Search Results (11,924)

Understanding how migratory waterbird species co-vary through time can reveal guild structure and guide monitoring in dynamic coastal wetlands, yet seasonal phenology can inflate simple co-occurrence signals. Here, we used standardized monthly bird counts from Yongan Wetland, Taiwan (36 survey months across two survey blocks: November 2014 and January–August 2015, and October 2016–December 2018) to infer de-seasonalized interspecific associations. We analyzed 50 regularly recorded species and a focal subset of 13 shorebirds and ducks. For each species, we transformed raw counts to monthly anomalies that remove recurrent seasonal patterns, then quantified pairwise Spearman correlations and controlled multiple testing using Benjamini–Hochberg FDR (q ≤ 0.05) to construct association networks. The anomaly-based network revealed strong guild structure: positive links were concentrated within dabbling ducks and within shorebirds, consistent with shared habitat use and foraging regimes, whereas negative links were fewer and suggested potential niche partitioning or spatiotemporal segregation. Robustness analyses (moving-block bootstrap stability, a circular-shift null comparison, and log-transformed anomaly sensitivity) supported that the main network patterns were unlikely to arise from chance alignment. Our framework provides a transparent, time-series–based approach for disentangling phenology from association inference, offering a practical framework for wetland monitoring and hypothesis generation about waterbird community dynamics. Full article

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems. Full article

In this work, an adaptive event-triggered-based consensus control strategy is proposed for the quadrotor unmanned aerial vehicle (QUAV) formation system in the presence of external disturbances and state constraints. Firstly, the disturbed QUAV formation system dynamic model is established. Then, to address the initial peaking explosion problem in the traditional active disturbance rejection control method, a time-varying gain extended state observer (TGESO) is designed to suppress external disturbances. Meanwhile, a novel barrier Lyapunov function (BLF) is constructed to cope with the adverse effects caused by state constraints. Furthermore, aiming to alleviate network congestion and reduce communication burden, the adaptive event-triggered mechanism (AETM) is adopted to design the formation flight controller. Finally, the stability of the developed consensus controller and the boundedness of all error signals are proved via Lyapunov theory. Comparative simulation results demonstrate the practicality of the presented control algorithm. Full article

The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the “state-flickering” issue inherent in traditional HMMs, we incorporate a “Sticky” parameter into the transition matrix, imposing a temporal penalty on spurious state switching to maintain continuity. Furthermore, we introduce a Dynamic Prior Strategy that adaptively calibrates self-transition probabilities by mapping frequency-domain features of the observed sequence to the model’s parameter space. The proposed decoding process employs a two-pass refinement strategy and the Viterbi algorithm in the logarithmic domain to ensure numerical stability. The model’s efficacy was validated using a high-fidelity dataset of simulated apnea events. This work provides a computationally efficient and mathematically rigorous approach that demonstrates strong potential for long-term respiratory health monitoring. Full article

Under severe ice and snow weather, ice-covered pantograph–catenary arcs affect the safe operation of high-speed trains. This study investigates the impact of ice-covered arc electrical characteristics, plasma parameters, and material ablation mechanisms. By constructing a comprehensive pantograph–catenary icing experimental platform, arc voltage, current signals, high-speed dynamic images, and emission spectra were synchronously collected under different icing thicknesses ranging from 0 to 15 mm. Research indicates that ice coverture causes frequent “extinction–reignition” phenomena during the arc initiation stage due to the latent heat absorbed by melting ice, significantly reducing the initial stability of arc combustion. Spectral analysis confirms that the arc excitation temperature and energy density are positively correlated with the concentration of hydrogen ions produced by water vapor ionization, reaching a peak under the 5 mm icing condition. Experimental results show that the average energy density of ice-covered arcs is approximately double that of the non-iced condition, causing the ablation pits on the carbon strip to exhibit characteristics of greater depth and wider copper deposition zones. This study reveals the unique mechanisms and damage characteristics of icing pantograph–catenary arcs, providing an important basis for the safe design and maintenance of pantograph–catenary systems in high-cold railway environments. Full article

