Search Results (1,076)

This work presents the first systematic development of the inverse scattering transform for matrix nonlinear Schrödinger equations in the case where the discrete spectrum has triple poles, under nonzero boundary conditions at infinity. These systems arise physically as reductions modeling spinor Bose-Einstein condensates with hyperfine spin $F=1$ and find applications in nonlinear optics. A uniformization variable is employed to map the underlying Riemann surface to the complex plane, enabling a complete characterization of the analyticity, symmetries, and asymptotic behaviors of the Jost functions and scattering data. Extending the established framework for simple and double poles, we show that $\mathrm{rank}$$P(x,t,z_n)=3$ requires a third-order zero of $deta\left(z\right)$ at $z=z_n$, while $\mathrm{rank}$$P(x,t,z_n)=2$ necessitates a fourth-order zero—a nontrivial feature absent in lower-order cases. The discrete spectrum for both rank configurations is fully characterized, and the full singular behavior near a triple pole is derived, respecting the quartet symmetry $z_n$, $z_n^{*}$, $v{k}_0^2/z_n$, $v{k}_0^2/z_n^{*}$ imposed by the nonzero boundary conditions. Solving the resulting matrix Riemann-Hilbert problem with triple poles yields the potential reconstruction formula and, in the reflectionless case, explicit expressions for general N-triple-pole soliton solutions, with a detailed example for $N=1$ presented to illustrate the construction. Full article

Object detection in unmanned aerial vehicle (UAV) imagery remains a crucial yet challenging task due to complex backgrounds, large scale variations, and the prevalence of small objects. Visible-spectrum images lack robustness under all-weather and all-illumination conditions; by contrast, multispectral sensing provides complementary cues (e.g., thermal signatures) that improve detection robustness. However, existing multispectral solutions often incur high computational costs and are therefore difficult to deploy on resource-constrained UAV platforms. To address these issues, SG-YOLO is proposed, a lightweight and efficient multispectral object detection framework that aims to balance accuracy and efficiency. First, a Spectral Gated Downsampling Stem (SGDS) is designed, in which grouped convolutions and a gating mechanism are employed at the early stage of the network to extract band-specific features, thereby maximizing spectral complementarity while minimizing redundancy. Second, a Spectral–Spatial Iterative Attention Fusion (SSIAF) module is introduced, in which spectral-wise (channel) attention and spatial-wise attention are iteratively coupled and cascaded in a multi-scale manner to jointly model cross-band dependencies and spatial saliency, thereby aggregating high-level semantic information while suppressing redundant spectral responses. Finally, a Spatial–Channel Synergistic Fusion (SCSF) module is designed to enhance multi-scale and cross-channel feature integration in the neck. Experiments on the MODA dataset show that SG-YOLOs achieves 72.4% mAP₅₀, outperforming the baseline by 3.2%. Moreover, compared with a range of mainstream one-stage detectors and multispectral detection methods, SG-YOLO delivers the best overall performance, providing an effective solution for UAV object detection while maintaining a favorable trade-off between model size and detection accuracy. Full article

Incipient fault signatures in rolling bearings are often compromised by intense background noise and stochastic impulses. Conventional resonance demodulation frequently relies on rigid frequency partitioning, which tends to disrupt the physical continuity of resonance bands and results in the incomplete capture of essential diagnostic information. Furthermore, the robustness of prevailing optimal demodulation frequency band (ODFB) selection indicators remains limited under heavy noise interference. This study develops the WLERgram framework, which utilizes regularized Fourier series to capture the global morphology of the vibration spectrum. By anchoring filter boundaries at natural energy troughs, the method mitigates spectral truncation based on inherent signal characteristics. The framework integrates an Adaptive Morphological Consensus (AMC) strategy, employing multi-scale operators to extract rotation-correlated components and enhance resistance to incoherent interference. By incorporating a Weighted Logarithmic Energy Ratio (WLER) metric, the method utilizes a nonlinear operator to implement differential mapping between coherent fault harmonics and stochastic noise, enabling autonomous optimization of the demodulation band. Validations using synthetic simulations and experimental benchmarks (CWRU and UORED) suggest that WLERgram offers reliable feature extraction performance and diagnostic robustness under harsh noise environments. Full article

