Search Results (127)

Adaptive life histories emerge through their environmentally dependent effects on fitness. Those effects are consequences of habitat quality and the density-dependent decisions that organisms make on habitat choice. Density dependence for ideal organisms maximizing fitness through habitat selection is uniquely revealed by their habitat isodars, lines in the state space of species’ densities that confer equal fitness between habitats coupled by dispersal. We use isodars to structure simple simulations of habitat selection in stable and stochastic environments. The simulations demonstrate an indirect effect of ideal habitat selection that can dampen otherwise wide fluctuations in abundance and their impact on pace-of-life strategies. The ability of habitat selection to equalize fitness between habitats also has a direct effect on life history evolution. Habitat selection can promote phenotypically plastic life histories between habitats that might otherwise convey divergent genetically fixed strategies. The direct and indirect effects on life history demonstrate that it is not just habitat that requires our concern in managing and conserving nature, but how those activities are likely to impinge on habitat selection. Full article

Enhancing forestry management efficiency is critical for global sustainable development goals, yet how institutional arrangements can effectively incentivize farmers’ performance requires deeper investigation. This study constructs an integrated framework to examine the effects of well-defined property rights and market certification on the output and technical efficiency of household bamboo management. Utilizing survey data from 1090 households in China, we employ stochastic frontier analysis (SFA), propensity score matching (PSM), and mediation models. The findings reveal a key divergence: (1) Forest tenure certificates significantly increased bamboo output but not technical efficiency. This “quantity-driven” effect stemmed from increased capital and land inputs. (2) Market certification enhanced both output and technical efficiency, operating via a “quality-driven” mechanism of standardized management. (3) Significant technical efficiency losses persist, indicating substantial potential for productivity gains through optimized practices. This study concludes that singular property rights institutions are insufficient to overcome the “output-without-efficiency” bottleneck. Complementary, market-based mechanisms are essential for a dual-pillar policy system. This research offers theoretical support for optimizing forestry policies and provides insights for other developing countries seeking sustainable resource management. Full article

To clarify microbial assembly during saline–alkali wetland degradation, we analyzed bacterial (16S rRNA) and fungal (ITS) communities across four habitats: pristine wetland (PW), transitional meadow wetland (TMW), halophytic herbaceous community (HHC), and converted farmland (CF). Soil water content collapsed from PW (42.22%) to ≤18.40% elsewhere, and soils were alkaline with pH highest in HHC (10.08). Nutrient pools and enzyme activities were highest in PW (SOC 35.03 g kg⁻¹; URE 142.58 mg g⁻¹; SUC 527.83 mg g⁻¹) but declined sharply under natural degradation, reaching minima in HHC (SOC 8.02 g kg⁻¹). ACP and CAT were also lowest in HHC. Bacterial communities were dominated by Actinomycetota and Pseudomonadota, with Acidobacteriota and Bacillota enriched in CF. Bacterial diversity peaked in CF, whereas fungal richness was highest in CF and Shannon diversity peaked in TMW. Ordination and redundancy analyses indicated stronger edaphic control on bacteria than fungi, with pH, SOC, and moisture as key drivers. Null-model analyses showed bacterial assembly shifted toward deterministic selection under saline–alkali stress and agricultural conversion, whereas fungal assembly remained predominantly stochastic. Co-occurrence networks further suggested higher bacterial vulnerability under extreme degradation but comparatively higher fungal robustness. Overall, bacteria and fungi follow divergent assembly rules during saline–alkali wetland degradation. Full article

