Stochastic Behavior Research Articles

Information Theory Quantifiers in Cryptocurrency Time Series Analysis.

This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity-entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.

Intelligent Clustering and Adaptive Energy Management in Wireless Sensor Networks with KDE-Based Deployment.

Wireless sensor networks (WSNs) are widely used in IoT, environmental monitoring, and industrial systems, but ensuring energy efficiency, extended network lifetime, and reliable communication under real-world constraints remains challenging. This work proposes a novel clustering framework that integrates kernel density estimation (KDE)-based adaptive node deployment, silhouette-optimized K-means clustering, Bayesian cluster head (CH) selection with Gaussian noise-based energy uncertainty modeling, energy-efficient coverage control, and carrier sense multiple access with collision avoidance-based data transmission. Unlike conventional approaches that rely on fixed clustering and uniform deployments, our framework supports terrain-aware node placement and dynamic CH selection based on residual energy and distance under imperfect sensing conditions. Simulation results demonstrate significant improvements in performance, including over 35% extension in network lifetime and higher coverage retention under energy constraints, compared to baseline methods such as LEACH and K-LEACH. While detailed metrics vary per run due to adaptive parameters and stochastic node behavior, these outcomes validate the scalability, robustness, and practical relevance of the proposed method for real-world WSN deployments.

Cell deformations generated by stochastic actomyosin waves drive in vivo random-walk swimming migration

Amoeboid cell migration drives many developmental and disease-related processes including immune responses and cancer metastasis. Swimming migration is a subtype of amoeboid migration observed in cells in suspension ex vivo. However, the mechanism underlying swimming migration in vivo is unknown. Using Drosophila fat body cells (FBCs) as a model, we show that FBCs actively swim to patrol the pupa by random-walk. Their migration is powered through actomyosin waves, that exert compressive forces as they travel to the cell rear causing cell deformations. Unlike in other types of amoeboid migration, RhoA, Cdc42 and Rac1, are all required for FBC migration to regulate formin-driven actin polymerisation. We find that RhoA at the cell rear induces actomyosin contractions via Rho kinase and myosin II. We show that contractile actin waves display a stochastic behaviour, inducing either cell elongation or rounding, suggesting that nonreciprocal cell deformations drive locomotion. Importantly, our work in a physiological system reveals that stochastic actomyosin waves promote random-walk swimming migration to enable fast, long-range cell dispersal. We propose that this individualist migration behaviour collectively allows patrolling of the pupal body.

Understanding Market Dynamics and Performance of Mobile Apps

Understanding Market Dynamics and Performance of Mobile Apps

Cross-validation-based sequential design for stochastic models.

Complex numerical models are increasingly being used in healthcare and epidemiology. To represent the complex features, modellers often make the decision to include stochastic behaviour where repeated runs of the model with identical inputs produce different outputs. When computational constraints limit the number of model runs and replications, heteroscedastic Gaussian processes can be used as a fast surrogate, allowing for efficient emulation of varying noise levels across the input space. The accuracy of any emulator is greatly dependent on the design of the training data, where sequential design algorithms increase the number of design points iteratively based on predefined criteria. For stochastic models, the design problem is more challenging due to the possibility of replicates at design points. This article develops a new sequential design method for stochastic models which scales well in high-dimensional input spaces. We build upon an existing method for deterministic models using an expected squared leave-one-out error criterion that balances exploration and replication. We compare our approach with existing sequential design methods as well as applying it to an agent-based model and a COVID-19 model. Results demonstrate that the proposed method performs well in noisy environments, offering a scalable alternative to existing methods.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

Thermo‐Electro‐Mechanical Modeling of Failure: Application to Long‐Term Reliability of Aging Transmission Lines

ABSTRACTThe integrity and reliability of a national power grid system are essential to society's development and security. Among the power grid components, transmission lines are critical due to exposure and vulnerability to severe external conditions, including high winds, ice, and extreme temperatures. The combined effects of external agents with high electrical load and the presence of damage precursors greatly affect the conducting material's properties due to a thermal runaway cycle that accelerates the aging process. In this paper, we develop a thermo‐electro‐mechanical model for long‐term failure analysis of overhead transmission lines. A phase‐field model of damage and fatigue, coupled with electrical and thermal modules, provides a detailed description of the conductor's temperature evolution. We define a limit state function based on maximum operating temperature to avoid excessive overheating and sagging. We study four representative scenarios deterministically and propose the Probabilistic Collocation Method (PCM) as a tool to understand the stochastic behavior of the system. We use PCM in forward parametric uncertainty quantification, global sensitivity analysis, and computation of failure probability curves in a straightforward and computationally efficient fashion, and we quantify the most influential parameters that affect failure predictability from a physics‐based perspective.

