The purpose of this research was to assess and utilize the bioactive compounds of garlic nanoparticles (Ga-NPs) as a natural antioxidant in sunflower oil (SFO) stored at 65 ± 1 °C for 24 days. The garlic nanoparticles (Ga-NPs) from the Balady cultivar were prepared, characterized, and added to SFO at three concentrations: 200, 600, and 1000 ppm (w/v), and they were compared with 600 ppm garlic lyophilized powder extract (Ga-LPE), 200 ppm BHT, 200 ppm α-tocopherol, and SFO without Ga-NPs (control). The QTRAP LC/MS/MS profile of Ga-NPs revealed the presence of four organosulfur compounds. Ga-NPs exhibited the highest capacity for phenolic, flavonoid, and antioxidant compounds. In Ga-NP SFO samples, the values of peroxide, p-anisidine, totox, conjugated dienes, and conjugated trienes were significantly lower than the control. The antioxidant indices of SFO samples containing Ga-NPs were higher than the control. The Ga-NPs enhanced the sensory acceptability of SFO treatments up to day 24 of storage. The shelf life of SFO treated with Ga-NPs was substantially increased (presuming a Q₁₀ amount). The results show that Ga-NPs are a powerful antioxidant that improves SFO stability and extends the shelf life (~384 days at 25 °C).

Fuel cells guarantee ecological ways of electricity production by promising zero emissions. Proton exchange membrane fuel cells (PEMFCs) are considered one of the safest methods, with a low operating temperature and maximum conversion efficiency. In order to harness the full potential of PEMFC, it is imperative to ensure the membrane’s safety through appropriate control strategies. However, most of the strategies focus on fuel economy along with viable fuel cell life, but they do not assure constant output voltage characteristics. A comprehensive design to regulate and boost the output voltages of PEMFC under varying load conditions is addressed with dynamic integral sliding mode control (DISMC) by combining the properties of both the dynamic and integral SMC. The proposed system outperforms in robustness against parametric uncertainties and eliminates the reaching phase along with assured stability. A hardware test rig consisting of a portable PEMFC is connected to the power converter using the proposed technique that regulates voltage for varying loads and power conditions. The results are compared with a proportional integral (PI) based system. Both simulation and hardware results are provided to validate the proposed technique. The experimental results show improvements of $35.4\%$, $34\%$ and $50\%$ in the rise time, settling time and robustness, respectively.

The p53 pathway has been the focus of many researchers in the last few decades owing to its pivotal role as a frontline cancer suppressant protein. It plays a vital role in maintaining cell cycle checkpoints and cell apoptosis in response to a broken DNA strand. This is why it is found in the mutated form in more than 50% of malignant tumors. To overcome this, various drugs have been proposed to revive the p53 pathway in cancer patients. Small-molecule-based drugs, such as Nutlin 3a, which are capable of performing this stimulation, are at the fore of advanced clinical trials. However, the calculation of their dosage is a challenge. In this work, a method to determine the dosage of Nutlin 3a is investigated. A control-systems-based model is developed to study the response of the wild-type p53 protein to this drug. The proposed strategy regulates the p53 protein along with negative and positive feedback loops mediated by the MDM2 and MDM2 mRNA, respectively, along with the reversible repression of MDM2 caused by Nutlin 3a. For a broader perspective, the reported PBK dynamics of Nutlin 3a are also incorporated. It has been reported that p53 responds to stresses in two ways in terms of concentration to this drug: either it is a sustained (constant) or an oscillatory response. The claimed dosage strategy turned out to be appropriate for sustained p53 response. However, for the induction of oscillations, inhibition of MDM2 is not enough; rather, anti-repression of the p53–MDM2 complex is also needed, which opens new horizons for a new drug design paradigm.

The prediction of material lifetime is central to nanomaterial engineering and reliability analysis. We propose the Marshall–Olkin Power Half-Logistic (MOPHL) distribution, obtained by applying a Marshall–Olkin transform to the Power Half-Logistic baseline. We derive core properties—including moments, hazard rate characterization, and Rényi entropy—and develop inference under generalized progressive hybrid censoring. Estimation is carried out via maximum likelihood and Bayesian methods using a Metropolis–Hastings sampler. Asymptotic results, Fisher information, and corresponding confidence/credible intervals are provided. A Monte Carlo study assesses bias, the mean squared error, and coverage across censoring scenarios and hazard regimes. In a case study on hydroxylated fullerene degradation, MOPHL outperforms nine competing models in goodness-of-fit and predictive reliability. We also report the mean time to failure and mean residual life to support engineering decision-making. The proposed framework offers a tractable and robust tool for degradation analysis under censored data, with applicability to materials, mechanical components, biomedical devices, and environmental monitoring.

Mathematical modeling of degradation data is essential for quantifying the lifetime, reliability, and long-term stability of advanced materials when a direct experimental assessment is costly or limited. This paper develops an applied statistical framework based on the proportional hazard Gumbel Type-II (PHGT-II) distribution for modeling positive degradation times under a generalized progressive hybrid censoring scheme. The proposed model extends the baseline Gumbel Type-II distribution through a proportional hazard structure, providing additional flexibility for representing non-monotone hazard behavior, heavy-tailed lifetime patterns, and heterogeneous degradation mechanisms. The probability density, survival, hazard, and mean time to failure functions were derived, and the likelihood function was formulated under generalized progressive hybrid censoring. Parameter estimation was performed using maximum likelihood estimation and Bayesian inference with independent Gamma priors. Bayesian estimates were obtained under squared error and general entropy loss functions using a Metropolis–Hastings algorithm. The model was applied to thermal degradation data of the hydroxylated fullerene nanocomposite Sc₃N@C₈₀(OH)₁₈, where the degradation time was defined through a 2% weight-loss threshold obtained from a thermogravimetric analysis. The PHGT-II model was compared with other distributions using several goodness-of-fit measures. The results show that the PHGT-II distribution provides the best fit to the observed degradation data and yields consistent reliability estimates across maximum likelihood and Bayesian approaches. The proposed framework offers a flexible and interpretable tool for modeling censored degradation data and can be extended to other reliability and lifetime applications in engineering and material science.