Search Results (1,900)

Indian mythological narratives contain long event chains, recurring characters, moral conflicts, interactions between human and divine agents, and source-specific narrative styles. General-purpose large language models can generate fluent text while losing character continuity, thematic relations, or source-supported events. This study presents MythoBiLLM, a parameter-efficient framework for summarization and continuation generation from Indian mythological texts. The framework combines a frozen Llama 3.2 3B-Instruct backbone, LoRA-based adaptation, and a gated BiLSTM narrative-memory adapter. A corpus of public-domain English translations from the Ramayana, Mahabharata, Bhagavad-Gita, Vishnupuranam, Harivamsha, Hindu Tales, and Indian Myth and Legend contains 3,684,838 word-level tokens and 6057 segmented passages. Evaluation covers language modeling, summarization, continuation generation, entity consistency, theme retention, component ablation, robustness, human assessment, and statistical testing. Relative to LLM+LoRA, the complete framework reduces average perplexity from 23.4 to 19.8. In controlled comparisons, the BiLSTM adapter achieves an MCS of 0.713 on both tasks, compared with 0.699 for the parameter-matched MLP adapter, 0.704 for independently trained long-context LoRA, and 0.708 for retrieval augmentation. Full MythoBiLLM reaches MCS values of 0.762 for summarization and 0.744 for continuation generation. After entity consistency and style alignment are excluded from MCS, the complete configuration retains the highest scores of 0.751 and 0.731. These findings support the complete framework on the evaluated corpus, while the controlled comparisons indicate a modest complementary contribution from the BiLSTM and do not identify it as the sole source of the performance gains. Full article

This paper develops a model-driven analytical framework for portfolio optimization under a multidimensional Black–Scholes model with time-varying parameters, where both the drift and volatility functions evolve linearly over time. Within this framework, explicit closed-form expressions are derived for the covariance matrix of normalized asset prices and subsequently incorporated into the classical Markowitz mean–variance framework to obtain analytical representations of the global minimum-variance portfolio, the mean–variance efficient portfolio, and the corresponding efficient frontier. The proposed methodology establishes a direct connection between continuous-time stochastic asset-price modeling and portfolio optimization through a model-implied covariance structure. Its practical implementation is investigated through both numerical experiments and an empirical study using daily stock price data from 20 constituents of the $S\&P$ 500 index over the period 2020–2024. Monte Carlo simulations demonstrate the finite-sample sensitivity of portfolio optimization to covariance estimation, while the empirical analysis illustrates how the estimated model parameters, obtained using the maximum likelihood framework of Aït-Sahalia for discretely sampled diffusion processes, can be incorporated into the analytical covariance matrix for constructing efficient frontiers under realistic market conditions. Overall, the proposed framework provides an analytically tractable methodology that integrates continuous-time asset pricing models with classical mean–variance portfolio optimization, offering a coherent model-based covariance representation for portfolio selection under time-varying market environments. Full article

Background/Objectives: To evaluate the association between demographic, ocular biometric, and systemic comorbidity variables with corneal endothelial morphometric parameters in a Caucasian adult population undergoing cataract surgery. Methods: Cross-sectional study in a large cohort of patients who were candidates for cataract surgery with no other concomitant ocular pathology. Endothelial cell density (ECD), hexagonality (HEX), the coefficient of variation (CV), and the presence of guttae were assessed using specular microscopy (Tomey EM-4000). Biometric parameters were obtained by partial coherence interferometry (IOLMaster 700), and systemic comorbidities were recorded. Multiple linear regression models adjusted for age and sex were applied, along with stratified analyses according to axial length (AL). Results: A total of 1032 eyes from 1032 patients were included. The mean age was 75.9 ± 9.3 years. The cohort comprised 366 men (35.5%) and 666 women (64.5%). The mean ECD was 2113 ± 544 cells/mm², HEX 42.7 ± 21.6%, and CV 43.2 ± 9.6%. The prevalence of guttae was 15.4% (159 eyes). ECD correlated negatively with age (r = −0.148; p < 0.001) and positively with central corneal thickness (r = 0.091; p = 0.004) and anterior chamber depth (r = 0.064; p = 0.030). In the multivariate model, age was independently associated with lower ECD (β = −8.40 cells/mm²/year; p < 0.001), whereas male sex was associated with higher ECD and lower CV. No significant associations were found with AL, keratometry, or systemic comorbidities. Stratified analysis by AL group showed consistent patterns with no relevant differences. Conclusions: Corneal endothelial morphometry in Caucasian adults is primarily associated with age, sex, central corneal thickness, and anterior chamber depth, with no significant association with axial length or systemic comorbidities. Full article

