IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Assessing the Levels of Clinical Reasoning Skills Using Self-Assessment of Clinical Reflection and Reasoning in Undergraduate Nursing Students: A Descriptive Comparative Study

Background: Clinical reasoning is a critical cognitive skill that enables undergraduate nursing students to make clinically sound decisions. A lapse in clinical reasoning can result in unintended harm to patients. The aim of the study was to assess and compare the levels of clinical reasoning skills between third year and fourth year undergraduate nursing students. Methods: The study utilized a descriptive comparative research design, based on the positivism paradigm. 410 undergraduate nursing students were systematically sampled and recruited into the study. The researchers used the Self-Assessment of Clinical Reflection and Reasoning questionnaire to collect data on clinical reasoning skills from third- and fourth-year nursing students while adhering to ethical principles of human dignity. Descriptive statistics were done to analyse the level of clinical reasoning and an independent sample t-test was performed to compare the clinical reasoning skills of the student. A p value of 0.05 was accepted. Results: The results of the study revealed that the mean clinical reasoning scores of the undergraduate nursing students were knowledge/theory application (M = 3.84;SD = 1.04);decision-making based on experience and evidence (M = 4.09;SD = 1.01);dealing with uncertainty (M = 3.93;SD = 0.87);reflection and reasoning (M = 3.77;SD = 3.88). The mean difference in clinical reasoning skills between third- and fourth-year undergraduate nursing students was not significantly different from an independent sample t-test scores (t = −1.08;p = 0.28);(t = −0.29;p = 0.73);(t = 1.19;p = 0.24);(t = −0.57;p = 0.57). Since the p-value is >0.05, the null hypothesis (H0) “there is no significantno significant difference in clinical reasoning between third year and fourth year undergraduate nursing students”, was accepted. Conclusion: This study has shown that the level of clinical reasoning skills of the undergraduate nursing students was moderate to low. This meant that the teaching methods have not been effective to improve the students clinical reasoning skills. Therefore, the training institutions should revise their curriculum by incorporating new teaching methods like simulation to enhance students’ clinical reasoning skills. In conclusion, evaluating clinical reasoning skills is crucial for addressing healthcare issues, validating teaching methods, and fostering continuous improvement in nursing education.

Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

Local-to-Global Causal Reasoning for Cross-Document Relation Extraction

Cross-document relation extraction(RE),as an extension of information extraction,requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts.Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity.However,similarity-based methods cannot distinguish valid information from highly similar retrieved documents well.How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue.To address this problem,we design a novel local-toglobal causal reasoning(LGCR)network for cross-document RE,which enables efficient distinguishing,filtering and global reasoning on complex information from a causal perspective.Specifically,we propose a local causal estimation algorithm to estimate the causal effect,which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE.Furthermore,based on the causal effect,we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning.Experimental results under the closed and the open settings of the large-scale dataset Cod RED demonstrate our LGCR network significantly outperforms the state-ofthe-art methods and validate the effectiveness of causal reasoning in confusing information processing.

Intelligent Color Reasoning of IOT Based on P-laws

Aiming at the dynamics and uncertainties of natural colors affected by the natural environment,a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoretical basis for plant dynamic color simulation and color sensor data transmission.Based on the HSL(Hue,Saturation,Lightness)color solid,the proposed method uses the function P-set to provide a color P-law generation model and an algorithm of the Dynamic Colors System(DCS),establishing the DCS modeling theory of the natural environment and the color P-reasoning simulation based on the HSL color solid.The experimental results show that based on the color P-law,for the DCS of the natural environment,when the external factors change,the color of the plant changes,accordingly,verifying the effectiveness of the color P-law generation model and the algorithm of the DCS.In the dynamic color intel-ligent simulation system,when external factors change,the dynamic change of plant color generally conforms to the basic laws of the natural environment.This enables the effective extraction of color data from the Internet of Things(IoT)-based color sensors and provides an effective way to significantly reduce the data transmission bandwidth of the IoT network.

An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine(SVM)to classify the tropical cyclone tracks into four types of classes namely straight,quasi-straight,curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology(TCTRO)described in this paper is a knowledge base which comprises of classes,objects and data properties that represent the interaction among the TC characteristics.A set of SWRL(Semantic Web Rule Language)rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore,we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset,the proposed ontology based SVM classifier achieves an accuracy of 98.3%with reduced classification error rates.

