The hyperconjugation effect on the graphene counterparts based on silicon and germanium

Bending of the A = A (A of the group IVA) double bond neighboring is rationalized by the hyperconjugation phenomenon analysis. The bending is also observed for the high sized linear, cyclic or graphene-like compounds that imply the conjugated double bonds. The electronic delocalization takes place between occupied σ(π) and unoccupied π*(σ*) orbitals especially for compound implying Si and Ge atoms. Leading to rippled structure, this phenomenon affects the silicene and germane thickness sheets and probably would have some consequences on the properties of such compounds when they will be involved in the industries in the future. However we introduce a new parameter to assess the thickness of graphenic structures when the hyperconjugation takes place in the bonding framework. The study has been undertaken at high levels of theory like B3LYP/6-311 + G(3df,2p).     

A Classical Formulation of Quantum Theory?

We explore a particular way of reformulating quantum theory in classical terms, starting with phase space rather than Hilbert space, and with actual probability distributions rather than quasiprobabilities. The classical picture we start with is epistemically restricted, in the spirit of a model introduced by Spekkens. We obtain quantum theory only by combining a collection of restricted classical pictures. Our main challenge in this paper is to find a simple way of characterizing the allowed sets of classical pictures. We present one promising approach to this problem and show how it works out for the case of a single qubit.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

Bilattice logic of epistemic actions and knowledge

Baltag, Moss, and Solecki proposed an expansion of classical modal logic, called logic of epistemic actions and knowledge (EAK), in which one can reason about knowledge and change of knowledge. Kurz and Palmigiano showed how duality theory provides a flexible framework for modeling such epistemic changes, allowing one to develop dynamic epistemic logics on a weaker propositional basis than classical logic (for example an intuitionistic basis). In this paper we show how the techniques of Kurz and Palmigiano can be further extended to define and axiomatize a bilattice logic of epistemic actions and knowledge (BEAK). Our propositional basis is a modal expansion of the well-known four-valued logic of Belnap and Dunn, which is a system designed for handling inconsistent as well as potentially conflicting information. These features, we believe, make our framework particularly promising from a computer science perspective.     

一种基于证据理论的结构可靠性分析方法

提出了一种基于证据理论的结构可靠性高效求解方法. 通过构造优化问题求解极限状态方程的非概率可靠性指标及设计验算点, 并构造一辅助区域. 通过辅助区域显著减少需要进行极值分析的焦元个数, 并基于区间分析方法减少焦元上极限状态方程的计算次数, 从而有效降低计算成本. 数值算例及工程应用验证了该方法的有效性.     

AN SEPARATION OF THE ALEATORY AND EPISTEMIC UNCERTAINTIES WITH CORRELATED NORMAL VARIABLES AND ANALYSIS OF THE INFLUENCE OF PARAMETERS

针对主观和客观不确定性同时存在且输入变量相关的结构系统,研究分析了分布参数的主观不确定性对系统输出响应的影响. 首先采用正交变换进行了相关输入变量情况下主、客观不确定性的分离,通过引入独立标准正态空间辅助变量,使得分布参数的主观不确定性能够从输入变量客观不确定性中分离,进而分析分布参数的主观不确定性对输出响应的主效应指标. 其次验证了分布参数对模型输出响应期望影响的一阶方差贡献与主、客观不确定性分离后其对响应量一阶方差贡献的相等关系. 算例结果表明了文中所用主、客观不确定性分离方法的合理性和有效性.     

Dynamic systems view of learning a three‐tiered theory in physics: robust learning outcomes as attractors

The process of learning scientific knowledge from the dynamic systems viewpoint is studied in terms probabilistic learning model (PLM), where learning accrues from foraging in the epistemic landscape. The PLM leads to the formation of attractor‐type regions of preferred models in an epistemic landscape. The attractor‐type states correspond to robust learning outcomes which are more probable than others. These can be assigned either to the high confidence in model selection or to the dynamic evolution of a learner's proficiency, which depends on the learning history. The results suggest that robust learning states are essentially context dependent, and that learning is a continuous development between these context dependent states. © 2016 Wiley Periodicals, Inc. Complexity 21: 259–267, 2016     

大概念的内涵解析及大概念教学设计与实施策略

将大概念作为一项重要的化学课程内容,是《义务教育化学课程标准(2022年版)》的重大变化之一。对大概念内涵从抽象知识到上位概念再到学科观念的发展过程进行了梳理,认为主题大概念对发展学生核心素养更具有独特价值;基于学科理解理论阐释了大概念的idea功能,认为大概念是对解决学科本原性问题的一系列idea的概括;从大概念素养发展功能的定位,基于大概念idea功能的教学内容的组织与呈现,以及探究式、建构式教学方式的运用等3个方面,探讨了大概念教学的重要实施策略。