This study introduces the Hybrid Integrated Prediction-Error Reconstruction-based Anomaly Detection (HIPER-CHAD) model, which addresses the challenge of reliably detecting subtle anomalies in noisy multivariate indoor environmental time-series data. The main objective is to separate temporal modeling of normal behavior from probabilistic modeling of prediction uncertainty, ensuring that the anomaly score becomes robust to stochastic fluctuations while remaining sensitive to truly abnormal events. The HIPER-CHAD architecture first employs a Long Short-Term Memory (LSTM) network to forecast the next time step’s sensor readings, subsequently forming a residual error vector that captures deviations from the expected temporal pattern. A Variational Autoencoder (VAE) is then trained on these residual vectors rather than on the raw sensor data to learn the distribution of normal prediction errors and quantify their probabilistic unicity. The final anomaly score integrates the VAE’s reconstruction error with its Kullback–Leibler (KL) divergence, yielding a statistically grounded measure that jointly reflects the magnitude and distributional abnormality of the residual. The proposed model is evaluated on a real-world multivariate indoor environmental dataset and compared against eight traditional machine learning and deep learning baselines using a synthetic ground truth generated by a 99th percentile-based criterion. HIPER-CHAD achieves an F1-score of 0.8571, outperforming the next best model, the LSTM Autoencoder (F1 = 0.8095), while maintaining perfect recall. Furthermore, a time-step sensitivity analysis demonstrates that a 20-step window yields an optimal F1-score of 0.884, indicating that the proposed residual-based hybrid design provides a reliable and accurate framework for anomaly detection in complex multivariate time-series data. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable to quantify predictive uncertainty due to their deterministic inference process. To address these limitations, this study proposes a novelty detection framework that integrates an out-of-distribution detector for neural networks (ODIN) with Monte Carlo (MC) dropout. ODIN mitigates model overconfidence and enhances the separability between known and unknown signals through softmax probability calibration, while MC dropout introduces stochasticity via multiple forward passes to estimate predictive uncertainty—an element critical for stable sensing in real-world underwater environments. The resulting probabilistic outputs are modeled using Gaussian mixture models fitted to ODIN-calibrated softmax distributions of known classes. The Kullback–Leibler divergence is then employed to quantify deviations of test samples from known class behavior. Experimental evaluations on the DeepShip dataset demonstrate that the proposed method achieves, on average, a 9.5% and 5.39% increase in area under the receiver operating characteristic curve, and a 7.82% and 2.63% reduction in false positive rate at 95% true positive rate, compared to the MC dropout and ODIN baseline, respectively. These results confirm that integrating stochastic inference with ODIN significantly enhances the stability and reliability of novelty detection in underwater acoustic environments. Full article

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques. Full article

In this research work, we present findings on fractional stochastic systems characterized by fractional Brownian motion, which is defined by a Hurst parameter $\mathbb{H}\in \left(\frac{1}{2},1\right)$. These systems are crucial for modeling complex phenomena that diverge from Markovian behavior and exhibit long-range dependence, particularly in areas such as financial engineering and statistical physics. We utilize the fixed-point iteration method to demonstrate the existence and uniqueness (Ex-Un) of mild solutions. Additionally, we investigate the approximate controllability of the system. We establish all results within the framework of the $\mu$-Caputo fractional derivative. This study makes a meaningful contribution to the existing body of literature by rigorously establishing the existence, uniqueness, and approximate controllability of mild solutions to generalized Caputo fractional stochastic differential equations driven by fractional Brownian motion. Full article

To address the uncertainty in power grid scheduling caused by the output variability of distributed energy resources (DERs) in microgrids, as well as the limitations of stochastic optimization relying on accurate probability distributions and the overly conservative nature of robust optimization leading to insufficient economic performance, this paper proposes a disseminated robust optimization method for microgrid operation that considers flexible demand-side resources. First, to address the uncertainty in the forecasting of wind and solar power scenarios, this paper launches a two-stage distributionally robust optimization (DRO) model based on a Kullback–Leibler (KL) divergence ambiguity set using a min–max–min framework. Then, the Column-and-Constraint Generation (C&CG) algorithm is employed to decouple the model for an iterative solution. Finally, simulation case studies are directed to validate the effectiveness of the proposed model. The demand response-based optimization model projected in the paper effectively enhances the flexibility of the Microgrid. Compared to robust optimization, this model reduces the daily operating cost by 2.86%. Although the cost is slightly higher (4.88%) than that of stochastic optimization, it achieves a balance between economy and robustness by optimizing the expected value under the worst-case probability distribution. Full article

