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.

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.

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。

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.

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.

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.

Correlation between Reasons for Prescription and Karyotype Results in Patients Referred for Suspected Chromosomal Abnormalities

Karyotype prescription is based on clinical signs (or reasons for karyotype prescription) which are phenotypic manifestations associated with chromosomal abnormalities. The aim of this study was to establish a correspondence between karyotype indications and their results in patients. This was a retrospective study that was carried out in the Histology-Embryology-Cytogenetics laboratory of the University Hospital of Cocody-Abidjan from 2014 to 2019. 58 patient files were identified and included the indication or reason for prescribing a constitutional karyotype and the biological result obtained. An individual data sheet was used to collect the data. 17 reasons for prescription were identified and divided into 2 groups. Sexual ambiguity was the most frequent reason (29.3%). The first group (G1) represented the 10 reasons for which the karyotype results were normal. The second group (G2) corresponded of the 7 motives with normal or abnormal karyotype results. Several anomalies were listed according to these reasons: inversions, mosaics (anomalies of number and structure) and trisomy 21. The last was the most frequent chromosomal anomaly (69.24%). It was found in several reasons for karyotype prescription: malformations, neurological disorders, suspected trisomy and cardiac pathology. Several factors could explain these results, among which are the limits of the karyotype and the non-genetic causes that can induce these abnormal phenotypes. Complementary examinations to the karyotype are molecular cytogenetic techniques, notably fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (Array-CGH).

The Reasons for Admission of Elderly Subjects in Intensive Care at the CHU Ignace Deen

Objective: To determine the reasons for admission of elderly subjects and the prognosis in general intensive care. Patients and Methods: Observational descriptive and analytical study with prospective collection of data over a period of one year from January 1 to December 31, 2021. Patients aged 65 or over were included. Abstract: During the study period, 223 cases were collected out of 587 patients admitted, giving a prevalence of 37.9%. The average age was 74.127.39 ± years with extremes of 65 and 96 years and a male predominance (58.7%). The comorbidities were dominated by arterial hypertension (71.3%). The patients were: transferred from medical and surgical emergencies (75.8%). The average admission time was 48.8 ± 29.8 hours. One hundred and eight patients had a Glasgow score between 3 and 7. The reasons for admission were dominated by vascular causes (51.6%). Strokes of any type accounted for 43.9% of these reasons for admission. The average time for carrying out the biological assessments and imaging was 41.8 ± 27.3 hours with the extremes of 3 and 89 hours, 37.2% had a complete assessment within 24 hours. The average duration of hospitalization was 7.10 ± 8.87 days with extremes of 1 and 72 days. The mortality rate was 71.7%. Conclusion: This study has made it possible to take stock of the reasons for the admission of elderly subjects to intensive care. It appears that vascular causes are the main reasons for admission with heavy comorbidities which results in high mortality.

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.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

A Formal Model for Analyzing Fair Exchange Protocols Based on Event Logic

Fair exchange protocols play a critical role in enabling two distrustful entities to conduct electronic data exchanges in a fair and secure manner.These protocols are widely used in electronic payment systems and electronic contract signing,ensuring the reliability and security of network transactions.In order to address the limitations of current research methods and enhance the analytical capabilities for fair exchange protocols,this paper proposes a formal model for analyzing such protocols.The proposed model begins with a thorough analysis of fair exchange protocols,followed by the formal definition of fairness.This definition accurately captures the inherent requirements of fair exchange protocols.Building upon event logic,the model incorporates the time factor into predicates and introduces knowledge set axioms.This enhancement empowers the improved logic to effectively describe the state and knowledge of protocol participants at different time points,facilitating reasoning about their acquired knowledge.To maximize the intruder’s capabilities,channel errors are translated into the behaviors of the intruder.The participants are further categorized into honest participants and malicious participants,enabling a comprehensive evaluation of the intruder’s potential impact.By employing a typical fair exchange protocol as an illustrative example,this paper demonstrates the detailed steps of utilizing the proposed model for protocol analysis.The entire process of protocol execution under attack scenarios is presented,shedding light on the underlying reasons for the attacks and proposing corresponding countermeasures.The developedmodel enhances the ability to reason about and evaluate the security properties of fair exchange protocols,thereby contributing to the advancement of secure network transactions.

