How Sense of Ritual in Tourism Impacts Tourists’Co-Creation of Tourism Experience Values:A Study Based on the Theory of Value Co-Creation

Establishing a sense of ritual within tourism consumption scenarios offers tourists the opportunity for interactive engagement.Drawing upon the value co-creation theory,this study constructed an influence mechanism model to examine tourists'active engagement in the process of co-creating tourism experience values.It employed Partial Least Squares Structural Equation Modeling(PLS-SEM)to empirically test the proposed hypotheses.The findings demonstrate that the model constructed in the present study exhibits robust reliability,validity,and explanatory power.The perception of the sense of ritual in tourism exerts a significant positive influence on tourists’co-creation of tourism experience values,thereby significantly enhancing both the communitas and flow experienced by tourists during their travels.Moreover,such communitas and flow can mediate the influence of the sense of ritual in tourism on tourists’co-creation of tourism experience values.This study contributes to advancing the current research on tourists’co-creation of tourism experience values and the sense of ritual in tourism,thereby providing theoretical foundations for cultivating a sense of ritual within tourism consumption scenarios.

Sensing as Knowing:Medicines,the Senses,and Practical Expertise in Late Imperial China

The development of the medicinal trade and markets in late imperial China increased anxiety among scholarly physicians about the authenticity of medicines.Even though the market was typically depicted by scholarly physicians as a place full of tricks and deceptions,it was a repertoire where practical knowledge about authentication was created and circulated.The specialized knowledge was mainly transmitted through oral tradition.But in some instances,it was also written down by scholarly physicians or merchants,allowing us to reconstruct the techniques and their underlying rationales.Authentication of medicines mobilized multiple sensory perceptions of the human body,consisting of observing,tasting,smelling,touching,and performing small tests.All these techniques played different roles in the practice of authentication.Even though these sensory techniques seemed like a collection of trivial and practical records without any coherent rationales,an underlying episteme could be detected through a close investigation.Merchants and practitioners in the market did not understand the nature and materiality of medicines by any established theories.Instead,they actively engaged with the tangible form of medicines through the senses and bodily techniques.This sensory form of knowing indicates a type of practical expertise that is distant from the scholarly tradition of materia medica in late imperial China.

Comparative study of self-compassion and sense of coherence in nurses of psychiatric hospitals

Objective:Nurses,due to the nature of their job,are subjected to various forms of stress and physical and psychological injuries.Self-compassion and a sense of coherence have attracted much attention in research in recent years as having high potential for improving the health and performance of people in the workplace.The present study aimed to compare self-compassion and the sense of coherence in nurses working in psychiatric hospitals and other hospitals of Kerman University of Medical Sciences.Methods:The present study was a descriptive-comparative study of applied type conducted on 100 nurses of Shahid Beheshti Psychiatric Hospital in Kerman by the census method and 100 nurses of other hospitals of Kerman University of Medical Sciences by the stratified random sampling method.The instruments included the self-compassion questionnaire and the sense of coherence questionnaire.Data were analyzed using the Statistical Package for Social Sciences software.Results:The results of the present study showed that self-compassion and sense of coherence were higher in psychiatric nurses(39.35±7.7 and 53.02±8.01)than among other nurses(36.03±5.81 and 49.76±6.30).There was a direct and moderate relationship between a sense of coherence and self-compassion(P-value<0.005).The higher the score of compassion for oneself,the higher was the score of sense of coherence.Furthermore,the sense of coherence had a direct and weak relationship with all dimensions of self-compassion,except for the extreme imitation dimension,which showed a direct and moderate relationship to other dimensions(P-value<0.001).Conclusions:Self-compassion and a sense of coherence,as a form of self-communication,increase nurses’satisfaction and reduce nurses’job stress and,as a result,job performance is improved.

WHY PRACTICE MAKES PERFECT SENSE:THE PAST,PRESENT AND POTENTIAL FUTURE OF THE PPP PARADIGM IN LANGUAGE TEACHER EDUCATION

Introduction Among,the many lesson planning paradigms usedin English language teacher education over the last 40 years,PPP has proven to be one of the mostpopular and most durable(see Figure 1)despite regular criticism in literature emanating from the Anglophone,centre,of ELT theory.After presenting a brief history of the paradigm andoutlining the main criticisms directed at PPP,especially in the 1990s,I discuss some important rescarch findings from SLA studics sincc the turn of the century that lend support to PPP-typelesson structures.

