Construction of the Classification and Grading Index System of Cultivated Land Based on the Viewpoint of Sustainable Development

In order to objectively and reasonably evaluate the actual and potential value of cultivated land, both social and ecological values are introduced into the classification and grading index system of cultivated land based on the viewpoint of sustainable development, after considering the natural and economic values of cultivated land. Index system construction of the sustainable utilization of cultivated land should follow the principles of economic viability, social acceptability, and ecological protection. Classification of cultivated land should take into account the soil fertility of cultivated land. Then, grading of cultivated land is carried out from the practical productivity (or potential productivity) of cultivated land. According to the existing classification index system of cultivated land, the soil, natural and environmental factors in plains, mountains and hills are mainly modified in the classification index system of cultivated land. And index systems for the cultivated land classification in plains, mountains and hills are set up. The grading index system of cultivated land is established based on the economic viability (economic value), social acceptability (social value) and protection of cultivated land (ecological value). Quantitative expression of cultivated land grading index is also carried out.

A Review on the Classification and Grading Criteria of Road Transport Natural Disasters

This review summarizes and analyzes the basic information of various types of road transport natural disaster emergencies,refers to various types of road transport emergency plans,and combines the actual needs of road transport emergency rescue with national emergency related laws.It also proposes the classification criteria and grading standard for the emergencies of road transport natural disasters based on the classification and grading standard of the regulations,which provide a basis to take reasonable and effective disposal measures in the emergency response of road transport emergencies under natural disaster conditions.

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.

Recursive Partitioning Analysis Classification and Graded Prognostic Assessment for Non-Small Cell Lung Cancer Patients with Brain Metastasis:A Retrospective Cohort Study

Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients with brain metastasis （BM）.Methods：From Jan 2008 to Dec 2009,the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation,systemic chemotherapy and tyrosine kinase inhibitors （TKIs） in two institutes were analyzed.Survival was estimated by Kaplan-Meier method.The differences of survival rates in subgroups were assayed using log-rank test.Multivariate Cox＇s regression method was used to analyze the impact of prognostic factors on survival.Two prognostic indexes models （RPA and GPA） were validated respectively.Results：All patients were followed up for 1-44 months,the median survival time after brain irradiation and its corresponding 95% confidence interval （95% CI） was 14 （12.3-15.8） months.1-,2-and 3-year survival rates in the whole group were 56.0%,28.3%,and 12.0%,respectively.The survival curves of subgroups,stratified by both RPA and GPA,were significantly different （P0.001）.In the multivariate analysis as RPA and GPA entered Cox＇s regression model,Karnofsky performance status （KPS） ≥ 70,adenocarcinoma subtype,longer administration of TKIs remained their prognostic significance,RPA classes and GPA also appeared in the prognostic model.Conclusion：KPS ≥70,adenocarcinoma subtype,longer treatment of molecular targeted drug,and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.

Apple grading method based on neural network with ordered partitions and evidential ensemble learning

In order to improve the performance of the automatic apple grading and sorting system,in this paper,an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed.As a non-destructive grading method,apples are graded into three grades based on the Soluble Solids Content value,with features extracted from the preprocessed near-infrared spectrum of apple serving as model inputs.Considering the uncertainty in grading labels,mass generation approach and evidential encoding scheme for ordinal label are proposed,with uncertainty handled within the framework of Dempster–Shafer theory.Constructing neural network with ordered partitions as the base learner,the learning procedure of the Bagging-based ensemble model is detailed.Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

New Classification and Visual Analysis of the Patella Cartilage during Total Knee Replacement

Objective: The aim of this prospective study is to evaluate how much damage the patellar cartilage presents during a total knee replacement. Methods: The damage of the articular patellar surface was analysed by visual inspection and photographs in 354 primary total knee replacements. The authors graded the degree of cartilage lesion in five groups. The cartilage status was analyzed and correlated with age, gender, side, body mass index (BMI), Kellgren-Lawrence radiographic scale and axial deviation. Results: After statistical analysis, we concluded: there was no evidence of an association between patellar arthrosis and age gender, side, weight and deformity. Conclusions: Articular cartilage was damaged in all 354 knees. Important subchondral bone exposure occurred in 274 knees (77.4%). Obese patients had more severe patellar osteoarthritis.

