The clinical study of solute rem oval index( SRI) asan index to quantitate the adequcacy of hemodialysi

The clinical study of solute removal index (SRI) was performed in 20 stable maintenance hemodialysis patients in order to find a proper hemodialydsis dosage and improve the life quality of the patients.Plasma BUN levels were tested pre-hemodialysis and 0, 1 hr post-hemodialysis. Urea generation rate (G)、protein catabolic rate(PCR)、solute removal amount(R)、SRI were calculated by double pools urea kinetic model . The result showed that urea rebound rate was 18.5±3.35 % 1 hr after hemodialysis,R was 13.82±5.48 g, G was 4.85±1.39 g, PCR was 0.94±0.29 g/kg.d, SRI was 71.33±6.8% respectively, which indicated that SRI was a better index to quantitate the adequacy of hemodialysis.

Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Characterization and quantification of multi-field coupling in lithium-ion batteries under mechanical constraints

The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Recent advances in quantifying the inactive lithium and failure mechanism of Li anodes in rechargeable lithium metal batteries

Lithium metal is considered as the ultimate anode material for the next generation of high-energy density batteries.However,non-uniform lithium dendrite growth,serious electrolyte consumption,and significant volume changes during lithium deposition/stripping processes lead to sustained accumulation of inactive lithium and poor cycling reversibility.Quantifying the formation and evolution of inactive lithium under different conditions and fully evaluating the complex failure modes are the key issues in this challenging field.This article comprehensively reviews recent research progress on the quantification of formation and evolution of inactive lithium detected by different quantitative techniques in rechargeable lithium metal batteries.The key research challenges such as failure mechanism,modification strategies and operando characterization of lithium metal anodes are systematically summarized and prospected.This review provides a new angle of view to understand failure mechanism of lithium metal anodes and inspiration and guidance for the future development of rechargeable lithium metal batteries.

Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Wood anatomy chronologies of Scots pine in the foothills of the Western Sayan(Siberia)

Recent methodological advances in quantitative wood anatomy have provided new insights into the climatic responses of radial growth at the scale of cell structure of tree rings. This study considered long-term chronologies of tracheid measurements, indexed by a novel approach to separate their specific climatic responses from signal recorded in cell production(closely reflected in tree-ring width). To fill gaps in understanding the impact of climate on conifer xylem structure, Scots pine(Pinus sylvestris L.)trees > 200 years old were selected within the forest-steppe zone in southern Siberia. Such habitats undergo mild moisture deficits and the resulting climatic regulation of growth processes. Mean and maximum values of cell radial diameter and cell wall thickness were recorded for each tree ring.Despite a low level of climatogenic stress, components of cell chronologies independent of cambial activity were separated to obtain significant climatic signals revealing the timing of the specific stages of tracheid differentiation. Cell expansion lasted from mid-April to July and was impacted similarly to tree-ring width(stimulated by precipitation and stressed by heat), maximum cell size formed late June. A switch in the climatic responses of mean anatomical traits indicated transition to latewood in mid-July. Secondary wall deposition lasted until mid-September, suppressed by end of season temperatures. Generally, anatomical climatic responses were modulated by a less dry May and September compared with summer months.

Analysis on the turning point of dynamic in-plane compressive strength for a plain weave composite

Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate of composites was established. Then the upper limit of strain rate, restricted by stress equilibrium and constant loading rate, was rationally estimated and confirmed by tests. Within the achievable range of 0.001/s-895/s, it was found that the strength increased first and subsequently decreased as the strain rate increased. This feature was also reflected by the turning point(579/s) of the bilinear model for strength prediction. The transition in failure mechanism, from local opening damage to completely splitting destruction, was mainly responsible for such strain rate effects. And three major failure modes were summarized under microscopic observations: fiber fracture, inter-fiber fracture, and interface delamination. Finally, by introducing a nonlinear damage variable, a simplified ZWT model was developed to characterize the dynamic mechanical response. Excellent agreement was shown between the experimental and simulated results.