A case-based reasoning method of recognizing liquefaction pits induced by 2021 M_(W) 7.3 Madoi earthquake

Earthquake-triggered liquefaction deformation could lead to severe infrastructure damage and associated casualties and property damage.At present,there are few studies on the rapid extraction of liquefaction pits based on high-resolution satellite images.Therefore,we provide a framework for extracting liquefaction pits based on a case-based reasoning method.Furthermore,five covariates selection methods were used to filter the 11 covariates that were generated from high-resolution satellite images and digital elevation models(DEM).The proposed method was trained with 450 typical samples which were collected based on visual interpretation,then used the trained case-based reasoning method to identify the liquefaction pits in the whole study area.The performance of the proposed methods was evaluated from three aspects,the prediction accuracies of liquefaction pits based on the validation samples by kappa index,the comparison between the pre-and post-earthquake images,the rationality of spatial distribution of liquefaction pits.The final result shows the importance of covariates ranked by different methods could be different.However,the most important of covariates is consistent.When selecting five most important covariates,the value of kappa index could be about 96%.There also exist clear differences between the pre-and post-earthquake areas that were identified as liquefaction pits.The predicted spatial distribution of liquefaction is also consistent with the formation principle of liquefaction.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

Similarity Intelligence:Similarity Based Reasoning,Computing,and Analytics

Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.

基于Reason模型的SIF-Q260型电子小肠镜故障原因分析

根据武汉大学中南医院内窥镜中心SIF-Q260型小肠镜故障率高且维修成本高的情况,应用Reason模型,从环境影响、不安全监督、不安全行为前兆及不安全行为4个层面分析造成SIF-Q260小肠镜故障的原因,针对各个层面故障原因,从定期培训以规范内窥镜的洗消及使用、设置专人管理内窥镜并定期对内窥镜使用洗消的规范性进行监督及评价、完善内窥镜监管系统3方面拟定改进措施,堵住系统“漏洞”,为内窥镜质量控制措施的制定提供依据,可预防和减少内窥镜故障的发生。

基于知识图谱多跳推理的中文矿物知识问答方法与系统

已有相关矿物数据库用于存储和查询相关矿物知识,常用的搜索引擎也可以对矿物知识进行查询,但无法回答用自然语言进行提问的矿物问题,查询返回的答案需要进一步筛选。亦有基于知识图谱进行矿物知识问答的相关研究,但只能回答涉及知识图谱中一个三元组的简单问题,无法回答涉及多个三元组的多跳复杂问题。为此,本文提出基于知识图谱多跳推理的矿物复杂知识问答方法,采用ComplEx模型将矿物实体、关系和问句表示为复数向量,以更好地获取相互之间的语义及推理关系。输入矿物问句后,通过Bert-LSTM-CRF获取其中心词,采用基于编辑距离及分词的方法获得中心词的候选实体集合,然后采用全连接网络确定最相关的实体作为推理起点,与矿物问句拼接后通过全连接网络获得当前跳的最相关关系。根据当前跳的起始实体及最相关关系,在矿物知识图谱中获得另一实体作为下一跳的推理起点,并将下一跳的问句更新为原问句,与当前跳最相关关系拼接,以将当前跳的推理信息带入到下一跳推理中,直到获得的最相关推理关系为预定义的结束标识符,推理结束,返回最后一跳的实体为答案,并给出推理路径。采用Python语言,在Tensorflow框架下实现了本文提出的矿物复杂知识问答并与相关模型进行对比,证明了本文方法的有效性。采用前后端分离架构,使用RESTful API、React、Ajax、echarts和Flask等框架和技术,开发了基于知识图谱多跳推理的矿物复杂知识问答系统,为矿物知识获取及相关地质研究提供了平台和工具。

平台经济领域强化反垄断的正当性与合理限度

近些年来,国内外在平台经济领域开展了声势浩大的反垄断行动,尤其是欧盟出台的《数字市场法》和我国强调“强化反垄断和防止资本无序扩张”备受关注。基于平台经济竞争的鲜明特点和数字平台对经济社会的巨大影响,强化平台经济领域的反垄断有其理论上的正当性和现实的必要性。与此同时,平台经济创新发展的内在要求又决定了在平台经济领域的反垄断有其合理的边界和必要的限度。美国和欧盟等在平台经济领域反垄断上的差异性反映了国际竞争的现实情况,我国需要从国家利益最大化角度出发对平台经济领域反垄断策略做出理性选择,以促进我国平台经济规范健康持续发展。

基于Bookmark标准设定方法的数学演绎推理能力表现研究

为了深入分析八年级学生的数学演绎推理能力表现情况,了解不同性别、不同学校地域学生的表现差异.通过Bookmark方法开展标准设定,基于IRT技术获得学生数学演绎推理能力表现的水平比例.结果显示,有30%的学生达到最高水平,有超过17%的学生处在低水平,尚未达到课程标准要求.总体表现中女生成绩略好于男生,但在水平分布上呈现明显差异性,女生较男生更稳定.城市和县镇学生的能力表现明显好于农村地区学生,3类地域的学生水平分布也呈现明显不同,农村地区高水平人数比例最低,且不合格水平人数比例高于高水平学生比例.结果表明需要关注数学演绎推理能力在性别、地域上的表现差异,教育测量评价研究中开展标准设定工作对测评结果分析有重要作用.

