Development of endemicity and conservation importance indices for tropical forests and the floral diversity assessment of Mt.Natoo in Benguet,Philippines

The scientific community faces the challenge of measuring progress toward biodiversity targets and indices have been traditionally used.However,recent inventories in secondary tropical mountain forests using traditional biodiversity indices have yielded results that are indistinct with primary ones.This shows the need to develop complementary indices that goes beyond species count but integrates the distribution and conservation status of the species.This study developed endemicity and conservation importance index for tropical forest that incorporated the distribution and conservation status of the species.These indices were applied to Mt.Natoo,a remnant primary mossy forest in Buguias,Benguet,Philippines,that resulted to endemicity index of 81.07 and conservation importance index of 42.90.Comparing these with secondary forest sites with comparable Shannon-Wiener,Simpson,Evenness and Margalef’s indices,our endemicity and conservation indices clearly differentiates primary forest(our study site)with higher values from secondary forests with much lower values.Thus,we are proposing these indices for a direct but scientifically-informed identification of specific sites for conservation and protection in tropical forests.Additionally,our study documented a total of 168 vascular plant species(79 endemic and 12 locally threatened species)in Mt.Nato-o.Majority are of tropical elements for both generic and species levels with some temperate elements that could be attributed to the site's high elevation and semi-temperate climate.These are important baseline information for conservation plans and monitoring of tropical mossy forests.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

The Importance of Breathing Training in Folk Dance Teaching

The importance of breathing training in dance teaching is reflected in the two aspects of enhancing the quality of dance movements and sublimating the connotation of dance movements.For example,high-quality breathing can help performers complete the dance movements and improve the coordination of the movements;at the same time,the unique body rhythm formed by breathing can strengthen the visual effect of the performance and convey its spirit and soul to the audience.This requires folk dance teachers to carry out relevant training and teaching activities based on the categories and skills of dance breathing,such as changing students’ideological cognition,developing periodic breathing training courses,providing personalized guidance to students,and allowing students to adjust their learning and practice methods in the evaluation.

Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.

基于Null Importance与GS-LGBM的糖尿病视网膜病变因素分析与风险预测

目的:通过机器学习算法分析糖尿病视网膜病变(DR)关键因素,构建DR风险预测模型,为DR的预防和诊断提供参考。方法:采用国家人口健康科学数据中心的《糖尿病并发症预警数据集》,基于Null Importance方法去除噪声特征,筛选出与DR有关的关键因素;基于GridSearch优化LGBM模型参数,构建GS-LGBM DR风险预测模型。以准确率、精确率、召回率、F1分数、AUC值作为评价标准,与XGBoost、随机森林、Logistic以及未调优的LGBM模型进行比较。结果:Null Importance方法筛选出30个关键因素;与XGBoost、随机森林、Logistic以及未调优的LGBM模型相比,本研究所构建的GS-LGBM DR风险预测模型各评价指标均最优,其在测试数据上的AUC值高达0.897。结论:相较传统的DR预测模型,经过超参数优化后的模型具有更好的DR风险预测能力,更有助于DR的临床诊断。

A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data:An Application to COVID-19

Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.

Importance of Urban Wetland Parks under the Development of Green Healthy Cities:A Case Study of Nanjing City

Wetlands are widely distributed all over the world,and have many wildlife resources,which are the main pieces of the puzzle for natural resource conservation and sustainable development on earth and have important irreplaceability.In this paper,through questionnaire survey,field research,literature review,etc.,importance weight analysis was conducted by using principal component analysis,and field survey and questionnaire were carried out to collect data on ecological environment function,environmental protection function,landscape beautification function,disaster prevention and mitigation function of urban wetlands.The problems in wetland parks of Nanjing were discussed,such as lack of awareness of landscape planning,deficient late management of wetland parks,weak ability of sustainable development,and unreasonable landscape layout and function.Finally,corresponding solutions were proposed,such as adhering to the planning and design of urban wetland parks with green as the base and health as the basis,persisting in the construction of a wetland system with high biodiversity and near-natural characteristics,adhering to the principle of sustainable development,adopting the construction idea of symbiosis and circulation of urban wetland parks,strengthening education and publicity work,and paying attention to the organic combination of system protection and coordinated construction.The research can build a new development direction for the model of urban wetland parks and green healthy cities,and provide theoretical support for urban sustainable construction.

