作为moral reasonability的道德理性及其优先性

通行的"道德理性"概念的所指及有效性并未局限在理论范围,还必然地延展到实践领域。对于其丰富内含,植根于西方传统形而上学的reason和rationality却不能给予充分表达。基于这两个概念的一些西方传统道德哲学(如康德的道义论)看起来至高至远,其实并不具有经验或实践层面的可普遍化性,因而在实际生活中难免走向反面。研究发现,如果使用英文词moral reasonability来理解和解释道德理性,或可弥补上述欠缺。因为,相较而言,这后一概念蕴含有一种着眼于"探究原因、提供理由"的最低限度的道德要求或"超底线准则",作为一种形式性的"前道德立场",它更易于落实到现实生活。这个概念不仅能够表达出道德理性中被前两个概念所忽略的含义,而且因为其历史特点具有优先性。

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。

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 comparative study on characteristics of retracted publications across different open access levels

Purpose:Recently,global science has shown an increasing open trend,however,the characteristics of research integrity of open access(OA)publications have rarely been studied.The aim of this study is to compare the characteristics of retracted articles across different OA levels and discover whether OA level influences the characteristics of retracted articles.Design/methodology/approach:The research conducted an analysis of 6,005 retracted publications between 2001 and 2020 from the Web of Science and Retraction Watch databases.These publications were categorized based on their OA levels,including Gold OA,Green OA,and non-OA.The study explored retraction rates,time lags and reasons within these categories.Findings:The findings of this research revealed distinct patterns in retraction rates among different OA levels.Publications with Gold OA demonstrated the highest retraction rate,followed by Green OA and non-OA.A comparison of retraction reasons between Gold OA and non-OA categories indicated similar proportions,while Green OA exhibited a higher proportion due to falsification and manipulation issues,along with a lower occurrence of plagiarism and authorship issues.The retraction time lag was shortest for Gold OA,followed by non-OA,and longest for Green OA.The prolonged retraction time for Green OA could be attributed to an atypical distribution of retraction reasons.A comparative study on characteristics of retracted publications across different open access levels Research limitations:There is no exploration of a wider range of OA levels,such as Hybrid OA and Bronze OA.Practical implications:The outcomes of this study suggest the need for increased attention to research integrity within the OA publications.The occurrences offalsification,manipulation,and ethical concerns within Green OA publications warrant attention from the scientific community.Originality/value:This study contributes to the understanding of research integrity in the realm of OA publications,shedding light on retraction patterns and reasons across different OA levels.

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.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

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.

Upper and Lower Bounds of theα-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.

An Exploration of How Practitioners Make Meaning in a Chinese Medicine Consultation and Treatment

In Chinese medicine, practitioners assess patients’ complaints, analyze their underlying problems, identify causes and come to a diagnosis, which then directs treatment. What is not obvious and not recorded in a consultation is the clinical reasoning process that practitioners use. The research filmed three practitioners in the UK while they conducted a consultation and treatment on new patients. The practitioners and researchers viewed the films and used them as aide-memoirs while the reasoning process throughout was discussed. In order to determine the pattern, practitioners used the four examinations to gather information from the patient in an iterative process;their aesthetic reasoning was highly developed. Through triangulation they checked the information they received against a detailed understanding of the qi-dynamic. They used highly analytical strategies of forward(inductive) and backward(deductive) reasoning against the prototypes of the signs and symptoms that indicate a specific Zheng. This was achieved through an abductive process that linked description with explanation and causal factors with pathological mechanisms. The feedback loop with the patient continued through the consultation and into the treatment. A process of translation and interpretation was needed to turn the patient’s story into the practitioner’s story of qi-dynamics that then directed the treatment. Awareness of our clinical reasoning process will mitigate against biases, improve our diagnoses and treatment choices and support the training of students.

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.

