Using Vector Representation of Propositions and Actions for STRIPS Action Model Learning

Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"plan traces".To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al.

Calculation of Particle Decay Times in the Standard Model

We present here a two-step method of classification and calculation for decay rates in the Standard Model. The first step is a phenomenological classification method, which is an extended and improved schematic experimental formula for decay width originally introduced by Chang. This schematic formula separates decays into seven classes. Furthermore, from it is derived a process-specific interaction energy mX. The second step is a numerical calculation method, which calculates this interaction energy mX numerically by minimization of action from the Lagrangian of the process, from which follows the decay width via the phenomenological formula. The Lagrangian is based on an extension of the Standard Model, the extended SU(4)-preon-model. A comparison of numerically calculated and observed decay widths for a large selection of decays shows a good agreement.

Calculation of the Standard Model Parameters and Particles Based on a SU(4) Preon Model

This paper describes an extension and a new foundation of the Standard Model of particle physics based on a SU(4)-force called hyper-color, and on preon subparticles. The hyper-color force is a generalization of the SU(2)-based weak interaction and the SU(1)-based right-chiral self-interaction, in which the W-and the Z-bosons are Yukawa residual-field-carriers of the hyper-color force, in the same sense as the pions are the residual-field-carriers of the color SU(3) interaction. Using the method of numerical minimization of the SU(4)-action based on this model, the masses and the inner structure of leptons, quarks and weak bosons are calculated: the mass results are very close to the experimental values. We calculate also precisely the value of the Cabibbo angle, so the mixing matrices of the Standard model, CKM matrix for quarks and PMNS matrix for neutrinos can also be calculated. In total, we reduce the 29 parameters of the Standard Model to a total of 7 parameters.

Human-Object Interaction Recognition Based on Modeling Context

This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.

Improved grey prediction model based on exponential grey action quantity

With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM（1,1） usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model（EOGM（1,1））, based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry＇s GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.

Dynamic simulation of hydrodynamic model of drum level wave action and sloshing

In order to build the model of the drum level wave action and sloshing, based on the method of modularization modeling, the hydrodynamic model of drum level wave action and sloshing was developed, and dynamic simulation researches were carried out based on the model. The results indicate that both drum level and drum length have functional relations with period of drum level wave action and sloshing. When the drum level decreases or drum length increases, the period of drum level wave action and sloshing increases, density of liquid and number of sub-module division have little influence on the period of drum level wave action and sloshing. The model was validated by the analytical solution theory of liquid’s wave action and sloshing in cuboid container, and the 3D graphics of drum level wave action and sloshing was also obtained. The model can dynamically reflect the rules of wave action and sloshing of water in the container exactly.

Action Recognition in Surveillance Videos with Combined Deep Network Models

Action recognition is an important topic in computer vision. Recently, deep learning technologies have been successfully used in lots of applications including video data for sloving recognition problems. However, most existing deep learning based recognition frameworks are not optimized for action in the surveillance videos. In this paper, we propose a novel method to deal with the recognition of different types of actions in outdoor surveillance videos. The proposed method first introduces motion compensation to improve the detection of human target. Then, it uses three different types of deep models with single and sequenced images as inputs for the recognition of different types of actions. Finally, predictions from different models are fused with a linear model. Experimental results show that the proposed method works well on the real surveillance videos.

Numerical modelling of the sea level under the actions of the tide and strong wind in the Bohai Sea

In this paper, the numerical modelling of the tidal level and current in the Bohai Sea was carried out with ADI method, by taking the sum of four main tidal components M2,S2K2,O1 as the open boundary condition. The calculated values were consistent with the predicted ones (the observed values in the case of calm) in the Tidal Table. On the basis of the modelling of the tide, the sea level and current fields under the effects of strong wind were simulated. The calculated results were also quite satisfactory.

母亲河复苏行动模式与关键技术研究

为推动重要江河湖库生态保护治理,加快复苏河湖生态环境,水利部从2022年起部署实施母亲河复苏行动,印发行动方案,明确母亲河复苏行动河湖名单(2022—2025年)。通过系统分析88条(个)实施母亲河复苏行动河湖的问题、复苏目标、重点任务与行动措施、组织与责任落实情况等,研究提出母亲河复苏行动问题诊断、复苏目标确定、复苏行动模式与关键技术体系框架,为推动各地母亲河复苏行动提供参考。

影响网在作战行动过程优选中的应用

影响网(IN)作为一种典型的概率网络建模方法,广泛应用于作战行动过程(COA)优选。首先,分析了IN相比于其他概率网络方法的优势,概述了基本IN在COA建模与生成中的应用;其次,从机制拓展和功能拓展两个角度出发,综述了拓展IN的研究现状;然后,分析了不确定性下的COA优选问题以保证其鲁棒性和适应性;最后,对基于IN的作战COA优选研究进行了展望。

