizontal HBS model and a model considering the HBS’s fluctuation characteristics with the data of the SH2 site in the Shenhu Sea area were first constructed in this paper.Then,their production behaviors were compared,and the huge impact of the fluctuation characteristics on HBS production was determined.On this basis,the key parameters affecting the depressurization production of the fluctuating HBSs were studied and optimized.The research results show that the fluctuation characteristics have an obvious influence on the hydrate production of HBSs by affecting their temperatures and pressure distributions,as well as the transmission of the pressure drop and methane gas discharge.Furthermore,the results show that the gas productivity of fluctuating HBSs was about 5%less than that of horizontal HBSs.By optimizing the depressurization amplitude,well length,and layout location of vertical wells,the productivity of fluctuating HBSs increased by about 56.6%.

Altered spontaneous brain activity patterns in hypertensive retinopathy using fractional amplitude of low-frequency fluctuations:a functional magnetic resonance imaging study

AIM:To study functional brain abnormalities in patients with hypertensive retinopathy(HR)and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations(fALFFs)method.METHODS:Twenty HR patients and 20 healthy controls(HCs)were respectively recruited.The age,gender,and educational background characteristics of the two groups were similar.After functional magnetic resonance imaging(fMRI)scanning,the subjects’spontaneous brain activity was evaluated with the fALFF method.Receiver operating characteristic(ROC)curve analysis was used to classify the data.Further,we used Pearson’s correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR.RESULTS:The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus(RO-MFG)and right lingual gyrus.In contrast,the values of fALFFs in the left middle temporal gyrus(MTG),left superior temporal pole(STP),left middle frontal gyrus(MFG),left superior marginal gyrus(SMG),left superior parietal lobule(SPL),and right supplementary motor area(SMA)were higher in the HR group.The results of a t-test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group(P<0.001).The fALFF values of the left middle frontal gyrus in HR patients were positively correlated with anxiety scores(r=0.9232;P<0.0001)and depression scores(r=0.9682;P<0.0001).CONCLUSION:fALFF values in multiple brain regions of HR patients are abnormal,suggesting that these brain regions in HR patients may be dysfunctional,which may help to reveal the pathophysiological mechanisms of HR.

Experimental study on temperature fluctuations on plate surface induced by coaxial-jet flow

In nuclear reactors,temperature fluctuations of fluids may cause fatigue damage to adjacent structures;this is referred to as thermal striping.Research on thermal striping in the upper plenum has mainly focused on fluid fields.Few experimental studies have been reported on solid structures in a fluid field with a coaxial jet.This study entailed an experimental study of the temperature fluctuations in the fluid and on a plate surface caused by a coaxial jet.The temperature fluctuations of the fluid and plate surfaces located at different heights were analyzed.The cause of the temperature fluctuation was analyzed using a transient temperature distribution.The results show that the mixing of the hot and cold fluids gradually becomes uniform in the positive axial direction.The average surface temperatures tended to be consistent.When the jet reaches the plate surface,the swing of the jet center,contraction and expansion of the cold jet,and changes in the jet shape result in temperature fluctuations.The intensity of the temperature fluctuation was affected by the position.More attention should be paid when the plate is located at a lower height,and between the hot and cold-fluid nozzles.

Regularity analysis of resilient modulus for hot-mix asphalt with large temperature fluctuations

To evaluate the regularity of resilient modulus for hot-mix asphalt(HMA)under large temperature fluctuations,back propagation(BP)neural network technology was used to analyze the continuous change of HMA resilient modulus.Firstly,based on the abundant data,the training model of HMA resilient modulus was established by using BP neural network technology.Subsequently,BP neural network prediction and regression analysis were performed,and the prediction model of HMA resilient modulus at different temperatures(50C to 60C)was obtained,which fully considered multi-factor and nonlinearity.Finally,the fitted theoretical model can be used to evaluate the HMA performance under the condition of large temperature fluctuations,and the rationality of theoretical model was verified by taking Harbin region as an example.It was found that the relationship between HMA resilient modulus and temperatures can be described by inverse tangent function.And the key parameters of theoretical model can be used to evaluate the continuous change characteristics of HMA resilient modulus with large temperature fluctuations.The results can further improve the HMA performance evaluation system and have certain theoretical value.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

