A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

Clustered Federated Learning with Weighted Model Aggregation for Imbalanced Data

As a promising edge learning framework in future 6G networks,federated learning(FL)faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors.Data imbalance is one of these challenges that can significantly degrade the learning efficiency.To deal with data imbalance issue,this work proposes a new learning framework,called clustered federated learning with weighted model aggregation(weighted CFL).Compared with traditional FL,our weighted CFL adaptively clusters the participating edge devices based on the cosine similarity of their local gradients at each training iteration,and then performs weighted per-cluster model aggregation.Therein,the similarity threshold for clustering is adaptive over iterations in response to the time-varying divergence of local gradients.Moreover,the weights for per-cluster model aggregation are adjusted according to the data balance feature so as to speed up the convergence rate.Experimental results show that the proposed weighted CFL achieves a faster model convergence rate and greater learning accuracy than benchmark methods under the imbalanced data scenario.

Relationship between effort-reward imbalance, job satisfaction, and intention to leave the profession among the medical staff of Qom University of Medical Sciences

Objective: The effort–reward imbalance(ERI) model claims that work that has the characteristics of high effort and low reward has a mutual defect between input and output and this imbalance may result in sustained and long-lasting results. This study aimed to investigate the relationship between the intention to leave the profession(ILP) and ERI and job satisfaction among the medical staff in Qom Province.Methods: A descriptive-correlative study was conducted on 202 medical staff in Qom Province based on random sampling in 2018. Demographics checklist, standard ILP, job satisfaction, and Siegrist’s ERI questionnaires were used for data collection. The chisquared test, independent t-test, and one-way Analyses of Variance(ANOVA) were used to analyze data.Results: The mean age of employees was 32.04 ± 7.9 years, and 165(87.1%) of the employees were women. The results showed that the medical staff was willing to leave their profession at a moderate level(40.7 ± 10.3). There was no significant relationship between demographics and ILP. Nevertheless, a significant and inverse relationship was observed between ERI(r:0.318, P < 0.01) and ILP(r: 0.197, P < 0.01). Leave the profession(LP) had a negative correlation with the dimensions of job descriptive index(JDI) such as job, manager, coworker and wage score(P < 0.01, r:-0.147, r:-0.262, r:-0.292, r:-0.271, r:-0.396).Conclusions: According to the results, managers need to make sure that their working staff is rewarded as they deserve. According to the results, managers need to ensure that the reward factor is observed for the staff, while an ERI imbalance may contribute to ILP of the staff. On the other hand, it leads to job satisfaction.

Primary logistics planning of oil products under the imbalance of supply and demand

This paper intends to complete the primary logistics planning of oil products under the imbalance of supply and demand. An integrated mathematical programming model is developed to simultaneously find the balance between supply and demand, and optimize the logistics scheme. The model takes minimum logistics cost and resource adjustment cost as the objective function, and takes supply and demand capacity, transportation capacity, mass balance, and resource adjustment rules as constraints.Three adjustment rules are considered in the model, including resource adjustment within oil suppliers,within oil consumers, and between oil consumers. The model is tested on a large-scale primary logistics of a state-owned petroleum enterprise, involving 37 affiliated refineries, 31 procurement departments,286 market depots and dedicated consumers. After the unified optimization, the supply and demand imbalance is eased by 97% and the total cost is saved by 7%, which proves the effectiveness and applicability of the proposed model.

mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as “acetylation”, “crotonylation”, “methylation” and “succinylation” and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.

面向类不均衡数据的多任务博弈概率分类向量机

在工程实际中获取的故障样本往往会呈现不均衡特点,同时传统的分类模型也会存在局限性。针对这些问题,基于稀疏贝叶斯理论、模糊隶属度等理论,提出了一种多任务博弈概率分类向量机(MGPCVM)分类方法。首先,在MGPCVM的目标函数中,设计了博弈因子,将不同类样本质心间的博弈信息赋予每个样本特定的样本质心敏感值,以解决传统分类器对不平衡数据集分类表现较差的问题;然后,在贝叶斯框架理论下,采用截断高斯先验分布的方法,使样本参数的正负与对应的标签信息相一致,且使样本质心敏感值产生了稀疏估计;最后,将MGPCVM方法应用于两种不同实验平台采集的滚动轴承实验数据处理,进行了故障诊断有效性验证。研究结果表明:在不同的不平衡比(IR)下,MGPCVM方法的准确率均保持在95%以上,相对于支持向量机(SVM)、概率分类向量机(PCVM)等方法提升了4%~8%;与典型向量式分类方法相比,MGPCVM方法可以在不平衡数据条件下表现出优越的分类性能,适用于实际工况中数据失衡的分类问题。

