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。

Deterioration Reason and Improvement Measure of the Retarding Effect of Protein Retarder on Phosphorus Building Gypsum

The retarding effect of protein retarder on phosphorus building gypsum(PBG)and desulfurization building gypsum(DBG)was investigated,and the results show that protein retarder for DBG can effectively prolong the setting time and displays a better retarding effect,but for PBG shows a poor retarding effect.Furthermore,the deterioration reason of the retarding effect of protein retarder on PBG was investigated by measuring the pH value and the retarder concentration of the liquid phase from vacuum filtration of PBG slurry at different hydration time,and the measure to improve the retarding effect of protein retarding on PBG was suggested.The pH value of PBG slurry(<5.0)is lower than that of DBG slurry(7.8-8.5).After hydration for 5 min,the concentration of retarder in liquid phase of DBG slurry gradually decreases,but in liquid phase of PBG slurry continually increases,which results in the worse retarding effect of protein retarder on PBG.The liquid phase pH value of PBG slurry can be adjusted higher by sodium silicate,which is beneficial to improvement in the retarding effect of the retarder.By adding 1.0%of sodium silicate,the initial setting time of PBG was efficiently prolonged from 17 to 210 min,but little effect on the absolute dry flexural strength was observed.

Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical diagnosis.However,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text.To better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic classification.In the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the input.In the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be diagnosed.To differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text subgraph.Themessage-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning process.Finally,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis results.Experimental results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods.

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.

The Effect of the Menstrual Cycle on Cognitive Performance: Spatial Reasoning, Visual & Numerical Memory

The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.

Challenges Experienced by Nurse Educators in Promoting Acquisition of Clinical Reasoning Skills by the Undergraduate Nursing Students: A Qualitative Exploratory Study

Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is difficult because of its complexity. This study, therefore aimed at exploring the challenges experienced by nurse educators in promoting acquisition of clinical reasoning skills by undergraduate nursing students. Methods: A qualitative exploratory research design was used in this study. The participants were purposively sampled and recruited into the study. Data were collected using semi-structured interview guides. Thematic analysis method was used to analyze the collected data The principles of beneficence, respect of human dignity and justice were observed. Results: The findings have shown that clinical learning environment, lacked material and human resources. The students had no interest to learn the skill. There was also knowledge gap between nurse educators and clinical nurses. Lack of role model was also an issue and limited time exposure. Conclusion: The study revealed that nurse educators encounter various challenges in promoting the acquisition of clinical reasoning skills among undergraduate nursing students. Training institutions and hospitals should periodically revise the curriculum and provide sufficient resources to facilitate effective teaching and learning of clinical reasoning. Nurse educators must also update their knowledge and skills through continuous professional development if they are to transfer the skill effectively.

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.

基于改进Reason模型的航空事故人为维修差错解析与预防

当今航空工业发展迅猛,航空器制造管理技术日趋成熟,重大航空事故发生率逐年下降,但维修差错相关事故占比却不降反升。本文首先利用数组数据,证明了因维修差错造成的事故在航空事故中的占比在逐渐提高。其次利用Reason模型提出了一种归纳方法,利用历史航空事故数据进行分析,通过讨论各类维修差错致因在航空事故Reason模型中的占比,给出减少维修差错的建议。

Analysis of the Impact of Inductive Reasoning on the Mathematical Thinking Style of Deaf and Hard-of-Hearing Students

In this paper,we combine the teaching and learning situation of deaf and hard-of-hearing students in the Linear Algebra course of the Computer Science and Technology major at the Nanjing Normal University of Special Education.Based on the cognitive style of deaf and hard-of-hearing students,we apply example induction,exhaustive induction,and mathematical induction to the teaching of Linear Algebra by utilizing specific course content.The aim is to design comprehensive teaching that caters to the cognitive style characteristics of deaf and hard-of-hearing students,strengthen their mathematical thinking styles such as quantitative thinking,algorithmic thinking,symbolic thinking,visual thinking,logical thinking,and creative thinking,and enhance the effectiveness of classroom teaching and learning outcomes in Linear Algebra for deaf and hard-of-hearing students.

REPRESENTATION PROPERTIES OF ABSTRACT DEFAULT REASONING FRAMEWORKS

presented The conceptions of abstract default reasoning frameworks （ADRFs） and D-consequence relations are Based on representation properties of D-consequence relations, it proves that any cumulative nonmonotonic consequence relation with the connective-free form can be represented by ADRFs.