Influence of blood glucose fluctuations on chemotherapy efficacy and safety in type 2 diabetes mellitus patients complicated with lung carcinoma

BACKGROUND Patients with type 2 diabetes mellitus(T2DM)have large fluctuations in blood glucose(BG),abnormal metabolic function and low immunity to varying degrees,which increases the risk of malignant tumor diseases and affects the efficacy of tumor chemotherapy.Controlling hyperglycemia may have important therapeutic implications for cancer patients.AIM To clarify the influence of BG fluctuations on chemotherapy efficacy and safety in T2DM patients complicated with lung carcinoma(LC).METHODS The clinical data of 60 T2DM+LC patients who presented to the First Affiliated Hospital of Ningbo University between January 2019 and January 2021 were retrospectively analyzed.All patients underwent chemotherapy and were grouped as a control group(CG;normal BG fluctuation with a mean fluctuation<3.9 mmol/L)and an observation group(OG;high BG fluctuation with a mean fluctuation≥3.9 mmol/L)based on their BG fluctuations,with 30 cases each.BGrelated indices,tumor markers,serum inflammatory cytokines and adverse reactions were comparatively analyzed.Pearson correlation analysis was performed to analyze the correlation between BG fluctuations and tumor markers.RESULTS The fasting blood glucose and 2-hour postprandial blood glucose levels in the OG were notably elevated compared with those in the CG,together with markedly higher mean amplitude of glycemic excursions(MAGE),mean of daily differences,largest amplitude of glycemic excursions and standard deviation of blood glucose(P<0.05).In addition,the OG exhibited evidently higher levels of carbohydrate antigen 19-9,carbohydrate antigen 125,carcinoembryonic antigen,neuron-specific enolase,cytokeratin 19,tumor necrosis factor-α,interleukin-6,and highsensitivity C-reactive protein than the CG(P<0.05).Pearson analysis revealed a positive association of MAGE with serum tumor markers.The incidence of adverse reactions was significantly higher in the OG than in the CG(P<0.05).CONCLUSION The greater the BG fluctuation in LC patients after chemotherapy,the more unfavorable the therapeutic effect of chemotherapy;the higher the level of tumor markers and inflammatory cytokines,the more adverse reactions the patient experiences.

Deformation and failure mechanism of Yanjiao rock slope influenced by rainfall and water level fluctuation of the Xiluodu hydropower station reservoir

With the construction of the Xiluodu hydropower station on the Jinsha River,the reservoir impoundment began in 2013 and the water level fluctuates annually between 540 m and 600 m above sea level.The Yanjiao rock slope which is located on the left bank of the Jinsha River 75 km upstream of the Xiluodu dam site,began to deform in 2014.The potential failure of the slope not only threatens Yanjiao town but also affects the safe operation of the Xiluodu reservoir.This paper is to find the factors influencing the Yanjiao slope deformation through field investigation,geotechnical reconnaissance,and monitoring.Results show that the Yanjiao slope can be divided into a bank collapse area(BCA)and a strong deformation area(SDA)based on the crack distribution characteristics of the slope.The rear area of the slope has been experiencing persistent deformation with a maximum cumulative displacement(GPS monitoring point G4)of 505 mm and 399 mm in the horizontal and vertical directions,respectively.The potential failure surface of the slope is formed 36 m below the surface based on the borehole inclinometer.The bank collapses of the Yanjiao slope are directly caused by the reservoir impoundment while the deformation area of the slope is affected by the combination of the rainfall and reservoir water level fluctuation.Based on mechanism of the Yanjiao slope,prestressed anchor combined with the surface drainage and slope unloading are recommended to prevent potential deformation.

Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression

To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.

