Lately, the development of green chemistry methods with high efficiency for metal nanoparticle synthesis has become a primary focus among researchers. The main goal is to find an eco-friendly technique for the production of nanoparticles. Ferro- and ferrimagnetic materials such as magnetite (Fe₃O₄) exhibit superparamagnetic behavior at a nanometric scale. Magnetic nanoparticles have been gaining increasing interest in nanoscience and nanotechnology. This interest is attributed to their physicochemical properties, particle size, and low toxicity. The present work aims to synthesize magnetite nanoparticles in a single step using extracts of green lemon Citrus Aurantifolia residues. The results produced nanoparticles of smaller size using a method that is friendlier to health and the environment, is more profitable, and can be applied in anticorrosive coatings. The green synthesis was carried out by a co-precipitation method under variable temperature conditions. The X-ray Diffraction (XRD) and Transmission Electron Microscopy (TEM) characterization showed that magnetite nanoparticles were successfully obtained with a very narrow particle size distribution between 3 and 10 nm. A composite was produced with the nanoparticles and graphene to be used as a surface coating on steel. In addition, the coating’s anticorrosive behavior was evaluated through electrochemical techniques. The surface coating obtained showed good anticorrosive properties and resistance to abrasion.

This paper presents the novel enhanced Wagner–Hagras interval type-3 Takagi–Sugeno–Kang fuzzy logic system with type-1 non-singleton inputs (EWH IT3 TSK NSFLS-1) that uses the backpropagation (BP) algorithm to train the antecedent and consequent parameters. The proposed methodology dynamically changes the parameters of only the alpha-0 level, minimizing some criterion functions as the current information becomes available for each alpha-k level. The novel fuzzy system was applied in two industrial processes and several fuzzy models were used to make comparisons. The experiments demonstrated that the proposed fuzzy system has a superior ability to predict the critical variables of the tested processes with lower prediction errors than those produced by the benchmark fuzzy systems.

This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output $y$ alpha using the average of the outputs $y$ alpha $k$ instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the backpropagation (BP) method to train the antecedent parameters. This methodology dynamically changes the parameters of only the level alpha 0, minimizing some criterion functions as new information becomes available to each level alpha $k$. The precursor sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha $k$ to dynamically construct and update the proposed EWH IT3 NSFLS-1 (OLS–BP) system. The proposed enhanced fuzzy system and the proposed hybrid learning algorithm were applied in a hot strip mill facility to predict the transfer bar surface temperature at the finishing mill entry zone using, as inputs, (1) the surface temperature measured by the pyrometer located at the roughing mill exit and (2) the time taken to translate the transfer bar from the exit of the roughing mill to the entry of the descale breaker of the finishing mill. Several fuzzy tools were used to make the benchmarking compositions: type-1 singleton fuzzy logic systems (T1 SFLS), type-1 adaptive network fuzzy inference systems (T1 ANFIS), type-1 radial basis function neural networks (T1 RBFNN), interval singleton type-2 fuzzy logic systems (IT2 SFLS), interval type-1 non-singleton type-2 fuzzy logic systems (IT2 NSFLS-1), type-2 ANFIS (IT2 ANFIS), IT2 RBFNN, general singleton type-2 fuzzy logic systems (GT2 SFLS), general type-1 non-singleton type-2 fuzzy logic systems (GT2 NSFLS-1), interval singleton type-3 fuzzy logic systems (IT3 SFLS), and interval type-1 non-singleton type-3 fuzzy systems (IT3 NSFLS-1). The experiments show that the proposed EWH IT3 NSFLS-1 (OLS–BP) system presented superior capability to learn the knowledge and to predict the surface temperature with the lower prediction error.

Peripartum cardiomyopathy (PPCM) is a rare clinical entity during pregnancy. PPCM is a diagnosis of exclusion. These patients do not have prior history of heart disease, and there are no other known possible causes of heart failure. It is more common in African countries, may be related to the consumption of kanwa, in the postpartum period. The multiparity, African descent and pregnancy-induced hypertension are a few risk factors for PPCM. The exact etiology of PPCM is not known; possible theories range from myocarditis to the maladaptation to the changes of pregnancy. The clinical manifestation varies from shortness of breath to thromboembolic phenomenon. Echocardiography is essential for diagnosis as well as differential diagnosis of PPCM. These patients preferably are managed in tertiary healthcare facilities. Anticoagulation and antiarrhythmic medications are pillars for the management of PPCM patients. If required, mechanical devices should be used temporarily. PPCM patients may need heart transplant. The beneficial role of bromocriptine and immunosuppression is not clear in PPCM patients. Subsequent pregnancies should be avoided to prevent the PPCM occurrence.