In this paper, we investigate the (1 + 1)-dimensional nonlinear truncated M-fractional FitzHugh–Nagumo model. The main objective is to analyze the dynamical behavior and obtain exact solutions for the model. First, a fractional transformation is applied to convert the governing partial differential equation into an ordinary differential equation. Subsequently, a Galilean transformation is employed to reduce the resulting equation to a dynamical system. The bifurcation structure and chaotic dynamics of the model are then examined. The presence of chaos is further confirmed through the phase portrait, basin of attraction, return map, Lyapunov exponent, permutation entropy, Poincaré map, power spectrum, attractor, fractal dimension, multistability, time analysis, and recurrence plot. In addition, the sensitivity of the system to the initial conditions is analyzed. Finally, exact solutions for the model are constructed using the unified Riccati equation expansion method. The obtained results are illustrated using two-dimensional, three-dimensional, and contour plots. Full article

The remarkable evolution of oncological therapies has dramatically improved cancer survival rates but has simultaneously introduced a significant burden of cardiovascular complications. Cardio-oncology has emerged as a critical multidisciplinary field focused on mitigating the “collateral damage” of life-saving anticancer treatments, ranging from traditional chemotherapeutics to novel immunotherapies. This review provides a comprehensive analysis of the pathophysiological mechanisms, clinical phenotypes, and evolving management strategies for cancer therapy-related cardiac dysfunction (CTRCD). An extensive synthesis of the current literature was conducted, focusing on the molecular pathways of cardiotoxicity, including Topoisomerase IIβ inhibition by anthracyclines, HER2 signaling disruption by targeted agents, and immune-mediated myocarditis triggered by checkpoint inhibitors (ICIs). Cardiotoxicity is increasingly recognized as a spectrum of phenotypes. Heart failure with reduced ejection fraction (HFrEF) remains a primary concern with cytotoxic agents, while heart failure with preserved ejection fraction (HFpEF) is emerging as a critical complication of radiation therapy and tyrosine kinase inhibitors (TKIs). The integration of advanced diagnostic tools—specifically Global Longitudinal Strain (GLS) and Cardiac Magnetic Resonance (CMR) mapping—has shifted the clinical focus toward subclinical detection. Furthermore, pivotal clinical trials such as PRADA and SUCCOUR have validated early pharmacological prophylaxis and strain-guided interventions. Emerging challenges, including the management of CAR-T cell-induced cytokine release syndrome and the specific cardiovascular needs of pediatric and geriatric populations, are also explored. The future of cardio-oncology lies in precision medicine, leveraging genomic profiling and artificial intelligence to identify high-risk individuals. A proactive, multidisciplinary approach is essential to ensure that the success of modern oncology is not compromised by irreversible cardiovascular morbidity. Full article

Background/Objectives: Comparative sociogenomics combines multiple scientific fields to investigate the genetic basis of social behavior across species. Our aim was to uncover the genetic roots of human sociability with possible implications for autism, a neurodevelopmental disorder characterized by social and communication deficits. Methods: We conducted molecular network analysis on 659 sociability-related genes from different animal species, including humans. Results: We identified a network of 240 genes strongly associated with autism (p < 10⁻¹⁵), with 194 inferred. These genes were grouped into 23 functional communities related to cell–cell junctions and communication, inflammatory and synaptic signaling, neurotransmitter receptors and semaphorin signaling among the more enriched meta-pathways. Some network genes were clustered in nine chromosomal bands (FDR < 0.25), indicating genes’ functional cooperation, shared evolutionary history, and coordinated regulation, and few genes are physically in linkage with ASD genes (within 0.5 cM) or controlled by human-accelerated regions. Conclusions: The most compelling inferred autism risk genes are MED12, FZD9, and DMD since they are differentially expressed in autistic brains, physically linked to key autism genes, controlled by human-accelerated regions, or mapped to chromosomal regions enriched in network genes. If validated, they could represent novel biomarkers, advancing the understanding of autism’s genetic makeup. Full article

To address the challenge of detecting weak targets with small radar cross-sections (RCS), this work explores an integrated framework that leverages cross-level multimodal fusion of radar echoes. This method considers the target’s motion properties via Doppler spectrum and phase sequences (direct physical level), and introduces the Gramian Angular Field (GAF) to map the echo amplitude sequence into two-dimensional (2D) structured images, thereby revealing the dynamic evolution characteristics of echo energy (abstract representation level). This approach integrates direct physical attributes and abstract system evolution features within a unified representation. To accommodate the structural differences among modalities, a heterogeneous branch processing network is designed: the Transformer is employed to capture long-range dependencies in one-dimensional (1D) sequences, while ResNet18 is used to extract spatial texture features from two-dimensional images. A self-attention mechanism is further introduced to achieve adaptive fusion of the multimodal data. Experimental results based on the IPIX dataset suggest that this cross-level strategy provides improved detection performance across various scenarios, as observed in complex marine environments. Full article