A new electromagnetic probe array diagnostic for analyzing electrostatic and magnetic fluctuations in EAST plasmas

A novel electromagnetic probe array(EMPA) diagnostic, which consists of a magnetic probe array and an electrostatic probe array, has recently been developed on EAST. The EMPA is fixed near the first wall at horizontal port P. The magnetic probe array of the EMPA consists of 24 identical magnetic probes, each of them capable of measuring toroidal, poloidal and radial magnetic fluctuations simultaneously, providing additional toroidal magnetic fluctuation measurements compared with the regular magnetic probes on EAST. With a higher sampling rate and self-resonant frequency, the EMPA magnetic probes can provide higher frequency magnetic fluctuation measurements. The magnetic probe array of the EMPA is composed of two parallel layers of magnetic probes with a radial distance of 63 mm, and each layer of magnetic probes is arranged in four poloidal rows and three toroidal columns. The compact arrangement of the EMPA magnetic probe array largely improves the toroidal mode number measurement ability from-8≤ n≤ 8 to-112≤ n≤ 112, and also improves the high poloidal wave number measurement ability of magnetic fluctuations compared with the regular high frequency magnetic probes on EAST. The electrostatic probe array of the EMPA consists of two sets of four-tip probes and a single-tip probe array with three poloidal rows and four toroidal columns. It complements the electrostatic parameter measurements behind the main limiter and near the first wall in EAST. The engineering details of the EMPA diagnostic, including the mechanical system, the electrical system, the acquisition and control system, and the effective area calibration, are presented. The preliminary applications of the EMPA in L-mode and H-mode discharges on EAST have demonstrated that the EMPA works well for providing information on the magnetic and electrostatic fluctuations and can contribute to deeper physical analysis in future EAST experiments.

Preparation of a beam emission spectroscopy diagnostic based on a Lyman-alpha line to diagnose core and edge-plasma density fluctuation on the HL-2A tokamak

In this article,the design of a Lyman-alpha-based beam emission spectroscopy(LAB)diagnostic on the HL-2A tokamak has been proposed for the first time.The purpose of this novel diagnostic is to measure density fluctuations of tokamak plasma.The light-collection system of LAB,which consists of the first mirror and two groups of coaxial double-mirror telescopes,can realize a twosegmented viewing field ofρ=0–0.2 andρ=0.75–1,which is optimized to measure plasma density fluctuation,not only in the edge transport barrier region but also in the internal transport barrier region,to investigate the underlying physics of turbulence in tokamaks.Spectrometers are developed to separate out the Doppler-shifted target line(122.03 and 122.17 nm)from the background Lyman-alpha line(121.53 nm).Here,30 Core-LAB channels and 30 Edge-LAB channels are under development on the HL-2A tokamak.It has high radial spatial resolutions of about 2.7 mm and 3.3 mm for the core and edge channels,respectively.Taking the high light intensity of this Lyman-alpha line into account,temporal resolution of 200 k Hz can be ensured by broad bandwidth amplifiers.This high spatio-temporal resolution makes LAB a potential keen tool to experimentally investigate tokamak plasma physics.

Stochastic fluctuations in the rotation of the bacterial flagellar motor

The bacterial flagellar motor is a nanometer-sized rotary motor that generates the torque to drive the rotation of the flagellar filament.The output torque is an important property of the motor.The motor rotation was usually monitored by attaching aμm-sized bead to a shortened flagellar filament,and the torque was extracted by calculating the torque due to the viscous drag of the medium on the bead rotation.We sought for an independent extraction of the torque from thermal fluctuation in the motor rotation using the fluctuation theorem(FT).However,we identified an overwhelming fluctuation beyond the thermal noise that precluded the use of FT.We further characterized the timescale and the amplitude of this fluctuation,finding that it was probably due to the stepping of the motor.The amplitude of torque fluctuation we characterized here provided new information on the torque-generating interaction potential curve.

Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches

The rapid expansion of the non-fungible token(NFT)market has attracted many investors.However,studies on the NFT price fluctuations have been relatively limited.To date,the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction.The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea,the world’s largest NFT marketplace.We used Python programs to collect data and summarized them as:NFT information,collection information,and related account information.AdaBoost and Random Forest(RF)algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models,respectively.We found that the NFT related account information,especially the number of favorites and activity status of creators,confer a good predictive power to both the models.AdaBoost in the regression model had more accurate predictions,the root mean square error(RMSE)in predicting NFT sale price was 0.047.In predicting NFT sale price fluctuations,RF performed better,which the area under the curve(AUC)reached 0.956.We suggest that investors should pay more attention to the information of NFT creators.We anticipate that these prediction models will reduce the number of investment failures for the investors.

