Search Results (3,718)

We study axial perturbations of Reissner–Nordström black holes within the general framework of parity-violating modified gravity theories. We derive the governing equations for a class of frame-dragging perturbations, focusing on the symmetry structure and radial dependence of the perturbed metric component, describing its behavior across three distinct regions: near the singularity ($r\to 0$), between the inner and outer Reissner–Nordström horizons ($r_{-}<r<r_{+}$), and in the asymptotic exterior regime ($r\to \infty$). Using a combination of analytical and numerical methods, we analyze the solutions for varying black hole charge-to-mass ratios ($Q/M$) and angular momentum parameters (l). Key findings include the suppression of perturbations by the electromagnetic field for higher $Q/M$; the emergence of radial resonance-like behavior for specific l values; and a high degree of symmetry for solutions in the extremal limit ($Q/M\sim 1$), attributed to the AdS₂$\times S^2$ near-horizon geometry. The WKB approximation is employed to study the high-l regime, revealing quantized radial resonance modes and singular behavior in the extremal limit. Additionally, we explore the role of boundary conditions and the possibility of a Chern–Simons field $\mathsf{\Theta }$ as the source of the parity violation, showing that consistency and the behavior of the perturbations under time reversal demand a constant field (and thus no actually observable Chern–Simons effects) at leading order. These results provide a basis for further analysis of the stability and dynamical properties of charged black holes in parity-violating theories, with potential experimental signatures in gravitational wave observations. Full article

Remote sensing object detection (RSOD) models play an increasingly important role in modern remote sensing systems. However, during model delivery, sharing, and deployment, RSOD models face increasing risks of unauthorized redistribution, illegal replication, and intellectual property infringement. To mitigate these threats, this paper proposes a white-box watermarking framework for RSOD models that enables reliable copyright verification while preserving the performance of the primary detection task. Specifically, a gradient-based sensitivity analysis of the detection loss is first performed to adaptively identify model parameters that minimally affect detection performance, which are then selected as watermark carriers. Subsequently, a parameter-ranking-based watermark encoding scheme is developed, where watermark bits are embedded by enforcing relative ordering constraints between parameter pairs. To further improve robustness under practical deployment conditions, an attack-simulation-driven training strategy is introduced, in which common perturbations and watermark removal attacks are simulated during the embedding process. In addition, a stealthiness enhancement strategy based on statistical distribution constraints is designed to maintain consistency between the distribution of watermarked parameters and those of the original model, thereby reducing the risk of watermark exposure and localization. Extensive experiments across multiple RSOD datasets and detection architectures demonstrate that the proposed method achieves a high copyright verification success rate with negligible impact on detection accuracy and exhibits strong robustness and stealthiness against a variety of watermark removal attacks. Full article

Proton Exchange Membrane (PEM) fuel cells represent a promising clean energy technology due to their high efficiency, environmental sustainability, and wide applicability in transportation and stationary power systems. Accurate parameter extraction from PEM fuel cell models is critical for reliable performance prediction, control, and optimization. However, this task is challenging because of the nonlinear, multimodal, and highly coupled characteristics of fuel cell models. To address this challenge, this paper proposes an Enhanced Elk Herd Optimizer (EEHO), incorporating a novel best-bull–guided differential reproduction mechanism to improve search accuracy, convergence speed, and robustness. The proposed enhancement enables a portion of offspring solutions to be generated by perturbing the global best solution using scaled differences between randomly selected herd members. This mechanism strengthens exploitation around promising regions while maintaining population diversity and preventing premature convergence. The EEHO is applied to extract seven unknown parameters of PEM fuel cell models by minimizing the sum of squared errors between experimental and simulated voltage data. The effectiveness of the proposed method is validated using two commercial PEM fuel cell stacks, namely a 250 W stack and a BCS 500 W stack. Extensive comparative evaluations against the conventional Elk Herd Optimizer and several well-established methods demonstrate that the EEHO achieves superior performance in terms of accuracy, convergence speed, robustness, and statistical consistency. The proposed algorithm attains lower error values, faster convergence, and more stable performance across multiple independent runs. Furthermore, the extracted parameters produce highly accurate voltage and power characteristics, closely matching experimental observations. The results confirm that the proposed EEHO provides an efficient, reliable, and robust optimization framework for PEM fuel cell parameter extraction and offers strong potential for broader applications in energy system modeling, intelligent optimization, and renewable energy optimization problems. Quantitatively, the proposed EEHO achieved a significant reduction in the averages of the Sum of Squared Errors (SSE) of up to 24.96% and 23.29% compared with the conventional EHO for the 250 W stack and a BCS 500 W stack, respectively, demonstrating its superior accuracy in parameter estimation. To further validate the robustness and generalization capability of the proposed EEHO, two additional commercial PEM fuel cell datasets, of Ballard Mark V and Modular SR-12, are investigated and compared against several state-of-the-art optimization algorithms. The results, supported by Wilcoxon and Friedman statistical tests and boxplot analyses, confirm that EEHO consistently achieves superior accuracy, stability, and convergence reliability across different operating conditions. Full article