FUZZY PARTITIONING OF FEATURE SPACE FOR PATTERN CLASSIFICATION BASED ON SUPERVISED C1USTERING

The choice of a fuzzy partitioning is crucial to the performance of a fuzzy system based on if-then rules. However, most of the existing methods are complicated or lead ,o too many subspaces, which is unfit for the applications of pattern classification. A simple but effective clustering approach is proposed in this paper, which obtains a set of compact subspaces and is applicable for classification problems with higher dimensional feature. Its effectiveness is demonstrated by the experimental results.

A Study on Associated Rules and Fuzzy Partitions for Classification

The amount of data for decision making has increased tremendously in the age of the digital economy. Decision makers who fail to proficiently manipulate the data produced may make incorrect decisions and therefore harm their business. Thus, the task of extracting and classifying the useful information efficiently and effectively from huge amounts of computational data is of special importance. In this paper, we consider that the attributes of data could be both crisp and fuzzy. By examining the suitable partial data, segments with different classes are formed, then a multithreaded computation is performed to generate crisp rules (if possible), and finally, the fuzzy partition technique is employed to deal with the fuzzy attributes for classification. The rules generated in classifying the overall data can be used to gain more knowledge from the data collected.

Deep Learning Model Ensemble for the Accuracy of Classification Degenerative Arthritis

Artificial intelligence technologies are being studied to provide scientific evidence in the medical field and developed for use as diagnostic tools.This study focused on deep learning models to classify degenerative arthritis into Kellgren–Lawrence grades.Specifically,degenerative arthritis was assessed by X-ray radiographic images and classified into five classes.Subsequently,the use of various deep learning models was investigated for automating the degenerative arthritis classification process.Although research on the classification of osteoarthritis using deep learning has been conducted in previous studies,only local models have been used,and an ensemble of deep learning models has never been applied to obtain more accurate results.To address this issue,this study compared the classification performance of deep learning models,includingVGGNet,DenseNet,ResNet,TinyNet,EfficientNet,MobileNet,Xception,and ViT,on a dataset commonly used for osteoarthritis classification tasks.Our experimental results verified that even without applying a separate methodology,the performance of the ensemble was comparable to that of existing studies that only used the latest deep learning model and changed the learning method.From the trained models,two ensembles were created and evaluated:weight and specialist.The weight ensemble showed an improvement in accuracy of 1%,and the proposed specialist ensemble improved accuracy,precision,recall,and F1 score by 5%,6%,6%,and 6%,respectively,compared with the results of prior studies.

Classification of facial wrinkles among Chinese women

It is generally recognized that Caucasians and Asians have different skin aging features. The aim of this study was to develop a facial wrinkle grading scale for Chinese women. Standard photographs were taken of 242 Chinese women. Six sets of 0 to 9 wrinkle scales with reference photographs and descriptions were selected, including grading scales for resting and hyperkinetic crow＇s feet, frontalis lines, glabellar frown lines, and nasolabial folds. To identify the scale by objective quantitative measurement, skin surface measurements from the Visioscan~ VC98 were used. To test the reliability and validity of our wrinkle scale, a multi-rater consensus method was used. A double-blind, randomized, vehicle-controlled 12-week study was conducted to use this clinical photo-score to evaluate the efficacy and safety of Centella triterpenes cream~ in treating crow＇s feet. A newly developed 10-point photographic and descriptive scale emerged from this study. The final atlas of these photographs contained a total of 6 sets with 10 pictures each. From 0 to 9, surface evaluation of smoothness （SEsm） parametric measurements decreased progressively, indicating that the scale increased inversely. Weighted kappa coefficients for intra-assessor were between 0.75-0.87. The overall Kendall＇s coefficient is 0.86 on the first rating and 0.87 on the second rating. Thirty- six volunteers were recruited and 35 subjects completed a 12-week trial. Clinical photo-score by investigator showed a significant difference （P 〈 0.05） between the treatment side and control side after 4 weeks. Use of these scales in clinical settings to evaluate facial wrinkles in Asians individuals is recommended.

Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities:A Survey

Cataracts are the leading cause of visual impairment and blindness globally.Over the years,researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading,aiming to prevent cataracts early and improve clinicians′diagnosis efficiency.This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images.We summarize existing literature from two research directions:conventional machine learning methods and deep learning methods.This survey also provides insights into existing works of both merits and limitations.In addition,we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

Ventral Hernia: An Innovative Grading System

Background: Ventral hernia is a complex and progressive condition that may lead to serious complications. However, no specified grading or classifying system is found to categorize the hernia, which leads to clinical complexities and may affect the patient outcome. Aim: The general aim of this paper is to build up an easy and comprehensive grading system to categorize ventral hernia. Methodology: By carrying out a secondary search over clinical presentation, physical examination, and imaging studies of ventral hernia, a valid grading system is developed. Results: Hanoon’s grading system is composed of seven grades, grades 1, 2A, 2B, 3A, 3B, 3C, and 4. Each grade entailed different clinical presentations, imaging characteristics, and progressivity of ventral hernia. Conclusion: Hanoon’s grading system for ventral hernia can be used to solve the clinical complexities of ventral hernia. Also, it can be a step forward in hernia research to build upon.

Time for a paradigm shift in the classification of muscle injuries

Muscle injuries remain one of the most common injuries in sport,yet despite this,there is little consensus on how to either effectively describe or determine the prognosis of a specific muscle injury.Numerous approaches to muscle classification and grading of medicine have been applied over the last century,but over the last decade the limitations of historic approaches have been recognized.As a consequence,in the past 10 years,clinical research groups have begun to question the historic approaches and reconsider the way muscle injuries are classified and described.Using a narrative approach,this manuscript describes several of the most recent attempts to classify and grade muscle injuries and highlights the relative strengths and weaknesses of each system.While each of the new classification and grading systems have strengths,there remains little consensus on a system that is both comprehensive and evidence based.Few of the currently identified features within the grading systems have relevance to accurately determining prognosis.

Clinicopathological classification and individualized treatment of breast cancer

Background The clinicopathological classification was proposed in the St. Gallen Consensus Report 2011. We conducted a retrospective analysis of breast cancer subtypes, tumor-nodal-metastatic （TNM） staging, and histopathological grade to investigate the value of these parameters in the treatment strategies of invasive breast cancer.

Large-scale OPF Based on Voltage Grading and Network Partition

In this paper,a model of a large-scale optimal power flow(OPF)under voltage grading and network partition and its algorithm is presented.Based on the principles of open loop operations,the node injecting current method is used to divide the large-scale power grid into voltage grading and district dividing structures.The power network is further divided into a high-voltage main network and several subnets according to voltage levels of 220 kV.The subnets are connected by means of boundary nodes,and the partition model is solved using the improved approximate Newton direction method,which achieves complete dynamic decoupling simply by exchanging boundary variables between the main network and the subnets.A largescale power grid thus is decomposed into many subnets,making the solution of the problem simpler and faster while helping to protect the information of individual subnets.The system is tested for correctness and effectiveness of the proposed model,and the results obtained are matched in real-time.Finally,the algorithm is seen to have good convergence while improving calculation speed.

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition

Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1H NMRS) can provide important information on tumor biology and metabolism.These metabolic fingerprints can then be used for tumor classification and grading,with great potential value for tumor diagnosis.We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies,including two astrocytomas (grade I),12 astrocytomas (grade II),eight anaplastic astrocytomas (grade III),three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS 1H NMRS.The results were correlated with pathological features using multivariate data analysis,including principal component analysis (PCA).There were significant differences in the levels of N-acetyl-aspartate (NAA),creatine,myo-inositol,glycine and lactate between tumors of different grades (P<0.05).There were also significant differences in the ratios of NAA/creatine,lactate/creatine,myo-inositol/creatine,glycine/creatine,scyllo-inositol/creatine and alanine/creatine (P<0.05).A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%.HRMAS 1H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

