Search Results (41,861)

A working-state model is proposed for the MnOx–Na₂WO₄/SiO₂ catalyst in oxidative coupling of methane (OCM), where a Na₂WO₄-rich surface environment forms an adaptive interphase that buffers the effective interfacial oxygen chemical potential and stabilizes cooperative MnO_x/Na–WO_x/Mn–O–W motifs. A thermodynamic-kinetic scheme is developed that relates (1) reaction-induced surface enrichment (structural stabilization), (2) oxygen spillover (damping of local oxygen gradients), and (3) Mn ↔ W redox exchange as an electron-oxygen buffer channel. Ex situ XPS/EDS/EPR data indicate a dynamically stratified near-surface region with chemically heterogeneous environments of Mn, W, and O. The W 4f region remains dominated by the W⁶⁺ contribution in the presence of a minor reduced component after OCM. In oxygen-deficient mixtures (CH₄/O₂ > 4), interfacial reconstruction becomes more pronounced: Mn-centered Mars–van Krevelen chemistry determines CH₄ activation and oxygen exchange, while the Na₂WO₄-rich phase ensures fast ion/oxygen transport. Observation of the EPR signal from W⁵⁺ ions in the tungstate matrix indicates the existence of reduced W intermediates at low oxygen potential. Optimization of C₂ selectivity and stability is suggested to require maintaining the catalyst within the selective window of effective interfacial μO by adjusting CH₂/O₂ and contact time, as well as controlling the architecture of the Na–W–O/MnO_x interfacial region. Full article

This article presents a generalized observer scheme for dynamic structures designed to estimate the dynamic behavior of various types of attacks, such as Denial of Service (DoS) attacks, False Data Injection (FDIA), and Random Data Injection (RDI). These attacks employ a Markovian distribution logic to alter the behavior of actuators and sensors in a cyber-physical system. A three-tank interconnected system is used to demonstrate the effectiveness in estimating these attacks, modeled under the Takagi–Sugeno representation. This approach allows for precise detection and diagnosis of the attacks, which is essential for the design of controllers that ensure the security and integrity of cyber-physical systems. Moreover, it lays the foundation for developing an attack-tolerant controller based on observers, offering a comprehensive and robust solution to address security challenges. Full article

Accurately evaluating how aero-engine performance supports upper-level capability remains a challenging issue in the digital planning, demonstration, and design of complex equipment systems-of-systems. Existing studies mainly rely on two-level analyses at the subsystem and system-of-systems levels, which are insufficient to characterize the cross-level transmission relationships among the aero-engine, aircraft performance, and overall capability. To address this limitation, this paper proposes a multilayer network-based contribution evaluation method for aero-engines oriented toward digital equipment planning and demonstration. First, a three-layer evaluation index system is constructed, including the overall capability layer, the aircraft performance layer, and the aero-engine performance layer, based on the OODA loop concept and aviation physical constraints. This provides a structured and traceable basis for cross-level requirement decomposition and scheme evaluation. Second, by integrating expert prior judgment with mechanism-based sensitivity analysis, the interrelationships among indicators at different layers are quantified, and a multilayer evaluation index network is established. Third, topological structure analysis is employed to identify key indicators in the aero-engine layer, and a cascading propagation model is introduced to evaluate the supporting roles and contribution rates of both individual indicators and the overall aero-engine layer with respect to the overall capability layer. Simulation results show that the proposed method can effectively reveal the structural characteristics, propagation paths, and dynamic influence patterns of aero-engine-layer indicators within the multilayer network. The proposed method provides methodological support for digital equipment planning, scheme demonstration, design optimization, and capability-oriented decision-making of aero-engines. Full article

