Research Title: Lean Six Sigma, hospital effectiveness, and errors: an empirical analysis of patient safety mediation role in Jordanian hospitals

Author: Audi Naji Khaled Al Smadi, Published Year: 2025

International Journal of Productivity and Performance Management,

Faculty: Business

Abstract: Purpose This paper investigates Lean Six Sigma initiatives, hospital effectiveness results, and hospital error sources, addressing the gap in the literature regarding the mediating role of patient safety results in Jordanian healthcare settings. Design/methodology/approach The study is based on data collected from 10 hospitals located in Amman, Jordan. Four hundred fifty hospital employees in managerial positions, including those in nursing, administration, quality management, and other areas, were given a survey questionnaire. Eighty percent of responses were received. The data were analyzed using structural equation modeling (Path analysis) on the AMOS version 21 software. Findings The research findings indicate that LSS significantly improves patient safety results and hospital effectiveness. Additionally, LSS is significantly associated with a reduction in hospital error sources in Jordanian hospitals. However, patient safety results were found to have no significant direct relationship with hospital error sources. Similarly, the results reveal that patient safety results partially mediate the relationship between LSS and hospital effectiveness. Research limitations/implications This study was limited to hospitals based in Amman, Jordan, and employed a non-probability sampling technique. Thus, the results may not be generalizable to other healthcare organizations, regions within Jordan, other countries, or different types of service organizations. Originality/value This study provides valuable insights for healthcare practitioners seeking to implement the LSS approach in hospital settings. It offers both theoretical and applied contributions to the thoughtfulness of LSS in hospitals. The findings demonstrate that LSS can be effectively applied across various hospital functions, resulting in quantifiable improvements in performance and safety.

Keywords: Hospitals, Lean six sigma, Patient safety, Hospital effectiveness, Hospital error sources

Research Title: The impact of financial inclusion on sustainable development in the MENA region: the moderating effect of digital finance

Author: Mohammad Oqlah Al-Smadi, Published Year: 2025

COGENT ECONOMICS & FINANCE, 13

Faculty: Business

Abstract: Although sustainable development has attracted growing attention from academics and practitioners, the impact of financial inclusion on sustainable development needs more studies in the MENA region. Therefore, this study aims to investigate the effect of financial inclusion on sustainable development and examines the moderating effect of digital finance on this impact in the MENA region. Financial inclusion was measured by the ratio of outstanding loans from commercial banks to GDP, while digital finance was measured by the number of automated teller machines per 100,000 individuals. Four dimensions of sustainable development were measured: general sustainable development, economic sustainable development, social sustainable development, and environmentally sustainable development. A system-generalized method of moment panel analysis is conducted using annual data from 12 countries in the MENA region for the period 2004 to 2023. Additionally, three control variables are used in the study. The results confirm the role of DF in enhancing all dimensions of sustainable development in the MENA countries. In addition, the results prove the importance of digital finance in strengthening the relationship between financial inclusion and sustainable development. This research can be used by financial regulators and institutions in MENA countries to further sustainable development.

Keywords: Financial inclusion; sustainable development; MENA countries; digital finance; System GMM

Research Title: A new approach for optimal sizing and allocation of distributed generation in power grids

Author: Mohammed Bani Younis, Published Year: 2025

International Journal of Power Electronics and Drive Systems, 16

Faculty: Engineering and Technology

Abstract: This paper presents a methodology for optimizing the allocation and sizing of distributed generators (DG) in electrical systems, aiming to minimize active power losses on transmission lines and maintain bus voltages within permissible limits. The approach consists of two stages. First, a sensitivity based analysis is used to identify the optimal candidate bus or buses for DG placement. In the second stage, a new random number generation method is applied to determine the optimal DG sizing. Moreover, a ranking for the optimal locations and sizes is given in case the optimal location is unavailable in real-world scenarios. The proposed methodology is demonstrated through a straightforward algorithm and tested on the IEEE 14-bus and IEEE 30-bus networks. Numerical simulations in MATLAB illustrate the effectiveness of the proposed approach in finding the optimal allocation of DG and the amount of active power to be allocated at the candidate buses, considering the inequality constraints regarding voltage limits and DG allowable power. The paper concludes with results, discussions, and recommendations derived from the proposed approach.

