Natural networks as thermodynamic systems

Natural networks are considered as thermodynamic systems that evolve from one state to another by consuming free energy. The least-time consumption of free energy is found to result in ubiquitous scale-free characteristics. The network evolution will yield the scale-independent qualities because the least-time imperative will prefer attachment of nodes that contribute most to the free-energy consumption. The analysis of evolutionary equation of motion, derived from statistical physics of open systems, reveals that evolution of natural networks is a path-dependent and nondeterministic process. Despite the noncomputability of evolution, many mathematical models of networks can be recognized as approximations of the least-time process as well as many measures of networks can be appreciated as practical assessments of the system's thermodynamic status. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

Systems Analysis and the Computer

The words ‘systems analysis’ are used in at least two senses in the literature. Whilst the general nature of the topic is well understood in the O.R. community, the nature of the term as used by computer scientists is less familiar. In this paper, the nature of systems analysis as it relates to computer-based systems is examined from the point of view that the computer system is an automaton embedded in a human system, and some facets of this are explored. It is concluded that O.R. analysts and computer analysts have things to learn from each other and that this ought to be reflected in their education. The important role played by change in the design of systems is also highlighted, and it is concluded that, whilst the application of techniques developed in the ‘artificial intelligence’ field have considerable relevance to constructing automata able to adapt to change in the environment, study of the human factors affecting the overall systems within which the automata are embedded has an even more important role.     

SOLVING DYNAMIC WILDLIFE RESOURCE OPTIMIZATION PROBLEMS USING REINFORCEMENT LEARNING

ABSTRACT. An important technical component of natural resource management, particularly in an adaptive management context, is optimization. This is used to select the most appropriate management strategy, given a model of the system and all relevant available information. For dynamic resource systems, dynamic programming has been the de facto standard for deriving optimal state‐specific management strategies. Though effective for small‐dimension problems, dynamic programming is incapable of providing solutions to larger problems, even with modern microcomputing technology. Reinforcement learning is an alternative, related procedure for deriving optimal management strategies, based on stochastic approximation. It is an iterative process that improves estimates of the value of state‐specific actions based in interactions with a system, or model thereof. Applications of reinforcement learning in the field of artificial intelligence have illustrated its ability to yield near‐optimal strategies for very complex model systems, highlighting the potential utility of this method for ecological and natural resource management problems, which tend to be of high dimension. I describe the concept of reinforcement learning and its approach of estimating optimal strategies by temporal difference learning. I then illustrate the application of this method using a simple, well‐known case study of Anderson [1975], and compare the reinforcement learning results with those of dynamic programming. Though a globally‐optimal strategy is not discovered, it performs very well relative to the dynamic programming strategy, based on simulated cumulative objective return. I suggest that reinforcement learning be applied to relatively complex problems where an approximate solution to a realistic model is preferable to an exact answer to an oversimplified model.     

The relativistic theory of chemical binding

Starting from Breit’s relativistic equation for a system of two electrons, it is shown that for a hydrogen molecule (or for a system of two electrons moving in a field of cylindrical symmetry) the component of the total angular momentum (J _x ) along the axis of the molecule (axis of symmetry) is a constant of motion. Thus every eigenstate of the system is simultaneously an eigenstate of J _x also, and a state of the system will specify, besides its energy, only the eigenvalue of the component of the angular momentum parallel to the axis of symmetry. The form of the four large components of the wave function relating to their dependence on the azimuthal co-ordinates has been given. The case of Russel-Saunders approximation has been considered in detail and the nature of the components of the wave function for the singlet and triplet states has been discussed. It is shown that the wave function for the ground state of the hydrogen molecule could be expressed as a sum of a set of symmetric functions of which the first term is the Heitler-London function, and that the wave function for a triplet state should be a superposition of anti-symmetric molecular orbitals. It is shown that relativistic theory brings about in a natural manner the facts relating to the ground state of the molecules C₂ and O₂. Finally, some remarks are made concerning the case of molecules for which the spinorbit and the spin-spin couplings are strong.     

