高校学生公寓空间环境情绪测度实证及优化设计策略研究

人的情绪与建成环境存在互动耦合关系。文章选取苏州大学独墅湖校区学生公寓楼为调研对象,采用个体报告法深入挖掘影响大学生情绪的空间环境要素,并提出合理的空间设计和改造策略,以促进健康大学校园的建设。     

一种基于CST技术的机翼参数化描述方法

为了确保翼型性能，需要采用恰当的翼型参数化方法生成所需要的翼型几何体。利用CST方法生成具有代表性的Clark-Y二维机翼，同时对CST方法生成三维几何体进行了详细研究。在原有的CST方法基础上对生成三维机翼的方法进行改进，用来解决机翼外形不连续变化的情况，生成可控分段变化三维机翼外形，控制效果良好。     

因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。     

战“疫”虽艰难 后市仍可期

春节以来,新型冠状病毒感染的肺炎肆虐,国内外宏观避险情绪升温,大宗商品市场遭受强烈冲击,有色金属行业供需两端受挫,价格普遍下跌,市场焦虑担忧情绪蔓延。有色金属行业在国民经济中的地位举足轻重。面对疫情,全行业积极贯彻党中央和国务院的各项决策部署,纷纷捐款捐物支援各级慈善机构和所在社区,统筹推进新冠肺炎疫情防控,努力保持生产运行,积极组织复工复产,充分调动自身资源降低疫情对生产经营造成的影响,保持了行业基本平稳有序运行。     

优质护理在剖宫产30例中的应用分析

目的:分析剖宫产产妇手术前后实施优质护理的效果。方法:选择行剖宫产术的单胎妊娠产妇60例,随机分成对照组与研究组各30例。对照组实施产科常规护理,研究组在对照组基础上实施优质护理。观察两组产妇焦虑情绪、术后观察指标以及护理服务满意率等情况。结果:研究组手术前产妇焦虑评分明显低于对照组(P<0.05);研究组术后并发症发生例数明显比对照组减少(P<0.05);研究组产妇的护理满意率明显高于对照组(P<0.05)。结论:对剖宫产产妇手术前后实施优质护理,可有效改善产妇的焦虑情绪,减少疼痛、乳液分泌不足等发生率,能有效促进产后恢复。     

人文关怀护理对血液肿瘤住院患者焦虑抑郁情绪的改善效果探究

目的:观察人文关怀护理干预对血液肿瘤住院患者焦虑抑郁情绪的改善效果。方法:选取2016年1月-2018年10月本院收治的血液肿瘤患者200例。按照随机数字表法将其分为常规组和关怀组,每组各100例。常规组采取常规护理干预直至出院,关怀组采取人文关怀护理干预直至出院。比较两组情绪状态(SDS、SAS评分)、睡眠情况(PSQI评分)、依从性。结果:护理后,关怀组SDS、SAS评分均低于对照组(P<0.001);护理后,关怀组PSQI各维度评分及总分均低于对照组(P<0.001);关怀组依从性好率100%,高于对照组的91.00%(X~2=9.424 1,P=0.002 2)。结论:在血液肿瘤住院患者中,采取人文关怀护理干预,可明显减轻患者焦虑、抑郁情况,改善患者睡眠质量,进而提高患者对治疗的依从性。     

基于扁铲侧胀试验材料指数的岩土类别研究

阐述了岩土工程勘察中岩土类别的划分情况,由于岩土体的复杂性和天然成因,其物理力学性质具有差异性和区域性,采用扁铲侧胀试验对其进行岩土类别的划分,需要建立区域性岩土参数的岩土材料分类公式。通过室内试验数据和扁铲侧胀试验,据现行国家规范提出的材料指数公式,总结了武汉地区岩土类别的划分规律和相关关系,以促进扁铲侧胀技术的推广应用。     

2019年重庆市食品生产许可现场核查分析及建议

目的 分析2019年重庆市食品生产许可现场核查中出现的问题。方法 依据《食品、食品添加剂生产许可现场核查评分记录表》六个部分条款梳理现场核查缺陷,对209个不予许可食品类别进行汇总分析。结果 六个部分条款出现零分项频率递减为生产场所、设备设施、管理制度、人员管理、设备布局和工艺流程、试制产品检验报告,生产场所缺陷频率略少于设备设施,不予许可食品类别依次递减为调味品、蔬菜制品、豆制品、速冻食品、其他食品。结论 通过梳理许可现场核查中的个性或共性问题并提出对策,以期能为相关工作技术人员提供指导和借鉴。     

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model北大核心CSCD

The Miao nationality is the sixth largest ethnic group in China which has a history of thousands of years. It has created a unique material culture and spiritual culture in its development process and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality. As one of the unique symbols of Miao culture the Miao costume has profound cultural heritage and cultural connotations. The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life but also symbolize the pursuit of the good spirit of the Miao people. However under the impact of modern pop culture and foreign culture these cultural symbols are gradually disappearing. In order to protect and inherit them the Miao costume pattern segmentation has become the most important work. However the Miao costume pattern segmentation is quite difficult. At present there are few studies on the extraction classification identification and preservation of the features of Miao costume pattern segmentation. With the excellent segmentation performance of the U-Net model and the advantages of easy deployment the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet Residual Selective-Kernel Parallel U-Net model. The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model. The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning. The paper not only combines the current research focus-deep learning and attention mechanism but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns. The RSKP-UNet model is better than other models in various segmentation indicators. Compared with the benchmark model U-Net the Dice coefficient IoU precision recall and accuracy are improved by 6. 98% 11. 07% 2. 89% 6. 75% and 3. 92% . The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns which can be used to build the Miao costume pattern database in this way thus helping designers relevant researchers and organizations to provide research foundation and completing the protection and inheritance of the Miao costume culture. The paper also provides some reference for the segmentation research of other minority costume patterns. © 2022 Authors. All rights reserved.