Mohammad Ali

In our investigation, we unveil a novel, eco-friendly, and cost-effective method for crafting a bio-derived electrode using discarded cotton fabric via a carbonization procedure, marking its inaugural application in a vanadium redox flow battery (VRFB). Our findings showcase the superior reaction surface area, heightened carbon content, and enhanced catalytic prowess for vanadium reactions exhibited by this carbonized waste cloth (CWC) electrode compared to commercially treated graphite felt (TT-GF). Therefore, the VRFB system equipped with these custom electrodes surpasses its treated graphite felt counterpart (61% at an equivalent current) and achieves an impressive voltage efficiency of 70% at a current density of 100 mA cm−2. Notably, energy efficiency sees a notable uptick from 58% to 67% under the same current density conditions. These compelling outcomes underscore the immense potential of the carbonized waste cotton cloth electrode for widespread integration in VRFB installations at scale.

Abstract: Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spatial reasoning.

Abstract In this work, a novel series of quinoline-thiosemicarbazone-1,2,3-triazole-aceamide derivatives 10a-n as new potent α-glucosidase inhibitors was designed, synthesized, and evaluated.

The production of medicinal plants under stressful environments offers an alternative to meet the requirements of sustainable agriculture. The action of mycorrhizal fungus; Funneliformis mosseae and zinc in stimulating growth and stress tolerance in medicinal plants is an intriguing area of research. The current study evaluated the combined use of nano-zinc and mycorrhizal fungus on the physiochemical responses of Dracocephalum moldavica under salinity stress. The study employed a factorial based on a completely randomized design with three replications. The treatments were different levels of salinity (0, 50, and 100 mM NaCl), two levels of mycorrhiza application (0 and 5 g kg− 1 of soil), and two levels of foliar spraying of nano zinc oxide (0 and 1000 ppm). Salinity decreased the photosynthetic pigments content, SPAD value, and chlorophyll fluorescence data (Fm, Fv, Fv/Fm). Plant dry weight, Na+ content, and essential oil content were significantly higher at 50 mM salinity + co-application of mycorrhiza and nano zinc oxide. Electrolyte leakage increased under salt stress, while mycorrhizal inoculation compensated for the trait. The main essential oil constituents were geranyl acetate, nerol, geranial, geraniol, viridiflorol, hexadecane, humulene, and germacrene D. Energy metabolism demonstrates the effectiveness of treatment combinations in promoting the biosynthesis and accumulation of essential oil components. The overall results with more comprehensive field-based studies would be advisable for the extension section to utilize marginal salty lands for the reliable production of a valuable medicinal plant.

Abstract Among the various cations, the Fe ion is one of the most critical transition metal ions in living cells for many cellular functions and enzymatic activities.

This study focuses on numerical modeling of the oleogelation process using grape seed oil and beeswax and its validation using experimental approach. The main goal is to investigate how the cooling rate affects this process. The necessary physical and thermal properties of the oleogel for modeling were determined through experiments. Additionally, differential scanning calorimetry was used to characterize phase transitions. The apparent heat capacity method was applied in the numerical modeling to simulate the phase change process, and the energy equation was solved using the finite element method. The numerical model demonstrated a maximum relative error of 5.4%, indicating a strong agreement between the numerical results and experimental data. After validating the numerical model, five different cooling rates were investigated. The findings showed that oleogelation begins near the bottom boundary of the setup and then propagates toward the center. Furthermore, the fraction of the total time required for the phase change to complete varied between 0.35 and 0.04 as the cooling rate decreased. This indicates that slower cooling rates provide more time for heat transfer, allowing for more thorough gelation and completing the phase transition in a smaller fraction of the total time. The proposed model can save time and costs while delivering accurate data on creating a beneficial oleogel.

Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE). A lightweight PDSCNN is then employed to extract relevant tumor-specific patterns while minimizing computational complexity. A hybrid RRELM model is proposed, enhancing the traditional ELM for improved classification performance. The proposed framework is compared with various state-of-the-art models in terms of classification accuracy, model parameters, and layer sizes. The proposed framework achieved remarkable average precision, recall, and accuracy values of 99.35%, 99.30%, and 99.22%, respectively, through five-fold cross-validation. The PDSCNN-RRELM outperformed the extreme learning machine model with pseudoinverse (PELM) and exhibited superior performance. The introduction of ridge regression in the ELM framework led to significant enhancements in classification performance model parameters and layer sizes compared to those of the state-of-the-art models. Additionally, the interpretability of the framework was demonstrated using Shapley Additive Explanations (SHAP), providing insights into the decision-making process and increasing confidence in real-world diagnosis.

Abstract: In this work, we investigate the efficacy of various adapter architectures on supervised binary classification tasks from the SuperGLUE

Abstract: This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example.

Department of Clinical Biochemistry, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran