Prediction and Control of Wrinkle and Fracture for Stamping Regular Polygonal Box

Based on the deformation characteristic of regular polygonal box stamped parts and the superfluous triangle material wrinkle model,the criterion of regular polygonal box stamped parts without wrinkle was deduced and used to predict and control the wrinkle limit.According to the fracture model,the criterion of regular polygonal box stamped parts without fracture was deduced and used to predict and control the fracture limit.Combining the criterion for stamping without wrinkle with that without fracture,the stamping criterion of regular polygonal box stamped parts was obtained to predict and control the stamping limit.Taken the stainless steel0Cr18Ni9(SUS304)sheet and the square box stamped part as examples,the limit diagram was given to predict and control the wrinkle,fracture and stamping limits.It is suitable for the deep drawing without flange,the deep drawing and stretching combined forming with flange and the rigid punch stretching of plane blank.The limit deep-drawing coefficient and the minimum deep-drawing coefficient can be determined,and the appropriate BHF(blank holder force)and the deep-drawing force can be chosen.These provide a reference for the technology planning,the die and mold design and the equipment determination,and a new criterion evaluating sheet stamping formability,which predicts and controls the stamping process,can be applied to the deep drawing under constant or variable BHF conditions.

Proteomic and metabolic prediction of response to therapy in gastric cancer

Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by implementing earlier screenings. Because many patients are asymptomatic during the early stages of gastric cancer,the diagnosis is often delayed and patients present with unresectable locally advanced or metastatic disease. Cytotoxic treatment has been shown to prolong survival in general,but not all patients are responders. The application of targeted therapies and multimodal treatment has improved prognosis for those with advanced disease.However,these new therapeutic strategies do not uniformly benefit all patients.Predicting whether patients will respond to specific therapies would be of particular value and would allow for stratifying patients for personalized treatment strategies.Metabolic imaging by positron emission tomography was the first technique with the potential to predict the response of esophagogastric cancer to neoadjuvant therapy.Exploring and validating tissue-based biomarkers are ongoing processes.In this review,we discuss the status of several targeted therapies for gastric cancer,as well as proteomic and metabolic methods for investigating biomarkers for therapy response prediction in gastric cancer.

Burden of Cardiometabolic Risk Factors on Cerebrovascular Events in a Southern Italian Population

Background: Stroke is the second leading cause of death in the world and the third due to disability. However, there are few data available that identify the risk factors associated with it and their weight in different populations (population risk). Aim: Contribute to the knowledge of burden risk factors in stroke in a large cohort of Southern Italy. Methods: The data refer to a randomized Campania cohort of 1200 subjects (35 - 74 years) enrolled in 2008-09. Ten years later (2018-19) they were re-evaluated. We analyzed data from 32 patients who reported a cerebrovascular event (stroke or TIA) with the event-free group of subjects (804 subjects: 378 men and 426 women). We evaluated: absolute risk, Odds Ratio (OR), Additional Risk (AR), Risk Attributable to the Population (PAR) and, finally, the Population Attributable risk Fraction (FAP). Results: In the comparison between the two groups (patients with events and patients without events) the risk factors with statistically significant differences were: age, Systolic Blood Pressure (SBP), BMI, cholesterol, triglycerides, glycemia and hyperinsulinemia. The ORs with the greatest impact were: blood glucose (5.1), BMI (3.3) and BPS (2.9). Linear regression analysis identified Glycemia and BMI as the only independent variables. The FAPs with the greatest impact were SBP (47.4%) and BMI (42.6%). Discussion and Conclusions: Our data confirm that the high incidence of stroke in Campania is particularly related to the high prevalence of obesity and hypertension. In the single patient, however, the risk factors with the greatest impact are: glycaemia BMI an SBP.

Optimal Portfolio Selection of Wind Power Plants Using a Stochastic Risk-Averse Optimization Model, Considering the Wind Complementarity of the Sites and a Budget Constraint

This work focuses on the best financial resources allocation to define a wind power plant portfolio, considering a set of feasible sites. To accomplish the problem formulation and solution, the first step was to establish a long-term wind series reconstruction methodology for generating scenarios of wind energy, applying it to study five different locations of the Brazilian territory. Secondly, a risk-averse stochastic optimization model was implemented and used to define the optimal wind power plant selection that maximizes the portfolio financial results, considering an investment budget constraint. In a sequence, a case study was developed to illustrate a practical situation of applying the methodology to the portfolio selection problem, considering five wind power plants options. The case study was supported by the proposed optimization model, using the scenarios of generation created by the reconstruction methodology. The obtained results show the model performance in terms of defining the best financial resources allocation considering the effect of the complementarity between sites, making it feasible to select the optimal set of wind power plants, characterizing a wind plant optimal portfolio that takes into account the budget constraint. The adopted methodology makes it possible to realize that the diversification of the portfolio depends on the investor risk aversion. Although applied to the Brazilian case, this model can be customized to solve a similar problem worldwide.

