Bayesian and unsupervised learning insights into pH- and temperature-driven sorption of fluoroquinolones and sulfonamides on marine algal biochar.

Abstract Despite numerous theories on pulsars, a consensus on a reliable emission mechanism explaining their diverse observational characteristics remains elusive. In this paper, we couple the plasma process of stimulated Raman scattering (SRS) with the emission geometry of radio pulsars. SRS, involving energy transfer from curvature radiation photons to weakly damped electron-plasma modes, allows us to simulate integrated pulse profiles. We compute the growth rate of plasma modes across phase factors and reproduce soliton pulse profiles using nonlinear Schrödinger equations, driven by the pondermotive force in a typical plasma environment. We also examine collisional damping across plasma species. Notably, we show that circular polarization (CP) decreases with lower nonlinear parameter η (density perturbation ratio along two orthogonal basis vectors in our synergy). A similar problem was addressed via Bayesian analysis of magnetar radio emission data in Nature Astronomy; our approach offers a theoretical explanation using a plasma-astrophysical-based mechanism. We argue that SRS can explain various pulsar phenomena, including polarization angle swing reversals, percentage changes in circular and linear polarization nature, and depolarization.

Abstract Precise real-time prediction of endpoint carbon content and temperature in converter steelmaking is vital for improving steel quality and efficiency. Given the process complexity and nonlinear parameter interactions, this study proposes an intelligent soft sensor modeling approach based on multi-objective optimization and feature combination strategies. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed for feature optimization, with SHAP value analysis innovatively integrated into crossover and mutation operations to retain high-interaction features. By minimizing redundancy and maximizing accuracy, representative feature subsets are selected. A multi-strategy heterogeneous model fusion framework is subsequently implemented, wherein distinct base models are constructed for different feature subsets and integrated using Stacking technology. Experimental results demonstrate high prediction accuracy: 90.4% within ±10°C for temperature and 90.4% within ±0.02% for carbon content. The proposed methodology exhibits strong stability and adaptability, providing reliable support for intelligent monitoring and precise control in converter steelmaking processes.Journal vv (yyyy) aaaaaa

Static component of nonlinear guided wave as a Preferable indicator of creep damage in superalloys.

SiC MOSFETs face prominent reliability issues due to higher voltage resistance requirements and continued device miniaturization. The lifetime prediction of SiC MOSFET plays a crucial role in improving the reliability of devices and systems. However, existing methods still face challenges in terms of adaptability, stability, and accuracy due to the complexity of the failure process in SiC MOSFET. This article proposes an improved grey wolf optimizer-based long short-term memory (IGWO-LSTM) model for SiC MOSFET lifetime prediction. The model introduces a Tent chaotic mapping to generate an initial population with optimal distribution, ensuring comprehensive search space coverage and enhancing dynamic search adaptability. Then, a nonlinear control parameter strategy and the principle of particle swarm optimization (PSO) are added. The feature extraction capability of the model is strengthened, and the exploration and exploitation phases are dynamically balanced. The optimizations enable faster discovery of the global optimum while maintaining solution quality, thereby improving prediction accuracy and stability. Finally, power cycling experiments were conducted on two types of SiC MOSFETs with different internal resistances to validate the effectiveness of the proposed model. The proposed IGWO-LSTM model achieves high prediction accuracy, with R2 values of 96.2%, 94.8%, 94.1%, and 93.9% for four SiC MOSFETs, and RMSE values as low as 0.0117, 0.0143, 0.0152, and 0.0158, respectively. This represents an average improvement in R2 by 16%, 8%, and 4%, and a reduction in RMSE by up to 67.03%, 50.39%, and 31.57% compared with other intelligent models. Similarly, IGWO-LSTM achieves reductions in MAE of approximately 68%, 50%, and 30%, with corresponding reductions in MAPE of about 70%, 48%, and 26%, respectively. The results demonstrate superior performance in prediction accuracy, stability, and adaptability of the proposed model.

In response to the challenges posed by multifactorial nonlinear relationships and uncertainties, and to address the limitations of the existing Belief Rule Base (BRB) in nonlinear fitting, uncertainty representation, and parameter optimization, this paper presents an improved reliable modeling method using a nonlinear belief rule base (R-NBRB). First, the linear inference mechanism is replaced by a smooth nonlinear S-function. This replacement better adapts to nonlinear dynamics in complex industrial systems. Second, attribute reliability is quantified through a reliability assessment method. Data, reliability, and expert knowledge are integrated using the Evidential Reasoning (ER) algorithm. Uncertainty is expressed in the form of belief degrees. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is applied to optimize the inference parameters. Decision bias caused by insufficient expert knowledge is thereby reduced. Experiments were conducted on a task involving the detection of a petroleum pipeline leak. The mean squared error (MSE) of the R-NBRB model is only 0.2569. This represents a 28.24% reduction compared with the BRB model. The proposed method's effectiveness and adaptability in complex industrial situations are confirmed.

