Ajloun National University

Accurate load sharing in tie beam foundations represents one of those challenges a structural engineer is bound to encounter regarding the transpiring infrastructure’s structural safety and reliability. Conventional approaches generally remain confined to empirical correlations alone, which are found unable to appropriately address high interaction among the individual properties of the soil and configuration and dimensions of the supporting units and environmental variations. The ML models used in this paper bridge these gaps by combining them with optimization algorithms, increasing the efficiency and accuracy of the predictive performance. Data Description The study dataset contained 21 feature variables representing characteristics related to soil, structural parameters, and environmental conditions. In contrast, one variable is the target, which accounts for the load distribution factor. Three machine learning models are developed for the analysis: Random Forest, Gradient Boosting, and ANNs. Furthermore, optimization algorithms such as SWO, FFOA, EVO, and SAO were implemented to select the features and optimize the hyperparameters to improve performance. The results illustrated that the performance of the models improved much after optimization; ANN outperformed others with the best accuracy of R2 = 0.941 with a minimum error metric of RMSE and MAE. Gradient Boosting and Random Forest also showed enhancements that again evidence the transformation after its optimization. Contributions are threefold: proposing two new optimization techniques and developing a robust predictive framework for the structural engineering domain. In this way, this work underlines the potential currently offered by the combination of ML and optimization in solving complex challenges in engineering. These results set the stage for further research by expanding data sets, considering advanced algorithms, and applying this framework within a wide range of geological contexts that will improve safety and efficiency for engineering practices.

Background This study explores the intersection of strategic management and artificial intelligence (AI) from 2014 to 2024. As AI technologies advance, their integration into strategic management becomes essential for sustaining competitive advantage. The research aims to understand how AI influences and reshapes strategic decision-making and competitive strategies. Objective The study aims to systematically examine how AI and strategic management converge. It seeks to identify key research trends, influential scholars, and major publications to clarify how AI is transforming strategic management and competitive dynamics. Methods We used a three-stage methodology: we used RStudio to analyze 326 scholarly articles, VOSviewer to perform citation and network analysis to identify key authors and research clusters, and Excel for data management and visualization to highlight emerging trends and findings. Results The study identifies three main research clusters: leveraging AI for competitive advantage, the impact of emerging technologies on strategic management, and the role of dynamic capabilities. It underscores AI’s growing importance in strategic foresight and the need for dynamic capabilities to adapt to technological changes. Implications The research offers a roadmap for academics and practitioners, highlighting crucial areas for future exploration and emphasizing the need to integrate AI into strategic management practices to navigate emerging trends and maintain competitive advantage.

The swift evolution of digital banking has underscored the urgent need for enhanced data security. Blockchain technology, with its inherent decentralized, transparent, and secure characteristics, offers a robust solution. This research aims to explore the integration of blockchain technology as a solution for enhancing data security in digital banking services within digital banking services. We began with a thorough literature review to identify existing challenges and opportunities. Following this, we developed a comprehensive blockchain framework specifically designed for digital banking, complete with detailed protocols and security measures. A prototype was developed and rigorously tested to evaluate its security, efficiency, and scalability. Our research includes case studies of blockchain applications in the banking sector, juxtaposed with traditional security methods, to highlight benefits and address any limitations. Performance metrics such as transaction speed, cost-effectiveness, and data integrity were meticulously assessed using real transaction data and simulations, ensuring compliance with privacy standards. The findings of this study provide practical insights and recommendations for implementing blockchain technology in digital banking, demonstrating its potential to significantly enhance data security and operational reliability.

The purpose of this research is to explore the influence of the implementation of total quality management on the development of intellectual capital among workers in Jordanian insurance companies. The method that was used was both descriptive and analytical. All of the workers in the group of insurance companies in Jordan were included in the study's population, which amounted to (214) individuals, and the comprehensive survey method was used. Overall, 192 questionnaires were retrieved for analysis, and the study used SPSS statistical analysis software to analyze the data, and the results of the study showed that the level of application of total quality management dimensions in developing intellectual capital for workers in Jordanian insurance companies came to a moderate degree, and the results of the study indicated that there is a moderate degree of application of the dimensions of total quality management in developing intellectual capital for workers in Jordanian insurance companies.

