Jadara University

The quantitative determination of thin film thickness using X-ray emission analysis provides an accurate, non-destructive alternative to conventional characterisation methods. This study established empirical relationships among the X-ray k-ratio, backscattering electron coefficient (η), and film thickness for two-dimensional (2D) silicon (Si) and silicon dioxide (SiO₂) films, and a silicon dioxide (SiO₂) layer on a Si substrate. Monte Carlo simulations using the McXRayLite to model the electron-solid interactions at primary beam energies of 3-10 keV and film thicknesses of 2-10 nm. The Si Kα₁ signal intensity was used to compute the k-ratios for unsupported Si and SiO₂ films, whereas both Si Kα₁ and O₂ Kα₁ intensities were used for SiO₂/Si. For the Si Kα₁ signal used, a strong linear correlation was obtained between the k-ratio and the thickness of the 2D Si and SiO₂ films, where an inverse linear relationship was observed for the SiO₂/Si sample. For the O₂ Kα₁ signal used, a strong linear correlation between the k-ratio and top-film SiO2 thickness on the Si substrate was observed. The k-ratio also exhibited a consistent dependence on the backscattering coefficient (η), confirming the influence of backscattered electrons on X-ray generation. Empirical equations were derived to predict the film thickness with uncertainties below 4%, demonstrating that the k-ratio method is a reliable and accurate tool for nanoscale thin film measurements.

This paper studies the existence and uniqueness of periodic solutions to a class of nonlinear neutral matrix difference systems with multiple delays. The analysis is based on the construction of a suitable Green operator combined with fixed-point methods under exponential dichotomy assumptions. The existence of periodic solutions is established using Krasnoselskii’s fixed-point theorem, while uniqueness is demonstrated under a natural contraction condition via Banach’s principle. The results extend previous contributions on neutral difference systems and provide discrete analogues of related differential models. Examples are included to illustrate the applicability of the theory.

Statistical methodologies play a crucial role in biomedical research, particularly in the analysis of clinical and survival data. In oncology, specialized statistical frameworks are often necessary to accurately characterize time-to-event outcomes. This study introduces a generalized model that yields a weighted probability distribution, specifically the Length-Biased Sujit (LBSJT) distribution, derived as an extension of the conventional Sujit model. The proposed distribution incorporates a length-biased mechanism, enhancing its flexibility for modeling survival times. A comparative analysis is conducted between the original and the length-biased versions to evaluate their respective fitting capabilities. Key theoretical properties of the LBSJT distribution are established, including raw moments, the moment generating function, and reliability-related measures. Additional analytical features such as the Bonferroni and Lorenz curves and incomplete moments are also derived. Parameter estimation is performed using the maximum likelihood approach, and a comprehensive Monte Carlo simulation is carried out to assess the precision and robustness of the estimators. The results demonstrate strong estimation accuracy, particularly with increasing sample size and parameter values. Finally, the applicability and effectiveness of the proposed distribution are demonstrated through its application to clinical remission data, where the LBSJT model provides a superior fit compared to several well-established distributions.

This paper introduces a Gauss–Seidel fixed-point iteration approach for estimating the parameters of the Epanechnikov–Burr XII distribution (EBD) probability density function using maximum likelihood principles. The proposed method updates the shape parameter θ and the scale parameter α in an alternating manner based on explicitly derived fixed-point equations. Numerical experiments are conducted to investigate the convergence behavior of the algorithm and to evaluate its performance in comparison with standard numerical optimization techniques.

The exponential growth of Internet of Things (IoT) deployments demands fog computing architectures that position computational resources at the network edge to ensure low-latency processing. However, optimal fog node placement in dynamic environments remains a significant challenge due to node mobility, random failures, and fluctuating traffic patterns, which require continuous adaptation to maintain service quality. This paper introduces the Dynamic Pufferfish Optimization Algorithm (D-POA), a bio-inspired metaheuristic that leverages behavioral strategies observed in pufferfish species to adaptively reposition fog nodes in real-time, balancing exploration of the solution space with refinement of promising configurations. D-POA formulates the placement problem as a continuous multi-objective optimization that simultaneously addresses network connectivity, area coverage, and movement costs to minimize service disruption during reconfiguration. Comprehensive experimental evaluation across five dynamic scenarios demonstrates that D-POA achieves 97.8% connectivity and 98.4% coverage while reducing movement costs by 38-57% compared to baseline algorithms, with near-linear scalability maintaining over 96% solution quality across networks ranging from 50 to 1000 nodes.

