Search Results (223)

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

The incorporation of fractional-order derivatives into neural networks presents a novel approach to improving gradient flow and adaptive learning dynamics. This paper introduces a fractional-order LSTM model, leveraging the Grünwald–Letnikov (GL) method to modify both activation functions and backpropagation mechanics. By redefining the transition functions of LSTM gates with fractional derivatives, the model achieves a smoother gradient adaptation while maintaining consistency across forward and backward passes. This is the first study integrating the Grünwald–Letnikov operator directly into both forward and backward LSTM computations, ensuring a consistent fractional framework throughout the entire learning process. We apply this approach to anomaly detection in fiber optic cable manufacturing, where small deviations in production parameters can significantly impact quality. A dataset containing time-series sensor measurements was used to train the fractional LSTM, demonstrating improved generalization and stability compared to classical LSTM models. Numerical stability analysis confirms that the fractional derivative framework allows convergent learning, preventing both vanishing and exploding gradients. Experimental results show that the fractional-order LSTM outperforms standard architectures in detecting manufacturing anomalies, with the optimal fractional order $\nu =0.95$ providing a balance between accuracy and computational complexity. The findings suggest that fractional calculus can enhance deep learning architectures by introducing a continuous and flexible transition between neuron activations, paving the way for adaptive neural networks with tunable memory effects. Full article

Background/Objectives: Adipose tissue (AT) plays significant roles in energy storage, metabolite signaling, and immunomodulation. The understanding of its underlying mechanisms of dysregulation can provide insight into complex disease processes through analysis with histology, flow cytometry, metabolomics, and proteomics. Tissue sampling in the clinical setting has largely shifted towards minimally invasive approaches to improve factors such as patient satisfaction, post-operative recovery, and procedure length. In contrast, preclinical animal models continue to rely on more invasive methods until refined, minimally invasive techniques are developed and systematically assessed. To improve animal welfare and enhance clinical translatability, there is a critical need to reverse translate these approaches into animal models. Methods: Our study evaluated the feasibility and performance of a commercially available vacuum-assisted biopsy (VAB) device for AT sampling in a preclinical nonhuman primate (NHP) model. Six rhesus NHPs successfully underwent three serial AT biopsies with a VAB device (n = 18). Results: All animals recovered without any serious or unexpected adverse events. The amount of adipose tissue collected per biopsy (0.5–2.7 g) was proportional to the number of individual tracks. Isolation of the stromal vascular fraction (SVF) from a subset of samples (n = 6) yielded 0.41 ± 0.12 × 10⁶ cells/g of tissue. Conclusions: The minimally invasive VAB technique is a safe and reliable method of AT collection in NHPs. This feasibility study demonstrated adequate volumes of tissue cores that are suitable for typical, downstream research applications including immunologic studies and pathology, while improving animal welfare. Full article

Glucagon-like peptide-1 receptor agonists (GLP1-RAs) have demonstrated significant cardiometabolic benefits, particularly in patients with type 2 diabetes and obesity. Their role in heart failure (HF) is gaining increasing attention, with growing evidence supporting their efficacy in HF with preserved ejection fraction (HFpEF). Recent trials have shown that semaglutide improves symptoms, functional capacity, and weight loss in patients with HFpEF. However, these trials did not demonstrate a reduction in HF hospitalizations or mortality. In contrast, tirzepatide has revealed a significant reduction in cardiovascular death and worsening HF events in patients with obesity-related HFpEF, suggesting broader cardioprotective effects. Concordantly, the benefit of GLP1-RAs in the setting of HF with reduced ejection fraction (HFrEF) remains uncertain. Although their mechanisms suggest potential advantages, particularly for patients with a cardiometabolic phenotype, clinical evidence supporting improvements in major clinical outcomes is lacking. Additionally, concerns regarding risk of increased HF hospitalizations, fluid retention and arrhythmic risk have led to a cautious approach in this population. As HF management continues to evolve, GLP1-RAs emerge as a promising yet complex therapeutic option. This review synthesizes the current evidence, highlights key knowledge gaps, and explores how these medications might be integrated into guideline-directed medical therapy (GDMT) to determine their optimal role across the LVEF spectrum in HF. Full article

