法舒地尔缓解β-淀粉样蛋白1-42诱导的SH-SY5Y细胞凋亡

背景:法舒地尔对阿尔茨海默病小鼠脑内的线粒体动力学有调节作用,并且可以抑制神经炎症,但能否调节线粒体自噬和NLRP3炎症小体进而减轻β-淀粉样蛋白毒性尚不清楚。目的:探究法舒地尔对β-淀粉样蛋白1-42诱导的人源神经母细胞瘤细胞株SH-SY5Y凋亡和线粒体自噬以及NLRP3炎症小体的调节作用。方法:将SH-SY5Y细胞接种于孔板内,细胞贴壁后分3组干预:对照组不加入任何药物,模型组加入20μmol/L β-淀粉样蛋白1-42,法舒地尔组同时加入20μmol/L β-淀粉样蛋白1-42与15 mg/L法舒地尔,干预24 h后,采用MTT法检测细胞活性,TUNEL染色检测细胞凋亡,qRT-PCR和Western blot检测凋亡相关蛋白表达,免疫荧光染色和Western blot检测线粒体自噬相关蛋白表达,免疫荧光染色和Western blot检测NLRP3炎症小体相关蛋白表达。结果与结论:(1)与对照组比较,模型组细胞活性降低、凋亡率升高(P<0.05);与模型组比较,法舒地尔组细胞活性升高、凋亡率降低(P<0.05);(2)qRT-PCR和Western blot检测结果显示,与对照组比较,模型组细胞Bax mRNA与蛋白表达升高(P<0.05),Bcl-2 mRNA与蛋白表达降低(P<0.05);与模型组比较,法舒地尔组细胞Bax mRNA与蛋白表达降低(P<0.05),Bcl-2 mRNA与蛋白表达升高(P<0.05);(3)免疫荧光染色和Western blot检测结果显示,与对照组相比,模型组细胞PINK1、帕金森病蛋白和LC3蛋白表达降低(P<0.05),p62蛋白表达升高(P<0.05);与模型组相比,法舒地尔组细胞PINK1、帕金森病蛋白和LC3蛋白表达升高(P<0.05),p62蛋白表达降低(P<0.05);(4)免疫荧光染色和Western blot检测结果显示,与对照组相比,模型组细胞NLRP3、ASC、Caspase-1和白细胞介素1β蛋白表达升高(P<0.05);与模型组相比,法舒地尔组细胞NLRP3、ASC、Caspase-1和白细胞介素1β蛋白表达降低(P<0.05);(5)结果表明,法舒地尔可以减轻β-淀粉样蛋白1-42诱导的SH-SY5Y细胞凋亡,其机制可能与激活线粒体自噬且抑制NLRP3炎症小体激活有关。

Ambient-Condition Strategy for Production of Hollow Ga_(2)O_(3)@rGO Crystalline Nanostructures Toward Efficient Lithium Storage

Crystallineγ-Ga_(2)O_(3)@rGO core-shell nanostructures are synthesized in gram scale,which are accomplished by a facile sonochemical strategy under ambient condition.They are composed of uniformγ-Ga_(2)O_(3)nanospheres encapsulated by reduced graphene oxide(rGO)nanolayers,and their formation is mainly attributed to the existed opposite zeta potential between the Ga_(2)O_(3)and rGO.The as-constructed lithium-ion batteries(LIBs)based on as-fabricatedγ-Ga_(2)O_(3)@rGO nanostructures deliver an initial discharge capacity of 1000 mAh g^(-1)at 100 mA g^(-1)and reversible capacity of 600 mAh g^(-1)under 500 mA g^(-1)after 1000 cycles,respectively,which are remarkably higher than those of pristineγ-Ga_(2)O_(3)with a much reduced lifetime of 100 cycles and much lower capacity.Ex situ XRD and XPS analyses demonstrate that the reversible LIBs storage is dominant by a conversion reaction and alloying mechanism,where the discharged product of liquid metal Ga exhibits self-healing ability,thus preventing the destroy of electrodes.Additionally,the rGO shell could act robustly as conductive network of the electrode for significantly improved conductivity,endowing the efficient Li storage behaviors.This work might provide some insight on mass production of advanced electrode materials under mild condition for energy storage and conversion applications.

自旋-轨道耦合作用下极化激元凝聚中的调制不稳定性

利用线性稳定性分析方法,对存在自旋-轨道耦合(SOS)作用的二维极化激元玻色-爱因斯坦凝聚(BEC)系统中的调制不稳定性(MI)进行了研究.分析了组分内部,组分之间以及SOC相互作用对系统调制不稳定性的影响.结果显示,当系统内部不存在SOC作用,组分之间的相互作用为0,组分内部存在排斥作用时,不会出现调制不稳定性,组分内部存在吸引作用时,会出现调制不稳定性,并且调制不稳定性区间长度随吸引作用的增强而增加;组分之间相互作用不为0时,组分之间的相互作用以平方形式出现,其正负不会对调制不稳定性产生实质性影响.存在SOC相互作用时,SOC相互作用会引起增益谱曲线的不规则振荡,破坏原来的调制不稳定性区间.

