Transition Function Research Articles

Matrix Model of Deterministic Synchronous Cellular Automata

The paper presents an approach for modeling deterministic synchronous cellular automata by matrices and operations on them. It is proposed to renumber the states of the automaton and write down a matrix, the elements of which are the current states (numbers associated with them) of the cells of the automaton. It is proposed to calculate the matrix of cell states in the next cycle in two stages: find the convolution of the current matrix with a special core, and map the resulting convolution matrix into the matrix of cell states in the next cycle. The convolution core is determined by the cell neighborhood pattern, and the convolution mapping to the matrix of the following states is based on the transition function of the automaton.

Study on the pore structure of eco-regenerated mortar using corn cob based on nuclear magnetic resonance

This study aims to investigate the influence of corn cob particles on pore structure of mortar. By using NMR technology, the pore parameters of fast-hardening sulfoaluminate cement mortar (SAC) and ordinary Portland cement mortar (PO) were measured and compared. The influence of corncob particles on the mechanical properties of eco-mortar was evaluated by uniaxial compression test. The results indicate that medium and large pores of both SAC and PO increases with corn cob particle, as well as the porosity. The fractal dimensions of harmful and multiple harmful pores are negatively correlated with the volume fraction of corn cob particles. The addition of corn cob particles reduced the overall fractal dimension of the mortar and decreased the complexity of internal pores. The probability density function of grayscale values transitions from being “thin and tall” to “short and wide”, with an increasing proportion of high grayscale values. This leads to an increase in the proportion of large pores within the mortar, resulting in a continuous deterioration of pore structure. The addition of corn cob particles greatly reduces the strength of SAC and PO, but the peak stress of SAC is higher than that of PO. When corn cob particle content is 30wt% and 50wt%, the peak stress of SAC is increased by 23.37% and 14.25%, respectively, compared with the peak stress of PO.

Self-Avoiding Walks on Cayley Graphs Through the Lens of Symbolic Dynamics

We study dynamical and computational properties of the set of bi-infinite self-avoiding walks on Cayley graphs, as well as ways to compute, approximate and bound their connective constant. To do this, we introduce the skeleton $X_{G,S}$ of a finitely generated group $G$ relative to a generating set $S$, which is a one-dimensional subshift made of configurations on $S$ that avoid all words that reduce to the identity. We provide a characterization of groups which have SFT skeletons and sofic skeletons: first, there exists a finite generating set $S$ such that $X_{G,S}$ is a subshift of finite type if and only if $G$ is a plain group; second, there exists $S$ such that $X_{G,S}$ is sofic if and only if $G$ is a plain group, $\mathbb{Z}\times\mathbb{Z}/2\mathbb{Z}$ or $\mathcal{D}_{\infty}\times\mathbb{Z}/2\mathbb{Z}$. We also characterize finitely generated torsion groups as groups whose skeletons are aperiodic. For connective constants, using graph height functions and bridges, we show that Cayley graphs of finitely generated torsion groups do not admit graph height functions, and that for groups that admit transitive graph height functions, the connective constant is equal to the growth rate of periodic points of the skeleton. Finally, we take a brief look at the set of bi-infinite geodesics and introduce an analog of the connective constant for the geodesic growth.

Simulation of stainless ferritic-martensitic and austenitic steel hardening after irradiation in ion accelerator. Part 2. Development of a methodology for determining the ion mode irradiation of austenitic steels

A methodology for determining the irradiation mode for ferritic-martensitic steels at ion accelerator has been developed and experimentally substantiated, providing radiation hardening of these steels, identical to that realized under neutron irradiation. The change in Vickers microhardness is used as a measure of radiation hardening. The paper presents the results of a study of radiation-induced changes in the microhardness of austenitic steels 08Kh18Н10Т and 08Kh16Н20М2Т irradiated in reactors SM-3, VVER-440, BOR-60, SM-3 + BOR-60 to damaging doses of 10.2–33.7 dpa in the interval of temperatures 60–500°C. A study of radiation-induced changes in microhardness in a wider range of irradiation temperatures, post-irradiation annealing of irradiated steels was carried out in the range from 400 to 600°C, simulating irradiation at temperatures equal to annealing temperatures. Data are presented on radiation-induced changes in microhardness after irradiation in the ion accelerator of the State Scientific Center of the Russian Federation – Institute for Physics and Power Engineering named after A.I. Leypunsky (IPPE) with Ni+4 ions and He+ ions up to concentrations of 0–7 appm/dpa at damaging doses of 13–30 dpa and temperatures of 300–650°. A transition function has been established that connects the irradiation of temperatures during neutron and ion irradiation at a given damaging dose, ensuring the same radiation hardening of austenitic steels.

