Facile synthesis of hierarchical NaX zeolite from natural kaolinite for efficient Knoevenagel condensation

Zeolite catalysts have found extensive applications in the synthesis of various fine chemicals.However,the micropores of zeolites impose diffusion limitations on bulky molecules,greatly reducing the catalytic efficiency.Herein,we explore an economic and environmentally friendly method for synthesizing hierarchical NaX zeolite that exhibits improved catalytic performance in the Knoevenagel condensation reaction for producing the useful fine chemical 2-cyano-3-phenylacrylate.The synthesis was achieved via a low-temperature activation of kaolinite and subsequent in-situ transformation strategy without any template or seed.Systematic characterizations reveal that the synthesized NaX zeolite has both intercrystalline and intra-crystalline mesopores,smaller crystal size,and larger external specific surface area compared to commercial NaX zeolite.Detailed mechanism investigations show that the inter-crystalline mesopores are generated by stacking smaller crystals formed from in-situ crystallization of the depolymerized kaolinite,and the intra-crystalline mesopores are inherited from the pores in the depolymerized kaolinite.This synthesis strategy provides an energy-saving and effective way to construct hierarchical zeolites,which may gain wide applications in fine chemical manufacturing.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

Structural Engineering of Hierarchical Aerogels Comprised of Multi-dimensional Gradient Carbon Nanoarchitectures for Highly Efficient Microwave Absorption

Recently,multilevel structural carbon aerogels are deemed as attractive candidates for microwave absorbing materials.Nevertheless,excessive stack and agglomeration for low-dimension carbon nanomaterials inducing impedance mismatch are significant challenges.Herein,the delicate“3D helix-2D sheet-1D fiber-0D dot”hierarchical aerogels have been successfully synthesized,for the first time,by sequential processes of hydrothermal self-assembly and in-situ chemical vapor deposition method.Particularly,the graphene sheets are uniformly intercalated by 3D helical carbon nanocoils,which give a feasible solution to the mentioned problem and endows the as-obtained aerogel with abundant porous structures and better dielectric properties.Moreover,by adjusting the content of 0D core-shell structured particles and the parameters for growth of the 1D carbon nanofibers,tunable electromagnetic properties and excellent impedance matching are achieved,which plays a vital role in the microwave absorption performance.As expected,the optimized aerogels harvest excellent performance,including broad effective bandwidth and strong reflection loss at low filling ratio and thin thickness.This work gives valuable guidance and inspiration for the design of hierarchical materials comprised of dimensional gradient structures,which holds great application potential for electromagnetic wave attenuation.

Hierarchical Domain Assignment BaseC on Word-Gloss

This paper proposes a hierarchical word domain assignment algorithm to automatically build domain dictionaries from Machine-Readable Dictionary(MRD).The process for word domain assignment can be divided into three steps:1) Hierarchical structure constructing;2) Classifier training;3) Word domain assigning.Compared with the traditional methods,the hierarchical word domain assignment algorithm enhances the accuracy of word domain assignment while reducing human efforts on collecting corpus.Experiments on WordNet 2.0 show that 62.53% of the first domain labels are matched with the WordNet Domains 3.0 by using gloss-based word domain assignment,and the performance can be further improved by utilizing the hierarchical relationships among the domain sets.

A Hierarchical Combination Method for Computer Understanding of Chinese Language

On the basis of the characteristics of Chinese language such.as simple and uniform structure, distinct hierarchy and construction by word order and function words, and in the view of the human cognitive mechanism, a hierarchical combination method for computer understanding of Chinese language is put forward in this paper. By this method, the whole information of a sentence is hierarchically combined from the partial information of the basic units of it, with the unification operation under attribute description frames. This method is perfect in combining syntax analysis with semantic analysis, easy to implement, and very suitable for the computer understanding system for processing Chinese language.

利用BERT和覆盖率机制改进的HiNT文本检索模型

为有效提升文本语义检索的准确度,本文针对当前文本检索模型衡量查询和文档的相关性时不能很好地解决文本歧义和一词多义等问题,提出一种基于改进的分层神经匹配模型(hierarchical neural matching model,HiNT)。该模型先对文档的各个段提取关键主题词,然后用基于变换器的双向编码器(bidirectional encoder representations from transformers,BERT)模型将其编码为多个稠密的语义向量,再利用引入覆盖率机制的局部匹配层进行处理,使模型可以根据文档的局部段级别粒度和全局文档级别粒度进行相关性计算,提高检索的准确率。本文提出的模型在MS MARCO和webtext2019zh数据集上与多个检索模型进行对比,取得了最优结果,验证了本文提出模型的有效性。

