Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Application of artificial intelligence to rock mechanics:An overview

Different artificial intelligence(AI)methods have been applied to various aspects of rock mechanics,but the fact that none of these methods have been used as a standard implies that doubt as to their generality and validity still exists.For this,a literature review of application of AI to the field of rock mechanics is presented.Comprehensive studies of the researches published in the top journals relative to the fields of rock mechanics,computer applications in engineering,and the textbooks were conducted.The performances of the AI methods that have been used in rock mechanics applications were evaluated.The literature review shows that AI methods have successfully been used to solve various problems in the rock mechanics field and they performed better than the traditional empirical,mathematical or statistical methods.However,their practical applicability is still an issue of concern as many of the existing AI models require some level of expertise before they can be used,because they are not in the form of tractable mathematical equations.Thus some advanced AI methods are still yet to be explored.The limited availability of dataset for the AI simulations is also identified as a major problem.The solutions to the identified problems and the possible future research focus were proposed in the study subsequently.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone:A review

In recent years,the application of smartphone in various fields has received great attention,and it has become a promising tool in virus detection,data processing and data exchange.During the rapid spread of COVID-19 around the world,many traditional detection methods have been combined with smartphone to assist in the analysis and detection of the novel coronavirus(SARS-CoV-2),including electrochemistry,fluorescence and colorimetry.With the gradual development of artificial intelligence(AI),the combination of AI and smartphone to analyze SARS-CoV-2 was also the focus of research.Based on the summary of the traditional methods combined with smartphone to detect SARS-CoV-2 virus,in addition to AI-based data processing,AI algorithms are also employed for SARS-CoV-2 detection itself.This review discussed both strategies and focused on the application of the former.The combination of AI algorithm and smartphone to detect SARS-CoV-2 has high accuracy,which is more conducive to meeting the needs of portable detection.In addition,the classification of SARS-CoV-2 virus samples in biological fluids such as blood and saliva was also discussed.Finally,this paper briefly discussed the limitations of using smartphone analysis to detect SARS-CoV-2,as well as the prospect and future development of virus detection.In conclusion,the detection methods based on smartphone and AI algorithms show great potential in the detection of SARS-CoV-2 and can be a valuable complement to traditional analysis methods.

A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Design methods for antimicrobial peptides with improved performance

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult.In this regard,developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority.Antimicrobial peptides(AMPs),a ubiquitous class of naturally occurring compounds with broadspectrum antipathogenic activity,hold significant promise as an effective solution to the current antimicrobial resistance(AMR)crisis.Several AMPs have been identified and evaluated for their therapeutic application,with many already in the drug development pipeline.Their distinct properties,such as high target specificity,potency,and ability to bypass microbial resistance mechanisms,make AMPs a promising alternative to traditional antibiotics.Nonetheless,several challenges,such as high toxicity,lability to proteolytic degradation,low stability,poor pharmacokinetics,and high production costs,continue to hamper their clinical applicability.Therefore,recent research has focused on optimizing the properties of AMPs to improve their performance.By understanding the physicochemical properties of AMPs that correspond to their activity,such as amphipathicity,hydrophobicity,structural conformation,amino acid distribution,and composition,researchers can design AMPs with desired and improved performance.In this review,we highlight some of the key strategies used to optimize the performance of AMPs,including rational design and de novo synthesis.We also discuss the growing role of predictive computational tools,utilizing artificial intelligence and machine learning,in the design and synthesis of highly efficacious lead drug candidates.

智能助产术教学法——以“智能苏格拉底会话机器人”教学实践为例

当前,生成式人工智能与学生的人机会话主要是“知识讲述”型会话关系,这会影响学生的高阶思维能力发展。解决这一问题的关键是,如何将人机“知识讲述”型会话关系,转变为“知识转化”型会话关系。为此,研究者以助产术理论、ChatGPT、学习分析和腾讯QQ工具为基础,探索智能助产术教学法的学习发生机制,开发智能苏格拉底会话机器人,构建智能助产术教学模式。本研究采用准实验方法,以“远程教育学”课程为教学内容,以教育技术学专业本科生为对象,开展以智能会话机器人支持的教学实验。实验结果表明,智能助产术教学与直接使用ChatGPT的教学相比,能显著提升学生的问题解决能力、创新能力和协作学习能力,但是对学习绩效、批判性思维能力和自我效能感的影响不显著。为提高教学效果,研究者需提升计算机的系统算力,开发批判性思维学习支架,构建人机适切性互动机制,研制自我效能感提升策略。

AI-Enhanced Performance Evaluation of Python, MATLAB, and Scilab for Solving Nonlinear Systems of Equations: A Comparative Study Using the Broyden Method

This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for researchers and practitioners in selecting tools for their unique challenges.