Keywords: DG allocation; jacobian matrix; loss reduction; sensitivity analysis; sizing of DG power

Research Title: A new approach for optimal sizing and allocation of distributed generation in power grids

Author: Mohammed Bani Younis, Published Year: 2025

International Journal of Power Electronics and Drive Systems, 16

Faculty: Engineering and Technology

Abstract: This paper presents a methodology for optimizing the allocation and sizing of distributed generators (DG) in electrical systems, aiming to minimize active power losses on transmission lines and maintain bus voltages within permissible limits. The approach consists of two stages. First, a sensitivity based analysis is used to identify the optimal candidate bus or buses for DG placement. In the second stage, a new random number generation method is applied to determine the optimal DG sizing. Moreover, a ranking for the optimal locations and sizes is given in case the optimal location is unavailable in real-world scenarios. The proposed methodology is demonstrated through a straightforward algorithm and tested on the IEEE 14-bus and IEEE 30-bus networks. Numerical simulations in MATLAB illustrate the effectiveness of the proposed approach in finding the optimal allocation of DG and the amount of active power to be allocated at the candidate buses, considering the inequality constraints regarding voltage limits and DG allowable power. The paper concludes with results, discussions, and recommendations derived from the proposed approach.

Keywords: DG allocation; jacobian matrix; loss reduction; sensitivity analysis; sizing of DG power

Research Title: Premature Avoidance in Genetic Algorithm using Dynamic Mutation Probability

Author: Rawan Nassri Abulail, Published Year: 2025

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 16

Faculty: Information Technology

Abstract: Evolutionary algorithms are optimization techniques based on biological and natural evolution mechanisms. These algorithms are a subset of evolutionary computation and fall under unsupervised learning. The Genetic Algorithm (GA) is one of the most common types of evolutionary algorithms. It begins with an initial set of candidate solutions and starts the evolutionary process by applying certain operators to generate new solutions. The newly produced solutions are expected to outperform the previous ones. Premature convergence is a problem encountered by most evolutionary algorithms, particularly genetic algorithms. It occurs when parental solutions fail to generate better offspring or children with superior traits. Self-adaptive mutations and Panmictic populations are the main factors contributing to premature convergence. Several approaches can be applied to avoid premature convergence and sustain population diversity, including the crowding method, incest prevention algorithm, scheduled sharing approach, cooperation-based approach, syntactic analysis of convergence, random offspring generation, selective mutation, and dynamic reproduction operators. The lack of population diversity leads directly to convergence, forcing the evolutionary algorithm to stop evolving and return the dominant value as the candidate solution. In most cases, this is not an optimal solution. One approach to sustaining population diversity is applying dynamic reproduction genetic operators. The main objective of this research is to propose an enhancement to the standard genetic algorithm to overcome premature convergence. A dynamic reproduction mutation operator is proposed to vary the probability of mutation based on the fitness value in each iteration. The methodology employed by the researcher involves conducting experiments to demonstrate the results achieved after applying the enhanced genetic algorithm (Rowe, 2008). Three different experiments with varying population sizes and mutation probability values were carried out to identify the best solution for an optimization problem. A total of 100 generations were produced by applying 10,000 iterations, and a binary genetic algorithm was used for running iterations with 16-bit chromosome lengths to represent candidate solutions. The results show that improvements in fitness scores were achieved, which enhanced the performance of the genetic algorithm for the produced generations (offspring). Moreover, population diversity was maintained.

