民族器乐合奏之“声融度”与“色融度”解析

民乐合奏中的"声融度"和"色融度",乃是决定一个乐队能否奏出和谐美妙声音的两个重要指标。本文对民乐队中的乐器性能、声部组合等进行研究分析,并与西方管弦乐队作一对比,从中检视民族乐队在"声融度"与"色融度"方面存在的缺憾与不足。

特殊教育学校师生心理互融度的调查与思考

师生心理互融度是教师与学生之间彼此情感的接受和容纳程度,以及对同一问题和行为认识的一致程度。特殊教育学校师生在情感接受(喜爱)度、学习困难的解决、课业负担、表扬与批评、教学活动认可度、校园文化生活几个维度上心理互融度是比较高的;在信任与帮助维度上,师生间出现了较大反差,显示出师生心理融合中出现的问题。

新媒体视域下团学活动深度融入高校大思政课教学策略研究

党的二十大报告提出“完善思想政治工作体系,推进大中小学思想政治教育一体化建设”,为高校思想政治工作指明了方向。为解决传统思政教学中的痛点问题,满足新媒体时代思政教育要求,高校应落实立德树人根本任务,全方位打造全过程“大思政”格局,引导学生树立正确世界观、人生观与价值观。与此同时,高校应提升对学生团学活动的重视度,发挥团学活动对学生思想境界的促进作用,将团学活动作为高校大思政课教学的重要组成部分,促进学生身心健康发展。随着新媒体的快速发展,高校还应将新媒体作为团学工作的重要工具,以“三创三融三度”为团学工作目标,提高高校思政教育效能。文章对新媒体对高校团学活动及大思政课的影响进行阐述,分析新媒体视域下团学活动与高校大思政课融合面临的挑战,结合“三创三融三度”目标提出团学活动发展方向,提出针对性优化路径,为提高高校思政教育成效奠定基础。

双高院校制造大类专业“岗课赛证”融合探索与实践——以江苏食品药品职业技术学院智控专业为例

以智能控制技术专业为例进行“岗课赛证”融合的人才培养路径的实践探索对“双高计划”背景下的专业建设有重要现实意义。明确“岗课赛证”融合的概念,结合智控专业现状促成“岗课赛证”融合,实现智控专业课程内容、教学模式、考核评价等各方面的兼容与协同推进,扎实培养学生的职业技能和素养,着力提升其岗位适应性和就业创业质量。构建“五度四融”和“四位一体”的“岗课赛证”融合式校企合作人才培养模式,为教师今后的专业建设实践提供思路借鉴。

引入像点融合度修补的图像边缘化参差拼接实现

研究引入像点融合度修补的图像边缘化参差拼接实现方法;图像参差拼接技术是实现多幅图像无缝拼接的关键;传统的图像参差拼接实现时,采用基于边缘的普通特征拼接融合方法,无法做到无缝连接;提出一种基于像点融合度修补的图像边缘化参差拼接实现,首先对图像边缘化参差拼接进行模块划分,提取各个模块之间的像点融合度修补特征,然后将像点修补特征进行融合处理,最终实现整个图像边缘化参差拼接;最后采用实际的图像拼接进行测试实验,结果显示,采用基于像点融合度修补的图像边缘化参差拼接实现方法,图像拼接的效果大大提高,具有很好的工程应用价值.

青藏高原中部冻融强度变化及其与气温的关系

使用青藏高原中部野外22个站点2010-2014年观测数据结合GLDAS-NOAH陆面模式1960-2014年3 h 0.25°×0.25°格网数据,通过线性拟合等方法分析了高原中部的冻结强度变化并探讨了其与气温的关系。选取典型站点资料,结合GLDAS-NOAH数据对四次冻融过程进行分析比较,结果表明:(1)冻结强年和冻结弱年,高原中部季节冻土区各站点冻结、消融过程的持续时间差异大。(2)1960-2014年,高原中部平均气温呈上升趋势,其速率为0.39℃·(10a)^(-1);冻结起始日以0.91 d·(10a)^(-1)的速率延后,冻结结束日则以2.88 d·(10a)^(-1)的速率提前,冻结结束日对气温变暖的响应更迅速。(3)垂直方向上,不同冻结强度年表层5 cm处土壤温度、湿度差异最大,差值随土壤深度的增加逐渐减小。冻结强、弱年土壤水分相变速率不同引起的热量差使得各层土壤温度的日变化产生明显差异。