基于深度学习SSD算法的高密度电法智能解译方法技术研究

高密度电法在探测灰岩区地下溶洞病害体方面得到广泛应用,但高密度电法反演结果依赖于初始模型,存在多解性,地质解译容易受专业人员主观因素影响。为此,本文从具有唯一性的视电阻率数据出发,研究了基于深度学习的SSD(Single Shot Multi-box Detector)目标检测算法的视电阻率异常智能解译方法技术。针对岩溶地质病害,设计了不同填充类型、形状、规模、数量的溶洞电性异常模型,利用Res2dmod软件进行视电阻率正演计算,构建了包含1400个样本的高密度电法视电阻率智能解译学习样本库(样本和标签)。基于TensorFlow框架,建立了基于深度学习SSD算法的高密度电法视电阻率异常智能解译方法技术,使用学习样本库训练网络权值,训练结束后对高密电法温纳装置视电阻率异常进行智能解译,单个视电阻率剖面异常智能解译耗时不到1 s,各类目标(填充型溶洞、未填充型溶洞)平均准确率为90.68%。研究结果表明:基于SSD算法的高密度电法视电阻率异常智能解译技术可显著提高高密度电法视电阻率解译效率,避免专业人员主观因素影响。

体教融合背景下陕北高校篮球运动队管理系统的设计研究

从校园篮球的推进到落实,从体教分离到体教融合,体现了体育与教育融合是时代所趋。该文主要在体教融合背景下,通过走访延安市各大高校,了解学生希望达成的目标和现在训练过程中遇到的困难。针对这些问题,利用互联网和大数据对高校篮球校队运动员开展训练日程管理,方便教师安排训练任务及跟踪运动员的训练任务完成情况,不断提高高校校队篮球运动员的训练质量。最后,通过SWOT分析,验证了该训练管理系统的有效性和可行性,为高校篮球运动队的可持续发展提供了有益的参考和借鉴。

煤中杂物的危害与洗选过程中的有效清除方法

煤炭中混入的各种杂物不仅影响煤炭质量,还会对煤炭加工设备、运输工具造成损害,甚至引发安全事故,有效排除煤中杂物是煤炭生产亟待解决的关键问题。杂质脱除的难易程度随着科学技术的发展而发生着变化,也决定着除杂选用的方法与手段。传统的除杂很大程度上依靠杂质的物理性质,金属类杂物一般采用除铁器清除,跳汰分选与浅槽分选过程可清除部分轻质杂物,拦杂钩和拦杂网也可清除部分轻质杂物,往往多种方法联合使用,发挥各自的优势。随着人工智能技术的发展,通过实时捕获杂物的图像和三维数据,运用计算机视觉算法对杂物进行快速、准确的识别与定位,随后指导机械手进行精准抓取,可实现杂物的精准分离。该方法实施的关键是需要建立较完备的数据集、设计精准识别算法和抓取控制策略。煤炭洗选过程中杂物的智能清除有利于推动煤炭行业的智能化转型、提高生产效率和产品质量、降低劳动强度和成本。

地震事件分类识别软件

非天然地震事件分类是地震监测业务部门的日常工作之一。本研究主要针对地震、爆炸和矿震的分类问题,在地震波数据处理、特征提取和人工智能综合分类的研究基础上,基于Qt开发框架,结合Python、 Matlab等多种编程语言,开发了一个具有良好的可移植性和可扩展性、具有自主知识产权的地震分类识别软件。该软件可以部署在不同操作系统上,由七个模块组成:地震数据导入模块、数据处理模块、特征提取模块、综合分类模块、特征分析模块、当量估算模块和结果分析模块。软件集成了多种时频特征提取技术和人工智能分类方法,形成了较为完整的地震类型判定流程。软件内置的地震事件分类模型准确率高于90%,适用范围较广,已推广应用于多个地震监测部门,并取得了较好的应用成果,提高了对非天然地震的快速分析能力。

人工智能辅助的苏格拉底式对话教学在医学分子生物学中的实践

文章探讨了在医学分子生物学教学中应用基于人工智能工具的苏格拉底式对话教学法的创新模式。通过结合苏格拉底式对话教学法和医学分子生物学教学的特点和挑战以及人工智能工具在促进教学效果和学习体验方面的关键作用,提出了针对大班额实施苏格拉底式对话教学法的挑战和对策,并进行了实践验证。研究结果表明,基于人工智能工具的苏格拉底式对话教学法能够提高学生的参与度和学习动力,促进批判性思维能力的培养,并实现个性化和差异化教学。这一创新模式将为医学分子生物学教学提供有效的教学方法和策略,推动医学教育的发展和进步。

