Al al-Bayt University

In this paper, we investigate the dynamical behaviors of the rational difference equation xn=(axn−1)/(b+cxnxn−1) with arbitrary initial conditions, where a, b, and c are real numbers. A general solution is obtained. The asymptotic stability of the equilibrium points is investigated, using a nonlinear stability criterion combined with basin of attraction analysis and simulation to determine the stability regions of the equilibrium points. The existence of the periodic solutions is discussed. We investigate the codim-1 bifurcations of the equation. We show that the equation exhibits a Neimark–Sacker bifurcation. For this bifurcation, the topological normal form is computed. To confirm our theoretical results, we performed a numerical simulation as well as numerical bifurcation analysis by using the Matlab package MatContM.

ABSTRACTThis paper introduces the modified dandelion optimizer (mDO), a novel adaptive metaheuristic algorithm designed to address complex engineering optimization challenges, with a focus on infinite impulse response (IIR) system identification. The proposed mDO incorporates three key advancements: an enhanced descending phase to improve global exploration, a novel exploration‐exploitation phase that balances search intensity and breadth, and a self‐adaptive crossover operator that refines solutions dynamically. These innovations specifically target the challenges associated with high‐order IIR modeling, enabling mDO to deliver more precise and efficient system identification. To validate its performance, mDO was rigorously evaluated across diverse testing environments, including the CEC2017 and CEC2022 benchmark functions, various IIR model identification scenarios, and real‐world engineering design problems such as multi‐product batch plant design, multiple disk clutch brake design, and speed reducer design. Comparative analyses reveal that mDO consistently outperforms leading optimization algorithms in terms of accuracy, robustness, and computational efficiency, particularly in complex, high‐dimensional landscapes. Statistical assessments further confirm mDO's superior capability in accurately identifying IIR system parameters even under noise and varying model orders. This study positions mDO as a competitive and versatile tool for engineering applications, offering significant improvements in optimization accuracy and adaptability for advanced system modeling and real‐world problem‐solving.

ABSTRACTRadiotherapy is a cornerstone of lung cancer management, though its efficacy is frequently undermined by intrinsic and acquired radioresistance. This review examines the complexity of lung tumors, highlighting their potential as a reservoir of novel targets for radiosensitization. Ionizing radiation (IR) primarily exerts its effects through oxidative damage and DNA double‐strand breaks (DSBs). Lung cancer cells, however, develop mutations that enhance DNA damage response (DDR) and suppress cell death pathways. Additionally, interactions between tumor cells and tumor microenvironment (TME) components—including immune cells, stromal cells, and molecular mediators such as cytokines, chemokines, and growth factors—contribute to resistance against IR. Understanding these intricate relationships reveals potential targets to improve radiotherapy outcomes. Promising targets include DDR pathways, immunosuppressive cells and molecules, hypoxia, proangiogenic mediators, and other key signaling pathways. This review discusses emerging strategies, such as combining radiotherapy with immunomodulators, hypoxia and proangiogenic inhibitors, DDR‐targeting agents, and other innovative approaches. By offering a comprehensive analysis of the lung TME, this review underscores opportunities to enhance radiotherapy effectiveness through targeted radiosensitization strategies.

ABSTRACTThree Co(II) complexes of terephthalate anion containing 1,10‐phenanthroline (phen) or 2,2′‐bipyridine (2,2′‐bipy), are prepared. The structures of dimeric [Co2(H2O)4(μ‐H2O)2(phen)2]4+·2(C8H4O42−), 1, and the polymeric [Co2(H2O)2(phen)2(μ‐C8H4O4)2]n, 2, complexes were confirmed by single crystal structure determination. Both complexes have CoN2O4 core in a distorted octahedral arrangement. Extensive hydrogen bonding and π–π stacking interactions consolidate 3‐D structures in 1 and 2. Complex 3, [Co(μ‐C8H4O4)(2,2′‐bipy)]n, was previously reported. It has CoN2O4 core in a distorted trigonal prismatic environment. Complexes 1, 2 and 3 exhibited good inhibition efficiencies for corrosion of carbon steel in 0.25‐M sulfuric acid solution. The order of corrosion inhibiting effect of the complexes was 2 (94.6%) > 1 (90.5%) > 3 (79.5%). The increase in Rp value and the decrease in Cdl were in parallel with the increase in the concentration of complexes. Tafel plot indicated that complex 1 behaved as a cathodic‐type corrosion inhibitor, whereas complexes 2 and 3 as anodic inhibitors for C‐steel in sulfuric acid medium. Weight loss measurement of steel samples in 1‐M HCl showed more inhibition of corrosion by complexes 1 and 2 than by complex 3. The adsorption mechanism of inhibitors on C‐steel followed Langmuir isotherm. The free energy changes indicated comprehensive physical and chemical adsorption for the three complexes on the surface of the C‐steel. SEM analysis of steel samples in 1‐M HCl proved the efficiency of complexes in retarding corrosion as the steel surface showed smoothness compared to the blank. Quantum chemical DFT study declared that the highest corrosion inhibition efficiency is observed for complex 2, which was strongly supported by the electrochemical anticorrosion studies. The results of the TGA analysis support the X‐ray crystal structures of the complexes.

