Surface and Content Validity of the Mentoring Function Scale for Novice Nurses

This study aimed to examine the surface and content validity of the Mentoring Function Scale for Novice Nurses, used to assess the mentoring of entry-level nurses, and to refine the scale items. In Study 1, six nurse education researchers, selected using convenience sampling, with five or more years of nursing experience and experience teaching novice nurses, were invited to an expert meeting in July 2015. A group interview was conducted that lasted approximately 120 minutes. Study 2 examined the content validity index. Between September and November 2015, we distributed a self-administered questionnaire survey to 11 participants selected by convenience sampling. The participants included five nurse education researchers with a minimum of five years of nursing experience and experience teaching novice nurses, as well as six clinical nurses with a master’s degree or higher. Finally, 81 questionnaire items were retained from the initial 125 items. The 81-item Mentoring Function Scale for Novice Nurses had higher content validity than the original scale. To further increase the scale’s applicability, future studies should assess its reliability, construct validity, and criterion-related validity.

Effects of Phenyl Content and Molecular Structure on the Refractive Index of Organic Silicon Polymer

The effects of phenyl content and group distribution on the refractive index of phenyl silicone oil were investigated by synthesis and characterization of silicone oils of different molecular structures.A group contribution function model was established to predict the refractive index. The results showed that refractive index of phenyl silicone oil increased as its phenyl content increased. A linear equation had been established to describe the quantitative relationship between the refractive index and phenyl content.Refractive index values from the group contribution function model showed good consistency with experimental results.

Assessment of Soil Water Content in Field with Antecedent Precipitation Index and Groundwater Depth in the Yangtze River Estuary

To better understand soil moisture dynamics in the Yangtze River Estuary （YRE） and predict its variation in a simple way, a field monitoring experiment was carried out along the north branch of the Yangtze River, where seawater intrusion was strong and salt-water variation is one of the limiting factors of local agriculture. In present paper, relation between antecedent precipitation index （API） and soil water content is studied, and effects of groundwater depth on soil water content was analyzed. A relatively accurate prediction result of soil water content was reached using a neural network model. The impact analysis result showed that the variation of the API was consistent with soil water content and it displayed significant correlations with soil water content in both 20 and 50 cm soil layer, and higher correlation was observed in the layer of 20 cm. Groundwater impact analysis suggested that soil moisture was affected by the depth of groundwater, and was affected more greatly by groundwater at depth of 50 cm than that at 20 cm layer. By introducing API, groundwater depth and temperature together, a BP artificial network model was established to predict soil water content and an acceptable agreement was achieved. The model can be used for supplementing monitoring data of soil water content and predicting soil water content in shallow groundwater areas, and can provide favorable support for the research of water and salt transport in estuary area.

Hyperspectral Inversion of Potassium Content in Apple Leaves Based on Vegetation Index

The aim of this study is to establish the estimation model of potassium content in apple leaves by using vegetation index. A total of 96 fresh apple leaves were collected from 24 orchards in Qixia County, Shandong Province. The spectral reflectance of the leaves was measured by ASD FieldSpec4. The difference vegetation index (DVI), ratio vegetation index (RVI) and normalized vegetation index (NDVI) were used to make the contour map through Matlab platform, and the combination of high correlation wavelength was selected to establish the random forest (RF) regression model of potassium content. The hyperspectral reflectance increased with the increase of leaf potassium content. The correlation between DVI and the content of potassium is higher than NDVI and RVI. The optimal vegetation index was DVI (364,740), the correlation coefficient was 0.5355. The random forest regression model established with DVI selected vegetation index was the best. R2 was 0.8995, RMSE and RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the random forest regression model to reverse the potassium content of apple leaves has achieved good results. It is important to determine the growth status of apple in hyperspectral and to determine the potash fertilizer of apple trees.

Research on the Contents and Indexes of Reservoir Operational Security

Reservoirs play an important role in the development of economy and society, as well as the maintenance of ecological balance. The reservoir operational security can make every function of reservoirs fully played. This paper makes a systematic analysis on the meaning of reservoir operational security and builds up a framework system of it from the perspective of organization and system. On this3 basis, the paper researches the contents and indexes of reservoir operational security from the microscopic, intermediate and macroscopic aspects. The results of this paper provide a foundation for further research on reservoir operational security management.