The stochastic behavior of electricity prices under scrutiny: Evidence from spot and futures markets

The stochastic behavior of electricity prices under scrutiny: Evidence from spot and futures markets

A Data-Driven Algorithm for Dynamic Parameter Estimation of an Alkaline Electrolysis System Combining Online Reinforcement Learning and k-Means Clustering Analysis

Determining the electrochemical, thermal, and mass transfer dynamics embedded in an alkaline electrolysis (AEL) system provides important information about the application of ancillary services provided by hydrogen energy for the elimination of carbon emissions. Therefore, there is an urgent need to develop methodologies for evaluating key parameters, such as overvoltage coefficients, stack transfer capacity, diaphragm thickness, and permeability, to accurately capture the system’s fluctuating characteristics. However, limited by the lack of superior sensor technology, some significant variables cannot be measured directly. In this context, comprehensively accurate parameters of an estimation strategy offer a novel alternative to characterize the system’s corresponding intrinsic nature. This paper was motivated by this arduous challenge and aims to address the large branching factors with irregular properties. Specifically, the associated mathematical models reflecting the transient operating parameters in terms of electrochemical, heat transfer, and mass transfer are first established. Subsequently, k-means clustering analysis is conducted to deduce the similarity of distribution of the measured variables, which can function as proxies of the separator to distinguish the working status. Furthermore, online reinforcement learning (RL), renowned for its ability to operate without extensive predefined datasets, is employed to conduct dynamic parameter estimation, thereby approximating the robust nonlinear and stochastic behaviors within AEL components. Finally, the experimental results verify that the proposed model achieves significant improvements in estimation errors compared to existing parameter estimation methods (such as EKF and UKF). The enhancements are 76.7%, 54.96%, 51.84%, and 31% in terms of RMSE, NRMSE, PCC, and MPE, respectively.

MNN-BasisONet: a moment-based operator learning framework for uncertainty quantification

Abstract Neural operators extend the application of neural networks to problems in infinite-dimensional spaces and have demonstrated excellent prediction capabilities. However, to handle the inherent randomness in real-world systems, it is crucial to incorporate mechanisms for quantifying uncertainty. A major paradigm in uncertainty quantification methods is the sampling-based approach, which uses statistical sampling to quantify uncertainty but comes with a high computational cost. This paper presents MNN-BasisONet, a novel framework integrating moment neural networks (MNN) with BasisONet to address uncertainty quantification in neural operators. Our method incorporates stochastic differential equations within neurons and approximates their stochastic behavior using statistical moments, which substantially reduces computational complexity while preserving performance. MNN-BasisONet requires only a few extra parameters to quantify uncertainty in a single forward pass without sampling. The efficiency of MNN-BasisONet is demonstrated through theoretical derivations and numerical experiments, which include noiseless and noisy PDE problems, as well as real-world black-box modeling. Our model achieved comparable performance in terms of accuracy and effective uncertainty quantification compared to sampling-based methods while significantly reducing computational cost. Furthermore, we validate the MNN approach as a general uncertainty quantification method by comparing it with its stochastic counterpart and applying it to other neural operator models.

Detection and Defense Mechanisms for Covert Timing communications

This study presents a literature overview on methods for discovering and removing covert channels. Data transmission methods known as covert channels take advantage of system resources already in place but weren't intended for this purpose, such as firewalls, to transport data undetected. By setting up a seemingly secure channel of communication between two parties, sensitive information could be leaked from the more secure party to the less secure party. Using a shared network, two parties can easily communicate and exchange information to send confidential data without being detected. As a result, discovering covert communications is difficult. Network protocols place restrictions on Covert Storage Channels (CSC), preventing it from deviating from a set of guidelines. On the other hand, CTCs have stochastic behavior, which makes detection more challenging. Analysis of the state of art systems is done in this paper & it is found that the most likely option is an active warden which is a specialized network security system that is designed to filter out a range of irregularities seen in network data.