机电一体化系统集成与融合

分析研究了机电一体化系统原理与组成，提出有关机电一体化系统集成与融合四个方面即信息集融、能量集融、物质集融和系统集融。分析研究了它的集成单元的层次性，集融规则等，建立了系统的质量评定标准：集融度。

作为研究者的“四度”空间——从社会学视角与老师们商谈教育写作

厚度、浓度、融度、零度,架构起作为研究者的一方可以自驭的纵横空间。研究者的厚度是一双历史之眼。有无历史之眼的厚度,主要差异在于是否能在遥远历史与现实教育既惊人相似又不言自明中追问一个个为什么,是否能够揭示诸多教育现象之中渗透骨髓的历史文化传统和在现今的意义表达。研究者的浓度是一副凝练之功。有无凝练之功的浓度,主要差异在于是否能在日复一日、平淡无奇的教育教学中具备"化熟为生"的思想本领,具备筛淘金子的思维路径。研究者的融度是一种想象之力。有无想象之力的融度,主要差异在于是否能既不画地为牢自己的有限时空,又能不断发现与其他学科、视野的交融点,带来更具震撼力和解释力的剖析。研究者的零度是一抹新生之韵。有无新生之韵的零度,主要差异在于是否能在教育的诸多关系与复杂中,不断发现异化、弱化、固化的问题空间,一再开始事实解剖的讲述找寻,实现对心灵秩序和生命意义的人文关怀。

杨树越冬死亡的有效冻融伤害累积效应

为了阐明杨树越冬死亡机制,根据5a田间定位观测结果,分析杨树越冬死亡、树干破腹与温度及光照的关系。采用室内控制试验的方法,研究低温和冻融循环对不同杨树无性系萌芽率、相对电导率的影响。结果表明,低温并非越冬死亡的充分条件,负积温与杨树越冬死亡无明确因果关系。冻融循环比持续低温对杨树造成的伤害更大。抗寒性不同的无性系耐冻融循环的能力不同。随着冻融循环次数的增加,杨树萌芽率逐渐降低。抗寒性较强的无性系相对电导率低。提出与杨树越冬死亡相关的抗冻度、有效冻融和抗冻融度3个概念及有效冻融伤害累积效应假说。

新疆丘陵区小流域春季融雪设计洪水估算

新疆是一个丘陵区春季融雪性洪水多发的省份,每年各地都会形成灾害。灾害性的春季融雪洪水,主要发生在由负气温稳定转为正气温初期的急剧增温期,本文以春季融雪洪水的发生时间与实测逐日气温的对应关系,从推理分析的角度,提出小流域春季融雪的设计洪水估算思路,对实现小流域融雪洪水的预警预报,防患小流域融雪洪水灾害,具有重要的应用意义。

Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.

氨茶碱中空栓的研制

研究了氨茶碱中空栓的工艺，并对重量差异限度、融变时限进行了检查，制定了氨茶碱中空栓的含量测定方法，并对所制得的中空栓进行含量测定。结果表明，氨茶碱中空栓主要理化指标符合栓剂的质量标准。

关于音程协和性的本质探讨

音程协和与不协和概念的界定，除运用数理方法揭示其质的规定外，还必须视其所用律制的不同、其一音程在某种调式中所处的位置不同，以及某些地域某个民族在听觉习惯上的不同为转移，尤其要指出的是，人们对某些音程协和度的感受和认同，会随着时代的变迁、乐艺的创新而变化，已往被认为是不协和的音程会渐渐变得“协和”起来，反之也一样．

F2上2^n-周期序列的k-错误序列

为了更好地刻画和研究序列的随机性,该文提出了序列的k-错误序列的概念,并对k=1,2,确定了F2上2n-周期序列的k-错误序列的计数,还给出了F2上2n-周期序列的1-错误序列个数的均值。