This study presents an experimental methodology for characterizing the crack-tip region using high-resolution Digital Image Correlation (DIC). The approach utilizes a stereoscopic microscope setup combined with 3D-DIC analysis to enable precise measurements within the small-scale region surrounding the crack tip. Two nonlinear parameters are evaluated: the plastic component of the crack-tip opening displacement (CTODp) and the cyclic plastic zone size. The investigation was conducted on disk-shaped compact tension specimens made of AISI 1020 steel under constant-ΔK fatigue testing. The results demonstrate a strong correlation between these nonlinear parameters and fatigue crack propagation, which was maintained stable, validating the proposed methodology. Furthermore, the relevance of crack-tip plasticity in fatigue crack propagation is verified under the tested conditions, highlighting its utility for fatigue life assessment under complex loading scenarios.

Abstract Nanocomposite thin films of AgIn 5 S 8 /Ag 3.4 In 0.6 were prepared and proportion controlled with different thicknesses by the thermal deposition technique and heat treated at 330 °C for 2 h. XRD patterns demonstrated that the prepared thin films crystallized in two cubic phases of AgIn 5 S 8 and Ag 3.4 In 0.6 . The EDX revealed data near stoichiometric composition with slightly excess S. The surface morphology by FE-SEM showed accumulated nanoparticles forming granules that increased in diameter with increasing film thickness and elongated at the highest thickness of 450 nm. The optical measurements of films revealed two optical direct transitions for each film, ranging from 1.67 to 1.53 and 2.89 to 2.57 eV, by increasing the film thickness from 225 to 450 nm. Other optical linear and nonlinear parameters were determined. Electrical investigation of the resistivity, conductivity, and heterojunction electrical parameters with a crystalline p-Si substrate showed a slight improvement in the fabricated device conductivity with increasing the film thickness. AgIn 5 S 8 /Ag 3.4 In 0.6 nanocrystalline thin films provide a novel and promising candidate for long-life photovoltaic devices and photoelectronic applications.

Renewable wind energy systems widely employ doubly fed induction generators (DFIGs), where efficient converter control ensures grid-integrated power system stability and reliability. Conventional proportional–integral (PI) controller tuning methods often encounter challenges with nonlinear dynamics and parameter variations, resulting in reduced adaptability and efficiency. To address this, we present an owl search optimization (OSO)-based tuning strategy for PI controllers in DFIG back-to-back converters. Inspired by the hunting behavior of owls, OSO provides robust global search capabilities and resilience against premature convergence. The proposed method is evaluated in MATLAB/Simulink and benchmarked against particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) under step wind variations, turbulence, and grid disturbances. Simulation results demonstrate that OSO achieves superior performance, with 96.4% efficiency, reduced power losses (~40 kW), faster convergence (<400 ms), shorter settling time (<345 ms), and minimal oscillations (0.002). These findings establish OSO as a robust and efficient optimization approach for DFIG-based wind energy systems, delivering enhanced dynamic response and improved grid stability.

Magnetic resonance water-fat separation is an important imaging technique for distinguishing water and fat signals, enabling accurate tissue characterization and fat quantification in both clinical and research settings. However, achieving robust separation remains challenging, especially in the presence of complex background noise and rapidly varying magnetic field inhomogeneities. To improve robustness in challenging acquisition scenarios, including low field strength systems and large field of view (FOV) imaging with complex boundary regions, by leveraging the inherent smoothness of field maps and fat fraction maps. We propose the FMFMS (Field Map and Fat Fraction Map Smoothing) algorithm, a robust water-fat separation method that incorporates multi-peak fat modeling, T2* decay correction, and field inhomogeneity compensation. The signal model accounted for chemical shift and relaxation effects, requiring at least three uniformly spaced echoes. Nonlinear parameters were estimated via a two-stage optimization framework, with water and fat amplitudes derived using a least-squares solution. To further improve robustness, we leveraged the spatial smoothness of both the field map and fat fraction map. Erroneous voxels were identified by local field discontinuities and refined through Local Polynomial Surface Fitting (LPSF) applied to neighboring field and fat fraction values. A final field value was selected based on agreement between smoothed and candidate fat fraction estimates within a narrow search window. Validation on the ISMRM 2012 Challenge dataset, an agar-based water-fat phantom dataset, and in vivo studies (including 0.35T standard FOV and 1.5T large FOV acquisitions), the proposed FMFMS algorithm outperforms existing methods such as the globally optimal surface estimation (GOOSE) algorithm and the field factor method (FFM). It achieved an average accuracy of 99.5% across all ISMRM Challenge cases, excluding case #3, with minimal water-fat exchange artifacts, and demonstrated strong performance in processing low-field acquisitions of the head, neck, lumbar spine, knee, and large FOV abdominal regions. The proposed FMFMS method demonstrates superior performance in low SNR scenarios and provides reliable water-fat separation across different field strengths and anatomical regions.