This study introduces the Intelligent Hybrid Global Optimization (IHGO) algorithm to improve the predictive accuracy of neural network models for estimating the fundamental period of vibration in masonry-infilled reinforced concrete (RC) frame structures. Using a dataset of 4,026 entries, which includes critical structural parameters such as the number of storeys (ranging from 2 to 15), span length (3–8 m), opening ratio (0–50%), and masonry wall stiffness (up to 10⁵ kN/m), the IHGO algorithm optimizes neural network hyperparameters. The IHGO-optimized neural network outperforms baseline models, achieving an R² value of 0.92, a Mean Absolute Error (MAE) of 0.012 s, and a Root Mean Square Error (RMSE) of 0.017 s, compared to 0.85 R², 0.018 MAE, and 0.026 RMSE for the standard neural network. The optimization balances exploration and exploitation, enhancing precision and revealing complex nonlinear relationships between structural features and seismic behavior. The study demonstrates the critical role of accurate period estimation in seismic design, supporting better assessments of structural vulnerabilities and compliance with safety standards. This work highlights the efficacy of hybrid optimization in structural engineering and suggests future research on adaptive tuning and broader seismic applications.

Alzheimer's disease (AD) arises from aberrant protein buildup in the brain and impairs cognition. AD is more susceptible to therapy early on, therefore early detection is crucial for effective treatments. In the last decade, machine learning (ML) methods have successfully detected AD and other medical imaging applications. These methods can automatically learn and recover features from large datasets, making them useful for medical picture analysis. Logistic Regression, Radial Basis Function Support Vector, Decision Tree, Random forest, Adaptive-Boost, EXtreme Gradient Boosting (XG-Boost), Voting Classifier, K-Nearest Neighbour (KNN), Stochastic Gradient Descent (SGD), Quadratic Discriminant Analysis (QDA), Gaussian Naive Bayes, Multi-layer Perceptron (MLP), Extra-Gaussian Naive Bayes, and For AD predictions, ML models are assessed using accuracy, precision, recall, and f1 score using the open access series of imaging studies (OASIS) dataset. The study shows that ML models may be used to generate clinically meaningful AD diagnosis methods in MRI pictures. This paper ranks selected ML models by accuracy scores. Random forest, Voting, and Boosting classifiers achieved 100% accuracy and excellent results, while Passive Aggressive and KNN classifiers had lower scores.

The main objective of this research is to analyze the relations among the Jordan Gen Z individuals Personal Financial (PF) Decisions and the Financial Literacy (FL). It mainly emphasis in determining the way that the Personal (DM) Decision Making gets impacted by the Financial Literacy (FL) level. As it gets impacted in DM expenditure, investments and savings. Then, 487 Gen Z individuals with age varied form 18 to 24 were enrolled in the Jordanian institutions according to the survey outcomes. For the purpose of gathering information about their FL status, Financial Attitudes (FA), Finanial Behavior (FB), skills and knowledges. For obtaining the relations among FL and PF decisions, the SEM was applied for this analysis. Then the outcomes of this study shows that there is a positive correlation among Jordan GenZ individuals PF decisions and FL. Improved financial outcomes, FA and FB due to the FL’s greater level. Various suggestions arrives due to the outcomes of the analysis, schools and universities inclusive financial education programs were implemented. Digital platforms and social media are improved by FL, provision of easily accessible and cost- effective financial services provided to the young clients demands. Thus, the study utilizes SEM for analyzing such complex relations among PF decisions and FL. This research focuses on developing FL over the Jordan’s young individuals through the practical knowledge and Empirical Analaysis (EA) as it helps to fodter the individuals in making financial decisions and it is considered to be its main goal.

