Yanbu University College

An innovative, sustainable analytical approach was developed by coupling an Epsom salt, E518 (magnesium sulfate)-facilitated salting-out assisted liquid-liquid extraction (SALLE) system with a safe molecular fluorescent probe, Celfia Pink B (CPB), employing a fluorescence turn-off sensing strategy. MgSO₄ acts as a green salting-out agent, enhancing phase separation efficiency and producing cleaner extracts with higher analyte recovery. CPB, a safe, food-grade dye, serves as a selective fluorogenic probe whose emission at 553nm is quenched upon electrostatic ion-pair complex formation with Cinacalcet HCl under mildly acidic conditions. The method exhibited excellent linearity (0.08-1.3µg/mL) with high sensitivity (LOD = 0.024µg/mL; LOQ = 0.075µg/mL). Validation according to ICH guidelines demonstrated robustness, precision, and accuracy across pharmaceutical formulations and biological matrices. Sustainability assessment using the WAC framework categorized the platform as "white," highlighting low environmental impact, safe operation, and simplicity. By minimizing organic solvent use and eliminating hazardous reagents, this MgSO₄-assisted SALLE/fluorescence turn-off approach provides a green, highly sensitive, and versatile tool for trace-level determination of Cinacalcet HCl in pharmaceutical and biological matrices.

ABSTRACT This paper investigates the stability and dynamic behavior of the Willamowski–Rössler (WR) chemical reaction model, a classical prototype for chaotic oscillations in chemical kinetics. While previous studies have primarily focused on isolated equilibria or local dynamical properties, this work provides a systematic and unified analysis of the WR model by characterizing the full coexistence structure of its equilibria and rigorously determining their stability across distinct parameter regions. In particular, we identify regions of full and partial equilibrium coexistence and derive exact analytical conditions for the occurrence of Hopf bifurcations, thereby clarifying the parameter regimes in which oscillatory and chaotic dynamics may arise. This framework yields a global perspective on the stability landscape of the WR system that is not available in earlier analyses. In addition, two backstepping‐based control schemes are developed to achieve stabilization and master‐slave synchronization of the WR model. Numerical simulations are presented to validate the theoretical results and demonstrate the effectiveness of the proposed control strategies. The results contribute to a deeper understanding of the WR system's dynamics and provide a structured basis for control‐oriented applications in chemical reaction engineering.

In this article, we introduce an enhanced version of the log-logistic model, termed the Kumaraswamy alpha-power log-logistic (KAPLL) distribution. The KAPLL model expands upon the traditional log-logistic distribution and several well-established distributions. We investigate the mathematical properties of the KAPLL model, highlighting its ability to effectively model various aging and failure criteria. The KAPLL distribution exhibits remarkable flexibility in modeling various types of hazard rate behaviors. It is capable of accommodating a wide range of shapes, including increasing, decreasing, J-shaped, reversed J-shaped, bathtub-shaped, inverted bathtub-shaped, and even more complex forms such as decreasing–increasing–decreasing failure rates. The KAPLL distribution is characterized by its capacity to exhibit both symmetric and asymmetric shapes in its density function. The proposed KAPLL model overcomes key limitations of existing LL-based generalizations by offering enhanced flexibility in modeling diverse hazard rate shapes and tail behaviors. We estimate the KAPLL parameters using eight classical estimation methods. Comprehensive simulation results are presented and ranked to identify the most effective approach for estimating KAPLL parameters, which we believe will be of great interest to engineers and applied statisticians. To further demonstrate the versatility of the KAPLL distribution, we analyze five real-world datasets from reliability, engineering, biomedical, and environmental sciences, highlighting its flexibility relative to other extensions of the log-logistic model. Likelihood ratio tests conducted across five real datasets confirm that the KAPLL model provides a statistically significant improvement over the baseline log-logistic distribution.

Metabolomics provides powerful means to analyze metabolite profiles in biological samples, enabling insights into biochemical changes under genetic, environmental, or pathological conditions. Nuclear Magnetic Resonance (NMR) spectroscopy is central to metabolomics, but its utility is often constrained by the strong and overlapping resonances of abundant components, such as sugars in plant‑ and food‑derived materials, which obscure signals of lower‑abundance metabolites. Here, we introduce a modified NMR acquisition method that increases sensitivity and specificity by selectively suppressing dominant signals, while enhancing weaker metabolite signals across the spectrum. The method integrates water presaturation with excitation sculpting (ES), yielding a robust 1D presat‑1H‑ES pulse sequence. Validation on a range of sugar-rich samples demonstrated 2–fourfold signal enhancement for low‑abundance metabolites compared with conventional 1H‑ES. Multivariate analyses show the method improves reproducibility and discrimination, enabling detection and comparison of low‑abundance metabolites not accessible with conventional approaches’. Beyond sugar‑rich systems, the method is broadly applicable to other spectral regions where dominant metabolite classes obscure lower‑concentration compounds, including primary metabolites and structurally diverse natural products. Overall, the 1D presat‑1H‑ES significantly enhances resolution and sensitivity of NMR‑based metabolomics, shortens analysis time, and supports more precise profiling for both fundamental studies and translational applications in metabolomics and natural‑products discovery.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1007/s13659-025-00570-3.