A Neighborhood Rough Set Attribute Reduction Method Based on Attribute Importance

Attribute reduction is a hot topic in rough set research. As an extension of rough sets, neighborhood rough sets can effectively solve the problem of information loss after data discretization. However, traditional greedy-based neighborhood rough set attribute reduction algorithms have a high computational complexity and long processing time. In this paper, a novel attribute reduction algorithm based on attribute importance is proposed. By using conditional information, the attribute reduction problem in neighborhood rough sets is discussed, and the importance of attributes is measured by conditional information gain. The algorithm iteratively removes the attribute with the lowest importance, thus achieving the goal of attribute reduction. Six groups of UCI datasets are selected, and the proposed algorithm SAR is compared with L2-ELM, LapTELM, CTSVM, and TBSVM classifiers. The results demonstrate that SAR can effectively improve the time consumption and accuracy issues in attribute reduction.

2023年中国天然气发展述评及2024年展望

为了助力产业高质量发展、助推中国式现代化建设,收集、整理、分析了中国天然气行业2023年的各项指标、数据和动态,论述和研判了2023年中国天然气产业链各环节以及天然气市场的热点和亮点,进而展望了2024年中国天然气行业的发展前景。研究结果表明,2023年中国天然气发展稳中有进、不乏亮点,主要表现在:(1)天然气勘探全面开花,预计新增天然气探明地质储量或创历史新高;(2)天然气生产稳中有进,全年共生产天然气2 324×10^(8)m^(3),年增产量近123×10^(8)m^(3);(3)天然气进口量增价跌(共进口天然气1 655.6×10^(8)m^(3)),其中LNG进口量重返世界第一(进口量为984.2×10^(8)m^(3));(4)天然气需求量小幅度反弹,天然气表观消费量达3 915.5×10^(8)m^(3),天然气对外依存度为40.6%,市场供应平稳;(5)天然气基础设施建设方兴未艾,LNG接收站、天然气长输管道和地下储气库建设有新进展;(6)在推进实现碳达峰碳中和目标和建设新型能源体系的形势下,油气生产企业大力拓展新能源业务;(7) LNG重卡销量创历史新高(共销售15.19万辆),用气增量异军突起;(8)国家问政于民,新版《天然气利用政策》面向全社会征求意见。结论认为:(1)尽管中国天然气增量市场面临着煤炭“压舱石”和新能源的竞争,但天然气仍然是中国能源结构低碳化发展过程中的桥梁和支柱,不可或缺;(2)预计2024年天然气勘探力度不减,探明天然气地质储量增势依旧;(3)天然气生产稳中有进,增产量与2023年相当;(4)天然气进口量将大幅度增加,天然气进口均价将继续下降;(5)天然气需求量平稳增长,市场供应宽松。

中国式现代化背景下文化和旅游融合发展的五重逻辑与重要议题

文化和旅游现代化是中国式现代化的组成部分,推进文化和旅游融合发展是现阶段建设文化强国和旅游强国的重要任务。要解决当前文化和旅游融合发展中存在的价值导向不突出、推进方式碎片化、市场体系不健全、治理水平不高、学理支撑不强等问题,须将文化和旅游及其融合发展置于中国式现代化的整体格局和历史进程中加以理解、审视和推进。文章在扼要梳理中国式现代化以及文化和旅游融合研究进展的基础上,论述了中国式现代化背景下推进文化和旅游融合发展的价值逻辑、历史逻辑、系统逻辑、实践逻辑和理论逻辑,并结合中国式现代化的总体要求与推进文化和旅游深度融合发展的现实需要,提出当前和未来一段时期亟须重点关注的8个重要议题。