Preventing Health Anxiety:The Role of Self-Evaluation,Sense of Coherence,Self-Rated Health and Perceived Social Support

Background:Components of Self,completed with the perceived social support determine the individual differences in theevaluation of a stressor and the behavioral responses toward it,such as health-related anxiety.The study set as a goal theanalysis of associations between the components of Self,such as self-evaluation,sense of coherence,perceived social support,and reported health-related anxiety in an adult sample.Methods:147 adults from the 18–73 age group(mean age 37.5)voluntarily completed the questionnaire through Qualtrics online platform containing the Short Health Anxiety Inventory,Core Self-Evaluation Scale,Social Support Assessing Scale,and one Health Self-Evaluation Item.Results:ANOVA foundrelevant differences in total scores and subscales’scores of the health anxiety scale depending on the positive self-evaluation.Linear regression shows that the analyzed variables were responsible for the prediction of a higher value on the“Perceivedprobability of becoming ill”subscale in a proportion of 45.6%and for the“Perceived consequence of illness”subscale in aproportion of 20.2%The predictive value of the linear regression model for the total score on the health anxiety scale was46.3%.Our findings show that negative Core Self-Evaluation is linked with perceived health anxiety.Conclusions:Selfevaluation,sense of coherence and perceived social support influence the perceived health and can explain the differences inthe reported health-related anxiety.

A novel three-step implicit iteration process for threeΦ-hemicontractive mapping in the intermediate sense

In this paper,we introduce a three-step composite implicit iteration process for approximating the common fixed point of three uniformly continuous and asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.We prove that our proposed iteration process converges to the common fixed point of three finite family of asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.Our results extends,improves and complements several known results in literature.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

Air Pollution Risk Assessment Using GIS and Remotely Sensed Data in Kirkuk City,Iraq

According to World Health Organization(WHO)estimates and based on a world population review,Iraq ranks tenth among the most air-polluted countries in the world.In this study,the authors tried to evaluate the outdoor air of Kirkuk City north of Iraq.The authors relied on two types of data:field measurements and remotely sensed data.Fifteen air quality points were determined in the study region representing the monthly average measurements im­plemented for the one-year dataset.Geographic information systems(GIS)based geo-statistic and geo-processing techniques have been applied to collected data.Spatial distribution data related to Air Quality Index(AQI),and Par­ticulate Matter(PM10 and PM2.5)were obtained by mapping collected records.Remotely sensed data of PM2.5 were analyzed and compared with the collected data.Health impacts were assessed per each air pollutant determined in the study.Spatial distribution maps revealed the hazardous air type in the study area.Overall AQI ranged between 300 and 472μg/m^(3)referring to unhealthy,very unhealthy,and hazardous classes of pollution.Also,PM10 ranged between 300 and 570μg/m^(3)indicating the same class of air pollution from unhealthy to hazardous.While PM2.5 ranged between 40 and 60μg/m^(3)which represents unhealthy air for sensitive persons and unhealthy air.The remotely sensed data re­vealed different air types for the study period ranging from 14.5 to 52.5μg/m^(3)represented in moderate and unhealthy air for sensitive persons.Significant correlations were obtained where the mean local R2(coefficient of determination)was obtained as 0.83.The assessed data were within high air pollution that requires immediate intervention for con­trolling causes and eliminating their effects.