Impact of psychrometry on the aerosol distribution pattern in human lungs

Abstract This article investigates the localized air quality of the workplace and its impacts on the stochastic behaviour of aerosol deposition. Related to the same, the dewpoint (DPT), wet bulb (WBT) and dry bulb (DBT) temperatures, vapour pressure, and relative and specific humidities of the air are being tested. The given problem investigates the regional and total deposition of aerosol particles in the extrathoracic (Ex), bronchioles (Br) and alveolar sacs (A) of the subjects working in the bioenergy plant. The oral and nasal (n) pathways were considered for the air to enter the extrathoracic region of the human body. The algorithm based on the Monte-Carlo technique is written on Rust version 1.79.0 to calculate the deposition fraction of aerosol particles in the human lungs. The particle is assumed to have a spherical geometry. Only the diffusion of water vapour onto the surface of aerosol is the limiting factor for the growth of aerosol particles and the surface reaction is omitted. The deposition fraction of smaller-sized particles was seen to be increased with nucleation in the Ex region. Similarly, the change in the dew point of air also favoured the likelihood of deposition of the aerosol particle in the Ex region. As compared to the nasal pathway, the accretion of aerosol particles in the Ex region through the oral pathway declined by 35.12 to 38.33 owing to the growth of the aerosol particles with time.

A novel approach to wind energy modeling in the context of climate change at Zaafrana region in Egypt

Global warming, driven by the excessive emission of greenhouse gases from the combustion of fossil fuels, has emerged as a critical environmental challenge which is considered as a motivation for this research. Where, the switch to sustainable energy sources is crucial because of the pressing need to slow down climate change and lower carbon footprints. Of all the renewable energy sources, wind energy is particularly important as a means of reducing carbon emissions from the generation of electricity. With the increase in the penetration of renewable energy resources in electrical power systems, the stochastic behavior of the renewable energy resources has to be taken into account for better analysis in power systems. However, the stochastic behavior of the renewable energy is also affected by the environmental conditions. In this context, The main objective of this paper is to present a novel wind energy modeling that includes the effect of ambient temperature on the wind turbine capabilities. This effect is presented as the de-rating curve for wind turbine output power to respect the thermal capabilities of the electrical components of the wind turbine. That’s why this novel model is developed to consider the effect of ambient temperature to represent the practical limitations of wind turbines which wasn’t considered by previous literature although the temperature has a siginicant impact on the wind turbine output power. In this Paper, Gamesa G80 wind turbine is used to perform the numerical analysis of the proposed new model. Moreover, Exponential Distribution Optimizer (EDO), Aquila Optimizer (AO), and Equilibrium Optimizer (EO) algorithms are used to find various probability distribution functions (PDFs) parameters to model wind speed data from Zaafrana region in Egypt using Root Mean Square Error (RMSE) and Coefficient of Correlation (R^2) as judging criteria. In addition, real temperature data from the same site are used to validate the proposed model compared to the manufacturer’s capabilities. The results show that mixed PDFs provide a better representation for the wind speed data. Moreover, the study demonstrates that ambient temperature cannot be neglected in wind power modeling, as the wind turbine output power varies significantly. Additionally, this work highlights the impact of climate change on the efficiency of renewable energy sources like the wind energy. The proposed wind energy model could be valuable to system operators as a decision-making aid when dealing with and analyzing complex power systems.

