Combined Optimal Dispatch of Thermal Power Generators and Energy Storage Considering Thermal Power Deep Peak Clipping and Wind Energy Emission Grading Punishment

Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal power generators and BESS(battery energy storage system)taking wind energy emission grading punishment and deep peak clipping into consideration.Firstly,in order to minimize wind abandonment,a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced,and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system.Secondly,considering BESS and thermal power,the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS.Finally,Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs,and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31 MWh and enhances the effect and economy of system peak clipping.

Relationship between neonatal respiratory distress syndrome pulmonary ultrasonography and respiratory distress score,oxygenation index,and chest radiography grading

BACKGROUND Accurate condition assessment is critical for improving the prognosis of neonatal respiratory distress syndrome(RDS),but current assessment methods for RDS pose a cumulative risk of harm to neonates.Thus,a less harmful method for assessing the health of neonates with RDS is needed.AIM To analyze the relationships between pulmonary ultrasonography and respiratory distress scores,oxygenation index,and chest X-ray grade of neonatal RDS to identify predictors of neonatal RDS severity.METHODS This retrospective study analyzed the medical information of 73 neonates with RDS admitted to the neonatal intensive care unit of Liupanshui Maternal and Child Care Service Center between April and December 2022.The pulmonary ultrasonography score,respiratory distress score,oxygenation index,and chest Xray grade of each newborn before and after treatment were collected.Spearman correlation analysis was performed to determine the relationships among these values and neonatal RDS severity.RESULTS The pulmonary ultrasonography score,respiratory distress score,oxygenation index,and chest X-ray RDS grade of the neonates were significantly lower after treatment than before treatment(P<0.05).Spearman correlation analysis showed that before and after treatment,the pulmonary ultrasonography score of neonates with RDS was positively correlated with the respiratory distress score,oxygenation index,and chest X-ray grade(ρ=0.429–0.859,P<0.05).Receiver operating characteristic curve analysis indicated that pulmonary ultrasonography screening effectively predicted the severity of neonatal RDS(area under the curve=0.805–1.000,P<0.05).CONCLUSION The pulmonary ultrasonography score was significantly associated with the neonatal RDS score,oxygenation index,and chest X-ray grade.The pulmonary ultrasonography score was an effective predictor of neonatal RDS severity.

Retrospective analysis based on a clinical grading system for patients with hepatic hemangioma:A single center experience

BACKGROUND The optimal approach for managing hepatic hemangioma is controversial.AIM To evaluate a clinical grading system for management of hepatic hemangioma based on our 17-year of single institution experience.METHODS A clinical grading system was retrospectively applied to 1171 patients with hepatic hemangioma from January 2002 to December 2018.Patients were classified into four groups based on the clinical grading system and treatment:(1)Observation group with score<4(Obs score<4);(2)Surgical group with score<4(Sur score<4);(3)Observation group with score≥4(Obs score≥4);and(4)Surgical group with score≥4(Sur score≥4).The clinico-pathological index and outcomes were evaluated.RESULTS There were significantly fewer symptomatic patients in surgical groups(Sur score≥4 vs Obs score≥4,P<0.001;Sur score<4 vs Obs score<4,χ^(2)=8.60,P=0.004;Sur score≥4 vs Obs score<4,P<0.001).The patients in Sur score≥4 had a lower rate of in need for intervention and total patients with adverse event than in Obs score≥4(P<0.001;P<0.001).Nevertheless,there was no significant difference in need for intervention and total patients with adverse event between the Sur score<4 and Obs score<4(P>0.05;χ^(2)=1.68,P>0.05).CONCLUSION This clinical grading system appeared as a practical tool for hepatic hemangioma.Surgery can be suggested for patients with a score≥4.For those with<4,follow-up should be proposed.

