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Study of surface texture wavelength slope spectra density distribution of micro-surfacing pavement related to vehicle interior noise

QSPR analysis of anticancer drugs using the Euler–Sombor index and theoretical insights on its minimum value for unicyclic graphs

Abstract Topological indices (TIs) are powerful tools for exploring the relationship between molecular structure and drug activity, offering a cost-effective alternative to experimental screening. The Euler–Sombor index, a recently introduced degree-based TI, captures key structural features of molecules that influence their physico-chemical behaviour. This study investigates the use of the Euler–Sombor index in quantitative structure–property relationship (QSPR) modeling of anticancer drugs. By applying linear, quadratic, and logarithmic regression models, we predict important properties such as boiling point (BP), melting point (MP), enthalpy (E), and molar refraction (MR). The results show a significant correlation between the Euler–Sombor index and these molecular properties. In addition, we extend the result of Su and Tang by characterizing unicyclic graphs that have the third minimum Euler–Sombor index.

Diagnostic and immunological roles of leptin gene rs7799039 polymorphism and cytokines in COVID-19, HCV, and dual infection

Abstract The identification of reliable diagnostic biomarkers is essential for improving the early detection, prognosis, and management of viral infections. Interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and Interferon-gamma (IFN-γ) are key cytokines involved in immune regulation and inflammatory responses, playing a pivotal role in the pathogenesis of hepatitis C virus (HCV), coronavirus disease 2019 (COVID-19), and co-infection with both viruses. This study aims to investigate the role of circulating leptin gene polymorphism (rs7799039, G > A), together with IL-6, TNF-α, and IFN-γ biomarkers, in the context of HCV, COVID-19, and dual HCV–COVID-19 infection. While previous research has examined rs7799039 in COVID-19, its contribution in co-infection remains unclear. By integrating cytokine profiling with genotypic analysis, this work provides additional insights into disease diagnosis, risk stratification, and the underlying immunopathological mechanisms of these infections. Biomarker levels were measured in plasma using enzyme-linked immunosorbent assay (ELISA). Nasopharyngeal swabs were collected for real-time polymerase chain reaction (RT-PCR) detection of COVID-19 ribonucleic acid (RNA), and blood samples were used for quantitative RT-PCR detection of HCV-RNA. Single nucleotide polymorphisms (SNP) genotyping assay for Leptin gene (rs7799039) was examined from genomic deoxyribonucleic acid (DNA) using RT-PCR. No cytokine correlations were found in HCV alone, but TNF-α and IFN-γ correlated in dual infections. IL-6, TNF-α, and IFN-γ levels were significantly elevated in all patient groups compared to controls, with mean ± SD values of IL-6: 199.2 ± 28 pg/mL (co-infected), 171 ± 31.9 pg/mL (COVID-19), and 46.8 ± 9 pg/mL (HCV); TNF-α: 64.6 ± 15.4 pg/mL, 33.5 ± 7.1 pg/mL, and 32.9 ± 6.2 pg/mL; IFN-γ: 166.2 ± 41.9 pg/mL, 116.8 ± 18.6 pg/mL, and 101 ± 14.4 pg/mL, respectively. TNF-α and IFN-γ were significant predictors of disease status across groups (OR = 1.107–1.482, P  < 0.001). IL-6 was predictive only in HCV patients (OR = 1.508, P  < 0.001). TNF-α performed well (> 85%) in all groups. IFN-γ performed well in co-infection and COVID-19 (> 83%), but was moderately accurate in HCV (about 60%). IL-6 showed perfect accuracy in co-infection and COVID-19. Genotype analysis of the leptin gene polymorphism (rs7799039, G > A) indicated that the GA genotype was the most common across all study groups. The AA genotype of the leptin gene (rs7799039, G > A) was more frequent in individuals with COVID-19, suggesting a potential association with increased disease susceptibility. Conversely, the GG genotype was more commonly observed in the control group, which may indicate a protective role against infection. Statistical analysis revealed a significant relationship between genotype and disease status (χ² = 13.52, P  = 0.035 < 0.05). This study highlights the distinct roles of IL-6, TNF-α, and IFN-γ in HCV, COVID-19, and co-infections, with more pronounced cytokine interactions in dual infections. Furthermore, the AA genotype may be associated with increased susceptibility to COVID-19, while the GG genotype was more frequent among controls, suggesting a possible protective trend that warrants further investigation. This study suggests differential roles of IL-6, TNF-α, and IFN-γ across HCV, COVID-19, and co-infections, with indications of more pronounced cytokine interactions in dual infections. Furthermore, the GA genotype appeared more common among patients, while the GG genotype was more frequent in controls, suggesting a possible protective trend. The AA genotype showed a slight increase in COVID-19 cases, but this association was not statistically significant and should be interpreted with caution.

