Sulieman Ibraheem’s research while affiliated with Al al-Bayt University and other places

This study's objective is developing and creating an active interaction between real and theoretical results of the opinion of 100 advanced level of clinical leader against the hospital performance by using high advanced mathematical model. Primary data were collected by one of the most famous and global questionnaires for the authentic leadership with systematic interview, from the top level of clinical leaders in one of the most important university hospitals in Jordan, from August-October 2019. The system represents the input variable, Transparency, Moral/Ethical, Balanced Processing, Self-Awareness, and hospital performance as production. identification system basis on the actual results to estimate the effects of Transparency (tr), Moral/Ethical (et), Balanced Processing (bp), Self-Awareness (sa) on the hospital performance were 69.25%, 82.05%, 67.65% and 99.35%, respectively. The proposed mathematical model has a high accuracy to represent all the active parameters of the system and optimize the conditions to produce high outcomes. The proposed mathematical model depends on experimental results have a real state to optimize the active input variables of tr, et, bp and sa to be equal 89.77%, 83.01%, 91% and 81.05% respectively to have optimum results 93.7%. Scientific studies are needed to satisfy the results of this survey using this dynamic technique.

To get the estimated values for parameters, this study use the recommended Ranking set sampling estimator method (RSSE), which is based on the Newton Raphson approach. For type one censored data, the parameters of the Exponential-Rayleigh distribution of order statistic are estimated. subsequently utilising real COVID-19 data from Health and Environment Ministry in Iraqi and Al Karkh General Hospital. Fuzzy numbers are obtained by estimating their values using the nonlinear membership function (S-function). Finally, the ranking algorithm is applied to turn the fuzzy numbers into crisp numbers.

To get the estimated values for parameters, this study use the recommended Ranking set sampling estimator method (RSSE), which is based on the Newton Raphson approach. For type one censored data, the parameters of the Exponential-Rayleigh distribution of order statistic are estimated. subsequently utilising real COVID-19 data from Health and Environment Ministry in Iraqi and Al Karkh General Hospital. Fuzzy numbers are obtained by estimating their values using the nonlinear membership function (S-function). Finally, the ranking algorithm is applied to turn the fuzzy numbers into crisp numbers.

Long and short text messages are widely transmitted through various communication media, and some of these messages may be very secret or of a special nature, which requires protecting them from the danger of abusers, intruders, or data hackers. Given the importance of protecting text messages, a novel method will be presented focusing on simplifying the procedures to protect data and make the hacking process difficult.A high-level protection method will be provided using the complicated variable content private key; this key can be easily changed without changing the sequence of operations used in the encryption and decryption phases.A secret color image is to be used to generate the private key;both the sender and receiver must save this image, and the selected image key can be easily resized to get a key equal to the secret message length.To increase the degree of protection, it is recommended to divide the text message into blocks with a selected size, the sender and receiver must determine the size, and the byte in each block is to be combined into one vector. This vector will be rotated left using a specified number of bits determined by the sender and receiver.The proposed method will be implemented using various color images and text messages, and the results will be compared with other methods to prove the achievements obtained by the proposed method.

Globally, breast cancer is a major type of cancer among women. This study focused on identifying factors associated with Quality of life among Jordanian women undergoing breast cancer treatment using data mining. Data mining was used to reveal variables by employing several algorithms and the Support Vector Machine to a secondary data set. The results indicated that the factors most strongly related to the physical well-being were pain and fatigue, in the psychological well-being were fear of metastasis, recurrence, and fear from future diagnostic tests, in the social well-being were cancer effect on the women's relationship, and household activity, and in the spiritual well-being were feeling of purpose in life and feeling hopeful. The study signaled the importance of using data mining in health-related studies, and it showed that the dimensions of quality of life of breast cancer women are interrelated and cannot be isolated. Therefore, providing of effective individualized holistic care approach for these women provided by a multidisciplinary team may ease the disease trajectory and improve the quality of life of women with breast cancer

