Al Maaref University College

The significance of accurately predicting the creep index coefficient for assessing the long-term settlement of soil is critical in geotechnical engineering. However, current empirical methods for determining the creep coefficient often lack precision, highlighting the need for a more accurate predictive model. This study employs the Multivariate Adaptive Regression Splines (MARS) model to predict the creep index in clay, a critical parameter in geotechnical engineering. The dataset was divided into training and testing subsets. Grid search hyperparameter tuning was applied to optimize the MARS model, as well as a black-box model (Support Vector Machine, SVM) and a white-box model (Lasso), with five-fold cross-validation used to assess their performance. MARS demonstrated superior predictive accuracy, as evidenced by the mean and median R2 and RMSE values obtained from the five-fold cross-validation results. The optimized MARS model was then applied to the test set, achieving excellent predictive accuracy. Finally, the model's performance was compared to previously developed machine learning models and empirical equations across the entire dataset. The MARS model outperformed all others based on RMSE, R2, MAE, and KGE metrics, highlighting its robustness and reliability in predicting the clay creep index.

Abstract The aim of this work to fabricate bamboo (B)/oil palm (O) fibers based biocomposites to assess mechanical, thermal and morphological properties. Flexural strength and modulus of hybrid biocomposite (7B:3O) exhibited a higher value (71.18 MPa and 5.24GPa) respectively among all biocomposite samples. On the other hand, pure biocomposite (B) showed a higher impact strength value (6977.8 J/m2) compared to other biocomposites. The results of TGA showed that thermal stability was observed significantly for hybrid biocomposites compared to other samples. In addition, the maximum decomposition temperature values were displayed in the hybrid biocomposite (5B5O), which was 377.82°C. Through DMA, it was likely to find the storage modulus curves (E′), loss modulus (E″) and damping factor (tan δ) for the biocomposites. The E′ showed an increase in the (3736.84 MPa) contrasted to other corresponding samples which were 3121.81 MPa, and 3209.67 MPa for 3B7O MPa and 5B5O samples, respectively. Overall, the results of DMA displayed an enhancement in the storage modulus (E′) in terms of the hybrid biocomposites representing greater stiffness and lower damping factor. SEM micrographs were utilized to understand the fiber bonding and fiber adhesion with the epoxy matrix. Furthermore, it confirmed SEM results that hybrid natural fibers enhance the complete characterisations of bio‐epoxy materials. Additionally, SEM images of the tensile fracture surfaces discovered voids and cracks. Finally, it can be concluded that the selection for the developed hybrid biocomposites provides excellent and economical lightweight biomaterials for automotive, aerospace, contractions and building components.Highlights Green hybrid biocomposites by oil palm/bamboo fiber/bioepoxy resin. Mechanical, thermal, DMA of hybrid composites carried out. Hybrid biocomposite(7B:3O) exhibited higher flexural strength value (71.18 MPa). TGA showed that the biocomposites are thermally further stable. DMA displayed an enhancement in the storage modulus (E′).

The messenger RNA(mRNA) vaccines have progressed from a theoretical concept to a clinical reality over the last few decades. Compared to conventional vaccination methods, these vaccines have a number of benefits, such as substantial potency, rapid growth, inexpensive production, and safe administration. Nevertheless, their usefulness was restricted up to now due to worries about the erratic and ineffective circulation of mRNA in vivo. Thankfully, these worries have largely been allayed by recent technological developments, which have led to the creation of multiple mRNA vaccination platforms for cancer and viral infections. The mRNA vaccines have been demonstrated as a powerful alternative to traditional conventional vaccines because of their high potency, safetyand efficacy, capacity for rapid clinical development, and potential for rapid, low-cost manufacturing. The paper will examine the present status of mRNA vaccine technology and suggest future paths for the advancement and application of this exciting vaccine platform as a common therapeutic choice.

Abstract Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameters is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines the Extreme Learning Machine (ELM) with the Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.9%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored to the ELM-DOA model, to aid engineers and researchers in effectively utilizing the predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

Cancer drug resistance remains a formidable challenge in modern oncology, necessitating innovative therapeutic strategies. The convergence of intricate regulatory networks involving long non-coding RNAs, microRNAs, and pivotal signaling pathways has emerged as a crucial determinant of drug resistance. This review underscores the multifaceted roles of lncRNAs and miRNAs in orchestrating gene expression and cellular processes, mainly focusing on their interactions with specific signaling pathways. Dysregulation of these networks leads to the acquisition of drug resistance, dampening the efficacy of conventional treatments. The review highlights the potential therapeutic avenues unlocked by targeting these non-coding RNAs. Developing specific inhibitors or mimics for lncRNAs and miRNAs, alone or in combination with conventional chemotherapy, emerges as a promising strategy. In addition, epigenetic modulators, immunotherapies, and personalized medicine present exciting prospects in tackling drug resistance. While substantial progress has been made, challenges, including target validation and safety assessment, remain. The review emphasizes the need for continued research to overcome these hurdles and underscores the transformative potential of lncRNA-miRNA interplay in revolutionizing cancer therapy.

Aim: Dry eye disease (DED) is a prevalent ocular condition that significantly impacts individuals' quality of life and performance. It is charac­terized by the instability of the tear film, which causes ocular surface inflamma­tion and damage that leads to ocular symptoms. However, this study aimed to determine the prevalence of DED and identify associated risk factors among university students in western Saudi Arabia.Methods: A total of 402 university students participated in this study. The sample size was determined using Raosoft software (Raosoft, Inc., Seattle, WA), considering an estimated student population of 20,000. Data were collected between January and March 2023 through an online questionnaire distributed to the participants. The questionnaire comprised three sections, covering general information, behaviors related to digital device (DD) use, and the validated Arabic version of the Ocular Surface Disease Index (OSDI) questionnaire. OSDI scores were calculated, and the severity of DED was categorized using established cutoff points.Results: Among the 402 university students who took part in the survey, the majority (63.2%) were aged between 21 and 25 years, with females representing the dominant gender (72.9%). Notably, 90.8% of participants reported using DDs at bedtime. Over 60% of students had been using DDs for more than 10 years, and approximately 61.7% reported having more than six hours of daily screen time. Mobile devices were the most commonly used electronic devices (67.2%), and TikTok emerged as the most frequently used application (35.6%). Based on the OSDI criteria, 21.1% of students had mild DED symptoms, 14.9% had moderate symptoms, and 38.6% had severe symptoms. Hence, the prevalence of students exhibiting positive DED symptoms was 74.6%, while 25.4% were negative.