Background: Type 2 diabetes mellitus is a growing epidemic condition that is expected to reach pandemic levels in the upcoming decades. Physical activity among individuals with type 2 diabetes is beneficial. A deeper understanding of physical activity among adults with type 2 diabetes in Jordan using a qualitative approach is needed. Aim: This study aimed at exploring physical activity among adults with type 2 diabetes in Jordan. Design: A constructivist grounded theory methodology guided this study. Methods: Data were collected using semi-structured and audio-recorded interviews and then analysed simultaneously using coding, constant comparative analysis and writing reflexive memos. Findings: Two themes emerged including ‘The Perception about Physical Activity’ and ‘Factors Influencing Adherence to Physical Activity’. The first theme included four sub-themes: physical activity definition; importance; duration and types. The second theme included five sub-themes: the belief that diet is superior to physical activity; ageing and presence of diabetes or comorbidities; job and family obligations; social support and weather. Conclusion: This study provided insights into patients’ perceptions and adherence to physical activity including facilitators and barriers. Clinicians and policymakers may consider the findings of this study to develop health promotion programmes and to suggest a suitable environment for individuals with type 2 diabetes to enhance their physical activity.

Abstract Background Healthcare organizations are distinguished by intricate systems that undergo continual modifications and unpredictability. This greatly hinders the ability to estimate the exact consequences of any changes accurately. Therefore, scholars prove that strategic foresight enables leaders to anticipate future challenges and possibilities. The utilization of artificial intelligence (AI) in management is on the rise, mostly because of its ability to provide intelligent services, reduce medical errors, and improve operational efficiency. Purpose To examine the impact of strategic foresight on the quality of healthcare services provided by Jordanian nurses in the context of AI solutions in governmental hospitals. Method A cross-sectional descriptive correlational analysis was conducted. A convenience sampling approach was used in the four selected Jordanian governmental hospitals. The study’s target population consisted of nurses. Over three weeks between January and February 2024, 240 self-reported questionnaires were received using a five-point Likert scale, with a response rate of 88.9%. The completed surveys were suitable for analysis using AMOS SPSS v. 26 and SPSS. Results Simple linear regression and (Pearson’s r) test results showed that (R = .279, R square = 0.078) between strategic foresight and the quality of healthcare services. (R = .543, R square = 0.295) between strategic foresight and the adoption of AI-based solutions. And (R = .432, R square = 0.187) between adopting AI-based solutions and the quality of healthcare services. That reveals a statistically significant, positive correlation coefficient relationship between the variables. In the presence of the mediator, the direct relationship between strategic foresight and healthcare service quality was not statistically significant (b = 0.063, p = .398). The path analysis test indicates a linear relationship between the variables sequentially, and the AI-based solutions completely mediate the relationship between strategic foresight and the quality of healthcare services. Conclusions A positive and significant correlation between the variables suggests that a simulation-proposed model for a healthcare quality forecasting system, which the researcher built and included in the study recommendations, has to be designed. Therefore, AI-based forecasting systems should incorporate health service quality parameters to facilitate high efficiency and prompt patient demand fulfillment.