Staphylococcus aureus is increasingly resistant to β-lactam antibiotics, making non-β-lactam cell-wall-targeting drugs crucial alternatives. Growing resistance to these agents highlights the need to identify genomic factors influencing susceptibility. Machine learning can integrate genomic and phenotypic data to predict minimum inhibitory concentrations (MICs) and uncover resistance mechanisms across time and regions. We obtained 112,360 S. aureus genomes from NCBI GenBank (March 2024), applying quality filters and standardizing metadata. Resistance genes and mutations were identified using AMRFinderPlus and CARD, focusing on glycopeptide, lipopeptide, bacitracin, and fosfomycin resistance. MICs for five antibiotics were compiled, standardized, and log₂-transformed for analysis. Allelic profiles for seven housekeeping genes were assigned using PubMLST's BIGSdb and MLST CLI v2.19.0. Temporal and geographic resistance trends were modeled using logistic regression and statistical tests. Machine learning models (Random Forest, XGBoost, Elastic Net, Partial Least Squares (PLS)) predicted MICs from genomic features, with performance assessed via cross-validation. Statistical analyses and visualizations were performed in R, with all data and scripts provided for reproducibility. We analyzed 111,350 S. aureus genomes from 137 countries, with 78% from clinical sources, 10% from environmental, veterinary, or food-related origins, and some from animals. Glycopeptide MICs were low across all sources: vancomycin (0.96µg/mL) and teicoplanin (0.52µg/mL), while daptomycin showed more variability (0.44µg/mL). Fosfomycin resistance genes, particularly fosB, were detected in 65.3% of genomes overall, with significantly higher prevalence in clinical isolates (32.5%) compared to environmental (2.1%), food (4.0%), and animal sources (7.5%). Bacitracin resistance genes (bcrAB) were detected in 6.2% of clinical isolates versus 1.3% environmental and 2.8% animal sources. However, phenotypic MIC data were severely limited (fosfomycin n = 1, bacitracin n = 1), precluding validation of genotype-phenotype correlations and limiting epidemiological interpretation to genetic prevalence alone. Resistance to glycopeptides and lipopeptides remained rare (< 0.1%). Fosfomycin resistance protein B (fosB) resistance increased by 0.20% annually, especially in clinical and animal sources, while other mutations like glpT_V213I and murA_D278E declined. Geographic trends showed fosB resistance exceeded 50% in North America, Europe, and South America, with MurA_G257D most prevalent in the Middle East. Machine learning models showed moderate predictive performance for daptomycin MICs (R² = 0.49), with mprF mutations as key predictors, but demonstrated poor accuracy for glycopeptides (vancomycin R² = 0.05; teicoplanin R² = -0.13) due to extremely limited MIC variability in the dataset. Fosfomycin and bacitracin models could not be trained due to insufficient phenotypic data (n = 1 each). This study highlights the growing challenge of S. aureus resistance, especially to non-β-lactam antibiotics such as fosfomycin and bacitracin, with clinical isolates showing the highest resistance. Geographic and temporal trends indicate an increasing prevalence of fosB resistance, particularly in clinical and animal sources. Machine learning models failed to predict glycopeptide MICs (vancomycin R² = 0.05; teicoplanin R² = -0.13) due to limited phenotypic variability, achieved moderate success for daptomycin (R² = 0.49), and could not be trained for fosfomycin and bacitracin (n = 1 MIC measurement each) despite high genetic prevalence of resistance determinants. These findings emphasize the critical need for integrated genomic and phenotypic surveillance, and highlight fundamental data requirements for ML-based resistance prediction.

Abstract Background Hepatitis B virus (HBV) infection is a primary global public health concern since it is commonly recognized as the primary risk factor for cirrhosis and liver cancer globally. This study aims to evaluate the level of knowledge and the prevailing attitudes of Jordanian nursing students toward hepatitis B, with a particular focus on identifying knowledge gaps and factors influencing their understanding and perceptions. Materials and methods A descriptive design was employed to collect data from December 1, 2023, to April 1, 2024. A self-administered questionnaire was used to conduct convenience sampling among senior Jordanian nurses. Result A total of 455 participants were analyzed, revealing a young cohort with a mean age of 22.5 years. The participants demonstrated moderate knowledge of hepatitis B, with specific misconceptions identified. Besides, participants demonstrated a moderately positive attitude toward hepatitis B. However, demographic characteristics showed significant differences in knowledge by sex, whereas certain factors did not. Still, attitudes significantly differed based on those infected with hepatitis B and family members infected with hepatitis B. Conclusion This study focused on training and awareness programs to enhance knowledge and foster a positive attitude towards hepatitis B amongst nursing students. Moreover, combining these training and awareness activities would help improve hospital safety protocols in Jordan and ensure that a hepatitis B virus outbreak is averted.

Enhancing the heat and mass transfer performance of working fluids remains a critical challenge to pursue sustainable and energy-efficient technologies. Although regular working fluids have superior thermo-physical properties to pure base fluids, they often face limitations that hinder their adoption in multifunctional applications. To overcome these challenges, this study develops a novel, comprehensive and physically consistent mathematical model for an unsteady, electrically conducting ternary hybrid nanofluid composed of graphene oxide (GO), cerium oxide (CeO[Formula: see text], and hexagonal boron nitride ([Formula: see text]-BN) suspended in an environmentally friendly ionic liquid (Ethyl-3-methylimidazolium tetrafluoroborate) [EMIM][BF 4 ]. The model integrates magnetic effects, radiation heat transfer, viscous dissipation, Joule heating, and coupled thermo-diffusion effects. A fractal–fractional derivative operator is employed to generalize the governing equations, while the local radial basis functions (RBF) scheme is used to solve them numerically. Computational and graphical results reveal that CeO 2 suppresses the fluid velocity due to increased inertial resistance, while the dispersion of [Formula: see text]-BN significantly enhanced the thermal profile, resulting in a higher Nusselt number. Furthermore, higher values of Dufour and Soret numbers enhance the coupled heat and mass transfer rates, indicating the model’s potential to design advanced heat exchangers and smart cooling devices. These findings provide valuable guidelines for designing compact heat exchangers and thermal energy storage systems for applications in renewable energy and microelectronics cooling.