Catalyst grading technology for promoting the diesel hydrocracking to naphtha

This paper reports the application of multi-component hydrocracking catalyst grading technology in diesel hydrocracking system to increase naphtha,and studies the influence of catalyst systems with different number of graded beds on the reaction process of diesel hydrocracking.Three hydrocracking catalysts with different physicochemical properties as gradation components,the diesel hydrocracking reaction on catalyst systems of one-component,two-component and three-component graded beds with different loading sequences are carried out and evaluated,respectively.The catalytic mechanism of the multi-component grading system is analyzed.The results show that,with the increase of the number of grading beds,the space velocity of reaction on each catalyst increases,which can effectively control the overreaction process;along the flow direction of feedstock,the loading sequences of catalysts with acidity decreasing and pore properties increasing can satisfy the demand of different catalytic activity for the conversion of reactant with changing composition to naphtha,which has a guiding role in the conversion of feedstock to target products.Therefore,the conversion of diesel,the selectivity and yield of naphtha all increase significantly on the multi-component catalyst system.The research on the grading technology of multi-component catalysts is of great significance to the promotion and application of catalyst systems in various catalytic fields.

LuNet-LightGBM:An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the retina.If not treated early,DR becomes a significant reason for blindness.To identify the DR and determine the stages,medical tests are very labor-intensive,expensive,and timeconsuming.To address the issue,a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper.Our proposal is based on lesion segmentation of the fundus images based on the LuNet network.Then a Refined Attention Pyramid Network(RAPNet)is used for extracting global and local features.To increase the performance of the classifier,the unique features are selected from the extracted feature set using Aquila Optimizer(AO)algorithm.Finally,the LightGBM model is applied to classify the input image based on the severity.Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets(MESSIDOR,APTOS,and IDRiD)using several performance metrics such as accuracy,precision,recall,and f1-score.The proposed classifier achieves 99.29%,99.35%,and 99.31%accuracy for these three datasets respectively.The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

YOLO-Banana:An Effective Grading Method for Banana Appearance Quality

The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.

An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt

To solve inefficient water stress classification of spinach seedlings under complex background,this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt(NCAM+MobileNetXt)network.Firstly,this study recon-structed the Sandglass Block to effectively increase the model accuracy;secondly,this study introduced the group convolution module and a two-dimensional adaptive average pool,which can significantly compress the model parameters and enhance the model robustness separately;finally,this study innovatively proposed the Normalization-based Channel Attention Module(NCAM)to enhance the image features obviously.The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%,and the number of parameters was decreased by 66%compared with the original MobileNetXt model.The N-MobileNetXt model was superior to other net-work models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification.It can provide a theoretical basis and technical support for automatic irrigation.

论Peer Grading and Peer Feedback在英语写作教学中的应用

本文作者以自己在多年英语教学实践过程中积累的经验和感悟,从自身的教学实践出发,通过案例分析法论述了同伴批改(PeerGrading)和同伴反馈(PeerFeedback)在英语写作教学中的积极作用及存在的一些局限性。认为peer grading和peer feedback作为英语写作课上的一种辅助评判手段,值得广大英语写作教学老师推广使用。

Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

Liver pathology of hepatitis C,beyond grading and stagingof the disease

Liver biopsy evaluation plays a critical role in management of patients with viral hepatitis C. In patients with acute viral hepatitis, a liver biopsy, though uncommonly performed, helps to rule out other nonviral causes of deranged liver function. In chronic viral hepatitis C, it is considered the gold standard in assessment of the degree of necroinflammation and the stage of fibrosis, to help guide treatment and determine prognosis. It also helps rule out any concomitant diseases such as steatohepatitis, hemochromatosis or others. In patients with chronic progressive liver disease with cirrhosis and dominant nodules, a targeted liver biopsy is helpful in differentiating a regenerative nodule from dysplastic nodule or hepatocellular carcinoma. In the setting of transplantation, the liver biopsy helps distinguish recurrent hepatitis C from acute rejection and also is invaluable in the diagnosis of fibrosing cholestatic hepatitis, a rare variant of recurrent hepatitis C. This comprehensive review discusses the entire spectrum of pathologic findings in the course of hepatitis C infection.