Fractional Brownian motion has been widely used in financial modeling to capture long-range dependence and persistent behavior in asset dynamics. In the fractional Black–Scholes framework, accurate estimation of the Hurst parameter is essential because estimation uncertainty can directly affect option pricing. In this paper, we propose a Bayesian framework for joint inference on the Hurst parameter and volatility in fractional stochastic differential equation models. Unlike approaches based solely on point estimation, the proposed framework propagates posterior uncertainty directly into option pricing distributions under the fractional Black–Scholes model. Simulation studies are conducted across multiple values of the Hurst parameter and sample sizes to evaluate estimation accuracy, posterior coverage, and pricing uncertainty. The results demonstrate stable posterior inference and coherent uncertainty quantification for both model parameters and option prices. The methodology is further illustrated using WTI crude oil and natural gas data under different market regimes. The empirical analysis indicates that differences in market behavior are driven primarily by changes in volatility rather than strong long-range dependence, while posterior option price distributions exhibit substantial variation in pricing uncertainty across regimes. These findings highlight the importance of incorporating joint parameter uncertainty into fractional financial models and demonstrate the practical value of Bayesian methods for option pricing. Full article

Time-reversed Young (TRY) interferometry reconstructs interference from a fixed detector by reading out a programmable source-label distribution. This work formulates the architecture as a source-coded Bayesian response sensor. For a perturbation parameter $\theta$, the detected source-label histogram is a posterior distribution determined by a programmed source prior, an optical likelihood for a fixed-detector click, and an evidence factor equal to the click probability. The key point is not the Bayesian identity itself, but its physical implementation: in TRY the prior is imposed before propagation and can therefore reshape the response ensemble actually sampled by the detector. The normalized posterior is shown to respond through a centered likelihood score, and the detected-event Fisher information is the posterior variance of this score. This identifies posterior-weighted score contrast, rather than local response magnitude alone, as the relevant sensing resource. The framework separates posterior-shape information from evidence information, giving a resource-aware way to judge near-null response enhancement. It also yields practical design rules: a two-label source code converts a weak perturbation into a fixed-detector label imbalance, while the multiparameter score covariance provides a route to nuisance rejection and gives a minimal-label rank condition for sensing multiple perturbations. A passive double-slit implementation with weak one-slit phase and loss perturbations is proposed, requiring only fixed-detector source scans before and after calibrated perturbations. Practical tolerances associated with source-programming error, drift, background, imperfect coherence, and polarization mismatch are analyzed, and extensions to multi-aperture and integrated photonic systems are formulated. The results position TRY as a source-programmable Bayesian sensing architecture complementary to conventional detector-plane Young interferometry. Full article

With the continuous growth of electricity demand, the structural safety of transmission towers under wind loads has become crucial. The wind-resistant design of transmission towers is mainly analyzed through the wind-induced response and wind vibration coefficients of the structure, but the applicability of the coherence function selected in the wind load simulation process has been overlooked. This study investigates the applicability of the Davenport and Shiotani coherence functions in wind load simulation for long-span transmission towers and also analyses the influence of tower-line coupling effects on transmission towers. The research results show that (1) the displacement mean square deviation response and wind vibration coefficient of the Davenport coherence function are significantly larger than those of the Shiotani coherence function. The Shiotani coherence function is not applicable to wind resistance-related studies of long-span high towers, and the design parameters are insufficiently safe. (2) Under wind directions of 60° and 90°, the coupling effect increases the wind vibration coefficient; under wind directions of 0° and 45°, the coupling effect reduces the wind vibration coefficient. (3) Based on the current codes and simulation results, a modified formula for the wind vibration coefficient of transmission towers is proposed, which can reflect the wind vibration effect of “spread-wing” transmission towers, and the overall error of the wind vibration coefficient can be controlled within 10%. The research in this paper is of great significance to the wind-resistant design of long-span transmission towers. Full article

