Human-artificial intelligence interaction in gastrointestinal endoscopy

The number and variety of applications of artificial intelligence(AI)in gastr-ointestinal(GI)endoscopy is growing rapidly.New technologies based on machine learning(ML)and convolutional neural networks(CNNs)are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures,in detection,diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators.Platforms based on ML and CNNs require regulatory approval as medical devices.Interactions between humans and the technologies we use are complex and are influenced by design,behavioural and psychological elements.Due to the substantial differences between AI and prior technologies,important differences may be expected in how we interact with advice from AI technologies.Human-AI interaction(HAII)may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability.Human factors influencing HAII may include automation bias,alarm fatigue,algorithm aversion,learning effect and deskilling.Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

How to Promote Students' English Achievements By Using Non-intelligence Factors

Psychological researches showed that learning activities are under the constant impact of both between learners’internal factors and external factors,and learners’internal factors,in specific,include intelligence factors and non-intelligence factors.This article explores the relations between 11 non-intelligence factors with students’English achievements by adopting the method of questionnaire.The research shows that non-intelligence factors and students’English achievements have a close relation.Therefore,teachers should cultivate students’non-intelligence factors to promote students’English achievements.

Non-intelligence Factors in Online English Learning

In the process of online English learning,learning efficiency is influenced by various non-intelligence factors.Non-intelligence factors include learning motivation,self-efficacy,cultural background knowledge and online learning strategies which play crucial roles in online English learning.This paper analyses the non-intelligence factors affecting students'online English learning and put forwards some measures to enhance online English learning.

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

Association between the Epidermal Growth Factor Gene and Intelligence in Major Depression Patients

Objective To study the association between the epidermal growth factor(EGF) gene and intelligence in patients with major depression.Methods Intelligence measurement using Wechsler Adult Intelligence Scale(WAIS) was perfor-med on 120 unrelated patients with major depression and 46 control subjects.Blood was collected from all subjects for extraction of genomic DNA.Four single nucleotide polymorphisms(SNPs) in the EGF gene were genotyped using polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry(MALDI-TOF-MS).Results Mean scores of both score lang and score task,two subtests in WAIS,differed significantly between major depression patients and controls(P<0.0001).Quantitative trait analysis showed that the genetype of rs2250724 was closely associated with score lang and score task in major depression patients.The associations were still significant after 10 000 permutations.Conclusions Although preliminary,our results provide evidence for association between the EGF gene and intelligence in patients with major depression.Genetic variation in the EGF gene may increase the susceptibility of major depression.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

MigroGrid(MG)has emerged to resolve the growing demand for energy.But because of its inconsistent output,it can result in various power quality(PQ)issues.PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources.Similarly,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system.Thus,this research focuses on power quality enhancement in the MG using hybrid shunt filters.However,the performance of the filter mainly depends upon the design,and stability of the controller.The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)controller.The performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this approach.Further,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in MG.From the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

发展新质生产力 推动我国经济高质量发展

中华人民共和国(新中国)成立以来,从毛泽东的《论十大关系》,到邓小平的“科学技术是第一生产力”,再到习近平的“整合科技创新资源,引领发展战略性新兴产业和未来产业,加快形成新质生产力”,我党对经济工作规律性的认识,随着时代的发展而不断深化。习近平总书记在2024年1月31日召开的中央政治局第十一次集体学习会议上的重要讲话,更是把这种认识推向了全新的高度。总书记在主持学习时明确指出“必须牢记高质量发展是新时代的硬道理”,“高质量发展需要新的生产力理论来指导,而新质生产力已经在实践中形成并展示出对高质量发展的强劲推动力、支撑力,需要我们从理论上进行总结、概括,用以指导新的发展实践”,并强调“科技创新能够催生新产业、新模式、新动能,是发展新质生产力的核心要素”。为了深入学习贯彻总书记讲话精神,围绕“发展新质生产力推动我国经济高质量发展”这个新时代经济发展的核心课题,本刊邀请国内著名专家、学者,撰写一组笔谈文章,以飨读者。

智能化转型、组织韧性与制造业企业高质量发展

新时期制造业企业高质量发展是推动制造强国建设的重要战略环节。智能化技术与制造业的深度融合,将成为制造业企业转型升级、创新发展的历史机遇。以2012—2022沪深A股制造业企业为样本,从制造业企业智能化转型出发,探讨其对企业组织韧性和企业高质量发展的影响机制,在对三者进行量化测算的基础上进行固定效应、中介效应、异质性等实证检验。研究发现:智能化转型能够显著提升制造业企业组织韧性,并带动企业全要素生产率的提升,实现制造业企业高质量发展。经实证检验,该结论稳健。同时,智能化转型对组织韧性的提升存在地区市场化程度、企业生命周期、资本密集度、股权性质的异质性。基于此,本文从宏观层面出发,提出了政府加强顶层设计,为制造业企业的数智化转型提供配套支持及企业发挥主体作用,积极推进全过程全生命周期的智能化转型的共性建议,从中观层面出发,提出了企业可通过建立稳健的财务策略,识别并规避各种风险、优化生产流程,提高生产效率,加强供应链条稳健性建设和企业文化建设等措施提升组织韧性,以应对环境不确定性日益加剧的风险。并从微观层面建议不同性质的制造业企业应依据不同特点合理审慎做出智能化转型的战略选择,以优化资源配置效率,提高市场竞争力、加大在智能化技术和设备方面的投入,赋能价值链共生共赢,支撑企业实现韧性增长。为制造业智能化转型推动产业韧性水平、实现制造业高质量发展提供理论和现实借鉴。