人工智能辅助评价技术在火山岩气藏开发中的应用

结合COMOST人工智能辅助评价技术对典型火山岩气藏区块开展了多因素敏感性分析,自动历史拟合,方案最优化以及不确定分析等评价,建立了一种评价火山岩气藏开发指标预测、方案优化及风险分析的新方法,为以后气藏的合理指标预测及开发技术界限的界定提供参考和依据。

动态不确定因果图在中医诊断中的应用探讨

动态不确定因果图(DUCG)已成为中医药领域新兴的、先进的知识表示与推理模型。为更好地应用DUCG为中医临床提供诊断推理与决策支持,在归纳总结DUCG中医药领域研究与应用情况的基础上,分析现阶段DUCG在中医诊断中存在的主要问题,包括中医术语规范统一和中医药知识库质量问题、推理算法和模型建造的方法选择与设计问题、DUCG中医诊断模型的平台化和产品化问题等,并据此展开应用思路与方法探讨,提出应深挖DUCG的技术内涵,根据临床实际需求选择精准、高效的推理建模方法,建立符合中医药理论思想、具有中医特色的智能辅助诊断模型,加强DUCG协同研究平台及产品的开发应用。

模式识别与人工智能研究生课程建设实践

随着信息技术的发展和智能制造的提出,模式识别与人工智能作为一门信息技术类的重要课程,已经成为助力新产业变革不可或缺的力量。面向研究生开设模式识别与人工智能课程,对完善交叉学科结构、提高学生的科研能力和就业竞争能力等具有重要意义。为激发学生的学习兴趣、高质量完成培养目标,提出了案例教学、思政入课、以赛促教的课程建设方案,围绕教学方法和课程建设进行了教学实践。根据问卷调查结果可知,绝大多数学生对课程建设的评价良好,认为通过课程学习提升了自主学习能力,锻炼了科研思维,增强了解决问题的能力。

人工智能场域下教师角色定位实现的研究

随着科技的进步,教育领域正在经历一场深刻的变革,人工智能作为这场变革的重要推动力,正在逐渐改变教师的教学方式和角色定位。研究旨在深入探讨教师在教育人工智能场域下如何实现知识信息、数字资源智能传播与呈现的掌舵者,基于证据的个性化教学决策者与分析者,以及智能时代学生辅导的情感补位者的角色定位,并提出具体的实现措施,对于教师适应智能时代,提高教学质量,满足学生个性化需求具有重要的实践意义和理论价值。

生成式人工智能教育应用政策比较:共识、差异与实施进路

生成式人工智能教育应用近年来备受瞩目,其强大的生成能力为教育领域注入新的活力。然而,我国关于生成式人工智能教育应用方面的法律法规仍处于空白状态,这无疑对生成式人工智能广泛应用带来了不小挑战。为弥补这一政策空白,运用内容分析法,对国际上已发布的具有代表性的三项政策,从教师教学、学生学习、教学评价、教学研究、学习辅导和教育管理六个核心维度进行分析。在此基础上,挖掘三项政策的共识、差异,以期为我国生成式人工智能教育应用的政策制定提供借鉴,推动其在教育领域的纵深发展和广度辐射,并提出五条实施进路:一是重塑教育蓝图,法规保障消除潜在隐患;二是强化监管体系,法律监管防范潜在风险;三是聚焦育人目标,不断提高教师数字胜任力;四是提升学生数字素养,落实人本主义理念;五是适配场景与对象,创新应用模式和方法。

人工智能赋能高校思想政治教育方法创新研究

人工智能赋能思想政治教育方法创新是适应社会发展、把握时代脉搏的现实之需,是高校思想政治教育改革创新的内在要求,是因材施教、培育时代新人的重要抓手。当前,人工智能赋能高校思想政治教育方法创新存在思想政治教育者数字素养“本领恐慌”、高校与人工智能建设的相关制度缺位、数据失范等困境,需坚持工具性和价值性相统一、虚拟性和现实性相统一、教育者主导性和受教育者主体性相统一的原则,揭示人工智能以数智化理念、多技术集成、全场域共生等优势为高校思想政治教育赋能的作用机理,并在人才培养、课程教学、日常管理、考核评价等方面展开具体实践。为解决人工智能赋能高校思想政治教育方法的困境,需从提升教育者数字素养、健全人工智能建设保障机制、加强人工智能风险管理上下功夫,从而加快高校思想政治教育数字化运用和创新,促进高校思想政治教育高质量发展。