Abstract To construct a chaotic image encryption algorithm resistant to plaintext attacks, a new two-dimensional chaotic map is proposed based on the characteristics of the Cubic and cosine functions. Additionally, a novel NL-PF(Nonlinear Parameter Feedback) pixel diffusion and scrambling method with a nonlinear dynamic model and parameter feedback is put forward, along with a mathematically rigorous proof of the algorithm. The encryption scheme includes NL-PF and DNA coding diffusion modules: first, NL-PF scrambling is used to diffuse pixel positions, and then encrypted images are generated using chaotic sequences based on DNA base coding. To verify the effectiveness in resisting plaintext attacks, this paper focuses on validating the performance of the proposed scheme through both statistical analysis and cryptographic analysis. Statistical analysis shows that the scheme has excellent resistance against statistical attacks; cryptographic analysis confirms that although this scheme does not possess formally cryptographically significant IND-CPA (Indistinguishability under Chosen Plaintext Attack) security, it exhibits good robustness in terms of statistics and diffusion when resisting Known Plaintext Attacks (KPA) and Chosen Plaintext Attacks (CPA).

An online data-driven method for predicting crack propagation and remaining fatigue life via combining linear and nonlinear ultrasonic.

Aerodynamic shape optimization of hypersonic aircraft using data-driven generative nonlinear parameterization

Poly-methylmethacrylate (PMMA)–polyethylene oxide (PEO)–tetrapropylammonium iodide (TPAI)-blended polymers loaded with nano Zn[Formula: see text]Mg[Formula: see text]O or Zn[Formula: see text]Mg[Formula: see text]X[Formula: see text]O (where X represents Mn, Ni, Cu) have been formed for optical and energy storage uses. The impact of Zn[Formula: see text]Mg[Formula: see text]O or Zn[Formula: see text]Mg[Formula: see text]X[Formula: see text]O (X [Formula: see text] Mn, Ni, Cu) loading on the structure and morphology of the host PMMA/PEO/TPAI-blended polymer was studied. The absorbance of the PMMA/PEO/TPAI-blended polymer rose based on the sort of filler used. The justification for the enhancement was that the filled samples would successfully obstruct UVA, UVB, and UVC radiation. The sample containing Ni exhibited the lowest [Formula: see text] values. In the visible spectrum, samples containing Zn[Formula: see text]Mg[Formula: see text]O or Zn[Formula: see text]Mg[Formula: see text]Ni[Formula: see text]O displayed the greatest n values, contingent upon the wavelength range. In the visible spectrum, the sample containing Zn[Formula: see text]Mg[Formula: see text]O had the utmost optical dielectric constant values, but the sample containing Ni demonstrated the greatest values in the UV range. When comparing the sample filled with either Zn[Formula: see text]Mg[Formula: see text]O or Zn[Formula: see text]Mg[Formula: see text]M[Formula: see text]O to the pure blend, it was found that the values of the nonlinear optical parameters were raised, as the blended polymer filled with either Zn[Formula: see text]Mg[Formula: see text]O or Zn[Formula: see text]Mg[Formula: see text]Ni[Formula: see text]O relied on the wavelength range. The host composite polymer can be infused with Zn[Formula: see text]Mg[Formula: see text]O to achieve the highest energy density values. The loaded PMMA/PEO polymer containing either Zn[Formula: see text]Mg[Formula: see text]O or Zn[Formula: see text]Mg[Formula: see text]X[Formula: see text]O showed enhanced capacitance. The sample containing Ni exhibited the maximum conductivity.

Abstract This study investigates the mechanisms of friction-induced vibration under periodic stress distribution variations using an improved fretting friction model. A fretting friction test system integrated with a total reflection method was developed to analyze interfacial contact behavior under dynamic loading conditions. An improved fretting friction model was established, incorporating three critical nonlinear parameters: hysteretic friction coefficient, tangential stiffness fluctuations, and stress distribution. Through systematic validation, the model demonstrates high-fidelity replication of experimental steady-state amplitude-frequency responses. Key findings reveal that stress distribution non-uniformity governs vibration response irregularity, and increased uniformity intensifies stick-slip instabilities. Near the stick-slip transition threshold, distinct vibration anomalies emerge due to the coupled effects of stress heterogeneity, friction hysteresis, and stiffness variations during state transitions. Furthermore, the magnitude of the normal contact force systematically alters the dominant interfacial contact mechanism. The different interfacial contact states at various frequencies lead to distinct steady-state responses. This shift elevates resonance frequencies and amplifies higher-order resonant peaks. The fretting friction model provides a predictive framework for vibration control under dynamic interfacial loading.