工业互联网使用如何促进中小企业智能化转型:驱动因素与赋能机制

工业互联网是中小制造企业智能化转型的重要载体。基于TOE框架与企业能力理论,构建工业互联网使用驱动因素及赋能机制综合研究模型。结果表明:第一,工业互联网使用的重要驱动因素包括资源就绪、数字能力、业务复杂度与政府支持。资源就绪是中小制造企业使用工业互联网的前提,数字能力是中小制造企业使用工业互联网的技术基础,业务复杂度可以反映中小制造企业使用工业互联网的内在需求,政府支持可为中小制造企业使用工业互联网提供保障。第二,工业互联网通过培育两种核心能力赋能企业转型升级。工业互联网使用对中小制造企业智能化转型具有显著赋能效应,集成互联能力通过促进企业互联互通实现智能化转型,协同融合能力通过促进企业内外协同实现智能化转型。结论可为中小企业“上云上平台”和智能化转型提供理论依据与实践启示。

养老机构服务智慧化转型驱动因素组态与路径识别——基于模糊集的定性比较分析

智慧化转型是人口老龄化背景下创新养老服务模式、重塑养老服务格局的重要战略选择。养老机构服务对象辐射范围广、服务供给形式灵活、运营管理形式多样,在推动养老服务智慧化转型进程中具有先天优势,养老机构服务智慧化转型可引领、带动实现养老服务体系整体优化升级。基于技术—组织—环境(TOE)框架,运用模糊集定性比较分析(fsQCA)方法探索养老机构服务智慧化转型的驱动因素组合,考察技术、组织、环境维度7个前因变量对养老机构服务智慧化转型意愿的联动效应及作用路径。结果显示,任何单一前因变量均不能单独成为养老机构服务智慧化转型的驱动因素,而是以技术—组织—环境驱动型和技术—组织驱动型两种驱动类型下的4种组态形式协同促进高转型意愿生成,且前因条件越完备,养老机构越倾向于提供智慧化服务;感知相对优势是养老机构服务智慧化转型核心驱动要素,技术兼容性和市场拉力可与特定前因条件“等效替代”共同推动养老机构服务智慧化转型。研究结论为激发养老机构服务智慧化转型动力、探索适宜路径、加速体系智慧化升级提供了极具实践价值的改革参考与管理启示。

数字孪生村落:传统村落全要素智慧化营建新范式

工业与生态文明对传统农耕文明的不断侵蚀,导致了传统农耕文明的不断消亡。为了实现对传统村落全方位要素的保护目标,以数字孪生技术为驱动内核,就传统村落全要素保护的内涵与外延、体系架构、建设路径及关键技术等展开讨论。结果表明,数字孪生村落的全要素智慧营建是一项复杂的系统性工程,需要从顶层设计、标准体系、建设路径与示范应用四个维度进行深化研究;新型测绘耦合数字孪生技术为传统村落的全要素智慧化管控与运维奠定了数据与技术基础,结合实际保护需求与建设目标的“二阶段”体系划分,以期为传统村落的产学研用一体化保护、协同发展与综合利用提供方法借鉴。

人工智能影响我国全要素生产率的机制与效应研究

人工智能作为引领新一轮科技革命与产业变革的战略性技术,具有溢出带动性很强的“头雁”效应,有利于提升全要素生产率。本文分别基于宏观省级层面和微观企业层面的数据,利用企业机器人数据和智能制造试点示范项目的准自然实验,采用交叠DID和双重机器学习等方法实证识别了人工智能对全要素生产率的作用效应。研究结果表明,人工智能发展应用在宏观和微观两个维度均较为显著地促进了我国全要素生产率的提升。在宏观层面,人工智能对东部沿海地区全要素生产率的作用效应强于对中西部地区的影响;对高市场竞争环境下的全要素生产率作用效应更明显。在微观层面,人工智能对低技术企业全要素生产率的影响强于对高技术企业的影响,对规模较大企业的影响强于对规模较小企业的影响。人工智能提升全要素生产率的机制包括提高要素配置效率、降低企业成本和增强企业研发能力。本文研究结论对于进一步深化人工智能与实体经济融合,提升全要素生产率,实现经济高质量发展具有明确意义。

