A Study on the Correlation Between LIWC Word Categories and Chinese Composition Writing Performance of Fourth,Fifth,and Sixth Grade Students

This study focuses on the analysis of the Chinese composition writing performance of fourth,fifth,and sixth grade students in 16 selected schools in Longhua District,Shenzhen during the spring semester of 2023.Using LIWC(Linguistic Inquiry and Word Count)as a text analysis tool,the study explores the impact of LIWC categories on writing performance which is scaled by score.The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students;while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students.Process word categories have a positive influence on the composition scores of all three grades,but the impact of complex process word categories increases as the grade level rises.

Scientific and Technological Civilization and Confucian Culture: Views of Modern Neo-Confucianism

In this article,the author intends to make a summary and brief criticism with respect to the appraisals of modern scientific and technological civilization and demonstrations of resurrecting Confucian culture performed by the representatives of modern Neo-Confucianism.

Association between cardiorespiratory fitness level and insulin resistance in adolescents with various obesity categories

BACKGROUND An association between cardiorespiratory fitness(CRF)and insulin resistance in obese adolescents,especially in those with various obesity categories,has not been systematically studied.There is a lack of knowledge about the effects of CRF on insulin resistance in severely obese adolescents,despite their continuous rise.AIM To investigate the association between CRF and insulin resistance in obese adolescents,with special emphasis on severely obese adolescents.METHODS We performed a prospective,cross-sectional study that included 200 pubertal adolescents,10 years to 18 years of age,who were referred to a tertiary care center due to obesity.According to body mass index(BMI),adolescents were classified as mildly obese(BMI 100% to 120% of the 95^(th)percentile for age and sex)or severely obese(BMI≥120% of the 95^(th)percentile for age and sex or≥35 kg/m^(2),whichever was lower).Participant body composition was assessed by bioelectrical impedance analysis.A homeostatic model assessment of insulin resistance(HOMA-IR)was calculated.Maximal oxygen uptake(VO_(2)max)was determined from submaximal treadmill exercise test.CRF was expressed as VO_(2)max scaled by total body weight(TBW)(mL/min/kg TBW)or by fat free mass(FFM)(mL/min/kg FFM),and then categorized as poor,intermediate,or good,according to VO_(2)max terciles.Data were analyzed by statistical software package SPSS(IBM SPSS Statistics for Windows,Version 24.0).P<0.05 was considered statistically significant.RESULTS A weak negative correlation between CRF and HOMA-IR was found[Spearman’s rank correlation coefficient(rs)=-0.28,P<0.01 for CRF_(TBW);(r_(s))=-0.21,P<0.01 for CRF_(FFM)].One-way analysis of variance(ANOVA)revealed a significant main effect of CRF on HOMA-IR[F(2200)=6.840,P=0.001 for CRF_(TBW);F_((2200))=3.883,P=0.022 for CRF_(FFM)].Subsequent analyses showed that obese adolescents with poor CRF had higher HOMA-IR than obese adolescents with good CRF(P=0.001 for CRF_(TBW);P=0.018 for CRF_(FFM)).Two-way ANOVA with Bonferroni correction confirmed significant effect of interaction of CRF level and obesity category on HOMA-IR[F_((2200))=3.292,P=0.039 for CRF_(TBW)].Severely obese adolescents had higher HOMA-IR than those who were mildly obese,with either good or poor CRF.However,HOMA-IR did not differ between severely obese adolescents with good and mildly obese adolescents with poor CRF.CONCLUSION CRF is an important determinant of insulin resistance in obese adolescents,regardless of obesity category.Therefore,CRF assessment should be a part of diagnostic procedure,and its improvement should be a therapeutic goal.