Histo-and clinico-pathological analysis of a large series of triple-negative breast cancer in a single center in China:Evidences on necessity of histological subtyping and grading

Objective: To investigate histo-pathological distribution and clinico-pathological significance in a large Chinese triple-negative breast cancer(TNBC) patients serials based on the latest understanding of its clinico-pathological diversity, and to provide more information to clinicians to improve precision of individualized treatment of TNBC.Methods: A retrospective analysis was performed on patients with TNBC at Breast Disease Center, Peking University First Hospital between January 2010 and December 2019. Histo-and clinico-pathological characteristics were analyzed by Chi-square test and Student's t-test, and prognoses were calculated using KaplanMeier method and a Cox proportionate hazards model. Bonferroni correction was used to correct for multiple comparison.Results: Conventional type of TNBC(c TNBC) were identified in 73.7% of 582 TNBC, while special type of TNBC(s TNBC) were 26.3%, including 71 apocrine carcinoma, 20 medullary carcinoma, 31 metaplastic carcinoma, 18 invasive lobular carcinoma, 7 invasive micropapillary carcinoma, 5 adenoid cystic carcinoma and 1 acinic cell carcinoma. Compared to s TNBC, c TNBC was associated with high histologic grade(P<0.001) and lower androgen receptor(AR) expression(P<0.001). TNM stage of low-grade c TNBC was significantly lower than that of high-grade c TNBC(P=0.002). Although no significant difference, there was a trend that the rate of 5-year disease-free survival(DFS) and 5-year overall survival(OS) were longer in high-grade c TNBC than in high-grade s TNBC(P=0.091 and 0.518), and were longer in low-grade s TNBC than in high-grade s TNBC(P=0.051 and0.350). Metaplastic carcinomas showed larger tumor size(P=0.008) and higher proliferative Ki67 index(P=0.004)than c TNBCs.Conclusions: Results from our cohort imply that sub-categorization or subtyping and histological grading could be meaningful in pathological evaluation of TNBC, and need to be clarified in more large collections of TNBC.

Enhancement parameters of contrast-enhanced computed tomography for pancreatic ductal adenocarcinoma: Correlation with pathologic grading

BACKGROUND Pancreatic ductal adenocarcinoma(PDA)is a malignancy with a high mortality rate and short survival time.The conventional computed tomography(CT)has been worldwide used as a modality for diagnosis of PDA,as CT enhancement pattern has been thought to be related to tumor angiogenesis and pathologic grade of PDA.AIM To evaluate the relationship between the pathologic grade of pancreatic ductal adenocarcinoma and the enhancement parameters of contrast-enhanced CT.METHODS In this retrospective study,42 patients(Age,mean±SD:62.43±11.42 years)with PDA who underwent surgery after preoperative CT were selected.Two radiologists evaluated the CT images and calculated the value of attenuation at the aorta in the arterial phase and the pancreatic phase(VAarterial and VApancreatic)and of the tumor(VTarterial and VTpancreatic)by finding out four regions of interest.Ratio between the tumor and the aorta enhancement on the arterial phase and the pancreatic phase(TARarterial and TARpancreatic)was figured out through dividing VT arterial by VAarterial and VTpancreatic by VApancreatic.Tumor-to-aortic enhancement fraction(TAF)was expressed as the ratio of the difference between attenuation of the tumor on arterial and parenchymal images to that between attenuation of the aorta on arterial and pancreatic images.The Kruskal-Wallis analysis of variance and Mann-Whitney U test for statistical analysis were used.RESULTS Forty-two PDAs(23 men and 19 women)were divided into three groups:Welldifferentiated(n=13),moderately differentiated(n=21),and poorly differentiated(n=8).TAF differed significantly between the three groups(P=0.034)but TARarterial(P=0.164)and TARpancreatic(P=0.339)did not.The median value of TAF for poorly differentiated PDAs(0.1011;95%CI:0.01100-0.1796)was significantly higher than that for well-differentiated PDAs(0.1941;95%CI:0.1463-0.3194).CONCLUSION Calculation of TAF might be useful in predicting the pathologic grade of PDA.