Contents Index to Volume 4

Research Papers Generating a multilingual taxonomy based on multilingual terminology clustering Chengzhi ZHANG 2011,4(2):27Construction of an evaluation system for information openness of local governments Fang WANG&Qun GU 2011,4(1):28Real-time virtual reference service based on applicable artificial intelligence technologies:The debut of the robot Xiaotu at Tsinghua University Library

Effects of Moisture Content and Plasticity Index on Duncan-Chang Model Parameters of Hydraulic Fill Soft Soil

In order to explore the effects of moisture content and plasticity index on Duncan-Chang model parameters?K,n,?C?and?Rf,?we selected 8 groups of soft soil with water content of 69.1%?-?94.3% and plasticity index of 32.2?-?54.1 for triaxial unconsolidated undrained shear test. The results show that?Cuu,?K?and?n?values all showed a downward trend, and?Rf?variation was not obvious with the increase of moisture content. The variation rule of each parameter is not obvious with the increase of plasticity index. When moisture content is constant,?Cuu?and?n?values do not change much,?K?increases with the increase of plasticity index within the range of 70%?-?80% moisture content, and does not change much with the increase of plasticity index when moisture content is greater than 80%,?Rf?has no obvious rule.?When the plasticity index is constant,?Cuu,?Kand?n?decrease with the increase of moisture content,?Rf?has no obvious rule. The maximum value of?Cuu?is 20.18?kPa, the minimum is 3.72?kPa, and the maximum to minimum ratio is 5.42. The maximum value of?K?is 0.517, the minimum is 0.022, and the maximum to minimum ratio is 23.5. The maximum value of?n?is 1.198, the minimum is 0.150, and the maximum to minimum ratio is 7.99. The maximum value of?Rf?is 0.872, the minimum is 0.679, and the maximum to minimum ratio is 1.28.

Indexing of Content-Based Image Retrieval System with Image Understanding Approach

This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases.

基于超分辨率重建与机器学习的油菜苗情监测方法

为优化养分管理和确保植株正常生长,以无人机遥感技术高效且非破坏采集田间作物苗情信息,监测油菜苗期的叶面积指数(LAI)与叶绿素相对含量(SPAD)。针对无人机因飞行高度与图像分辨率相互制约,监测效率与监测精度难以兼顾的问题,采用超分辨率重建方法,融合较低飞行高度拍摄高分辨率影像,重建较高飞行高度拍摄影像,建模完成后可通过拍摄飞行影像监测LAI和SPAD。试验设置3个氮肥梯度、3个播期、3个种植密度处理,在苗期利用无人机分别采集20m及40m 2个飞行高度的油菜苗影像,采用SRRestnet方法,对40m影像进行超分辨率重建。基于20m、40m及40m重建影像中提取的3种特征组合,利用偏最小二乘回归(PLSR)、随机森林(RF)、支持向量回归(SVR)3种机器学习方法对LAI和SPAD进行监测。结果表明,超分辨率重建后的图像在表型苗情监测中表现出良好效果,PLSR监测LAI、RF监测SPAD的监测精度最高,且40m重建图像的作业效率相比于20m图像提高48.6%。

开采保护层与预抽煤层瓦斯防突效果分析

为比较开采保护层与预抽煤层瓦斯防突效果,以1124(3)运顺开采保护层段和预抽煤层瓦斯段煤巷掘进为试验对象,统计了试验地点煤巷掘进期间残余瓦斯含量、钻屑瓦斯解吸指标K1、钻屑量等防突指标,采用假设检验方法推断了各防突指标是否有差别,分析了开采保护层段和预抽煤层瓦斯段各防突指标产生差别的原因,验证了开采保护层区域防突措施比预抽煤层瓦斯区域防突措施更加安全可靠。

基于扩散模型的联珠团窠纹再生设计研究

生成式人工智能正广泛应用于图像生成领域,为解决传统纹样设计方法创作联珠团窠纹耗时耗力的问题,研究扩散模型应用于联珠团窠纹再生设计的方法。通过对联珠团窠纹艺术结构特征的提取和扩散模型中交叉注意力模块的研究,设计有效的描述文本并重构扩散模型,提出以结构相似度作为参考标准,采用高斯函数确定阈值进行筛选的再生设计方法。主客观评价结果证明了运用重构的扩散模型进行纹样再生设计的可行性。为联珠团窠纹的再生设计提供了新的思路与方法。

