This study presents a mathematical model describing the interaction between human immunodeficiency virus (HIV) and CD4+ T cells within a predator–prey framework, where HIV acts as the predator and CD4+ T cells serve as the prey. Unlike previous simplified models that treat infected cell death as a constant process, this work introduces an explicit density-dependent cytotoxic immune response mechanism that eliminates infected CD4+ T cells in proportion to both the infected cell and viral populations. The model comprises three ordinary differential equations representing uninfected CD4+ T cells, infected CD4+ T cells, and free virus particles. Mathematical analysis reveals two equilibrium points: a virus-free equilibrium and an endemic equilibrium where all three populations coexist. Stability analysis using linearization and Routh-Hurwitz criteria demonstrates that the virus-free equilibrium is locally asymptotically stable when the basic reproduction number, R0 falls below unity, while the endemic equilibrium achieves stability under sufficient conditions involving the immune response strength. Numerical simulations employing parameter values informed by recent immunological studies confirm that robust cytotoxic immune responses promote stable chronic infection with reduced viral burden. Global stability analysis via Lyapunov functions and bifurcation analysis further validate these findings. The results highlight the critical role of adaptive immunity in determining HIV persistence and provide a theoretical foundation for understanding immune control mechanisms in untreated HIV infection.
During vehicle operation, noise signals present challenges such as a broad frequency range, strong non-stationarity, and the limited effectiveness of traditional passive noise reduction methods in the low-frequency band. To address these issues, this study proposes an active noise control model that integrates the least mean square algorithm with support vector machines. The noise control architecture incorporates a least mean squares branch for real-time suppression of linear noise components, while a support vector machine branch models and compensates for nonlinear residuals and frequency drift. Outputs from both branches are coupled via a hybrid coefficient, forming a closed-loop control mechanism integrated with secondary channel modeling. Experimental results demonstrated that the proposed model achieved significantly faster convergence speed and reduced steady-state error compared to the standard FxLMS system across both datasets, with in-band noise reduction increasing by approximately 2.0–2.5 dB. Furthermore, it achieved faster stabilization control while maintaining an average computational delay of around 3.3 ms. Under frequency drift conditions, the method exhibited markedly shorter error recovery times during step and ramp transitions than the benchmark algorithm, indicating superior rapid tracking capability and control stability in complex non-stationary environments. In summary, this research not only effectively enhances noise reduction performance and nonlinear adaptability under complex automotive conditions but also provides an efficient and feasible technical approach for low-frequency noise mitigation in intelligent driving environments.
Abstract This study explores entropy generation in the magnetohydrodynamic flow of a tetra hybrid nanofluid. ( $${\text{Al}}_{2} {\text{O}}_{3} - {\text{Cu}} - {\text{SiO}}_{2} - {\text{TiO}}_{2} /{\text{H}}_{2} {\text{O}}$$ ) over a permeable surface embedded in a Darcy–Forchheimer porous medium, motivated by the need to enhance thermal system efficiency and minimise energy losses. The model incorporates key physical effects, including multiple dissipation mechanisms (Ohmic, viscous, Darcy, and Forchheimer), thermal radiation, internal heat generation, activation energy, and chemical reaction. The transformed nonlinear equations are solved numerically using the bvp4c technique. Results reveal that thermal radiation and heat source significantly elevate temperature and entropy generation, indicating increased irreversibility, while magnetic field and porous resistance strongly suppress fluid motion. Activation energy enhances species concentration, whereas a higher Schmidt number reduces mass diffusion. The Bejan number decreases under stronger magnetic and porous effects but increases with radiation and inertial resistance. The combined influence of these parameters provides deeper insight into controlling heat and mass transfer in advanced nanofluid systems. The findings are particularly relevant for applications in energy systems, cooling technologies, and thermal management, where minimising entropy generation is crucial for improving performance and efficiency.