数字化转型赋能乡村教师专业发展:现实阻力与实践路向——基于“AGIL”模型视角的分析

乡村教师是乡村教育振兴的关键力量。教育数字化转型赋予乡村教师专业发展全新内涵和有利契机,同时对建设高质量乡村教师队伍提出更迫切要求。为进一步强化数字化转型赋能乡村教师专业发展效果,推动乡村教育数字化体系实现高效和标准化发展,运用帕森斯AGIL模型解析数字化转型赋能乡村教师专业发展实践的现实阻力,发现存在内生发展动力不足,数字行动进展迟缓;研修培训存在偏差,教师角色难以转变;共建主体参与有限,支持体系保障乏力;数字价值认同缺失,数字责任有待强化等困境。基于AGIL模型的乡村教师专业发展实践路向包括激活专业发展自觉,确保数字行动落地;创新教师培育方式,明确数字行动策略;释放主体支持活力,畅通资源支持体系;形塑数字价值认同,践行数字社会责任。

知信行护理模式结合细节化干预在胃癌根治术患者围手术期的应用效果分析

目的:探讨知信行护理模式结合细节化干预在胃癌根治术患者围手术期的应用价值.方法:选取2022年1月至2023年5月本院收治的89例胃癌根治术患者为研究对象.根据护理方式将其分为观察组(n=46,采用知信行护理模式结合细节化干预)和对照组(n=43,采用常规护理干预).采用统计学方法,比较两组患者临床指标、胃肠功能恢复情况及并发症总发生率.结果:干预后,观察组患者尿管留置时间、首次下床活动时间、住院时间均短于对照组,差异均有统计学意义(P<0.05).干预后,观察组患者肛门排气时间、排便时间均短于对照组,差异均有统计学意义(P<0.05).干预后,观察组患者并发症总发生率(1/46,1.89%)低于对照组(6/43,13.94%),差异有统计学意义(P<0.05).结论:知信行护理模式结合细节化干预,可促进胃癌根治术围手术期患者身体及胃肠功能恢复,降低并发症发生风险.

家庭代际学习:模式、要素与作用机理

家庭代际学习是代际学习在家庭场域中的具体应用,它贯穿家庭的各个生命阶段,是真正意义上的终身学习实践。当前家庭代际学习存在开放共享、基于尊重的自上而下、基于尊重的自下而上和开放非共享四种典型模式,其与家庭的构成要素,即家庭结构、互动层次、学习目标与内容密切相关。从家庭结构来看,复合家庭、直系家庭、核心家庭三种家庭结构相对传统和稳定,任何家庭代际学习模式都可能发生;不完全家庭多表现为开放共享或非共享家庭代际学习模式。从互动层次来看,基于尊重的自上而下模式和基于尊重的自下而上模式均以家庭内学习为主;开放共享模式和开放非共享模式均存在个体参与形式的家庭外学习,而以家庭为单位参与的家庭外学习只发生在开放共享模式中。从学习目标与内容来看,无论哪种家庭代际学习模式,都包含知识、技能与意识提升等学习目标,以及知识传授、技能传递与意识形态建设等学习内容。家庭代际学习的有效发生受家庭凝聚力、灵活性和沟通三大因素影响。因此,推进家庭代际学习,一是要注重回归家庭深度联结,激发家庭代际学习原生动力;二是要明晰多元构成要素,选择适合的家庭代际学习模式;三是要重视有效调节机制,形成家庭学习可持续性生态。

商业模式创新对网络视频平台用户价值共创的作用机理

数字经济发展需要互联网技术和数字技术作支撑,引导消费者网络视频消费沿着“简单用户搜索—网络平台展示推荐—网络平台算法推荐”的路径演变,这对企业的商业模式创新提出了挑战。本文基于企业内外部价值网络理论,以“平台质量-用户认知-用户行为”的逻辑分析思路,构建商业模式创新对网络视频平台价值共创的作用机制模型并检验其作用机理。结果显示:商业模式创新的内容质量创新、交互质量变革及创作激励创新对用户认知有促进作用,网络视频平台用户认知对价值共创有促进作用。因此建议:以网络视频平台需求为依据,选择相匹配的商业模式创新方案;加大投入力度,鼓励网络平台关联企业创作视频内容;构建有效链接纽带,畅通平台企业与用户的沟通渠道。

传统文化资本对山东经济增长的贡献及作用机制研究

选取1993—2019年间山东经济增长相关数据,基于柯布—道格拉斯生产函数,将传统文化资本纳入经济增长核算框架,进而从长期静态和短期动态视角揭示传统文化资本对山东经济增长的贡献及作用机制。结果表明,传统文化资本与山东经济增长之间存在长期均衡关系,传统文化资本对山东经济增长存在显著的促进作用;资本投入是山东经济增长的重要驱动力,其中,传统文化资本对经济增长的贡献相对较高,年均贡献达36.3%;传统文化资本可通过促进物质资本和人力资本积累推动山东经济增长。因此,在保持山东经济稳定发展的基础上,应深入挖掘山东传统文化内涵,充分利用传统文化资本的积极作用,通过制定科学合理的传统文化发展规划,促进山东经济的可持续发展。

