Effect of the interval between two shocks on ejecta formation from the grooved aluminum surface

This work focuses on the effect of the interval between two shocks on the ejecta formation from the grooved aluminum(Al_(1100))surface by using smoothed particle hydrodynamics numerical simulation.Two unsupported shocks are obtained by the plate-impact between sample and two flyers at interval,with a peak pressure of approximately 30 GPa for each shock.When the shock interval varies from 2.11 to 7.67 times the groove depth,the bubble velocity reduces to a constant,and the micro jetting factor R_(J) from spike to bubble exhibits a non-monotonic change that decreases initially and then increases.At a shock interval of 3.6 times the groove depth,micro jetting factor R_(J) from spike to bubble reaches its minimum value of approximately 0.6.While,the micro jetting factor R_(F) from spike to free surface decreases linearly at first,and stabilizes around 0.25 once the shock interval surpasses 4.18 times the groove depth.When the shock interval is less than 4.18 times the groove depth,the unloading wave generated by the breakout of the first shock wave is superimpose with the unloading part of the second shock wave to form a large tensile area.

Optimizing profile line interval for enhanced accuracy in rock joint morphology and shear strength assessments

2D profile lines play a critical role in cost-effectively evaluating rock joint properties and shear strength.However, the interval(ΔI_(L)) between these lines significantly impacts roughness and shear strength assessments. A detailed study of 45 joint samples using four statistical measures across 500 different ΔI_(L)values identified a clear line interval effect with two stages: stable and fluctuation-discrete.Further statistical analysis showed a linear relationship between the error bounds of four parameters,shear strength evaluation, and their corresponding maximum ΔI_(L)values, where the gradient k of this linear relationship was influenced by the basic friction angle and normal stress. Accounting for these factors,lower-limit linear models were employed to determine the optimal ΔI_(L)values that met error tolerances(1%–10%) for all metrics and shear strength. The study also explored the consistent size effect on joints regardless of ΔI_(L)changes, revealing three types of size effects based on morphological heterogeneity.Notably, larger joints required generally higher ΔI_(L)to maintain the predefined error limits, suggesting an increased interval for large joint analyses. Consequently, this research provides a basis for determining the optimal ΔI_(L), improving accuracy in 2D profile line assessments of joint characteristics.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

The burden of upper motor neuron involvement is correlated with the bilateral limb involvement interval in patients with amyotrophic lateral sclerosis:a retrospective observational study

Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives and may lead them to be confined to bed.However,the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear.To address this issue,we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022.A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis.We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients.Multiple factor analyses revealed that higher upper motor neuron scores(hazard ratio[HR]=1.05,95%confidence interval[CI]=1.01–1.09,P=0.018),onset in the left limb(HR=0.72,95%CI=0.58–0.89,P=0.002),and a horizontal pattern of progression(HR=0.46,95%CI=0.37–0.58,P<0.001)were risk factors for a shorter interval until bilateral limb involvement.The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients.These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.

可撤销属性加密的区块链数据访问控制方法

针对区块链数据共享中存在的粗粒度访问控制问题,提出一种基于属性撤销密文策略属性基加密的区块链数据访问控制方法。在现有方案基础上进行改造,引入预解密过程,结合属性撤销列表实现属性实时撤销;基于非对称群下的DBDH困难问题假设进行安全性证明;基于超级账本Fabric进行系统设计,结合星际文件系统采用链上链下存储方式解决区块链容量不足和系统效率问题。实验结果表明,所提方案撤销属性时无需更新密钥密文重复上链,仅需要6次Pairing操作进行预解密和解密,且在大规模属性集下,预解密时间和解密时间平均保持在百毫秒左右的常量级上,实现区块链数据高效、细粒度的访问控制。

基于GRA-GCRITIC和改进加权双向投影的区间Fermatean模糊多属性群决策方法

针对司法案件决策环境的复杂性及评价信息的模糊性,提出一种基于GRA-GCRITIC(grey relational analysis-group criteria importance through intercriteria correlation)和改进加权双向投影的区间Fermatean模糊Hamacher-TODIM多属性群决策方法。首先考虑到各位专家对各评价方案下各指标评价信息的差异性,提出一种GRA-GCRITIC方法,该方法将灰色关联融入到CRITIC中,以确定指标综合权重比单一获取的指标权重更加客观可靠;其次,结合信任关系对加权双向投影法进行改进,兼顾主客观关系,利用专家个体与群体评价信息的隶属度及非隶属度矩阵间的相似度得到专家权重;最后,考虑到决策者的损失规避心理,将融合了Hamacher算子的TODIM方法拓展至区间Fermatean模糊环境中,通过具体算例可得到其综合优势度及排序,验证了所提方法的可行性及灵活性。