基于句子层次结构的语义句模研究

汉语句模对计算机在处理自然语言时具有重要作用,可以使其更好地理解和分析汉语文本、抽取文本信息,提高自然语言处理的准确性和效率。但由于中文文本表达存在一定的灵活性和多样性,使得目前现有的句模存在匹配精度低、句模数量多、难以完全覆盖所有句子等问题,阻碍了句模在具体领域实现广泛应用和发展。针对以上的不足,文章提出基于句子层次结构的语义句模(HSST)。该句模以句子是嵌套的、有层次结构的,句子结构并非单向线性的为构建依据,使用多个句模且根据句子的层次结构对这些句模进行组合,大大减少了句模的数量,提高句模的匹配精度,并使得其对中文文本的覆盖面更广,使机器能更准确有效地理解与抽取文本语义信息。

基于多维语义特征与层次注意力机制的讽刺识别

讽刺是一种复杂的语言表达方式,在日常交流中发挥着重要作用。随着人工智能和社交网络的快速发展,讽刺识别已成为自然语言处理领域的热点研究课题之一。现有的讽刺识别研究往往从单一维度对讽刺文本特征进行表示,忽视了讽刺文本特征的细微差异及其重要程度。本文将讽刺识别视为文本分类任务,在特征提取阶段,将讽刺文本根据其不一致性特征、情感特征、句法结构特征和风格特征进行多维语义特征表示。在特征融合阶段,针对不同维度特征对整体特征贡献和关联程度不同,采用层次注意力机制调整不同讽刺语言学特征对模型整体性能的影响。实验结果表明,所提出的模型能够从多个维度提取讽刺文本的潜在语义特征,其在公开数据集IAC、Tweets和Reddit上的实验性能均有明显提升。

融合异构信息的自动国际疾病分类编码方法

针对自动国际疾病分类(ICD)编码中医学电子健康记录(EHR)的结构多样性以及编码间复杂的关联关系等特点,提出一种融合异构信息的自动ICD编码方法AIC-HI(Automatic ICD Coding integrating Heterogeneous Information)。首先,针对编码任务中结构化编码、半结构化描述、非结构化医学文本这3种异构数据的不同特性设计了多种特征提取器;其次,构建编码知识图谱拟合编码的层次结构关系,将不同分支间关联关系转化为包含头尾编码的三元组;再次,运用表征学习融合编码和描述信息计算标签特征;最后,通过注意力机制提取在非结构化文档中与编码标签最为相关的特征表示。实验结果表明,与次优的基线模型MARN(Multitask bAlanced and Recalibrated Network)相比,AIC-HI在真实临床数据集MIMIC-Ⅲ上所有编码的微观F1值提升了4.3个百分点。

大型企业自然灾害应急管理评估模型构建

根据自然灾害应急管理工作实际,基于鱼骨分析原理,构建了大型企业自然灾害应急管理评估指标体系,在此基础上,基于层次分析和模糊评价理论,建立了自然灾害应急管理评估模型。以某大型企业A为例,对自然灾害应急管理体系进行系统评估,梳理存在的问题与薄弱环节,提出加强和完善A企业自然灾害应急管理体系的对策和建议。研究成果可为大型或超大型企业自然灾害应急管理评估提供一定的理论和技术支撑。

Geological reservoir and resource potential(10^(13)m^(3))of gas hydrates in the South China Sea

A detailed understanding of the distribution and potential of natural gas hydrate(NGHs)resources is crucial to fostering the industrialization of those resources in the South China Sea,where NGHs are abundant.In this study,this study analyzed the applicability of resource evaluation methods,including the volumetric,genesis,and analogy methods,and estimated NGHs resource potential in the South China Sea by using scientific resource evaluation methods based on the factors controlling the geological accumulation and the reservoir characteristics of NGHs.Furthermore,this study compared the evaluation results of NGHs resource evaluations in representative worldwise sea areas via rational analysis.The results of this study are as follows:(1)The gas hydrate accumulation in the South China Sea is characterized by multiple sources of gas supply,multi-channel migration,and extensive accumulation,which are significantly different from those of oil and gas and other unconventional resources.(2)The evaluation of gas hydrate resources in the South China Sea is a highly targeted,stratified,and multidisciplinary evaluation of geological resources under the framework of a multi-type gas hydrate resource evaluation system and focuses on the comprehensive utilization of multi-source heterogeneous data.(3)Global NGHs resources is n×10^(15)m^(3),while the NGHs resources in the South China Sea are estimated to be 10^(13)m^(3),which is comparable to the abundance of typical marine NGHs deposits in other parts of the world.In the South China Sea,the NGHs resources have a broad prospect and provide a substantial resource base for production tests and industrialization of NGHs.

