How to Strengthen Child Learners' Self-confidence

Self-confidence is closely related to L2 learning.In order to make their learners enjoy English learning,English teach ers need to strengthen the learners’English learning confidence.This article is to find out English teachers’role to strengthening learners’self-confidence in ELL by means of class observation.The writer concludes improving speaking ability;giving praise and offering gentle error correcting can help to strengthen the learners’confidence.

Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

Students＇ Satisfaction with Simulation Learning Environment in Relation to Self-confidence and Learning Achievement

The evolution in patient simulation as educational tool is being driven by a number of factors. Priority of patient safety, patient availability, and the ever increasing body of medical knowledge presents new challenges to curriculum planners. Student＇s satisfaction is an important element of the investigation of simulation learning environment efficacy. There are suggestions that student satisfaction may have some correlation with self-confidence and learning achievement. This is a prospective exploratory study that evaluates learners＇ satisfaction with simulation learning environment and self-conference utilized satisfaction with SSE （simulation experience scale） and pre and post simulation test for learning achievement. Participants was third-year medical students （n = 45） participating in their regular simulation-based session at Center of Excellence for Simulation Education and Innovation （CESEI）, University of British Columbia. A comparison between pre- and post-test results was conducted on the basis of t-test for related samples. Correlation was used to explore the relationships among students satisfaction with the simulation environment and students＇ self-confidence and achievement. The simulation exercise, completed by 45 students, increased correct test answers on average from 72% to 89% （P-Value 〈 0.0001 by paired t-test）. Increases in test results were between pre- and post- simulation identical multiple choice questions. The mean score for satisfaction with simulation environment items was 4.47, SD （0.45）, using a 5-point Likert scale with 5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, and 1 = strongly disagree. The mean score for self-confidence in performing BLS, evaluating and managing acute cardiac patient was 3.83, SD （1.02）. For evaluating the relationship between students satisfaction with simulation learning environment and learning achievement, bivariate analysis revealed a significant positive relationship between satisfaction with simulation learning environment and learning achievement （Pearson r = 0.80, P-Value 〈 0.01）. The magnitude or strength of the correlation coefficient （r = 0.80） indicated satisfaction with simulation environment and learning achievement have a strong effect and positive correlation. Simulation learning environment for medical students is effective in improving students＇ overall comprehension and better learning achievements. Furthermore, students＇ basic clinical skills are improved associated with higher self-confidence.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Recombinant chitinase-3-like protein 1 alleviates learning and memory impairments via M2 microglia polarization in postoperative cognitive dysfunction mice

Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.

Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

The Effect of Self-confidence in SLA

Krashen's affective filter theory drew researchers' attention to the fresh field of internal factors of learners.But researchers seem to pay too much attention to the other factors while tending to neglect a factor that we cannot afford to play down in language learning research-self-confidence.This paper is designed to discuss the close relationship between self-confidence and some of the other important affective factors,and furthermore raise appropriate concern over self-confidence.

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。

Cultivation of Cultural Subjectivity from the Perspective of Cultural Self-Confidence

Economic and cultural globalization has strong impacts on the culture of each country.How to maintain the integrity of the local culture in the course of globalization and how to develop on the basis of the inheritance and innovation are the questions worthy of being studied.This paper firstly expounds the important meaning of cultural subjectivity in the view of cultural self-confidence,then explores the roots for the loss of cultural subjectivity consciousness and finally indicates the strategies of cultivating cultural subjectivity.Cultivating cultural subjectivity is an effective way to maintain the integrity of the local culture and the cultural self-confidence.

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。

M-learning结合CBL在消化科规培教学中的探讨及应用

目的探究移动学习平台(M-learning,ML)结合案例教学(Case-based Learning,CBL)在消化科住院医师规范化培训(简称规培)教学中的应用效果。方法选取2021年1月—2023年1月于上海市徐汇区中心医院消化科参加规培学习的80名医师作为研究对象,将其按照随机数表法分为研究组和对照组,每组40名。对照组给予传统讲授式教学法,研究组给予M-learning结合CBL教学法,对比两组医师的理论考试成绩、实践技能考试成绩和学习满意度。结果研究组的理论成绩和实践技能考试成绩均高于对照组,差异具有统计学意义(P均<0.05);研究组的学习满意度明显高于对照组,差异具有统计学意义(P<0.05)。结论将Mlearning结合CBL教学法应用于消化科规培教学中,不仅能够提升医师的理论考试成绩和实践技能考试成绩,还能够有效提高医师学习满意度。

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。

基于Q-learning的自适应链路状态路由协议

针对大规模无人机自组网面临的任务需求多样性、电磁环境复杂性、节点高机动性等问题,充分考虑无人机节点高速移动的特点,基于无人机拓扑稳定度和链路通信容量指标设计了一种无人机多点中继(multi-point relay,MPR)选择方法;为了减少网络路由更新时间,增加无人机自组网路由策略的稳定性和可靠性,提出了一种基于Q-learning的自适应链路状态路由协议(Q-learning based adaptive link state routing,QALSR)。仿真结果表明,所提算法性能指标优于现有的主动路由协议。

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。