Investigations of moiréartifacts induced by flux fluctuations in x-ray dark-field imaging

X-ray dark-field imaging using a grating interferometer has shown potential benefits for a variety of applications in recent years.X-ray dark-field image is commonly retrieved by using discrete Fourier transform from the acquired phasestepping data.The retrieval process assumes a constant phase step size and a constant flux for each stepped grating position.However,stepping errors and flux fluctuations inevitably occur due to external vibrations and/or thermal drift during data acquisition.Previous studies have shown that those influences introduce errors in the acquired phase-stepping data,which cause obvious moiréartifacts in the retrieved refraction image.This work investigates moiréartifacts in x-ray dark-field imaging as a result of flux fluctuations.For the retrieved mean intensity,amplitude,visibility and dark-field images,the dependence of moiréartifacts on flux fluctuation factors is theoretically derived respectively by using a first-order Taylor series expansion.Results of synchrotron radiation experiments verify the validity of the derived analytical formulas.The spatial frequency characteristics of moiréartifacts are analyzed and compared to those induced by phase-stepping errors.It illustrates that moiréartifacts can be estimated by a weighted mean of flux fluctuation factors,with the weighting factors dependent on the moiréphase and different greatly for each retrieved image.Furthermore,moiréartifacts can even be affected by object’s features not displayed in the particular contrast.These results can be used to interpret images correctly,identify sources of moiréartifacts,and develop dedicated algorithms to remove moiréartifacts in the retrieved multi-contrast images.

UAV-Assisted FSO Communication System with Amplify-and-Forward Protocol under AOA Fluctuations:A Performance Analysis

Free space optical(FSO)communication has recently aroused great interest in academia due to its unique features,such as large transmission band,high data rates,and strong anti-electromagnetic interference.With the aim of evaluating the performance of an FSO communication system and extending the line-of-sight transmission distance,we propose an unmanned aerial vehicle(UAV)-assisted dual-hop FSO communication system equipped with amplifyand-forward protocol at the relay node.Specifically,we consider impairments of atmospheric absorption,pointing errors,atmospheric turbulence,and link interruptions due to angle-of-arrival fluctuations in the relay system.The Gamma-Gamma and Malaga distributions are used to model the influence of atmospheric turbulence on the source-to-UAV and UAVto-destination links,respectively.We derive closedform expressions of the probability density function(PDF)and cumulative distribution function(CDF)for the proposed communication system,in terms of the Meijer-G function.Based on the precise PDF and CDF,analytical expressions for the outage probability,average bit error rate,and ergodic capacity are proposed with the aid of the extended generalized bivariate Fox’s H function.Finally,we show that there is a match between the analytical results and numerical results,and we analyze the influence of the system and channel parameters on the performance.

Delayed response to the photovoltaic performance in a double quantum dots photocell with spatially correlated fluctuation

A viable strategy for enhancing photovoltaic performance is to comprehend the underlying quantum physical regime of charge transfer in a double quantum dots(DQD) photocell. This work explored the photovoltaic performance dependent spatially correlated fluctuation in a DQD photocell. The effects of spatially correlated fluctuation on charge transfer and output photovoltaic efficiency were explored in a proposed DQD photocell model. The results revealed that the charge transport process and the time to peak photovoltaic efficiency were both significantly delayed by the spatially correlated fluctuation, while the anti-spatially correlated fluctuation reduced the output peak photovoltaic efficiency. Further results revealed that the delayed response could be suppressed by gap difference and tunneling coefficient within two dots. Subsequent investigation demonstrated that the delayed response was caused by the spatial correlation fluctuation slowing the generative process of noise-induced coherence, which had previously been proven to improve the quantum photovoltaic performance in quantum photocells. And the reduced photovoltaic properties were verified by the damaged noise-induced coherence owing to the anti-spatial correlation fluctuation and a hotter thermal ambient environment. The discovery of delayed response generated by the spatially correlated fluctuations will deepen the understanding of quantum features of electron transfer, as well as promises to take our understanding even further concerning quantum techniques for high efficiency DQD solar cells.