基于模型融合和生成网络的有效阵位智能决策方法

军事智能技术是当前最具活力的前沿领域和未来无人装备发展的必然趋势。针对无人平台在复杂环境下自主决策可靠性和实时性的双重需求和现有基于规则推演的作战仿真技术在动态性和灵活性方面的不足,采用原理分析与实验验证的研究方法,在某型无人平台射击实验数据集的基础上,围绕攻击决策的有效阵位识别环节,将其转换为机器学习领域类别不平衡的二分类问题,综合采用相关性分析、特征工程、模型融合技术构建高实时性和灵活性的有效阵位智能决策模型,并提出基于ICGAN-Stacking不平衡分类架构对少数类样本进行定向扩充,实现数据增强和模型性能提升。实验结果表明:所提方法召回率提升了4.1%、精确度提升了0.4%、F1值提升了1.5%、AUC值达到90.9%,能够满足无人平台执行作战任务实时性和可靠性需求。

中国耕地补充与撂荒的空间关系识别及其失衡归因

研究目的:探究耕地补充与耕地撂荒的空间匹配关系,为耕地保护和开发提供科学依据。研究方法:遥感监测、空间自相关分析、空间失衡指数、地理探测器和增强回归树模型。研究结果:(1)近20年中国耕地年均补充263.22×10^(4) hm^(2),年均补充率为1.39%,集中分布于西北、东北和西南地区。耕地年均撂荒226.68×10^(4) hm^(2),年均撂荒率为1.19%,主要分布于黄河中游和西南地区。(2)耕地补充和耕地撂荒之间以轻度失衡和不失衡为主,但极度失衡和重度失衡的单元数量逐年增加。至2020年,中度、重度和极度失衡的单元占16.87%,主要分布在西北、黄河中游、西南和南部沿海地区。(3)高—高集聚和低—低集聚是耕地补充和撂荒失衡的主要空间集聚模式。其中,高—高集聚区集中在西北、黄河中游、西南和南部沿海地区,低—低集聚区集中在东北、长江中游、西南和北部沿海地区。(4)坡度和年平均降水量为耕地补充与撂荒空间失衡主要的自然资源驱动因子,非农从业人数、农业机械总动力是主要的社会经济驱动因子。研究结论:耕地补充和撂荒之间空间失衡具有复杂性且受当地自然条件和经济社会因素的交互影响,未来应基于不同的情景和目标综合确定最优的利用模式,为高质量耕地保护提供科学依据。

高等教育治理现代化的张力失衡及其调适路径——基于公共价值战略三角模型的分析框架

高等教育治理现代化是一个涉及价值、主体、结构等多元治理要素的动态发展过程,对其主要特征的全面把握是新时代推进高等教育治理现代化的前提和基础。借助公共价值战略三角模型,从使命管理、政治管理、运营管理三个维度构建分析框架,阐释我国高等教育治理现代化的主要特征,剖析高等教育治理现代化进程中出现的价值张力失衡、权力张力失衡和结构张力失衡问题。要有效解决我国高等教育治理现代化的张力失衡问题,需要以价值耦合凝聚高等教育“公共性”价值共识,以利益共融打造共建共享的高等教育利益共同体,以结构优化构建纵向连通、横向协作的高等教育治理机制,促进高等教育公共价值最大化的实现。

婚姻市场性别结构对女性婚姻流动距离的影响及异质性分析

在关于婚姻流动距离的研究中,个体对于经济社会资源等方面的偏好因素的影响已经被广泛认同。然而地区性别结构因素作为地区婚姻市场的一种重要的宏观因素,关于其对于婚姻流动距离的影响还值得深入探讨。基于婚姻搜寻理论,利用多层模型证实了地区婚姻市场性别结构对中国女性婚姻流出距离的影响。研究发现:(1)总的来看,未婚人口性别比高的地区,女性婚姻流出距离相对更短,当地女性更可能在较近的地区结婚。(2)性别结构的影响在不同收入水平、城乡之间和不同受教育程度的女性之间存在异质性,户籍地为低收入地区、乡村地区或受教育程度较低的女性婚姻流动距离受到婚姻市场性别结构的影响较大。

不平衡电网电压下虚拟同步发电机模型预测控制

虚拟同步发电机(Virtual Synchronous Generator,VSG)控制算法,可为电网提供惯性并参与电压及频率调节,但在电网电压出现不平衡时会导致并网电流不平衡及有功、无功功率波动的问题,严重影响了并网电能质量。针对此问题,提出了一种不平衡电网下VSG模型预测控制策略,通过基于快速电压矢量选择的模型预测控制策略来控制所重构的正序及负序电流分量,达到有功功率恒定、无功功率恒定及电流平衡的控制目标。此外还引入负序电流调节系数,进而实现这三个目标的协调控制。