Enhanced projectile path estimation using multi-vehicle FMCW radar sensors

Abstract This paper presents an enhanced approach to projectile path estimation using Frequency-Modulated Continuous Wave (FMCW) radar sensors distributed across multiple vehicles in a tactical formation. Building upon established FMCW radar signal processing techniques, we implement and analyze a multi-sensor approach that significantly improves the accuracy of key path parameters: pass range, pass time, and velocity. Through detailed simulation, we demonstrate that a four-vehicle formation achieves approximately 75% error reduction across all parameters compared to single-vehicle sensing. This improvement directly enhances the performance of active protection systems for military vehicles by enabling more precise threat assessment and countermeasure deployment. Our results validate theoretical predictions that triangulation from multiple sensing positions provides more robust parameter estimation, particularly for projectiles with linear trajectories. The methods described can be implemented with existing FMCW radar technology and standard data fusion algorithms, making this approach practical for near-term deployment.

Daily briefing: Scientists delve into the smells of history

Spatial and temporal variation of benthic ecological quality evaluation in the Bohai Bay (China) using benthic indices

How learning handwriting trains the brain: the science behind the cursive wars

Prognostic value of nine inflammatory biomarkers for 30-day mortality in critically ill elderly patients with osteoporotic hip fracture

Spatiotemporal heterogeneity-aware meta-parameter interaction learning for traffic flow forecasting

Is the journal legitimate? Aletheia-Probe can help you decide

PARP-1 couples β-catenin/TCF4 signaling to epithelial–mesenchymal transition in endometriosis

See the Sun expand and contract like a pufferfish — January’s best science images

Computational analysis of X2CaZnH6 (X = K, Rb and Cs) hydrides for hydrogen storage

OpenAI-backed firm to use ultrasound to read minds. Does the science stand up?

Dynamic simulation of gas-lock instability in an electrical submersible pump induced by annulus valve closure

Ablation of cancer cell secreted neuropeptide PTHLH/PTHrP provokes anti-tumor immunity in murine tongue squamous cell carcinoma

Hybrid Grey Wolf Optimizer with discrete prism dispersion strategy for solving flexible job-shop scheduling problem

Abstract The Flexible job-shop scheduling problem (FJSP) is a quintessential NP-hard problem in the field of production scheduling. With the development of intelligent manufacturing industry, minimizing the total completion time in workshops has become a crucial research focus. Swarm intelligence algorithms have been widely used to solve the FJSP. However, they still suffer from issues such as premature convergence and a tendency of trapping in local optimum. In addition, as iterations increase, the basic parameters of the algorithm still need to be flexibly adjusted. To address these challenges, we propose a hybrid grey wolf optimization algorithm incorporating a discrete prism dispersion strategy (HGWO-DPDS). Inspired by the optical dispersion of light through a prism, this strategy simulates a multi-directional refraction process to diversify the population and improve global exploration. First, in the position update stage, a critical-path-guided mechanism is introduced in the operation sequencing stage to identify and perturb bottleneck operations, while in the machine selection stage, machine-guided convergence enhances the search toward the current best solution. Secondly, the prism-inspired dispersion strategy expands the search directions through multiple reference centers. Finally, an adaptive mutation operator is applied to maintain population diversity and avoid stagnation. We conduct a comprehensive evaluation of the proposed model through benchmark experiments on three widely used datasets—MK, Kacem, and Lawrence instances. HGWO-DPDS is compared with several existing algorithms. The experimental results demonstrate that the proposed framework achieves near-optimal makespan values on most instances, while maintaining stable and reliable performance in solving the FJSP, particularly excelling at escaping local optima compared to existing methods.

Exploring the mechanism of Platycladi Cacumen in intervening androgenetic alopecia based on network pharmacology, molecular docking, and molecular dynamics simulation

Enhancing aerodynamic performance using biomimetic wavy trailing edges on aircraft wing at low Reynolds number

Abstract This study investigates the aerodynamic effects of biomimetic wavy trailing edges inspired by natural designs, focusing on their application to a swept-back NACA 0012 airfoil under free-flight conditions. Numerical simulations were conducted using a three-dimensional numerical model with k−ω SST turbulence modeling at a Reynolds number of 3 × 10 4 . The study was performed under steady-state conditions. Reynolds-Averaged Navier–Stokes (RANS) were used in the simulation. The baseline wing was modified with sinusoidal trailing edges, varying wave amplitude and wavelength in both chordwise and spanwise directions to assess their impact on lift, drag, and stall characteristics. Experimental validation was conducted in a low subsonic speed wind tunnel using 3D-printed scaled models, with comprehensive data collection on lift and drag coefficients, pressure distribution, and flow visualization. The results indicate that the wavy trailing-edge configuration maintains comparable lift to the clean wing at low angles of attack (AOA < 8°), while providing substantial aerodynamic enhancement beyond 8°, with improved lift generation and delayed stall. This demonstrates its potential for improving post-stall stability and aerodynamic efficiency in low-Reynolds-number flight regimes. Results demonstrate that a moderate wave amplitude of 20% tip chord length at 8° angle of attack enhances lift by 11.8%. The optimum parametric wavy wing delays the stall by approximate 6° associated with an increase in C Lmax by 31% compared to a conventional straight edge. The findings highlight the potential of wavy trailing edges for improving aerodynamic efficiency and stability, particularly for small aircraft and UAVs.

Characteristics of strontium adsorption onto aquatic sediments in Southwest China