长春市高校足球后备人才培养风险管理研究

采用访谈法、逻辑分析法等,研究长春市高等教育阶段足球后备人才培养过程中的风险因素,建立长春市高等教育阶段足球后备人才培养Reason模型。研究表明:需要采取成立高等教育阶段足球后备人才培养专项工作组、减轻运动员学习负担、优化高校足球赛事建设、改进足球教练员培训与考核、健全“一条龙”足球人才培养体系等措施,以突破长春市高等教育阶段足球后备人才培养困境。

Reason模型在肝癌介入术病人围术期护理安全管理中的应用分析

目的:探讨Reason模型在肝癌介入术病人围术期护理安全管理中的应用效果。方法:应用Reason模型对2020年6月-2021年6月22例次肝癌介入术病人围术期护理安全不良事件进行回顾性分析,从不安全行为、不安全性前提、不安全监督、组织管理4个方面分析原因并制定解决方案,比较方案实施前后护理安全不良事件发生率。结果:实施Reason模型后肝癌介入术病人围术期护理安全不良事件发生率为0.135%,显著低于实施前的0.586%(P<0.05)。结论:基于Reason模型的管理有利于深度挖掘护理管理问题,降低护理安全不良事件发生率,有助于改变医护人员对不良事件上报的态度,保障病人安全。

Can ChatGPT Boost Artistic Creation:The Need of Imaginative Intelligence for Parallel Art

CHATGPT,one of the leading Large Language Models(LLMs),has acquired linguistic capabilities such as text comprehension and logical reasoning,enabling it to engage in natural conversations with humans.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

改进YOLOv5的红外船舶目标检测算法

针对传统算法依赖于对红外船舶目标与环境背景的精确分离和信息提取,难以满足复杂背景和噪声等干扰环境下的船舶目标检测需求,提出一种基于改进YOLOv5的红外船舶目标检测算法。在YOLOv5网络中添加Reasoning层,以一种新的提取图像区域间语义关系来预测边界框和类概率的体系结构,提高模型的检测精度,同时对YOLOv5目标检测网络的损失函数进行改进,从而达到进一步提高模型准确率的目的。验证结果表明,改进后的YOLOv5算法训练出来的模型,检测精确率和速度与实验列出的几种目标检测算法相比均有明显提升,其中平均精度均值(mAP)可达94.65%。该模型经过验证,既能满足检测的实时性要求,又能保证高精度。

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

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.

Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

Soft computing(SC)refers to the ability of a digital computer or robot to perform functions that are normally associated with intelligent individuals,such as reasoning and problem-solving.An example of this would be a project aimed at creating systems capable of reasoning,discovering meaning,generalising,or learning from past experience.Science and engineering problems that are both non-linear and complex can be solved using these methodologies.It has been proven that these algorithms can be used to solve numerous real-world problems.The techniques outlined can be used to increase the accuracy of existing models/equations,or they can be used to propose a newmodel that can address the problem.

Radiation protection measures during endourological therapies

Objective The objective of this narrative review was to search the existing literature for studies reporting measures to minimize radiation use during endoscopic management of stone disease and present ways of reducing the exposure of both patients and operating room staff.Methods A literature review in PubMed was performed to identify studies describing protocols or measures to reduce radiation received during endourological procedures from January 1970 to August 2022.Eligible studies were those that reported outcomes for ureteroscopy or percutaneous nephrolithotripsy regarding measures to minimize radiation doses used intraoperatively,performed either in real-life theatres or using phantoms.Both comparative and non-comparative studies were deemed eligible.Results Protection can be achieved initially at the level of diagnosis and follow-up of patients,which should be done following an algorithm and choice of more conservative imaging methods.Certain protocols,which follow principles for minimized fluoroscopy use should be implemented and urologists as well as operating room staff should be continuously trained regarding radiation damage and protection measures.Wearing protective lead equipment remains a cornerstone for personnel protection,while configuration of the operating room and adjusting X-ray machine settings can also significantly reduce radiation energy.Conclusion There are specific measures,which can be implemented to reduce radiation exposure.These include avoiding excessive use of computed tomography scans and X-rays during diagnosis and follow-up of urolithiasis patients.Intraoperative protocols with minimal fluoroscopy use can be employed.Staff training regarding dangers of radiation plays also a major role.Use and maintenance of protective equipment and setting up the operating room properly also serve towards this goal.Machine settings can be customized appropriately and finally continuously monitoring of exposure with dosimeters can be adopted.