Keywords: Evolutionary Algorithms, Genetic Algorithms, Premature Convergence, Dynamic Mutation Probability

Research Title: Enhanced Fitness Proportionate Selection Algorithm for Parent Selection in Genetic Algorithms

Author: Rawan Nassri Abulail, Published Year: 2025

Journal of Internet Services and Information Security (JISIS), 15

Faculty: Information Technology

Abstract: Enhanced Fitness Proportionate Selection Algorithm for Parent Selection in Genetic Algorithms A genetic Algorithm is an evolutionary algorithm that models and simulates biological behavior, whether evolution or genetics, to reach a high-quality solution for search and optimization problems. There are many areas and applications to which genetic algorithms can be applied, like machine learning, feature selection, engineering design, and function optimization. Three leading operators must be applied to each generation's reproduction process; the first is the Selection process, which is applied to the initial population to select the candidate parents to mate and recombine to produce the next generation(offspring). The second operator is a crossover, which is applied to the selected parents from the previous operation (Selection) to make new individuals (offspring) carrying the same traits from parents by combining the parent's chromosomes; the last operator is a mutation, which is applied to the new offspring after crossover. Mutation operation aims to change the value of the chromosome gene randomly. In this research, the selection process will be demonstrated in detail. Then, fitness proportionate selection (FPS) will be presented as one of the most popular methods used in the selection process. The main problem of FPS is the candidate parent, which will mate and recombine to reproduce the next generation; in some cases, a strong individual can mate with a weak one and produce offspring with lower quality traits than the strong parents as a consequence of trait exchange, which happens between that pair. The researcher proposed an enhancement of the FPS algorithm to ensure that strong parents will mate and reproduce strong offspring and propagate their strong traits to the next generations; the proposed enhancement can be summarized as adding a step to the standards FPS to sort the selected individual in ascending or descending order after selection process and before applying cross over and mutation phases. The researcher conducted three experiments to prove the improvements in the fitness value as a consequence of applying that additional step in the selection algorithm; the experiments were performed with three different population sizes and reproduced 100 generations. The fitness score was measured in each generation, and the researcher presented the fitness score evolution over the GA iterations. The results were precise, proving that the sorted individuals after Selection gave better fitness scores than those obtained by applying the standard FPS.

Keywords: Genetic Algorithm, Reproduction Operators, Selection, Fitness Proportional Selection (FPS).

Research Title: Exploring the Factors Influencing AI Adoption Intentions in Higher Education: An Integrated Model of DOI, TOE, and TAM

Author: Rawan Nassri Abulail, Published Year: 2025

Computers, 14

Faculty: Information Technology

Abstract: This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI), the Technology–Organization–Environment (TOE), and the Technology Acceptance Model (TAM) combined framework were proposed and tested using data collected from 367 higher education students, faculty members, and employees. SPSS Amos 24 was used for CB-SEM to choose the best-fitting model, which proved more efficient than traditional multiple regression analysis to examine the relationships among the proposed constructs, ensuring model fit and statistical robustness. The findings reveal that Compatibility “C”, Complexity “CX”, User Interface “UX”, Perceived Ease of Use “PEOU”, User Satisfaction “US”, Performance Expectation “PE”, Artificial intelligence “AI” introducing new tools “AINT”, AI Strategic Alignment “AIS”, Availability of Resources

Keywords: AI adoption; diffusion of innovation theory (DOI); higher education; structural equation modeling (SEM); technology–organization–environment (TOE) framework; technology acceptance model (TAM)

Research Title: A new approach for optimal sizing and allocation of distributed generation in power grids