We investigate the dynamics of a driven quantum field coupled to a finite-temperature reservoir. The corresponding master equation is solved using superoperator techniques, yielding an analytical expression for the density operator. To obtain a compact and physically transparent description of the dynamics, we adopt a phase-space representation based on the Husimi Q-function. For an initially coherent state, we derive a closed-form Gaussian expression for the Husimi Q-function whose stationary limit corresponds to a displaced thermal state. This approach also enables an analytical study of quantum-interference dynamics for an initial superposition of coherent states. Furthermore, we derive the corresponding Fokker–Planck equation for the Husimi Q-function and obtain closed-form expressions for relevant statistical quantities, including the mean photon number, the photon-number standard deviation, and the Mandel parameter. We also investigate the Wehrl and linear entropies, which quantify the loss of phase-space information and purity induced by the thermal environment. The framework provides a complete analytical characterization of the phase-space dynamics, photon statistics, and entropic properties of driven–dissipative quantum fields while avoiding the explicit manipulation of the density operator. Full article

The COVID-19 pandemic underscored the need to better characterize immune and molecular responses following SARS-CoV-2 infection and vaccination. Beyond antibody and cellular immunity, COVID-19 involves oxidative stress and DNA damage, affecting repair mechanisms and metabolic adaptation linked to immune resilience. Here, we present a multidimensional analysis of 20 individuals who recovered from mild COVID-19, integrating clinical features with humoral and cellular immune responses, T cell and myeloid phenotypes, oxidative stress, DNA damage, and metabolomic and lipidomic profiles. Although most individual parameters fell within physiological ranges, network modeling revealed structured associations spanning multiple biological domains. A central finding was a coherent cluster organized around vaccine dose number, linking anti-Spike antibody titers, oxidative stress, bioenergetic signatures, and granulocyte activation. Higher vaccination was associated with stronger humoral responses, lower oxidative stress, and a more balanced myeloid–metabolic profile, suggesting a potential protective role extending beyond antibody induction. Additional associations linked symptom patterns to T cell differentiation states, anti-nucleocapsid responses to systemic inflammation, and anaerobic signatures to DNA damage markers, revealing interconnections between immunometabolism, clinical expression, and genomic stress. Despite the small sample size, these findings offer a preliminary systems-level perspective on mild COVID-19 recovery and illustrate the value of integrative exploratory frameworks in infectious disease research, laying the groundwork for validation in larger longitudinal cohorts. Full article

Urban traffic anomaly detection is essential for intelligent transportation systems, particularly in smart city environments where fast identification of abnormal events can improve road safety and traffic management. This work proposes a novel ULSTM-driven architecture that explicitly models temporal dependencies across consecutive traffic frames to achieve more stable and temporally coherent reconstructions. The proposed framework leverages sequential spatio-temporal representations to improve the distinction between normal traffic patterns and anomalous events. To further enhance reliability, we introduce a Hybrid Weighted Fusion strategy that synergistically combines structural, perceptual and pixel-wise metrics. The framework’s parameters are optimized using a Discrete Dirichlet Sampling approach, achieving a peak F1 Score of 70.28%. Evaluations were conducted on a manually curated traffic anomaly dataset with frame-level annotations. Experimental results demonstrate that the ULSTM framework significantly outperforms frame-independent generative models by suppressing high-frequency reconstruction noise, providing a robust solution for real-world smart city deployments. While highly effective in complex scenarios, the proposed framework is strictly applicable to highly dynamic traffic environments with active motion, as static background ensembles can degrade performance. Full article

Laser shock peening (LSP) is an advanced surface modification technique that significantly enhances the fatigue resistance of metallic components through the synergistic implantation of deep compressive residual stresses (CRSs) and gradient microstructural refinement. Existing investigations have shown that LSP can generate strengthening layers extending from several hundred micrometers to approximately 1 mm in depth, with affected zones reaching 5–6 times the depth typically achieved by conventional shot peening in representative titanium alloys. In specific cases, LSP has increased the fatigue limit from 483.2 MPa to 593.6 MPa, corresponding to an improvement of approximately 22.8%, while optimized treatment of Ti-17 compressor blades has extended fatigue life by more than two orders of magnitude. This review systematically elucidates the anti-fatigue strengthening mechanisms of LSP across a range of metallic systems, with emphasis on three key aspects: (i) the mechanistic retardation of fatigue crack initiation and propagation, mediated by CRS-induced reductions in the stress intensity factor and enhanced crack closure effects; (ii) the parametric sensitivity of surface integrity and stress field homogeneity to laser energy density, spot overlap ratio, and multiple-impact sequencing; and (iii) the process-specific characteristics of emerging LSP variants, including laser peening without coating, warm laser shock peening, and cryogenic laser shock peening. Furthermore, we critically evaluate the role of multiscale numerical simulations—encompassing macroscopic finite element analysis, mesoscopic crystal plasticity modeling, and molecular dynamics—in optimizing process parameters and predicting fatigue life. By integrating experimental, computational, and theoretical perspectives, this review establishes a coherent process–structure–property framework to guide the rational design of LSP protocols for targeted fatigue performance enhancement. Full article