Antimicrobial resistance (AMR) remains a global health crisis, yet treatment outcomes cannot be explained by resistance genes alone. Increasing evidence highlights the importance of variability at two levels: within bacterial populations and across patients. At the microbial level, cell-to-cell variability including genetic mutations, stochastic gene expression, persister cell formation, heteroresistance, and spatial heterogeneity within biofilms creates phenotypic diversity that allows subsets of bacteria to survive antimicrobial stress. At the host level, patient-to-patient variability including differences in genetic background, immune competence, comorbidities, gut microbiome composition, and pharmacokinetics shapes both susceptibility to resistant infections and the likelihood of treatment success. Together, these dimensions explain why infections with the same pathogen can lead to divergent clinical outcomes. Understanding and integrating both microbial and host variability offers a path toward more precise diagnostics, personalized therapy, and novel strategies to counter AMR. Full article

White-gill disease has emerged as one of the major health threats in large yellow croaker Larimichthys crocea (Richardson, 1846) aquaculture, yet its underlying microbial mechanisms remain poorly understood. In this study, we investigated the gut microbiota of healthy and white-gill diseased L. crocea across different growth stages and aquaculture locations using 16S rRNA gene amplicon sequencing and bioinformatics analysis. Across both juvenile and adult fish, as well as multiple sampling locations, diseased individuals consistently exhibited significantly reduced microbial richness and evenness compared to healthy counterparts, along with a clear divergence in community composition. Notably, the relative abundance of Photobacterium damselae subsp. damselae was markedly increased in diseased fish, especially juveniles, accompanied by a decline in beneficial genera such as Bacillus. Co-occurrence network analysis revealed simplified microbial interactions and decreased community stability in gut of diseased fish. Null model analysis further indicated that stochastic processes dominated gut microbial assembly, with a higher contribution in diseased individuals, suggesting weakened host selection pressure and enhanced random colonization under disease conditions. These findings highlight the important role of gut microbiota dysbiosis in the development of white-gill disease and provide new insights into microbiota-based diagnostics and ecological strategies for disease prevention in marine aquaculture. Full article

In this study, we introduce and rigorously formalize the notion of (P, m)-superquadratic stochastic processes, representing a novel and far-reaching generalization of classical convex stochastic processes. By exploring their intrinsic structural characteristics, we establish advanced Jensen and Hermite–Hadamard ($\mathbb{H}.\mathbb{H}$)-type inequalities within the mean-square stochastic calculus framework. Furthermore, we extend these inequalities to their fractional counterparts via stochastic Riemann–Liouville ($\mathbb{RL}$) fractional integrals, thereby enriching the analytical machinery available for fractional stochastic analysis. The theoretical findings are comprehensively validated through graphical visualizations and detailed tabular illustrations, constructed from diverse numerical examples to highlight the behavior and accuracy of the proposed results. Beyond their theoretical depth, the developed framework is applied to information theory, where we introduce new classes of stochastic divergence measures. The proposed results significantly refine the approximation of stochastic and fractional stochastic differential equations governed by convex stochastic processes, thereby enhancing the precision, stability, and applicability of existing stochastic models. To ensure reproducibility and computational transparency, all graph-generation commands, numerical procedures, and execution times are provided, offering a complete and verifiable reference for future research in stochastic and fractional inequality theory. Full article