考虑参数权重与分层映射的光伏组件健康程度检测

光伏系统的健康程度检测对预防故障及保障系统安全运行至关重要。选取光伏组件的光生电流、串联电阻和并联电阻为其健康参数,该文提出了一种考虑健康参数权重与辐照度分层映射的光伏组件健康程度检测方法,来提高健康程度检测的精度与适用范围。首先依据光伏组件I-V曲线,通过蝠鲼觅食优化(MRFO)算法进行参数辨识;其次根据辐照度高低将辨识结果分层映射至两种辐照度参考状态,提高低辐照状态下的映射精度;然后根据组件预期寿命内允许的功率衰减情况建立光伏组件自然衰减模型,估算参数预期值作为参考,再结合实际的参数提取结果计算参数劣化度;最后使用熵权法确定参数权重,计算表征光伏组件健康程度的健康指数。仿真与实验结果表明,该方法可对任何环境参数下的光伏组件健康程度进行有效检测。

东莞市自然保护区保护评估体系构建与应用

自然保护区是我国重要的生态屏障,充分发挥自然保护区的保护成效是实现生物多样性保护目标的关键,开展保护成效评估对促进自然保护区的有效管理和宏观决策具有重要意义。文章以东莞市6个自然保护区为研究对象,基于现有研究成果和技术标准规范,采用层次分析法构建了包括3个基准层、10个因素层和28个指标层的评估体系和综合评估模型,对东莞市自然保护区的保护成效进行评估。结果表明,东莞市16.67%的自然保护区评估为A级,66.66%的自然保护区评估为B级,16.67%的自然保护区评估为C级;东莞市的自然保护区网络对区内自然生境景观的保护效益显著,减缓了边界范围内景观破碎化趋势。该研究可为地市级区域内的自然保护区保护成效评价方法的探索提供参考,研究结果可为地市级区域内自然保护区保护成效提升与优化调整提供科学支撑。

在产房质量控制中应用助产士分级授权管理模式的效果分析

目的:探讨助产士分级授权管理模式在产房质量控制中的应用效果。方法:将临沂市妇幼保健院2021年1月—2022年12收治的产房产妇80例作为研究对象,抽签法分为两组。对照组给予常规护理,试验组给予助产士分级授权管理模式。比较两组护理前后产妇负面情绪积分、产妇护理满意度、自然分娩率、会阴侧切率、纠纷发生率。结果:试验组产妇负面情绪积分低于对照组,产妇护理满意度高于对照组,自然分娩率高于对照组,会阴侧切率、纠纷发生率低于对照组(P<0.05)。结论:产房产妇实施助产士分级授权管理模式效果确切,可以有效提高自然分娩率,降低会阴侧切率、纠纷发生率,减轻产妇的焦虑和抑郁情绪,提高产妇对助产护理服务的满意度。

基于层次化主题分析的铁路敏感数据智能识别与分类分级方法

为了对铁路不同敏感等级数据实施差异化保护,文章提出了一种基于层次化主题分析的铁路敏感数据智能识别与分类分级方法,旨在为铁路网络数据分级保护提供依据。该方法利用数据语义和分类分级规则建立主题词库,通过主题分析初步判断数据敏感级别。考虑到铁路网络数据的敏感级别分布不平衡,设计分级概率向量加权聚合机制,利用凝聚层次聚类算法实现准确定级。经实验验证,与基于语义和K-means聚类的传统主题分析方法相比,该方法可有效缓解分布不平衡问题,实现细粒度、动态可调整的铁路敏感数据智能识别与准确定级,从而为落实铁路网络数据分级管理要求、确保铁路网络数据安全可控提供技术支撑。

基于层次分析法和GIS的地质灾害易发性评价

结合层次分析法,选取地层岩性、地质构造、降雨、地形坡度、NDVI(归一化植被指数)、人类工程活动6项评价指标,构建五峰县地质灾害易发性评价指标体系,进行地质灾害易发性评价和分区。结果表明:地质灾害中高易发区约占31.40%,主要分布于河流流域及附近的斜坡地带、道路和断裂构造带两侧、工程活动密集区;低易发区约占68.60%,主要分布在山间谷地、植被覆盖率高、工程活动较少的区域;评价结果可为五峰县地质灾害区划和预防提供参考。

Evaluation and reasonable exploitation on the natural view resources of Lushan Mountain scenic spot

The paper carried on the classified and rating evaluation primarily on natural landscape resources in Lushan Mountain. According to the evaluation, exploiting and utilizing the situation of scenic spot natural landscape resources, some reasonable advices were given on further exploiting Lushan Mountain natural scenic spot, expecting that it could supply some theoretical references for the natural landscape resources sustainable development in Lushan Mountain in the future.