Dynamic community detection seeks to identify changing structural groups in temporal graphs; however, current neural methodologies are susceptible to misinterpreting transient edges, noisy temporal variations, or unusual spectral disturbances as authentic structural changes. This research introduces TriMeta-BFNet, a tri-meta stacked atypical-frequency Bayesian Fourier neural network designed for hallucination-resistant community discovery. The proposed system presents a three-dimensional meta-counterbalance mechanism that includes topological consistency, Fourier-domain atypical frequency modeling, and Bayesian posterior uncertainty estimation. Initially, temporal graph signals are converted into the Fourier domain to distinguish stable low-frequency community patterns from erratic high-frequency disturbances. Secondly, unusual frequency points are detected by spectral energy deviation and integrated into a stacked neural representation module, enabling the model to differentiate significant structural alterations from extraneous oscillations. Third, Bayesian inference is employed to assess posterior uncertainty regarding community assignments, therefore mitigating overconfident predictions in the presence of ambiguous or noisy graph evolution. The three components are simultaneously optimized via a cohesive objective function that integrates community detection loss, structural consistency regularization, atypical-frequency penalty, temporal stability management, and Bayesian calibration loss. The resultant structure offers both resilient community divisions and comprehensible hallucination-risk assessments. TriMeta-BFNet theoretically conceptualizes hallucination in dynamic community detection as an imbalance of structural, spectral, and uncertainty factors, and it develops a mathematically rigorous counterbalance mechanism to mitigate erroneous community evolution. The suggested model presents a novel approach to uncertainty-aware, frequency-sensitive, and interpretable dynamic graph learning. Full article

We propose a Bayesian kernel machine regression (BKMR) framework for count outcomes with dynamic spatiotemporal dependence. The proposed model, termed Negative Binomial BKMR with spatiotemporal effects (NB-BKMR), integrates (i) a negative binomial likelihood to accommodate overdispersion, (ii) a kernel-based exposure–response surface for complex mixtures, (iii) hierarchical group-wise variable selection and (iv) a dynamic spatiotemporal random effect structure based on a Leroux conditional autoregressive (CAR) prior evolving over time. Posterior inference is conducted in a fully Bayesian framework using Polya-Gamma data augmentation. Through simulation studies, under varying nonlinear exposure–response functions, correlation structures, and spatiotemporal dependence patterns, we show that NB-BKMR yields well-calibrated uncertainty quantification and robust identification of dominant mixture drivers, even when exposures are highly correlated. An application to the U.S. state-level traffic fatality counts (1982–1988) illustrates how the model uncovers nonlinear effects and interactions among socioeconomic and behavioral predictors while improving predictive performance relative to generalized additive models with spatiotemporal smooths. This work extends existing BKMR methodology by unifying mixture modeling, count outcomes, and dynamic spatial dependence in a single coherent framework, with particular relevance for areal public health surveillance data. Full article

KRAS is one of the most frequently mutated oncogenes in human cancer and has long been considered an “undruggable” therapeutic target because of its high affinity for guanine nucleotides and limited druggable binding pockets. Recent advances in structural biology and molecular pharmacology have transformed this paradigm, leading to the successful development of KRAS G12C inhibitors such as sotorasib and adagrasib. These agents established proof-of-concept for direct KRAS inhibition and marked an important advance in precision oncology. However, intrinsic and acquired resistance mechanisms, adaptive signaling reactivation, and tumor heterogeneity continue to limit the durability of clinical responses. Therapeutic development has rapidly expanded beyond KRAS G12C toward broader strategies including KRAS G12D inhibitors, pan-RAS and RAS(ON) inhibitors, degraders, and biomarker-guided combination approaches. In parallel, circulating tumor DNA (ctDNA) and other biomarker-driven strategies are increasingly enabling dynamic monitoring of treatment response, minimal residual disease, and resistance evolution. In this review, we summarize the molecular biology and conformational regulation of KRAS signaling, recent advances in allele-specific and pan-RAS therapeutic strategies, mechanisms of resistance, and emerging precision oncology frameworks for KRAS-mutant cancers. Full article