This research paper delves into the intricate dynamics between the personality characteristics of social media users and investor behaviour within the Athens Stock Exchange (ASE). In an era where information dissemination occurs at unprecedented speeds through platforms like Twitter, Reddit, and financial forums, the study aims to unravel the impact of personality characteristics on investor sentiment and subsequent stock market volatility. By incorporating the renowned Big Five personality characteristics, including openness, conscientiousness, extraversion, agreeableness, and neuroticism, the research examines how individual personality profiles shape investment decisions. The unique economic and cultural context of the ASE further enriches the investigation, offering insights into the region-specific nuances of investor behaviour. The study applied a quantitative analysis using structural equation modeling and concluded that all personality characteristics of the Big Five model had a positive impact on investor behaviour. The findings not only contribute to the academic discourse on investor behaviour but also offer practical implications for investors, financial institutions, and regulators operating within the Southeastern European market.

This study aims to fill the gap in the literature by investigating the role of using accounting information systems (AIS) on performance in Jordanian Companies. Companies in Jordan might play a significant part in the development of systems, programmers, and communications in the formulation of service performance levels. On the other side, not adopting AIS may hurt the company’s market share, performance, and competitive position. This study contributes to the literature on information systems by giving evidence on the utility of employing AIS to improve performance. This study aims to give evidence that organizations should learn about acceptable information quality aspects for AIS usage to increase job performance and help organizations generate money. The issue of inadequate AIS utilization may be ascribed to the initial goal of information technology adoption, which was mainly to replace the manual accounting process, which has now hampered future usage and study of the system advantages that boost performance.

As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems. We evaluated our model using the RT-IoT2022 dataset, which includes various devices, standard operations, and simulated attacks. Our research’s significance lies in the comprehensive evaluation metrics, including Cohen Kappa and Matthews Correlation Coefficient (MCC), which underscore the model’s reliability and predictive quality. Our model surpassed traditional machine learning algorithms and the state-of-the-art, achieving over 99.6% precision, recall, F1-score, False Positive Rate (FPR), Detection Time, and accuracy, effectively identifying specific threats such as Message Queuing Telemetry Transport (MQTT) Publish, Denial of Service Synchronize network packet crafting tool (DOS SYN Hping), and Network Mapper Operating System Detection (NMAP OS DETECTION). The experimental analysis reveals a significant improvement over existing detection systems, significantly enhancing IoT security paradigms. Through our experimental analysis, we have demonstrated a remarkable enhancement in comparison to existing detection systems, which significantly strengthens the security standards of IoT. Our model effectively addresses the need for advanced, dependable, and adaptable security solutions, serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape. This achievement marks a significant stride towards protecting the integrity of IoT infrastructure, ensuring operational resilience, and building privacy in this groundbreaking technology.

Raising public awareness of environmental issues is a crucial step toward ensuring a sustainable future for both human society and the environment. Universities, along with their students and graduates, play a vital role in this process. As Environmental Education (EE) is a critical subject in education that requires further investigation, this paper seeks to examine students’ levels of knowledge, values, and environmental behaviors. It also aims to assess the role universities play in increasing students’ environmental awareness. The study’s population and sample consisted of full-time students at Ajloun National University, with 100 students randomly selected as the study sample. The results revealed that students demonstrated a high level of environmental awareness and held positive attitudes toward the environment. Furthermore, the findings highlighted the university’s essential role in developing environmental knowledge, attitudes, and behavior. This was achieved through initiatives such as the inclusion of environmental education as an optional subject, the integration of environmental topics in compulsory course syllabi, and the provision of scientific activities. These activities included applied research projects, lecture series, documentary screenings, environmental day celebrations, exhibitions, nature visits, and seminars. These opportunities allowed students to explore solutions to various environmental problems in a practical setting.