This study presents a systematic comparative benchmark between two distinct paradigms for solving nonlinear partial differential equations (PDEs): the data-driven Physics-Informed Neural Networks (PINNs) and the analytical Homotopy Analysis Method (HAM). We apply both methods to a unified family of canonical PDEs, the Burgers, Huxley, Fisher, Burgers–Huxley, and Burgers–Fisher equations, under identical problem setups, domain discretization, and validation metrics. PINNs incorporate physical laws directly into neural network training by minimizing a loss function that enforces PDE residuals, yielding physically consistent solutions even for strongly nonlinear problems. HAM provides approximate analytical solutions using a unified framework, and the same initial guess, auxiliary linear operator, and auxiliary function across all equations despite their distinct nonlinearities. The controlled, consistent application of both methods enables a fair, reproducible comparison across this equation family. The results provide a quantitative performance map under identical conditions, delineating when PINNs (high accuracy, long-term stability, and generalization capability) are preferable, versus when HAM (computational speed, short-term analytic approximation, and lower memory footprint) offers advantages. While the finite radius of convergence of the truncated HAM series is theoretically expected, our controlled comparison quantifies for the first time how this degradation varies across equation types, revealing that the choice between methods depends on specific problem requirements including error tolerance, available computational resources, and temporal horizon. The novelty lies not in solving each equation individually, but in deriving a performance taxonomy that systematically connects equation features (shocks, stiffness, and reaction–diffusion coupling) to optimal solver choice—providing previously unavailable, evidence-based guidance for the scientific computing community. This study establishes the first rigorous, controlled comparative benchmark between analytic and data-driven PDE solvers across a spectrum of nonlinearities, providing a reproducible baseline for future hybrid scientific machine learning solvers. Full article

Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) hyperspectral imaging (HSI; 449.54–2399.17 nm; 563 bands) was used to classify 32 grain–legume accessions (n = 3200 seeds; 100 seeds per accession), comprising 30 common bean (Phaseolus vulgaris L.) landraces plus two outgroup legumes (Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession (n = 2240) reserved for fully independent training and 30 seeds per accession (n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R² of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54–456.30 nm and 577.02–597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification. Full article

This paper presents a taxonomy-based survey of AI-driven network optimization mechanisms relevant to the transition from fifth generation (5G) to sixth generation (6G) mobile communication systems. In contrast to earlier generational shifts that are often described as technology replacement cycles, the 5G-to-6G evolution is increasingly characterized in the literature as a prolonged period of coexistence, hybrid operation, and progressive integration of new capabilities across radio, edge, core, and service layers. To structure this transition, the paper organizes prior work into a transition-oriented taxonomy covering migration strategies, AI-enabled closed-loop control, RAN disaggregation and edge intelligence, core virtualization and slice orchestration, spectrum-aware coexistence, service-driven requirements, and security-aware governance. Rather than introducing a new optimization algorithm or an experimentally validated architecture, the contribution of this survey is analytical and integrative. Specifically, it consolidates fragmented research directions into a reference view of how AI-driven control mechanisms are distributed across spectrum, RAN, edge, and core domains during hybrid 5G–6G operation. In addition, the paper includes a structured evidence synthesis of performance trends, deployment maturity signals, and recurring methodological limitations reported across the literature. The review indicates that meeting anticipated 6G objectives, including ultra-low latency, high reliability, scalability, and improved energy efficiency, depends less on isolated enhancements at individual protocol layers and more on coordinated cross-layer optimization supported by AI-native control loops. At the same time, the surveyed literature reveals persistent gaps in service-to-control mapping, security-aware orchestration, interoperability across heterogeneous domains, and reproducible evaluation methodologies for hybrid 5G–6G environments. The survey is intended to provide researchers, network operators, and standardization stakeholders with a structured analytical basis for assessing how AI-driven optimization can support the staged evolution from 5G systems toward 6G-ready infrastructures. Full article