In satellite and space debris laser ranging, photon-counting time-of-flight sequences exhibit spatio-temporal echo coherence and deterministic orbital constraints. We propose an unsupervised framework exploiting this spatial-kinematic coupling to extract weak returns under high background noise. First, a fuzzy clustering regression employs a dynamic energy functional and cross-entropy-regularized photon attribution, guided by target motion priors. To enhance low signal-to-noise ratio sensitivity, we introduce a low-gradient sampling strategy that theoretically guarantees a signal-to-background ratio exceeding 1/2. Furthermore, a dual-stream autoencoder fuses orbital kinematic parameters and multi-scale echo densities via noise-adaptive latent gating. Validation on 88 satellite and 24 debris datasets achieves F1-scores of 0.73 and 0.93, respectively. The sampling strategy reduces computational latency by ∼5.2% with no loss in tracking precision. This label-free, physically grounded approach enables robust weak signal detection in ground-based photon-counting lidar. Full article

Heart failure (HF) classification is still primarily based on left ventricular ejection fraction, even though this parameter only partially reflects the biological mechanisms determining disease progression and therapeutic response. The aim of this review was to present a conceptual interpretive framework that analyzes the HFrEF (heart failure with reduced ejection fraction) and HFmrEF (heart failure with mildly reduced ejection fraction) phenotypes through the lens of the network organization of pathophysiological processes. This review integrates data on molecular and cellular mechanisms, clinical phenotypes, clinical trial results, and therapeutic recommendations. Particular attention was paid to the concepts of network coherence, pathway dominance, and the relationship between the disease’s biological architecture and treatment response. The proposed conceptual framework suggests that HFrEF is more often characterized by a relatively coherent pathophysiological architecture, in which neurohormonal activation, disturbances in calcium metabolism, mitochondrial dysfunction, and extracellular matrix remodeling constitute mutually reinforcing processes. HFmrEF, on the other hand, is presented as a heterogeneous category, encompassing patients with partial improvement of previous systolic dysfunction, patients progressing towards HFrEF, and HFpEF (heart failure with preserved ejection fraction)—like phenotypes associated with inflammation, endothelial dysfunction, microcirculatory disturbances, and metabolic dysregulation. In this approach, therapeutic response depends on whether the targeted pathway occupies a central position in the disease network. This review proposes a hypothesis-generating conceptual framework that complements, rather than replaces, the current ejection fraction-based classification of heart failure. Although the proposed framework requires prospective validation, it may facilitate a more mechanistic interpretation of HF phenotypes, support future biologically informed therapeutic strategies, and stimulate the design of mechanistically oriented clinical studies. Full article