Additive manufacturing (AM) technology has evolved considerably and its major impact on the substitution of AM products over conventional parts. Fused deposition modeling (FDM) process falls under the classifications AM technique and predominantly produces thermoplastic-based products. The existing studies have explored the performance of Carbon fiber reinforced Polylactide, the understanding of correlation between the structure and property needs to be much focused which motivates to perform this study. In the present work, Carbon fiber reinforced Polylactide was used as a filler to fabricate the tensile specimen by optimize the printing conditions includes layer thickness, infill orientation, infill density, and printing strategy. From the results it has been observed that the sample printed with 0.3 mm layer height, 45° infill orientation, 30
To address the problems of perceptual redundancy estimation in digital image watermarking, which are poor adaptability to fixed-size inputs and insufficient coordination between local and global visual features, a perceptual redundancy estimation based on perceptually sensitive size optimization is proposed. First, BI and block aggregation are combined to achieve size adaptation of visual saliency maps. A multi-scale saliency map fusion mechanism is then used to combine local details with global structural information. An adaptive weighting strategy based on edge complexity is introduced to dynamically modulate feature contributions. Finally, the optimized model is integrated into an extended transform dithering modulation watermarking framework to achieve perceptually adaptive embedding. The proposed method is validated on the USC-SIPI and BSDS300 datasets. Experimental results show that the proposed method improves structural similarity and peak signal-to-noise ratio by 5.38
To reduce the interaction noise of the cylinder-airfoil configuration, longitudinal grooves are applied to the upstream cylinder. In this work, the noise and flow characteristics are numerically investigated, with a focus on three key factors: groove profile, groove size ratio, and groove number. It is found that when the groove profile is circular, the groove size ratio is 0.01, and the groove number is 60, the far-field noise is reduced by approximately 14 dB—the maximum reduction observed. Shallow grooves exhibit a better noise reduction effect than deep ones. The investigation of transient flow fields shows that shedding vortices have a significant impact on far-field noise levels. The groove structure, especially the circular groove profile, can break down large-scale vortices into smaller ones, which contributes to noise reduction. This investigation provides new insights and methods for noise control in wind turbines and landing gear systems.
Segmentation of medical images is a fundamental step towards quantification and clinical decision support. Many approaches have been proposed and successfully demonstrated on benchmark datasets. However, reliable application of current approaches in real clinical practice remains challenging. Medical image segmentation is not a single task but rather a group of different tasks with different input and output characteristics, which call for dedicated solutions. Task difficulty and failure modes vary with anatomical priors, boundary ambiguity, and clinically meaningful error types. Therefore, rather than providing only a method summary, this review presents a task overview of medical image segmentation research from 2015 to 2026. We organize the literature according to clinical task properties and examine how different methodological choices influence benchmark results and real-world applicability. We categorize existing work into major task families, including organ segmentation, lesion and tumor segmentation, vascular segmentation, histopathology and cellular segmentation, and multi-task and cross-domain settings. For each category, we summarize representative modeling strategies and identify common challenges specific to each task. We further review widely used public datasets and evaluation metrics. In particular, we analyze how overlap, boundary, topology, and instance-level metrics capture different clinical priorities. Finally, we discuss challenges such as annotation burden, inter-institution variability, and clinical reliability. We then discuss emerging research directions, including foundation models, multi-modal and multi-task learning, and privacy-preserving federated collaboration, which may contribute to improved public health and clinical decision-making.
