卷积神经网络模型在儿科疾病预测中的应用

目的:针对儿童看病需求量大导致的儿科诊疗服务效率和准确率偏低等问题,利用自然语言处理和深度学习技术,从儿科历史病历数据中自动\"学习\"专家医生诊断模式,形成智能辅助诊断模型,从而对新的儿科病历数据输出疾病诊断决策。结果:基于深度卷积神经网络的七分类疾病智能诊断模型的正确率为84.26%,F1-score为84.33%,基本达到可投入实际应用的级别。结论:智能诊断决策作为预诊信息提供给医生进行确诊参考,对提升医生诊断速度效果明显。国家自然科学基金面上项目(编号:71571056);;福建省自然科学基金面上项目(编号:2012J01274)~

基于现代学徒制的园林工程课程实践教学改革探索

城市现代化建设和发展,人们物质生活水平急剧升高,对于生活环境要求随之提升,建设生态型城镇成为主要目标。园林工程是改善城市环境、提升城市整体形象的有效途径,行业发展离不开高素质人才的支持,与时俱进转变理念和方法,在园林工程课程实践教学改革中应用现代学徒制,便于进一步推动校企合作,在理论知识指导下推动实践教学改革,对于培养更多理论知识储备丰富、实践技能强的人才具有积极意义。本文就园林工程课程实践教学改革相关内容展开分析,在剖析其中的问题同时,充分发挥现代学徒制优势,提升实践教学质量。</jats:p

单层网壳结构初始几何缺陷场模拟方法研究进展

单层网壳结构的安全性很大程度上取决于其稳定承载力，而其制作及安装过程中产生的节点几何偏差初始缺陷对其稳定承载力具有重大影响。对单层网壳结构初始几何缺陷场模拟方法的研究进展和现行规范的规定进行了系统综述和分析，结果表明既有的随机性模拟法、确定性模拟法和半随机性模拟法均存在一定问题，规范所推荐的初始缺陷模拟方法脱离实际，因而无法准确确定单层网壳结构稳定承载力的真实概率模型。因此，亟待提出一种考虑结构拓扑约束、基本假定合理、计算成本可控的初始几何缺陷场模拟方法，为完善单层网壳结构整体稳定分析方法提供科学依据，并为分析其稳定可靠度提供理论基础

单层网壳结构初始几何缺陷场模拟方法研究进展

单层网壳结构的安全性很大程度上取决于其稳定承载力，而制作及安装过程中产生的节点几何偏差初始缺陷对其稳定承载力具有重大影响。对单层网壳结构初始几何缺陷场模拟方法的研究进展和现行规范规定进行了系统综述和分析，结果表明既有随机性模拟法、确定性模拟法和半随机性模拟法均存在一定问题，规范所推荐的初始缺陷模拟方法欠实际，从而无法准确确定单层网壳结构稳定承载力的真实概率模型。因此，亟待提出一种考虑结构拓扑约束、基本假定合理、计算代价可控的初始几何缺陷场模拟方法，为单层网壳整体稳定分析方法的完善提供科学依据，并为其稳定可靠度的分析提供理论基础

Prediction of Energy Resolution in the JUNO Experiment

International audienceThis paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors

Determination of the number of ψ(3686) events taken at BESIII

The number of ψ(3686) events collected by the BESIII detector during the 2021 run period is determined to be (2259.3±11.1)×106 by counting inclusive ψ(3686) hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of ψ(3686) events collected during the 2009 and 2012 run periods are updated to be (107.7±0.6)×106 and (345.4±2.6)×106, respectively. Both numbers are consistent with the previous measurements within one standard deviation. The total number of ψ(3686) events in the three data samples is (2712.4±14.3)×10^