Mohammad Ali

Improving the quality of extracts derived from medicinal plants is a critical concern due to their extensive use across various industries. The Arvaneh plant (Hymenocrater platystegius Rech. F), a species native to the Khorasan province in Iran and belonging to the mint family, was the focus of this study. We optimized the extraction of Arvaneh plant using both microwave (MW) and pulsed electric field (PEF) techniques. The MW method was tested at different processing times (90, 180, and 270 s) and power levels (180, 540, and 900 W), while the PEF method was evaluated with varying electric field intensities (0.25, 3.25, and 6.25 kV/cm) and pulse numbers (10, 45, and 80) using a central composite design (CCD). The results revealed that extraction efficiency, total phenolic content, and total flavonoid content were significantly higher with the PEF method compared to the MW method (p < 0.05). Moreover, the PEF technique showed superior performance in preserving the antioxidant properties of the extract, as assessed by DPPH and FRAP methods. GC/MS analysis confirmed the presence of 27 bioactive compounds in the Arvaneh extract obtained through PEF-assisted extraction. In conclusion, the PEF method proved to be highly efficient for extracting bioactive compounds from the Arvaneh plant.

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank's short-term resilience to liquidity shocks. Despite limited prior research, particularly in anticipating upcoming positions of bank liquidity risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the liquidity coverage ratio (LCR) under Basel III reforms, focusing on its direction (up, down, stable) rather than on exact values, thus distinguishing itself from previous studies. The research objectively explores the influence of external signals, particularly news sentiment, on liquidity prediction, through novel data augmentation, supported by empirical research, as qualitative factors to build a model predicting LCR positions using AI techniques such as deep and convolutional neural networks. Focused on a semi-private Islamic bank in Iran incorporating 4,288,829 Persian economic news articles from 2004 to 2020, this study compared various AI algorithms. It revealed that real-time news content offers valuable insights into impending changes in LCR, particularly in Islamic banks with elevated liquidity risks, achieving a predictive accuracy of 88.6%. This discovery underscores the importance of complementing traditional qualitative metrics with contemporary news sentiments as a signal, particularly when traditional measures require time-consuming data preparation, offering a promising avenue for risk managers seeking more robust liquidity risk forecasts.

In this paper, an efficient computational discretization approach is investigated for nonlinear fourth-order boundary value problems using beam theory. We specifically deal with nonlinear models described by fourth-order boundary value problems. The proposed method is applied on three different types of problems, i.e., the problem when an elastic bearing is non-zero (Case I), the problem under homogeneous boundary conditions of the unknown function and its second derivative (Case II), and the problem with integral boundary conditions (Case III). Moreover, the convergence analysis of the proposed method is provided. Finally, illustrative examples are included to demonstrate the applicability and validity of the technique and the comparison is made with the existing methods to show the efficiency and accuracy of the proposed method.

Imperialism in the contemporary world

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Abstract: Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have

The idea of governments representing the aspirations of the people in true democratic fashion has always been more of an ideal than a reality.

Abstract α -Glucosidase inhibitors are important in the treatment of type 2 diabetes by regulating blood glucose levels and reducing carbohydrate absorption.

Brucellosis is a major worldwide zoonotic disease that is caused by Brucella spp. and threatens the health of communities. Novel methods for rapid detection of Brucella bacteria are beneficial and necessary in preventing infection and subsequent economic losses. Constructing biosensors with nanoparticles is a promising approach for identification of pathogenic bacteria in a short time. This study aimed to introduce a new detection method of Brucella cells using a biosensor, based on gold nanoparticles and a specific aptamer, via a colorimetric reaction. In this work, gold nanoparticles (GNPs) were synthesized and attached to the aptamer through electrostatic bonding. The binding of aptamer to gold nanoparticles was confirmed by Uv/vis spectrophotometry, FT-IR, transmission electron microscope (TEM) and zeta sizer (DLS).

Introduction Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the central nervous system (CNS), which may lead