地缘政治视域下中国参与亚运会历程中的重要议题及杭州亚运会的时代成就

已经胜利落幕的杭州亚运会是在中国境内举办的第3届亚运会。采用文献资料法与历史研究法,回顾中国的亚运会参与史,进一步分析亚运会与亚洲地缘政治格局和体育格局的互动影响后发现,参与亚运会是中国走向世界体育、参与国际体育事务的重要桥梁;是中国施展体育外交坚实且富有弹性的“第一步”,是推动亚洲地缘关系,巩固在亚地位的重要渠道;是处理两岸关系的重要场域。在两个大局背景下,杭州亚运会在加强中国与亚洲国家的联系,促进亚洲国家的交流与团结,树立“可亲可爱”的中国形象,宣传“心心相融”的在亚外交倡议,推动两岸交流,夯实和平统一基础等方面体现出重要的时代价值,并为中国以体育促外交的未来发展提出新的议题。

系统重要性金融科技公司的法律规制

随着金融科技的加速应用,金融科技的系统性风险已成为监管重点。金融科技巨头以关键技术服务商、金融基础设施和互联网金融控股集团等样态深度参与金融活动,在规模性、复杂性、可替代性和关联性等方面开始具备系统重要性特征,成为金融科技时代的“大而不能倒”。有必要参考系统重要性金融机构的监管经验,并结合金融科技公司系统性风险的生成机理,对系统重要性金融科技公司予以特殊规制。在有效识别系统重要性金融科技公司的基础上,整合金融监管、数据治理、网络安全和市场竞争等四大规制场域,建立以协调性为目标的监管体制、以可靠性为目标的审慎监管体系、以可承受性为目标的风险处置机制,构建系统重要性金融科技公司的监管框架和规制方案,有效防范其运行失灵给金融稳定带来的冲击。

疫情背景下中国蔬菜进出口贸易变化分析及对策

通过分析我国2020—2022年的蔬菜进出口数据发现,我国蔬菜出口额稳步增长,年均增长了9.13%,2021年蔬菜出口量受疫情影响稍有下降,2022年疫情形势缓和后有所回涨;进口贸易中进口额和进口量先升后降,蔬菜进口贸易的升降与疫情相关。出口市场中,我国蔬菜出口量超10万t的支柱市场的出口量受新冠疫情影响较大;出口产品中,优势产品集中度明显,辅助产品占比持续较小,其中鲜冷冻蔬菜和加工保藏蔬菜出口量止降回涨,干蔬菜和蔬菜种子稳步发展。优势进口市场数量减少,进口量占比持续下降,优势进口蔬菜产品变化较大,进口量集中度明显下降。另外,3年来我国蔬菜进出口均价有所上涨,其中加工保藏蔬菜进出口均价持续上涨,其他品类皆有波动。提高农产品在世界市场上的国际竞争力,促进农业现代化发展,是我国在后疫情时代中拓展蔬菜对外贸易市场、巩固我国主要的蔬菜进口市场的重要方法之一。

航空发动机滑油系统LRU级故障树建模与分析

滑油系统是航空发动机的关键系统之一,一旦发生故障,严重影响飞行安全。滑油系统结构复杂,排故难度较大。对滑油系统进行LRU级故障树建模和分析,能够为快速定位LRU级故障源提供指导性建议。根据滑油系统工作原理、组成与运行特点,构建了以LRU级故障为底事件故障树模型,运用下行法求解故障树最小割集,采用不交化的最小割集表达式,计算滑油系统顶事件故障发生概率,以及滑油压力异常、油液污染、滑油消耗量大、滑油系统部件损坏等典型故障发生概率;在不交化最小割集矩阵中,与求顶事件概率相对应,分析LRU级故障树底事件概率重要度和相对概率重要度;通过概率重要度分析明确航空发动机滑油系统的易损部件,为日常维护与管理提供参考。

我国重要数据认定制度的探索与完善

重要数据认定是国家数据安全监管的基础性制度,明确该制度是维护国家数据安全的必然要求,是落实数据安全保护义务的客观需要,也是促进数据要素价值释放的重要举措。对此,我国相关配套规定和国家标准进行了积极探索,从早期尝试列举重要数据具体范围,到目前已转向规定重要数据认定规则,但尚未形成明确统一的规则体系。完善我国重要数据认定制度,要在厘清重要数据概念的基础上,构建基于风险的认定规则体系,主要包括明确重要数据认定的领导体制、细化重要数据认定的条件标准、厘清重要数据认定的疑难界分以及健全重要数据认定的程序机制。

