Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Mixed-Metal MOF-74 Templated Catalysts for Efficient Carbon Dioxide Capture and Methanation

The vast chemical and structural tunability of metal–organic frameworks (MOFs) are beginning to be harnessed as functional supports for catalytic nanoparticles spanning a range of applications. However, a lack of straightforward methods for producing nanoparticle-encapsulated MOFs as efficient heterogeneous catalysts limits their usage. Herein, a mixed-metal MOF, NiMg-MOF-74, is utilized as a template to disperse small Ni nanoclusters throughout the parent MOF. By exploiting the difference in Ni O and Mg O coordination bond strength, Ni²⁺ is selectively reduced to form highly dispersed Ni nanoclusters constrained by the parent MOF pore diameter, while Mg²⁺ remains coordinated in the framework. By varying the ratio of Ni to Mg in the parent MOF, accessible surface area and crystallinity can be tuned upon thermal treatment, influencing CO₂ adsorption capacity and hydrogenation selectivity. The resulting Ni nanoclusters prove to be an active catalyst for CO₂ methanation and are examined using extended X-ray absorption fine structure and X-ray photoelectron spectroscopy. By preserving a segment of the Mg²⁺-containing MOF framework, the composite system retains a portion of its CO₂ adsorption capacity while continuing to deliver catalytic activity. The approach is thus critical for designing materials that can bridge the gap between carbon capture and CO₂ utilization.     

Probing the Structural and Antimicrobial Study on a Sol–Gel Derived Velosef-Loaded Bioactive Calcium Magneso-Silicate Xerogel

Silicon - The nature of the opening silicate- based surface affects the chemical interaction, spectroscopic and antimicrobial efficiency. The aim of this approach was to evaluate the spectroscopic...     

Modern Template Design and Biological Evaluation of Cephradine-loaded Magnesium Calcium Silicate Nanocomposites as an Inhibitor for Nosocomial Bacteria in Biomedical Applications

Silicon - Magnesium calcium silicate nanostructures (MCSNS) loaded with (0.0, 0.6, 0.9, and 1.2 wt%) of Cephradine-drug consisting of mesoporous particles were functionally prepared by sol-gel...     

Photocatalytic mineralisation of organic compounds: a comparison of flame-made TiO2 catalysts

A comparative photocatalytic analysis was carried out on TiO₂ made in a Flame Spray Pyrolysis (FSP) process and flame-made Degussa P25. Both have similar crystallinity, phase composition, phase segregation and a non-porous surface. Hence comparison was made based on their difference in specific surface area, organic adsorption and the amount of OH• generated upon illumination. The photocatalytic activity tests were carried out using the following series of organic compounds: sucrose, glucose, fructose, maleic acid, glyoxylic acid, oxalic acid, isobutyric acid, phenol and methanol. FSP-made TiO₂ outperformed P25 for saccharides mineralisation, while for phenol and methanol mineralisation P25 was better than FSP-made TiO₂. Similar mineralisation rates were observed for both FSP-made and P25 TiO₂ for the mineralisation of carboxylic acids. This shows that the relative performance of the photocatalysts depends on the type of organic compounds to be degraded. The high surface area and possibly a more efficient interfacial charge transfer of FSP-made TiO₂ provided an efficient pathway for saccharides mineralisation. As for phenol and methanol, the mineralisation rates were higher when using P25 due to the greater amount of OH• radicals generated by this photocatalyst. The fast mineralisation rates of carboxylic acids made degradation of these organic compounds to be less affected by the TiO₂ photocatalyst properties and conditions tested in this work.     

Pt-V2O5-WO3/TiO2 catalysts supported on SiC filter for NO reduction at low temperature

The catalytic filter, V₂O₅-WO₃-TiO₂ supported on a ceramic filter, is known as a promising material for treating particulates and NO _x simultaneously at optimum temperatures around 320°C. In order to improve its catalytic activity at low temperatures, the effect of Pt addition on the catalytic filter has been investigated. Catalytic filters, Pt-V₂O₅-WO₃-TiO₂/SiC, were prepared by co-impregnation of Pt, V, and W precursors on TiO₂ coated-SiC filter by vacuum aided-dip coating. The Pt-added catalytic filter shifted the optimum working temperature from 280–330°C (for the non Pt-impregnated filter) to 180–230°C, providing N _x slip concentration less than 20 ppm for the treatment of 700 ppm NO at a face velocity of 2 cm/s with the same value over the non Pt-added catalytic filters. The promotional effect following the addition of Pt is believed to result from electrical modification of the catalyst maintaining a high electron transfer state. Ammonia oxidation was also observed to be dominant above the optimal temperature for SCR.     

Understanding the role of restructuring in flocculation: The application of a population balance model

The effect of shear on floc properties was observed through population balance to comprehend the mechanisms of flocculation, in particular the role of restructuring. Little fundamental attention has been given before to the shear influence responsible for creating compact aggregates, while the floc characteristics might differ in other conditions. It is crucial to understand how aggregates evolve to steady state, if their properties are to be ‘tailored’ to suit subsequent solid-liquid separation. From a previous experimental study (Langmuir 18(6) (2002) 1974), restructuring was observed to occur extensively in the flocculation of latex particles in couette-flow, and was proposed to be responsible for the decrease in floc size on their transition to equilibrium. On the other hand, flocs of larger primary particles were more susceptible to breakage, with densification occurring as a result of fragmentation and re-aggregation. Denser flocs were found when structural deformation dominated, particularly in the initial stage of the process, while comparatively tenuous ones were observed when formation and breakage kinetics were the governing mechanisms. The distinct manners in which aggregates of different primary particle sizes evolved with time, were replicated with a population balance that incorporated the floc structural variation; verifying that restructuring indeed played a crucial role under certain flocculation conditions.     

The effect of platinum and silver deposits in the photocatalytic oxidation of resorcinol

The effects of platinum (Pt) and silver (Ag) metallisation in the photocatalytic oxidation of resorcinol at pH 3 ± 0.5 have been investigated. The photocatalytic degradation of resorcinol was significantly improved by Pt/TiO₂, while the presence of Ag/TiO₂ enhanced the initial photocatalytic degradation rate of resorcinol slightly. Likewise, the photocatalytic mineralisation of resorcinol continued to be enhanced by Pt/TiO₂, but it was retarded when Ag/TiO₂ was used.

The function of Pt and Ag deposits on the surface of TiO₂ has been found to be markedly influenced by the interaction of resorcinol and its degradation products with the metal deposits. The presence of Pt or Ag on the surface of TiO₂ altered the distribution of degradation products of resorcinol as well as the production of photoactive species for the photocatalytic oxidation of resorcinol. The X-ray photoelectron spectroscopy (XPS), zeta potential and transmission electron microscopy (TEM) analyses have indicated that the contrasting effect of Pt and Ag deposits were governed by the oxidation states and the catalytic property of metal deposits. In addition to that, it has been found that the roles of metal deposits are specific and should not be generalised.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.