苏浙两省人口城镇化和土地城镇化失衡问题研究:不同发展模式视角

本文从不同发展模式视角,探讨了苏浙两省的城镇化失衡问题,得出以下主要结论:一是江苏省城镇化的发展与“苏南模式”的阶段性演变密切相关,第一阶段是人口城镇化与土地城镇化双抑制阶段,第二阶段受股份制改革及吸引外资的影响,是对人口城镇化和土地城镇化双推动阶段,第三阶段是政府引导下人口城镇化和土地城镇化协调发展阶段;二是“温州模式”下的浙江省人口城镇化与土地城镇化协调发展没有明显的阶段性特征,总体上“商业为主+民营经济”的经营方式对土地城镇化贡献不大,反而会通过增加农民收入等方式制约人口城镇化的发展。本文在以上结论基础上,提出了相应的对策,以期为矫正城镇化失衡问题提供帮助。

财政纵向失衡对城市绿色全要素生产率的影响——双重机器学习下来自土地财政视角的理论阐释

本文在推动绿色发展的时代背景下,从土地财政的角度为财政纵向失衡如何影响城市绿色全要素生产率提供一种新的理论阐释,并进一步量化分析异质性环境规制情境下影响机制的适用范围。具体而言,本文在利用网络爬虫技术手工整理匹配百万条土地交易信息以及使用两期前沿技术下的曼奎斯特-卢恩伯格指数测度城市绿色全要素生产率的基础上,基于2007—2019年的城市面板数据,运用双重机器学习模型、倾向得分匹配方法等进行了多维检验。研究发现,财政纵向失衡推动了土地财政规模的扩张,阻碍了城市绿色全要素生产率,并且土地财政是重要影响渠道。进一步分析发现,传导渠道会受到环境规制的调节影响,在低环境规制情境下,作用机制的适用性更强。考虑城市异质性特征发现,财政纵向失衡对三线以下城市、非资源型城市以及内陆城市绿色全要素生产率的阻碍作用更强。由此,本文提出完善纵向转移支付体系,降低地方政府对土地财政过度依赖的政策建议。

基于CatBoost的空铁联运中转城市推荐研究

随着交通网络的快速发展,越来越多的旅客选择空铁联运出行,对空铁联运中转城市推荐方法提出了更高的要求。文章设计了符合空铁联运中转城市数据特点的数据不平衡处理方法,采用能够处理类别型特征的CatBoost算法构造基准模型,在2个不同数据分布的测试集上对该模型进行评估,模型准确率均超过85%。通过与其他算法的对比分析,证明了该模型具有较好的稳定性和更优的性能,提高了空铁联运中转城市的推荐效果,可更好地满足旅客的出行需求;通过对特征贡献度的分析发现,下单人的姓名特征会对模型预测带来影响,从而进一步提高空铁联运中转城市的个性化推荐效果。

北京市医务人员付出回报失衡与工作压力的关系研究

背景医务工作具有挑战性高、任务重和压力大的特点。付出-回报失衡(ERI)模型认为,如果组织环境造成了ERI,会对员工产生负面影响。医务人员调查是2018年全国第六次卫生服务调查的重要组成部分,医务人员的良好工作感受和工作状态是居民服务利用和健康改善的重要决定因素,提高医务人员的获得感和满意度也是新医改的重要目标。目的探讨北京市医务人员ERI与工作压力的关系,为降低医务人员的工作压力提供参考。方法本研究数据来源于2018年全国第六次卫生服务调查中对北京市4156名医务人员的抽样调查结果。根据医务人员的ERI量表和工作压力量表得分,构建工作压力结构方程预测模型,使用偏最小平方法估计相关参数,分析ERI与工作压力之间的关系。结果参与付出回报比(ERI指数)计算的有效记录为4098条,根据ERI指数计算结果,1333名(32.53%)医务人员处于付出与回报平衡状态,2765名(67.47%)医务人员处于付出与回报失衡状态。北京市医务人员46.00%的工作压力可通过ERI模型进行解释。内在付出、外在付出、工作回报对工作压力均具有直接影响(P<0.001),其中内在付出的总效应为0.409(95%CI=0.373~0.443),外在付出的总效应为0.583(95%CI为0.559~0.606),工作回报的总效应为-0.199(95%CI=-0.227~-0.171)。另外,内在付出是外在付出和工作回报对工作压力影响的中介因子(P<0.001)。结论研究结果支持ERI是北京市医务人员工作压力重要来源的研究假设。鉴于内在付出对工作压力影响最大,卫生管理部门和医疗机构应针对性地改善相关管理制度,解决ERI问题,降低医务人员的工作压力。