ABSTRACTBackgroundUnlike other medical practitioners, nurses working in pediatric intensive care units face uniquely challenging workplace conditions because they care for preterm newborns and critically ill patients. These workplace challenges led to increased compassion fatigue (i.e., burnout and secondary traumatic stress) and decreased compassion satisfaction. Compassion fatigue and compassion satisfaction strongly influence the quality of care and patient outcomes, and these need to be addressed through effective interventions such as mindfulness‐based interventions.AimThis study aimed to examine the impact of a brief mindfulness‐based intervention on compassion fatigue and compassion satisfaction among pediatric intensive care nurses.MethodsA quasi experimental study with a pretest posttest design was used to recruit 204 nurses: 102 in the intervention group and 102 in the control group with randomization by hospital (n = 4). The brief mindfulness‐based intervention was delivered over 6 weeks. Data were collected using a demographics questionnaire and the Professional Quality of Life Scale, Version 5.ResultsThe intervention group's mean scores of burnout and secondary traumatic stress were significantly lower postinterventions compared with the control group. Similarly, the mean compassion satisfaction score for the intervention group indicated a significant improvement post‐intervention compared with the control group. Additional evidence for the effectiveness of the intervention was the disappearance of low compassion satisfaction, high burnout, and high secondary traumatic stress categorizations postintervention in the intervention group, contrary to the control group.Linking Evidence to ActionImplementing brief mindfulness‐based interventions can improve pediatric intensive care nurses' well‐being by reducing burnout and secondary traumatic stress while enhancing compassion satisfaction. By using the study's findings, nurse managers can make these practices essential for high‐quality care and effective workforce management.Trial RegistrationClinicalTrials.gov identifier: ACTRN12622000389707

Recently, several researchers have estimated the Maclaurin coefficients, namely q2 and q3, and the Fekete–Szegö functional problem of functions belonging to some special subfamilies of analytic functions related to certain polynomials, such as Lucas polynomials, Legendrae polynomials, Chebyshev polynomials, and others. This study obtains the bounds of coefficients q2 and q3, and the Fekete–Szegö functional problem for functions belonging to the comprehensive subfamilies T(ζ,ϵ,δ) and J(φ,δ) of analytic functions in a symmetric domain U, using the imaginary error function subordinate to Euler polynomials. After specializing the parameters used in our main results, a number of new special cases are also obtained.

Assessing Environmental, Social, and Governance (ESG) practices in the banking sector is becoming increasingly important. This study aims to analyze the correlation between ESG scores and the performance of banks. The ESG data were gathered using a Bloomberg database. Using fixed-effect estimation for a static model, this study examines a balanced panel sample of 15 Jordanian-listed banks from 2009 to 2023. Based on multivariate regression, the study outcomes suggest that Jordanian banks with higher ESG scores perform better in operating and market performance. Stakeholder theory supports this. Accordingly, the R2 values for the study models were 23.9% for the ROA model and 18.7% for Tobin’s Q, respectively, showing the high explanatory power of both models. Therefore, an increase of one point in ESG scores leads to a corresponding rise in ROA and Tobin’s Q 0.496 and 0.370, respectively. Regarding control variables, leverage has a negative correlation coefficient of –0.169 and –0.253, respectively, in both the ROA and Tobin’s Q models. According to the ROA model, a one-unit increase in bank size leads to a 0.309-unit increase in bank performance and a 0.115-unit increase, according to Tobin’s Q model. Similarly, as the bank ages by one year, its performance improves, with the ROA and Tobin’s Q models showing increases of 0.216 and 0.116 units, respectively. Additionally, the financial development showed correlation coefficients of 0.108 and 0.045 for the ROA and Tobin’s Q models, respectively. However, the ESG committee does not affect the performance of banks. AcknowledgmentThis research was funded through the annual funding track by the Deanship of Scientific Research, from the Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant NO. KFU242703].

Abstract C12H9Br2N3O3, monoclinic, P21/c (no. 14), a = 9.0255(5) Å, b = 14.9112(5) Å, c = 10.9438(5) Å, β = 109.223(5)°, V = 1390.71(12) Å3, Z = 4, Rgt (F) = 0.0482, wRref (F 2) = 0.0948, T = 293(2) K.

The investigation of adsorption properties of dopamine has aroused significant attention in search for the design of effective biosensors. In this work, arsenene-based biosensors functionalized with Ag atoms are modeled and optimized using DFT calculations. The adsorption and surface reactivity of dopamine molecule can be enhanced over the Ag-functionalized arsenene nanostructures compared with the pristine systems. Charge density difference plots and total charge distribution analysis indicate the significant accumulation of charge densities over the adsorbed Ag atom and dopamine molecule. Compared with pure arsenene, surface modification by Ag adatom enhances the reactivity of arsenene, which is very conducive to biosensing of dopamine by arsenene surface. Moreover, in the PDOS analysis, there are great peak overlaps between Ag–O and Ag–N atoms, confirming the formation of chemical bonds between the Ag and O or N atoms. Our results indicate the substantial efficiency of Ag-functionalized arsenene nanosheets for sensing dopamine molecules.

Hand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students.