变电运行人因事故分析的拟REASON模型

REASON模型是航空事故调查与分析的理论模型之一,引入进来并建立了变电运行人因事故分析的拟REASON模型,提出了事故链上的六个要素,包括环境、信息、组织、危险点、监护人、操作人。分别针对这六个要素提出防止变电运行人因事故发生的具体措施,并指出各方面安全措施的协调是追求"事故零目标"的必然要求。所提模型对变电运行人因事故分析具有重要的指导意义。

基于FTA-Reason的施工作业高空坠落风险预控研究

为了对施工作业中高空坠落风险进行预控,采用故障树法对施工作业高空坠落事故进行了故障树的构建、分析,继而通过对经典Reason模型进行改造,建立了施工作业高空坠落事故的预控模型,包括组织因素、安全监督、操作条件、安全行为的前提以及安全行为5个防御层次。在风险预控的过程中,通过运用建立的施工作业高空坠落风险模型,可以对施工作业中的高空坠落风险进行针对性地预控,为施工安全风险管理提供参考。

Reason模型在空中交通管制中的应用

指出了Reason模型及人的因素分析与分类系统的不足,提出对Reason模型进行修正的思路;结合中国民航业的实际情况,构建了空中交通管制不安全事件的分析框架;对防御系统失效、不安全行为、不安全行为的条件、管理失效4个层次的缺陷进行了论述,并给予详细的实证分析。研究有助于调查分析民航空管行业不安全事件中的原因以及为空管安全管理中的危险识别提供依据。

基于Reason模型的航空器事故调查分析软件设计

通过对航空器事故进行分析与研究,从而提出相应的改进措施,对于减少飞行事故、保障飞行安全具有重要意义。在现有航空器事故分析的基础上,提出了基于Reason模型的航空器事故调查分析软件设计。在分析Reason模型基本概念的基础上,详细介绍了该软件的系统结构、软件的界面和相应的操作步骤与方法,并结合实际的案例介绍了该软件在分析航空器事故调查方面的优势。该软件对于调查分析民航业不安全事件中的人为因素具有一定的借鉴作用。

基于Reason模型的煤矿事故致因分析

阐述了Reason组织事故病理学模型的基本原理和构造,并以一起典型煤矿事故为案例,分别从组织因素、管理因素、不安全行为的直接前提、操作者的不安全行为和防御系统等5个方面,对其致因进行了分析,认为我国煤矿事故频发的根源在于组织错误的存在。

REASON模型在航空维修事故调查中的应用

分析和运用Reason模型,从系统安全角度对一起典型航空维修事故案例,按照事件链因果逻辑展开调查分析,构建出事故致因分析模型。

食品安全事件的影响因素及治理路径——基于REASON模型的QCA分析

本文基于REASON模型,构造“监管主体、监管过程、监管环境、监管对象”的监管闭环,探寻食品安全事件发生的逻辑链条。首先,建立“环节分布—责任主体”的组合分析框架,从9989个食品安全事件统计分析入手,提炼出8个高发节点。其次,筛选出60个案例进行模糊集定性比较分析,得到8个食品安全事件的影响因素——日常监管、多元主体参与、检测水平、质量控制、宣传教育、风险控制、企业或个人行为、环节分布安全隐患,验证了“不安全的行为”是食品安全事件的直接因素。再次,通过条件变量的组合探究路径,从影响因素及监管的维度入手,根据“行为—原因—实践”的逻辑链条总结食品安全治理的5条路径,回答“如何监管”及“监管谁”的问题。

“国家理由”还是“国家理性”——思想史脉络中的“reason of state”

作为现代早期政治思想史的核心概念,"国家理性"与现代早期西方世界的理性化是联系在一起的,它意味着工具理性(国家理性是其重要表现)取代传统的"道德理性"居支配地位。在当代中国学术中,国家理性没有在中文语境下获得恰当的呈现。

基于Reason模型的医疗器械不良事件的影响因素研究

目的探讨在用医疗器械不良事件的影响因素。方法基于文献分析法,在CNKI、万方等数据库检索相关文献273篇,并采用其中74篇作为证据文献;应用Reason模型,从组织影响、不安全监督、不安全的行为前提和不安全的行为方面分析不良事件成因。结果结合证据文献的案例分析,对学者分析的导致医疗器械不良事件的原因进行归纳,并构建了医疗器械不良事件影响因素的Reason致因模型。结论医疗器械不良事件的影响因素映射为产品在组织管理、临床试验监管、产品设计、物流管理和临床使用环节中出现的"漏洞"中的隐形失效,应该加强医疗器械上市前可靠性要求和临床使用风险管控。