Search Results (900)

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

In this paper, we study the pricing of vulnerable options, which are exposed to the option issuer’s default risk. We develop a pricing framework that integrates a reduced-form model for default risk with the 4/2 stochastic volatility model for the underlying asset. A feature of our model is the correlation between the issuer’s default intensity and the systematic component of the stochastic volatility. Using the characteristic function method and properties of the Grasselli transform, we derive an analytical pricing formula for a European vulnerable call option. Finally, we conduct numerical experiments to illustrate the impact of significant parameters, such as the recovery rate, default intensity, and the specific parameters of the 4/2 model. The results show that the 4/2 model component, which distinguishes it from the standard Heston model, has a significant effect on option prices. Full article

The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete-time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is carried out through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW-simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and the robust system-level performance evaluation of PLC networks. Full article

This study examines how connectionist AI reshapes architectural rationality, focusing on the under-theorised epistemic implications of generative technologies. It positions latent space as the convergent medium of representation, cognition, and computation to investigate how learning-based models reorganise architectural reasoning. Employing a qualitative hermeneutic methodology suited to interpreting epistemic transformation, and analysing four emblematic cases, the study identified a tripartite shift: representation moves from symbolic abstraction to probabilistic, feature-based latent descriptions; cognition evolves from individual, rule-defined schemas to collective, data-inferred structures; and computation reorients from deterministic procedures to stochastic generative exploration. In this framework, type and style emerge not as fixed classifications but as continuous distributions of similarity, redefining the designer’s role from originator of form to curator of datasets, navigator of latent spaces, and interpreter of model outputs. Ultimately, the paper argues that connectionism introduces a distinct epistemic orientation grounded in correlation and probabilistic reasoning, thereby prompting critical reflection on the ethical, curatorial, and disciplinary responsibilities of AI-mediated design. 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

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

Threats to forest ecosystems from pest insects are supposed to become more severe due to climate change. Therefore, understanding the dynamics of forest pest insects and the mechanisms of their outbreaks is going to be of even greater importance. To understand these phenomena and cope with the consequences, the question of which patterns show meta-populations of pest insects before and after outbreaks is of high interest. Therefore, long-term studies have been carried out in two research areas in North-West Poland with the aim of studying the fluctuations of meta-populations of the Nun moth (Lymantria monacha L.) (Lepidoptera: Erebidae) using pheromone traps. Synchronization of the fluctuations at the individual study plots was tested for correlations with the numbers of the Nun moth per trap, changes in the numbers of the Nun moth per trap, and the growth factors. The studied Nun moth meta-populations showed a certain pattern in fluctuations of their sub-populations (interaction groups) with phases of asynchronous and synchronous fluctuations; the latter seem to be important when it comes to distinctive peaks in Nun moth numbers in the meta-populations. We conclude that predicting population dynamics of the Nun moth demands long-term studies, including research on both density-dependent factors and stochastic processes. Full article

To investigate the soil displacement rule caused by shield tunneling in soil–rock composite strata, the convergence mode of the shield excavation surface was analyzed. The research accounts for the variations in the slopes of the tunnel and the rock–soil interface along the excavation direction. Based on the stochastic medium theory, the calculation formula of soil displacement under different depths is derived. Surface subsidence was computed and evaluated using three engineering case studies. The results show that the calculated surface subsidence curves exhibit strong symmetry and are similar to the distribution pattern of the measured data. When tunneling through composite strata, the segments are prone to an upward floating motion, leading to a convergence pattern in the cross-section that tends toward a non-equal radial convergence mode with top tangency. Within the same project context, the grouting filling rate (δ) diminishes as the hard rock ratio (B) increases, exhibiting an approximate linear correlation. An increase in the hard rock ratio results in reduced values for lateral and longitudinal subsidence, the width of the lateral subsidence trough, and the main impact zone of the shield tunneling operations. Full article