Oil and gas exploration conducted in the main fault zone of the Fuman Oilfield has yielded large-scale and high-production results. Against this background, the non-fault zone has emerged as a new domain for oil exploration endeavors. Nevertheless, the establishment of a unified sequence division scheme for the study area remains unachieved, primarily constrained by two key factors: first, the high costs associated with ultra-deep high-density coring operations; and second, the inconspicuous response characteristics exhibited by logging curves. This absence of a standardized scheme has further impeded the progress of oil and gas exploration in the non-main fault inter-region within the study area. Consequently, the present study is based on multi-source data, including seismic data, logging data, and field outcrop data. Magnetic susceptibility measurements from the cement plant section and natural gamma-ray logging data from the Yangjikan section were systematically analyzed to establish cyclostratigraphic frameworks. A sedimentary noise model (SNM) was employed to reconstruct Holocene sea-level fluctuations, enabling precise sequence stratigraphic subdivision within the Fuman Area. Results demonstrate that the Middle-Lower Ordovician Yijianfang–Penglaiba Formations retain robust astronomical cyclicity, validated by high-fidelity orbital forcing signals. Notably, the DYNOT (Dynamic Noise After Orbital Tuning) model effectively decouples orbital-driven sea-level oscillations from local depositional noise, offering a novel approach for sequence boundary identification. This methodology reveals a hierarchical sequence architecture comprising four third-order sequences and 11 fourth-order sequences within the Yijianfang–Penglaiba Formations. Such a framework provides critical insights into facies distribution patterns and non-fault-controlled exploration potential in the Fuman Basin. Full article

WRKY is a crucial transcription factor involved in plant growth, development, and responses to abiotic stress. In the present study, a total of 182 AaWRKY transcription factor members were identified across the Artemisia argyi genome and found to be distributed across 17 chromosomes. Evolutionary analysis revealed that segmental duplication served as the primary driver for family expansion, with the evolutionary trajectory shaped by strong purifying selection (Ka/Ks < 1.0). Phylogenetic classification categorized these members into seven highly conserved subgroups, while physicochemical analysis indicated that most AaWRKYs are unstable, hydrophilic proteins, consistent with the rapid turnover required for transcriptional switches. Transcriptomic profiling unveiled significant tissue-specific expression patterns, with over 50% of the members predominantly enriched in roots and specific genes, such as AaWRKY11, implicated in the regulation of leaf senescence. Protein–protein interaction (PPI) network analysis identified AaWRKY110 as a central regulatory hub linking the MAPK signaling pathway with the isoflavonoid biosynthetic machinery. Furthermore, comparative transcriptomic analysis between aphid-resistant (Ai20K) and susceptible (Ai72G) cultivars demonstrated that resistance is conferred by a priming mechanism involving high basal expression of key candidates, including AaWRKY82, 108, 128, and 71. In contrast, the susceptible genotype exhibited a delayed and ineffective hypersensitive-like response. Collectively, these findings elucidate the evolutionary dynamics of the AaWRKY family and provide critical genetic targets for the concurrent improvement of medicinal metabolite accumulation and biotic stress resilience in Artemisia argyi via molecular breeding. Full article

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

We examine whether speculative bubbles in shadow banking institutions contribute to the buildup and materialization of systemic risk. Using the Phillips–Shi–Yu (BSADF) bubble detection methodology and market-based systemic risk measures (ΔCoVaR and Marginal Expected Shortfall), we analyze daily data for 17 publicly listed U.S. shadow banking firms over the period 2010–2026. We document a pronounced pro-cyclical measurement puzzle. During bubble periods, firms exhibit higher market exposure and greater tail risk—Beta increases by 4.9% and Expected Shortfall by 7.9%—yet widely used systemic risk measures decline, with ΔCoVaR falling by 6.6%. This pattern suggests that conventional systemic risk metrics may underestimate vulnerabilities during speculative expansions. However, when bubbles burst, systemic risk materializes rapidly. During burst windows, ΔCoVaR increases by 7.9% and MES by 8.6%, indicating that vulnerabilities accumulated during bubble phases translate into significant systemic spillovers once speculative dynamics collapse. Our findings highlight a pro-cyclical bias in commonly used systemic risk indicators: these measures capture realized financial stress but fail to detect the buildup of fragility during expansion phases. Monitoring bubble dynamics in shadow banking may therefore provide valuable complementary signals for macroprudential surveillance. Full article

