Mutural Learning Among Civilizations with Great Rivers as the Medium 2023 World Great Rivers Civilizations Forum

The 2023World Great Rivers Civilizations Forum(Zhengzhou,China)was organized by the Ministry of Culture and Tourism,People's Daily,the National Cultural Heritage Administration and the Henan Provincial People's Government.

文明1(civilized)、文明2(civilization)、文明3(civitas)——论“文明”一词的三种不同含义

学术界对于“什么是文明”存有争议的原因,是把文明(civilized)、文明社会(civilization)和国家(civitas)三个不同的概念混为一谈造成的。“文明1”大致相当于英语civilized(文明的)。文化是人群的生活方式,但是不仅彬彬有礼、琴棋书画、轻歌曼舞、美味佳肴是文化,愚昧粗鲁、恶俗陋习、野风蛮舞、吃糠咽菜也是生活方式,所以文明1不完全等同于文化,而只有被认可与受推崇的文化才被称为文明。无论古今中外,作为文化的文明1都是褒义的,文明1即“文明”的本义,也是对文化的价值判断。“文明2”相当于英语civilization(文明社会),摩尔根用以指称继蒙昧社会与野蛮社会之后出现的、高于蒙昧与野蛮状态的社会阶段。作为社会形态的文明2当然比蒙昧与野蛮社会要进步,但是文明社会并非一切都是文明的,其中充斥着种种不文明的蒙昧与野蛮,所以不能说文明2就是“文明的”社会。“文明3”相当于英语civitas(国家),是中国学者根据恩格斯的“国家是文明社会的概括”得出的概念,系指继部落联盟或酋邦之后出现的社会组织形态。因为不同国家的国体与政体各不相同,有了国家之后并非一切都变得比野蛮社会更文明了,所以作为国家的文明3也不都是“文明的”国家。

Civil3d技术在河道治理中的应用分析

为了切实加强水环境保护及水资源管理与利用,近年来我国一直在加快和加大力度落实河道治理,要想保证河道治理达到预期效果,并最大程度提高河道治理工程生态效益,首先要确保河道治理设计与施工方案的科学合理性,可见借助现代化技术手段,优化和改进河道治理设计与规划是很有必要的。基于此本文首先进行Civil3d技术的梳理与说明,继而详细分析Civil3D软件在某河道治理项目的具体应用,旨在与相关工作人员相互交流、共同探讨。

基于Q-Learning的航空器滑行路径规划研究

针对传统算法规划航空器滑行路径准确度低、不能根据整体场面运行情况进行路径规划的问题,提出一种基于Q-Learning的路径规划方法。通过对机场飞行区网络结构模型和强化学习的仿真环境分析,设置了状态空间和动作空间,并根据路径的合规性和合理性设定了奖励函数,将路径合理性评价值设置为滑行路径长度与飞行区平均滑行时间乘积的倒数。最后,分析了动作选择策略参数对路径规划模型的影响。结果表明,与A*算法和Floyd算法相比,基于Q-Learning的路径规划在滑行距离最短的同时,避开了相对繁忙的区域,路径合理性评价值高。

LEARNS模式健康教育对初产妇育儿胜任感及母乳喂养的影响

目的探究LEARNS模式健康教育对初产妇育儿胜任感及母乳喂养的影响。方法2023年3-6月,采用便利抽样法选取安徽省某医院产科两个病区收治的130例初产妇为研究对象,将2023年3-4月收治的65例初产妇作为对照组,给予常规健康教育;2023年5-6月收治的65例初产妇作为观察组,采用LEARNS模式健康教育。比较两组初产妇育儿胜任感、母乳喂养情况及产后抑郁水平。结果观察组初产妇产后育儿胜任感总分及各维度分数均高于对照组(均P<0.05);观察组初产妇首次母乳喂养成功率及产后42 d内纯母乳喂养率较对照组更高(均P<0.05);观察组初产妇产后7 d的抑郁量表得分低于对照组(P<0.05)。结论LEARNS模式健康教育可提高初产妇的育儿胜任感,改善初产妇母乳喂养情况、减轻其产后抑郁情绪。

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

改进Q-Learning的路径规划算法研究

针对Q-Learning算法学习效率低、收敛速度慢且在动态障碍物的环境下路径规划效果不佳的问题,本文提出一种改进Q-Learning的移动机器人路径规划算法.针对该问题,算法根据概率的突变性引入探索因子来平衡探索和利用以加快学习效率;通过在更新函数中设计深度学习因子以保证算法探索概率;融合遗传算法,避免陷入局部路径最优同时按阶段探索最优迭代步长次数,以减少动态地图探索重复率;最后提取输出的最优路径关键节点采用贝塞尔曲线进行平滑处理,进一步保证路径平滑度和可行性.实验通过栅格法构建地图,对比实验结果表明,改进后的算法效率相较于传统算法在迭代次数和路径上均有较大优化,且能够较好的实现动态地图下的路径规划,进一步验证所提方法的有效性和实用性.

基于LEARNS模式的多元化教育对龈上洁治术患者应激反应和满意度的影响

目的:研究基于LEARNS模式的多元化教育对龈上洁治术患者应激反应和满意度的影响。方法:选取笔者医院2018年1月-2023年1月收治的60例龈上洁治术患者作为研究对象。将患者随机化分为观察组和对照组,各30例。对照组采取常规健康教育方式,观察组采取基于LEARNS模式的多元化教育方式,比较两组患者应激反应、对龈上洁治术的满意度、口腔清洁度、自我效能及自护能力。结果:相比于干预前,干预后两组患者心理应激反应得分均有下降,且观察组患者相较于对照组更低(P<0.05);观察组患者相较于对照组满意度更高(P<0.05);相比于干预前,干预后两组患者口腔清洁度得分均降低(P<0.05),且观察组相较于对照组更低(P<0.05);相比于干预前,干预后两组患者自我效能得分均有升高(P<0.05),且观察组相较于对照组更高(P<0.05);相比于干预前,干预后两组患者自护能力得分均有上升,且观察组患者相较于对照组更高(P<0.05)。结论:相比于应用常规健康宣教,应用基于LEARNS模式的多元化教育对龈上洁治术患者而言,更能减轻心理应激反应、提升满意度和口腔清洁度、提高自我效能、增强自护能力,适合在临床应用推广。

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

LEARNS认知图式教育对乳腺癌MRI增强检查患者心理状态的影响

目的:探讨乳腺癌磁共振成像(MRI)增强扫描患者应用LEARNS模式下认知图式教育的效果。方法:以便利抽样法采集2023年1月31日-7月31日在某院接受MRI增强扫描检查的乳腺癌患者为对照组,2023年8月1日-2024年2月29日在该院接受MRI增强扫描检查的乳腺癌患者为研究组。对照组接受常规护理,研究组给予LEARNS模式下认知图式教育,对比两组心理状态、病耻感水平、应对方式、不良事件。结果:研究组坚韧性、力量性及乐观性及总分均高于对照组(t=50.575,44.798,19.077,86.704;P<0.001);研究组社会排斥、经济不安全感、内在羞耻感、社会隔离及总分均低于对照组(t=17.492,10.741,13.396,18.143,33.372;P<0.001);研究组简易应对方式问卷(SCSQ)中积极应对得分高于对照组、消极应对得分低于对照组(t=32.750,34.481;P<0.05);研究组不良事件发生率低于对照组(χ^(2)=6.093,P<0.05)。结论:在乳腺癌MRI增强扫描患者中应用LEARNS模式下认知图式教育有助于改善心理状态,降低患者病耻感水平,促进患者采用积极应对方式,并降低不良事件的发生率。

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Creating a New Era for Global Civilization Development by Deepening Exchanges and Mutual Learning

On March 15, 2023, General Secretary Xi Jinping proposed for the first time the Global Civilization Initiative(GCI) at the CPC in Dialogue with World Political Parties High-level Meeting. The GCI, like the Global Development Initiative and the Global Security Initiative, is another important public good provided by China in the new era to address common global challenges and build a shared future for humanity.

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

基于LEARNS模式的健康教育对PCI术后病人自我管理能力及生活质量的影响

目的:探讨应用LEARNS健康教育模式对经皮冠状动脉介入治疗(PCI)术后病人自我管理能力及生活质量的影响。方法:选取某三级甲等医院2022年1月—6月80例PCI术后病人为研究对象,按照随机数字表法将其分为观察组和对照组,每组40例。对照组采用术后常规健康教育模式,观察组在对照组基础上使用LEARNS健康教育模式对病人进行健康宣教,在病人干预前、后分别使用中国心血管病人生活质量评定问卷和冠心病自我管理行为量表对病人进行评估。结果:观察组病人自我管理能力和生活质量得分高于对照组(P<0.05)。结论:LEARNS健康教育模式在PCI术后病人中的应用效果显著,能够明显提高病人的自我管理能力和生活质量。

Flood Velocity Prediction Using Deep Learning Approach

Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.