Improved statistical fluctuation analysis for two decoy-states phase-matching quantum key distribution

Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution,breaking through the traditional linear key-rate bound.In practical applications,finite data size can cause significant system performance to deteriorate when data size is below 1010.In this work,an improved statistical fluctuation analysis method is applied for the first time to two decoy-states phase-matching quantum key distribution,offering a new insight and potential solutions for improving the key generation rate and the maximum transmission distance while maintaining security.Moreover,we also compare the influence of the proposed improved statistical fluctuation analysis method on system performance with those of the Gaussian approximation and Chernoff-Hoeffding boundary methods on system performance.The simulation results show that the proposed scheme significantly improves the key generation rate and maximum transmission distance in comparison with the Chernoff-Hoeffding approach,and approach the results obtained when the Gaussian approximation is employed.At the same time,the proposed scheme retains the same security level as the Chernoff-Hoeffding method,and is even more secure than the Gaussian approximation.

Influencing mechanism and hydrogeological implications of water level fluctuation of lakes in the northern Qaidam Basin,Qinghai-Tibet Plateau

The Qaidam Basin(QB)is a concentrated distribution area and chemical industrial bases of salt lakes in China.Lakes in the QB have been expanding during the past 20 years.Rapid lake expansion resulted in some considerable scientific issues on the protection of salt lake resources and infrastructure,and monitoring of hydrological processes at the lake-basin scale.Although the spatial-temporal trends of lake changes on the Qinghai-Tibet Plateau(QTP)have been well documented,the underlying influencing mechanism and hydrogeological implications of rapid lake changes in the QB are not well understood.Three lakes in the northern QB were selected to investigate lake water level fluctuations on different time scales based on extensive in-situ monitoring and satellite observations.The influencing mechanism and hydrogeological implications of rapid changes of terminal lakes were discussed in combination with the reported increasing precipitation rate and mass balance of glaciers in the northern QTP.Results reveal the following:(1)the fluctuation pattern of Sugan Lake was asynchronous and out of phase with that of Xiao Qaidam and Toson lakes during the monitoring period;(2)Sugan Lake water rose gradually,and the rise interval was from late April to early July.In contrast,Xiao Qaidam and Toson lakes took on a rapid and steep rise,and the rise intervalwas from late July to September;(3)the influencing mechanisms for rapid lake fluctuations are controlled by different factors:glacier and snow melting with increasing temperature for Sugan Lake and increasing precipitation for Xiao Qaidam and Toson lakes;(4)in accordance with different intervals and influencing mechanisms of rapid lake expansions in the QB,hydrological risk precaution of lakes and corresponding river catchments was conducted in different parts of the basin.This study provided an important scientific basis for assessing the hydrological process and hydrological risk precaution,and protection of salt lake resources along with rapid lake expansions in the arid area.

A Cross-Seasonal Linkage between Arctic Sea Ice and Eurasian Summertime Temperature Fluctuations

This study explores the linkage between summertime temperature fluctuations over midlatitude Eurasia and the preceding Arctic sea ice concentration (SIC) by utilizing the squared norm of the temperature anomaly, the essential part of local eddy available potential energy, as a metric to quantify the temperature fluctuations with weather patterns on various timescales. By comparing groups of singular value decomposition (SVD) analysis, we suggest a significant linkage between strong (weak) August 10-to-30-day temperature fluctuations over mid-west Asia and enhanced (decreased) Barents-Kara Sea ice in the previous February. We find that when the February SIC increases in the Barents-Kara Sea, a zonal dipolar pattern of SST anomalies appears in the Atlantic subpolar region and lasts from February into the summer months. Evidence suggests that in such a background state, the atmospheric circulation changes evidently from July to August, so that the August is characterized by an amplified meridional circulation over Eurasia, weakened westerlies, and high- pressure anomalies along the Arctic coast. Moreover, the 10-to-30-day wave becomes more active in the North Atlantic-Barents-Kara Sea-Central Asia regions and manifests a more evident southward propagation from the Barents- Kara Sea into the Ural region, which is responsible for the enhanced 10-to-30-day wave activity and temperature fluctuations in the region.

Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network

Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.