Journal of Experimental and Theoretical Analyses — Advanced Methods for Science, Engineering, and Technology

Vibration-based methods offer a promising nondestructive approach for monitoring lithium-ion battery (LIB) condition; however, most existing approaches rely primarily on linear or locally linear vibration models. This study develops a theoretical framework for characterizing the nonlinear vibration response of a cased LIB cell under impact, harmonic, and random excitations. A lumped-parameter model is formulated with a general constitutive relation incorporating nonlinear elastic and hysteretic behavior, while state of charge (SOC), state of health (SOH), and temperature are treated as quasi-static over the vibration time scale. Polynomial nonlinearities are analyzed using single-mode approximations. Quadratic and cubic stiffness terms generate distinct harmonic components and amplitude-dependent frequency shifts, while harmonic excitation can produce resonance bending and jump phenomena. Random excitation is represented by a bounded narrow-band stochastic process as a basis for analyzing stochastic amplitude and phase behavior. Hysteresis is examined using a bilinear internal-variable formulation that introduces history-dependent stiffness, amplitude-dependent effective properties, energy dissipation, and higher-harmonic components. The resulting analytical relationships provide theoretical baselines for nonlinear vibration features that may support LIB condition monitoring. Experimental identification and validation are required before these features can be associated with specific battery states or failure mechanisms.

Green synthesized silver nanoparticles (AgNPs) offer potential applications as eco-friendly biostimulants in agriculture. This study evaluated the effects of direct applications of neem (Azadirachta indica) flower extract-mediated AgNPs on early growth and physiological responses of wheat (Triticum aestivum L.) seedlings. The AgNPs were characterized using UV-Visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), and Field-emission scanning electron microscopy (FESEM), confirming their successful formation, surface chemistry, morphology, and particle size. The nanoparticles exhibited diameters ranging from 37.50 to 63.10 nm, with an average size of 49.32 ± 7.3 nm. Three-day-old wheat seedlings were treated with 20, 40, and 80 mg L⁻¹ AgNPs to assess their growth parameters, photosynthetic pigments, soluble protein, secondary metabolites, oxidative stress markers, antioxidant enzyme activities, and stomatal and epidermal cell characteristics. ICP-MS analysis showed a dose-dependent increase in total root-associated Ag, reaching a maximum of 1460.34 mg kg⁻¹ dry weight at 80 mg L⁻¹. AgNP treatment improved growth, pigment accumulation, stomatal traits and antioxidant response, indicating enhanced physiological activity during early seedling development. These findings provide insights into the relationship between Ag accumulation and the physiological, biochemical and structural responses of wheat seedlings, contributing to a better understanding of plant–AgNP interactions. Furthermore, neem flower-mediated AgNPs show potential as an environmentally compatible nanomaterial for improving early wheat seedling establishment and performance, highlighting their prospective application in sustainable agricultural practices.

Prolonged exposure to high sound pressure levels (SPLs) in agricultural tractors, especially when the power take-off (PTO) is engaged, poses serious health risks to operators. While machine learning has been applied in agriculture, little progress has been made in predicting SPLs in agricultural tractors or detecting patterns that may indicate early mechanical issues. The main goal of this study was to spatially predict SPLs and identify observations whose SPLs departed from the model-expected level for normally operating tractors under different stationary pre-check conditions, as a preliminary basis for future condition-monitoring applications. SPLs were measured at the operator’s seat and at distances up to 15 m in three tractors at PTO speeds of 540, 750, and 1000 rpm. Statistical tests and machine learning models were used to model spatial noise distribution. The RF model performed best (R² = 0.93, RMSE = 1.65 dB(A)), with spatial position being the key predictor of SPL. In this sample, open-station tractors exceeded 100 dB(A) at and near the operator’s position under the loudest tested conditions, far above the 85 dB(A) screening criterion, whereas no statistically significant effect of PTO speed was detected under the tested conditions. Because the reported values are short-duration source measurements rather than full-shift personal exposures, they are interpreted as screening indicators of potentially hazardous conditions rather than as a formal daily noise-dose assessment. In addition, an out-of-range analysis identified observations exceeding the 99th percentile of positive residuals as atypical, indicating SPLs substantially higher than expected for a normally operating tractor. The proposed approach demonstrates a novel way to identify screening distances and conditions, providing a practical tool to protect workers’ health, monitor equipment, and inform maintenance strategies.