The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

In this article we address two related issues on the learning of probabilistic sequences of events. First, which features make the sequence of events generated by a stochastic chain more difficult to predict. Second, how to model the procedures employed by different learners to identify the structure of sequences of events. Playing the role of a goalkeeper in a video game, participants were told to predict step by step the successive directions-left, center or right-to which the penalty kicker would send the ball. The sequence of kicks was driven by a stochastic chain with memory of variable length. Results showed that at least three features play a role in the first issue: (1) the shape of the context tree summarizing the dependencies between present and past directions; (2) the entropy of the stochastic chain used to generate the sequences of events; (3) the existence or not of a deterministic periodic sequence underlying the sequences of events. Moreover, evidence suggests that best learners rely less on their own past choices to identify the structure of the sequences of events.

Synchronization is one of the most striking instances of collective behavior, occurring in many natural phenomena. For example, in some ant species, ants are inactive within the nest most of the time, but their bursts of activity are highly synchronized and involve the entire nest population. Here we revisit a simulation model that generates this synchronized rhythmic activity through autocatalytic behavior, i.e., active ants can activate inactive ants, followed by a period of rest. We derive a set of delay differential equations that provide an accurate description of the simulations for large ant colonies. Analysis of the fixed-point solutions, complemented by numerical integration of the equations, indicates the existence of stable limit-cycle solutions when the rest period is greater than a threshold and the event of spontaneous activation of inactive ants is very unlikely, so that most of the arousal of ants is done by active ants. Furthermore, we argue that the persistent oscillations observed in the simulations for colonies of finite size are due to resonant amplification of demographic noise.

Delays in nerve transmission are an important topic in the field of neuroscience. Spike signals fired or received by the dendrites of a neuron travel from the axon to a presynaptic cell. The spike signal then triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, regenerates electrical signals via a chemical reaction through ion channels, and transmits them to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme-order statistics. By considering the statistical variance in the time constant of the leaky Integrate-and-Fire model, a deterministic time evolution model for spike signals, we enabled randomness in the time interval of the spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, one of the three forms of extreme-value statistics. We further confirmed that the histogram of the maximum time interval followed a Fréchet distribution when the time interval of the spike signal followed a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and can therefore be used as a new indicator of transmission delay.

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID-19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread-anchored in physical considerations-lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing-type instabilities, and the concomitant emergence of steady-state patterns under the interplay between three critical model parameters-the saturation parameter, the noise intensity, and the transmission rate. Employing a second-order perturbation analysis to investigate stability, we uncover both diffusion-driven and noise-induced instabilities and corresponding self-organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

Cellular Potts models are broadly applied across developmental biology and cancer research. We overcome limitations of the traditional approach, which reinterprets a modified Metropolis sampling as ad hoc dynamics, by introducing a physical timescale through Poissonian kinetics and by applying principles of stochastic thermodynamics to separate thermal and relaxation effects from athermal noise and nonconservative forces. Our method accurately describes cell-sorting dynamics in mouse-embryo development and identifies the distinct contributions of nonequilibrium processes, e.g., cell growth and active fluctuations.

When the costs of the inputs and outputs of the units under evaluation are known, the evaluation of the profit efficiency of the units is one of the most significant evaluations that can provide valuable information about them. In this research, first, a new definition of the optimal scale size based on the maximization of the average measure of profit efficiency is presented. The average measure of profit efficiency develops the concept of economic efficiency measure by introducing a more accurate measure of efficiency compared to the measure of comparative and profit efficiency. It has been shown that the average measure of profit efficiency in a convex space is equivalent to the measure of profit efficiency in constant returns to scale technology, and then, some models are presented to calculate profit efficiency in a stochastic environment, to increase the ability of profit models in real examples by considering the calculation errors of inputs and outputs. Finally, the proposed method is used in an empirical example to calculate the average profit efficiency of a set of postal areas in Iran.

In the current study, the fish farm model perturbed with time white noise is numerically examined. This model contains fish and mussel populations with external food supplied. The main aim of this work is to develop time-efficient numerical schemes for such models that preserve the dynamical properties. The stochastic backward Euler (SBE) and stochastic Implicit finite difference (SIFD) schemes are designed for the computational results. In the mean square sense, both schemes are consistent with the underlying model and schemes are von Neumann stable. The underlying model has various equilibria points and all these points are successfully gained by the SIFD scheme. The SIFD scheme showed positive and convergent behavior for the given values of the parameter. As the underlying model is a population model and its solution can attain minimum value zero, so a solution that can attain value less than zero is not biologically possible. So, the numerical solution obtained by the stochastic backward Euler is negative and divergent solution and it is not a biological phenomenon that is useless in such dynamical systems. The graphical behaviors of the system show that external nutrient supply is the important factor that controls the dynamics of the given model. The three-dimensional results are drawn for the various choices of the parameters.

Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer\'s and Parkinson\'s. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.

Understanding diversity patterns and underlying drivers is one of the central topics in the fields of biogeography and community ecology. Aquatic macroinvertebrates are widely distributed in various wetlands and play vital ecological roles. Previous studies mainly have focused on macroinvertebrate diversity in a single type of wetland. Our understanding of the differences in diversity patterns and underlying drivers between different wetland types remains limited. Here, we compared diversity patterns and community assembly of floodplain wetlands (FWs) and non-floodplain wetlands (NWs) in the Sanjiang Plain, Northeast China. We found that the taxonomic richness and abundance were higher in NWs than those in FWs. Nineteen taxa were identified as habitat specialists in the NWs, whereas only four taxa were designated as habitat specialists in the FWs. In addition, the FW and NW assemblages exhibited contrasting compositions. Spatial and environmental variables explained the largest variations in the macroinvertebrate assemblages of NWs and FWs, respectively. Normalised stochasticity ratios and Sloan neutral models confirmed that the macroinvertebrate community assembly of both wetland types was driven largely by stochastic processes. Stochastic processes were more prominent in shaping macroinvertebrate communities of FWs, whereas a stronger dispersal limitation was detected in NWs. Our results revealed contrasting diversity patterns and assembly mechanisms of macroinvertebrate communities in FWs and NWs. We underscore the importance of flood disturbance in shaping wetland ecosystems in the Sanjiang Plain and highlight that conservation and restoration actions cover different types of wetland habitats.