Simulation of stainless ferritic-martensitic and austenitic steel hardening after irradiation in ion accelerator. Part 1. Development of a methodology for determining the ion mode irradiation of ferritic-martensitic steels

A methodology for determining the irradiation mode for ferritic-martensitic steels at ion accelerator has been developed and experimentally substantiated, providing radiation hardening of these steels, identical to that realized under neutron irradiation. The change in Vickers microhardness is used as a measure of radiation hardening. A study was carried out of radiation-induced changes in the microhardness of ferritic-martensitic steels 07Kh12NMFB and EP-823 after neutron and ion irradiation to damaging doses of 10–30 dpa in the temperature range 350–600°C. These materials were irradiated with neutrons in the reactors BOR-60, BN-600 and in the ion accelerator of the State Scientific Center of the Russian Federation – Institute for Physics and Power Engineering named after A.I. Leypunsky (IPPE) with Fe3+, Fe4+ ions and He+ ions to concentrations of 0.2 and 4 appm/dpa. A transition function has been established that connects the irradiation temperatures for neutron and ion irradiation at a given damaging dose, ensuring the same radiation hardening.

On monomorphisms of hypergraphic automata

Hypergraphic automata are automata, state sets and output symbol sets of which are hypergraphs, being invariant under actions of transition and output functions. Universally attracting objects in the category of hypergraphic automata are called universal hypergraphic automata. The semigroups of input symbols of such automata are derivative algebras of mappings for such automata. So their properties are interconnected with properties of the algebraic structures of the automata. This paper describes the structure of monomorphisms of such automata and their semigroups of input signals.

Priority-based intelligent resolution method of multi-aircraft flight conflicts

Abstract The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

"Timely help" or "one disaster after another": The impact of potential and transition interim CEO succession on corporate performance.

The succession mode of interim CEO is highly uncertain. The smooth transition function of interim CEO is particularly important in the process of enterprise reform. We divide interim CEOs into potential successors and transition successors. Then we empirically analyze the heterogeneous impact and mechanism of interim CEOs on firm performance. The results show that compared with transition CEO, potential CEO has less negative impact on corporate performance. Interim CEO has a negative impact on enterprise performance by reducing agency efficiency. The mechanism analysis of this paper are as follows. For the potential CEO, strong incentive constraints can effectively mitigate the negative impact of the succession on corporate performance. For a transitional CEO, superior personal capabilities coupled with an effective oversight mechanism represent more optimal solutions. In today's corporate landscape, the frequencies of executive changes and interim successions are on the rise annually. Our research is tailored to aid enterprises in navigating these transitions seamlessly and in expediting their recovery processes.

A criterion for selecting the appropriate one from the trained models for model‐based offline policy evaluation

AbstractOffline policy evaluation, evaluating and selecting complex policies for decision‐making by only using offline datasets is important in reinforcement learning. At present, the model‐based offline policy evaluation (MBOPE) is widely welcomed because of its easy to implement and good performance. MBOPE directly approximates the unknown value of a given policy using the Monte Carlo method given the estimated transition and reward functions of the environment. Usually, multiple models are trained, and then one of them is selected to be used. However, a challenge remains in selecting an appropriate model from those trained for further use. The authors first analyse the upper bound of the difference between the approximated value and the unknown true value. Theoretical results show that this difference is related to the trajectories generated by the given policy on the learnt model and the prediction error of the transition and reward functions at these generated data points. Based on the theoretical results, a new criterion is proposed to tell which trained model is better suited for evaluating the given policy. At last, the effectiveness of the proposed criterion is demonstrated on both benchmark and synthetic offline datasets.

Dynamic Docking Anti‐Disturbance Control of Overactuated AUV: System, Method, and Lake Trails

ABSTRACTDynamic docking control technology is crucial for autonomous underwater vehicles (AUV) to perform tasks underwater. To enhance the docking success rate of AUVs during dynamic docking, this paper presents a robust anti‐disturbance control algorithm specifically designed for overactuated AUV dynamic docking scenarios. During a dynamic docking mission, the AUV's depth control is adversely affected by the complex flow field generated by the underwater recovery device. To address this issue, this research proposes an AUV control scheme that combines an extended state observer (ESO) with a combined disturbance rejection method of the elevator‐vertical tunnel controller. First, an ESO is constructed to estimate and compensate for complicated disturbances such as model uncertainty and environmental disturbances. These estimations are then incorporated into the control law to mitigate the effects of the complicated flow field interference experienced during the AUV's dynamic docking process. Second, as turbulence intensifies at the end of the docking stage, the vertical thrust allocation is achieved using a hyperbolic tangent transition function. This ensures the stability of the AUV's attitude and depth, thereby enabling precise docking. Finally, the effectiveness of the proposed control algorithm is verified through lake trials and compared against the classic proportional‐integral‐differential (PID) and active disturbance rejection control (ADRC) methods. The trial results indicate that the proposed control algorithm significantly reduces the pitch and depth errors of the AUV, resulting in a remarkable 91% success rate for dynamic docking (based on 45 tests). The lake trials demonstrate that the proposed control algorithm is highly precise and robust.