Hybrid coordinate approach for the modelling and simulation of multibody systems

The main goal of the present work is to provide an add-on scheme for the formulation of multibody dynamics, based on natural coordinates, in regard to ideally balanced rigid bodies with high rotational spin, e.g. gyroscopes. The underlying aim of this approach is to achieve higher numerical accuracy whenever the preferred axis of rotation coincides with the balanced main axis of the body. This will be achieved by seperating the spin of the balanced rigid body along the denoted axis as an additional angular coordinate, whereas the other rotations will be covered by a carried frame, parameterized via natural coordinates. At the same time the carried frame provides a link to the existing modelling framework in terms of natural coordinates, enabling a straightforward implementation into existing multibody systems (e.g. rotary crane [2]). (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Scheduling problem using genetic algorithm,simulated annealing and the effects of parameter values on GA performance

Genetics algorithms have been designed as general purpose optimisation methods. Simulated annealing simulates the cooling process of solid materials-known as annealing. However this analogy is limited to the physical movement of the molecules without involving complex thermodynamic systems. Physical annealing refers to the process of cooling a solid material so that it reaches a low energy state. Initially the solid is heated up to the melting point. Then it is cooled very slowly, allowing it is to come to thermal equilibrium at each temperature. This process of slow cooling is called annealing. The goal is to find the best arrangement of molecules that minimises the energy of the system, which is referred to as the ground state of the solid material. If the cooling process is fast, the solid will not attain the ground state, but a locally optimal structure. In this paper presents a general purpose schedule optimiser for manufacturing shop scheduling using genetic algorithms and simulated annealing. Then, the ‘uniform order-based’ crossover and mutation operators and novel general effects of parameter values on minimised objective value are presented.     

A fuzzy opportunity and threat aggregation approach in multicriteria decision analysis

Economic expansion in developed countries coupled with dramatically growing economies in countries such as China and India have precipitated a steady increase in demand for oil and natural gas. The Caspian Sea region holds large quantities of both oil and natural gas. Because the Caspian Sea is landlocked and the region’s nations are distant from the largest energy markets, transportation must at least begin by pipeline. While some lines currently exist, pipelines with the capacity of transporting larger amounts of energy resources must be constructed to meet the global demand. This study is conducted for a multinational oil and natural gas producer to develop a multicriteria decision analysis (MCDA) framework for evaluating five possible pipeline routes in the Caspian Sea region. The proposed MCDA model considers a large number of conflicting criteria in the evaluation process and captures decision makers’ (DMs’) beliefs through a series of intuitive and analytical methods such as the analytic network process and fuzzy scoring. A defuzzification method is used to obtain crisp values from the subjective judgments and estimates provided by multiple DMs. These crisp values are aggregated and synthesized with the concept of entropy and the theory of the displaced ideal. The alternative routes are plotted on a diagram in a polar coordinate system and a classification scheme is used along with the Euclidean distance to measure which alternative is closer to the ideal route.     

Continuous-time limit of repeated interactions for a system in a confining potential

We study the continuous-time limit of a class of Markov chains coming from the evolution of classical open systems undergoing repeated interactions. This repeated interaction model has been initially developed for dissipative quantum systems in Attal and Pautrat (2006) and was recently set up for the first time in Deschamps (2012) for classical dynamics. It was particularly shown in the latter that this scheme furnishes a new kind of Markovian evolutions based on Hamilton’s equations of motion. The system is also proved to evolve in the continuous-time limit with a stochastic differential equation. We here extend the convergence of the evolution of the system to more general dynamics, that is, to more general Hamiltonians and probability measures in the definition of the model. We also present a natural way to directly renormalize the initial Hamiltonian in order to obtain the relevant process in a study of the continuous-time limit. Then, even if Hamilton’s equations have no explicit solution in general, we obtain some bounds on the dynamics allowing us to prove the convergence in law of the Markov chain on the system to the solution of a stochastic differential equation, via the infinitesimal generators.     

Is the statement of Murphy's law valid?