Oleocanthal (OC), an anti-inflammatory and antioxidant phenolic compound exclusively found in extra virgin olive oil (EVOO), has emerged as a potential anticancer agent through multiple mechanisms of action, yet its impact on key processes such as cellular metabolism remains insufficiently characterized. Here, we investigated the metabolic and mitochondrial responses to OC across different breast cancer molecular subtypes. Triple-negative (MDA-MB-231) and luminal (MCF7, T47D) breast cancer cell lines were treated with OC to evaluate cell viability, cell cycle progression, metabolic enzyme expression, mitochondrial respiration, and mitochondrial network organization. OC responsiveness differed, being highest in MDA-MB-231 and lowest in T47D cells. Lactate dehydrogenase levels decreased in all cell lines, while mitochondrial response varied. MDA-MB-231 mitochondrial function was fully impaired, while MCF7 cells showed increased respiratory activity, with marked mitochondrial fragmentation, and T47D cells largely preserved mitochondrial integrity and function. Notably, the magnitude of OC effects correlated with MET expression, an established target of OC and a prognostic factor associated with reduced relapse-free survival within the triple-negative subtype. Collectively, these findings identify OC as a modulator of cancer cell metabolism and mitochondrial dynamics, with particular relevance in MET-high triple-negative breast cancers. Full article

Background and Objectives: Psychological resilience is increasingly recognized as a determinant of how patients respond to surgical stress, yet its role in perioperative medicine remains poorly defined. This narrative review aims to synthesize current evidence on resilience in surgical populations from a psychobiological perspective, spanning conceptual models, measurement approaches, clinical correlates, biological mechanisms, and intervention strategies. Materials and Methods: This narrative review was conducted to examine psychological resilience in adult surgical populations from an integrated psychobiological and perioperative perspective. A structured literature search was performed in December 2026 using PubMed, Scopus, and PsycInfo, combining resilience-related constructs with surgical, perioperative, biological, and clinical outcome keywords. Eligible publications included observational, longitudinal, interventional, translational, and conceptually relevant studies addressing resilience in adult surgical settings. Evidence was synthesized qualitatively across predefined domains, including conceptualization and measurement of resilience, associations with perioperative outcomes, neuroendocrine and inflammatory mechanisms, and resilience-modulating interventions within perioperative and Enhanced Recovery After Surgery (ERAS) frameworks. Results: Contemporary models conceptualize resilience as a dynamic, context-dependent process supported by interacting psychological, biological, and social factors. In surgical cohorts, higher resilience is consistently associated with better patient-reported outcomes, including quality of life, pain control, and emotional adjustment, and in some studies with survival and functional recovery. Preoperative depression, anxiety, maladaptive coping, and low social support converge as components of a broader “resilience profile” linked to poorer postoperative trajectories. Biologically, resilient phenotypes are characterized by more regulated hypothalamic–pituitary–adrenal and autonomic responses and reduced inflammatory activation. Psychological therapies, prehabilitation programs, and selected pharmacological strategies show convergent, though heterogeneous, signals of benefit and can be interpreted as indirect resilience-enhancing interventions. Conclusions: Resilience appears to be a clinically meaningful, potentially modifiable construct that links psychosocial functioning, biological vulnerability, and postoperative outcomes. Incorporating resilience assessment into preoperative risk stratification and systematically embedding resilience-building strategies within perioperative and ERAS pathways may support more personalized, psychologically informed surgical care. Prospective, multidomain studies are needed to validate measurement tools, clarify mechanisms, and test resilience-targeted interventions in surgical populations. Full article