Aesthetical Meanings of the Central Notion Ti Ren in Cultivation Theory of Neo-Confucianism

Ti Ren體認is a commonly used and very important notion in traditional Chinese philosophy and intellectual history,nevertheless with its connotation unclear in the ancients’discourses.This text tries to analyze the method and process of this notion via the perspective of Neo-Confucianism in Song Dynasty.The basic hypothesis of this research is that Ti Ren is the basis of the cultivation theory that helps confucians improve their moral integrity.Under such assumption,this text proposes the core connotation of Ti Ren in Neo-Confucianism lies in personal grasp of the heavenly principle.Based on this,from Zhou Dunyi to Shao Yong,Zhang Zai and the Cheng brothers,this kind of grasp gradually developed into a clear clue from appreciation of objects to that of inner mind,and then epitomized by Zhu Xi.Zhu Xi’s Ti Ren consists of two approaches,extroversive and introversive,and built a way of self-cultivating to be the saint from methods like investigation of things and reading books to the realization of grasping saints’minds.The end of the road is the aesthetic world of eagles soaring in the sky and fishes diving in water,the great pleasure,and on passage,it is overflowing with both physical and spiritual feelings,so that we know Ti Ren possesses aesthetic meanings.Further,based on the differentiation and analysis of the connotation of this concept,this text preliminarily compares Zhu Xi’s Ti Ren with Gadamer’s“Erlebnis”,promoting the possibility of dialogue between east and west philosophy.

Profile of the Functional Categories of Food Additives in Industrial Foods Marketed in Senegal

Food additives, whether natural or artificial substances, are widely used around the world to improve the sensory quality of products, extend their shelf life and make them more competitive. However, the abusive and uncontrolled consumption of food additives is the cause of numerous illnesses and diseases such as poisoning, allergies, diabetes and numerous cancers. So, in addition to setting up control and regulatory bodies, it is becoming essential to keep a constant watch on the presence of food additives on the market. The aim of this study is to highlight the main categories of food additives in food products frequently sold on the Senegalese market. The methodology of the study is based on the identification of food additives from the information given on the labels of food packaging. Information was collected in markets in two (2) major regions of Senegal: Dakar and Saint-Louis. The results of our study show the presence of 153 food additives on the labels of 514 samples collected. Moreover, the frequency and diversity of additives depended on the food category. On the other hand, beyond their important technological and functional roles, some additives such as aspartame and monosodium glutamate have been implicated in pathologies, and others, such as titanium dioxide, are the subject of much controversy and even withdrawal in certain legislations for their impacts deemed potentially negative on consumer health.

Refined Sparse Representation Based Similar Category Image Retrieval

Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.

A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

基于微信平台的健康信息行为研究:基本特征、主题范畴与展望

目的/意义梳理基于微信平台的健康信息行为研究现状,把握研究动态、拓宽媒介视角。方法/过程系统搜集国内外文献,运用内容分析法统计其发文趋势、研究方法和研究对象,归纳分析主题范畴并展望未来发展方向。结果/结论问卷调查法和访谈法广泛应用于微信平台健康信息行为研究中;老年微信用户和公众号功能成为热点研究对象;主要研究主题可归纳为健康信息使用行为、健康信息采纳行为、健康信息传播行为和持续使用行为4个范畴。未来应进一步丰富研究方法、细分研究对象、厘清主题内涵及外延。

驾驶场景下结合运动速度以及外观特征的多类多目标跟踪方法

基于相机传感器的多目标跟踪算法是自动驾驶的重要组成部分。驾驶场景下,基于交并比进行前后帧关联的方案一直存在大量的身份切换,此现象在对向来车以及自车转弯时更加明显。本文将目标在二维平面上的运动速度作为扩展匹配空间的变量,设计了基于目标速度变化的交并比算法:Velocity IoU,从而优化前后帧目标关联。同时,使用自监督的外观模型提取不同目标的外观特征编码。基于上述的运动模型以及外观模型,提出了一种互补的关联策略,最终实现驾驶场景下多类别多目标跟踪。在BDD100K上验证了该方法,对应mMOTA为45.2,mIDF1为55.2,IDs为8793,优于大部分跟踪算法。

“数据”可否纳入知识产权客体范畴?

《民法总则》(草案)第108条将“数据信息”纳入知识产权客体范畴,虽然该规定最终未获通过,但却引发了学界和业界的热议,数据可否纳入知识产权客体范畴的讨论方兴未艾。根据“信息链”理论,事实、数据、信息、知识和情报之间是呈现一种由下至上逐步提炼加工的链式关系,数据与信息、知识、情报之间依次包含。但数据并不符合知识产权客体的一般特征,若将“数据”整体直接纳入知识产权客体的范畴,会冲击已有的权能和体系,影响知识产权客体家族的统一性和协调性。应对数据进行分类保护,在现有知识产权保护基础之上,增设数据产权保护模式,数据主体就个人原始数据享有数据人格权和数据财产权;企业就其加工处理取得的衍生数据享有数据财产权,企业这种财产权利取得的门槛低于知识产权取得,不必要求独创性、创造性或显著性等知识产权客体的一般特征,宜采取弱保护模式。

模糊集范畴

在模糊集的理论和应用研究中,一个基本问题是怎样把模糊集组织成一个范畴,相应的范畴具有什么样的性质?这一问题自Zadeh引入模糊集的概念之后,一直受到人们的关注.简要介绍2个具有代表性的解决方案,其一是由Goguen提出的Goguen范畴,其二是受topos理论影响发展起来的Q-集理论.这2种方案差异很大,Goguen范畴把模糊集的隶属函数看作一个集合到单点集的模糊关系,因而2个模糊集之间的态射就是2个集合之间满足一定条件的模糊关系;Q-集范畴则把隶属函数看作类型函数,因而2个模糊集之间的态射就是2个类型函数之间满足一定条件的模糊关系.这2种方案的共同特点在于充分利用逻辑值域的逻辑结构,这也是模糊集理论区别于其他数学分支的一个基本特征.

ADASYN与类别逆比例加权法在阿尔茨海默病不平衡数据中的应用

目的利用自适应合成抽样(adaptive synthetic sampling,ADASYN)与类别逆比例加权法处理类别不平衡数据,结合分类器构建模型对阿尔茨海默病(alzheimer′s disease,AD)患者疾病进程进行分类预测。方法数据源自阿尔茨海默病神经影像学计划(Alzheimer′s disease neuroimaging initiative,ADNI),经随机森林填补缺失值,弹性网络筛选特征子集后,利用ADASYN与类别逆比例加权法处理类别不平衡数据。分别结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)构建四种模型:ADASYN-RF、ADASYN-SVM、加权随机森林(weighted random forest,WRF)、加权支持向量机(weighted support vector machine,WSVM),与RF、SVM比较分类性能。模型评价指标为宏观平均精确率(macro-average of precision,macro-P)、宏观平均召回率(macro-average of recall,macro-R)、宏观平均F1值(macro-average of F1-score,macro-F1)、准确率(accuracy,ACC)、Kappa值和AUC(area under the ROC curve)。结果ADASYN-RF的分类性能最优(Kappa值为0.938,AUC为0.980),ADASYN-SVM次之。利用ADASYN-RF预测得到的重要分类特征分别为CDRSB、LDELTOTAL、MMSE,在临床上均可得到证实。结论ADASYN与类别逆比例加权法都能辅助提升分类器性能,但ADASYN算法更优。

按病种分类的疾病诊疗难度测度及应用研究

本研究首次界定了按病种分类的疾病诊疗难度的内涵,运用按病种的疾病发生概率、平均总费用与治愈好转率对病种诊疗难度系数进行测算。采用七县(市)区2017年新农合医保报销数据计算住院病种诊疗难度系数,并绘制“病例数—诊疗难度系数”分布图与拟合曲线,结果符合病种发生的实际状况。对其中两个样本县分别计算相同病种的诊疗难度系数,并进行配对资料秩和检验,计算结果不存在显著差异(P=0.73),提示该算法结果稳健,能够较好地描绘病种的发生与分布规律。测度结果能够用于明确医疗机构病种诊疗范围,刻画医疗服务供给与需求行为,但也存在较为依赖样本量、权重确定较为主观等问题。

