Uniqueness and Reproducibility of Semantic Intelligence: New Approach to the Notion of Self-Organization

A novel notion of self-organization whose major property is that it brings about the execution of semantic intelligence as spontaneous physico-chemical processes in an unspecified ever-changing non-uniform environment is introduced. Its greatest advantage is that the covariance of causality encapsulated in any piece of semantic intelligence is provided with a great diversity of its individuality viewed as the properties of the current response and its reproducibility viewed as causality encapsulated in any of the homeostatic patterns. Alongside, the consistency of the functional metrics, which is always Euclidean, with any metrics of the space-time renders the proposed notion of self-organization ubiquitously available.

Harnessing the power of artificial intelligence for human living organoid research

As a powerful paradigm,artificial intelligence(AI)is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary transformations.Living human organoids(LOs)have a great potential for in vitro reshaping many aspects of in vivo true human organs,including organ development,disease occurrence,and drug responses.To date,AI has driven the revolutionary advances of human organoids in life science,precision medicine and pharmaceutical science in an unprecedented way.Herein,we provide a forward-looking review,the frontiers of LOs,covering the engineered construction strategies and multidisciplinary technologies for developing LOs,highlighting the cutting-edge achievements and the prospective applications of AI in LOs,particularly in biological study,disease occurrence,disease diagnosis and prediction and drug screening in preclinical assay.Moreover,we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary technologies.The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle,narrow the gap between in vitro microphysiological models and the real human body,accurately predict human-related responses to external stimuli(cues and drugs),accelerate the preclinical-to-clinical transformation,and ultimately enhance the health and well-being of patients.

Transforming organic chemistry research paradigms:Moving from manual efforts to the intersection of automation and artificial intelligence

Organic chemistry is undergoing a major paradigm shift,moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence(AI).This transformative shift is being driven by technological advances,the ever-increasing demand for greater research efficiency and accuracy,and the burgeoning growth of interdisciplinary research.AI models,supported by computational power and algorithms,are drastically reshaping synthetic planning and introducing groundbreaking ways to tackle complex molecular synthesis.In addition,autonomous robotic systems are rapidly accelerating the pace of discovery by performing tedious tasks with unprecedented speed and precision.This article examines the multiple opportunities and challenges presented by this paradigm shift and explores its far-reaching implications.It provides valuable insights into the future trajectory of organic chemistry research,which is increasingly defined by the synergistic interaction of automation and AI.

Machine learning in solid organ transplantation:Charting the evolving landscape

BACKGROUND Machine learning(ML),a major branch of artificial intelligence,has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation.ML provides revolutionary opportunities in areas such as donorrecipient matching,post-transplant monitoring,and patient care by automatically analyzing large amounts of data,identifying patterns,and forecasting outcomes.AIM To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.METHODS On July 18,a thorough search strategy was used with the Web of Science database.ML and transplantation-related keywords were utilized.With the aid of the VOS viewer application,the identified articles were subjected to bibliometric variable analysis in order to determine publication counts,citation counts,contributing countries,and institutions,among other factors.RESULTS Of the 529 articles that were first identified,427 were deemed relevant for bibliometric analysis.A surge in publications was observed over the last four years,especially after 2018,signifying growing interest in this area.With 209 publications,the United States emerged as the top contributor.Notably,the"Journal of Heart and Lung Transplantation"and the"American Journal of Transplantation"emerged as the leading journals,publishing the highest number of relevant articles.Frequent keyword searches revealed that patient survival,mortality,outcomes,allocation,and risk assessment were significant themes of focus.CONCLUSION The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation.This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes.Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

The Entropy Modelling and Evaluation of Organization Intelligence

Organization intelligence is an important index for evaluating competition ability of organization. This paper applies the Entropy concept to evaluate the level of organization intelligence and set up the framework of entropy evaluation theory related to the environment, inter-structure and behavior fields.

Overview on artificial intelligence in design of Organic Rankine Cycle

Converting thermal energy into mechanical work by means of Organic Rankine Cycle is a validated technology to exploit low-grade waste heat.The typical design process of Organic Rankine Cycle system,which commonly in-volves working fluid selection,cycle configuration selection,operating parameters optimization,and component selection and sizing,is time-consuming and highly dependent on engineer’s experience.Thus,it is difficult to achieve the optimal design in most cases.In recent decades,artificial intelligence has been gradually introduced into the design of energy system to overcome above shortcomings.In order to clarify the research field of arti-ficial intelligence technique in Organic Rankine Cycle design and guide artificial intelligence technique to assist Organic Rankine Cycle design better,this study presents a preliminary literature summary on recent progresses of artificial intelligence technique in organic Rankine cycle systems design.First,this study analyzes four main procedures which constitute a typical design process of Organic Rankine Cycle systems and finds that design problems encountered during design process can be divided into three categories:decision making,parameter optimization and parameter prediction.In the second section,a detailed literature review on each design proce-dures using artificial intelligence algorithms is presented.At last,the state of art in this field and the prospects for the future work are provided.