An optical vortex is a typical structured light field characterized by a helical wavefront and a central phase singularity. With its expanding applications in modern information technology, the demand for generating vortex beams with diverse topological charges continues to grow. Existing methods for modulating the topological charges of vortex beams involve complex operations and high costs. This study proposes a novel approach to modulate the topological charges of optical vortices through edge diffraction of a high-order Hermit–Gaussian (HG) mode laser. First, a high-order HG mode laser is built using off-axis pumping configuration. By selectively obscuring specific lobes of the high-order HG beam, various optical vortices are generated using a cylindrical lens mode converter. The topological charge can be continuously tuned by controlling the number of obscured lobes. This method substantially improves the efficiency of topological charge modulation, while also enabling the generation of fractional vortex states. These advancements show potential in mode-division-multiplexed optical communications and encryption. Full article

This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization. Full article

Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of networked interactions. In this work, we introduce a fractional-order discrete computer virus (FDCV) model, derived from a three-dimensional continuous integer-order formulation and reformulated into a two-dimensional fractional discrete framework. We analyze its rich dynamical behaviors under both commensurate and incommensurate fractional orders. Leveraging a comprehensive toolbox including bifurcation diagrams, Lyapunov spectra, phase portraits, the 0–1 test for chaos, spectral entropy, and $C_0$ complexity measures, we demonstrate that the FDCV system exhibits persistent chaos and high dynamical complexity across broad parameter regimes. Our findings reveal that fractional-order discrete models not only enhance the dynamical richness compared to integer-order counterparts but also provide a more realistic representation of malware propagation. These insights advance the theoretical study of fractional discrete systems, supporting the development of potential technologies for cybersecurity modeling, detection, and prevention strategies. Full article

Computer viruses continue to threaten the security of digital networks, and their complex propagation dynamics require refined modelling tools. Most existing models rely on integer-order dynamics or assume uniform memory effects, which limit their ability to capture heterogeneous behaviours observed in practice. To address this gap, we propose a discrete incommensurate fractional-order virus model based on Caputo-like delta differences, where each compartment is assigned a distinct fractional order to represent mismatched time scales. The model’s dynamics are analysed in terms of stability, bifurcation, and chaos. Numerical results reveal the emergence of rich chaotic attractors, emphasizing the impact of fractional memory on system evolution. To quantify complexity, we employ Approximate Entropy and Spectral Entropy and relate these indicators to the maximum Lyapunov exponent, confirming the system’s sensitivity and unpredictability. All numerical simulations and visualizations were performed using MATLAB (R2015a). The findings highlight the importance of heterogeneous memory in computer-virus modeling and offer new insights for developing theoretical foundations of robust cybersecurity strategies. Full article

This paper proposes a numerical technique to study dynamical systems and uncover new behaviors in chaotic fractional-order models, a field that continues to attract significant research interest due to its broad applicability and the ongoing development of innovative methods. Through various types of simulations, this approach is able to uncover novel dynamic behaviors that were previously undiscovered. The results guarantee that initial conditions and fractional-order derivatives have a significant contribution to system dynamics, thus distinguishing fractional systems from traditional integer-order models. The approach demonstrated has excellent consistency with traditional approaches for integer-order systems while offering higher accuracy for fractional orders. Consequently, this approach serves as a powerful and efficient tool for studying complex chaotic models. Fractional-order dynamical systems (FDSs) are particularly noteworthy for their ability to model memory and hereditary characteristics. The method identifies new complex phenomena, including new chaos, unusual attractors, and complex time-series patterns, not documented in the existing literature. We use Lyapunov exponents, bifurcation analysis, and Poincaré sections to thoroughly investigate the system dynamics, with particular emphasis on the effect of fractional-order and initial conditions. Compared to traditional integer-order approaches, our approach is more accurate and gives a more efficient device for facilitating research on fractional-order chaos. Full article