A New Classifier for Imbalanced Data Based on a Generalized Density Ratio Model

Achieving higher true positive rate when decreasing false positive rate is always a great challenge to the imbalance learning community.This work combines penalized empirical likelihood method,lower bound algorithm and Nyströmmethod and applies these techniques along with kernel method to density ratio model.The resulting classifier,density ratio classifier(DRC),is a combination of kernelization,regularization,efficient implementation and threshold moving,all of which are critical to enable DRC to be an effective and powerful method for solving difficult imbalance problems.Compared with other methods,DRC is competitive in that it is widely applicable and it is simple and easy to use without additional imbalance handling skills.In addition,the convergence rate of the estimate of log density ratio is discussed as well.And the results of numerical analysis also show that DRC outperforms other methods in AUC and G-mean score.

无热子空心阴极点火过程的放电维持失衡机理研究

无热子空心阴极(HHC)的放电维持失衡是目前制约其工程应用的主要问题。为研究上述问题的核心机理,本文建立一种等离子体放电与固体传热耦合的非稳态数值模型,基于电子路径的网格划分方法,该模型可将放电过程和传热过程的时间步长同时设定在1 ms量级,同时提升计算效率和精度。在真空舱内开展HHC的发射体测温与阳极电流诊断试验,以试验数据对数值模型进行验证和修正,修正后的模型计算误差为:发射体温度2.5%~8.4%;阳极电流5.6%~12.9%。在此基础上,通过数值模型针对HHC在不同工况下的点火过程进行数值模拟,发现HHC是否存在电子发射衰弱区是引起放电维持失衡的主要机制,而降低气体流率、降低触持极管-阴极管间隙距离以及增加阴极管材料的传热能力,是增加电子发射衰弱区存在可能性的因素。

江苏沿海经济带高质量发展水平测度、非均衡性及障碍因子识别

从江苏区域发展实际出发,以新发展理念为引领构建江苏沿海经济带高质量发展水平评价指标体系,选用熵权法测度江苏沿海经济带高质量发展水平。运用Dagum基尼系数和障碍度模型揭示其高质量发展非均衡性特征和障碍因子。主要研究结论:(1)2008—2022年,该经济带整体高质量发展水平长期向好,但仍处于较低水平,空间上表现为“南高北低”,南通市县域发展优于连云港市和盐城市县域;(2)各子系统中,绿色发展水平最高,协调发展水平次之,共享发展水平稳中向好,创新发展和开放发展水平不佳;(3)组内差异贡献度最小,且变化不明显,组间差异是总体差异的主要来源,贡献度有所下降,超变密度贡献度有所提升,组内和组间差异具有一定程度的交叉重叠现象;(4)各县域的经济发展水平、战略定位和发展目标存在差异,导致每个县域的主要障碍因子不尽相同,但也有一定的相似性,障碍因子集中在创新发展、开放发展和共享发展中。最后,研究从加强科技创新、深化双向开放等角度给出建议,以期为科学谋划未来发展提供参考依据,助推江苏区域协调发展和中国式现代化建设。

Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

The lithofacies classification is essential for oil and gas reservoir exploration and development.The traditional method of lithofacies classification is based on"core calibration logging"and the experience of geologists.This approach has strong subjectivity,low efficiency,and high uncertainty.This uncertainty may be one of the key factors affecting the results of 3 D modeling of tight sandstone reservoirs.In recent years,deep learning,which is a cutting-edge artificial intelligence technology,has attracted attention from various fields.However,the study of deep-learning techniques in the field of lithofacies classification has not been sufficient.Therefore,this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks(CNN)and the excellent ability to describe time-dependent features of long short-term memory networks(LSTM)to conduct lithological facies-classification experiments.The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3%and the best classification effect compared to the CNN,LSTM or the three commonly used machine learning models(Support vector machine,random forest,and gradient boosting decision tree).In addition,the borderline synthetic minority oversampling technique(BSMOTE)is introduced to address the class-imbalance issue of raw data.The results show that processed data balance can significantly improve the accuracy of lithofacies classification.Beside that,based on the fine lithofacies constraints,the sequential indicator simulation method is used to establish a three-dimensional lithofacies model,which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area.According to this comprehensive analysis,the proposed CNN-LSTM model,which eliminates class imbalance,can be effectively applied to lithofacies classification,and is expected to improve the reality of the geological model for the tight sandstone reservoirs.