Search Results (7,535)

Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper presents a smart energy management system for agricultural sensor nodes integrating a machine learning model for adaptive sampling and a batching strategy to optimize energy usage. A lightweight Stochastic Gradient Descent (SGD) regressor trained on temperature dynamics runs on-device to predict the sampling interval ($T_s$). In parallel, the node adjusts the number of buffered samples as the battery state of charge ($SOC$) decreases, reducing Long Range (LoRa) transmissions. Field experiments show that the proposed approach reduces energy consumption by 77.8% compared with fixed-interval sampling, while maintaining good temperature fidelity with Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction. Full article

Branching processes are stochastic models describing the evolution of populations in which individuals reproduce and die independently over time. In the classical setting, an individual’s reproductive capacity is fixed throughout its lifetime. However, in real-world situations, fertility typically rises during a juvenile phase, peaks at maturity, and subsequently declines. In order to capture this feature, we introduce a branching random walk with ageing, as an extension of the classical branching random walk, by assigning each individual an age-dependent reproductive rate. Our model differs from classical age-dependent processes such as the Bellman–Harris model, where the remaining lifespan depends on age, while the rate of reproduction is fixed within that lifetime. As in the classical case, branching random walks with ageing are parametrised by $\lambda >0$, which tunes the reproductive speed and may be seen as a characteristic of the population. The thresholds of $\lambda$ separating extinction and survival are the global and local critical parameters. We characterise the value of the local critical parameter and provide a lower bound for the global critical parameter. We identify a class of ageing branching random walks for which this lower bound coincides with the global critical parameter. We study how local modifications to the reproduction and ageing rates may change the critical parameters. This is of practical interest: in species preservation, one may want to lower the critical parameters, so that $\lambda$ exceeds them, and there is a positive probability of survival. On the other hand, in epidemic control, the goal is to increase the critical parameters, since if $\lambda$ is below them, then the epidemic is eventually going to disappear. We compute the expected number of individuals alive in a branching process with ageing and show that, contrary to the behaviour of classical branching processes, it may exhibit an initial growth even when the population is ultimately destined for extinction. Full article

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets. Full article

Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs) is justified by performance gains over established feature engineering paradigms (e.g., XGBoost) under the constraints of sparse altimetry supervision. We established a rigorous comparative framework across four mainstream products—ALOS World 3D, Copernicus DEM, SRTM GL1, and TanDEM-X—using Sichuan Province, China, as a representative natural laboratory. Our results reveal a fundamental scale mismatch (where the ~485 m average spacing of sampled altimetry footprints dwarfs the local terrain resolution): despite their topological complexity, Hybrid GNN models fail to establish a statistically significant accuracy advantage over the systematically optimized XGBoost baseline, demonstrating RMSE parity. Mechanistically, we uncover a critical divergence in decision logic: XGBoost relies on a stable “Physics Skeleton” consistently dominated by deterministic features (terrain aspect and vegetation density), whereas GNNs exhibit severe “Attribution Stochasticity” ($\rho \approx$ 0.63–0.77). The GNN component acts as a residual-dependent latent feature learner rather than discovering universal topological laws. We conclude that for geospatial regression tasks relying on sparse supervision, “Physics Trumps Geometry.” A “Feature-First” paradigm that prioritizes robust, domain-knowledge-based physical descriptors outweighs the indeterminate complexity of “Black Box” architectures. This study underscores the imperative of prioritizing explanatory stability over marginal accuracy gains to foster trusted Geo-AI. Full article

We introduce the one-parameter bounded p-exponential distribution on $(0, p+1)$, which includes the uniform model as a special case and converges pointwise to the exponential law as $p\to \infty$. Closed-form expressions are derived for the CDF and PDF, the survival function, an explicit increasing-failure-rate hazard function, the quantile function (enabling inversion-based simulation), moments, and entropy, along with a constructive scaled beta or Kumaraswamy representation. We also establish stochastic ordering with respect to p in stop-loss and increasing convex order, formalizing how dispersion varies with the parameter while preserving the mean scale. Inference is discussed under parameter-dependent support, a non-regular setting, and we develop and compare several estimation procedures, including a likelihood-based boundary MLE, a variance-matching method-of-moments estimator, and Bayesian estimation under a gamma prior implemented via numerical quadrature or MCMC. Monte Carlo simulation studies evaluate finite-sample performance and interval behavior, and two real-world applications in survival and reliability analysis illustrate competitive goodness-of-fit relative to standard benchmark models. Full article

Gene transcription is inherently stochastic, and promoter-switching-induced transcriptional bursting generates substantial cell-to-cell variability in mRNA abundance. Such variability is commonly characterized by the mean and variance; however, these low-order statistics fail to capture the geometric features of mRNA copy number distributions and may obscure mechanistic differences in promoter dynamics. In this work, we analyze a two-state stochastic gene transcription model and derive explicit analytical expressions for higher-order moments of mRNA abundance. We show that skewness and kurtosis provide mechanistically informative signatures of transcriptional bursting, explicitly depending on promoter switching kinetics and burst size. Our results demonstrate that distinct promoter dynamics can produce identical mean expression levels and variances while exhibiting markedly different skewness and kurtosis. The explicit analytical expressions derived here reveal how higher-order moments encode mechanistically informative signatures of transcriptional bursting through distributional asymmetry and heavy-tailed behavior. These results demonstrate that higher-order moments encode mechanistic information beyond mean–variance statistics and provide a powerful framework for distinguishing between different promoter-switching mechanisms in stochastic gene transcription. Full article