Construction projects are inherently complex and prone to delays, significantly impacting project timelines and costs. This study addresses the critical issue of construction delays in Jordan by leveraging advanced methodologies such as Gaussian Process Regression (GPR) and the Analytical Hierarchy Process (AHP). The problem of accurately predicting and managing delays in construction projects has long challenged the industry, with existing approaches often failing to account for the multifaceted nature of delay factors. This research integrates GPR, a machine learning technique, with AHP, a Multi-Criteria Decision Analysis (MCDA) tool, to evaluate and predict the impact of delay factors on project duration. The study employs a comprehensive dataset comprising 191 construction projects in Jordan, with critical variables identified through expert evaluations and literature reviews. The GPR model demonstrated superior predictive capabilities, achieving an R² value close to 1, indicating its high accuracy in forecasting time and cost overruns. The AHP model, on the other hand, prioritized weather conditions and unrealistic contract requirements as the most significant contributors to delays. The findings suggest that the combined application of GPR and AHP offers a robust framework for predicting and managing construction delays, providing valuable insights for improving project management practices. Future work should focus on expanding the dataset and refining the models to enhance their applicability across different regions and project types.

This paper aims to find special cases of some inequalities for numerical radii and spectral radii of a bounded linear operator on a Hil-bert space, we focus on numerical radii inequalities for restricted linear operators on complex Hil-bert spaces for the case of one and two operators, and study the numerical range of an operator K on a complex Hil-bert space H, after that we present some inequalities for numerical radii and spectral radii and studied it to find new results. At the end of this paper we find several inequalities for numerical radii by using the spectral norm, this study is necessary to find other bound for zeros of polynomials and this study is necessary to find new bounds for the zeros of polynomials by a playing the new results to the companion matrix.

This research introduces the notion of complex Pythagorean fuzzy subgroup (CPFSG). Both complex fuzzy subgroup (CFSG) and complex intuitionistic fuzzy subgroup (CIFSG) have significance in assigning membership grades in the unit disk in the complex plane. CFSG has a limitation solved by CIFSG, while CIFSG deals with a limited range of values. The important novelty of the CPFSG lies in its ability to solve the above limitations simultaneously and gets a wider range of values to be engaged in CPFSG. This work has introduced and investigated CPFSG as a new algebraic structure via the conditions that the sum of the square membership and non-membership lies on the unit interval for both the amplitude term and phase term. The result as any CIFSG is CPFSG but the convers is not true has been proved. Complex Pythagorean fuzzy coset has been defined and complex Pythagorean fuzzy normal subgroup (CPFNSG) and their algebraic characteristic has been demonstrated. Homomorphism on the CPFSG is shown. Some results as the inverse image of CPFSG and CPFNSG under iso-morphism function are also a CPFSG and CPFNSG, respectively.

BACKGROUND: This study delves into the academic literature regarding the significance of e-leadership transformation within corporate environments. OBJECTIVE: The primary objective is to analyze and synthesize existing research on e-leadership transformation, identifying key trends, contributors, and thematic clusters. The study aims to provide a comprehensive understanding of the processes and impacts associated with e-leadership, as well as highlight areas for future research. METHODS: We used a dual-method approach incorporating bibliometric analysis as a part of the Systematic Literature Review (SLR) to examine 269 journal articles published between 2010 and 2024, with a focus on the period from 2019 onwards. RESULTS: The analysis identifies significant trends, influential articles, top journals, authors, and leading countries in the field. We identify thematic clusters such as digital leadership and strategic transformation, organisational communication and performance frameworks, behavioural dynamics, and transformational leadership strategies. IMPLICATIONS: Insights from this study offer a deeper understanding of e-leadership transformation’s relevance in corporate settings, highlighting future research prospects and avenues for further exploration in this dynamic and evolving field.