The Necrópolis de Wari Kayan, at the Paracas site in the coastal desert of south–central Peru, is a large archeologically excavated mortuary complex with fine textile preservation, dated approximately to 2000 BP. This study investigates loose yarns associated with textiles from Wari Kayan tomb 12 (bundle 382), collected by the late Dr. Anne Paul in 1985 at what is now the Museo Nacional de Arqueología Antropología e Historia del Perú (MNAAHP). Sequencing multiple state-of-the-art analyses, including direct analysis in real time mass spectrometry (DART-MS), high performance liquid chromatography (HPLC) with diode array detection, and accelerator mass spectrometry, on the same small sample, seeks to “squeeze out every drop” of information. Six mantles from the outer layer include different sets of color hues and values, representing either different time periods or different producer groups. Plasma oxidation at low temperature (<100 °C) prepared carbon dioxide for AMS radiocarbon analysis. Fibers remaining after oxidation were combusted for light-stable isotope analysis. The sequential analysis results in fiber and dye composition, radiocarbon age, and stable isotope fractionation values may suggest fiber origin, continuing and updating a project started over 40 years ago. Full article

Cutaneous malignancies represent the most common cancers worldwide and pose a growing public health burden. While surgical excision remains the primary curative modality, radiotherapy offers an effective adjuvant therapy for high-risk histopathologic features and an established, organ-preserving alternative for patients with inoperable disease or lesions in cosmetically or functionally sensitive sites. Advances in radiotherapeutic techniques, including brachytherapy and proton therapy, have expanded the therapeutic armamentarium, allowing tailored treatment based on tumor depth, extent, and anatomical location. Contemporary evidence highlights favorable local control and toxicity outcomes with modern radiation therapy approaches, yet data remain fragmented, with most studies limited by small cohorts, heterogeneous methodologies, and limited follow-up durations. Furthermore, the role of radiotherapy in complex scenarios, such as perineural invasion, recurrent disease, and previously irradiated fields, continues to evolve. This review synthesizes the current literature on radiotherapeutic management of skin cancer, critically evaluates dosimetric and clinical outcomes across modalities, and identifies key gaps in evidence. Emphasis is placed on the need for prospective, multicenter investigations to better define comparative effectiveness, optimize dose-fractionation regimens, and integrate emerging technologies into clinical practice. Radiotherapy remains an indispensable modality in dermatological oncology, offering curative potential with preservation of cosmesis and function, yet its optimal utilization demands further high-quality research to refine patient selection and therapeutic strategies. Full article

The Wind Farm Layout Optimization Problem (WFLOP) aims to improve wind energy utilization and reduce wake-induced power losses through optimal placement of wind turbines. Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely adopted due to their suitability for discrete optimization tasks, yet they suffer from limited global exploration and insufficient convergence depth. Differential evolution (DE), while effective in continuous optimization, lacks adaptability in discrete and nonlinear scenarios such as WFLOP. To address this, the fractional-order differential evolution (FODE) algorithm introduces a memory-based difference mechanism that significantly enhances search diversity and robustness. Building upon FODE, this paper proposes FQFODE, which incorporates reinforcement learning to enable adaptive adjustment of the evolutionary process. Specifically, a Q-learning mechanism is employed to dynamically guide key search behaviors, allowing the algorithm to flexibly balance exploration and exploitation based on problem complexity. Experiments conducted across WFLOP benchmarks involving three turbine quantities and five wind condition settings show that FQFODE outperforms current mainstream GA-, PSO-, and DE-based optimizers in both solution quality and stability. These results demonstrate that embedding reinforcement learning strategies into differential frameworks is an effective approach for solving complex combinatorial optimization problems in renewable energy systems. Full article