时频电磁技术在黄土塬覆盖区河道砂油藏分布预测中的应用

鄂尔多斯盆地西部黄土塬覆盖区的中生界侏罗系油藏成藏主控因素复杂,在未开展三维地震勘探的区域,仅利用二维地震资料进行钻探的成功率较低。为了提高钻井成功率,针对该区侏罗系河道砂油藏特点,采用时频电磁双侧、双源激发、多线接收的油气检测技术方案。在鄂尔多斯盆地西部ZB地区进行攻关试验,总结了适用于黄土塬覆盖区的时频电磁采集、处理、解释技术方案,即两项针对性采集方案设计和三项针对性处理、解释技术。利用GeoEast软件系统,基于深度学习开展时频电磁多参数智能油气预测。根据时频电磁解释成果,在时频电磁强异常区域部署并完钻Z6井,获得工业油气流,表明时频电磁预测结果与实钻结果吻合,证实了在黄土塬覆盖区利用时频电磁技术对侏罗系薄储层进行油气预测的可靠性。

Modeling Cyber Loss Severity Using a Spliced Regression Distribution with Mixture Components

Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.

PREDICTION OF PUMP CHARACTERISTICS ACCORDING TO GEOMETRICAL PARAMETERS OF IMPELLER AND VOLUTE CASING

The geometrical parameters of impeller or volute casing (including guide vane ofmultistage pump) have a great effect on pump characteristics, but ultimately. the pump characteris-tics are determined by the geometrical parameters of impeller and volute casing cooperatively. Inthis essay the effect of impeller and volute casing on pump characteristics will be studiedquantitatvely from the angle cf optimal matching of them.

金融法视阈下的行政执法和解制度探析

《中华人民共和国金融稳定法(草案)》规定了行政执法当事人承诺制度,正在为金融领域行政执法和解制度的构建提供法律依据。与此同时,虽然行政和解暂未形成理论共识,但金融法视阈下行政执法和解的实践已然先行于理论。针对金融风险处置和证券期货领域中的金融纠纷,行政机关在符合依法行政的要求下通过行使自由裁量权,借助协商民主、公众参与等私法化手段采取的行政执法和解措施,可有效化解纠纷且实现各方获益的经济及社会效果。通过对金融领域行政执法和解措施的实践检视,提出以各方均可获益作为启动行政执法和解制度的前提,通过举证责任的倾斜、“自认”的可适用性与行刑衔接的合规机制相结合,将有助于构建更加符合实践理性的行政执法和解制度。

结构性资产负债表衰退及其宏观经济效应与政策协整

疫后恢复政策组合拳,有力承托了中国宏观经济运行的积极因素,但当前经济下行压力依然突出,部分领域发展呈现明显波动,亟须有能够“对症下药”的理论解释和宏观政策应对。构建NK-DSGE模型,探讨结构性资产负债表衰退的宏观经济效应以及相应政策协整问题。研究结果展示,典型事实蕴含中国不存在总体资产负债表衰退,但存在结构性资产负债表衰退的证据。在宏观层面,结构性资产负债表衰退冲击会导致宏观经济较长时期下滑,乃至引发债务-通缩风险,而在微观层面,减轻资产负债表衰退企业的债务厌恶和改善正常经营企业的融资约束有助于减弱上述影响。调节效应分析表明,结构性资产负债表衰退会抑制技术进步与需求扩张对产出的促进作用,但在技术进步情形下,上述抑制作用有限。为减小福利损失,财政政策应盯住衰退类企业债务,货币政策应锚定高融资约束企业债务,并提高政策反应力度,宏观审慎政策应提高对资产价格的敏感度。上述结论为理解微观层面债务厌恶所产生的宏观合力以及设计实施相应政策搭配提供参考。

基于多层级条件风险价值的高渗透可再生能源电力系统低碳运行联合优化

在双碳目标下,风电渗透率增加给新型电力系统的安稳运行带来挑战,而条件风险价值法(Conditional Value-at-Risk,CVaR)可对风电的随机风险进行分类量化评估,以用于系统调度运行。根据CVaR提出了改进的多层级条件风险价值法(Multi-layer Conditional Value-at-Risk,MCVaR),并建立了基于MCVaR的高渗透可再生能源电力系统低碳运行联合优化模型。仿真算例结果表明,MCVaR相较于CVaR能够显著降低系统的弃风与切负荷量,同时系统的极端损失下降了49.45%。所提方法能够在满足系统可控风险约束的前提下,实现运行经济效益与低碳环保收益的综合最优化。

