Al-Ahliyya Amman University (AAU) (or Amman University, or Amman Private University) is located in Amman, Jordan. Founded in 1990, it was the first private university in Jordan. The university is accredited by the Ministry of Higher Education and Scientific Research, Jordan, and is a member of four university associations.[Note 1] AAU have students from across 30 plus countries across the world.
This study addresses the challenge of accurately predicting oil–water interfacial tension through the integration of surfactant physicochemical descriptors and machine learning algorithms. The main objective was to establish quantitative relationships between surfactant structure, concentration, and oil characteristics to determine their collective effect on interfacial energy minimization. A dataset of 260 experimentally reported points was compiled from peer-reviewed studies, encompassing seven inputs (Surfactant MW, Charge, HLB value, CMC, Concentration, oil ZPC, and Oil API) against measured IFT as output. After ensuring dataset uniformity through leverage-based outlier detection, six algorithms (DT, AdaBoost, RF, KNN, CNN, and MLP-ANN) and one hybrid framework were trained and evaluated using 5-fold cross-validation with performance indices R2, MSE, and AARE%. The Ensemble Learning model achieved the highest accuracy (R2test = 0.986, MSEtest = 4.09), demonstrating superior generalization compared with single learners. SHAP analysis confirmed surfactant concentration as the dominant factor with a strong negative association to IFT, followed by Oil API and ZPC, consistent with Gibbs adsorption theory. The results emphasize that interfacial behavior is mainly dictated by surfactant molecular architecture and concentration rather than oil composition. This unified data-driven approach provides a reproducible framework for evaluating and optimizing surfactant formulations to minimize IFT effectively in industrial applications.
This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561–1739 rpm), primary air flow (98.87–351.13 m3/h), and secondary air flow (6–74 m3/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15–18 μm and 18–21 μm. The Pareto results revealed a clear trade-off: CAI decreased from ∼2.30 to ∼1.65 as CS increased slightly in the fine separation regime and stabilized at ∼1.58–1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate–high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.
The persistent global burden of tuberculosis (TB) and the context-dependent efficacy of the Bacillus Calmette–Guérin (BCG) vaccine necessitate the development of innovative prophylactic strategies. mRNA vaccine platforms have emerged as a transformative toolkit, offering unprecedented versatility in antigen design and manufacturing scalability. This inclusive innovation review synthesizes the molecular engineering and immunological mechanisms of mRNA TB vaccines, evaluating their capacity to address the unique challenges posed by the intracellular lifestyle of Mycobacterium tuberculosis (Mtb). mRNA platforms realistically offer superior endogenous antigen production for CD8⁺ T-cell activation and the flexibility to encode multi-stage fusion antigens targeting both active and latent bacilli. However, significant constraints remain; mRNA technology alone cannot resolve the spatial sequestration of Mtb within necrotic granulomas or the "recruitment lag" of systemic immunity to the lung parenchyma. Achieving sterile protection requires a transition toward mucosal delivery systems capable of inducing lung TRM cells. Furthermore, translational success must be measured beyond classical interferon-gamma (IFN-γ) readouts, prioritizing correlates of protection that reflect site-specific immunity, safety in latently infected populations, and the deployment of thermostable formulations in endemic regions. By integrating mRNA constructs into heterologous prime-boost regimens and host-directed therapies, the field moves toward a precision vaccinology framework capable of curtailing the TB epidemic.
Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental pollutants with high persistence and significant toxic effects on ecosystems and human health. Despite numerous regional studies, a comprehensive understanding of their global distribution across major environmental compartments—soil, water, air, and sediment—is still lacking. This systematic review and meta-analysis address this gap by synthesizing worldwide data to reveal spatial patterns and identify regions with higher contamination levels. Comprehensive searches of PubMed, Scopus, Web of Science, and grey literature identified 15,084 records, of which 79 studies met the inclusion criteria. Random-effects meta-analysis was conducted to estimate pooled PAH concentrations across environmental media and evaluate heterogeneity, sensitivity, and publication bias. The results revealed marked regional disparities, with the highest levels generally reported in Nigeria, Iran, China, and Egypt. Water and soil were the most frequently investigated media (28.7
The tumor microenvironment (TME) is a central regulator and driver of lung cancer progression. Within this TME, cancer-associated fibroblasts (CAFs) serve as key mediators of crosstalk between tumor cells and the surrounding stroma. CAFs promote immunosuppression, remodel the extracellular matrix (ECM), induce abnormal hypoxia and altered metabolism, and contribute to therapeutic resistance. These effects arise through dynamic interactions with cancer cells, cancer stem cells, and other stromal and immune components in the TME. Recent studies have revealed substantial heterogeneity among lung CAFs, with distinct subsets identified by specific marker proteins. This heterogeneity is associated with distinct secretory profiles that support tumor growth. The present review summarizes current understanding of the roles of CAFs in lung cancer progression and therapy resistance. We outline emerging strategies for targeting lung CAFs, including disrupting their signaling pathways, inhibiting ECM remodeling, and blocking CAF-derived secreted factors. In addition, we address the conflicting roles of CAFs in responses to immunotherapy, chemotherapy, and radiotherapy. Finally, we discuss the therapeutic potential of novel approaches, including nanoparticle-based delivery systems, small-molecule inhibitors, natural compounds, and repurposed drugs.