环状RNA在羊重要性状形成中的研究进展

环状RNA(circular RNA, circRNA)是一类经过反向剪接形成的闭合环状RNA分子,具有结构稳定、表达量丰富、在不同组织及其不同发育阶段具有表达特异性等特征,备受研究者关注。该文了总结近年来circRNA在羊重要性状中的研究进展,有助于揭示circRNA调控重要性状形成的分子机制,为最终构建羊重要性状形成的分子调控网络提供理论支撑。

基于自动重要抽样方法的减方差技巧体系构建与验证

屏蔽计算问题根据求解目标不同一般可分为源-探测器问题、区域问题和全局问题。MCShield研究团队针对3类问题中存在的深穿透问题提出了相应的减方差技巧,本文以此为基础构建了基于自动重要抽样(AIS)方法的减方差技巧体系,并开展了验证工作。针对源-探测器问题,采用NUREG/CR-6115 PWR压力容器计算基准题对小探测器自动重要抽样(SDAIS)方法进行验证。结果表明,SDAIS方法的计算效率约为AIS方法的7倍。此外还提出并验证了基于AIS伴随蒙特卡罗的耦合减方差(AIS-CADIS)方法,将AIS方法引入到蒙特卡罗伴随计算中,取得了良好的效果。针对全局问题,提出网格化-AIS方法并使用简化反应堆屏蔽计算算例进行验证,结果表明,网格化-AIS方法的计算效率是AIS方法的12倍左右,是直接蒙特卡罗方法的290倍左右。

引入重要度修正因子的复杂光电系统可靠性分配研究

以某新型复杂光电系统为研究对像,针对传统评分分配法客观性差、分配偏差大的缺点,构建重要度修正因子,提出了一种引入重要度修正因子的可靠性评分分配法。首先,运用模糊层次分析法,得到功能单元对上级功能单元/系统可靠性影响的相对重要度;然后,基于相对重要度,按照专家评分规则,得到功能单元对上级功能单元/系统可靠性影响的绝对重要度;最后,基于相对重要度和绝对重要度,构建功能单元重要度修正因子,以此修正各功能单元的评分结果。经对比验证,关重功能单元可靠性分配值较传统评分分配值至少提高6%,引入重要度修正因子的评分分配法能有效削弱传统评分分配法的主观影响,释放关重功能单元可靠性余量,提高功能单元可靠性分配的科学性、合理性,进而提高复杂系统可靠性设计水平。

加强涉外法治体系建设的顶层擘画——论学习贯彻习近平总书记重要讲话精神

习近平总书记在中央政治局第十次集体学习时就加强涉外法治建设所作系列重要论述,是马克思主义法治理论中国化的最新成果。“长远所需”和“当务之急”,凸显了新形势下加强涉外法治建设的重要性和紧迫性。中国涉外法治建设成就巨大、来之不易,但仍存在不少短板弱项,亟待弥补。我国海外利益全方位多层次高速度拓展,外部法律风险与挑战前所未有,干涉与反干涉、制裁与反制裁外交斗争激烈,客观上要求强化法治思维,善用法治方式,“以法斗法”“以法制法”,丰富外交斗争法律“工具箱”,一体推进涉外立法、执法、司法、守法和法律服务,形成涉外法治工作大协同格局。必须坚持统筹推进国内法治和涉外法治,不断完善涉外法治体系和能力建设,为中国式现代化行稳致远营造有利法治条件和外部环境。

我国重要农产品供需变化趋势与供给保障能力提升策略

基于对我国重要农产品生产、消费和进口历史变化情况的分析,以及对2035年前变化趋势的预测,认为我国重要农产品供给保障面临国内增产边际效应递减和利用国际市场风险增大的双重挑战,应从统筹国内和国际两个大局、发展和安全两件大事出发,谋划好现代化新征程重要农产品供给保障思路。一方面,应从供需两侧入手扩产能、控消费,构建农业综合生产能力内在增长机制,针对农民务农种粮和地方重农抓粮逻辑采取差异化扶持政策,推进节粮减损、倡导节约消费;另一方面,应实施农产品进口多元化战略,提高海外供应链稳定性可靠性;此外,还应推动建立统一、联动的全国农产品大市场,完善农产品储备调节制度。