Amend:An integrated platform of retracted papers and concerned papers

Purpose:The notable increase in retraction papers has attracted considerable attention from diverse stakeholders.Various sources are now offering information related to research integrity,including concerns voiced on social media,disclosed lists of paper mills,and retraction notices accessible through journal websites.However,despite the availability of such resources,there remains a lack of a unified platform to consolidate this information,thereby hindering efficient searching and cross-referencing.Thus,it is imperative to develop a comprehensive platform for retracted papers and related concerns.This article aims to introduce“Amend,”a platform designed to integrate information on research integrity from diverse sources.Design/methodology/approach:The Amend platform consolidates concerns and lists of problematic articles sourced from social media platforms(e.g.,PubPeer,For Better Science),retraction notices from journal websites,and citation databases(e.g.,Web of Science,CrossRef).Moreover,Amend includes investigation and punishment announcements released by administrative agencies(e.g.,NSFC,MOE,MOST,CAS).Each related paper is marked and can be traced back to its information source via a provided link.Furthermore,the Amend database incorporates various attributes of retracted articles,including citation topics,funding details,open access status,and more.The reasons for retraction are identified and classified as either academic misconduct or honest errors,with detailed subcategories provided for further clarity.Findings:Within the Amend platform,a total of 32,515 retracted papers indexed in SCI,SSCI,and ESCI between 1980 and 2023 were identified.Of these,26,620(81.87%)were associated with academic misconduct.The retraction rate stands at 6.64 per 10,000 articles.Notably,the retraction rate for non-gold open access articles significantly differs from that for gold open access articles,with this disparity progressively widening over the years.Furthermore,the reasons for retractions have shifted from traditional individual behaviors like falsification,fabrication,plagiarism,and duplication to more organized large-scale fraudulent practices,including Paper Mills,Fake Peer-review,and Artificial Intelligence Generated Content(AIGC).Research limitations:The Amend platform may not fully capture all retracted and concerning papers,thereby impacting its comprehensiveness.Additionally,inaccuracies in retraction notices may lead to errors in tagged reasons.Practical implications:Amend provides an integrated platform for stakeholders to enhance monitoring,analysis,and research on academic misconduct issues.Ultimately,the Amend database can contribute to upholding scientific integrity.Originality/value:This study introduces a globally integrated platform for retracted and concerning papers,along with a preliminary analysis of the evolutionary trends in retracted papers.

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.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

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.

On the “Reasonable” Handling of Personal Information Disclosed According to the Law

The model for protection of personal information dis-closed according to the law has changed from indirect protection to direct protection.The indirect protection model for traditional repu-tation rights and privacy rights was not enough to meet the practical needs of governance.However;due to the ambiguity in the application of the“reasonable”processing requirements,the direct protection model centered on Article 27 of the Personal Information Protection Law also is not enough to effectively respond to practical disputes.The essence of the problem is to resolve the tension between informa-tion circulation and risk control and reshape the legal order for the protection of personal information disclosed according to the law.The determination of“reasonable”should be centered on the scenario theory and holism interpretation and carried out by using the interpre-tation technique of the dynamic system under Article 998 of the Civil Code.With the support of scenario-based discussions and comparative propositions,the crawling and tag extraction of personal information.disclosed according to the law should be considered as reasonable processing;profiling and automated decision-making should not be covered in the scope of reasonable processing,in principle;for behav-iors such as correlation analysis,elements like information subject,identifiability and sensitivity should be comprehensively considered to draw open and inclusive conclusions in individual cases.

International law obligations for the disposal of Fukushima nuclear-contaminated water under the principles of nuclear safety

The disposal of contaminated water from Japan’s Fukushima nuclear power plant is a significant international nuclear safety issue with considerable cross-border implications.This matter requires compliance not only with the law of the sea but also with the principles of nuclear safety under international law.These principles serve as the overarching tenet of international and China’s domestic nuclear laws,applicable to nuclear facilities and activities.The principle of safety in nuclear activities is fully recognized in international and domestic laws,carrying broad legal binding force.Japan’s discharge of nuclear-contaminated water into the sea violates its obligations under the principle of safety in nuclear activities,including commitments to optimum protection,as low as reasonably practicable,and prevention.The Japanese government and the International Atomic Energy Agency(IAEA)have breached the obligation of optimum protection by restricting the scope of assessments,substituting core concepts,and shielding dissenting views.In the absence of clear radiation standards,they have acted unilaterally without fulfilling the obligation as low as reasonably practicable principle.The discharge of Fukushima nuclear-contaminated water poses an imminent and unpredictable risk to all countries worldwide,including Japanese residents.Japan and the IAEA should fulfill their obligations under international law regarding disposal,adhering to the principles of nuclear safety,including optimum protection,the obligation as low as reasonably practicable,and prevention through multilateral cooperation.Specifically,the obligation to provide optimum protection should be implemented by re-evaluating the most reliable disposal technologies and methods currently available and comprehensively assessing various options.The standard of the obligation as low as reasonably practicable requires that the minimization of negative impacts on human health,livelihoods,and the environment should not be subordinated to considerations of cutting costs and expenses.Multilateral cooperation should be promoted through the establishment of sound multilateral long-term monitoring mechanisms for the discharge of nuclear-contaminated water,notification and consultation obligations,and periodic assessments.These obligations under international law were fulfilled after the accidents at the Three Mile Island and Chernobyl nuclear power plants.The implications of the principles of nuclear safety align with the concept of building a community of shared future for nuclear safety advocated by China.In cases of violations of international law regarding the disposal of nuclear-contaminated water that jeopardize the concept of a community of a shared future for nuclear safety,China can also rely on its own strength to promote the implementation of due obligations through self-help.

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。