We present an analytical and numerical study of nonlinear wave localization in a one-dimensional rhombic (diamond) waveguide array that combines forward- and backward-propagating channels. This mixed-index configuration, realizable through Bragg-type couplers or corrugated waveguides, produces a tunable spectral gap and supports nonlinear self-localized states in both transmission and forbidden-band regimes. Starting from the full set of coupled-mode equations, we derive the effective evolution model, identify the role of coupling asymmetry and nonlinear coefficients, and obtain explicit soliton solutions using the method of multiple scales. The resulting envelopes satisfy a nonlinear Schrödinger equation with an effective nonlinear parameter θ, which determines the conditions for soliton existence (θ>0) for various combinations of focusing and defocusing nonlinearities. We distinguish solitons formed outside and inside the bandgap and analyze their dependence on the dispersion curvature and nonlinear response. Direct numerical simulations confirm the analytical predictions and reveal robust propagation and interactions of counter-propagating soliton modes. Order-of-magnitude estimates show that the predicted effects are accessible in realistic integrated photonic platforms. These results provide a unified theoretical framework for soliton formation in mixed-index lattices and suggest feasible routes for realizing controllable nonlinear localization in Bragg-type photonic structures.

Feature selection represents a complex multi-objective optimization challenge aimed at identifying the optimal subset of features while maintaining high accuracy within the domain of machine learning, a task known for its difficulty. In this study, we devise a cost function that simultaneously optimizes classification accuracy and the selected features through linear weighting. Subsequently, we introduce an enhanced meta-heuristic approach named i mproved b ald e agle s earch (IBES) algorithm to effectively optimize the designed cost function. IBES incorporates the opposition-based learning, levy flight, and a nonlinear control parameter strategy into the various stages of the conventional BES algorithm, to develop a wrapper-based feature selection method. Additionally, an efficient binary optimization called V -shape transfer function is employed to convert the solution space from continuous to binary. To assess effectiveness, we utilize 24 well-known data sets from the UCI repository and compare the performance of the proposed methods with a selection of established algorithms from the literature in terms of cost value, classification accuracy, computational time, and selected features. The results affirm the superiority of the proposed method across the majority of the tested data sets.

Renormalization Method for Variable Stiffness in Fractal 2DOF Nonlinear Parametric Oscillators

ABSTRACTThe physicochemical behavior of L‐Isoleucine in aqueous solutions when two different inorganic electrolytes magnesium nitrate 0.1 m and magnesium sulphate all at the same concentration 0.10 mol/kg are added to the solutions under investigation over the range of concentrations (0.02–0.20 mol/kg) at two temperatures namely 288.15 and 298.15 K in both cases to measure the solutions density (ρ) in 10 mL of specific gravity bottle. Out of these data, a complete set of thermo‐acoustic and volumetric parameters was obtained, viz., adiabatic compressibility (β), nonlinear parameters (B/A), relative association (RA), and surface tension (Ϭ). The findings are the prominent intermolecular interactions that depend on temperature, the concentration of the solute, and the type of electrolyte. The significant reduction in isothermal compressibility with the rise of the concentration and temperature is evidence of good electrostriction effect and contraction of volume in the binary systems. By comparative analysis, it is clear that L‐Isoleucine + system of Mg(NO3)2 undergoes greater solute—solvent interaction, which is confirmed by the fact that surface tension values are higher and compressibility is lower. This work offers new insights into the molecular‐level interactions between amino acids and divalent electrolytes, contributing to the understanding of solvation behavior in biomolecular systems. The findings are relevant for macromolecular chemistry, particularly in the design of biomaterials, protein–electrolyte interactions, and pharmaceutical formulations.

The mechanical properties of dental implants are critical for their durability. The purpose of this study was to determine the maximum force required to induce full pull-out of a titanium implant from the bone and to characterize the mechanical behavior during this process. First, pull-out tests were performed on monolithic implants embedded in bovine ribs and foam blocks that mimic the mechanical parameters of human bone, allowing a quantitative evaluation of implant–bone interface strength and a comparison of geometric variants. Second, the extraction process was recreated in a three dimensional finite element model incorporating nonlinear interface contact and parameterization, enabling the reproduction of load–displacement curves; the results obtained showed good agreement with the experiment. Third, the fracture surfaces were observed macroscopically and by scanning electron microscopy/energy dispersive spectroscopy. The results demonstrated significant distinctions in the forces required to extract implants with varying thread geometries, clearly indicating the impact of implant design on their mechanical stability. The presented FEM-based methodology provides a reliable tool to study mechanical interactions at the implant–bone interface. The findings obtained can improve our understanding of implant behavior in biological systems and provide a basis for further optimization of their design.