Al al-Bayt University

MXene quantum dots (MQDs) provide a chemically versatile platform for metal-ion sensing because their surface coordination environments, defect-associated states, and size-dependent electronic structure can couple molecular recognition to measurable optical responses. This review develops a mechanism-centered framework for understanding that coupling rather than treating sensing performance as a collection of isolated analytical metrics. It first examines how surface terminations, coordination-site accessibility, electronic-state organization, solvation, and interfacial binding thermodynamics define the recognition landscape of MQDs. It then analyzes how recognition events are translated into fluorescence or electrochemiluminescence responses through charge transfer, energy transfer, static or dynamic quenching, inner-filter effects, and related excited-state perturbations. Particular emphasis is placed on structure–property–signal relationships, mechanistic distinguishability, and trade-offs among sensitivity, selectivity, reversibility, and robustness. Evidence from reported metal-ion sensing systems is comparatively assessed to identify recurring design trends and limitations in mechanistic validation, cross-platform benchmarking, and real-matrix translation. The analysis indicates that future progress depends less on maximizing signal magnitude than on establishing reproducible coordination environments, experimentally resolved transduction pathways, and standardized performance criteria. This perspective positions MQD sensing as a recognition-to-signal engineering problem governed by coupled surface chemistry and excited-state dynamics.

This systematic review examines the relationship between transformational leadership and extra-role behaviors among nurses, focusing on how this leadership style influences nurses’ engagement in activities beyond their formal job responsibilities. A systematic review was conducted across databases, including PubMed, CINAHL, Scopus, and Web of Science, covering studies from 2014 to 2024. The PRISMA guidelines were followed, with study selection, data extraction, and quality assessment using standardized tools and the Cochrane Risk of Bias and Joanna Briggs Institute checklists. Eighteen studies, primarily cross-sectional, from various countries were included. The findings consistently show that transformational leadership positively influences extra-role behaviors by enhancing nurses’ job satisfaction, motivation, and organizational commitment. Transformational leadership significantly impacts extra-role behaviors among nurses, improving patient care and organizational outcomes. Leadership development tailored to specific cultural and organizational contexts is crucial for maximizing these benefits. Nursing managers should prioritize the development and implementation of transformational leadership training programs tailored to their specific cultural and organizational contexts to foster extra-role behaviors among nurses, thereby enhancing both patient care and organizational effectiveness.

Solar energy is a clean, abundant, and sustainable power source that forms the foundation of energy sustainability. Researchers have focused on examining various factors affecting solar energy generation and storage to improve the efficiency of solar collectors. They have evaluated different design criteria, considering environmental elements such as wind speed, solar radiation, and ambient temperature. Both experimental methods and numerical simulations, including Computational Fluid Dynamics (CFD), have been used. ANSYS-Fluent CFD modeling, in particular, provides a cost-effective alternative to experiments by simulating fluid flow and heat transfer within solar collectors. This article reviews recent advances in numerical modeling of concentrating solar systems, using ANSYS-Fluent, detailing the models and methods employed while discussing current challenges. It covers various solar concentrators, including evacuated tube collectors (ETC), Linear Fresnel reflectors (LFR), Compound Parabolic Collectors (CPC), and Solar Towers (ST). Summaries of previous studies are tabulated, highlighting different CFD models, techniques, and assumptions. The main goals and results of these studies are outlined. The article also discusses validation techniques and compares experimental data with simulation outcomes, assessing the employed numerical models and methods. It emphasizes common physical models, solution strategies, and assumptions used in analyzing different solar concentrating systems. Additionally, it identifies current challenges, suggests future research directions, and offers perspectives to help advance understanding. This work aims to support researchers in understanding current trends in the numerical simulation of high-concentration solar collectors. Scholars can use this resource to select appropriate models and methods, leveraging their strengths and avoiding common pitfalls in CFD analysis of solar collectors with ANSYS-Fluent.

In the context of univalent function theory, special functions play an important role and have been studied by a number of researchers earlier. This article presents and examines two subfamilies of bi-univalent functions that are governed by Bernoulli polynomials in the open unit disk. We obtain limits on initial coefficients for functions in the specified subfamilies. The FeketeSzeg & ouml; problem is also addressed for the elements of the subfamilies that have been defined. We also present some new results and discuss pertinent links to earlier findings.

Abstract Osteoarthritis is one of the most prevalent degenerative joint diseases that causes an estimated 60 million people pain and limited ability to move throughout the United States alone. Due to increasing numbers of people being diagnosed with Osteoarthritis, it is essential to detect the condition earlier than previously possible to provide better patient outcomes as well as lower costs associated with health care. Researchers have been exploring ways to utilize machine learning and deep learning to determine if Osteoarthritis can be identified utilizing a collection of approximately 2350 labeled X-ray images of joints. The researchers utilized several forms of artificial neural networks that included; a stochastic gradient descent optimizer and adaptive moment estimation (ADAM) in conjunction with a variety of machine learning techniques, which included; random forest (RF), logistic regression (LR), gradient boosting (GB), and decision trees (DT). For consistency across all of the models tested, the researcher divided the dataset into a training group (70%) and a testing group (30%). Additionally, the researcher tested each of the models utilizing a wide array of evaluation metrics (auc, accuracy, f measure, precision, sensitivity, and specificity). Results indicated that the ANN-SGD model provided the greatest accuracy (99.50%) compared to the rf model (99.40%), dt model (98.70%) and the remaining models tested. These results demonstrate the capability of both deep learning and machine learning techniques to analyze and process complex medical image data, and further illustrates their potential to transform clinical practices. Providing accurate predictions of osteoarthritis via X-rays allows healthcare practitioners to make informed decisions regarding patient treatment in a timely manner and develop individualized treatment plans for patients with osteoarthritis. Ultimately, this research demonstrates the potential use of deep learning and machine learning in the diagnosis of osteoarthritis and highlights the potential integration of predictive models developed through these technologies in clinical decision making, and ultimately, improved patient outcomes, and represents a significant advancement in the management of osteoarthritis.