Lyapunov-exponent-based diagnostics are widely used to quantify deterministic chaos in optically injected semiconductor lasers (OISLs). In most numerical implementations, the optical field is represented either in phasor coordinates $(A,\psi ,N)$ or in Cartesian quadrature coordinates $(X,Y,N)$. Although these representations are mathematically related through a smooth coordinate transformation away from vanishing field amplitude, their numerical realizations can exhibit markedly different robustness in variational calculations, directly impacting the reliability of Lyapunov exponent estimation and chaoticity maps. In this work, we present a systematic assessment of the numerical reliability of Lyapunov-based chaos analysis in master-slave optically injected semiconductor lasers using both phasor and quadrature formulations. The full Lyapunov spectrum was computed via a noise-free variational method that integrates the nonlinear dynamics together with the corresponding Jacobian equations using a fourth-order Runge-Kutta scheme combined with periodic QR orthonormalization. High-resolution Lyapunov maps were constructed in the injection strength-frequency detuning parameter space, and the consistency between both formulations was quantitatively evaluated. While both approaches reproduce the overall structure of chaotic and non-chaotic regions, the phasor formulation may generate spurious positive Lyapunov exponents in regimes where the optical field amplitude approaches low values. These discrepancies originate from singular terms proportional to $1/A$ and $1/A^2$ in the variational Jacobian of the phasor model, which can lead to numerical amplification and artificial chaotic signatures. The quadrature formulation avoids these singularities and provides numerically stable and physically consistent Lyapunov spectra across the explored parameter space. The results establish practical guidelines for robust chaos quantification in optically injected semiconductor lasers and highlight the importance of representation choice in variational Lyapunov analysis of nonlinear photonic systems. Full article

This study presents a comprehensive investigation of exact fractional wave solutions and bifurcation analysis for the (3 + 1)-dimensional extended shallow water wave (3D-eSWW) equation with $\beta$-derivative, which models nonlinear wave phenomena in fluid dynamics and coastal engineering. Leveraging the flexibility of the fractional derivative, the model provides a more generalized and adaptable framework for describing shallow water wave propagation. The Modified Extended Direct Algebraic Method (MEDAM) is systematically employed to derive a broad spectrum of novel exact analytical solutions. These include the following: dark solitary waves, singular solitons, singular periodic waves, periodic solutions expressed via trigonometric and Jacobi elliptic functions, polynomial solutions, hyperbolic wave patterns, combined dark–singular structures, combined hyperbolic–linear waves, and exponential-type wave profiles. Each solution family is presented with explicit parameter constraints that ensure both mathematical consistency and physical relevance, thereby offering a robust classification of wave regimes under diverse conditions. A thorough bifurcation analysis is conducted on the reduced dynamical system to examine parametric dependence and stability transitions. Critical bifurcation thresholds are identified, and distinct solution branches are mapped in the parameter space spanned by wave numbers, nonlinear coefficients, external forcing, and the fractional order $\beta$. The analysis reveals how solution dynamics undergo qualitative transitions—such as the emergence of solitary waves from periodic patterns or the appearance of singular structures—driven by the interplay of nonlinearity, dispersion, and fractional-order effects. These insights are crucial for understanding wave stability, predictability, and the onset of extreme events in shallow water contexts. Graphical representations of selected solutions validate the analytical results and illustrate the influence of $\beta$ on wave morphology, propagation, and stability. The simulations demonstrate that varying the fractional order can significantly alter wave profiles, highlighting the role of fractional calculus in capturing complex real-world behaviors. This work demonstrates the efficacy of the MEDAM technique in handling high-dimensional fractional nonlinear PDEs and provides a systematic framework for predicting and classifying wave regimes in real-world shallow water environments. The findings not only enrich the solution inventory of the 3D-eSWW equation but also advance the analytical toolkit for studying complex spatio-temporal dynamics in fractional mathematical physics and fluid mechanics. Ultimately, this research contributes to the development of more accurate models for coastal protection, tsunami forecasting, and marine engineering applications. Full article