The emergence of financial technology has resulted in the rapid expansion of digital financial services and products that are accessed and delivered through digital channels. However, such technology has also exposed changes in the financial behaviors of the millennial generation. Hence, this article analyzes how digital financial literacy impacts financial behavior in the Jorda-nian millennial generation. A survey method was performed with 627 individuals of millennial generations from several cities in Jordan. Structural equation modeling (SEM) was conducted to estimate the relationship between the research variables. The findings revealed that digital financial literacy with its dimensions, i.e., digital knowledge, digital experience, digital skills, and digital awareness had a positive impact on financial behavior. This article depicts the level of digital financial literacy among Jordan's millennial generation. Therefore, it suggested a set of recommendations revolving around developing and promoting financial education programs for Jorda-nian millennials in collaboration with educational institutions, government agencies, and financial institutions.

Recent technological advancements are endless and have had a profound influence on everyone in every part of life throughout the preceding decades. Artificial intelligence is one such invention that has the potential to change the world. Now, artificial intelligence is being used in almost all commercial operations. Hence, this research attempted to investigate the impact of artificial intelligence dimensions, including natural language processing, machine learning, expert systems, and computer vision on the financial decisions of pharmaceutical companies in Jordan. A cross-sectional approach was used through a comprehensive survey to collect research data from 148 accountants and financial managers in pharmaceutical companies listed on the Amman Stock Exchange with a response rate of 81.3%. The research hypotheses were examined using structural equation modeling of the collected quantitative data. The results indicated that the dimensions of artificial intelligence positively impact financial decisions. Accordingly, companies should spend on building strong artificial intelligence infrastructure and skills. Access to modern artificial intelligence technology, data analysis tools and cloud computing resources are also essential to rationalizing financial decision-making. Besides, Jordan's pharmaceutical sector can overcome these limitations and realize the full potential of artificial intelligence in financial decision-making by solving data privacy issues, encouraging ethical AI research , investing in artificial intelligence expertise, and enhancing collaboration.

This research examined how virtual reality technology affects the marketing performance of the fashion industry in Jordan. Based on a sample composed of 528 online customers, structural equation modeling (SEM) was utilized as a statistical approach for hypothesis testing. The research findings demonstrated that virtual reality technology had a positive significant impact on marketing performance. This examination provides many implications that managers in the fashion industry in Jordan could recognize, the most notable was encouraging them to focus on increased product demos utilizing virtual reality technology in order to improve customers' comprehension of the product and boost their confidence when making purchase decisions.

The present study was done to synthesise an adsorbent, i.e. magnetic graphene oxide (MGO) nanocomposite, which was performed based on a facile precipitation method and was utilised in experiments for removing amoxycillin (AMX). The characteristics of the prepared adsorbent were defined based on commonly utilised analyses (SEM, XRD, BET, TEM, FTIR, VSM, and pH pzc). According to kinetic studies, the PSO model was found as an applicable model for describing data. Moreover, the two-step diffusion process, i.e. diffusion in the boundary layer and the porous structures, was perceived for the evaluated process based on the IPD model. The isotherm models, including Langmuir, Freundlich, Temkin, and D-R, were employed for fitting data and calculating AMX adsorption capacity, among which Langmuir was the best one; using this model, the maximum adsorption capacities for MGO were 91.4, 103.9, 112.3, and 122.5 mg/g, which were achieved at 20, 30, 40, and 50°C. In addition, a feasible, spontaneous, and endothermic process was found for the adsorption of AMX ions, according to thermodynamic studies. The highest percentage of removal (100%) was obtained for the initial concentration of 25 mg/L at 50°C using the adsorbent dose of 1.5 g/L at a pH of 5 and a contact time of 90 min. The values of 74.4 m 2 /g and 27.74 emu/g were detected for the specific surface area and saturation magnetisation values of the MGO, respectively. The overall results were representative of the suitability of the MGO as an adsorbent for removing AMX from aqueous media. ARTICLE HISTORY