There are several methods in the literature to find the fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems. However, in all these methods, it is assumed that the product of two trapezoidal (triangular) fuzzy numbers will also be a trapezoidal (triangular) fuzzy number. Fan et al. (“Generalized fuzzy linear programming for decision making under uncertainty: Feasibility of fuzzy solutions and solving approach”, Information Sciences, Vol. 241, pp. 12–27, 2013) proposed a method for finding the fuzzy optimal solution of FFLP problems without considering this assumption. In this paper, it is shown that the method proposed by Fan et al. (2013) suffer from errors and to overcome these errors, a new method (named as Mehar method) is proposed for solving FFLP problems by modifying the method proposed by Fan et al. (2013) . To illustrate the proposed method, some numerical problems are solved.

Fe3+-doped nano-TiO2 powders were prepared by sol-gel method. The photocatalytic activity of Fe3+-doped TiO2 nanoparticles was studied by using UV lamp as light source and methylene blue as degradation target. The photocatalytic activity of Fe3+-doped TiO2 was studied by degradation of 4L methylene blue solution with initial concentration of 10mg · L - 1. The results show that the photocatalytic activity of TiO2 can be improved by the addition of Fe3+. When the molar ratio of Fe3+ is 0.5-1%, the calcination temperature is 500 ℃. The photocatalytic degradation of methylene blue is the best.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

The objective of this research was to analyze several reading and writing methods used in educational settings, evaluating their pedagogical approaches and their effectiveness in the process of learning to read and write in school-age children. A systematic review was carried out in the open databases Dialnet and ScieELO, using different inclusion and exclusion criteria, which resulted in 164 documents, applying the PRISMA protocol, 20 were selected. A narrative synthesis analysis was carried out on the following dimensions: reading and writing methods, applied strategies, similarities with other methods and impact on the development of literacy. It is concluded that the combined application of the methods of synthetic and analytical approaches to reading and writing paves the way to attend to the diversity of learning styles, facilitates the strengthening of specific linguistic skills, and strengthens reading comprehension and writing competence.

In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.