Murphy's Law is not a law in the formal sense yet popular science often compares it with the Second Law of Thermodynamics as both the statements point toward a more disorganized state with time. In this article, we first construct a mathematically equivalent statement for Murphy's Law and then disprove it using the intuitive idea that energy differences will level off along the paths of steepest descent, or along trajectories of least action. © 2015 Wiley Periodicals, Inc. Complexity 21: 374–380, 2016     

On the incorporation of adversarial reasoning in course of action analysis and resource allocation

Course of Action analysis and Resource Management are concerned with the allocation of resources over time to effect desired actions as a result of the perceived situation awareness. Decision Support Systems provide automated recommended courses of action to decision makers, considering relevant resource capabilities and constraints. Incorporating potential adversary actions and reactions to the current course of action decision (and the resources effecting the actions) in the decision making process will make the decision support system more robust and increase confidence that the recommended decisions are appropriate responses to the unfolding situations. We discuss research results from the inclusion of possible adversary actions and reactions into the course of action/resource allocation decision making framework. The overall decision problem is formulated as a multi-stage mathematical program. As the problem is NP-hard, an heuristic is developed through a natural problem decomposition. Simulated results show the effectiveness of the heuristic in producing good-quality solutions in an efficient manner.     

气液耦合系统中固有频率的实验研究

流体砰击现象广泛存在于海洋环境、航空航天等自然界与工程中.流体砰击大尺度结构过程中,自由液面破碎时会包裹气体进入流场,气液混合易导致局部砰击荷载增大,引起结构破坏的危险.砰击过程中,气室压力对自由液面固有模态的影响尚未有系统的研究报道.该文采用物理模型实验方法在二维储舱内设计并开展一系列实验,系统研究了两种不同的气室压力对耦合系统的固有频率和阻尼的影响.实验中采用高速摄影机记录了自由液面振荡过程,通过自主研制的图像处理软件提取自由液面波高.结果表明:在低气室压力下,晃荡能量主要集中于一阶固有频率;在高气室压力下,晃荡能量主要集中于二阶固有频率.随着气室压强的增大,影响液体晃荡的主要固有频率提高,而对应的阻尼比却随之降低.因此,气体可压缩性是研究流体晃荡的一个重要因素.     

Active controlled structural systems under delta-correlated random excitation: Linear and nonlinear case

Reduction of structural vibration in active controlled dynamical system is usually performed by means of convenient control forces dependent of the dynamic response. In this paper the existent studies will be extended to dynamical systems subjected to non-Gaussian random process accounting for the time delay involved in the application of active control actions. Control forces acting with time-delay effects will be expanded in Taylor series evaluating response statistics by means of the extended Itô differential rule to consider the effects of the non-normality of the input processes. Numerical application provided shows the feasibility of the proposed method to analyze stochastic dynamic systems with delayed actions under delta-correlated process contrasting statistics of response with estimates from Monte Carlo simulation.     

Optimal inspection and perfect repair

Models are developed for decision making about monitoring andmaintenance of systems whose performance through time is describedby a general stochastic process. The system is monitored andpreventive and corrective maintenance actions are carried outin response to the observed system state. The decision processis simplified by using the maximum process as a decision variable.The models developed generalize age replacement models and othersimple maintenance strategies. The approach can deal with failuresthat prevent the system functioning further, and also failuresthat are defined by regulation or economic considerations. Attentionis restricted to perfect repair and inspection, but the structureprovides the framework for further developments.     

On a resource-constrained scheduling problem with application to distributed systems reconfiguration

This paper is devoted to the study of a resource-constrained scheduling problem, the Process Move Programming problem, which arises in relation to the operability of certain high availability real-time distributed systems. Informally, this problem consists, starting from an arbitrary initial distribution of processes on the processors of a distributed system, in finding the least disruptive sequence of operations (non-impacting process migrations or temporary process interruptions) at the end of which the system ends up in another predefined arbitrary state. The main constraint is that the capacity of the processors must not be exceeded during the reconfiguration. After a brief survey of the literature, we prove the NP-hardness of the problem and exhibit a few polynomial special cases. We then present a branch-and-bound algorithm for the general case along with computational results demonstrating its practical relevance. The paper is concluded by a discussion on further research.     