In this paper, we propose a multilevel stochastic model for the energy consumption of public proof-of-work blockchains. The main novelty is the proposal of a closed form for the expected energy consumption in one proof of work mining round. In the case of homogeneous per-hash efficiency, this proposition shows that the expected spending is $e_0/p$ depending only on the protocol difficulty and not on the distribution of the hash power among the miners. The proposal connects three levels of analysis: a local model of mining at the node level, a semi-global model of competitive block discovery and propagation, and a global stochastic model of workload, computational capacity, network connectivity and power consumption. This leads to the above closed form energy result. The mining process is approximated locally by exponential waiting times of Bernoulli hash trials. This extends to the semi-global model where the competition among miners and the delay in the propagation lead to the wasted computation. The global layer is modeled as a set of stochastic differential equations which models the interaction between workload dynamics, capacity constraints and communication overheads. The core analysis does not need Bayesian or Markov decision components but these are recommended for modeling estimation and adaptive control. We start with preliminary simulations on the VIBES platform and find qualitative properties of the full model: the total energy cost scales roughly linearly with the size of the network, the average energy per node decreases with increasing network size, the propagation latency is the primary source of wasted computation due to stale blocks and nodes tend to operate in a capacity-depleted regime with the workload-induced degradation being substantially higher than the recovery rate. The results give a structural analysis of how the design of the protocol and the network conditions affect the energy consumption and emphasize the importance of quantitatively calibrating with empirical data from Bitcoin. Full article

In modern cloud-native systems, a single root cause can trigger cascading anomalies across multiple entities (e.g., microservices, databases, and hosts), generating alert storms with hundreds or thousands of heterogeneous alerts. Alert convergence (automatically grouping these alerts into actionable incident tickets) is critical for reducing operator burden and recovery time. Existing graph-based methods construct a topological graph from known entity dependencies and then leverage Graph Neural Networks (GNNs) for information propagation, but they rely on static physical topologies that fail to capture implicit fault propagation paths. Large Language Model (LLM)-based methods focus on reasoning about the textual information of alerts, yet they do not incorporate global topological structure and struggle with consistency at scale. Motivated by these limitations, we propose LLM-Guided Graph Structure Learning (LLM-GSL), a novel framework that combines the semantic reasoning ability of LLMs with the structural modeling power of GNNs for alert convergence. Specifically, LLM-GSL first leverages an LLM to evaluate pairwise entity relationships and discover implicit fault propagation paths that are absent from static topologies, thereby enhancing the physical-topology graph into a more complete structure. A Graph Attention Network (GAT) then refines alert representations over this enhanced graph via graph message passing, guided by a self-supervised graph affinity loss with continuous multi-modal supervision targets that fuse adjacency structure, textual affinity, and temporal affinity. Finally, density-based clustering groups the learned representations into incident tickets. Experiments on five public datasets, including four LogHub-derived datasets and one RCAEval microservice fault-injection subset, demonstrate that LLM-GSL achieves an average F1-score of 96.2%, outperforming six baselines including both traditional clustering and LLM-based methods by at least 14.0 percentage points. Full article

Commensalism, a frequent type of interaction among freshwater invertebrates, remains poorly studied. Some turbellarians (Platyhelminthes: Temnocephalidae) are specialized obligate commensals of crustaceans, mollusks, insects, and turtles. In the Neotropics, Temnocephala iheringi inhabits the mantle cavity of snails (Mollusca: Gastropoda) from Pantanal (Brazil) to the Pampean region of Argentina, where several species serve as hosts. This study aimed to molecularly characterize several populations of T. iheringi and to analyze their genetic and morphological variability across different host species and geographic areas. Using the mitochondrial COI marker, we assessed populations associated with five of its seven known host species through phylogenetic reconstructions, species delimitation approaches, and haplotype network analyses. Combined with morphological data, results support COI as an effective identification tool for Temnocephalidae. Several genetic lineages were identified and were largely congruent with collection localities. However, specimens associated with hosts displaying high dispersal capabilities (i.e., Pomacea canaliculata and P. maculata) showed low mitochondrial genetic differentiation and minimal phylogenetic structure across large distances, which may be consistent with recent dispersal and/or ongoing connectivity among populations. These findings provide new insights into the evolutionary dynamics of this specific temnocephalid–snail association. Given that some hosts are highly invasive and even considered pests in several countries, the data and genetic sequences generated in this study may prove valuable for future research on symbiont diversity and dispersal. Full article