Stochastic vibration behaviors of functionally graded graphene platelets reinforced composite joined conical-cylindrical-conical shell with variable taper under moving random loads

Stochastic vibration behaviors of functionally graded graphene platelets reinforced composite joined conical-cylindrical-conical shell with variable taper under moving random loads

Elastic Mass Model Approach for Fuel Dynamics in Microgravity Environments

Managing fuel in the challenging conditions of zero gravity necessitates a profound grasp of fluid dynamics, particularly the pivotal role of surface tension. This study introduces a novel mechanical model to replicate fuel dynamics within a partially filled tank in zero gravity. Unlike traditional models, this model, with its six degrees of freedom and the implication of realistic liquid parameters, effectively captures the stochastic behavior of propellant mass. The model accounts for surface tension effects and elastic collisions with tank walls during orbital maneuvers. This novel elastic mass model overcomes the limitations of existing methods like the constrained surface model, pendulum-based, and spring-mass models, by leveraging a conceptual analogy rooted in Hooke’s Law and improved Hertz theory. Established simulations have demonstrated the model’s capability to imitate wobbling fuel motions and the resulting forces on orbiting spacecraft. Comparison with computational fluid dynamics results showed the auspicious potential of the model.

Assessing the suitability of the Langevin equation for analyzing measured data through downsampling

Abstract The measured time series from complex systems are renowned for their complex stochastic behavior, characterized by random fluctuations stemming from external influences and nonlinear interactions. These fluctuations take diverse forms, ranging from continuous trajectories reminiscent of Brownian motion to noncontinuous trajectories featuring jump events. The Langevin equation is a versatile framework for modeling stochastic systems, effectively describing the complex behavior of measured data that exhibit continuous stochastic variability and adhere to Markov properties. However, the traditional modeling framework of the Langevin equation falls short when it comes to capturing the presence of abrupt changes, particularly jumps, in trajectories that exhibit non-continuity. Such non-continuous changes pose a significant challenge for general processes and have profound implications for risk management. Moreover, the discrete nature of observed physical phenomena, measured with a finite sample rate, adds another layer of complexity. In such cases, data points often appear as a series of discontinuous jumps, even when the underlying trajectory is continuous. In this study, we present an analytical framework that goes beyond the limitations of the Langevin equation. Our approach effectively distinguishes between diffusive or Brownian-type trajectories and non-diffusive trajectories such as those with jumps. By introducing downsampling techniques, where we artificially lower the sample rate, we derive a set of measures and criteria to analyze the data and differentiate between diffusive and non-diffusive behaviors. To further demonstrate its versatility and practical applicability, we have applied our proposed method to real-world data in various scientific fields, such as trapped particles in optical tweezers, market price, neuroscience, turbulence and renewable energy. For real-world data that lack Markov properties, we estimate the functions and parameters using the generalized Langevin equation, which incorporates a memory kernel to account for non-Markovian dynamics.

Stochastic behaviour of Ghana power grid with massive electro-mobility penetration

Stochastic behaviour of Ghana power grid with massive electro-mobility penetration

Nowcasting the next hour of residential load using boosting ensemble machines

Accurate residential load forecasting is a key to achieve grid stability and efficient energy management. However, it becomes challenging due to the non-linear and seasonally fluctuating energy usage of domestic users. Existing statistical and machine learning-based forecasting models struggle to produce accurate forecasts due to dynamic and stochastic user behaviors for energy usage. On the other hand, pairwise ensemble methods can achieve higher forecasting accuracy in short-term load forecasting, but are not scalable and face generalization issues that often lead to overfitting and complexity in managing multivariate data. To address these limitations, we propose to integrate LightGBM, XGBoost and CatBoost models to systematically address the limitations of existing ensemble-based forecasting models. This integration aims to leverage the strengths of each ensemble method, where LightGBM handles generalization across multiple sites, XGBoost avoids overfitting the model, and CatBoost effectively manages categorical features. We implement our proposed model using a real-world, publicly available dataset for 13 residential locations in North America and Europe. The proposed model outperforms other state-of-the-art algorithms with the lowest root mean squared logarithmic error (RMSLE) values of 0.1898, while the coefficient of determination (R2) calculated from the data is 0.9745. Other evaluation measures such as root mean square error (RMSE), coefficient of variation of the root mean square error (CVRMSE) and mean bias error (MBE) also support the proposed approach regarding the efficiency of the model.Finally, we also perform an ablution study to show predictive efficacy of incremental model integration.