基于OCTA构建糖尿病肾脏病临床预测模型

【目的】探讨基于光学相干断层扫描血管成像技术(OCTA),筛查糖尿病肾脏病(DKD)高风险人群的临床预测模型。【方法】本研究以567例糖尿病患者为研究对象,在逐步logistic回归分析的基础上,运用随机森林算法筛选纳入建模的指标,构建基于OCTA糖尿病肾脏病临床预测模型。通过受试者工作特征曲线评价模型区分度,通过决策曲线分析评估模型临床有效性。【结果】构建基于OCTA的DKD临床预测模型,ROC曲线下面积为0.878,Brier=0.11。【结论】本研究构建了基于OCTA结果进行糖尿病肾脏病临床预测的列线图预测模型,并从多维度验证模型,从而达到早期预警、提前实施干预的目的。

Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

数字经济背景下企业社会责任信息披露、风险承担与创新投入

本文基于社会资本理论,以2007~2022年我国A股上市公司为样本,运用Heckman两阶段模型,探讨数字经济背景下,企业社会责任信息披露影响创新投入的内在机理,解释了企业风险承担能力和管理者风险承担意愿的中介作用,以及公司治理的调节效应,以拓展战略性慈善行为推动企业创新的相关研究。结果表明:社会责任信息披露促进创新投入;管理者风险承担意愿和企业风险承担水平均发挥中介作用;CEO二元性、董事会规模、管理层持股比和股权制衡度均对社会责任信息披露与创新投入的关系起促进作用。本文将社会责任信息披露的影响从伦理层面拓展到战略层面,为企业数字化战略、社会责任战略和创新战略的协同作用提供了重要启示。

基于改进PCA-BP神经网络模型的海宁市需水预测

需水预测是地区水资源规划中的重要部分,对于实现水资源合理有序开发,保障社会经济的可持续发展有重要的指导意义。采用改进PCA-BP神经网络模型对影响需水量的9个影响因子进行降维处理,并分别以海宁市2001~2014、2015~2020年数据作为训练样本和检验样本完成模型训练,其中,综合灰色预测模型GM(1,1)对降维后的影响因子独立预测,从而预测海宁市规划年需水量,并与传统定额法的需水预测结果进行对比分析。结果表明,人口、GDP、居民生活用水量、城镇公共用水量为影响海宁市需水量的主要因子;通过构建改进PCA-BP神经网络模型得到的2025、2030、2035年需水结果,比传统定额法更为真实、合理,进一步证实了预测模型的合理性,可为海宁市未来水资源规划提供指导。

鼻咽癌放疗相关性口腔黏膜炎危险因素及预见性护理的研究进展

放疗导致的口腔黏膜炎是鼻咽癌患者常见的并发症之一,不仅在一定程度上影响患者的口腔功能,且严重时还会延迟治疗进程,从而对患者的预后、生活质量及生存构成威胁。基于放疗相关性口腔黏膜炎的危险因素进行早期识别和预防有助于降低其发生率及严重程度,改善患者口腔状况及保证治疗效果。因此,本文综述放疗相关性口腔黏膜炎的危险因素和预见性护理措施,旨在提高患者的生活质量和生存获益,为口腔黏膜炎的早期预防和管理提供依据。

基于InVEST和PLUS模型下的土地利用变化及生境质量演变分析:以汉中盆地为例

汉中盆地是中国南水北调工程中的重要源头区,分析区内土地利用及生境质量,能提高对该地区演变规律的认识,进一步保护下游研究区的自然资源和生态环境安全。笔者基于2000~2020年20年内共3期土地利用数据,建立了研究区多类型土地利用数据库和生境质量评估体系,从土地利用转移矩阵、景观格局指数、生境质量等多个方面对研究区内的土地利用的时空变化进行研究,并利用PLUS模型进行土地利用扩张和预测分析。结果表明:2000~2020年,土地利用类型变化较大,变化转移主要发生在水田和农村居民点用地、城镇用地之间;蔓延度指数逐年减少,景观破碎化程度较高;生境质量结果呈逐年下降趋势,高值面积减少并向南部扩展,低值面积增多并以城镇为中心向外辐射扩散,高等级的生态质量景观由水域和林草为主,主要沿汉江流域分布。

基于矩不确定模糊集的分布鲁棒风险-回报优化模型研究

本文研究随机变量分布不确定下的风险-回报优化模型。针对传统的风险-回报三类典型问题和分布不确定性背景,提出了更一般性条件下的分布鲁棒风险-回报优化新模型;基于矩不确定集合和优化对偶理论,化简复杂的新优化模型为常规结构的非线性优化问题。理论上证明了分布鲁棒风险-回报三类优化模型效率前沿的等价性。数值实验验证了理论分析的有效性。