Author: Wasseem Hani Al Rousan, Published Year: 2025

International Journal of Power Electronics and Drive System, Vol 16 No 3

Faculty: Engineering and Technology

Abstract: This paper presents a methodology for optimizing the allocation and sizing of distributed generators (DG) in electrical systems, aiming to minimize active power losses on transmission lines and maintain bus voltages within permissible limits. The approach consists of two stages. First, a sensitivity based analysis is used to identify the optimal candidate bus or buses for DG placement. In the second stage, a new random number generation method is applied to determine the optimal DG sizing. Moreover, a ranking for the optimal locations and sizes is given in case the optimal location is unavailable in real world scenarios. The proposed methodology is demonstrated through a straightforward algorithm and tested on the IEEE 14 bus and IEEE 30 bus networks. Numerical simulations in MATLAB illustrate the effectiveness of the proposed approach in finding the optimal allocation of DG and the amount of active power to be allocated at the candidate buses, considering the inequality constraints regarding voltage limits and DG allowable power. The paper concludes with results, discussions, and recommendations derived from the proposed approach.

Keywords: DG allocation, Jacobian matrix, Loss reduction, Sensitivity analysis, Sizing of DG power

Research Title: Exploring the Intentions of Jordanian Patients Diagnosed with Hyperlipidemia to Engage in Physical Activity

Author: Maha Mohammed Wahbi Atout, Published Year: 2025

Healthcare , 13

Faculty: Nursing

Abstract: Background: The aim of this study was to explore the intention of Jordanian patients diag- nosed with hyperlipidemia to engage in physical activity. This objective was achieved via an in-depth analysis of how patient attitudes, subjective norms, and perceived behavioral control can influence patient intentions to exercise. Additionally, this research examined how sociodemographic factors and perceived barriers can impact patient participation in physical activity. Methodology: This study employed a cross-sectional approach on a con- venience sample of Jordanian patients diagnosed with hyperlipidemia. To gain the required data, a 15-item questionnaire (derived from the Theory of Planned Behavior) was presented to the participants in the form of an online survey (via several platforms, including What- sApp, Facebook, and email). Results: The results indicate that perceived behavioral control had a significant correlation with the participants’ intentions to participate in physical activ- ity. Additionally, the findings revealed that there were no significant correlations between demographic features (age, marital status, level of education, and monthly income) and intention to engage in physical activity. However, the results ascertained the existence of several facilitators to exercise (such as financial resource availability, self-interest, beneficial weather conditions, and supportive friends or exercise partners). The most commonly reported barriers to physical activity included time constraints, work commitments, and limitations imposed by existing health conditions. Conclusions: These findings provide valuable insights that can be employed to develop physical activity programs that address the cultural needs of Jordanian patients diagnosed with hyperlipidemia and enhance their levels of physical activity.

Keywords: intention; physical activity; Jordanian patients; hyperlipidemia

Research Title: Evaluating wavelet decomposition techniques in protective relays under continuous run-time simulated operation

Author: Wasseem Hani Al Rousan, Published Year: 2025

Results in Engineering, 27

Faculty: Engineering and Technology

Abstract: As modern power systems continue to evolve with the integration of advanced technologies and a growing share of renewable energy sources, maintaining grid stability has become increasingly challenging. Variations in the fundamental frequency and the presence of harmonics can significantly affect the performance of electrical networks. To ensure a stable and reliable grid, continuous monitoring and protection are essential through digital measurements and frequency analysis. This study introduces a run-time testing model for protective frequency relays, utilizing the Wavelet transform as a signal decomposition. Known for its ability to analyze non-stationary signals, the Wavelet transform is applied in three testing configurations: run-time Simulink operation, a combination of Simulink and function-based testing, and run-time sampling for assessing over- and under-frequency relay performance. The model's effectiveness is evaluated based on several key factors, including system complexity, run time responsiveness, data sampling resolution, reaction time, and overall performance. Unlike traditional methods, this approach supports real-time signal decomposition, effective noise filtering, and accurate detection of transient faults. As a result, it delivers more reliable, responsive, and practical protection under the dynamic conditions of modern power grids.

Keywords: Run time operation, Wavelet analysis, Protective relay, Dynamic modeling, Data-driven analysis models, Dynamic condition monitoring