引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法

目前基于通用YOLO系列的遥感目标检测算法存在并未充分利用图像的全局上下文信息,在特征融合金字塔部分并未充分考虑缩小融合特征之间的语义鸿沟、抑制冗余信息干扰的缺点。在结合YOLO算法优点的基础上提出GUS-YOLO算法,其拥有一个能够充分利用全局上下文信息的骨干网络Global Backbone。除此之外,该算法在融合特征金字塔自顶向下的结构中引入Attention Gate模块,可以突出必要的特征信息,抑制冗余信息。另外,为Attention Gate模块设计了最佳的网络结构,提出了网络的特征融合结构U-Net。最后,为克服ReLU函数可能导致模型梯度不再更新的问题,该算法将Attention Gate模块的激活函数升级为可学习的SMU激活函数,提高模型鲁棒性。在NWPU VHR-10遥感数据集上,该算法相较于YOLOV7算法取得宽松指标mAP^(0.50)1.64个百分点和严格指标mAP^(0.75)9.39个百分点的性能提升。相较于目前主流的七种检测算法,该算法取得较好的检测性能。

无人机多光谱影像的小麦倒伏信息多特征融合检测研究

为探究多特征融合方法在作物倒伏领域快速精准识别中的适用性,利用无人机获取多田块冠层尺度的不同倒伏率麦田多光谱数据,对原始倒伏图像进行图像拼接、辐射校正、几何校正等预处理,并利用重归一化差值植被指数和阴影指数分别剔除土壤和阴影背景,提取小麦倒伏DSM模型和植被指数分别与多光谱图像进行多特征图像主成分变换融合,筛选差异性较大的纹理特征,采用支持向量机(SVM)、人工神经网络(ANN)和最大似然法(MLC)监督分类模型对多光谱和DSM融合图像、多光谱和归一化植被指数(NDVI)融合图像、多光谱图像和纹理特征图像进行监督分类,并采用总体精度(OA)、 Kappa系数和提取误差综合评价各监督模型的分类性能和倒伏提取精度。分类结果表明:各监督分类方法在不同倒伏区域提取结果建模效果趋势一致,SVM和ANN整体提取精度高于MLC,在高倒伏区域,多光谱与NDVI融合图像的SVM监督模型(OA:92.63%, Kappa系数:0.85,提取误差:1.11%)提取效果最好;在中倒伏区域,多光谱与DSM融合图像的SVM监督模型(OA:90.35%, Kappa系数:0.79,提取误差:9.34%)提取效果最好;在低倒伏区域,均值纹理特征图像的ANN监督模型(OA:91.05%, Kappa系数:0.82,提取误差:8.20%)提取结果较好。本研究将DSM模型、植被指数、纹理特征与多光谱图像进行融合对比,并对多特征融合方法能否高精度有效提取小麦倒伏信息进行了探究,结果表明无人机多光谱遥感结合特征融合技术能有效提取小麦倒伏面积,提取效果优于单特征小麦倒伏图像。本研究结果可为助力小麦倒伏灾情调查数据的精确获取方法提供参考。

中国式现代化视域下铸牢中华民族共同体意识探索

走中国式现代化发展道路,必将为铸牢中华民族共同体意识奠定坚实的物质基础,反之,铸牢中华民族共同体意识也将为实现中国式现代化提供强大的精神动力。为了确保铸牢中华民族共同体意识这一民族工作的“纲”得到有效落实,必须了解其核心——中华民族的基本特征,这是提升铸牢中华民族共同体意识自觉性、自信心的前提和基础。高质量城镇化和实施乡村振兴战略是促进各民族大杂居状态充分实现、不断提升铸牢中华民族共同体意识的有力助推;实现共同富裕是增强各族人民自信心、自豪感,不断提升中华民族共同体的向心力和凝聚力的物质基础;大力传承弘扬以社会主义核心价值观为引领的中华优秀文化,则是铸牢中华民族共同体意识的强大精神纽带。

多源遥感数据在战场环境智能态势感知的现状及展望

面对复杂战场环境信息的快速变化,及时全面准确地掌握复杂战场环境态势,是未来打赢信息化战争的前提条件。为了探索多源遥感数据对战场环境态势感知的发展方向,基于广泛调研战场环境态势感知的遥感需求,首先对战场环境态势智能感知的概念和内涵进行了阐述,然后对多源遥感数据的处理与战场态势感知应用现状进行概述,分析了遥感在战场态势智能感知面临的挑战,最后提炼总结了遥感在战场态势感知的进展和发展趋势。遥感为战场环境态势感知提供多种观测数据,遥感与人工智能算法结合可以为军事战场环境态势感知提供多种应用,为指挥人员决策提供数据支持,但仍然存在大量挑战需要解决。在分析现阶段成果和面临挑战的基础上,提出了遥感在战场环境智能态势感知的前瞻,指出了基于遥感的深度学习可解释性和智慧战场是遥感在战场智能态势感知发展所面临的困难。