Conventional embankment dam safety assessments under reservoir drawdown conditions are typically based on deterministic finite element and limit equilibrium analyses. That use single representative values of hydraulic and shear strength parameters. Such approaches often neglect the inherent uncertainty and spatial variability of geomaterials leading to potential inaccuracies in predicting seepage behavior, slope stability and failure risk. Although probabilistic methods can better represent these uncertainties, their computational complexity limits their application in routine dam safety evaluations. Furthermore, the integration of physical based numerical modeling with machine learning for probabilistic seepage failure assessment remains insufficiently explored. To address these limitations, this study proposes a hybrid deterministic probabilistic framework that integrates transient seepage and stability analyses with probabilistic risk assessment and explainable machine learning. The framework was applied to the Ribb Embankment Dam in Ethiopia under steady state, rapid drawdown (RDD) and slow drawdown (SDD) conditions. Transient hydraulic and stability responses were simulated using SEEP/W and SLOPE/W based on Richards’ equation and the Morgenstern Price method. The generated hydraulic and stability indicators were then incorporated into a probabilistic failure model and used to train Random Forest (RF), XGBoost and Support Vector Machine (SVM) models. Probabilistic analysis indicated that 85.75
The use of artificial intelligence (AI) and machine learning (ML) in diagnosing and predicting lung cancer prognosis has changed the way images are interpreted, risks predicted, and decisions made for clinical purposes. Despite increased research in this area, there is little to no bibliometric analysis evaluating the global state of affairs. The purpose of this study was to examine this research area. In this study, a literature search was conducted by using the Scopus database, with emphasis on the role of AI and ML in the diagnosis and prognosis of lung cancer. Collaboration maps were analyzed using VOSviewer 1.6.20 and Biblioshiny. There was a significant growth in global scientific production with an exponential growth in number of publications during 2020–2024 period. The United States, China, and India became the major contributors to scientific production, and analysis of collaboration networks indicated the existence of effective international collaborations. Through citation analysis, the most impactful sources, authors, and institutions were found. Through keyword and thematic evolution analysis, the shift from computer-aided diagnosis and pulmonary nodule detection to radiomics, deep learning, convolutional neural networks, explainable AI, and precision medicine was identified. This paper is an exhaustive bibliometric analysis of the literature on the use of artificial intelligence and machine learning for diagnosing and prognosticating lung cancer. By identifying publication patterns, major contributors, collaboration networks, and research hot spots, this study has provided important insights into the field’s development and the future of research in this area. Bibliometric analysis mapped global AI and ML research trends in lung cancer. Scientific output increased exponentially, with rapid growth after 2021. Identified leading authors, institutions, journals, and influential studies. Highlighted deep learning and radiomics as a future direction for precision oncology.
Interdisciplinary digital media platforms receive concurrent image, speech, and text interaction tasks whose semantic urgency, computational demand, and service deadlines vary over time. This study formulates the problem as online scheduling of multimodal interaction tasks across heterogeneous GPU, CPU, and edge-computing nodes under queue-capacity, node-compatibility, and deadline constraints. A cross-modal attention module is used to generate a unified task descriptor and estimate the semantic consistency of the available modalities. The resulting semantic information is introduced into task pre-ranking and system-state construction rather than being treated as an independent recognition output. A tabular Q-learning scheduler then selects among ten fixed scheduling-rule and resource-node combinations, thereby avoiding an action space that changes with the number of pending tasks and available nodes. The experimental benchmark is constructed from public image, speech, and text datasets through an explicit intent-matching protocol, while a constraint-optimization oracle provides reference decisions for evaluating scheduling accuracy. Under the reported high-concurrency setting, the proposed method obtains an average queue-to-dispatch latency of 192 ms and an oracle-consistent scheduling accuracy of 87.2
Optical components play a pivotal role in precision industries; however, conventional polishing methods often struggle to achieve nano-level finishing. The increasing demand for nano-scale precision in optical components has led to the exploration of novel processes such as Magnetic Float Polishing (MFP). This paper addresses the research gap concerning the application of MFP for polishing plano-optical windows (POW) and reveals the complex dynamics governing MFP. This innovative method eliminates workpiece clamping forces and using passive magnetic levitation as well as control mechanisms such as hydrodynamic lift force to achieve nanoscale roughness. Parameters such as fluid flow speed, viscosity, and density of the magnetic abrasive fluid (influenced by magnetic particle volume fraction), as well as hydrodynamic forces, have been controlled during the process on the POW. Finite Element Analysis simulations illustrated that impeller rotation speed and magnetic particle volume fraction are key parameters influencing hydrodynamic forces. The vertical component of the hydrodynamic force, known as the normal force, is also affected by the distance between the workpiece and impeller, while the passive magnetic levitation force is influenced by the distance between the workpiece and the vessel bottom. It was discovered that the magnitude of the normal forces significantly affects surface roughness; to minimize surface roughness, the normal hydrodynamic forces must be optimized within a specific range. This study introduce the hydrodynamic lift forces of the abrasive fluid as a predictive mechanism to optimize the normal forces, achieving minimum surface roughness around 26 nm Ra.