Clustering techniques are widely applied to equity markets to uncover sectoral structures and regime shifts, yet most studies rely solely on empirical returns. This paper introduces a novel perspective by using diffusion-based parameters from the Black–Scholes model, namely monthly drift and diffusion, as clustering features. Using SET100 stocks in 2020, we applied k-means clustering and evaluated performances with silhouette scores, the Adjusted Rand Index, Wilcoxon tests, and an out-of-sample portfolio exercise. The results showed that diffusion-based features achieved higher silhouette scores in turbulent months, where they revealed sectoral divergence that log-returns failed to capture. The partition for November 2020 provided clearer sector separation and smaller portfolio losses, demonstrating predictive value beyond in-sample fit. Practically, the findings indicate that diffusion-based parameters can signal early signs of market stress, guide sector rotation decisions during volatile regimes, and enhance portfolio risk management by isolating persistent volatility structures across sectors. Theoretically, this model-based framework bridges equity clustering with stochastic diffusion representations used in derivatives valuation, offering a unified and interpretable tool for data-driven market monitoring. Full article

The objective of this study is to analyze spatial entropy flows that reveal the directional dynamics of climate change—patterns that remain obscured in traditional statistical analyses. This approach enables the identification of pathways for “climate information transport”, highlights associations with atmospheric circulation types, and allows for the localization of both sources and “informational voids”—regions where entropy is dissipated. The analytical framework is grounded in a quantitative assessment of long-term climate variability across Europe over the period 1901–2010, utilizing Shannon entropy as a measure of atmospheric system uncertainty and variability. The underlying assumption is that the variability of temperature and precipitation reflects the inherently dynamic character of climate as a nonlinear system prone to fluctuations. The study focuses on calculating entropy estimated within a 70-year moving window for each calendar month, using bivariate distributions of temperature and precipitation modeled with copula functions. Marginal distributions were selected based on the Akaike Information Criterion (AIC). To improve the accuracy of the estimation, a block bootstrap resampling technique was applied, along with numerical integration to compute the Shannon entropy values at each of the 4165 grid points with a spatial resolution of 0.5° × 0.5°. The results indicate that entropy and its derivative are complementary indicators of atmospheric system instability—entropy proving effective in long-term diagnostics, while its derivative provides insight into the short-term forecasting of abrupt changes. A lag analysis and Spearman rank correlation between entropy values and their potential supported the investigation of how circulation variability influences the occurrence of extreme precipitation events. Particularly noteworthy is the temporal derivative of entropy, which revealed strong nonlinear relationships between local dynamic conditions and climatic extremes. A spatial analysis of the information entropy field was also conducted, revealing distinct structures with varying degrees of climatic complexity on a continental scale. This field appears to be clearly structured, reflecting not only the directional patterns of change but also the potential sources of meteorological fluctuations. A field-theory-based spatial classification allows for the identification of transitional regions—areas with heightened susceptibility to shifts in local dynamics—as well as entropy source and sink regions. The study is embedded within the Fokker–Planck formalism, wherein the change in the stochastic distribution characterizes the rate of entropy production. In this context, regions of positive divergence are interpreted as active generators of variability, while sink regions function as stabilizing zones that dampen fluctuations. Full article

Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force–metric–bias (FMB) law: $\mathbf{\Delta }\mathit{\theta }=\mathbf{M}\mathbf{f}+\mathbf{b}+\mathit{\xi }$. The force $\mathbf{f}$ drives improvement in parameters, $\mathbf{\Delta }\mathit{\theta }$, in proportion to the slope of performance with respect to the parameters. The metric $\mathbf{M}$ rescales movement by inverse curvature. The bias $\mathbf{b}$ adds momentum or changes in the frame of reference. The noise $\mathit{\xi }$ enables exploration. This framework unifies natural selection, Bayesian updating, Newton’s method, stochastic gradient descent, stochastic Langevin dynamics, Adam optimization, and most other algorithms as special cases of the same underlying process. The Price equation also reveals why Fisher information, Kullback–Leibler divergence, and d’Alembert’s principle arise naturally in learning dynamics. By exposing this common structure, the FMB law provides a principled foundation for understanding, comparing, and designing learning algorithms across disciplines. Full article