Heart failure with a preserved ejection fraction (HFpEF) accounts for nearly half of all heart failure cases. It continues to impose a significant global cardiovascular burden due to its rising prevalence, complex pathophysiology, and limited treatment options. The absence of effective disease-modifying therapies is primarily attributable to the complex and heterogeneous pathophysiology underlying HFpEF. The hallmark of HFpEF is systemic inflammation, mostly originating from extracardiac comorbidities, which initiates and sustains the process of myocardial fibrosis, resulting in diastolic dysfunction. Recent evidence has identified specific micro ribonucleic acids (miRNAs) as key regulatory molecules in this inflammation–fibrosis cascade. Particularly, miR-21 and miR-29 play a central role in modulating these pathological processes by regulating the post-transcriptional expression of genes involved in inflammation, cardiac fibrosis, and remodeling. The inflammation-fibrosis axis in HFpEF offers multiple therapeutic opportunities ranging from direct anti-fibrotic strategies to the modulation of inflammation and fibrosis-related miRNA signatures. Such targeted approaches, especially miRNA modulation, hold potential to disrupt fundamental molecular mechanisms driving disease progression, moving beyond conventional HFpEF management. This narrative review explores the roles of miRNAs in modulating inflammation and fibrosis in HFpEF, critically assesses their potential as diagnostic and prognostic biomarkers, and evaluates their therapeutic application. Given the urgent clinical need for efficient HFpEF treatment strategies, understanding miRNA-mediated regulation of the inflammation–fibrosis axis is essential for developing personalized, mechanism-based therapies for HFpEF that could fundamentally change the HFpEF management paradigm. Full article

Background: Radiation exposure in the cardiac catheterization laboratory remains a critical occupational hazard for interventional cardiologists and staff, contributing to orthopedic injuries, cataracts, and malignancy. In parallel, procedural complexity continues to increase, demanding both precision and safety. Robotic-assisted percutaneous coronary intervention (R-PCI), alongside advanced shielding systems and imaging integration, has emerged as a transformative strategy to minimize radiation and enhance operator ergonomics. Objective: This state-of-the-art review synthesizes the current clinical evidence and technological advances that support a radiation-reduction paradigm in percutaneous coronary intervention (PCI), with a particular focus on the role of R-PCI platforms, procedural modifications, and emerging shielding technologies. Methods: We reviewed published clinical trials, registries, and experimental studies evaluating robotic PCI platforms, contrast and radiation dose metrics, ergonomic implications, procedural efficiency, and radiation shielding systems. Emphasis was given to the integration of CT-based imaging (coronary computed tomography angiography—CCTA, fractional flow reserve computed tomography—FFR-CT) and low-dose acquisition protocols. Results: R-PCI demonstrated technical success rates of 81–100% and clinical success rates up to 100% in both standard and complex lesions, with significant reductions in operator radiation exposure (up to 95%) and procedural ergonomic burden. Advanced shielding technologies offer radiation dose reductions ranging from 86% to nearly 100%, while integration of (CCTA), (FFR-CT), and Artificial Intelligence (AI) -assisted procedural mapping facilitates further fluoroscopy minimization. Robotic workflows, however, remain limited by lack of device compatibility, absence of haptic feedback, and incomplete integration of physiology and imaging tools. Conclusions: R-PCI, in combination with shielding technologies and imaging integration, marks a shift towards safer, radiation-minimizing interventional strategies. This transition reflects not only a technical evolution but a philosophical redefinition of safety, precision, and sustainability in modern interventional cardiology. Full article

This paper investigates the stability of continued fractions with complex partial denominators and numerators equal to one. Such fractions are an important tool for function approximation and have wide application in physics, engineering, and mathematics. A formula is derived for the relative error of the approximant of a continued fraction, which depends on both the relative errors of the fraction’s elements and the elements themselves. Based on this formula, using the methodology of element sets and their corresponding value sets, conditions are established under which the approximants of continued fractions are stable to perturbations of their elements. Stability sets are constructed, which are sets of admissible values for the fraction’s elements that guarantee bounded errors in the approximants. For each of these sets, an estimate of the relative error that arises from the perturbation of the continued fraction’s elements is obtained. The results are illustrated with an example of a continued fraction that is an expansion of the ratio of Bessel functions of the first kind. A numerical experiment is conducted, comparing two methods for calculating the approximants of a continued fraction: the backward and forward algorithms. The computational stability of the backward algorithm is demonstrated, which corresponds to the theoretical research results. The errors in calculating approximants with this algorithm are close to the unit round-off, regardless of the order of approximation, which demonstrates the advantages of continued fractions in high-precision computation tasks. Another example is a comparative analysis of the accuracy and stability to perturbations of second-order polynomial model and so-called second-order continued fraction model in the problem of wood drying modeling. Experimental studies have shown that the continued fraction model shows better results both in terms of approximation accuracy and stability to perturbations, which makes it more suitable for modeling processes with pronounced asymptotic behavior. Full article