模块化建筑刚性桩复合地基共同作用试验分析

本文依据某模块化示范楼,制作了简化后的1∶10的模块化建筑刚性桩复合地基的室外缩尺模型,考虑了上部模块结构与地基基础共同作用。通过模型试验分析了上部模块刚度变化对上部模块钢柱、筏板基础和刚性桩复合地基各受力体的影响。结果表明:模块化建筑中部柱轴力>边部柱轴力>角部柱轴力;随上部模块刚度的增加,中部柱轴力逐渐减小,边部柱和角部柱轴力逐渐增大;筏板基础中部沉降最大,边部沉降最小,整体沉降随上部模块楼层的增加逐渐增大;角部桩身轴力>边部桩身轴力>中部桩身轴力,桩身轴力沿着桩深度方向先增大后减小;桩身上部一定范围内产生负摩阻力。分析结果能够为模块化建筑单元构件设计及刚性桩复合地基基础优化设计提供参考。

我国煤层瓦斯运移模型研究进展

煤层瓦斯运移是矿井瓦斯抽采与煤矿区煤层气开发领域的基础科学问题。从理论分析、试验方法 2个方面,阐述了我国煤层瓦斯运移理论及其渗透特性测试方法的研究现状,重点围绕煤层瓦斯非线性渗流特性、多场耦合作用下的渗流理论等关键问题的最新成果开展论述;进一步对当前普遍采用的煤层瓦斯运移特性的试验方法进行分析。研究建议:深入研究钻孔周围煤体真实应力-应变状态、瓦斯渗透过程中煤孔隙率的演化特性测试等问题,深化对煤层瓦斯非线性渗流模型、与增透或增产措施相适应的瓦斯运移特征演化等问题的认识。

“枫桥经验”与诉源治理法治化

在习近平法治思想的指引下,“枫桥经验”与时俱进,创造性地解决了新时代的系列治理问题,逐渐发展为党领导基层治理的重要经验。当前我国基层矛盾纠纷仍呈高发态势,法院面临的“案多人少”问题依然严重,坚持和发展新时代“枫桥经验”,推进矛盾纠纷前端预防化解和多元综合治理相结合的诉源治理,是防止我国成为“诉讼大国”的有效途径。应当在党委领导下构建诉源治理大格局,形成多元化解矛盾纠纷体系,推动矛盾调处机制法治化、智能化发展。

球磨纳米生物质炭在饱和多孔介质中的迁移与滞留机制

利用球磨纳米生物质炭模拟老化产生的纳米生物质炭,通过一维柱迁移试验研究了不同背景溶液离子强度(IS)、pH和流速对球磨纳米生物质炭在饱和多孔石英砂介质中迁移与滞留行为的影响,并运用对流弥散模型拟合穿透与滞留曲线得到沉积速率系数等运移参数,结合DLVO理论研究影响作用机制。结果表明:背景溶液IS降低,pH和流速升高,球磨纳米生物质炭的迁移率明显提高。这是由于在低IS和高pH条件下,球磨纳米生物质炭和石英砂的电负性增加,从而增强了两者之间的静电斥力,同时球磨纳米生物质炭之间具有更好的分散性,有助于球磨纳米生物质炭的迁移;在高流速条件下,球磨纳米生物质炭在石英砂柱中的滞留时间因为平流扩散而减短,同时水流剪切作用力增大不利于颗粒在石英砂上的吸附,有助于球磨纳米生物质炭的迁移。

中小学教师行动研究能力的结构模型及提升路径

行动研究是一线中小学教师较为青睐的一种研究方式。教师缺乏对行动研究的深入理解,导致他们在实际研究过程中遭遇了各种各样的困惑。建立行动研究能力结构模型,有助于一线中小学教师精准地把握行动研究的核心要义,从而真正弄懂行动研究究竟是什么以及如何做。基于对相关文献的研究及分析,结合教学实际,提炼出了以问题意识、沟通合作能力、科研能力、分析与总结能力、反思与改进能力等为核心能力要素的“阶梯状-多相关”能力结构模型。该模型可以对中小学教师行动研究能力进行可视化展示。基于模型启示,提出了提升教师行动研究能力、促进教师专业发展的四条实施路径:构建行动研究实践共同体,促进教师沟通合作参与能力;以科研项目为驱动力,让教师在“做中学”的实践中发展科研能力;保持全过程反思,激发教师问题意识及研究自觉性;恪守终身学习理念,在学习与行动的循环中提升教师能力。