Computational intelligence:From mathematical point of view

A simple but illustrative survey is given on various approaches of computational intelligence with their features, applications and the mathematical tools involved, among which the simulated annealing, neural networks, genetic and evolutionary programming, self-organizing learning and adapting algorithms, hidden Markov models are recommended intensively. The common mathematical features of various computational intelligence algorithms are exploited.Finally, two common principles of concessive strategies implicated in many computational intelligence algorithms are discussed.

Biologically inspired self-organizing networks

Information networks are becoming more and more complex to accommodate a continuously increasing amount of traffic and networked devices,as well as having to cope with a growing diversity of operating environments and applications. Therefore,it is foreseeable that future information networks will frequently face unexpected problems,some of which could lead to the complete collapse of a network. To tackle this problem,recent attempts have been made to design novel network architectures which achieve a high level of scalability,adaptability,and robustness by taking inspiration from self-organizing biological systems. The objective of this paper is to discuss biologically inspired networking technologies.

有组织教研:人工智能环境下教师研修的有序进化

传统教师教研形式因客观性不强、持续性不够、衔接性不足,与时代所需要的高质量教研相悖,影响了教师的专业发展。基于生态进化理论,建构以人工智能技术为教师提供选题、过程支持、资源制度、工具辅助和协同机制的有组织教研模式,以提升教研与教师的个体适配,形成有组织的多主体行动网络,并在有序原理和整体原理的框架下,建构了智能技术支持的教师有组织教研运行模型。有组织教研的关键问题是教师动态需求的系统捕获、内容进化的智能控制、自适应路径的有效调节、研修空间的适应性调整、语义关联的建立,并形成面对理论与实践冲突、转化机制异化、主体与相关要素不适切等问题的处理机制。要实现人工智能支持的有组织教研,需要按照有组织教研的运行建立设计人工智能元平台,布局大数据采集平台,开发教育人工智能的算力,并在制度和理念的双向演进中通过实践进行优化,实现人工智能下教师研修模式的迭代。

作物农艺性状与形态结构表型智能识别技术综述

[目的/意义]作物农艺性状与形态结构表型智能识别是作物智慧育种的主要内容,是研究“基因型—环境型—表型”相互作用关系的基础,对现代作物育种具有重要意义。[进展]大规模、高通量作物表型获取设备是作物表型获取、分析、测量、识别等的基础和重要手段。本文介绍了高通量作物表型主流平台和感知成像设备的功能、性能以及应用场景。分析了作物株高获取、作物器官检测与技术等农艺性状智能识别和作物株型识别、作物形态信息测量以及作物三维重建等形态结构智能识别技术的研究进展及挑战。[结论/展望]从研制新型低成本田间智能作物表型获取与分析装备、提升作物表型获取田间环境的标准化与一致性水平、强化田间作物表型智能识别模型的通用性,研究多视角、多模态、多点连续分析与时空特征融合的作物表型识别方法,以及提高模型解释性等方面,展望了作物表型技术主要发展方向。

传统知识组织方法的智能力

[目的/意义]通过对传统知识组织方法的体系、规则分析,凝练出传统知识组织方法的智能能力,并将其融入人工智能技术,以增强人工智能信息处理的精准与高效。[方法/过程]文章通过回顾知识组织的发展过程,分析传统知识组织方法的内在结构,深入探讨了传统知识组织方法的机理。[结果/结论]研究认为,知识组织经过数百年的发展演进,能从不同角度去反映各学科的知识体系、学科体系,能从知识多样化的关联建立知识间的语义关系,可采用科学的知识组织方法将不同形式、不同类型、不同结构的知识关联并融合在一起,这些能力为人工智能把握知识体系、探索知识间的关系、推理科学问题中的相关可能性,以及拓展或聚焦知识和词汇、分析事物之间的关系等提供了更加有效的途径。人工智能在信息处理领域的发展应当与知识组织密切配合,充分发挥知识组织的潜在智能力。