Background: Gene expression and cellular identity are regulated by epigenetics that occurs through chromatin modifications, RNA changes, chromatin accessibility, and three-dimensional genome organization. Although DNA methylation has been the focus of most epigenetics studies in the past, other non-methyl epigenetic processes, including histone post-translational modifications (PTMs), epitranscriptomic marks, and chromatin remodeling, are dynamic, reversible, and context-dependent, and thus are difficult to accurately interrogate using endpoint sequencing-based assays, especially in heterogeneous tissues, developing systems, and therapeutic response environments. Scope and Approach: The present review discusses epigenetic modifications other than DNA methylation regarding sensor-based technologies that can measure live, dynamic, and spatially resolved measurements. Epigenetic sensors include any genetically encoded sensors (GECs) based on resonance energy transfer, CRISPR/dCas-derived sensors, or aptamer-based sensors, and hybrid biochemical/imaging sensors that can be used in live or semi-live settings. It lays emphasis on the technologies, which have been developed recently, that allow real-time kinetic measurements, working in three-dimensional and organoid models, and being applied to disease-relevant perturbations. On these platforms, performance properties such as specificity, sensitivity, spatial and temporal resolution, ability to perform dynamic versus locus-specific interrogation, and perturbed endogenous chromatin states are compared. Key Conclusions and Outlook: Together, these sensing strategies are complementary to the traditional methods of measuring epigenomics in that they show epigenetic dynamics unobservable with static measurements. We list the important technical issues, including specificity, quantitation, multiplexing, and chromatin perturbation, and report the barriers and solutions in development and design. Lastly, we provide a conceptual map of how live epigenetic sensing and multi-omics and translational models can be integrated, and how the two methodologies can be used to develop functional epigenetics and guide disease modeling and drug development. Full article

Vessel vibrations have serious safety risks and must be effectively mitigated. This study investigates the reduction in ship pitch–roll vibrations modeled as a two degrees of freedom of nonlinear spring–pendulum system subjected to multi-parametric excitation, using proportional–derivative controller. The main objective is to develop a rapid and efficient analytical approach to nonlinear vibration analysis. A non-perturbative approach is employed to transform weakly nonlinear oscillators of ordinary differential equations into equivalent linear ones without using Taylor expansions. He’s frequency formula plays a central role in this transformation. The resulting parametric solutions are validated using Mathematica Software (v13) and show a strong agreement with the original nonlinear model. The effects of various parameters on stability are examined. Theoretical analysis is conducted using the multiple time scales method to identify worst resonance conditions and derive frequency response equations. Stability near simultaneous sub-harmonic resonance is assessed using Routh–Hurwitz criterion. Numerical simulations based on the fourth-order Runge–Kutta method confirm the effectiveness of proportional–derivative control. Excellent agreement between analytical and numerical results demonstrates the accuracy, efficiency, and practical applicability of the proposed method. Full article

Recent studies have shown that deep neural networks can be misled by adversarial examples that involve only imperceptible perturbations. Among these, one-pixel attacks (OPA) represent an extreme yet powerful threat, as they alter only a single pixel of an input image while causing misclassification. While prior work has demonstrated the effectiveness of OPAs on low-resolution datasets, extending these attacks to high-resolution images poses a significant challenge due to the dramatic increase in the number of pixels and the resulting expansion of the search space. In this paper, we address this challenge by proposing a scalable one-pixel attack framework for deep neural networks on high-resolution images. The key difficulty in high-resolution OPAs lies in identifying a vulnerable pixel among tens of thousands of candidates under a black-box setting, where exhaustive pixel-wise probing is prohibitively expensive. To overcome this limitation, we decompose the attack into two phases. In the first phase, we efficiently identify a small set of promising pixel locations using a hierarchical patch-based search strategy, which iteratively prunes large image regions via coarse-grained patch perturbations, thereby substantially reducing the number of required model queries. In the second phase, for each selected pixel candidate, we search for adversarial RGB values using a black-box optimization method based on momentum-accelerated finite-difference gradient estimation. We evaluate our method on popular deep neural network architectures using high-resolution ImageNet images. The experimental results demonstrate that our approach achieves high attack success rates while significantly reducing query cost and improving the quality of the resulting adversarial perturbations compared to existing strategies. Full article