This study aims to investigate the dynamic mechanical properties and energy absorption evolution of fractured red sandstone under natural conditions and acidic environments with varying pH levels. Impact loading tests were conducted on red sandstone specimens with different fracture angles (0°, 30°, 45°, 60°, and 90°) using a Split Hopkinson Pressure Bar (SHPB) system. Based on stress wave theory and the principle of energy conservation, the dynamic response characteristics and energy dissipation mechanisms of the specimens were thoroughly analyzed. The results indicate that the coupling effect of fractures and acid corrosion significantly degrades the strength characteristics of red sandstone. With an increase in the fracture angle, the dynamic compressive strength of the specimens exhibits an upward trend, while the acidic environment further exacerbates the deterioration of strength. Regarding energy evolution, an increase in the fracture angle leads to a decrease in the reflected energy ratio and an increase in the transmitted energy ratio. Furthermore, acid corrosion intensifies energy dissipation, thereby weakening the mechanical properties of the sandstone. The findings of this study can provide a scientific basis for evaluating the stability of underground engineering structures in complex environments, preventing geological hazards, and improving the fundamental theories of rock dynamics.
Accurately quantifying methane (CH4) emissions from intense point sources at fine spatial and temporal scales remains a significant challenge for current monitoring techniques. In this study, we present an integrated methodology that combines UAV-based AirCore sampling with ground-based methane differential absorption light detection and ranging system (CH4-DIAL) measurements. By leveraging the high spatial resolution, sensitivity, and rapid scanning capabilities of lidar system, together with the flexibility and vertical profiling strengths of UAV-AirCore, our approach enables precise identification and quantification of methane emissions from localized sources. A hybrid estimation framework, incorporating genetic algorithms for initial source estimation and trust-region optimization for refinement, is developed to process the joint observational data. Field validation was conducted in two industrial areas in Dongying City. The results demonstrate that the integrated approach significantly enhances the accuracy and reliability of methane emission quantification for strong point sources, compared to conventional single-platform methods. This methodology offers a robust and scalable solution for atmospheric methane monitoring in complex industrial environments.
Abstract Bread wheat is considered one of the most important crops in Egypt and worldwide and improving its productivity under water stress is a major breeding objective. This study aimed to evaluate advanced bread wheat lines under water stress and to characterize heat shock protein ( HSP ) and vernalization ( Vrn ) genes, as well as to assess genetic diversity and population structure among 19 lines using Start codon targeted (SCoT) markers. Analysis of variance revealed highly significant differences for all tested traits. Two lines (L3 and L14) recorded the lowest values for days to heading and reached maturity in a shorter period compared to the check cultivar. Seven lines (L27, L25, L20, L21, L5, L19, and L13) showed the highest grain yield. A weak relationship was observed between plant height and grain yield; however, high-yielding lines tended to be shorter. Spike number per plant showed the strongest positive correlation with grain yield ( r = 0.77), indicating that it is the main determinant of yield. In addition, 1000-grain weight showed a weak positive correlation with grain yield ( r = 0.11). In contrast, spike number and 1000-grain weight were weakly negatively correlated ( r = -0.15). Furthermore, a single nucleotide polymorphism (SNP) marker for the HSP16.9 gene was utilized to assess heat tolerance in 19 wheat lines. All lines produced a single band of 197 bp, indicating a lack of allelic polymorphism at this locus among the evaluated genotypes. Furthermore, three gene-specific primers targeting Vrn genes ( Vrn-A1b , Vrn-B1a , and Vrn-B1b ) were used to characterize 19 wheat lines. All lines carried the dominant Vrn-A1b allele (147 bp), using primer Vrn-p2, indicating a spring growth habit and the ability to flower without a prolonged cold requirement. At the Vrn-B1a locus, 15 lines possessed the Vrn-B1a allele (709 bp), using primer Vrn-P5. Using the co-dominant marker Vrn-P7, one line (L10) exhibited the Vrn-B1a allele (215 bp), while 15 lines showed the dominant Vrn-B1b allele (252 bp). In addition, three lines (L5, L8, and L17) were heterozygous, displaying both 215 and 252 bp fragments. On the other hand, Jaccard’s genetic distance (GD) coefficient was employed to evaluate the genetic divergence among 19 wheat lines based on SCoT loci. The GD values ranged from 0.62 to 0.97. Therefore, the specific wheat genotypes possess valuable germplasm characteristics, making them excellent candidates for breeding programs focused on increasing grain yield and developing new, high-performing wheat varieties.