Continuous Markovian Model for Unexpected Shift in SPC

Various process models for discrete manufacturing systems (parts industry) can be treated as bounded discrete-space Markov chains, completely characterized by the original in-control state and a transition matrix for shifts to an out-of-control state. The present work extends these models by using a continuous-state Markov chain, incorporating non-random corrective actions. These actions are to be realized according to the statistical process control (SPC) technique and should substantially affect the model. The developed stochastic model yields Laplace distribution of a process mean. Real-data tests confirm its applicability for the parts industry and show that the distribution parameter is mainly controlled by the SPC sample size.     

Practical stabilization for piecewise-affine systems: A BMI approach

We propose in this paper, a systematic switching practical stabilization method for PWA switched systems around an average equilibrium. For these systems, the main difficulty comes from the fact that to end in BMI formulation, it is necessary to represent the system in an augmented state space but a restricted one. However, the derived stabilizing conditions are not tractable as BMI in the restricted domain. We will present a method that overcomes this difficulty and drives asymptotically system states into a ball centered on the desired non-equilibrium reference. The efficiency of this practical stabilization method is showed by the ball smallness and the good robustness against disturbances. The design control searches for a single Lyapunov-like function and an appropriate continuous state space partition to satisfy stabilizing properties. Therefore, the method constitutes a simple systematic state feedback computation; it may be useful for on-line applications. As a direct application, satisfactory simulation results are obtained for two illustrative examples, a Buck-Boost converter and a multilevel one. Due to their functioning nature, these devices constitute good examples of switched systems. They are electrical circuits controlled by switches to produce regulated outputs despite the load disturbances and power supply irregularities.     

Methods of statistical modeling in the process of sieve separation of heterogeneous particles

In the article we undertook the attempt of using the theory of random fields in the analysis of the separation process of particles in non-contiguous systems. The presentation of the complete model is a complicated problem. We can say that this article is start point of the theory, which will be supplemented gradually in the future. The separation process of particles on sieves is a phenomenon with random character. It is a result of random character of cleaned mixture and the nature of the separation process. Undoubtedly, an important part is played by the theory of random fields in which system-events, inherently not determined, are described in undetermined-probabilistic way. However, we can perceive one paradox in such actions – correctly constructed probabilistic models serve to impose deterministic frames on the given random process. We can say that stochastic models generate the matrix of the causal-consecutive dependence, which will give the ability of controlling a given process.     

On the Nature of OR — Entering the Fray

In 1980 a seminar of operational researchers and practitioners from a variety of different countries concluded that there was a remarkable consensus about the way that the process of operational research and systems analysis should be understood and practised. Since then the development of new methods has continued apace, stimulated by interaction with the social sciences and paradigms other than natural science. This paper enters the debate, started in this journal, about the nature of OR's status as science or technology. It recognizes the historic claims of science on OR, but it suggests that for ‘private’ consumption, technology will prove more fruitful, and for ‘public’ consumption practitioners will increasingly adopt the label ‘consultant’. Ackoff's original six-step method of OR is updated to reflect current OR practice. The process suggested acknowledges the importance of context, socio-political factors and the growing role of facilitation.     

Optimal maintenance scheduling for a complex manufacturing system subject to deterioration

We address the problem of determining inspection strategy and replacement policy for a deteriorating complex multi-component manufacturing system whose state is partially observable. We develop inspection and replacement scheduling models and other simple maintenance scheduling models via employing an imperfect repair model coupled with a damage process induced by operational conditions. The system state in performance of the imperfectly repaired system is modelled using a proportional intensity model incorporating a damage process and a virtual age process caused by repair. The system is monitored at periodic times and maintenance actions are carried out in response to the observed system state. Decisions to perform imperfect repair and replacement are based on the system state and crossing of a replacement threshold. The model proposed here aims at joint determination of a cost-optimal inspection and replacement policy along with an optimal level of maintenance which result in low maintenance cost and high operational performance and reliability of the system. To demonstrate the use of the model in practical applications a numerical example is provided. Solutions to optimal system parameters are obtained and the response of the model to these parameters is examined. Finally some features of the model are demonstrated. The approach presented provides a framework so that different scenario can be explored.