可重构智能表面辅助通信系统时变级联信道估计

针对可重构智能表面(RIS)辅助通信系统时变级联信道的估计中需解决的级联信道稀疏表示、时变信道参数跟踪和信号重构等关键问题,提出了一种结合Khatri-Rao积的分层贝叶斯卡尔曼滤波(KR-HBKF)算法。该算法首先利用信道的稀疏特性,通过Khatri-Rao积和克罗内克积变换得到RIS级联信道的稀疏表示,将RIS级联信道估计问题转化为低维度的稀疏信号恢复问题。然后,根据RIS级联信道的状态演化模型,在HBKF算法的预测模型中引入了时间相关性参数,应用改进的HBKF解决时变信道参数跟踪和信号重构问题,完成时变级联信道的估计。KR-HBKF算法综合利用了信道的稀疏性和时间相关性,能以较小的导频开销获得更好的估计精度。仿真结果表明,与传统压缩感知算法相比,所提算法具有约5 dB的估计性能提升,且在不同的时变信道条件下具有更好的鲁棒性。

基于深度学习的认知物联网频谱感知算法研究

针对认知物联网(Internet of Things, IoT)对低信噪比(Signal to Noise Ratio, SNR)的频谱感知性能低下以及传统卷积神经网络(Convolutional Neural Network, CNN)频谱感知方法提取数据特征不充分导致感知性能差等问题,提出了一种改进残差网络——ResNeXt的单节点频谱感知算法,ResNeXt只需要设置少量超参数且高度模块化,将该网络在图像处理上的优势应用在频谱感知问题上,先将接收信号转成二维矩阵并归一灰度化处理,得到灰度图像作为网络的输入。通过训练ResNeXt来提取灰度图像特征,将在线数据输入完成频谱感知。将各个次用户(Secondary User, SU)得到的评分向量矩阵直接用融合中心SoftCombinationNet(SCN)融合获得协作频谱感知结果,有效解决了传统硬融合方法检测性能低、软融合处理复杂等问题。实验结果表明,所提方法在低SNR仍能实现低虚警率、高检测概率,优于传统频谱感知方法。

基于准连续域束缚态的全介质超构表面双参数传感器

本文设计了由不对称半圆柱对阵列组成的全介质超构表面,获得了两个高品质因子的准连续域束缚态模式(quasi-bound states in the continuum,QBIC).通过选择不同形式的对称破缺,在近红外频段均可产生两个稳健的QBIC,并且二者的谐振波长、品质因子、偏振依赖等表现出不同的特性.模拟计算表明,通过测量两个QBIC的谐振波长,能够实现折射率和温度的双参数传感;通过调节不对称参数,利用QBIC的品质因子依赖于不对称参数的二次方反比关系,理论上能够提高品质因子到任意的数值,从而实现传感性能的提升和调节.该超构表面的折射率传感灵敏度、品质因子和优值分别达到194.7 nm/RIU,45829和8197,其温度传感灵敏度达到24 pm/℃.

文化生态保护区建设推进铸牢中华民族共同体意识

文化生态保护区建设贯穿人民生产生活行动,体现了非物质文化遗产从区域整体保护到中华民族共同体建设的文化逻辑。各族人民以非物质文化遗产突出各民族共有共享的中华文化符号体系和价值理念。文化生态保护区建设表现出各民族共同生活之上兼收并蓄的特性,为铸牢中华民族共同体意识提供丰沛资源。文化生态保护区建设就是以各种形式的非物质文化的活化实践来提升各族人民生活质量的过程。以非物质文化遗产为纽带将各族人民凝聚起来,在于强调共同性,在尊重和包容差异性中构筑中华民族共有精神家园,进而发挥文化生态保护区建设推进铸牢中华民族共同体意识的强劲动力。