The evolving Gleason grading system

The Gleason grading system for prostate adenocarcinoma has evolved from its original scheme established in the 1960s-1970s, to a significantly modified system after two major consensus meetings conducted by the International Society of Urologic Pathology （ISUP） in 2005 and 2014, respectively. The Gleason grading system has been incorporated into the WHO classification of prostate cancer, the AJCC/ UICC staging system, and the NCCN guidelines as one of the key factors in treatment decision. Both pathologists and clinicians need to fully understand the principles and practice of this grading system. We here briefly review the historical aspects of the original scheme and the recent developments of Gleason grading system, focusing on major changes over the years that resulted in the modern Gleason grading system, which has led to a new ＂Grade Group＂ system proposed by the 2014 ISUP consensus, and adopted by the 2016 WHO classification of tumours of the prostate.

Low-depth whole genome sequencing reveals copy number variations associated with higher pathologic grading and more aggressive subtypes of lung non-mucinous adenocarcinoma

Objective:Histology grade,subtypes and TNM stage of lung adenocarcinomas are useful predictors of prognosis and survival.The aim of the study was to investigate the relationship between chromosomal instability,morphological subtypes and the grading system used in lung non-mucinous adenocarcinoma(LNMA).Methods:We developed a whole genome copy number variation(WGCNV)scoring system and applied next generation sequencing to evaluate CNVs present in 91 LNMA tumor samples.Results:Higher histological grades,aggressive subtypes and more advanced TNM staging were associated with an increased WGCNV score,particularly in CNV regions enriched for tumor suppressor genes and oncogenes.In addition,we demonstrate that 24-chromosome CNV profiling can be performed reliably from specific cell types(<100 cells)isolated by sample laser capture microdissection.Conclusions:Our findings suggest that the WGCNV scoring system we developed may have potential value as an adjunct test for predicting the prognosis of patients diagnosed with LNMA.

Evaluation indices of sour flavor for apple fruit and grading standards

In order to establish grading standards of evaluation indices for sour flavor of apples, 10 indices of samples from 106 apple cultivars were tested, including： malic acid（Mal）, oxalic acid（Oxa）, citric acid（Cit）, lactic acid（Lac）, succinic acid（Suc）, fumaric acid（Fum）, total organic acids（To A, the sum of the six organic acids tested）, titratable acid（TiA）, acidity value（AcV）, and pH value. For most of the cultivars studied（85.8%）, the order of the organic acid contents in apples was Mal〉Oxa〉Cit〉Lac〉Suc〉Fum. Mal was the dominant organic acid, on average, accounting for 94.5% of To A. Among the 10 indices, the dispersion of pH value was the smallest with a coefficient of variation of only 8.2%, while the coefficients of variation of the other nine indices were larger, ranging between 31 and 66%. There were significant linear relationships between Mal and two indices（ToA and AcV） as well as between ToA and AcV. There were significant logarithmic relationships between pH value and four indices： Mal, TiA, ToA, and AcV. All the equations had very high fitting accuracy and can be used to accurately predict related indices. According to this study, Mal, ToA, and AcV of apple were normally distributed, TiA was close to normally distributed, whereas pH value had a skewed distribution. Using the fitted normal distribution curves, the grading standards of Mal, TiA, ToA, and AcV were established. The grading node values of pH value were obtained using the logarithmic relationship between pH value and Mal. The grading standards of these five indices can be used to evaluate the sour flavor of apple. This study provides a scientific basis for evaluating apple flavor and selecting apple cultivars.