冠层活性相关性状全基因组关联分析及其对产量性状遗传效应的解析

【目的】冠层活性是反映作物生长发育的重要指标,发掘小麦冠层活性相关基因并分析其与产量性状的关系,为解析产量遗传结构和辅助育种提供理论支撑。【方法】以166份国内外小麦品种为材料,将其种植于河南安阳和安徽濉溪,结合已构建的包含326570个来源于小麦90K和660K SNP芯片标记的高密度整合物理图谱,对苗期和花后10 d归一化植被指数(NDVI-S和NDVI-10)及花后10 d旗叶叶绿素含量(Chl-10)进行关联分析,并与前期利用相同材料得到的产量相关性状结果进行比较。【结果】NDVI-S、NDVI-10和Chl-10在基因型、环境及基因型×环境互作间变异方差均达极显著(P<0.01)差异,广义遗传力(hb2)分别为0.81、0.81和0.91。分别检测到13、12和15个与NDVI-S、NDVI-10和Chl-10显著相关的位点,其中,有12、11和12个为新位点,5个位点与2个以上性状有关。166份小麦品种含有NDVI-S、NDVI-10和Chl-10优异等位基因数变幅分别为4-11、3-11和4-12,且随着优异等位基因数目增加其对应表型值呈递增趋势。NDVI-S与千粒重、粒长和粒宽显著正相关(P<0.01);Chl-10与产量和旗叶宽显著正相关(P<0.01),与单位面积穗数、株高和穗下节长显著负相关(P<0.01)。检测到7个同时与产量和冠层活性相关的多效性位点。【结论】NDVI-S可用于品种产量性状的选择,检测到的稳定位点和多效性位点可在开发标记后用于育种材料的检测。

不同成熟度桑葚的品质分析

【目的】为桑葚的合理化应用和适时采收提供参考。【方法】以2个新疆良种‘黑妞’和‘贵妃’成熟度1~6的果实为材料,测定了果实营养相关指标和功能性相关指标,分析比较不同成熟度桑葚中各指标的差异,探讨成熟度与果实品质间的相关性。【结果】‘黑妞’和‘贵妃’果实在成熟度为4时内源物质含量具有显著差异,同时‘黑妞’果实内源物质含量总体要高于‘贵妃’;总酚、总黄酮和花青素含量整体上与果实成熟度具有正相关性,并且,这3类物质的含量在成熟度为6时均最高,分别为54.8、40.1和557.9 mg/g;‘黑妞’和‘贵妃’果实的蛋白质含量与果实成熟度均负相关,‘黑妞’果实在成熟度为5时含量最低(0.2 g/kg),‘贵妃’果实在成熟度为6时含量最低(0.1 g/kg);‘黑妞’果实的抗坏血酸含量与果实成熟度负相关,含量变化幅度小,在成熟度为6时含量最低(0.7 g/kg),而‘贵妃’果实在成熟度前3阶段未检测出抗坏血酸,从成熟度为4时开始出现抗坏血酸,说明这个阶段是抗坏血酸积累阶段;酚酸类含量和总酸含量与果实成熟度均负相关,在成熟度为6时达最低值,其中‘黑妞’和‘贵妃’果实中酚酸类含量最低值分别为30.3和2.2 mg/kg,总酸含量最低值分别为10.6和7.5 g/kg;在各种氨基酸中,‘黑妞’和‘贵妃’6个成熟度果实中天门冬氨酸的平均含量最高,分别为0.055和0.047 mg/g,其中成熟度为1时天门冬氨酸含量最高,分别为0.078和0.104 mg/g,该指标可被作为果实不同成熟期的风味评价指标。【结论】随成熟度的增加,各果实品质性状指标呈现明显的变化规律。‘黑妞’果实的营养物质总含量显著高于‘贵妃’,且‘黑妞’和‘贵妃’果实的内源物质含量均在绿果期开始出现显著性变化。

践踏对5个植丝深度高羊茅草坪生理指标的影响

为了解践踏对不同植丝深度草坪草生理指标的影响,本研究以高羊茅(Festuca arundinacea)植丝式混合草坪为试验对象,设置5种不同人造草丝深度6、9、12、15、18 cm和不植丝(CK),通过模拟践踏处理的方式,对比研究5种植丝深度混合草坪与天然草坪在践踏胁迫下草坪草的坪观质量、生理指标以及践踏后草坪草的恢复状况。未践踏情况下,植丝深度18 cm草坪质量显著高于对照和其他深度植丝处理,随着践踏次数增加,其他各深度植丝处理也对草坪质量有一定提升作用。植丝15 cm处理高羊茅叶绿素含量均能更快恢复,9 cm以上处理组在抑制丙二醛含量、可溶性蛋白和抗氧化酶活性恢复方面均有优势。综合试验结果表明,践踏胁迫条件下植丝式混合草坪的坪观质量总体表现高于天然草坪,植入人造草丝深度9 cm以上能在不同指标上有效加强草坪草抵抗践踏胁迫的能力,提升草坪草践踏后恢复速率。本研究为植丝式混合草坪的技术探索及应用推广提供了理论和实践依据。

基于混合方法的失效专利质量评价方法

失效专利对中小企业开展技术研发具有重要参考价值,识别高质量失效专利并加以利用,对弥补企业研发资源短板、规避侵权风险至关重要。文章首先从技术价值、经济价值、专利权人创新活动规模三个维度,选取13个失效专利质量评价指标;随后结合熵权TOPSIS模型计算失效专利质量综合得分,采用四分位距法设定阈值进行分级评价,从指标计算角度进行失效专利质量评价;接着应用基于深度学习的预训练模型Sentence-BERT对专利摘要进行向量化表征,依托基于密度的离群点检测算法识别离群专利,实现从内容分析角度评价失效专利质量;最后以“人工智能”领域的授权失效专利为例,对所提方法进行实证,从指标维度、方法维度和政策维度对结果进行检验,验证了所提方法的科学性和有效性,能够为中小企业快速遴选高质量失效专利、弥补自身技术短板提供创新方案。