深度卷积网络在航空高光谱岩性识别中的应用——以塔木素铀矿床北部地区为例

岩矿信息识别是高光谱遥感在地质勘探领域的主要应用方向。传统高光谱遥感方法尽管在矿物识别中取得了良好效果,但对于岩性识别存在瓶颈。深度学习是当前人工智能领域的研究热点,卷积神经网络是适用于图像识别的重要网络架构。以巴音戈壁盆地西部塔木素铀矿床北部区域为试验区,以SASI航空高光谱影像为数据源,将深度卷积神经网络引入航空高光谱遥感岩性识别,测试和评估其应用效果。基于预处理后的SASI航空高光谱影像,以试验区地质图及野外调查为参考,制作了8类样本,包括:印支期花岗岩、华力西晚期花岗岩、华力西晚期花岗闪长岩、华力西中期石英闪长岩、石炭系碎屑岩、中下侏罗统火山凝灰岩、第四系沉积物和绢云母化蚀变岩。构建了基于光谱特征的一维卷积神经网络、基于图-谱联合特征的一维+二维卷积神经网络和三维卷积神经网络3种模型结构,分别进行模型训练、测试和试验区岩性分类应用。模型测试结果表明:一维卷积神经网络、一维+二维卷积神经网络和三维卷积神经网络的总体精度分别为82.13%、86.46%和90.90%。通过评价分析三种卷积神经网络模型的岩性分类识别结果,三维卷积神经网络的识别结果与真实参考最为接近,对试验区各类岩性的区分识别效果最优,一维+二维卷积神经网络的识别效果次之,表明利用卷积神经网络引入高光谱图像空间信息,进行图-谱特征的联合挖掘,有利于提高影像的识别精度和实际应用效果。同时,一维卷积神经网络和一维+二维卷积神经网络的识别结果因航空高光谱影像拼接后的条带效应,影响了它们的实际应用效果,而三维卷积神经网络较好地克服了这种影响,表明其对于大面积航空影像处理具有相对较好的应用前景。

面向6G通信感知一体化的40~50 GHz毫米波信道反射和透射特性研究

为解决毫米波信道反射和透射特性测量数据不足、多层材料传播系数计算不准确和传播特性表征不明的问题,该文开展面向6G通信感知一体化(ISAC)的40~50 GHz毫米波信道反射和透射特性研究。首先,基于菲涅尔理论和射线弹跳追踪原理,提出一种室内多层建筑材料传播系数计算方法;然后,利用基于矢量网络分析仪的毫米波信道测量平台,开展40~50 GHz频率范围内多层木板和多层玻璃的反射和透射系数测量活动。结果表明,该方法与测量值间高度吻合,传播系数误差低于0.1,能够准确地刻画毫米波信道反射和透射特性变化规律。此外,研究还发现反射系数谐振特性和有效布儒斯特角特性依赖于电波极化、入射角和材料厚度。

用户体验视角下的文创产品互动设计研究

目的 基于用户体验视角探究文创产品的设计,尤其是探索文创产品的互动性设计策略,以优化文创设计,创造能更符合用户需求、更好地满足用户体验的产品。方法 采用文献资料法对当前用户体验视角下的文创产品研究现状展开研究,并采用案例法进一步了解基于用户体验的文创设计成功案例,为设计提供参考。结论 用户体验包括感官体验、行为体验、情感体验、思考体验和关联体验,文创产品设计应该关注用户体验,设计符合用户需求的、具有互动性的文创产品。

基于感通算融合和信息年龄优化的车联网多节点协同感知

面向未来自动驾驶系统中的实时性业务需求(如高清地图更新),基于感知-通信-计算融合,引入信息年龄作为实时性度量,设计感通算融合的车联网多节点协同感知机制。在通信-计算资源和车辆能耗约束下,优化调度感知节点信息采集和传输处理,最小化感知信息的平均信息年龄;提出基于李雅普诺夫的在线调度算法,将复杂的长期随机优化问题转化为单时隙在线优化问题,并设计低复杂度算法求解。仿真表明,与现有仅考虑通信与计算融合的机制相比,所提机制信息实时性可提高9%~50%。