行政复议变更决定的运用

2023年《行政复议法》修改的重要内容即构建了以变更决定为核心的行政复议决定体系。这一修改更加贴切行政复议的功能定位,行政复议也能够进一步避免程序空转,真正发挥出解决行政纠纷的“主渠道”作用。但是,抽象而出的规则面对复杂具体的个案,仍会存在诸多理解和适用上的不确定性,有必要对新法中的变更决定条款作出解释,进一步细化其实践运用。具体而言,在事证明确类案件中,行政复议机关可以基于两种理由作出变更决定,一是适用依据正确,但是行政行为内容不适当,包括明显不当和一般意义上的“不适当”,以是否具有规则性条款或者裁量基准,建立双重审查标准体系;二是未正确适用依据,包括违法和不当适用依据的情形。在事证瑕疵类案件中,行政机关原则上适用变更决定,但事实认定过程对专业性要求高以及案件事实疑难复杂的情形,可以适用撤销决定。

论行政复议的适当性审查

合法性审查、适当性审查是行政复议机关审查被申请行政行为的两个审查标准。适当性审查与行政诉讼合理性审查表述不同但内容一致。合法性审查是形式合法性审查,适当性审查是实质合法性审查,因此,适当性审查与合法性审查构成了一种从“从形式违法到实质违法”的双阶审查方法。在形式合法性已经确认的前提下,针对被申请行政行为的内容,行政复议机关可以从合目的性、结果可接受性和利益权衡三个面向作适当性审查。

基于事故表征和案例推理的煤矿瓦斯爆炸预测研究

为更好地通过案例预测煤矿瓦斯爆炸事故结果,在融合事故树分析和防御性悲观思想后,提出煤矿瓦斯爆炸事故表征方法,包括事故表征结构模型、表征规范和案例库编码规则;为提升案例推理精度,通过专家置信度改进层次分析法的主观影响,提出针对不同类型表征信息的相似度计算方法,引入时间衰退系数修正案例时效性。通过山西某煤矿瓦斯爆炸事故案例对该方法进行验证,结果表明,该方法的预测结果与实际结果相吻合,并能推荐事故预防措施。研究结果可为煤矿安全管理提供支持。

基于超声子宫内膜容受性参数和淋巴细胞免疫表型对复发性流产再孕早期流产的影响因素分析

目的基于超声子宫内膜容受性参数和淋巴细胞免疫表型探究不明原因复发性流产患者再妊娠后妊娠结局的影响因素。方法选择2022年4-8月南通市妇幼保健院收治的不明原因复发性自然流产再次妊娠患者166例。根据妊娠结局分为成功分娩组和再次流产组,检测子宫内膜容受性参数及外周血T淋巴细胞,筛查不明原因复发性流产患者再次妊娠流产的影响因素。结果166例患者中成功分娩102例(61.45%),再次流产64例(38.55%)。成功分娩组患者的子宫内膜厚度、内膜容积、血管化血流指数(VFI)均高于再次流产组(P<0.05)。成功分娩组患者的CD4^(+)、CD4^(+)/CD8^(+)低于再次流产组(P<0.05)。CD4^(+)、CD4^(+)/CD8^(+)是妊娠失败的危险因素(P<0.05),子宫内膜厚度、内膜容积、VFI是妊娠失败的保护因素(P<0.05)。结论不明原因复发性流产患者再妊娠的妊娠结局与子宫内膜厚度、内膜容积、VFI、CD4^(+)、CD4^(+)/CD8^(+)有关。

刑事案件事实认定中经验法则的运用

经验法则是证据评价的依据、建立证据与事实之间联系的桥梁以及事实认定合理性的检验标准,在刑事案件事实认定中经验法则的运用不可或缺。经验法则的适当选取是其规范运用的前提和基础,对于指向一致的经验法则应以盖然性程度为选取的基准,而内容迥异的经验法则则以具体条件的符合性为选取标准。裁判文书应当展示经验法则的内容,并结合案件的事实与证据论证经验法则选取的准确性与运用的合理性;对于控辩双方援引的经验法则,裁判文书应区别情况选择回应的方式,以增强裁判结论的正当性。对于经验法则运用的错误,可以通过启动第二审程序、审判监督程序实现救济。经验法则的规范运用有赖于控辩双方运用经验法则的公开与论辩、人民陪审员制度功能的充分发挥、直接审理原则的彻底贯彻等程序支撑规则。

道德作为审美如何可能?

将康德“实践理性”与中国文化“实用理性”精神进行简略比较,我们会得出中西不同文化意义的“道德作为审美”都是“可能”的这一初步结论。二者具有相异的文化背景与哲学机制。对于西方文化而言,“道德作为审美”之所以“可能”,是因为人格道德的至善与美,决定于“纯粹理性”即“上帝”的“在场”;就中国文化来说,是因为经过先秦“史”文化的“祛魅”即“理性化”过程,以“良知”即内在觉悟作为心灵根因的缘故,是心体与性体的合一于道。“道德作为审美”的可能实现,取决于“幸福”与“崇高”同时两栖于道德与审美的缘故。