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Highly Aligned Graphene Aerogels for Multifunctional Composites

Stemming from the unique in-plane honeycomb lattice structure and the sp^(2)hybridized carbon atoms bonded by exceptionally strong carbon–carbon bonds,graphene exhibits remarkable anisotropic electrical,mechanical,and thermal properties.To maximize the utilization of graphene’s in-plane properties,pre-constructed and aligned structures,such as oriented aerogels,films,and fibers,have been designed.The unique combination of aligned structure,high surface area,excellent electrical conductivity,mechanical stability,thermal conductivity,and porous nature of highly aligned graphene aerogels allows for tailored and enhanced performance in specific directions,enabling advancements in diverse fields.This review provides a comprehensive overview of recent advances in highly aligned graphene aerogels and their composites.It highlights the fabrication methods of aligned graphene aerogels and the optimization of alignment which can be estimated both qualitatively and quantitatively.The oriented scaffolds endow graphene aerogels and their composites with anisotropic properties,showing enhanced electrical,mechanical,and thermal properties along the alignment at the sacrifice of the perpendicular direction.This review showcases remarkable properties and applications of aligned graphene aerogels and their composites,such as their suitability for electronics,environmental applications,thermal management,and energy storage.Challenges and potential opportunities are proposed to offer new insights into prospects of this material.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Mangrove wetlands distribution status identification, changing trend analyzation and carbon storage assessment of China

This research investigates the ecological importance,changes,and status of mangrove wetlands along China’s coastline.Visual interpretation,geological surveys,and ISO clustering unsupervised classification methods are employed to interpret mangrove distribution from remote sensing images from 2021,utilizing ArcGIS software platform.Furthermore,the carbon storage capacity of mangrove wetlands is quantified using the carbon storage module of InVEST model.Results show that the mangrove wetlands in China covered an area of 278.85 km2 in 2021,predominantly distributed in Hainan,Guangxi,Guangdong,Fujian,Zhejiang,Taiwan,Hong Kong,and Macao.The total carbon storage is assessed at 2.11×10^(6) t,with specific regional data provided.Trends since the 1950s reveal periods of increase,decrease,sharp decrease,and slight-steady increases in mangrove areas in China.An important finding is the predominant replacement of natural coastlines adjacent to mangrove wetlands by artificial ones,highlighting the need for creating suitable spaces for mangrove restoration.This study is poised to guide future mangroverelated investigations and conservation strategies.

Ionic liquids as the effective technology for enhancing transdermal drug delivery: Design principles, roles, mechanisms, and future challenges

Ionic liquids (ILs) have been proven to be an effective technology for enhancing drug transdermal absorption. However, due to the unique structural components of ILs, the design of efficient ILs and elucidation of action mechanisms remain to be explored. In this review, basic design principles of ideal ILs for transdermal drug delivery system (TDDS) are discussed considering melting point, skin permeability, and toxicity, which depend on the molar ratios, types, functional groups of ions and inter-ionic interactions. Secondly, the contributions of ILs to the development of TDDS through different roles are described: as novel skin penetration enhancers for enhancing transdermal absorption of drugs;as novel solvents for improving the solubility of drugs in carriers;as novel active pharmaceutical ingredients (API-ILs) for regulating skin permeability, solubility, release, and pharmacokinetic behaviors of drugs;and as novel polymers for the development of smart medical materials. Moreover, diverse action mechanisms, mainly including the interactions among ILs, drugs, polymers, and skin components, are summarized. Finally, future challenges related to ILs are discussed, including underlying quantitative structure-activity relationships, complex interaction forces between anions, drugs, polymers and skin microenvironment, long-term stability, and in vivo safety issues. In summary, this article will promote the development of TDDS based on ILs.