In this paper, we investigate the spectrum distribution and asymptotic behavior of an M/$G^B$/1 bulk service queueing system. In this system, the server processes customers in batches of a fixed maximum capacity B, and the time required to serve a batch is governed by a general distribution with a service rate function $\eta (·)$, which determines the instantaneous probability of service completion. The system dynamics are described by an infinite set of partial integro-differential equations. First, by introducing the probability generating function and employing Greiner’s boundary perturbation method, we establish that the time-dependent solution (TDS) of the system converges strongly to its steady-state solution (SSS) in the natural Banach state space. To this end, when the service rate $\eta (·)$ is a bounded function, we prove that zero is an eigenvalue of both the system operator and its adjoint operator, with geometric multiplicity one. Moreover, we show that every point on the imaginary axis except zero belongs to the resolvent set of the system operator. Second, we analyze the spectrum of the system operator on the left real axis. When the service rate $\eta (·)$ is constant and the fixed maximum capacity B equals 2, we apply Jury’s stability criterion for cubic equations to demonstrate that the system operator possesses an uncountably infinite number of eigenvalues located on the negative real axis. Additionally, we prove that an open interval near zero on the left real axis is not part of the point spectrum of the system operator. Consequently, these results imply that the semigroup generated by the system operator is not compact, eventually compact, quasi-compact, or essentially compact. Full article

This paper is concerned with the leader-following consensus of time-delay multi-agent systems (MASs) with stochastic perturbation over a directed network. Different from existing literature subject to the conventional Lipschitz condition, the one-sided Lipschitz nonlinear MASs with delay are discussed. First, to address the challenge, in combination with current and delay information, the composite control law is constructed. By employing the Lyapunov function and using the Itô formula, this proves that the followers can eventually track the leader. Second, in the presence of external disturbance, sufficient conditions are established for the H-infinity leader-following consensus of one-sided Lipschitz nonlinear stochastic MASs. Further, the method to handle the one-sided Lipschitz nonlinearities is directly applicable to the stochastic MASs with conventional Lipschitz nonlinear dynamics, and the corresponding results are easily obtained. Finally, the relationship between one-sided Lipschitz scalars and time-delay parameters are presented, and the simulation results are given to verify the theoretical algorithms. Full article

Transformer-based detectors have demonstrated exceptional accuracy in visible-object detection tasks. However, adversarial patches, specific types of adversarial examples, can disrupt these detectors by introducing unrestricted perturbations into specific image regions. Traditional methodologies focus on placing patches directly on objects and increasing attention scores between the patch and all areas of the image to impair detector performance. Nevertheless, these approaches are suboptimal due to significant discrepancies between background and object features, which contradict optimization objectives. Moreover, they overlook the impact of cross-attention mechanisms on detection results. To address these limitations, we introduce a novel approach named Localized Query Attack (LQA), designed to interfere with both self-attention within the encoder and cross-attention in the decoder. Unlike conventional global interference methods, LQA targets object features specifically, enhancing self-attention interactions between the adversarial patch and foreground regions to redirect model focus toward the patch. In the context of decoder cross-attention, we compute the joint attention matrix connecting encoder outputs with object queries. By diminishing the influence of encoder outputs and residual components in this matrix, we amplify the relative importance of the adversarial patch, thereby intensifying the attack’s effectiveness. Our experiments show that LQA achieves an approximately 20% improvement in transfer attack performance compared to the second-best method across various transformer-based detectors. The practical efficacy of LQA is further substantiated through real-world scenario validations, underscoring its applicability. Full article

Background: Main risk factors associated with the development of sarcopenia (coexistence of muscle mass loss and dysfunction) are a sedentary lifestyle coupled with obesity. Associated mitochondrial dysfunction leads to energy deficits and perturbations in the balance between protein synthesis and degradation, thereby triggering muscle dysfunction or atrophy. Aside from exercise, which is challenging to implement and maintain, particularly in women, treatments for diminishing sarcopenia are scarce. The objective of the present study was to evaluate the effect of the flavanol (−)-epicatechin (EC) in a hypercaloric diet-induced obese female rat model. Muscle strength and endurance, as well as relative mitochondrial DNA content in skeletal muscle, were assessed. Methods: Female rats were fed a hypercaloric diet to induce obesity, as evidenced by increases in body weight, Lee index, and lipid profile alterations, and by abdominal fat accumulation, and to promote a sarcopenic phenotype. Functional tests of grip strength and mobility (treadmill) were performed. Mitochondrial relative content was evaluated by measuring the ratio of mtDNA/nuclear DNA, and the expression of genes related to mitochondrial biogenesis (Pgc1-α, Tfam), fusion (Mfn1 and Opa1), fission (Drp1 and Fis1), and mitophagy (Pink1 and Pkn), and function; citrate synthase and Ucp3 were also evaluated. Results: A significant decrease in mobility and strength was observed in obese female rats, accompanied by reduced mitochondrial numbers, activity, and dynamics, but not by changes in muscle size or weight. Treatment with EC induced mitochondrial biogenesis and positive changes in mitochondrial dynamics (fission and fusion) and activity, as measured indirectly by changes in citrate synthase and Ucp3 expression. Discussion: Results reinforce the potential of EC as a modulator of mitochondrial function in dysfunctional conditions associated with obesity, thereby attenuating the mechanisms underlying sarcopenia. Full article