数智经济推动共同富裕的机制与效应研究

共同富裕是社会主义的本质要求,数字化、智能化是引领共同富裕的重要驱动力。本文从数字化和智能化两个维度出发构建了数智经济发展水平的指标体系,并利用2012~2022年我国省际面板数据,实证分析了数智经济推动共同富裕的作用路径与影响效应。研究发现:数智经济通过提高农业全要素生产率推动共同富裕。在区域经济发达程度较低时,数智经济的发展可能会妨碍共同富裕目标的实现,但这种抑制效果将进一步削弱,直到区域经济发展达到一定水平,数智经济发展才有助于促进共同富裕。同时,数智经济发展还存在明显的空间溢出效应,可以提高邻近地区的共同富裕程度。因此,各地区应加快数智经济发展进度,使数智经济的建设更有效地服务于共同富裕目标。

类风湿关节炎治疗达标率的影响因素

目的:基于智能疾病管理系统分析影响类风湿关节炎治疗达标的因素,为进一步调整慢性病管理策略提供参考。方法:回顾性分析2018年1月—2020年12月在重庆市某三级甲等医院风湿病科接受慢性病管理的471例类风湿关节炎病人,根据是否达标分为达标组和未达标组,收集性别、年龄、病程、是否因药物副作用调整方案、是否规律用药、是否有经济负担、是否受其他疾病影响、是否受行动不便或路途远影响、是否定期复诊等资料,并分析其影响因素。结果:基于智能疾病管理系统对类风湿关节炎病人进行慢性病管理总达标率为57.3%,不同年龄、规律用药、定期复诊病人类风湿关节炎病人治疗达标率不同(P<0.05)。结论:应加强对老年病人的关怀,提高老年病人的依从性,指导病人规律用药和定期复诊有利于提高类风湿关节炎病人治疗的达标率。

AI时代阅读偏食现象研究:驱动因素与治理策略

阅读偏食作为信息偏食投射到阅读领域的一种具体现象,随着AI技术的发展愈发突显,探究AI时代读者阅读偏食现象对深化全民阅读推广、推行阅读服务实践等具有重要意义。文章以有关阅读偏食的网络公开语料为数据源,利用MAXQDA(2022版)质性分析软件对语料进行归纳式内容编码,识别相关要素,引入“物理一事理一人理”(WSR)系统方法论,构建阅读偏食驱动因素WSR模型,并进行阐释分析。研究发现AI时代读者阅读偏食表现为阅读失衡、阅读偏好和阅读“厌食”,并受不同驱动因素影响:物理层面表现为内容题材、读物形式和内容质量,事理层面表现为阅读推荐、阅读引导和市场环境,人理层面表现为阅读习惯、阅读能力和阅读素养等。据此,文章提出物理维应优化阅读资源组合、事理维应加强外部有力引导、人理维应强化内部素养提升的治理策略。

智能制造计划可以跨越“生产率悖论”吗:来自智能制造试点示范项目的准自然实验

新一代人工智能是新质生产力的典型代表和提升全要素生产率(TFP)的重要源泉。基于微观企业视角,首先对“生产率悖论”现象的出现与消失进行了一体化的理论机制阐释。随后,以我国工信部实施的智能制造试点为准自然实验,利用2011-2021年A股制造业上市公司面板数据,借助交错双重差分法实证评估了智能制造计划影响企业TFP的总效应、机制、异质性及动态影响。研究发现:(1)智能制造计划虽在短期内对企业TFP的影响不显著,但在长期和总体上提升了企业TFP;(2)机制分析表明,通过促进企业创新,智能制造计划在短期、长期和总体上均能提升企业TFP;智能制造计划在初期和总体上扭曲了企业资源配置,但这一扭曲效应随时间推移逐渐减弱,从而对企业TFP的影响呈现出U型演变特征;(3)异质性分析发现,企业人力资本结构或数字化水平越高,智能制造计划可以在总体上越大且在时序上越快地提升企业TFP。研究结果为从微观企业层面去理解“生产率悖论”现象的出现与否、内在机制、严重程度和持续时长提供了理论解释和经验证据,对探寻提升TFP的新动力源和微观机制具有政策启示意义。

人工智能时代高校教师专业发展动力因素及发展路径

该文论述人工智能时代高校教师信息智能素养在专业发展中起着举足轻重的重用,指出面对人工智能时代教育行业的风暴,高校教师思想紧跟时代、以奋斗热情勇于接受新使命的必要性,分析人工智能时代促进高校教师专业发展的三种动力因素,关注社会环境、学习与创新和共情能力,提出以人为本的高校教师专业发展新路径。

基于人工智能技术的智慧烟草农业发展探究

在新一轮科技革命和产业变革的历史背景下,发展智慧烟草农业是现代烟草农业的内在要求,也是实现我国农业现代化发展的必然趋势和正确选择。人工智能作为智慧烟草农业发展的关键技术,在烟草农业领域有着巨大的潜力。因此,文章结合智慧烟草农业发展内涵,选取育苗、种植、烘烤、分级四个烟叶生产关键环节概述了人工智能技术,特别是目标检测技术、图像识别技术、智能控制技术的应用现状。在此基础上,分析人工智能技术发展的制约因素,并从产业应用角度展望了未来发展趋势,以期为烟草农业智慧化发展提供思路。