Infra-hepatic inferior vena cava (IVC) balloon occlusion is an effective strategy for reducing intraoperative bleeding during precision liver surgery, yet rapid balloon inflation can produce abrupt transient deviations in downstream venous pressure that are not yet quantitatively characterized. Current practice relies on operator experience, with no quantitative framework to balance occlusion efficacy against downstream pressure safety. A computational fluid dynamics (CFD) model of the balloon-occluded IVC was developed in ANSYS 2025 R2 with two-way fluid–structure interaction (FSI), Carreau–Yasuda blood rheology, and a balloon described by an Ogden hyperelastic model; the flow regime was laminar (Re ≈ 254). Reduced-order ARX models of four input–output subsystems were identified from CFD-generated data, and a model predictive control (MPC) strategy was formulated to penalize downstream pressure overshoot through a weighted cost function. The identified models achieved training normalized root-mean-square errors of 0.0363 to 0.1164 and out-of-sample validation errors of 0.1224 to 0.2381. Conventional sigmoid inflation induced a 45.82% overshoot in downstream pressure (P_aft); the optimal input signal (q = [⁰,¹,⁰,⁰], λ = 0.1) reduced this to 6.05%, a reduction of 39.77 percentage points, while preserving >90% flow occlusion at U_F = 3 × 10⁴ Pa. The proposed framework offers a quantitative basis for balloon-occlusion device design that limits downstream pressure overshoot, motivating subsequent benchtop, ex vivo, and in vivo validation. Full article

High-speed imaging offers a non-intrusive approach for monitoring falling-film flows over horizontal tube bundles, but reflective images are difficult to quantify because grayscale variations are jointly affected by film geometry, interfacial curvature, surface slope, viewing angle, and local highlights. This study proposes an interpretable visual-proxy sensing framework for comparative state assessment of such flows. Isothermal water experiments were conducted on a five-row horizontal tube bundle over Re_Γ = 184 − 960. For each condition, grayscale frames were acquired at fps and analyzed within five fixed row-wise regions of interest. The image sequence was transformed by temporal-median background subtraction, local spatiotemporal mapping, moving-average detrending, and median-absolute-deviation normalization. The resulting normalized map M_n and dynamic renewal field G were used to extract four scalar descriptors: noise-corrected apparent renewal intensity I_R, high-frequency fraction R_HF, spectral peak frequency f_p, and burst-event rate F_B. Results show that M_n and G capture the transition from sparse column flow to more continuous sheet flow and reveal row-dependent activity organization. The descriptors provide complementary information on renewal intensity, frequency composition, dominant time scale, and intermittent events. Zero-response, noise-correction, and sensitivity tests confirm that the framework avoids structured pseudo-waves and maintains stable row-wise comparisons. The method provides a low-calibration visual sensing tool for relative falling-film state assessment. Full article

Traditional dynamic community detection methods often struggle to simultaneously preserve local structural consistency, capture global topological relationships, and efficiently adapt to continuous graph updates in large-scale environments. To solve these limitations, this paper proposes a novel dynamic community detection framework called Embedded Clustering Boundary Refinement (ECBR). The proposed method integrates unsupervised GraphSAGE and Node2Vec embeddings to jointly capture local neighborhood aggregation patterns and global structural equivalence among nodes. The generated embeddings are fused through feature concatenation and z-score normalization to construct a unified latent representation space. Subsequently, Mini-Batch KMeans clustering is employed to efficiently generate the initial community structure while maintaining scalability for large-scale graphs. To further improve partition quality, ECBR introduces a boundary-aware refinement mechanism that identifies structurally ambiguous nodes using neighborhood consistency analysis and reassigns them according to embedding-space similarity. In addition, the framework incorporates an adaptive dynamic update strategy capable of distinguishing between major topological shifts and localized structural changes. Significant graph perturbations trigger complete model retraining, whereas minor modifications are handled through computationally efficient incremental updates on local subgraphs. Experimental evaluations were conducted on synthetic LFR benchmark networks and several real-world dynamic interaction datasets, including high school, workplace, and hospital contact networks. The results demonstrate that ECBR consistently outperforms several state-of-the-art methods, including QCA, DyPerm, DCDID, IncNSA, and DCDBFE, achieving better NMI and ARI scores across diverse network conditions. The experimental findings confirm that ECBR provides a scalable, robust, and highly effective solution for dynamic community detection in evolving large-scale social networks. Full article