Background/Objectives: Severe acquired brain injury (sABI) disrupts early rehabilitation because arousal fluctuates, trunk control is fragile, and agitation limits therapy tolerance; land-based practice is frequently constrained by fall risk and staffing. We aim to reframe aquatic therapy as a programmable multisensory environment to stabilize arousal and support axial alignment before conventional impairment targets are feasible. Here, programmable denotes the deliberate titration and reporting of water depth, turbulence or perturbation, temperature, body orientation, and flotation and manual support as intervention inputs. Methods: This perspective integrates principles from neurobehavioral assessment, motor control, and immersion physiology to propose the Arousal–Alignment–Action loop as a falsifiable model and to define manipulable aquatic inputs (water depth, turbulence or perturbation, temperature, body orientation, and flotation and manual support) as dosing parameters. We outline a pragmatic testing ladder (within-session micro-experiments, feasibility studies, and embedded evaluations) and a minimal outcomes and confounder set to support cumulative evidence. Results: The framework links state regulation to alignment and goal-directed behavior, specifies predictions that can fail, and highlights boundary conditions (sedation, autonomic instability, pain, recent surgery or wounds, and cervical or cardiopulmonary constraints). A minimal outcome package spanning arousal/responsiveness, trunk control, behavioral dysregulation, participation/tolerance, and basic physiology is proposed, with optional objective adjuncts for mechanism-oriented studies. Conclusions: Treating water as a measurable and titratable medium, rather than a generic modality, may reduce early intensity bottlenecks and improve implementability and comparability of aquatic neurorehabilitation research in medically stable sABI; however, the model is intended as hypothesis-generating until supported by stronger direct clinical evidence. Full article

Background: Congested tournament schedules impose substantial physiological stress in team sports; however, the integrated endocrine and inflammatory responses to real competitive match load in female handball players remain insufficiently characterized. Objective: This study aimed to characterize the acute biochemical responses, including hormonal, inflammatory, muscle damage, and bone metabolism markers, elicited by competitive tournament load in female handball players and to provide practical insights for optimizing recovery strategies and load management during short-term competitive periods. Methods: In a pre–post study design, venous blood samples were collected from competitive female athletes (n = 8; age 20.83 ± 2.93 years) before the first match and after the fourth consecutive match of an official university qualification tournament. Biochemical analyses included cortisol, insulin, IL-6, creatine kinase (CK), IGF-1, irisin, lactate dehydrogenase (LDH), osteocalcin, and testosterone. Pre-to-post changes were assessed using paired t-tests and effect sizes. Results: Tournament load induced substantial multisystem physiological perturbations. Significant increases were observed in cortisol (p < 0.001), insulin (p = 0.044), IL-6 (p < 0.001), CK (p < 0.001), and osteocalcin (p = 0.005), indicating activation of the hypothalamic–pituitary–adrenal axis, systemic inflammation, muscle membrane disruption, and enhanced bone turnover. Conversely, IGF-1 (p < 0.001) and testosterone (p = 0.004) significantly decreased, reflecting suppression of anabolic signaling and a shift toward a catabolic hormonal environment under cumulative match stress. LDH significantly decreased (p = 0.002), while irisin showed no significant change (p > 0.05). Conclusions: These findings demonstrate that congested tournament schedules provoke an integrated endocrine–inflammatory stress response in female handball players. Importantly, the observed anabolic–catabolic imbalance highlights the need for individualized recovery strategies, optimized load management, and adequate recovery periods to mitigate maladaptation and reduce injury risk during short-term competitive tournaments. Full article

