The Physical Mandate for Belief-Goal Psychology

Robert Rosen has proposed several characteristics to distinguish “simple” physical systems (or “mechanisms”) from “complex” systems, such as living systems, which he calls “organisms”. The Memory Evolutive Systems (MES) introduced by the authors in preceding papers are shown to provide a mathematical model, based on category theory, which satisfies his characteristics of organisms, in particular the merger of the Aristotelian causes. Moreover they identify the condition for the emergence of objects and systems of increasing complexity. As an application, the cognitive system of an animal is modeled by the “MES of cat-neurons” obtained by successive complexifications of his neural system, in which the emergence of higher order cognitive processes gives support to Mario Bunge’s “emergentist monism.”

The existing reliability models of the cyber physical distribution systems, which are mostly based on some simplified assumptions, cannot accurately evaluate the reliability of complex cases in practical engineering applications. To solve this problem, an elaborate reliability evaluation method considering the whole process of fault location, isolation and supply restoration is proposed. This paper establishes reliability models of components and the two systems, summarizes the mapping relationship between various cyber system failures and the physical fault handling into several laws, proposes the reliability evaluation procedure in the framework of Monte Carlo method, and verifies the feasibility and effectiveness of the method in an actual distribution network cyber physical system. In the proposed method, the multiple component characteristics, complex topological structure, coupling relationship of the cyber-physical distribution systems, and the actual human-computer joint participation are considered in the analysis of fault location, isolation and supply restoration process, which provides a effective and accurate theory for the application of reliability evaluation in the actual distribution network and prosumer energy management system.

Generalized Second Law of Thermodynamics and its Applications in Social Science

The desire to understand the mathematics of living systems is increasing. The widely held presupposition that the mathematics developed for modeling of physical systems as continuous functions can be extended to the discrete chemical reactions of genetic systems is viewed with skepticism. The skepticism is grounded in the issue of scientific invariance and the role of the International System of Units in representing the realities of the apodictic sciences. Various formal logics contribute to the theories of biochemistry and molecular biology and genetics. Various paths of extension are invoked in these formal logics in order to express the information of biological apodicticism. Symbolizing the appropriate notations for invariant relations and for biological extensions of relations is fundamental to the exact generating functions of discrete algebraic biology. Aspects of philosophical perspectives of the relation scientific number systems are contrasted. The deep distinction between physical motion and biological motion is expressed in terms the roles of Aristotelian causes. The interior motion within perplex numbers is contrasted with the exterior motion of physical systems. The need for a new mathematics for biology is suggested.

Chaos exists widely in meteorological, economic, and physical systems. However, its inherent characteristics, such as sensitivity to initial conditions, long-term nonstationarity, and complex nonlinear dynamics, severely limit the accuracy of chaotic data forecasting. Traditional diffusion models typically use independently trained modules and assume overly simplistic Gaussian distributions in latent variable spaces. These simplified assumptions are inadequate for capturing the intricate topological structures of chaotic attractors, leading to a significant drop in forecasting performance. To address this issue, this paper proposes a model named ChaosDiff, an end-to-end jointly-trained diffusion model framework. In this model, the conventional Gaussian assumption is removed, allowing the latent variable space to more accurately reflect the complex topological and dynamic features of chaotic time series. Experimental results on classical chaotic systems, including Lorenz, Rössler, and a piecewise-linear hyperchaotic system, show that ChaosDiff significantly outperforms existing methods in terms of forecasting accuracy and generalization ability.

Evolving random graph R∞ as introduced and studied by Erdös and Rényi, represent the limit of random f-graphs Rf when f→∞. The latter have been studied mainly by chemists. Such systems show an abrupt transition with the appearance of a giant component (1-connected subgraph) which models transitions in physical systems. It is known that further abrupt transitions in Rf (and R∞) occur with the appearance of giant k-connected subgraphs and these transitions also appear to have their counterparts in physical systems. Cycle length distributions in Rf and R∞ (following the k=1 transition) appear to be inconsistent with the use of these random graphs as physical models and have led to the use of random lattice-graphs RL(f). Results from percolation theory in physics relate to an abrupt transition for 1-connected subgraphs and lead to some interesting conclusions about the use of random lattice-graphs when these systems are compared to the transition in Rf. Further progress in applying random graph theory to model physical systems requires that similar results on abrupt transitions for k-connected subgraphs and on cycle distributions in RL(f) be obtained. We report here on progress and problems of this type in the setting of the applicability of random graphs to model highly interesting physical, chemical and biological systems.