Stochastic Free Vibration Behavior of Multi-Layered Helicoidal Laminated Composite Shells Under Thermal Conditions

The present work aims to quantify the influence of uncertainties in the ply orientation of multi-layered bio-inspired helicoidal laminated composite conical, hemispherical, and toroidal shells under thermal conditions. Any change in the ply orientation affects the free vibration behavior of the laminates. The present investigation focuses on the different levels of uncertainties in the ply orientation on the free vibration behavior of the shell. Moreover, the study also focuses on the sensitivity of the uncertainties in ply orientations on the free vibration behavior of the shells, which is also quantified. To quantify the stochastic free vibration behavior of the shells, the Gaussian process regression (GPR) machine learning algorithm-based surrogate model is developed to predict the frequencies of the shells. The surrogate is created in the framework of higher-order shear deformation theory. The uncertainties in the ply orientations are introduced using bootstrapping. The present results are compared with the stochastic frequencies obtained using Monte Carlo simulations (MCS) to determine the model’s accuracy. The study highlights the influence of the temperature, type of shell, and end conditions on the stochastic free vibration behavior of bio-inspired laminated shells.

Leveraging stochasticity in memristive synapses for efficient and reliable neuromorphic systems

Neuromorphic computing is inspired by the human brain’s architecture to develop power-efficient and optimized neural networks. Different characteristics of synapses and neurons are emulated in the neuromorphic hardware or software model to mimic the behavior of the synaptic brain. Noise in the neuromorphic system is an important constraint to evaluate its overall performance. A reliable READ operation is essential to secure an acceptable performance from a neuromorphic system. In addition, a memristive synapse is very prone to stochastic behavior. Moreover, a dynamically reconfigurable READ operation is proposed for our 3T1R synapse to explore the effect of stochasticity on neuromorphic applications. Our proposed method allows for relevant applications in neuromorphic computing to utilize the stochastic behavior of the memristive 3T1R synapse. Performance evaluations show ~8.9x energy optimization with our proposed device. Optimization algorithm (EONS) is utilized for training and a hardware framework (RAVENS) is used for hardware simulation during training.

Three-State Non-Stationary Hidden Markov Model for an Improved Spectrum Inference in Cognitive Radio Networks

Spectrum manufacturers, operators and regulators are faced with the challenge of meeting the astronomical increase in demand by spectrum users due to the limited available radio spectrum already fixed for licensed or Primary Users (PUs). The emergence of Cognitive Radio Network (CRN) allows unlicensed or Secondary Users (SUs) to opportunistically access spectrum holes left unused by the PUs through spectrum sensing, management, sharing and mobility functionalities with the aid of algorithms and protocols. However, CRN suffers prolonged delay with negative impact on spectral efficiency. In order to improve the spectral efficiency, spectrum inference was introduced. Yet, inaccurate spectrum inference by existing mechanisms could not solve spectrum underutilization effectively due to persistent false alarm, interference and missed detection of PUs. Two-state Non-Stationary Hidden Markov Model (NSHMM) focused only on idle and busy states of PUs while previous work on three-state Stationary Hidden Markov Model (SHMM) did not consider the time-varying property of channel states obtainable in real scenarios where the state transition probability of a PU is time-varying. This work has proposed three-state NSHMM for spectrum inference in CRNs by formulating its parameters and modelling PU's dwell time distributions to realize the time-varying property of the stochastic PU behavior apart from the fuzzy state that takes care of noisy effects and undetermined or incomplete observations in the existing mechanisms where only idle and busy states were mostly recognized. The performance of the proposed mechanism was evaluated using Probability of Detection (PD), Prediction Accuracy (PA) and Spectrum Utilization Efficiency (SUE). The results were compared to the performance metrics obtained from spectrum inference of existing 2-state NSHMM and 3-state SHMM. The simulation results obtained revealed that the proposed three-state NSHMM spectrum inference mechanism gave the best performance with the highest PD, PA and SUE which curtailed PU collision because of its least possible chances of incorrect detection of primary users and least false alarm. The outstanding performance of the proposed NSHMM was due to its non-stationarity as well as the fuzzy state incorporated in the development of the mechanism. Therefore, the proposed three-state NSHMM for an improved spectrum inference in CRNs has grossly abated PU collision, false alarm and spectrum underutilization.