职业教育科教融汇的动力机制

职业教育科教融汇是科学知识与技术知识、科学技术与教育要素、科学研究与教学过程融合汇聚的过程,是优化职业教育类型定位的新方向,也是建设教育强国、科技强国、人才强国的迫切需要和题中之义。职业教育科教融汇动力机制由引力、行动力、支持力和阻力构成,四种要素相互作用、相互影响,决定科教融汇的方向和速度。当前由于体制机制错位缺位、院校能力整体偏弱、办学主体共建不足、办学实践路径依赖等原因,职业教育科教融汇动力机制存在引力有限、行动力式微、支持力薄弱、阻力突显等阻滞困境。为保障职业教育科教融汇长远可持续发展,需要建立健全体制机制,深化科教融汇发展格局;提升核心办学能力,强化科教融汇内生力量;协同多元主体,聚合科教融汇外部供给;创新发展路径,生成科教融汇特色内容。

基于“学科-科教-产教”多维融合的能源动力拔尖创新人才培养模式探索与实践

在我国“双碳”战略和能源战略的指导下,能源动力类别研究生培养尤为重要。而目前我国能源动力类别研究生培养所面临的主要问题是能源动力类别研究生培养模式与新时代拔尖创新人才要求不匹配,主要问题包括学科背景单一,课程体系陈旧;教学内容陈旧,知识体系不新;实践能力不强,实践环节薄弱。该文针对上述三方面问题开展研究,提出“学科-科教-产教”融合的多维融合思想,构建创新型能源动力类别研究生培养模式,该模式有助于将能源动力类别研究生培养成为新时代拔尖创新人才,满足新时代的迫切需要。该文所提出的思想和培养模式对于能源动力类别研究生培养具有重要的参考价值。

中国式现代化理论的标识性范畴与体系化构建

中国式现代化范畴既有从探索提出到形成确立的逻辑演进过程,也有从简单范畴到范畴体系的逻辑展开过程。中国式现代化是新时代党的理论创新的标识性范畴,但作为一个科学理论体系,中国式现代化理论自身也包含着一系列标识性范畴,这些范畴揭示了中国式现代化的内涵、特征和本质,使中国式现代化更加清晰、科学、可感可行。中国式现代化理论的标识性范畴为构建中国式现代化理论的科学体系,提炼中国式现代化理论的基本原理,提升中国式现代化理论的话语说服力和影响力,进而推进中国式现代化理论的体系化、学理化,奠定了坚实的概念基石和理论基础。提炼并阐明中国式现代化理论的标识性范畴,是构建具有学理性的中国式现代化理论体系的基础工作和必要环节。

能源动力类别研究生产教融合联合培养基地建设模式与实践

培养出满足新时代要求的具有创新精神和实践能力的高水平能源动力类别研究生是我国早日实现“双碳”目标的迫切需求。构建产教融合联合培养基地,是深化产教融合、实现能源动力类别研究生教育高质量发展的关键环节。该文总结长安大学在能源动力类别(动力工程及电气工程)研究生培养中基于产教融合建设联合培养基地的模式及实践工作,提出软-硬件协同构建产教融合联合培养实践基地的理念,具体阐述该理念在动力工程及电气工程等能源动力类别研究生培养中的具体实践与应用,以期为能源动力类别研究生实践创新能力的培养提供参考。

中国画品评中的避忌范畴探赜

中国画论中,历代画家、理论家们探讨的避忌范畴极为繁杂,本文立足于品评标准,将其归纳为“有形病”与“无形病”二类,而“陋劣中有至好”,避忌范畴多具有向正面范畴转化的可能性。论文从以上几个方面对中国画品评中具有代表性的避忌范畴作大致的梳理探析,并揭示其本质特征。

基于信息平台的发热门诊就诊模式优化实践

通过医院信息化建设整合患者诊疗数据,并用于智能服务设备、信息自动采集、信息区域共享。实现基于医院信息平台的发热门诊就诊模式,优化资源配置,减少患者候诊时长,提高患者满意度,同时强化发热门诊在新型病毒监测方面的哨点作用。该模式为发热门诊的整体诊疗流程提供了有力的支持,在改善患者就医体验、节约医院成本、院感防控方面均具有一定意义。