矫正眼镜的垂直屈光参差对患者垂直融像度影响分析

目的分析矫正眼镜的垂直屈光参差对患者垂直融像度的影响。方法样本来自我院验光配镜中心的105名患者，按双眼矫正眼镜的垂直屈光参差将研究对象分为两组：无垂直屈光参差组（双眼的垂直屈光参差差别〈1．0D）和垂直屈光参差组。在征得其本人同意的情况下，对其进行屈光检查并用综合验光仪进行平滑的垂直融像度的测定。收集的数据用相关分析探讨双眼的垂直屈光参差度与垂直融像度的关系；结果对照组的垂直融像度（3．61±1．01）D；垂直屈光参差组的垂直融像度为（4．45±1．50）D（t=3．432，P=0．001）；结论当暴露于垂直屈光参差的刺激时，垂直融像度会相应增加，因此在屈光参差验配中要尽量避免干扰患者的双眼视功能，以保证舒适的视觉。

Mean shift algorithm based on fusion model for head tracking

To solve the mismatch between the candidate model and the reference model caused by the time change of the tracked head, a novel mean shift algorithm based on a fusion model is provided. A fusion model is employed to describe the tracked head by sampling the models of the fore-head and the back-head under different situations. Thus the fusion head reference model is represented by the color distribution estimated from both the fore- head and the back-head. The proposed tracking system is efficient and it is easy to realize the goal of continual tracking of the head by using the fusion model. The results show that the new tracker is robust up to a 360°rotation of the head on a cluttered background and the tracking precision is improved.

Evolution model of concrete failure surface under coupling effect of seawater freeze-thaw and erosion

In order to effectively assess the mechanical properties of concrete with freeze-thaw and seawater erosion, tests about basic mechanical properties of concrete after freeze-thaw and seawater erosion are conducted based on the large-scale static and dynamic stiffness servo test set. 50, 100, 200 and 300 cycles of freeze-thaw cycling are made on normal concrete, and the artificial seawater is produced. The reasonable wet and dry accelerate system is selected. 10, 20, 30, 40, 50 and 60 cycles of wet and dry cycling are made to concrete after freeze-thaw cycling. The degeneration law of the concrete elastic modulus and compressive strength is studied. The Ottosen tri-axial strength criterion considering cycles of freeze-thaw and wet and dry cycling is deduced based on uniaxial mechanical properties of concrete and damage theory. Experimental results show that with the increase in the number of wet and dry cycles and freeze-thaw cycles, the concrete axial compressive strength and the elastic modulus decline gradually. Tensile and compressive meridians of concrete shrink gradually. The research can be referenced for anti-crack design of actual structures eroded by seawater at cold regions.

聚散灵敏度与融像性聚散的相关性研究

目的探讨聚散灵敏度与融像性聚散的相关性，为临床聚散系统异常的诊断提供更直接、更有意义的指标。方法临床病例自身对照研究。对2012年10～12月在天津医科大学眼科中心志愿随机抽取50名20—28周岁在校近视大学生，应用电脑验光仪和综合验光仪行规范验光后，在屈光不正全矫的基础上分别进行聚散灵敏度检测和融像性聚散检测，并利用Flashed Von Graefe法测量视近隐斜，利用SPSS13．0统计软件对相关数据进行分析，确定聚散灵敏度与融像性聚散的相关性。结果（1）视近隐斜偏高组（〉6EXO）和视近隐斜正常组（0-6EXO）的聚散灵敏度差异显著有统计学意义（P〈0．05），而融合性聚散（BI）恢复点数值差异无统计学意义（P〉0．05）。（2）BI模糊点和破裂点与聚散灵敏度无相关性，而其恢复点与聚散灵敏度有相关性。结论可以通过被检者的恢复点数值推测患者的聚散灵敏度是否异常，可使聚散检测中恢复点数据得到充分的使用，从而减少了专项的聚散灵敏度检查，使双眼视异常的诊断更为简便。

Seismic velocity inversion based on CNN-LSTM fusion deep neural network

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.

Neural Network Approach to Predict Melt Temperature in Injection Molding Processes

Among the processing conditions of injection molding, temperature of the melt entering the mold plays a significant role in determining the quality of molded parts. In our previous research, a neural network was developed to predict the melt temperature in the barrel during the plastication phase. In this paper, a neural network is proposed to predict the melt temperature at the nozzle exit during the injection phase. A typical two-layer neural network with back propagation learning rules is used to model the relationship between input and output in the injection phase. The preliminary results show that the network works well and may be used for on-line optimization and control of injection molding processes.