We propose a fair transmission expansion cost allocation (CA) algorithm and a fair process to build alternative transmission expansion plans. We define fairness such that each participant’s payment does not exceed its own benefit and the total payment equals the total TEP cost. In our framework, excessive payments over generator benefits are minimized. Owners of renewable energy resources (RES)s can choose the point of interconnection via the CA algorithm; owners in the same interconnection queue may form an intermediate coalition to persuade owners of expensive bottleneck plans to change at reduced allocation cost. Fairness is implemented using stochastic cooperative game theory (SCGT); the fair CA is obtained by recursively minimizing the largest unfairness, which is the difference between payments and benefits, through coalitions. Benefits consider transmission usage, transmission-induced gains, and the variability of RESs and demand. We design spatially and temporally correlated RESs and demand scenarios using Gibbs sampling specialized for long-term interconnection studies, validate plausibility against a benchmark from the Global Probabilistic Mid-term Load Forecasting Competition 2017, and verify fairness by showing that entities with greater benefits pay larger costs. Full article

Are your perceptions, memories and observations merely a statistical fluctuation arising from of the thermal equilibrium of the universe, bearing no correlation to the actual past state of the universe? Arguments are given in the literature for and against this “Boltzmann brain” hypothesis. Complicating these arguments have been the many subtle—and very often implicit—joint dependencies among these arguments and others that have been given for the past hypothesis, the second law, and even for Bayesian inference of the reliability of experimental data. These dependencies can easily lead to circular reasoning. To avoid this problem, since all of these arguments involve the stochastic properties of the dynamics of the universe’s entropy, we begin by formalizing that dynamics as a time-symmetric, time-translation invariant Markov process, which we call the entropy conjecture. Crucially, like all stochastic processes, the entropy conjecture does not specify any time(s) which it should be conditioned on in order to infer the stochastic dynamics of our universe’s entropy. Any such choice of conditioning times and associated entropy values must be introduced as an independent assumption. This observation allows us to disentangle the standard Boltzmann brain hypothesis, its “1000CE” variant, the past hypothesis, the second law, and the reliability of our experimental data, all in a fully formal manner. In particular, we show that these all make an arbitrary assumption that the dynamics of the universe’s entropy should be conditioned on a single event at a single moment in time, differing only in the details of their assumptions. In this aspect, the Boltzmann brain hypothesis and the second law are equally legitimate (or not). Full article

This paper proposes an evolutionary modeling method of scattering clusters based on Geometric-Based Stochastic Modeling (GBSM). In the single-bounce scenario of vacuum pipeline maglev train communication, the dynamic generation and extinction process and statistical behavior of multiple clusters at high speed are analyzed. The model abstracts the multipath component into a cluster structure. By iteratively updating the channel state and the birth and death cluster information after initialization, a dynamic model of the evolution process of scattering clusters in time-varying channels is constructed, which depicts the time evolution process of multipath clusters. Under the framework of GBSM, the correlation statistical characteristics of the scattering cluster birth and death process are further derived, and analytical integral form expression of the channel time autocorrelation function (ACF) is theoretically solved. The simulation results reveal the inherent law of channel time-varying characteristics under the joint action of high-speed train operation and closed pipe structure, and the results show that the proposed method can effectively capture the transient dynamic characteristics and long-term statistical trends of multipath clusters. The proposed model provides a practical basis for channel modeling in vacuum tube maglev wireless communication systems. Full article

This article presents a theoretical study of the scaling properties of the kinetic energy spectrum in compressible turbulence. From the fundamental symmetries and linear transformations of the microscopic action, we derive exact relations between the correlation functions and their generators. These relations put strong constraints on the possible scaling relations in the system as a function of scale. One of the main results of this study is that the action can be split between an incompressible part, which is the same as the usual stochastic Navier–Stokes theory in whatever the value of the Mach number is, and a longitudinal part, whose behavior is to be compared to the three-dimensional Burgers equation, which presents a much richer phase diagram as its usually discussed one-dimensional counterpart. Full article

Indoor air quality (IAQ) in residential settings is often dominated by high-concentration pollutant events from activities such as cooking and occupancy, which are overlooked by traditional 24 h average assessments. In this, we have designed and implemented a low-cost unit for remote IAQ monitoring. We deployed these units for high-resolution remote monitoring of CO₂, particulate matter (PM), and volatile organic compounds (VOCs) in three different domestic environments: a kitchen, a living room, and a bedroom. The monitoring campaign confirmed that, while daily averages frequently remained below guideline limits, transient peaks (e.g., CO₂ exceeding 2800 ppm in bedrooms and significant increases in PM during cooking) posed acute exposure risks. This dataset was used to train and evaluate machine learning models for 10 min ahead pollutant forecasting. Ensemble tree-based methods (Random Forest) and gradient boosting algorithms (XGBoost, LGBM, and CatBoost) were effective and robust. The predictability of the models correlated with room dynamics: performance improved under clear cyclical patterns (bedroom) and remained stable under stochastic events (kitchen). This work shows that integrating low-cost IoT sensing with machine learning enables proactive IAQ management, supporting health interventions driven by predictive risk rather than static averages. Full article