The paper is a landmark in earthquake and structural engineering, with modern machine-learning techniques applied to introduce innovative investigations into forecasting seismic behavior for vertically uneven structures using sophisticated machine-learning methodologies. The research constructs a very accurate model for making predictions using the XGBoost algorithm with the Owl Search algorithm (OSA) for hyperparameter tuning, which explicitly considers complex behavior in the structures under seismic stresses. The variety within the dataset is broad and covers all kinds of irregularities in the structures, such as stiffness and mass irregularities; thus, it has been used to accurately represent the complex characteristics of actual buildings. The results indicate a strong dependence of base shear capacity and seismic performance on the irregularity of stiffness and mass. The test accuracy of the optimized XGBoost model was 98.8%. The result was better than that of conventional models, thus proving the effectiveness of integrating the Owl Search Algorithm in further fine-tuning the parameters. These results give new variables as insight into affecting earthquake resilience and represent practical applications that enhance building design and retrofitting processes. This is further underlined by the proposal of future research directions that would extend the model’s applicability to other structural anomalies and include additional machine-learning methodologies. Through AI-driven approaches, this study captured complicated structural dynamics with the utmost precision, thus opening new insights that could be brought into practice to improve building design and retrofitting strategies in a way that would diminish the impact of seismic events. Graphical abstract

This study empirically analyses the demand for soybean imports from Argentina, the USA, and other countries to Egypt using the Almost Ideal Demand System (AIDS model) during the period (2001-2021). The model parameters used to calculate the Marshallian elasticities for which the own-price elasticities of soybean imports are 1.175, -0.857, and 0.101 for Argentina, the USA, and other countries, respectively. The cross-price elasticity of soybeans imported from Argentina and the USA are complementary relationships; and the soybeans imported from the USA have a substitutability relationship with other countries. Expenditure elasticities are elastic for soybeans imported from the USA and inelastic for soybeans imported from Argentina. If the expenditure on soybean imports increases by 10%, the soybeans imported from the USA and Argentina will increase by 11.23% and 7.36%, respectively. So, the research suggests that, in order to avoid becoming dependent on a small number of nations, Egypt must diversify the sources of its soybean imports. Additionally, it can lower imports in the future by boosting domestic manufacturing and using a variety of supply sources for goods That alludes to the primary innovation of the research, which is that soybeans imported from the USA have a substitutability relationship with other nations and a complementing cross-price elasticity with soybeans imported from Argentina. Keywords Soybeans, Demand system, AIDS model, Marshallian elasticities, expenditure elasticities.

The study’s goal was to determine the impact of administrative skills on employee performance. The descriptive-analytical method was used for the current study’s objectives. A questionnaire was created to gather data based on theoretical literature and prior research that evaluated the study factors. The study population consisted of 410 employees in Jordanian insurance companies, where the questionnaire was distributed to all of them, and 388 questionnaires valid for statistical analysis were retrieved. The study’s findings showed a statistically significant effect at the level (0.05) of the administrative skills with their dimensions (connection, planning, digital knowledge, team building, and integrity) combined on employee performance in its dimensions (performance efficiency, performance size, and performance type) with Jordanian insurance companies. The study recommends increasing the significance of taking into consideration personal characteristics in administrative work since they play a significant influence in obtaining outstanding performance and emphasizing the importance of factors of employee performance in insurance companies.

Gaussian process regression (GPR) models, with their desirable mathematical properties and outstanding practical performance, are increasingly favored in statistics, engineering, and other domains. Despite their advantages, challenges arise when applying GPR to extensive datasets with repeated observations. This study aims to develop models for predicting Finland's soft-sensitive clays’ undrained shear strength (Su). The study presents the first correlation equations for Su of Finnish clays, derived from a multivariate dataset compiled using field and laboratory measurements from 24 locations across Finland. The dataset includes key parameters such as Su from field vane tests, reconsolidation stress, vertical effective stress, liquid limit, plastic limit, natural water content, and sensitivity. The GPR model demonstrated high accuracy, with a mean squared error (MSE) of 0.11% and a correlation coefficient (R²) of 0.98, indicating excellent predictive performance. These findings highlight the strong interactions between Su, consolidation stresses, and index parameters, establishing a robust foundation for practical GPR implementation. The GPR model is recommended for forecasting Su due to its high learning performance and ability to display prediction outputs and intervals. This research has significant implications for various civil engineering applications, including transportation, geotechnical, construction, and structural engineering, offering a valuable tool for improving engineering practices and decision-making.