Natural Selection and Thermodynamics of Biological Evolution

The author of this article proposes that the representation of Charles Darwin and Alfred Wallace’s theory on “variation and selection” in the living world is a reflection of the action of hierarchical thermodynamics. Hierarchical thermodynamics is based on the law of temporal hierarchies and on the principle of substance stability. This principle enables the transmission of thermodynamic information between lower and higher structural hierarchies, in both forward and reverse direction: from nucleic acids to higher structural hierarchies and back. The principle of substance stability, in fact, is the main dynamical and thermodynamic mechanism of natural selection. It is alleged that the natural selection of atoms, molecules, organisms, populations, and other hierarchical structures takes place under the action of a variety of internal factors within organisms and the external environmental factors that can be considered as tropisms. Forms (design) of living organisms are formed as a result of spontaneous and non-spontaneous processes that lead to the adaptation of living systems to the environment. The selection is carried out as a result of the impacts of different energy types and the principle of substance stability at all levels of hierarchical structures. Actions of tropisms are presented by various members of the generalized Gibbs equation.

A Hierarchical Two-Level Feature Fusion Approach for SMS Spam Filtering

SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam.The system comprises five steps,beginning with the preprocessing of SMS data.RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis.Feature extraction is performed using a Convolutional Neural Network(CNN)for word-level analysis and a Bidirectional Long Short-Term Memory(BiLSTM)for sentence-level analysis.The two-level feature extraction enables a complete understanding of individual words and sentence structure.The novel part of the proposed approach is the Hierarchical Attention Network(HAN),which fuses and selects features at two levels through an attention mechanism.The HAN can deal with words and sentences to focus on the most pertinent aspects of messages for spam detection.This network is productive in capturing meaningful features,considering both word-level and sentence-level semantics.In the classification step,the model classifies the messages into spam and ham.This hybrid deep learning method improve the feature representation,and enhancing the model’s spam detection capabilities.By significantly reducing the incidence of SMS spam,our model contributes to a safer mobile communication environment,protecting users against potential phishing attacks and scams,and aiding in compliance with privacy and security regulations.This model’s performance was evaluated using the SMS Spam Collection Dataset from the UCI Machine Learning Repository.Cross-validation is employed to consider the dataset’s imbalanced nature,ensuring a reliable evaluation.The proposed model achieved a good accuracy of 99.48%,underscoring its efficiency in identifying SMS spam.

基于GIS的河南省沿黄区域农业面源污染分析

【目的】探究河南省沿黄区域农业面源污染空间分布及重点污染源的排放特征,为黄河流域农业面源污染治理和高质量发展提供依据。【方法】基于河南省DEM 90 m(数字高程模型地图)数据和河南省行政区划图,采用ArcGIS水文分析方法,确定以灵宝市等25个县(市、区)为研究区域,运用排污系数法、等标污染负荷法计算并分析了河南省沿黄区域25个县(市、区)化学需氧量(COD)、氨氮(NH_(3)-N)、总氮(TN)和总磷(TP)4种污染物的排放量、排放强度以及等标污染负荷,在对畜禽养殖业、种植业及水产养殖业3个污染源进行全面分析基础上,将河南省沿黄区域的污染负荷与河南省总污染负荷进行宏观对比得出的重点污染源,并通过ArcGIS自然断裂法和层次聚类分析法反映区域内污染排放分布特征。【结果】河南省沿黄区域25个县(市、区)COD、NH_(3)-N、TN和TP 4种污染物的总排放量分别为16.22×10^(4)、0.23×10^(4)、1.64×10^(4)和0.25×10^(4) t,其中,TN的等标污染负荷比率最高,为41.67%;污染物主要源自畜禽养殖业,等标污染负荷比率为70.52%;同时,河南省沿黄区域畜禽养殖业和水产养殖业在总负荷中的比率高于河南省。COD、NH_(3)-N和TP在总负荷中的比率高于河南省。空间上,污染负荷较大的区域主要分布在黄河北岸,黄河南岸的污染负荷分布呈“西低东高”的趋势。沿黄区域农业面源污染排放特征可划分为畜禽养殖业主导污染Ⅰ型、畜禽养殖业主导污染Ⅱ型、水产养殖业较高占比Ⅰ型和水产养殖业较高占比Ⅱ型4种污染类型区。【结论】河南省沿黄区域重点污染源为种植业源和畜禽养殖业,重点污染物为TN,TP和COD;畜禽养殖业源污染是需要优先控制的污染源,COD是需要优先控制的污染物。