Cardiovascular and vascular diseases remain major causes of morbidity and mortality worldwide, despite substantial advances in prevention, diagnosis, and treatment. In recent years, cardiovascular imaging has moved beyond the traditional assessment of anatomy and morphology toward a multidimensional evaluation of function, tissue composition, haemodynamics, inflammation, and individualized risk. This evolution has been driven by technological progress in echocardiography, cardiovascular magnetic resonance, computed tomography, nuclear imaging, intravascular imaging, and point-of-care ultrasound, together with the rapid development of artificial intelligence, radiomics, and predictive analytics. Advanced echocardiographic techniques, including contrast stress echocardiography and emerging methods for myocardial scar detection, may improve functional and prognostic assessment in patients with suspected or established coronary artery disease. Cardiac magnetic resonance, through tissue mapping, late gadolinium enhancement, and 4D flow imaging, provides unique information on myocardial fibrosis, perfusion, ventricular remodelling, and vascular haemodynamics. Computed tomography, particularly with the introduction of photon-counting technology, is expanding the non-invasive characterization of coronary plaques, vascular calcification, and thromboembolic disease. Hybrid imaging with PET/CT and PET/MR offers additional insight into vascular inflammation, myocardial metabolism, and active disease processes. At the same time, intravascular ultrasound, optical coherence tomography, and augmented-reality-supported imaging are refining interventional guidance, while point-of-care ultrasound is broadening access to rapid bedside cardiovascular and vascular assessment. The integration of imaging findings with circulating biomarkers, clinical scores, lipid profiles, coagulation parameters, and machine-learning models represents a promising strategy for personalized risk stratification, particularly in complex conditions such as coronary artery disease, venous thromboembolism, pulmonary embolism, and bleeding risk during antithrombotic therapy. This review summarizes current advances in cardiovascular imaging, discusses their translational implications, and highlights future directions for integrating imaging, artificial intelligence, and precision medicine into daily clinical practice. Full article

Hyperspectral image denoising must reconcile global spectral coherence with spatially heterogeneous scene content. This paper presents Adaptive Heterogeneity-Aware Tensor Decomposition (AHTD), a refinement module that augments a global Tucker initialization with heterogeneity-guided local tensor shrinkage. The method estimates a global spectral subspace, detects heterogeneous regions via combined variance and edge analysis, and applies adaptive weighted singular-value shrinkage modulated by regional patch complexity. To isolate the effect of the heterogeneity-aware selection mechanism itself, experiments employ a controlled internal ablation protocol comparing three pipeline variants under identical noise realizations, ranks, and parameter settings on the Pavia_80, Indian Pines corrected, and Salinas corrected benchmarks under synthetic mixed noise ($\sigma =0.03$ Gaussian with stripe and impulse noise): a global Tucker baseline, a dense full-local refinement variant, and the proposed selective AHTD. AHTD achieves peak signal-to-noise ratio (PSNR) gains of 0.35–0.40 dB over the global Tucker baseline while maintaining or improving structural similarity index (SSIM), spectral angle mapper (SAM), and relative dimensionless global error in synthesis (ERGAS), at approximately threefold lower runtime than dense full-local refinement, demonstrating its value as a computationally efficient, interpretable refinement stage for tensor-based hyperspectral processing. We note that matched comparisons against externally published denoising methods are not included in this study; these results establish the benefit of heterogeneity-aware selective refinement within the proposed Tucker-based pipeline. Full article

Image reconstruction from incomplete measurements is a fundamental problem in signal and image processing, with applications ranging from medical imaging to computational photography. In recent years, deep learning approaches have shown promising performance, particularly when combined with physics-inspired formulations such as deep unrolling. This paper presents a systematic comparative study of classical interpolation and variational reconstruction methods, direct convolutional neural networks (CNNs), and unrolled data-consistency CNN architectures for image reconstruction under different sampling patterns. We consider three representative mask types: structured block masks, nonuniform masks, and random sampling patterns, with sampling ratios ranging from 10% to 50%. Experiments are conducted on the public BSDS500 image dataset, using a fixed grayscale preprocessing pipeline and a reproducible train/validation/test split. Experimental results demonstrate that reconstruction performance strongly depends on the sampling pattern. For random masks, the full unrolled DC-CNN achieves the best quantitative and qualitative performance, reaching a PSNR of 30.98 dB and an SSIM of 0.921 at 50% sampling. In contrast, for structured block and nonuniform masks, TV-based inpainting provides the strongest overall performance, showing that classical model-based reconstruction remains highly competitive when the sampling pattern contains spatially coherent missing regions. A block-size sensitivity analysis further confirms that the difficulty of structured-mask reconstruction is governed by the geometric severity of the missing region. Statistical analysis using paired tests with Holm correction confirms that the main performance differences are significant across the evaluated configurations. Furthermore, we show that a lightweight unrolled model with shared weights and reduced depth achieves a substantially lower parameter count and lower computational cost than the full unrolled architecture, although with reduced accuracy in the most favorable random-sampling cases. These findings provide practical insights into the relationship between sampling strategies and reconstruction performance, offering guidance for the design of efficient and robust learning-based reconstruction systems. Full article