This study aimed to investigate the legacy effects of prolonged brackish irrigation on rhizobacterial communities and agricultural productivity in wheat. Here, we conducted pot experiments to investigate the mechanisms through which different irrigation regimes (irrigation using brackish groundwater and normal water) regulate wheat production. We applied four irrigation treatments across different stages of wheat growth (early stages, seedling-to-jointing, and late stages, jointing-to-maturity). This included irrigation exclusively using normal water during both stages (RR), using normal water followed by brackish groundwater (RW), exclusively using brackish groundwater (WW), and using brackish groundwater followed by normal water (WR). Under the premise of retaining 10 seedlings per pot, the average number of effective spikes per 10 plants in the RR, RW, and WR treatments was approximately 1.3, 1.1, and 1.1 times that of WW (19 ± 1), respectively. The spike weight per 10 plants in the RR, RW, and WR treatments was approximately 1.8, 1.5, and 1.3 times that of WW (12.75 ± 1.74 g), respectively. Compared with brackish groundwater irrigation, the use of normal water during the early stages significantly reduced the relative abundance of Pseudomonadota and increased that of Chloroflexota (p < 0.05). The number of effective spikes was positively correlated with the relative abundances of Actinobacteriota, Acidobacteriota, Chloroflexota, and Bacteroidota, but negatively correlated with the abundance of Pseudomonadota (p < 0.05). Irrigation regimes altered the rhizobacterial community structure. However, the legacy effect of long-term irrigation using brackish groundwater resulted in the dominance of stochastic processes in bacterial community assembly and stability of the Shannon diversity across all treatments. The complexity of the rhizobacterial co-occurrence network was lower in the RW treatments than that in the WW treatments (p < 0.05). Structural equation modeling revealed that irrigation using normal water during early stages boosted the number of effective spikes in wheat. This enhancement was achieved by increasing rhizobacterial diversity, reducing rhizosphere sodium, and simplifying the microbial network. This study challenges the “legacy effect” of brackish water irrigation by demonstrating that optimal irrigation timing is key to enhancing crop yield. Full article

Pseudogymnoascus destructans (Pd) invades the skin tissue of bats, leading to severe population declines. The skin microbiome plays a crucial role in protecting hosts from fungal infection and exhibits pronounced spatiotemporal dynamics in its structure and function. Meanwhile, metabolites derived from microbial communities reflect the host physiological state and participate in microbe–pathogen interactions. In this study, we investigated the spatiotemporal dynamics of skin bacterial communities and metabolites during hibernation in Rhinolophus ferrumequinum by integrating 16S rRNA sequencing with untargeted metabolomics and experimentally verified the antifungal effects of microbially derived potential metabolites against Pd. Our results revealed that the structure of the skin bacterial community varied significantly across sampling contexts, with its assembly primarily governed by stochastic processes. Bacterial diversity reached its lowest level during middle hibernation, accompanied by a simplified co-occurrence network dominated by cooperative or mutualistic interactions. Additionally, metabolomic analyses demonstrated systematic metabolic remodeling of bat skin across hibernation stages, marked by significant enrichment of multiple pathways closely involved in host antimicrobial defense. Furthermore, metabolite profiles differed across locations, and the abundance patterns of several metabolites were strongly correlated with Pd infection levels. Integrated analyses identified multiple metabolites that showed significant correlations with bacterial genera capable of synthesizing the corresponding compounds. In vitro validation confirmed that nine metabolites effectively inhibited the growth of Pd, among which melatonin exhibited the strongest antifungal activity. Collectively, this study reveals the dynamics of the skin microbiome and metabolites of R. ferrumequinum during hibernation, providing novel insights into the defensive role of skin-associated microbes and metabolites in maintaining population health and resilience against fungal pathogens. Full article