This paper presents the first study on the fatigue damage behavior of seawater sea-sand concrete (SSC) and its modeling. Experimental tests were conducted on cylindrical specimens subjected to uniaxial compression, investigating the effects of maximum stress level and material variability. The results indicate that the maximum stress-fatigue life curve for SSC can be well represented by a straight line, while the secant stiffness of SSC degrades in a two-phase process: initially in a decelerating manner, followed by an accelerating degradation until failure. Compared to ordinary concrete, SSC exhibits a significantly longer fatigue life. Due to material variability, the fatigue life of SSC shows considerable randomness, which can be effectively modeled using a Weibull distribution. A modification was made to a recently proposed damage model by the author and Li to capture the stochastic fatigue damage evolution behavior of SSC. The modified model successfully simulates both the maximum stress-fatigue life curve and the secant stiffness degradation curve, including their inherent randomness. Future research should explore the underlying specific factors contributing to the significantly longer fatigue life of SSC compared to ordinary concrete. Full article

Aging and stress-related factors affect sperm DNA methylation in regions associated with genes responsible for embryonic development. The stochastic epigenetic variation hypothesis holds potential to explain these patterns, proposing that, in response to stressors, naturally variable methylation regions (VMRs) associated with morphogenetic genes exhibit increased methylation variation to diversify phenotypes and improve the chances of survival of the genetic lineage. Here, we test predictions from this hypothesis using mouse and rat sperm DNA methylation data from publicly available sources. Specifically, we identify VMRs and analyze their overlap with regions differentially methylated (DMRs) in response to aging, stressors, and with various genomic elements. We demonstrate that the nature of the DNA regions, rather than the nature of the stressor, determines the response of the sperm methylome to aging and stress, and propose a model that explains shifts in methylation within VMRs through stochastic changes, whereby initially hypermethylated regions lose methylation and initially hypomethylated regions gain methylation. VMRs are depleted of open chromatin regions and histones in male germ cells and are enriched for a binding motif for ZFP42, an epigenetic remodeler. This knowledge may open opportunities for the development of interventions to control epigenetic information transfer via germ cells. Full article

During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions. Full article

This paper constructs and estimates a novel two-sector Dynamic Stochastic General Equilibrium (DSGE) model to analyze the macroeconomics of Kazakhstan’s dual-energy structure, where a large fossil fuel sector coexists with an emerging renewable segment. The model’s key innovation is its integration of an endogenous, depletable oil stock and a dual-inflation Taylor-type rule, which together capture the specific transmission channels between hydrocarbon dependence and green investment. By differentiating between oil-driven and core inflation, the framework quantifies how oil price volatility transmits monetary conditions to the renewable sector. Bayesian estimation, using sectoral data from national accounts, reveals a pronounced asymmetry: oil stock/discovery dynamics and oil revenue fluctuations dominate macroeconomic volatility, while the renewable sector exhibits stable output but remains vulnerable to oil-driven monetary tightening transmitted mainly through indirect channels. The results indicate that Kazakhstan’s ongoing energy transition offers a stabilizing diversification benefit in principle but remains structurally constrained by macroeconomic dynamics and fiscal patterns anchored to hydrocarbon conditions. These findings provide a quantitative basis for designing transition policies that mitigate cross-sector spillovers and support effective diversification in resource-dependent economies. Full article

We introduce the discrete asymmetric double Lindley distribution, a new two-parameter family on the integer line designed to model signed counts and net changes with flexible asymmetric tail behavior. This statistical model is obtained by merging two Lindley-type linear-geometric kernels on the negative and non-negative half-lines, with tail decay rates that are coupled through a simple two-parameter mechanism. This construction yields an analytically tractable probability mass function with an explicit normalizing constant, as well as closed-form expressions for the cumulative distribution function and one-sided tail probabilities. We further provide a transparent stochastic representation based solely on Bernoulli and geometric random variables, leading to an exact and efficient simulation algorithm that is convenient for Monte Carlo studies and validating numerical likelihood routines. Graphical illustrations highlight the role of the asymmetry parameter in controlling the imbalance between the two tails and the resulting skewness on $\mathbb{Z}$. The proposed family offers a practical and interpretable alternative to existing integer-line models for asymmetric discrete data, with direct applicability to likelihood-based inference and real-world datasets. Full article