Rainfall-index-based disaster insurance is an efficient approach to mitigating hydrometeorological losses. However, conventional premium pricing models generally assume memoryless stochastic dynamics that do not fully capture the long-range dependence inherent in rainfall data. This study develops a hydrometeorological disaster insurance model within a fractional Black–Scholes framework to incorporate long-memory effects. The model is formulated using fractional differential equations and solved semi-analytically by integrating the Daftardar–Jafari Method (DJM) with the Kashuri–Fundo (KF) transform, yielding a closed-form solution expressed in terms of the Mittag–Leffler function. The proposed contract is structured as parametric rainfall insurance with a multi-layer payout mechanism based on percentiles corresponding to minor, moderate, and severe housing damage. The results show that variations in the fractional-order parameter significantly affect premium estimation. In particular,  δ = 0.5 recovers the classical model and tends to generate higher premiums than the fractional model with δ = 0.23153, whereas the model with δ = 0.73153 yields lower premiums. These findings indicate that fractional-order parameterization can accommodate diverse risk characteristics and policyholders’ economic capacities, enabling more adaptive, risk-sensitive premium structures. In line with SDG 13 (Climate Action), the proposed framework offers a climate-responsive disaster-mitigation strategy through accessible, actuarially relevant insurance design.  recovers the classical model and tends to generate higher premiums than the fractional model with , whereas the model with  yields lower premiums. These findings indicate that fractional-order parameterization can accommodate diverse risk characteristics and policyholders’ economic capacities, enabling more adaptive, risk-sensitive premium structures. In line with SDG 13 (Climate Action), the proposed framework offers a climate-responsive disaster-mitigation strategy through accessible, actuarially relevant insurance design. Full article

Carbon dioxide (CO₂) conversion in non-thermal plasma is a promising route for carbon utilisation under mild conditions. This study investigates the performance and dynamic behaviour of kaolin-based catalysts modified with Ni (nickel), Ni–Ce (nickel-cerium), and Fe–Cu (iron-copper) oxides in a Dielectric Barrier Discharge (DBD) reactor. Materials were characterised using X-ray diffraction, energy-dispersive X-ray fluorescence, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. CO₂ conversion was evaluated at varying Plasma Energy Numbers (PEN = 1.65–20) with time-resolved gas analysis over a 10 min period. Results demonstrate that the kaolin support is not inert; its dielectric properties actively influence discharge characteristics. Ni-based catalysts exhibited the highest stable activity, reaching ~53% conversion for samples calcined at 500 °C. Conversely, adding cerium oxide significantly decreased conversion and induced temporal instabilities, contrasting with its typical role in thermal catalysis. Time-resolved measurements revealed that Ni–Ce and Fe–Cu systems exhibit initial activity followed by gradual deactivation, suggesting plasma-induced surface restructuring. These findings highlight that catalyst performance in DBD is governed by a complex interplay of chemical activity and plasma–material interactions. The generated time-series data provide a robust foundation for machine learning applications in predictive modelling and stability classification of plasma-catalytic systems. Full article

The increasing availability of large language models (LLMs) is transforming how users create and share content on social media platforms. Beyond enabling text generation, LLMs introduce a new paradigm in which content is deliberately optimized for engagement through algorithmically suggested phrasing, structure, and tone. This paper investigates the emerging shift from authentic self-expression toward engagement-driven optimization in LLM-assisted social media use. It examines whether and how LLM-generated or LLM-assisted posts systematically outperform human-authored content in engagement metrics and at what cost to informational quality, diversity, and authenticity. Using a mixed-methods approach, controlled experiments with human participants are combined with large-scale analysis of social media posts to compare organic and LLM-optimized content. Differences in engagement outcomes (e.g., likes, shares, comments), linguistic features, and perceived credibility and informativeness are evaluated. The findings suggest that while LLM-assisted content consistently increases short-term engagement, it tends to reduce informational depth and perceived authenticity while exhibiting changes in stylistic characteristics associated with engagement-oriented optimization. This creates a potential feedback loop in which users increasingly rely on optimization strategies that privilege attention over substance. The findings suggest that widespread adoption of LLM-driven optimization could contribute to changes in the dynamics of the social media attention economy. Future research is needed to determine whether these effects emerge at scale and persist over longer periods of platform use. Implications are discussed for platform design, content moderation, and the future of human–AI co-creation in digital communication. Full article