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

Clinical Value of the Quantitative Flow Ratio to Predict Long-term Target Vessel Failure in Patients with In-stent Restenosis after Drug-coated Balloon Angioplasty

Objective The study sought to investigate the clinical predictive value of quantitative flow ratio(QFR)for the long-term target vessel failure(TVF)outcome in patients with in-stent restenosis(ISR)by using drug-coated balloon(DCB)treatment after a long-term follow-up.Methods This was a retrospective study.A total of 186 patients who underwent DCB angioplasty for ISR in two hospitals from March 2014 to September 2019 were enrolled.The QFR of the entire target vessel was measured offline.The primary endpoint was TVF,including target vessel-cardiac death(TV-CD),target vessel-myocardial infarction(TV-MI),and clinically driven-target vessel revascularization(CD-TVR).Results The follow-up time was 3.09±1.53 years,and 50 patients had TVF.The QFR immediately after percutaneous coronary intervention(PCI)was significantly lower in the TVF group than in the no-TVF group.Multivariable Cox regression analysis indicated that the QFR immediately after PCI was an excellent predictor for TVF after the long-term follow-up[hazard ratio(HR):5.15×10−5(6.13×10−8−0.043);P<0.01].Receiver-operating characteristic(ROC)curve analysis demonstrated that the optimal cut-off value of the QFR immediately after PCI for predicting the long-term TVF was 0.925(area under the curve:0.886,95%confidence interval:0.834–0.938;sensitivity:83.40%,specificity:88.00;P<0.01).In addition,QFR≤0.925 post-PCI was strongly correlated with the TVF,including TV-MI and CD-TVR(P<0.01).Conclusion The QFR immediately after PCI showed a high predictive value of TVF after a long-term follow-up in ISR patients who underwent DCB angioplasty.A lower QFR immediately after PCI was associated with a worse TVF outcome.

Selenium Regulates Antioxidant Capacities and Diterpenoid Biosynthesis in the Medicinal Plant Isodon rubescens

Dōng líng căo,the dried aboveground parts of Isodon rubescens(Hemls.)Hara.,is commonly consumed as a med-icinal decoction or tea beverage.Natural beverages can be an important source of human dietary selenium(Se).However,how I.rubescens plants respond to exogenous Se remains unknown.In this study,a pot cultivation experiment was employed to investigate the phenotypic and physiological responses of I.rubescens plants exposed to Se.Fifteen days after applying different concentrations of sodium selenate to the soil,the Se enrichment capa-city,growth indices,antioxidant capacities,and the content offlavonoids and diterpenoids were measured in the plants.Further,the oridonin content was quantified using the high-performance liquid chromatography method,and the expression levels of key diterpenoid synthesis genes were analyzed by quantitative real-time PCR(qRT-PCR).I.rubescens plants efficiently accumulated Se,with the Se content increasing proportionally to the applied dose,reaching levels of nearly 200 mg·kg^(-1) dry leaves as Se concentration increased.None of the three Se treat-ments significantly altered the phenotypic indices,except a longer root length occurred in the 3μM·kg^(-1) Se group.Among three Se doses,6μM·kg^(-1) Se gave the highest accumulation offlavonoids,diterpenoids,and oridonin,with the increase of 2.0-,1.8-,and 1.9-fold in aboveground parts,respectively.Selenium application boosted the activities of antioxidant enzymes and antioxidant capacities according to 2,2-Diphenyl-1-picrylhydrazyl(DPPH),ferric reducing/antioxidant power,and tea brewing color experiments.Four key synthase genes were upregulated significantly by 6μM·kg^(-1) Se treatment,notably 1-deoxy-D-xylulose 5-phosphate reductoisomerase(IrDXR),with a 5-fold increase,and kaurene synthase-like 4(IrKSL4),with a 6-fold increase.Thus,Se application in I.rubescens cultivation may be a potential biofortification method to supplement Se while increasingflavonoid and diterpenoid contents.

Circulating proteomic biomarkers for diagnosing sporadic amyotrophic lateral sclerosis:a cross-sectional study