Exogenous glucose functions not only as a carbon source but also as a key signaling molecule involved in regulating root development and metabolism in plants. To elucidate the molecular mechanisms underlying this response in cherry rootstock (Prunus cerasus), we performed RNA-seq on lateral roots collected at 0, 6, 12, 24, 48, and 72 h after glucose treatment. Transcriptome profiling revealed a dynamic and sustained transcriptional reprogramming, with a total of 461 differentially expressed genes (DEGs) consistently altered across all post-treatment time points relative to the control (T0). Weighted gene co-expression network analysis identified five modules strongly correlated with glucose exposure, notably enriched for genes involved in nitrogen, carbon, and sulfur metabolism. Functional enrichment analyses further revealed a pronounced overrepresentation of pathways associated with nutrient utilization, as well as carbon fixation, glycolysis, amino acid biosynthesis, and stress-responsive processes such as glutathione metabolism and MAPK signaling. Intriguingly, key transcription factors and signaling components were consistently co-enriched across multiple functional categories, suggesting the presence of a tightly coordinated regulatory network that links sugar sensing to metabolic reprogramming, redox homeostasis, and developmental plasticity. Notably, glucose treatment induced both activation and repression of nitrogen-related genes in distinct co-expression modules, indicating fine-tuned modulation of nutrient uptake in response to carbon availability. Together, these findings suggest that exogenous glucose triggers a systems-level shift in root physiology, coordinating primary metabolism with stress adaptation and growth regulation through tightly interconnected carbon–nitrogen–sulfur metabolic circuits. Full article

AI travel assistants are increasingly designating hotels as “eco”, yet when the evidence is not independently verifiable, these recommendations may serve as persuasive cues or credible decision support. We present a preregistered 2 × 2 between-subject laboratory experiment (n = 63) that manipulates autonomy framing (Recommend vs. Plan) and evidence verifiability (verifiable vs. non-verifiable) in a realistic hotel-booking workflow with a standardized “Verify eco-claim” drawer. Phasic arousal was recorded at recommendation onset (E1) and verification initiation (E3), employing eye-tracking indexed verification behavior (verify clicks, time-to-verify, verification depth) and event-locked galvanic skin response (GSR). Verifiability did not directly speed up or deepen verification (H1 not supported), but verification was common (74.6% clicked Verify). Rather, autonomy influenced checking: Plan slowed verification and altered verification depth. E1 SCR revealed an Evidence × Autonomy interaction, which is consistent with an autonomy-boundary account (H4), rather than credibility stress emerging as a simple evidence main effect at E1 (H2 not supported as stated). Verification served as a repair moment: depending on the availability of diagnostic cues, arousal dynamics from E1 to E3 supported differential “repair” (H3). SCR dynamics explained incremental variance in perceived manipulation/greenwashing concern beyond condition and eye-tracking indices (H5b supported), but verification depth did not mediate effects on trust or delegation (H5a not supported). Overall, users’ interpretation of AI sustainability advice is influenced by autonomy, and multimodal process measures offer useful signals for auditing eco-recommendation designs in travel platforms. Full article

As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, $X_d$, $X_q$, $X_d^{\prime }$, $X_q^{\prime }$, $T_{do}^{\prime }$, $T_{qo}^{\prime }$) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment. Full article

The comprehension of the complex dynamics of degassing is critical for volcano monitoring and assessing volcanic hazards. In this study, we apply visibility graph analysis (VGA) to a decadal, high-resolution time series of daily soil CO₂ flux recorded by a standardized monitoring network at Mt. Etna volcano (Italy). By mapping these time series into complex networks, we demonstrate that the connectivity degree distributions follow a power law described by the exponent $\gamma$, which reveals a self-similar behavior of gas emissions. We introduce the $\gamma$-deviation, namely the variation of the scaling exponent from its long-term site-specific baseline, as a novel proxy for degassing efficiency. The long-term baseline is interpreted as a site-specific measure of flux efficiency, while its variations are attributed to other factors, such as fluctuations in the sources or changes in the efficiency of fluids transport pathways. Our results identify a transition from a period of discordance across the monitoring sites (pre-2016) to a phase of network-wide concordance (after 2016). The striking correlation between topological $\gamma$-deviations and the established normalized network signal (${\Phi }_{norm}$) validates the methodology, suggesting that VGA is able to capture the same underlying magmatic drivers. This study establishes VGA as a robust and reliable tool for medium- and long-term monitoring, potentially capable of identifying the occurrence of large-scale magmatic processes and refining the characterization of fluid transport dynamics in active volcanic systems. Full article