Cold atmospheric plasma (CAP) has emerged as a versatile platform at the intersection of plasma physics, materials engineering, and biomedicine, enabling both therapeutic and surface modification applications. Operating at near-room temperature and atmospheric pressure, CAP generates reactive oxygen and nitrogen species (RONS), charged particles, ultraviolet radiation, and electric fields that collectively induce complex physicochemical interactions with biological systems and material surfaces. This review presents an integrated framework linking plasma operating parameters, reactive species generation, surface physicochemical modification, and biological responses, highlighting CAP as a multifunctional physicochemical system. Recent advances in CAP applications are discussed in sterilization and disinfection, oncology, wound healing, dentistry, and biomaterials engineering, with emphasis on mechanisms such as surface activation, wettability enhancement, biofunctionalization, and selective cytotoxicity toward cancer cells. Emerging plasma-activated materials, including hydrogels, nanocomposites, and polymeric coatings, are also reviewed as promising therapeutic and biomedical platforms. Compared with conventional technologies, CAP offers advantages such as non-thermal operation, tunability, multi-modal action, and potential selective biological targeting. However, challenges remain in standardizing plasma parameters, improving reproducibility, and understanding long-term biological and material responses. This review provides a structured perspective for advancing CAP toward clinically and technologically relevant applications.
High temperature resistance and thermal stability are the significant characteristics of the polymeric materials which are employed in elevated temperature applications. Such resistance and stability of the polymer composites reinforced with natural fibers are measured using their degradation temperature and residual masses. Polymer composites reinforced with natural fibers like jute, sisal, or banana might be assessed for their thermal stability using thermogravimetric analysis (TGA). The thermal behaviour of the natural fiber reinforced composites depends on the nature and origin of the natural fibers, their hybridization and application. The thermal resistance of sisal composites can be further increased through chemical treatments. While individual natural fiber composites break down at relatively low temperatures, polymer-based hybrid systems can increase stability to higher temperatures. Decomposition temperatures varied for the fiber-reinforced composites, with the range being affected by additives, matrix type, and fiber type. Addition of natural fillers to the natural fiber composites and the cellulose-based nanocomposites exhibit better thermal stability owing to their scalability factor. An examination of the WIPO patent database reveals an increasing focus on TGA in creating bio-based composites that are thermally stable and flame-retardant. This review caters the material scientists working on natural fiber composites to understand the thermal behaviour of various natural fiber composites and hybrid composites based on the aforesaid parameters. With the help of International Patent Classification (IPC) codes, TGA will play an increasingly important role in the study of composite materials in the future.
Precise control of musculoskeletal robotic arms is challenging due to strong nonlinearity, redundant multi-muscle actuation, long-term temporal dependencies, and frequent disturbances. This paper proposes NeuroBayes-Former, a dual-layer adaptive control framework that decouples high-frequency motion generation from low-frequency online regulation. A lightweight Transformer with linear attention acts as the real-time feedback controller, while a Bayesian Optimization (BO) meta-regulator performs sample-efficient online hyperparameter tuning when performance degrades. Experiments in standard musculoskeletal simulators (MyoSuite and OpenSim) show that NeuroBayes-Former achieves high-precision tracking (RMSE 0.007 m on Arm26 circular tracking) and strong disturbance recovery (1.51 s after a sudden 0.5 kg payload), outperforming PID, LSTM, and ablation variants. Additional robustness evaluations under multiple random seeds, sensor noise, and actuator delay further demonstrate stability and reproducibility. These results indicate that combining efficient sequence modeling with online black-box optimization provides a practical pathway toward precise and adaptive control in bioinspired robotic systems.
