Al Jouf University

Compared to driver-specific and vehicle-specific characteristics, physiological signal features exhibit superior efficacy in detecting driver fatigue, particularly electroencephalogram (EEG) signals, which are less influenced by subjective human factors and directly reflect brain neural activity. Currently, many detection methods primarily focus on the analysis of EEG signals but fail to consider the interdependencies between signal acquisition channels. To improve the accuracy of driver state detection, a Graph Attention Convolutional Neural Network (GAT-CNN) is proposed in this study. The method incorporates channel relationships and performs an end-to-end learning process, which eliminates the need for manual feature extraction. The preprocessed EEG signals and the adjacency matrix based on mutual information serving as the input to the GAT-CNN. Finally, the EEG signals are classified into two states namely alert and fatigued by fully connected layers and a softmax classification layer. The performance of the method is validated on the SEED-VIG dataset with an average accuracy of 90.14 %, a peak accuracy of 99.23 % and an average F1 score of 91.54 %. Additionally, the Brier Score, used as an evaluation metric, yields an average value of 0.0841, which indicates high predictive accuracy and strong generalization ability. Compared to existing state-of-the-art methods, the GAT-CNN demonstrates superior performance.

Breast cancer is the second leading cause of cancer-related deaths throughout the world, and it remains one of the significant health challenges due to treatment limitations such as drug resistance and adverse side effects. Instead of developing novel treatment approaches, including radiotherapy and chemotherapy, their efficacy is trapped by non-specific toxicity and resistance. This study includes designing a novel chimeric protein combining Interleukin-24 (IL-24) and p18 peptides that are well-being to exhibit significant anticancer properties. The fusion protein, IL-24-P18, was developed to enhance specificity and minimize off-target effects in breast cancer treatment. We used silico techniques to predict this novel fusion protein construct's structural, functional and therapeutic properties. The P18 peptide was fused to the N-terminal of IL-24 using an AEAAAKEAAAKA linker. The fusion protein was found to be nontoxic, non-allergenic and non-antigenic. The fusion protein was hydrophilic with a neutral charge, exhibiting soluble expression in E. coli and stable at varying conditions. Secondary and tertiary structure predictions showed well-defined alpha-helices and a stable 3D conformation. Molecular docking and interaction analysis further confirmed strong binding affinities with a score of -847.5 Kcal/mol between IL-24-P18 and breast cancer receptor proteins, indicating the potential for selective targeting. Moreover, the MD simulation results revealed the interaction stability of the designed fusion protein and receptor. The overall results of this Insilco study showed that IL-24-P18 fusion is a prospective novel candidate against breast cancer that has the potential to inhibit breast cancer with higher accuracy and fewer side effects as compared to already available treatments. Further, in vitro and clinical studies are required to validate its clinical applicability.

Lower limb motion recognition plays a vital role in intelligent prosthetic control and wearable assistance systems. While traditional methods typically utilize multichannel surface electromyography (sEMG) signals to ensure recognition performance, such systems often involve complex hardware setups, are uncomfortable to wear, and require high computational resources. To address these challenges, this study aims to develop an efficient, low-complexity motion recognition approach based on single-channel sEMG signals. The proposed method integrates fast iterative filtering decomposition (FIFD) with a hybrid deep learning framework. FIFD is employed to decompose raw single-channel sEMG signals into multiple subcomponents, allowing the extraction of informative intrinsic features. These features are then processed by a deep neural network that combines convolutional neural networks (CNNs), attention mechanisms, and long short-term memory (LSTM) units to jointly capture spatial and temporal characteristics of the sEMG signal. Experiments were conducted on a self-constructed dataset comprising sEMG recordings of lower limb movements. The proposed method achieved a recognition accuracy of 99.35%, outperforming conventional multichannel approaches and other baseline models. The model demonstrated strong robustness and generalizability using only single-channel input. This study presents a novel single-channel sEMG motion recognition method that significantly reduces system complexity while maintaining high recognition accuracy. The approach offers a promising solution for developing low-cost, efficient, and user-friendly wearable systems, particularly suitable for lower limb rehabilitation and humancomputer interaction in resource-constrained environments.

In this study, novel ultrafiltration (UF) membranes were developed by incorporating magnesium-based metal--organic frameworks (Mg-MOFs) coated with polyaniline (PANI) and sulfonated polyaniline (SPANI) within a polyethersulfone (PES) matrix. The objective was to address the persistent trade-off between permeability and fouling resistance in conventional UF membranes. The hybrid fillers significantly improved membrane morphology, hydrophilicity, and separation performance. ATR-FTIR and FESEM analyses confirmed the successful integration and uniform dispersion of the hybrid fillers, thereby enhancing the pore structure and interfacial compatibility. DFT analysis confirmed that sulfonation improves interfacial compatibility and electronic interactions, enhancing the performance of Mg-MOF@SPANI-modified PES membranes. The addition of Mg-MOF@SPANI at 5 wt% yielded the best performance, resulting in a well-developed asymmetric structure with increased porosity (up to 51 %) and a reduced water contact angle (down to 54 degrees), indicating excellent surface wettability. Consequently, the pure water flux increased by over 60 % compared to neat PES, reaching a maximum of 270 L/m(2)& sdot;h. The modified membranes also exhibited outstanding antifouling properties, with a flux recovery ratio (FRR) exceeding 86 % and a significantly lower irreversible fouling resistance. Furthermore, the membranes maintained high humic acid rejection (>95 %) while minimizing foulant adsorption. These improvements are attributed to the synergistic effects of the MOF's high porosity and the hydrophilic functional groups of the conducting polymer shell, which together promote the formation of a stable hydration layer and enhance pore connectivity. This work demonstrates that incorporating Mg-MOF@SPANI hybrid fillers is a promising strategy for fabricating next-generation UF membranes with superior permeability, fouling resistance, and operational stability for efficient water treatment.

Staphylococcus aureus is among the most frequent causes of dangerous infections. Methicillin-resistant Staphylococcus aureus (MRSA) continues to be a significant public health issue. We critically need to use innovative antimicrobial scaffolds to treat MRSA. Utilizing benzimidazoles and/or 1,3,4-oxadiazole hybrids with MRSA inhibitory activity is crucial for this reason. The current study aims to prepare new benzimidazole-based bis (1,3,4-oxadiazoles) by using a two-step high-yielding tandem protocol. It involves condensing the appropriate bis (aldehydes) with benzimidazole-based acetohydrazides in refluxing dioxane for 3-5 h. Next, the crude bis(Nacetylhydrazones) was collected, redissolved in DMSO, and then refluxed for 4-6 h to undergo a subsequent oxidative cyclization utilizing chloramine trihydrate to give the products in 74-89 % yields. The new hybrids were subjected to antibacterial screening, and a structure-activity relationship was employed. The (2-methyl-1Hbenzo[d]imidazole)-based bis(1,3,4-oxadiazoles) 7e-7h that are attached to different alkane spacers, ranging from propane to hexane, displayed the most powerful antibacterial potency against different ATCC bacteria. They have a comparable potency to ciprofloxacin, with MIC and MBC ranging from 2.81-2.99 and 5.62-5.98 mu M, respectively, against S. aureus. Moreover, they demonstrate promising MRSA inhibitory activity that exceeds linezolid, with MIC and MBC ranging 2.81-2.99 and 11.24-11.96 mu M, respectively, against MRSA ATCC:33591 and ATCC:43300. According to the Ames test, the previous hybrids are not mutagenic to the TA 98 or TA 100 strain at the tested concentrations in the ranges from 2.5 to 7.5 mu M.