产权视角下林业碳汇资产的属性确认与计量探讨

“双碳经济”新格局下,林业碳汇作为当下成本最低的“碳吸收”手段已成为学术研究中的“新宠”。解决林业碳汇在碳汇交易过程中的会计核算问题有助于实现林业碳汇多重效益。文章基于产权理论,探讨了林业碳汇资产的会计属性及计量方式。首先,作为一项特殊自然资源,林业碳汇具备确认为资产的特征,依据交易过程可“二分”会计主体:碳汇需求方与开发经营方分别以“环境资产”与“交易性环境资产”为资产确认类型。其次,在计量方式上,以森林碳储量和市场价格为基础进行林业碳汇资产技术计量和经济计量,获得实物产权和财权对应的实物量和价值量。最后,在公允价值模式下探究了碳汇买卖双方针对林业碳汇资产的初始计量和后续计量。

敏感属性波形反演技术在大庆长垣水敏储层中的应用

针对大庆长垣油田萨零组储层水敏强、含油性差、厚度薄,以及河道窄小等问题,以长垣油田杏北开发区杏3区块为例,结合测井资料、室内实验及取心井综合解释资料,通过两步法协同敏感属性分析,判别与含油性及水敏强弱关联性最高的属性参数,然后结合属性参数范围,利用基于波形特征指示的井震联合反演技术,精准预测萨零组弱水敏高含油优质储层,从而指导井位部署。结果表明:声波时差与电阻率组合能够划分油层、差油层和非储层,自然伽马可以判别水敏强弱;利用反演预测结果部署1口新井,岩心分析数据表明该井水敏指数为0.62,为中等偏强水敏,含油级别以油浸和含油为主,含油饱和度平均为61.4%,反演结果精准可靠;该新井投产1 a,平均日产油3.5 t,累计年产油1090.6 t,投产效果较好。研究成果为萨零组开发奠定基础,对大庆油田的高质量稳产意义重大。

不完全劳动关系法律属性及制度构建探究

梳理学界及相关政策文件对不完全劳动关系的定义与解释,将劳动从属性与新就业形态劳动者特征以及外包用工模式相结合,总结出不完全劳动关系的本质是新就业形态下特有的具备弱人格从属性、经济从属性、组织从属性的新型用工关系。当前,该领域研究存在两大问题:一是不完全劳动关系的法律属性及地位不清晰;二是不完全劳动关系的制度构建方案不明确。参考“类雇员”制度及多要素权衡规则,在劳动三分体系基础之上提出三项建议:一是将不完全劳动关系纳入未来《劳动法典》中专门规制;二是引入考察要素制定不完全劳动关系认定标准;三是将不完全劳动关系劳动者权利划分为基础权利和特有权利。

秦药品牌内在属性和产业价值研究

秦药是三秦大地孕育的承载着厚重秦文化的重要战略资源,是陕西省中医药产业发展的重要资源保障。打造秦药品牌对推动陕西省中医药产业发展、提升产业链水平具有重要意义。从秦药品牌遴选、文化价值、战略需求、资源保障和产业发展5个方面论述了秦药品牌内在属性和产业价值。以发展秦药产业、树立秦药品牌为引领,破解陕西省中药产业长期存在的基础能力薄弱和产业链水平较低的问题,探索形成绿色产业发展模式,提升产业发展水平,更好地服务于生态保护、健康中国等国家战略。

基于属性的概念化:制度本体研究的必要性与路径

制度本体的研究现状与制度之于人类生活的重要性并不相称,制度研究有必要返回本体,明确存在。解读制度理论的制度理解并指出它所引发的本体认知困惑,确立了制度本体研究的必要性。本体在于内涵,内涵首要是本质,有抱负的研究者都试图给出事物的本质性定义,但此定义往往可欲难求。一个可行的操作化步骤是,确定本体的属性。制度学界普遍接受的“制度即结构”可以明确为“制度即要素结构”,并可解构为要素之制、载体之制与结构之制。确定本体属性、要素化属性并将其概念化,应是可行的本体研究策略或路径。由此获得的概念语言暨制度本体词汇有助于校准本体描述,同时可作为中介术语沟通制度研究,推动制度知识的共享与交流。

基于Kano模型的三级中医院住院患者中医护理服务需求属性研究

[目的]分析三级中医院住院患者的中医护理服务需求属性,为提升三级中医院中医护理服务提出策略。[方法]基于Kano模型设计三级中医院住院患者中医护理服务需求调查问卷,对天津市某三级中医院469例住院患者进行调查,采用定量与定性的方法确定三级中医院住院患者中医护理服务需求属性分类及优先级排序。[结果]通过Kano模型和Better-Worse二维矩阵图进行需求分析,最终获得三级中医院住院患者中医护理服务必备属性需求14个,魅力属性需求9个,期望属性需求6个。[结论]三级中医院医院管理者应依据住院患者中医护理服务需求优先级顺序精准施策,保障必备型需求项目供应,加大期望型需求投入,同时发展创新魅力型需求,确保中医护理服务需求与供给高效匹配。

省级中药饮片炮制规范法律属性探析

目的为省级中药饮片炮制规范法律地位的明确以及《药品管理法》中“药品标准”法律表述的立法技术完善提供参考。方法结合我国药品地方标准法律渊源,对中药饮片国家药品标准构成要件以及现行省级中药饮片炮制规范构成进行分析,同时基于法律解释的方法,对《药品管理法》中省级中药饮片炮制规范的法律内涵进行探讨。结果与结论在药品监管实务中,省级中药饮片炮制规范实质为地方药品标准,2019年新修订的《药品管理法》对药品标准进行了专门规定,但对于省级中药饮片炮制规范的药品标准法律属性并未作出规定,存在一定的立法技术瑕疵。建议在《药品管理法》修正时,对省级中药饮片炮制规范的药品标准法律属性予以明确。

智能制造的生产成本属性异变及边际成本递减律

智能科技革命驱动的智能制造,是新时期推进制造强国建设的不二选项。智能制造以工业智能体取代人力作业,采用“车间智能体+镜像网络”的单要素虚实融合生产模式,这将带来迥异于传统制造业的生产特征和成本属性,进而改变成本函数形态。比较研究发现,智能制造具有“高固定成本、弱可变成本”属性,并据此推导出随产量弱增长的弱成本函数,以及相应产生的边际成本递减规律。智能制造边际成本递减律,主要源于智能制造的单要素虚实融合生产模式对可变成本的冲淡与弱化效应。从政策层面,建议有序开展智能制造系统的众筹建造模式,积极推行智能制造系统的产能共享模式,尽早谋划智能时代产业工人的全新就业模式,大力引导消费者不断介入生产活动,实现消费者向“消产者”的身份转变。

社交属性在科技期刊出版全流程中的体现与应用

【目的】探讨社交属性在科技期刊出版全流程中的具体体现形式及意义,帮助期刊出版人员充分认识并利用这一特点,更好地促进新形势下科技期刊的发展。【方法】将科技期刊出版流程按同行评议、编校过程、传播推广及质量评价分为四部分,分析ScienceOpen平台、Mendeley平台、社交媒体及补充计量学在相应流程中的社交属性。【结果】同行评议过程中,ScienceOpen平台等新型网络平台将科研活动与社交活动有效结合起来,既符合开放科学的发展趋势,又极大地提高了同行评议效能。编校环节中,Mendeley平台以文献管理为基础功能,同时为用户推荐相近领域的研究人员并支持群组功能,为用户提供了便捷的交流场景。期刊传播途径方面,微信公众号通过异质性社交网络打破圈层关系壁垒,实现科研信息的“破圈”“拓圈”传播;微博、知乎、丁香园等社区借助学术背景较强的意见领袖放大信息的传播效力,成为针对全人群传播的重要途径。最后,以社交影响力为基础的补充计量学(Altmetrics)通过追踪、分析科研成果的去向(收藏、分享、评论、转载、引用等),全面、及时反馈科研成果的影响力,在期刊评价领域受到越来越多的关注。【结论】随着社交行为在各行业人群中的影响不断增强,跨圈层、跨专业的科学共同体逐渐形成,科技期刊从业人员应顺应该趋势并充分利用,以便更好地促进信息交流、实现传播目的。

基于U-Net和CNN深度学习求取地质属性建模变差函数参数

在油气藏地质属性建模中,变差函数的求取尤为关键,一般是通过拟合实验变差函数求取变程、方位角、基台值等参数的方式获得,但当研究区内样本点数量过少时,实验变差函数拟合效果较差,从而影响属性建模质量。为了克服传统方法的不足,最大限度地利用空间数据,文中提出一种基于U-Net和CNN网络求取变差函数参数的新方法:以孔隙度属性建模为例,首先选用球状模型并利用序贯高斯模拟(SGS)算法模拟生成多组孔隙度模型,以所得孔隙度平面模型抽取的数据点为基准构成样本集;然后采用U-Net进行孔隙度模型重构,保证孔隙度分布的空间相关性;最后利用CNN对样本集进行深度学习,从而建立求取变差函数的模型。实际应用表明,利用所提方法取得的主变程方位角与通过实验变差函数拟合求取的方位角仅相差1.52°,与沉积微相展布方向一致,得到的主次变程与实验变差函数非常契合,证明求取的变差函数结果可靠。同时,该方法简化了地质建模工作流程,减少了求取实验变差函数主观性,降低了研究区内样本点数量的局限性,为变差函数的预测提供了一种新的思路。

跨境电商下基于商品属性-情境的推荐算法

在传统协同过滤推荐算法的基础上,结合跨境电商行业特点,提出了一种基于商品属性和情境权重的混合推荐算法.首先,根据用户商品的历史购买金额和购买次数生成用户偏好评分,并结合用户商品属性相似度和情境化用户相似度得到目标用户的最近邻集,最后将通过变异系数法得到的情境权重纳入评分预测当中,进而生成推荐结果.实证分析表明,本算法能有效提升商品推荐结果的预测准确度,相较基于商品属性的协同过滤推荐算法,本算法可降低商品预测评分平均绝对误差平均达2.72%,提高了跨境电商商品推荐效果.本研究为推荐系统在跨境电商领域的应用提供了新方法.

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