基于多序列MRI影像组学与深度迁移学习特征的脑胶质瘤分级预测研究

目的:探讨基于多序列磁共振成像(magnetic resonance imaging,MRI)手工影像组学(hand⁃crafted radiomic,HCR)和深度迁移学习(deep transfer learning,DTL)特征的机器学习(machine learning,ML)模型在术前预测脑胶质瘤分级的效能。方法:选取BraTS2019数据集中332例患者的影像数据[高级别胶质瘤(high⁃grade glioma,HGG)258例,低级别胶质瘤(low⁃grade glioma,LGG)74例],随机抽取30例HGG和8例LGG作为测试数据集,其余294例作为训练集和验证集。从T1、T2、T1c和Flair序列中提取病灶的HCR特征和DTL特征,并筛选出影响力前10的特征子集,基于HCR特征、DTL特征和两者组合的深度学习影像组学(deep learning radiomics,DLR)特征,分别建立7种ML模型,评估模型预测HGG和LGG的效能。选择最佳模型后,使用SHAP法对模型特征重要性进行量化及归因分析。结果:基于HCR和DTL组合的DLR特征构建的ML模型预测效能最高,当使用支持向量机的递归特征消除(support vector machine⁃recursive feature elimination,SVM⁃RFE)筛选特征后,使用T2+T1c+Flair序列组合的支持向量机(support vector machine,SVM)分类器的预测模型效果最佳。在验证集上,受试者工作特征曲线下面积达到0.996(95%CI:0.991~1.000),约登指数、准确度、灵敏度和特异度分别为0.920、0.976、0.988和0.932,在测试集上同样具有较高的分级预测效能。SHAP特征权重分析显示Flair序列的特征贡献较大,其次为T2及T1c序列,HCR和DTL特征均有重要贡献。结论:基于多序列MRI的DLR特征构建的ML模型可有效预测脑胶质瘤的肿瘤分级,其中经过SVM⁃RFE筛选后的T2+T1c+Flair序列组合的SVM分类器模型效能最佳。

流动条件下X65钢在含氧体系下的腐蚀行为及腐蚀预测模型

X65钢注水海管介质含有溶解氧,导致管道内发生严重腐蚀。针对该问题,基于现场注水海管基础数据,考虑流速、温度、溶解氧浓度和Cl^(-)浓度对X65钢腐蚀行为的影响,设计9组正交试验,采用高温高压反应釜、扫描电子显微镜和X射线衍射仪进行失重测试和腐蚀产物表征;结合多元线性回归法,建立腐蚀预测模型。结果表明:4种影响因素对腐蚀速率的影响程度由大到小排序为:溶解氧浓度>温度>流速>Cl^(-)浓度,溶解氧浓度是腐蚀主控因素;随着流速、温度、溶解氧浓度和Cl^(-)浓度的增大,腐蚀速率增大;腐蚀产物表征结果表明,腐蚀产物主要为Fe3O4和Fe2O3;考虑上述4个因素的影响,基于7组均匀腐蚀速率值,建立腐蚀预测模型;采用剩余2组均匀腐蚀速率值对腐蚀预测模型进行验证,误差最大为8.9%,表明该腐蚀速率预测模型的准确性较高。

ST段抬高型心肌梗死术后彩超左心房容积指数的检测及其与复发心房颤动的关系

目的探讨ST段抬高型心肌梗死(STEMI)经皮冠状动脉支架置入术后患者彩超下左心房容积指数(LAVI)检测的意义及其与复发心房颤动的关系。方法选取2019年5月至2021年5月萍乡市人民医院收治的82例经皮冠状动脉支架置入术治疗的STEMI患者作为研究对象,根据术后随访结果是否复发心房颤动分为未复发组(n=61)与复发组(n=21)。比较STEMI患者治疗前后左心房参数[左心房容积(LAV)、LAVI、左心室射血分数(LVEF)及左心房内径(LAD)],比较两组左心房参数,采用多元Logistic回归分析STEMI患者术后复发心房颤动的影响因素。结果治疗后,患者LAV、LAVI、LAD低于治疗前,而LVEF高于治疗前,差异有统计学意义(P<0.05)。复发组LAV、LAVI大于未复发组,LAD长于未复发组,LVEF低于未复发组,差异有统计学意义(P<0.05)。多元Logistic逐步回归分析结果显示,LAVI、LAV、LAD是心房颤动复发的危险因素(OR>1,P<0.05),而LVEF心房颤动复发的保护因素(OR<1,P<0.05)。结论经皮冠状动脉支架置入术可改善STEMI患者心肌结构,且术后心房颤动复发与左心房参数相关,临床可根据LAV、LAVI及LAD指标的监测,为疾病诊治及术后预防心房颤动复发提供依据,值得临床推广。