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。

基于改进Q-Learning的移动机器人路径规划算法

随着移动机器人在生产生活中的深入应用,其路径规划能力也需要向快速性和环境适应性兼备发展。为解决现有移动机器人使用强化学习方法进行路径规划时存在的探索前期容易陷入局部最优、反复搜索同一区域,探索后期收敛率低、收敛速度慢的问题,本研究提出一种改进的Q-Learning算法。该算法改进Q矩阵赋值方法,使迭代前期探索过程具有指向性,并降低碰撞的情况;改进Q矩阵迭代方法,使Q矩阵更新具有前瞻性,避免在一个小区域中反复探索;改进随机探索策略,在迭代前期全面利用环境信息,后期向目标点靠近。在不同栅格地图仿真验证结果表明,本文算法在Q-Learning算法的基础上,通过上述改进降低探索过程中的路径长度、减少抖动并提高收敛的速度,具有更高的计算效率。

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。

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.

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。

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模式构建维持性血液透析病人营养健康教育方案

目的:构建基于LEARNS模式的维持性血液透析病人营养健康教育方案,为临床实施“以病人为中心”的营养健康教育提供参考。方法:研究小组在文献研究、小组讨论基础上初步制定营养健康教育方案,采用德尔菲专家函询法对23名专家进行函询,结合条目筛选标准和专家建议,利用层次分析法确定条目权重,构建基于LEARNS模式的维持性血液透析病人营养健康教育方案。结果:2轮咨询专家积极系数为100.0%和91.30%,权威系数为0.874和0.891;变异系数为0.00~0.38和0.00~0.22,肯德尔和谐系数为0.205和0.222(P<0.01)。最终形成包括3个一级条目、32个二级条目的维持性血液透析病人营养健康教育方案。层次分析各级条目一致性系数(CR值)均<0.1。结论:构建的维持性血液透析病人营养健康教育方案具有较高的科学性和可行性,可以为维持性血液透析病人营养健康教育提供借鉴。

LEARNS框架下的健康教育对慢性肾脏病患者的影响

目的研究LEARNS框架下的健康教育对慢性肾脏病患者的影响。方法选取郑州大学第二附属医院2022年5月至2024年1月90例慢性肾脏病患者并进行随机分组,对常规组45例给予常规健康教育,对研究组45例给予LEARNS框架下的健康教育,对比两组患者的自我管理能力、肾功能、主观幸福感、生活质量。结果研究组干预后成年人健康自我管理能力评测量表(AHSMSRS)评分高于常规组(P<0.05);研究组干预后胱抑素C(CYSC)水平低于常规组,血肌酐(Scr)、尿素氮(BUN)水平高于常规组;研究组干预后总体幸福感量表(GWB)评分高于常规组(P<0.05);研究组干预后肾脏病生活质量量表(KDQOL-36)评分高于常规组(P<0.05)。结论LEARNS框架下的健康教育能够促进慢性肾脏病患者肾功能的恢复,提高患者的自我管理能力和生活质量。

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.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

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.

糖尿病肾病开展Triangle分层分级管理+LEARNS模式的效果及心理状态的观察

目的探究在对糖尿病肾病患者护理时开展Triangle分层分级管理+LEARNS模式对患者血糖水平、肾功能水平以及心理状态等改善作用。方法在2022年2月至2023年1月云南省第三人民医院收治糖尿病肾病患者中随机抽选74例为对象,Excel表随机排序划分对照组(n=37,治疗期间接受常规护理)和观察组(n=37,接受Triangle分层分级管理+LEARNS模式护理)。针对患者血压水平、肾功能水平等改善情况进行评估。结果对比2组干预前后血糖水平,餐后2 h血糖、空腹血糖,干预后观察组低于对照组,差异有统计学意义(P<0.05)。对比患者生存质量,干预前无差异,干预后观察组QOLIE-31量表评分高于对照组,差异有统计学意义(P<0.05)。对比患者负面情绪评分,干预前BAI以及BDI评分无差异,干预后观察组评分低于对照组,差异有统计学意义(P<0.05)。对比患者肾功能水平,干预后观察组肾功能水平高于对照组,差异有统计学意义(P<0.05)。对比2组自我管理能力,干预后观察组高于对照组,差异有统计学意义(P<0.05)。结论在对糖尿病肾病患者护理时开展Triangle分层分级管理+LEARNS模式,可改善患者血糖、肾功能水平,缓解患者治疗期间负面情绪,有助于患者进行恢复。

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

以LEARNS模式为基础的健康教育在急性心肌梗死介入术后患者中的干预效果分析

目的:探讨以LEARNS[聆听(L)、建立(E)、应用(A)、提高(R)、反馈教学(N)、强化(S)]模式为基础的健康教育在急性心肌梗死(acute myocardial infarction,AMI)患者PCI术后中的干预效果。方法:选取2021年1月至2022年3月,在河北省张家口市第一医院,接受PCI治疗的120例AMI患者,用随机数字表法分为A组及B组,每组例数均为60例。采用常规措施对A组患者进行干预,采用以LEARNS模式为基础的健康教育联合A组患者常规措施对B组患者进行干预。两组患者出院后的随访时间均为12个月。比较两组知信行水平、疾病感知情况、心理状态及自我管理能力(干预前、随访12个月后),遵医情况(干预期间)。结果:两组随访12个月后的疾病同一性、疾病急慢性、情绪陈述、严重后果、疾病周期性评分、心理状态相关评分均低于干预前,且相比于A组,B组更低;两组治疗控制性、个人控制性、疾病相关性、健康知识、健康信念、健康行为评分及自我管理能力各项评分均提高,且B组变化明显(P<0.05)。干预期间,相比于A组,B组服药、饮食、作息规律、运动锻炼、心绞痛预防管理、戒烟戒酒及定期复查遵医率均更高(P<0.05)。结论:AMI患者PCI术后经LEARNS模式为基础的健康教育可有效促进疾病感知及知信行水平的改善,改善患者心理状态,并提高患者自我管理能力及遵医情况。

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.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.