21st century critical care medicine:An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units(ICUs).This abstract provides a concise summary of the latest developments in critical care,highlighting key areas of innovation.Recent advancements in critical care include Precision Medicine:Tailoring treatments based on individual patient characteristics,genomics,and biomarkers to enhance the effectiveness of therapies.The objective is to describe the recent advancements in Critical Care Medicine.Telemedicine:The integration of telehealth technologies for remote patient monitoring and consultation,facilitating timely interventions.Artificial intelligence(AI):AI-driven tools for early disease detection,predictive analytics,and treatment optimization,enhancing clinical decision-making.Organ Support:Advanced life support systems,such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support.Infection Control:Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections.Ventilation Strategies:Precision ventilation modes and lung-protective strategies to minimize ventilatorinduced lung injury.Sepsis Management:Early recognition and aggressive management of sepsis with tailored interventions.Patient-Centered Care:A shift towards patient-centered care focusing on psychological and emotional wellbeing in addition to medical needs.We conducted a thorough literature search on PubMed,EMBASE,and Scopus using our tailored strategy,incorporating keywords such as critical care,telemedicine,and sepsis management.A total of 125 articles meeting our criteria were included for qualitative synthesis.To ensure reliability,we focused only on articles published in the English language within the last two decades,excluding animal studies,in vitro/molecular studies,and non-original data like editorials,letters,protocols,and conference abstracts.These advancements reflect a dynamic landscape in critical care medicine,where technology,research,and patient-centered approaches converge to improve the quality of care and save lives in ICUs.The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

铁路三维电子沙盘搭建及其应用研究

为解决目前铁路行业电子沙盘存在的表达方式不一、应用内容单一、数据泄露风险大、数据加载效率低等问题,研究海量地理地质及BIM模型的数据处理、管理和应用方法,建立以BIM+GIS为技术核心的标准化电子沙盘,从而更好地服务于铁路设计和建造。根据鲁南高铁、拉林铁路、成渝中线高铁等项目实践总结,在中国铁路BIM联盟和国铁集团相关标准基础上提出了面向电子沙盘应用的地形、影像、BIM模型、专题图的数据制作标准,从坐标转换、数据转换、数据切片、知识图谱建立、模型发布、前端数据汇集的方法完成了电子沙盘的搭建。最后,在电子沙盘中实现了百公里级、多专业数据的在线高效组织和管理,提出的方法和标准适用于铁路设计、建设过程的管理和分析应用。

脑卒中后构音障碍患者构音器官运动功能与言语清晰度的相关性

目的 探讨脑卒中后构音障碍患者构音器官运动功能与言语清晰度(SI)之间的关系。方法 选择2020年11月至2023年10月北京博爱医院收治的住院脑卒中后构音障碍患者67例,采用SI测试和Frenchay构音障碍评价(FDA)进行评定,SI≤65%为低SI组,SI> 65%为高SI组。结果 除颌位置外,高SI组FDA各项评分均低于低SI组(Z> 1.543, P <0.05)。FDA各项评分均与SI呈负相关(r <-0.343, P <0.001),其中与唇运动、舌运动和喉控制3项评分的相关性最大(r <-0.6)。结论 脑卒中后构音障碍患者构音器官运动功能与SI相关,特别是唇、舌、喉的功能。

基于组织-技术依存网络的技术融合机理

技术融合能够显著提高企业的创新能力,对技术融合机理的深入探索有助于选择合适的创新伙伴和融合技术。构建依存型网络分析模型,探究技术特征和技术依附的组织特征与技术融合的关系,并对人工智能产业进行实证研究。研究表明:吸收能力强或扩散能力强的技术容易吸收或流向其他技术;技术成熟度高、技术邻近性强的两种技术容易发生双向流动;被多个组织拥有的共性技术不容易吸收其他技术,但容易流向其他技术,单个组织内拥有的技术间容易发生双向流动。

智能网联车辆组队与离队行为对混合交通流影响特性分析

针对智能网联车队的组队和离队场景从宏观基本图与微观跟驰行为2方面进行建模:首先在宏观层面,研究含有智能网联车队的混合交通流组态概率分布,紧接着对网联车渗透率p以及车队长度S这2个重要参数分别研究其对混合交通流通行能力的影响;其次,从微观角度出发,分别对车队的组队场景和分离场景进行仿真研究其微观运行状况。结果表明:当网联车渗透率为1时,道路通行能力可达到3777辆/h,是不含车队交通流通行能力的1.5倍以上;车辆组成车队所需的时间主要受车间距离以及车队规模影响;车队分离场景的效率以及稳定性均高于组队场景。研究结论可支撑未来车队动态演化过程的研究,同时也可为车队的宏观与微观运行管理提供理论支撑。