Computer vision-based apple grading for golden delicious apples based on surface features

In this paper,a computer vision-based algorithm for golden delicious apple grading is proposed which works in six steps.Non-apple pixels as background are firstly removed from input images.Then,stem end is detected by combination of morphological methods and Mahalanobis distant classifier.Calyx region is also detected by applying K-means clustering on the Cb component in YCbCr color space.After that,defects segmentation is achieved using Multi-Layer Perceptron(MLP)neural network.In the next step,stem end and calyx regions are removed from defected regions to refine and improve apple grading process.Then,statistical,textural and geometric features from refined defected regions are extracted.Finally,for apple grading,a comparison between performance of Support Vector Machine(SVM),MLP and K-Nearest Neighbor(KNN)classifiers is done.Classification is done in two manners which in the first one,an input apple is classified into two categories of healthy and defected.In the second manner,the input apple is classified into three categories of first rank,second rank and rejected ones.In both grading steps,SVM classifier works as the best one with recognition rate of 92.5%and 89.2%for two categories(healthy and defected)and three quality categories(first rank,second rank and rejected ones),among 120 different golden delicious apple images,respectively,considering K-folding with K=5.Moreover,the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases.

新旧世界葡萄酒质量表达演变及形成

葡萄酒等级是全球葡萄酒行业的关注点。在旧世界葡萄酒等级表达中,产品等级不仅反映产品内在质量,还与具有质量风格的地理特征紧密相关;新世界葡萄酒等级表达中,葡萄酒地理特征仅表示产品来源真实性,葡萄酒质量以产品口感为基础并满足消费者消费偏好需求。葡萄酒官方等级分级具有从产区到列级酒庄的纵向化结构,葡萄酒等级分类具有从品种、年份到酒庄的横向化结构,前者分级以生产者视角下“为何分级”并同时聚焦分级内生形成关键要素,后者分类以消费者视角下“为何分类”并锚定产品质量的某一关键要素,两者之间关注点存在差异。这种差异给国际葡萄酒市场创造竞争空间,形成一种质量表达的实践共创、智慧共存的竞争格局。

面向乳腺超声分类的低尺度形态特征重校准方法

针对乳腺超声图像具有类内差异大、类间差异小以及结节形状复杂多变等问题,提出一种形状特征重校准的乳腺超声图像算法,实现乳腺超声的自动化诊断.首先,构建端到端的网络模型,采用渐进训练方式,充分学习图像中更具辨别力的区域,获取更细粒度的特征信息;其次,提出分区打乱机制,降低网络中打乱图像时破坏结节区域所产生的噪声;然后,将模型底层提取的特征与通过掩膜图像获得的形状特征进行重校准,提出低尺度重校准损失函数;最后,构建一个包含1550张乳腺超声图像数据集LSRD(low-scale recalibration database),验证所提方法的有效性.实验结果表明:本文模型在LSRD上准确率94.3%、敏感性91.2%、特异性93.6%、ROC(receiver operator characteristic curve)与坐标围成的面积(area under curve,AUC)为0.941,均优于对比模型;在BUSI(breast ultrasound image)数据集上,相较于对比模型,其分类精度提升3.3%.

混合编班“大学物理”分级教学分类评价研究与实践探索

“大学物理”是为新疆大学16个理工科学院44个理工科本科专业开设的通识教育必修课程。“大学物理”课程所教授的基本概念、基本方法和基本理论是构成新时代大学生科学素养的重要组成部分,在学校各理工科专业高质量发展中发挥着夯基铸魂和启智润心的协同育人作用。基于新疆大学生源结构和专业特点,从混合编班“大学物理”分级教学分类评价的现实意义、具体举措及运行实效三个方面论述了混合编班“大学物理”分级教学分类评价的最新研究成果和实践经验,将为边疆民族地区高等学校混合编班“大学物理”分级教学分类评价提供有益参考。