双偶氮苯-二苯并[b,i]噻蒽-[2,3-b]苯-5,7,12,14-四酮衍生物分子的二阶非线性光学性质

使用密度泛函理论(DFT)M06-2X方法、采用6-311+g(d,p)基组,分别对26个双偶氮-二苯并[b,i]噻蒽-[2,3-b]苯-5,7,12,14-四酮衍生物分子进行结构优化与频率计算;使用含时密度泛函理论(TD-DFT)TD-M06-2X方法计算了a1~d6分子的前线分子轨道与电子吸收光谱,采用有效场FF方法研究了二阶非线性光学性质(NLO).研究结果表明,26个噻蒽四酮类衍生物分子的能隙在1.33—2.02 eV范围,归属于有机半导体;最低能量吸收峰波长在601.8~609.5nm范围;在增大分子的二阶非线性光学系数β_(μ)(或β_(0))值方面,含相同偶氮苯基团或含不同偶氮苯基团分别引入到二苯并[b,i]噻蒽-[2,3-b]苯-5,7,12,14-四酮分子两侧的2,10位优于2,9位,在2,10位分别端接含推、拉基团的偶氮苯优于含相同给电子基团的偶氮苯.在偶氮苯苯环对位分别端接强吸电子基(-NO_(2))与强供电子基(如-N(CH_(3))_(2)、-N(Ph)_(3)、-N-苯基咔唑等)可增强体系的二阶非线性光学性能,获得性能良好的非线性光学材料.

Oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions via Prufer transformation

A class of Sturm-Liouville problems with discontinuity is studied in this paper.The oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions are obtained.The main method used in this paper is based on Prufer transformation,which is different from the classical ones.Moreover,we give two examples to verify our main results.

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Design and high-power testing of offline conditioning cavity for CiADS RFQ high-power coupler

To validate the design rationality of the power coupler for the RFQ cavity and minimize cavity contamination,we designed a low-loss offline conditioning cavity and conducted high-power testing.This offline cavity features two coupling ports and two tuners,operating at a frequency of 162.5 MHz with a tuning range of 3.2 MHz.Adjusting the installation angle of the coupling ring and the insertion depth of the tuner helps minimize cavity losses.We performed electromagnetic structural and multiphysics simulations,revealing a minimal theoretical power loss of 4.3%.However,when the cavity frequency varied by110 kHz,theoretical power losses increased to10%,necessitating constant tuner adjustments during conditioning.Multiphysics simulations indicated that increased cavity temperature did not affect frequency variation.Upon completion of the offline high-power conditioning platform,we measured the transmission performance,revealing a power loss of 6.3%,exceeding the theoretical calculation.Conditioning utilized efficient automatic range scanning and standing wave resonant methods.To fully condition the power coupler,a 15°phase difference between two standing wave points in the condition-ing system was necessary.Notably,the maximum continuous wave power surpassed 20 kW,exceeding the expected target.

Multi-soliton solutions of coupled Lakshmanan-Porsezian-Daniel equations with variable coefficients under nonzero boundary conditions

This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan–Porsezian–Daniel equations with variable coefficients under nonzero boundary conditions.These equations are utilized to model the phenomenon of nonlinear waves propagating simultaneously in non-uniform optical fibers.By analyzing the Lax pair and the Riemann–Hilbert problem,we aim to provide a comprehensive understanding of the dynamics and interactions of solitons of this system.Furthermore,we study the impacts of group velocity dispersion or the fourth-order dispersion on soliton behaviors.Through appropriate parameter selections,we observe various nonlinear phenomena,including the disappearance of solitons after interaction and their transformation into breather-like solitons,as well as the propagation of breathers with variable periodicity and interactions between solitons with variable periodicities.

Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

Research on shell-side heat and mass transfer with multi-component in LNG spiral-wound heat exchanger under sloshing conditions

The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.