Comparison of fundus fluorescein angiography and fundus photography grading criteria for early diabetic retinopathy

AIM:To compare the assessment outcomes of the characteristics of mild to moderate non-proliferative diabetic retinopathy(NPDR) established by fundus photography and fundus fluorescein angiography(FFA).METHODS:The fundus photos and FFA results of 260 patients with diabetes mellitus were reviewed.Diabetic retinopathy(DR) severity was graded based on the international classification standard.The microaneurysms,hemorrhages,and intraretinal microvascular abnormalities(IRMA) in FFA images of patients with mild to moderate NPDR were observed.The differences between the fundus photos and the FFA results were summarized,analyzed,and compared.RESULTS:The counting of intraretinal hemorrhages identified by FFA revealed that only 9 eyes(1.9%) had more than 20 intraretinal hemorrhages in all four quadrants;15 eyes(3.1%) had more than 20 intraretinal hemorrhages in three quadrants;26 eyes(5.4%) had over 20 intraretinal hemorrhages in two quadrants;and 37 eyes(7.7%) had more than 20 intraretinal hemorrhages in only one quadrant.Furthermore,the number of IRMAs appeared ≥4 in 17 eyes,3 in 35 eyes,2 in 69 eyes,and 1 in 93 eyes.CONCLUSION:FFA has higher detection accuracy of retinal angiopathy than fundus photography.FFA grading results are helpful for timely detection and proper treatment of lesions easily missed by fundus photography.

Visual grading criteria for Japanese larch ( Larix kaempferi ) structural timber from Spain

Larch wood is structurally classifi ed in many countries as one of conifers with the highest load-bearing capacity(strength class of C30).The Spanish visual classifi cation regulation only assigns a strength class to 4 pine woods:Laricio pine(Pinus nigra Arn.var.Salzmannii),Silvestre pine(Pinus sylvestris L.),Radiata pine(Pinus radiata D.Don),and Pinaster pine(Pinus pinaster Ait.).This work adds to the number of structurally characterised species by creating a visual classifi cation table for Japanese larch wood(Larix kaempferi(Lamb.)Carr.)which diff erentiates between 2 visual classes,MEG-1 and MEG-2.Characteristic strength values were calculated for each class(fk,MEG-1=31.80 MPa,f k,MEG-2=24.55 MPa),mean module of elasticity(E 0,mean,MEG-1=13,082 MPA,E 0,mean,MEG-2=12,320 MPA)and density(ρk,MEG-1=456.6 kg m−3,ρk,MEG-2=469.1 kg m−3),before fi nally assigning a strength class of C30 to visual class MEG-1,and a strength class of C24 to visual class MEG-2.

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different grades.Besides,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)classifiers.This study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.

Radiographic assessment of leg alignment and grading of knee osteoarthritis:A critical review

Knee osteoarthritis(OA) is a progressive joint disease hallmarked by cartilage and bone breakdown and associated with changes to all of the tissues in the joint,ultimately causing pain,stiffness,deformity and disability in many people.Radiographs are commonly used for the clinical assessment of knee OA incidence and progression,and to assess for risk factors.One risk factor for the incidence and progression of knee OA is malalignment of the lower extremities(LE).The hipknee-ankle(HKA) angle,assessed from a full-length LE radiograph,is ideally used to assess LE alignment.Careful attention to LE positioning is necessary to obtain the most accurate measurement of the HKA angle.Since full-length LE radiographs are not always available,the femoral shaft-tibial shaft(FS-TS) angle may be calculated from a knee radiograph instead.However,the FS-TS angle is more variable than the HKA angle and it should be used with caution.Knee radiographs are used to assess the severity of knee OA and its progression.There are three types of ordinal grading scales for knee OA:global,composite and individual feature scales.Each grade on a global scale describes one or more features of knee OA.The entire description must be met for a specific grade to be assigned.The KellgrenLawrence scale is the most commonly-used global scale.Composite scales grade several features of knee OA individually and sum the grades to create a total score.One example is the compartmental grading scale for knee OA.Composite scales can respond to change in a variety of presentations of knee OA.Individual feature scales assess one or more OA features individually and do not calculate a total score.They are most often used to monitor change in one OA feature,commonly joint space narrowing.The most commonly-used individual feature scale is the OA Research Society International atlas.Each type of scale has its advantages;however,composite scales may offer greater content validity.Responsiveness to change is unknown for most scales and deserves further evaluation.