In a physical system with conformal symmetry, observables depend on cross-ratios, measures of distance invariant under global conformal transformations (conformal geometry for short). We identify a quantum information-theoretic mechanism by which the conformal geometry emerges at the gapless edge of a 2+1D quantum many-body system with a bulk energy gap. We introduce a novel pair of information-theoretic quantities (\mathfrak{c}_{\textrm{tot}}, \eta) ( 𝔠 tot , η ) that can be defined locally on the edge from the wavefunction of the many-body system, without prior knowledge of any distance measure. We posit that, for a topological groundstate, the quantity \mathfrak{c}_{\textrm{tot}} 𝔠 tot is stationary under arbitrary variations of the quantum state, and study the logical consequences. We show that stationarity, modulo an entanglement-based assumption about the bulk, implies (i) \mathfrak{c}_{\textrm{tot}} 𝔠 tot is a non-negative constant that can be interpreted as the total central charge of the edge theory. (ii) \eta η is a cross-ratio, obeying the full set of mathematical consistency rules, which further indicates the existence of a distance measure of the edge with global conformal invariance. Thus, the conformal geometry emerges from a simple assumption on groundstate entanglement. We show that stationarity of \mathfrak{c}_{\textrm{tot}} 𝔠 tot is equivalent to a vector fixed-point equation involving \eta η , making our assumption locally checkable. We also derive similar results for 1+1D systems under a suitable set of assumptions.

Recent attention to aviation cyber physical systems (ACPS) is driven by the need for seamless integration of design disciplines that dominate physical world and cyber world convergence. System convergence is a big obstacle to good aviation cyber-physical system (ACPS) design, which is due to a lack of an adequate scientific theoretical foundation for the subject. The absence of a good understanding of the science of aviation system convergence is not due to neglect, but rather due to its difficulty. Most complex aviation system builders have abandoned any science or engineering discipline for system convergence they simply treat it as a management problem. Aviation System convergence is almost totally absent from software engineering and engineering curricula. Hence, system convergence is particularly challenging in ACPS where fundamentally different physical and computational design concerns intersect. In this paper, we propose an integrated approach to handle System convergence of aviation cyber physical systems based on multi-dimensions, multi-views, multi-paradigm and multiple tools. This model-integrated development approach addresses the development needs of cyber physical systems through the pervasive use of models, and physical world, cyber world can be specified and modeled together, cyber world and physical world can be converged entirely, and cyber world models and physical world model can be integrated seamlessly. The effectiveness of the approach is illustrated by means of one practical case study: specifying and modeling Aircraft Systems. In this paper, We specify and model Aviation Cyber-Physical Systems with integrating Modelica, Modelicaml and Architecture Analysis & Design Language (AADL), the physical world is modeled by Modelica and Modelicaml, the cyber part is modeled by AADL and Modelicaml.

Cyber Physical System (CPS) is Informatics and computer science. Cyber physical manufacturing system is a new research field. In the fields of computer science and manufacturing science and technology Promote the Fourth Industrial Revolution known as Industry 4.0. CPS is generally focused. About the integration of the physical world and cyberspace. It`s the integration of communication, Computational, control, and physical elements. Currently, CPS is Science, government and industry. Systematic literature review on cyber. The physical system of the manufacturing system is not available. The purpose of the chapters in this bookis to gain insight into manufacturing systems and develop cyber-physical systems. Physical systems for intelligent manufacturing. CPS is written using the concept of CPS. A 5-tier architecture system for manufacturing systems. Continuous Key Release. It also describes the technology of cyber physical manufacturing systems. Combined with manufacturing, CPS enables intelligent manufacturing and provides technical support for manufacturing upgrades and conversions. This can be summarized in three aspects: internet-based manufacturing, process intelligence, and product intelligence. Future manufacturing is said to be personalized manufacturing based on cyber physical systems, intelligent manufacturing, digital manufacturing, and network-based manufacturing. CPS continues to transform the manufacturing trends of the industry, creating even more amazing value for future global manufacturing scenarios

How to model and implement connections between physical and virtual models for digital twin application

Cyber-physical vehicle systems (CPVSs) are advancing due to progress in real-time applications, control and artificial intelligence. Multi-objective design optimisation maximises CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modelling errors and uncertainties. CPVS optimisation occurs at design-time and at run-time. We will survey the run-time cooperative optimisation or co-optimisation of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilised in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimisation and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined. Cyber-physical systems will transform how we interact with the physical world around us. Many grand challenges wait in the economically vital domains of transportation, healthcare, manufacturing, agriculture, energy, defence, aerospace and buildings.

Cyber-physical vehicle systems (CPVSs) are advancing due to progress in real-time applications, control and artificial intelligence. Multi-objective design optimisation maximises CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modelling errors and uncertainties. CPVS optimisation occurs at design-time and at run-time. We will survey the run-time cooperative optimisation or co-optimisation of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilised in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimisation and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined. Cyber-physical systems will transform how we interact with the physical world around us. Many grand challenges wait in the economically vital domains of transportation, healthcare, manufacturing, agriculture, energy, defence, aerospace and buildings.

We formalize a cryptographic primitive called functional commitment (FC) which can be viewed as a generalization of vector commitments (VCs), polynomial commitments and many other special kinds of commitment schemes. A non-interactive functional commitment allows committing to a message in such a way that the committer has the flexibility of only revealing a function of the committed message during the opening phase. We provide constructions for the functionality of linear functions, where messages consist of vectors over some domain and commitments can later be opened to a specific linear function of the vector coordinates. An opening for a function thus generates a witness for the fact that the function indeed evaluates to a given value for the committed message. One security requirement is called function binding and requires that no adversary be able to open a commitment to two different evaluations for the same function. We propose a construction of functional commitment for linear functions based on constantsize assumptions in composite order groups endowed with a bilinear map. The construction has commitments and openings of constant size (i.e., independent of n or function description) and is perfectly hiding - the underlying message is information theoretically hidden. Our security proofs build on the Deja Q framework of Chase and Meiklejohn (Eurocrypt 2014) and its extension by Wee (TCC 2016) to encryption primitives, thus relying on constant-size subgroup decisional assumptions. We show that FC for linear functions are sufficiently powerful to solve four open problems. They, first, imply polynomial commitments, and, then, give cryptographic accumulators (i.e., an algebraic hash function which makes it possible to efficiently prove that some input belongs to a hashed set). In particular, specializing our FC construction leads to the first pairing-based polynomial commitments and accumulators for large universes known to achieve security under simple assumptions. We also substantially extend our pairing-based accumulator to handle subset queries which requires a non-trivial extension of the Deja Q framework.

Internet of things (IoT) and cyber-physical system (CPS) have a common intend of integrating network connectivity and computational capability to physical devices and systems. This seamless integration is the foundation of CPS and helps deploy more capable, adaptable, and reliable systems. Depending on the application CPS can be small and closed or an exceptionally large and complex system. In general, a CPS includes a network of devices that interact with a physical system that simultaneously controls or monitor communication and computing resources. In the recent decade with the advent of technology such as IoT/IET and artificial intelligence (AI) the interaction and connectivity b\etween people, places, and things have taken a whole new perspective. The progress of IoT has made it possible to collect, store, and analyze plenty of data and provide feedback from cyberspace to physical space. Initially, the internet was a medium to connect humans, but with the integration of sensors, the internet, IoT/IET forming CPS it is possible to connect the physical world and cyberspace without human intervention.

Spatial reasoning is an essential part of human interaction with the physical world. Of the many models that have been developed to support automated spatial reasoning, most rely on numerical descriptions of a spatial scene. This dissertation addresses problems where only qualitative descriptions of a spatial scene are available, such as natural language understanding, qualitative design, and physics problem-solving. We provide the first set of solutions, given only a qualitative description of a spatial scene, for reasoning about dynamic change in both the spatial and non-spatial properties of a physical system. We use diagrams to compactly input the spatial scene for a problem, and text to describe any non-spatial properties. To match diagram and text objects so their descriptions can be integrated, we have developed a method for describing the conceptual class of objects directly in diagrams. Then, diagram and text objects can be matched based on their conceptual class. The given problem is solved through qualitative simulation, and all spatial reasoning is done with respect to an extrinsic Cartesian coordinate system. We model the relative positions of objects through inequality constraints on the coordinates of the points of interest. Changes due to translational motion are detected by noting changes in the truth values of inequality constraints. We model the orientation of an object through knowledge of its extremal points and its qualitative angle of rotation with respect to each coordinate axis. This model has been used to reason qualitatively about the effects of rotational motion, such as changes in the area projected by one object onto another. We have implemented our spatial representation as production rules and as model fragments in the QPC qualitative modeling system. The former has been used for solving static-world problems such as understanding descriptions of an urban scene. The latter has been used to reason about situations where changes in spatial properties play a critical role, such as the operation of transformers, oscillators, generators, and motors. To support dynamic spatial reasoning, we have expanded the modeling capabilities of QPC to include methods for modeling piecewise-continuous variables, non-permanent objects, and variables with circular quantity spaces.