Current structural design codes use a stochastic approach (SC) in load combination and reliability calculation. This article presents a design model with a deterministic (DC) approach. Currently, design codes have variable load factors (γ_G ≠ γ_Q) and constant safety factors (γ_M) for all loads. This approach makes two significant approximations: It assumes a single material factor for all ratios of permanent to variable loads and across all types of variable loads. This research proposes the opposite strategy: instead of a single material factor, each load is assigned to its own material factor. Such a shift removes those approximations, simplifies codes, and reduces calculation effort. A central challenge in reliability assessment is combining loads. Existing SC methods differ in their reference time and target reliability. The fundamental fact of load–material interaction is that each load acts independently, without influencing others. This deduction results in the DC combination. Safety factors within the Eurocode framework are derived using today’s semi-probabilistic SC methods and a fully probabilistic DC approach. The proposed design model is straightforward, requires minimal computation, meets the target reliability, and yields significant savings in material use. Full article

Lamport’s Bakery algorithm is a well-known, simple, and elegant solution to the mutual exclusion problem for N ≥ 2 concurrent/parallel processes. However, the algorithm generates an unbounded number of tickets, even when only 2 processes are arbitrated. Various proposals in the literature were introduced to bound the number of tickets. Anyway, almost all these proposals prove to be correct when operated with atomic registers (AR) only. They become incorrect when working with non-atomic registers (NAR), as may occur in embedded hardware platforms with multi-port memory and relaxed memory-bus control, such as microcontrollers, FPGA-based systems, or specialized network devices. A notable solution with bounded tickets is Taubenfeld’s Black-White Bakery (BWB) algorithm. BWB relies on tickets which are couples <number,mycolor> where mycolor can be Black or White and number ranges in [0, N]. BWB, too, was confirmed, through informal reasoning, it is correct with AR only. The original contribution of this paper is a reformulation of BWB, which is formally modelled and exhaustively verified by timed automata in the Uppaal toolbox. In the reformulation, a ticket’s couple is coded as a single integer, and decoded and processed according to the BWB logic. The reformulated BWB remains fully correct with AR regardless of the number N of processes, but it is also correct with NAR for N = 2 processes. As a further original contribution, the paper demonstrates that the BWB version for 2 processes can be embedded in a general, state-of-the-art solution, based on a binary tournament tree (TT), to become AR/NAR correct, that is, RW-safe, for any number of processes. However, due to model complexity, the correctness of the TT versions of BWB, that is, based on atomic and non-atomic registers, is mainly studied by stochastic simulation of the formal model reduced to actors in Java. Full article

Current security solutions predominantly rely on cloud-based implementations, often neglecting computational resource constraints and operational efficiency. While contemporary methodologies typically require additional training, the few that operate without retraining frequently yield suboptimal performance. To address these limitations, this work leverages a pre-trained MTCNN architecture to detect faces of copyright-protected individuals. We construct a facial landmark database comprising five critical fiducial points, which serves as a supplementary module integrated into the stable diffusion framework, enabling real-time security filtering for synthesized video content. The proposed system utilizes MTCNN models pre-trained in the cloud to build a repository of copyrighted facial signatures, generating a geometric parameter database of facial landmarks. This database, coupled with a parallel verification unit, functions as a plugin within the standard Stable Diffusion pipeline. By leveraging Stable Diffusion’s native decoder, we decode stochastic frames from the U-Net latent representations and perform real-time comparative analysis to identify potential copyright violations in generated video sequences. Upon detecting an infringement, an on-screen display (OSD) alert notifies the user and immediately halts the text-to-video (T2V) generation process. Experimental evaluations demonstrate that our framework effectively mitigates the resource constraints and latency issues inherent in edge deployment scenarios of prior security implementations. Leveraging MTCNN’s proven robustness and extensive edge compatibility for facial recognition, the proposed detection and obfuscation plugin integrates seamlessly with Stable Diffusion while preserving generation quality. Full article

Modern computing platforms increasingly rely on random number generators (RNGs) for modeling probabilistic processes in simulation, probabilistic computing, and system validation. They are also essential for cryptographic operations such as key generation, authenticated encryption, and digital signatures. Deterministic Random Bit Generators (DRBGs), as specified in the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90A, provides a standardized method for expanding entropy into cryptographically strong pseudorandom sequences. This work presents the design and Field Programmable Gate Array (FPGA) implementation of a hash-based DRBG using Ascon-Hash256, a lightweight, quantum-resistant hash function from the NIST-standardized Ascon cryptographic suite. It implements hash-based derivation, instantiation, generation, and reseeding of the generator via iterative hash invocations and state updates. Leveraging Ascon’s sponge-based structure, the design achieves efficient entropy absorption and diffusion while maintaining an area-efficient FPGA architecture, making it well suited for resource-constrained platforms. The diffusion properties of the proposed DRBG are evaluated through avalanche and reproducibility analyses, confirming strong sensitivity to input variations and secure, repeatable operation. Moreover, Monte Carlo and stochastic-diffusion evaluation of the generated bitstreams demonstrates correct convergence and statistically consistent behavior. These results confirm that the proposed hash-based DRBG provides reproducible, hardware-efficient, and cryptographically secure random numbers suitable for next-generation neuromorphic, probabilistic computing systems, and Internet of Things (IoT) devices. Full article