Urban water systems are increasingly upgraded to withstand extreme weather events; however, existing studies often insufficiently address the necessity of localized pre-screening and the marginal utility of storage sizing. This study proposed an optimization framework to determine the optimal location and volume (V) of storage facilities based on long-term environmental benefits (E). Specifically, E was quantified as the reduction in downstream Non-Compliance Duration (NCD)—defined as the cumulative time during which Chemical Oxygen Demand (COD) concentrations exceed the regulatory limit of 30 mg/L. Using a coupled drainage–river model for a case study in Jiujiang, China, initial scenarios demonstrated that situating storage near outlets with the highest pollutant loads yielded the greatest E, validating the pre-screening step. Furthermore, marginal utility analysis revealed a non-linear E-V relationship: the incremental volume effectiveness ratio (IVER, intuitively reflecting the days of compliance gained per cubic meter of added storage) peaked at a site-specific critical volume threshold before diminishing returns set in. Rather than relying on arbitrary sizing, the study demonstrated that by intersecting a targeted minimum effectiveness constraint with a marginal efficiency threshold (e.g., 80
To address the limited dynamic adaptability of single-stage state-of-health (SOH) models for backup batteries in distribution-network automation terminals, this study proposes a lifecycle-aware diagnostic framework that combines strictly causal stage detection, stage-specific SOH tracking, capacity–power dual-dimensional diagnosis, probabilistic GPR output, and hierarchical safety decision logic. For online tracking, the first 20 observed target-cell cycles establish a frozen robust log-curvature baseline; the prediction at cycle k is generated before the observation at cycle k is ingested. Stage II is activated only after consecutive threshold exceedances, and the model-to-model continuity offset does not use the measured target capacity. In the B0005-to-B0007 case, the proposed method reduces RMSE from 0.0307 for SVR and 0.0294 for single-stage GPR to 0.0207. Across six directed source–target pairs, the mean RMSE is 0.0740, compared with 0.0795 for the strongest single-model baseline, Random Forest, corresponding to a 6.92
Zinc (Zn) deficiency in both soil and grain remains a constraint to crop productivity and human nutrition across the alluvial soils of eastern India. The strong fixation of Zn in soil limits its availability, creating interest in microbial Zn solubilization, but the low survivability of inoculants in conventional solid carriers has restricted its use. In this backdrop, we assessed the survivability, and biofortification potential of three rice rhizosphere Zn-solubilizing bacterial (ZnSB) strains (B1: Burkholderia caribensis and B2 and B3: Bacillus subtilis), formulated in liquid medium. Liquid formulations containing polyvinylpyrrolidone, glycerol, and phosphate buffer maintained high population (> 1 × 108 cfu/mL) even after nine months, whereas the microbial population showed rapid decline in solid carriers. To understand the effect of ZnSB, a pot experiment with rice was conducted with different treatments, including vermicompost (applied vs not applied), bacterial inoculation (no inoculation vs B1, B2, and B3), and Zn sources (ZnO, ZnS, ZnCO3, and Zn3(PO4)2) arranged in a three-factor factorial design. ZnSB inoculations significantly enhanced soil available Zn (1.7 folds) and Zn solubilization potential (2.7 folds) over the uninoculated control. Maximum concentration of Zn in rice grain (23.75–29.30 µg/g) and straw (19.10–21.40 µg/g) was recorded with the combined application of ZnSB, vermicompost, and ZnO. Principal component analysis identified B1 + ZnO + vermicompost as the most effective treatment for maximizing Zn availability and rice yield. Overall, the integration of liquid ZnSB bioformulation, ZnO, and organic amendment provides a sustainable solution for improving Zn biofortification and rice productivity in Zn-deficient alluvial soils of eastern India.