Biomarke rs are required for the early detection,prognosis prediction,and monitoring of amyotrophic lateral sclerosis,a progressive disease.Proteomics is an unbiased and quantitative method that can be used to detect neurochemical signatures to aid in the identification of candidate biomarke rs.In this study,we used a label-free quantitative proteomics approach to screen for substantially differentially regulated proteins in ten patients with sporadic amyotrophic lateral scle rosis compared with five healthy controls.Su bstantial upregulation of serum proteins related to multiple functional clusters was observed in patients with spo radic amyotrophic lateral sclerosis.Potential biomarke rs were selected based on functionality and expression specificity.To validate the proteomics profiles,blood samples from an additional cohort comprising 100 patients with sporadic amyotrophic lateral sclerosis and 100 healthy controls were subjected to enzyme-linked immunosorbent assay.Eight substantially upregulated serum proteins in patients with spora dic amyotrophic lateral sclerosis were selected,of which the cathelicidin-related antimicrobial peptide demonstrated the best discriminative ability between patients with sporadic amyotrophic lateral sclerosis and healthy controls(area under the curve[AUC]=0.713,P<0.0001).To further enhance diagnostic accuracy,a multi-protein combined discriminant algorithm was developed incorporating five proteins(hemoglobin beta,cathelicidin-related antimicrobial peptide,talin-1,zyxin,and translationally-controlled tumor protein).The algo rithm achieved an AUC of 0.811 and a P-value of<0.0001,resulting in 79%sensitivity and 71%specificity for the diagnosis of sporadic amyotrophic lateral scle rosis.Subsequently,the ability of candidate biomarkers to discriminate between early-stage amyotrophic lateral sclerosis patients and controls,as well as patients with different disease severities,was examined.A two-protein panel comprising talin-1 and translationally-controlled tumor protein effectively distinguished early-stage amyotrophic lateral sclerosis patients from controls(AUC=0.766,P<0.0001).Moreove r,the expression of three proteins(FK506 binding protein 1A,cathelicidin-related antimicrobial peptide,and hemoglobin beta-1)was found to increase with disease progression.The proteomic signatures developed in this study may help facilitate early diagnosis and monitor the progression of sporadic amyotrophic lateral sclerosis when used in co mbination with curre nt clinical-based parameters.

GSW3.1,a novel gene controlling grain size and weight in rice

Grain size and weight are closely related traits determining yield in rice(Oryza sativa L.).Since indica and japonica rice varieties differ significantly in multiple traits,a high-generation recombinant inbred line(RIL)population derived from the crossing LH9(indica)and RPY(japonica)was used to map grainrelated traits in six environments.Pyramiding of the quantitative trait loci(QTL)for thousand-grain weight showed that combinations of multiple QTL significantly increased the phenotypic effect.A novel gene named GSW3.1 controlling grain size and weight was discovered using the major QTL for the colocalization of grain width and thousand-grain weight on chromosome 3.Gene editing revealed that GSW3.1(LOC_Os03g16850)was pleiotropic,positively regulating grain size and weight while affecting several other agronomic traits.Haplotype analysis indicated that some traits,including grain width and weight,were highly correlated with indica-japonica differentiation.

Combined use of fly ash and silica to prevent the long-term strength retrogression of oil well cement set and cured at HPHT conditions

The long-term strength retrogression of silica-enriched oil well cement poses a significant threat to wellbore integrity in deep and ultra-deep wells, which is a major obstacle for deep petroleum and geothermal energy development. Previous attempts to address this problem has been unsatisfactory because they can only reduce the strength decline rate. This study presents a new solution to this problem by incorporating fly ash to the traditional silica-cement systems. The influences of fly ash and silica on the strength retrogression behavior of oil well cement systems directly set and cured under the condition of 200°C and 50 MPa are investigated. Test results indicate that the slurries containing only silica or fly ash experience severe strength retrogression from 2 to 30 d curing, while the slurries containing both fly ash and silica experience strength enhancement from 2 to 90 d. The strength test results are corroborated by further evidences from permeability tests as well as microstructure analysis of set cement. Composition of set cement evaluated by quantitative X-ray diffraction analyses with partial or no known crystal structure(PONKCS) method and thermogravimetry analyses revealed that the conversion of amorphous C-(A)-S-H to crystalline phases is the primary cause of long-term strength retrogression.The addition of fly ash can reduce the initial amount of C-(A)-S-H in the set cement, and its combined use with silica can prevent the crystallization of C-(A)-S-H, which is believed to be the working mechanism of this new admixture in improving long-term strength stability of oil well cement systems.