Objective: Emphysema is an important chronic obstructive pulmonary disease (COPD) phenotype characterized by the destruction of air spaces distal to the terminal bronchiole. Aiming to detect potential emphysema biomarkers and to assess the systemic effects of emphysema in blood plasma, we conducted a small cross-sectional shotgun proteomics study. Methods: This study included N = 40 participants divided into four subgroups (N = 10 per group): patients with emphysema and COPD (CE), patients with COPD but without emphysema (CN), healthy smokers (HS) and healthy never-smokers (HN). The participants were sampled non-probabilistically to be similar in terms of age, sex and comorbidities. Participants’ blood plasma was analyzed using liquid chromatography–mass spectrometry. Bioinformatic analysis included detection of differentially expressed proteins (DEPs) and overrepresentation analysis (ORA). Results: Across all groups, a total of 994 proteins were identified, with NADP-dependent malic enzyme (NADP-ME; encoded by ME1) being the only DEP in the CE vs. CN contrast. Proteins such as BMP1, ADAMTSL-2, -4 and IGFBP4, -5, 6 were identified to be upregulated in CE vs. HN. Fibulin-1, -3 and several immunoglobulin components were identified to be downregulated in the CE vs. HN contrast. ORA revealed several enriched processes, including serine-type endopeptidase activity, insulin-like growth factor I and II binding, and signaling receptor binding. Conclusion: We propose NADP-ME, an important enzyme of intermediary metabolism and redox homeostasis, as a potential biomarker candidate of emphysema. Notably, NADP-ME is also implicated in anoikis resistance. Additionally, changes in the expression levels of BMP1, ADAMTSL-2 and -4, and fibulin suggest potential major systemic effects of extracellular matrix perturbation. As all data was derived from LC-MS analysis, these findings need to be further evaluated with complementary methods. Full article

In this article, we investigate the existence and approximate controllability of a class of Sobolev-type fractional stochastic differential equations of order $1<\delta <2$ with infinite delay. The analysis is carried out in an abstract Hilbert space framework, incorporating fractional dynamics together with stochastic perturbations. By employing techniques from fractional calculus, semigroup theory, and fixed point theory, particularly the Banach contraction principle along with compactness arguments, we establish the existence of mild solutions for the proposed system. Subsequently, sufficient conditions for approximate controllability are derived by combining operator-theoretic methods with stochastic analysis. The novelty of this work lies in extending controllability results to Sobolev-type fractional stochastic systems of order $1<\delta <2$, where both the higher-order fractional structure and stochastic effects are treated simultaneously within a unified framework. This generalizes and complements several existing results in the literature that mainly address deterministic systems or fractional differential equations of order $0<\delta \le 1$. Finally, an illustrative example is presented to demonstrate the applicability and effectiveness of the theoretical findings. Full article

Background: The perioperative period in cancer surgery is characterized by transient metabolic and inflammatory perturbations that may influence early postoperative biochemical dynamics. Surgical stress induces insulin resistance, hyperglycemia, cytokine activation, and metabolic shifts that interact with tumor cell signaling pathways. Intravenous lidocaine has been associated with anti-inflammatory and systemic stabilizing effects beyond analgesia. We investigated whether perioperative lidocaine administration during robotic-assisted radical prostatectomy (RARP) is associated with early postoperative prostate-specific antigen (PSA) dynamics within the context of perioperative metabolic–inflammatory modulation. Methods: In this single-center retrospective cohort study, 180 patients undergoing RARP for localized or locally advanced prostate cancer were stratified according to perioperative intravenous lidocaine exposure. The primary endpoint was undetectable PSA (<0.1 ng/mL) at 6–12 weeks postoperatively. Secondary endpoints included PSA detectability at 3 and 6 months and time to first detectable PSA. Multivariable logistic and Cox regression models were adjusted for established oncologic risk factors. Perioperative glycemic variation, intraoperative lactate dynamics, and postoperative IL-6 levels were analyzed as indicators of stress-induced metabolic activation. Results: Lidocaine exposure was independently associated with higher odds of undetectable PSA at 6–12 weeks (OR 2.10, 95% CI 1.15–3.85) and at subsequent time points. In Cox analysis, lidocaine was associated with a reduced hazard of PSA detectability (HR 0.58, 95% CI 0.37–0.92). Patients receiving lidocaine demonstrated significantly attenuated perioperative hyperglycemia, lower lactate elevation, and reduced IL-6 response. Conclusions: Perioperative intravenous lidocaine administration during RARP was associated with more favorable early PSA dynamics and attenuation of perioperative metabolic–inflammatory activation. Given the retrospective and non-randomized design of the study, these findings should be interpreted as associative and hypothesis-generating, and warrant confirmation in prospective controlled investigations. Full article