Influence of tectonic preservation conditions on the nanopore structure of shale reservoir:A case study of Wufeng-Longmaxi Formation shale in western Hubei area,south China

Tectonism is one of the dominant factors affecting the shale pore structure.However,the control of shale pore structure by tectonic movements is still controversial,which limits the research progress of shale gas accumulation mechanism in the complex tectonic region of southern China.In this study,34 samples were collected from two exploratory wells located in different tectonic locations.Diverse experiments,e.g.,organic geochemistry,XRD analysis,FE-SEM,low-pressure gas adsorption,and high-pressure mercury intrusion,were conducted to fully characterize the shale reservoir.The TOC,Ro,and mineral composition of the shale samples between the two wells are similar,which reflects that the shale samples of the two wells have proximate pores-generating capacity and pores-supporting capacity.However,the pore characteristics of shale samples from two wells are significantly different.Compared with the stabilized zone shale,the porosity,pore volume,and specific surface area of the deformed zone shale were reduced by 60.61%,64.85%,and 27.81%,respectively.Moreover,the macroscopic and fine pores were reduced by 54.01%and 84.95%,respectively.Fault activity and uplift denudation are not conducive to pore preservation,and the rigid basement of Huangling uplift can promote pore preservation.These three factors are important reasons for controlling the difference in pore structure between two wells shales.We established a conceptual model of shale pores evolution under different tectonic preservation conditions.This study is significant to clarify the scale of shale gas formation and enrichment in complex tectonic regions,and helps in the selection of shale sweet spots.

Impact of wetting-drying cycles and acidic conditions on the soil aggregate stability of yellow‒brown soil

Soil aggregate is the basic structural unit of soil,which is the foundation for supporting ecosystem functions,while its composition and stability is significantly affected by the external environment.This study was conducted to explore the effect of external environment(wetting-drying cycles and acidic conditions)on the soil aggregate distribution and stability and identify the key soil physicochemical factors that affect the soil aggregate stability.The yellow‒brown soil from the Three Gorges Reservoir area(TGRA)was used,and 8 wetting-drying conditions(0,1,2,3,4,5,10 and 15 cycles)were simulated under 4 acidic conditions(pH=3,4,5 and 7).The particle size distribution and soil aggregate stability were determined by wet sieving method,the contribution of environmental factors(acid condition,wetting-drying cycle and their combined action)to the soil aggregate stability was clarified and the key soil physicochemical factors that affect the soil aggregate stability under wetting-drying cycles and acidic conditions were determined by using the Pearson’s correlation analysis,Partial least squares path modeling(PLS‒PM)and multiple linear regression analysis.The results indicate that wetting-drying cycles and acidic conditions have significant effects on the stability of soil aggregates,the soil aggregate stability gradually decreases with increasing number of wetting-drying cycles and it obviously decreases with the increase of acidity.Moreover,the combination of wetting-drying cycles and acidic conditions aggravate the reduction in the soil aggregate stability.The wetting-drying cycles,acidic conditions and their combined effect imposes significant impact on the soil aggregate stability,and the wetting-drying cycles exert the greatest influence.The soil aggregate stability is significantly correlated with the pH,Ca^(2+),Mg^(2+),maximum disintegration index(MDI)and soil bulk density(SBD).The PLS‒PM and multiple linear regression analysis further reveal that the soil aggregate stability is primarily influenced by SBD,Ca^(2+),and MDI.These results offer a scientific basis for understanding the soil aggregate breakdown mechanism and are helpful for clarifying the coupled effect of wetting-drying cycles and acid rain on terrestrial ecosystems in the TGRA.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Classification research of TCM pulse conditions based on multi-label voice analysis

Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

The Impact of Physiotherapy Interventions on the Elderly Population with Mental Health Conditions at Chainama Hills College Hospital in Zambia. A Pre-Post Single Sample Study Design

Background: Physical functional decline is common among elderly individuals with mental disorders, worsening their symptoms. Physiotherapy interventions have shown some evidence in improving physical function and mental health outcomes in this population. This study aimed to assess the impact of physiotherapy interventions on the elderly with mental health conditions at Chainama Hills College Hospital in Zambia. Methods: A pre-post single sample design was used to track patient progress over six weeks, with 10 physiotherapy sessions. The study population (N = 30) comprised of all elderly individuals with mental health conditions, encompassing both men and women, who were hospitalized during the research period. The Katz Index of Activities of Daily Living and the six-minute walk test were evaluated before and after the intervention. The IBM SPSS version 26 was used to analyze data and results were presented as mean ± SD with a 95% confidence interval. The variables were described in terms of their mean, SD, and range. A significance level of 0.05 was used for a paired T-test to detect changes and multiple logistic regression was used to identify factors associated with mental health. Results: Following the intervention, the percentage of participants achieving full function and independence increased significantly to 96.7% from the initial 73.3%, supported by a 95% CI = [0.82 - 0.99]. There was also a notable decrease in the proportion of individuals experiencing moderate impairment, dropping from 26.7% to just 3.3%, with a corresponding 95% CI = [0.00 - 0.17]. Conclusion: The findings derived from this study illustrate an enhancement in the aspects of participants’ overall health and functional condition, including blood pressure, heart rate, and respiratory rate. Consequently, physiotherapy exercises can be employed as a tactic to ameliorate the functional status and physical well-being of older individuals afflicted with mental disorders in Zambia.

Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.