Background/Objectives: Plasmids are key vehicles for the dissemination of antimicrobial resistance (AMR), yet their contribution to the global resistome architecture of Escherichia coli remains poorly resolved. This study aimed to quantify how plasmid backbones shape the distribution, mobility, and stabilization of resistance genes across diverse phylogenetic backgrounds. Methods: We analyze 9700 high-quality genomes spanning major phylogroups and sequence types. Plasmidome reconstruction was integrated with lineage-resolved antimicrobial resistance gene (ARG) mapping to characterize plasmid–ARG associations and evolutionary patterns. Results: Although most antimicrobial resistance genes (ARGs) are chromosomal, plasmids disproportionately encode clinically important determinants including bla_NDM-5, mcr-1.1, and multiple bla_CTX-M alleles that show strong, recurrent associations with a restricted set of backbone families, most notably IncX3, IncX4, IncI, and IncF. These conserved plasmid–gene modules recur across phylogenetic backgrounds and continental scales. We identify a marked divergence in evolutionary strategies: generalist phylogroups (A, B1, D) maintain plasmid-rich and highly diverse resistomes, whereas globally dominant Extraintestinal Pathogenic E. coli (ExPEC) clones such as ST131 and ST410 exhibit reduced plasmid dependency and frequent chromosomal integration of extended-spectrum β-lactamase (ESBL) genes, particularly bla_CTX-M-15, consistent with a shift toward vertically stabilized resistomes. By integrating plasmidome reconstruction with lineage-resolved ARG mapping, this study delivers the most extensive plasmid-focused resistome analysis to date, revealing highly modular plasmid–ARG networks structured around a small number of high-risk backbone types. These backbones account for the majority of globally relevant ARGs, including 64.6% of bla_NDM-5 and 76.4% of mcr-1.1 detections. Conclusions: Together, our findings establish plasmid lineages rather than individual genes or clones as central units of AMR dissemination and critical targets for future genomic surveillance and intervention strategies. Full article

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense RNA virus, and its genome includes a highly conserved 5′ untranslated region (5′UTR). This region contains the so-called ‘leader sequence’, a crucial genomic region responsible for the viral replication and the synthesis of all subgenomic RNAs (sgRNAs). It has been demonstrated that targeting highly conserved genomic regions is essential for developing broad-spectrum antiviral therapies that resist viral mutation and evasion. Hypothesis: Given the high level of nucleotide homology between SARS-CoV and SARS-CoV-2, particularly in essential regions like the 5′UTR, the identification of a perfect sequence alignment across SARS-CoV-2 variants within this conserved region would provide a robust, mutation-resistant target for novel RNA-based drugs, such as small interfering RNAs (siRNAs) or microRNAs (miRNAs). Materials and Methods: Sequence alignment was performed across the different SARS-CoV-2 strains (i.e., the different variants that have appeared so far) to identify conserved genomic areas, leading to the selection of potential target sites for antiviral molecules. Specifically, computational analyses were utilized to map available binding sites for human miRNAs within the SARS-CoV-2 5′UTR. Results: Comparative alignments revealed that the leader sequence/5′UTR region is highly stable and conserved in all the considered SARS-CoV-2 sequences, representing a common therapeutic target across different variants and strains. Discussion: The perfect alignment observed in the 5′UTR confirms that this region is a highly critical target, less prone to mutations in all the considered variants. This property makes the region ideal for therapeutic intervention using non-coding RNAs. If endogenous miRNAs were found to bind this region (e.g., miR-638, miR-3150b-3p, etc.) and promote viral replication similarly to mechanisms observed in viruses like hepatitis C virus (HCV), their activity could be inhibited using chemically modified antisense analogs, such as locked nucleic acid (LNA) oligonucleotides. Full article

By 2050, the global population is expected to reach approximately 10 billion, leading to a projected 50% increase in food demand relative to 2013 levels. If not adequately anticipated, this growing demand will place significant strain on agri-food systems worldwide, with disproportionate impacts on low- and middle-income countries. Moreover, current projections may underestimate the accelerating effects of climate change, political instability, and civil unrest, which continue to disrupt food production and distribution systems. In this context, technological advancements offer a promising pathway to enhance efficiency, improve transparency, and mitigate risks related to food safety, adulteration, and counterfeiting. Emerging innovations can decouple food production from environmental degradation while strengthening monitoring, verification, and accountability across supply chains. This review examines state-of-the-art technologies developed to support traceability and anti-counterfeiting in agri-food supply chains, considering their application across the full spectrum of stakeholders. To provide a system-level perspective, the review adopts a five-layer socio-technical traceability and anti-counterfeiting framework, comprising identity, sensing, intelligence, integrity, and interaction layers, which is used to map enabling technologies and reinterpret the evolution of traceability systems (TS 1.0–TS 4.0) as a progression of functional capabilities rather than isolated technological upgrades. Using this framework, the review analyzes the advantages and limitations of current solutions and clarifies how traceability and anti-counterfeiting functions emerge through technology integration. It further identifies gaps that hinder large-scale and equitable adoption. Finally, future research directions are outlined to address current technical, economic, and governance challenges and to guide the development of more resilient, trustworthy, and sustainable agri-food traceability systems. Full article