基于网络药理学和HPLC法建立宠物用复方石淋通片中多指标成分的质量控制方法

建立宠物用复方石淋通片多指标成分含量测定方法。利用网络药理学方法筛选复方石淋通片治疗尿石症、膀胱炎的关键活性成分,结合相关文献及《中国药典》相关要求,确定指标性成分。利用HPLC分析方法,使用CAPCELL PAK C18 MG(S-5)4.6×250 mm色谱柱,柱温30℃,进样量10μL,流速为1.0 mL·min^(-1),检测波长335 nm,以乙腈-0.1%甲酸水为流动相,梯度洗脱,建立指标性成分含量测定的方法。通过网络药理学筛选出槲皮素、咖啡酸、绿原酸等十个关键成分。结合《中国药典》及相关文献选出新绿原酸、绿原酸、隐绿原酸、咖啡酸、夏佛塔苷、异夏佛塔苷六种指标性成分。含量测定方法学结果表明:各成分线性关系良好(r≥0.999);精密度良好,RSD为0.03%~0.21%;重复性良好,RSD为0.70%~2.02%;供试品在24h内稳定性良好,RSD为0.30%~2.02%;平均加样回收率(RSD)在98.31%~104.31%(0.36%~2.28%);三批样品中六种成分的含量分别在0.990~1.434 mg·g^(-1)、1.533~2.083 mg·g^(-1)、1.311~2.023 mg·g^(-1)、0.332~0.353 mg·g^(-1)、1.594~1.768 mg·g^(-1)、0.787~1.057 mg·g^(-1)之间。该含量测定方法有较好的重复性和稳定性,可用于宠物用复方石淋通片的质量分析。

机器学习结合高光谱植被指数与SPAD值估算冬小麦氮含量

冬小麦叶片氮含量与叶片光合作用和营养状况密切相关,直接影响植株生长发育,而茎秆中的氮含量与茎秆中纤维素、半纤维素和木质素的比例和含量密切相关,直接影响茎秆质量及植株的抗倒伏能力。然而,有关对冬小麦茎秆氮含量估算研究较为有限,限制了从氮含量角度判断茎秆质量及对倒伏的预测能力。为精准估算冬小麦不同器官(叶片、茎秆)氮含量,该研究通过2年田间试验,获取冬小麦4个关键生育期(拔节期、抽穗期、开花期、灌浆期)和3种施氮水平条件下(N1、N2和N3)的冠层光谱反射率、叶片、茎秆氮含量及叶片SPAD(soil and plant analyzer development,SPAD)值。分析了不同生育期和施氮水平条件下高光谱植被指数对叶片和茎秆氮含量的敏感性,并结合5种常用的机器学习算法:随机森林回归(random forest regression,RFR)、支持向量回归(support vector regression,SVR)、偏最小二乘回归(partial least squares regression,PLSR)、高斯过程回归(gaussian process regression,GPR)、深度神经网络回归(deep neural networks,DNN)构建冬小麦叶片和茎秆氮含量估算模型。结果表明:高光谱植被指数对叶片和茎秆氮含量的敏感性受到生育期和施氮水平的影响。在灌浆期,最佳植被指数双峰冠层植被指数DCNI(double-peak canopy nitrogen index)对叶片氮含量的敏感性最高,R^(2)为0.866。对茎秆氮含量,在抽穗期的敏感性最高,最佳植被指数归一化叶绿素比值指数NPQI(normalized phaeophytinization index)与氮含量决定系数R^(2)=0.677。施氮水平的提升增加了光谱植被指数对茎秆氮含量的敏感性。结合SPAD值的机器学习算法提升了氮含量的估算精度,对叶片氮含量,在不同生育期和施氮水平条件下估算精度提升了1%~7%,其中在全生育期的归一化均方根误差NRMSE从0.254降低到0.214,抽穗期的NRMSE提升最大,从0.201降低到0.128。对茎秆氮含量,全生育期的NRMSE从0.443降低到0.400,抽穗期的NRMSE变化最大,从0.323降低到0.268。在全生育期,结合SPAD值的DNN模型对叶片(R^(2)=0.782、NRMSE=0.214)和茎秆(R^(2)=0.802、NRMSE=0.400)氮含量的估算精度最佳。研究说明,SPAD值与光谱植被指数结合有利于提升冬小麦不同生育期和施氮水平条件下叶片和茎秆氮含量的估算精度。