Off-Policy Evaluation in Doubly Inhomogeneous Environments

– This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions—temporal stationarity and individual homogeneity are both violated. To handle the “double inhomogeneities”, we propose a class of latent factor models for the reward and transition functions, under which we develop a general OPE framework that consists of both model-based and model-free approaches. To our knowledge, this is the first article that develops statistically sound OPE methods in offline RL with double inhomogeneities. It contributes to a deeper understanding of OPE in environments, where standard RL assumptions are not met, and provides several practical approaches in these settings. We establish the theoretical properties of the proposed value estimators and empirically show that our approach outperforms state-of-the-art methods. Finally, we illustrate our method on a dataset from the Medical Information Mart for Intensive Care. An R implementation of the proposed procedure is available at https://github.com/ZeyuBian/2FEOPE . Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Оптимизация инвестиционного портфеля бинарным методом роя пчел

<p>The problem of forming a stock portfolio is considered as a binary optimization problem. The solution is formed using the developed modification of the bee swarm method, supplemented by a binarization procedure using various transition functions. The efficiency of the proposed method is studied using model examples and the applied problem of maximizing portfolio profitability is solved taking into account constraints on the funds used and the risk value.</p>

Rapid design method for composite emergency steel piers with thin-walled steel tube framework and discussion on "temporary-permanent" transition

To address the conflict between the reserve of repair materials for existing bridge piers and the demands for emergency repairs, as well as the challenge of converting temporary emergency structures into permanent ones without significant disassembly, a novel composite emergency steel pier has been developed. This pier utilizes commonly available thin-walled steel tube sections as the framework, I-beams as the connecting elements, and incorporates a "temporary-permanent" transition function. Taking the emergency steel pier for medium-height bridge piers in high-speed rail systems as an example, this study introduces three evaluation metrics—security coefficient (n), stability coefficient k), and variation coefficient (V)—analyzing the factors influencing the design and application thresholds of the emergency steel piers from perspectives of strength, stability, and load distribution uniformity. Based on this, a rapid design method for composite emergency steel piers with thin-walled steel tube frameworks that does not require numerical simulation has been developed and experimentally validated. Research findings indicate that when used for the emergency repair of medium-height or lower bridge piers in high-speed railways, the column line stiffness of the thin-walled steel tube framework composite emergency steel piers should range between 0.34 × 10 exp7 kN/mm to 4.15 × 10 exp7 kN/mm. The transverse and longitudinal spacing of the columns should be 1 m to 3 m and 1.5 m to 3 m respectively. The line stiffness of the plinth beams I and II should be greater than 0.69 kN/mm and 1.01 kN/mm respectively. Within these parameter ranges, the emergency steel pier will not experience strength failure or instability, and the load distribution among the columns remains uniformly favorable. In applications to other scenarios, the rapid generation of composite emergency steel piers with thin-walled steel tube frameworks can be achieved by following three steps: parameter selection and structural configuration determination, calculation of the pier-beam linear stiffness ratio, and criterion-based result verification. The rapid design and implementation of composite emergency steel piers help alleviate the contradiction between material stockpiling and repair demands. Furthermore, the pre-designed "temporary-permanent" transition functionality facilitates integrated operations for emergency repairs and permanent restorations.

Understanding the role of the human gut microbiome in overweight and obesity.

The gut microbiome may be related to the prevalence of overweight and obesity, but high interindividual variability of the human microbiome complicates our understanding. Obesity often occurs concomitantly with micronutrient deficiencies that impair energy metabolism. Microbiota composition is affected by diet. Host-microbiota interactions are bidirectional. We propose three pathways whereby these interactions may modulate the gut microbiome and obesity: (1) ingested compounds or derivatives affecting small intestinal transit, endogenous secretions, digestion, absorption, microbiome balance, and gut barrier function directly affect host metabolism; (2) substrate availability affecting colonic microbial composition and contact with the gut barrier; and (3) microbial end products affecting host metabolism. The quantity/concentration, duration, and/or frequency (circadian rhythm) of changes in these pathways can alter the gut microbiome, disrupt the gut barrier, alter host immunity, and increase the risk of and progression to overweight and obesity. Host-specific characteristics (e.g., genetic variations) may further affect individual sensitivity and/or resilience to diet- and microbiome-associated perturbations in the colonic environment. In this narrative review, the effects of selected interventions, including fecal microbiota transplantation, dietary calorie restriction, dietary fibers and prebiotics, probiotics and synbiotics, vitamins, minerals, and fatty acids, on the gut microbiome, body weight, and/or adiposity are summarized to help identify mechanisms of action and research opportunities.

Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement Learning.

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this article, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.

Plücker coordinates and the Rosenfeld planes

The exceptional compact hermitian symmetric space EIII is the quotient E6/Spin(10)×Z4U(1). We introduce the Plücker coordinates which give an embedding of EIII into CP26 as a projective subvariety. The subvariety is cut out by 27 Plücker relations. We show that, using Clifford algebra, one can solve this over-determined system of relations, giving local coordinate charts to the space.Our motivation is to understand EIII as the complex projective octonion plane (C⊗O)P2, whose construction is somewhat scattered across the literature. We will see that the EIII has an atlas whose transition functions have clear octonion interpretations, apart from those covering a sub-variety X∞ of dimension 10. This subvariety is itself a hermitian symmetric space known as DIII, with no apparent octonion interpretation. We give detailed analysis of the geometry in the neighbourhood of X∞.We further decompose X=EIII into F4-orbits: X=Y0∪Y∞, where Y0∼(OP2)C is an open F4-orbit and is the complexification of OP2, whereas Y∞ has co-dimension 1, thus EIII could be more appropriately denoted as (OP2)C‾. This decomposition appears in the classification of equivariant completion of homogeneous algebraic varieties by Ahiezer [2].

Scheduling as a Constraint Satisfaction Problem (Using the Example of Open-Pit Minе Production Scheduling Problem)

The research described in the work is aimed at developing methods for Scheduling. The fundamental disadvantage of the existing methods of Mixed-Integer Linear Programming in application to the problems under consideration is the fact that they are too demanding on the amount of RAM. The difficulty of applying local search procedures to such high-dimensional problems is to develop an effective way to find an acceptable initial approximation and determine the neighboring state transition function, which would allow achieving the optimum fast enough. In the Operations Research Theory, adding additional conditions to a problem can lead to a fundamental change in the problem-solving scheme. The methods proposed in the study are implemented within the framework of the Constraint Programming Paradigm which makes it possible to represent the subject domain dependencies saving RAM, as well as to provide the ability to step-by-step take into account heterogeneous problem conditions without essentially changing the scheme of finding solutions. A significant part of the research deals with methods of logical inference on constraints to reduce the search space and speed up the computational process. The approach to scheduling is illustrated by the Open-Pit Mine Production Scheduling Problem, which was first proposed to be solved as a Constraint Satisfaction Problem. In order to find the first feasible solution, a «greedy» search method is proposed, the result of which can be improved using the developed hybrid method. Both methods rely on original procedures of inference on constraints. The proposed approach has proven its efficiency for block models with sizes of tens and hundreds of thousands of blocks.

Statistical Inference for Networks of High-Dimensional Point Processes

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes processes. The key ingredient for the inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarize the entire history of the process. Combining recent martingale central limit theorem with the new concentration inequality, we then characterize the convergence rate of the test statistics in a continuous time domain. Finally, to account for potential non-stationarity of the process in practice, we extend our statistical inference procedure to a flexible class of Hawkes processes with time-varying background intensities and unknown transition functions. The finite sample validity of the inferential tools is illustrated via extensive simulations and further applied to a neuron spike train dataset. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Dermal white adipose tissue: Development and impact on hair follicles, skin defense, and fibrosis.

Dermal white adipose tissue (DWAT) is a distinctive adipose depot located within the lower dermis of the skin. Its significance as an ancillary fat in skin homoeostasis has recently received increased attention. New research has revealed that DWAT responses to skin pathology and physiology changes, impacting skin development, hair cycling, defense mechanisms, and fibrotic conditions. In this review, we explore the developmental process of DWAT and the adipose commitment timing of hypodermal. We explore the development process of DWAT and its pivotal role in regulating the hair cycle. We conclude the antibacterial activity and reversible dedifferentiation of dermal adipocytes in response to skin defense. Furthermore, we underscore the potentially crucial yet underestimated anti-fibrotic functions of DWAT-derived adipokines and adipocyte-myofibroblast transition.

Parameter tuning for active disturbance rejection control of fixed-wing UAV based on improved bald eagle search algorithm

PurposeThis paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.Design/methodology/approachA six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.FindingsInspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.Practical implicationsThe proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.Originality/valueThe CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.