我国研究生教育管理组织的历史演进与嬗变

研究生教育管理组织是研究生教育改革发展的重要保障。随着我国研究生教育改革发展的不断深入,研究生教育管理组织也在不断地调整和优化。从1978年恢复研究生教育至今,我国研究生教育管理组织大致经历了奠基期、完善期、转型期三个阶段。我国研究生教育管理主要有三部分即研究生教育、学科建设和学位授予,其管理组织分为研究生管理组织、学科管理组织和学位管理组织。不同历史阶段三种类型组织的形态以及关系具有不同的表现。数智时代的来临以及建设研究生教育强国步伐的加快,我国研究生教育管理组织进入了一个形态重塑的变革时期。

电子生命与一般机体学——智能时代生命导向的本体论

智能时代的来临带来了一个新的问题,即智能的数字存在物是否具有与人类一样的“意识”?本文认为,人工智能的意识问题,在目前还言之过早,意识在生物学上也是一个相对高阶的产物,而在有机物发展的早期,更重要的是生命问题,我们或许更应该思考如何界定数字空间中的生命问题,即通过各种电子元件和数字代码和信号构成的数字生命。为了理解这种电子生命,我们需要突破传统。因为,今天的生命概念已经超越了纯粹有机体的器官身体的生命,构成了有机与无机混成在一起的机体学。当我们从卢克莱修、薛定谔、马图拉纳和瓦雷拉、雷蒙·鲁耶的生命哲学讨论中,将生命理解为一个自生系统,进一步提出电子生命在其自生系统内在部分与外在环境之间的建立了熵与负熵的平衡。而通过西蒙东的技术物则实现了人类生命体与外在缔结环境之间的物质交往,会极大地拓展了超越有机体的生命概念。而在智能时代的今天,技术物已经演化为数码物,进一步进化为斯蒂格勒的一般机体学,生命变成了盖娅式的整体生命。

技术-组织互构下的乡村数智治理:一种基于理论与行动的审思

数智技术嵌入乡村治理能够推动乡村振兴战略的实施,是乡村治理现代化的客观需求。在乡村数智治理中,软硬件建设、基层行动者以及治理结构与数智技术存在一定程度的不适应或不匹配。技术组织互构理论为乡村数智治理走出多重困境提供了新的行动进路指导。技术组织互构的实质在于技术推动组织变革,组织为技术的发展设定规范,技术和组织的双向互构共同形成乡村的数智治理逻辑。因此,未来的乡村数智治理行动可以从“技术组织双向协同”“技术+组织合力”以及“技术与组织互制”三个方向进行路径设计。

蒙陕接壤区煤层顶板涌水水源智能判别方法

蒙陕接壤区煤炭高强度开采诱发的煤层顶板水害问题日益凸显,高效智能地判别煤层顶板涌水水源是顶板水害防治的关键。以蒙陕接壤区3个典型矿井为研究对象,将无机指标K^(+)+Na^(+)、Ca^(2+)、Mg^(2+)、Cl^(-)、SO_(4)^(2-)、HCO_(3)^(-)、TDS和有机指标UV_(254)、TOC、溶解性有机质(DOM)的荧光光谱作为判别指标,利用主成分分析法(PCA)对80组地下水水样数据进行主成分提取,提出一种人工鱼群算法(AFSA)改进随机森林(RF)的PCA-AFSA-RF顶板涌水水源智能判别方法。首先,建立PCA-RF判别模型,其准确率(A_(c))、精确率(P_(r))、召回率(R_(c))和F-measure指数(f_(1))分别达到了83.00%、83.17%、80.42%和79.57%;其次,通过AFSA对PCA-RF判别模型中决策树数目、树深和内部节点分裂所需的最小样本数进行寻优,在AFSA中引入遗传机制以避免陷入局部最优,建立基于PCA-AFSA-RF的煤层顶板涌水水源智能判别模型,该模型A_(c)、P_(r)、R_(c)、f_(1)分别达到92.18%、91.11%、87.58%和88.82%,较PCA-RF分别提高9.18%、7.94%、7.16%和9.25%,回代准确率达到97.50%;最后,利用该模型对12个矿井水水样进行判别,结果与现场实际相一致,表明AFSA改进后的PCA-RF模型具有更好的准确性和泛化能力。研究结果可为煤层顶板涌水水源的准确判别提供新方法。