In this paper, we will focus to one of the recent applications of PU-algebras in the coding theory, namely the construction of codes by soft sets PU-valued functions. First, we shall introduce the notion of soft sets PU-valued functions on PU-algebra and investigate some of its related properties.Moreover, the codes generated by a soft sets PU-valued function are constructed and several examples are given. Furthermore, example with graphs of binary block code constructed from a soft sets PU-valued function is constructed.

    Soft clays are generally sediments deposited by rivers, seas, or lakes. These soils are fine-grained plastic soils with appreciable clay content and are characterized by high compressibility and low shear strength. To deal with soft soil problems there is more than one method that can be used such as soil replacement, preloading, stone column, sand drains, lime stabilization and Prefabricated Vertical Drains, PVDs. A numerical modeling of PVD with vacuum pressure was analyzed to investigate the effect of this technique on the consolidation behavior of fully and different depths of partially saturated soft soils.  Laboratory experiments were also conducted by using a specially-designed large consol

The fuzzy assignment models (FAMs) have been explored by various literature to access classical values, which are more precise in our real-life accomplishment. The novelty of this paper contributed positively to a unique application of pentagonal fuzzy numbers for the evaluation of FAMs. The new method namely Pascal's triangle graded mean (PT-GM) has presented a new algorithm in accessing the critical path to solve the assignment problems (AP) based on the fuzzy objective function of minimising total cost. The results obtained have been compared to the existing methods such as, the centroid formula (CF) and centroid formula integration (CFI). It has been demonstrated that operational efficiency of this conducted method is exquisitely develo

This survey investigates the thermal evaporation of Ag2Se on glass substrates at various thermal annealing temperatures (300, 348, 398, and 448) °K. To ascertain the effect of annealing temperature on the structural, surface morphology, and optical properties of Ag2Se films, investigations and research were carried out. The crystal structure of the film was described by Xray diffraction and other methods.The physical structure and characteristics of the Ag2Se thin films were examined using X-ray and atomic force microscopy (AFM) based techniques. The Ag2Se films surface morphology was examined by AFM techniques; the investigation gave average diameter, surface roughness, and grain size mutation values with increasing annealing temperature

Nanocrystalline TiO 2 and CuO doped TiO 2 thin films were successfully deposited on suitably cleaned glass substrate at constant room temperature and different concentrations of CuO (0.05,0.1,0.15,0.2) wt% using pulse laser deposition(PLD) technique at a constant deposition parameter such as : (pulse Nd:YAG laser with λ=1064 nm, constant energy 800 mJ, with repetition rate 6 Hz and No. of pulse (500). The films were annealed at different annealing temperatures 423K and 523 K. The effect of annealing on the morphological and electrical properties was studied. Surface morphology of the thin films has been studied by using atomic force microscopes which showed that the films have good crystalline and homogeneous surface. The Root M

Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error inject