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Most existing adaptive information security approaches focus on simplified behavioral patterns and work as isolated models. This limits their effectiveness against advanced and dynamic cyber threats. Therefore, there is an emergent requirement for a mathematically unified framework that can dynamically capture and forecast the aggregate behavior of both the attacker and the defender in a complex environment. The paper proposes a mathematical modeling approach that combines composite behavior models into adaptive information security strategies. The framework encapsulates heterogeneous behavioral patterns into a unified dynamic model that can adapt to an ever-changing threat landscape. This result in novel adaptation rules derived from system dynamics and game theory, with the aim of enabling proactive defense mechanisms that can adapt to real-time challenges posed by adversary actors. The outcomes presented in this paper demonstrate strong improvements in threat detection, mitigation speed, and resource optimization through systematic model implementation, comprehensive simulation, and positive statistical hypothesis testing. The comparison reveals that the proposed method is generally superior to existing methods in scalability and effectiveness. It presents a new class of adaptive cybersecurity models that have deeper behavioral insights and enhanced resilience in complicated threat environments.

Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO2 emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments.

Cyber range environments are key platforms for cybersecurity training, research and testing. This can enable the emulation of realistic cyberattacks and incident response scenarios. Most of the traditional approaches to simulation are based on predefined or rule-based models. These approaches do not allow for adaptation and fail to account for the complexity of evolving threats. An artificial Intelligence-Driven Multi-Agent System (MAS) has been proposed in this paper. The framework autonomously simulates sophisticated cyberattacks and coordinates automated incident response within a cyber range. CICIDS2017 and UNSW-NB15 datasets are combined and integrated into a cyber range simulator CyDER 2.0. Reinforcement learning and anomaly detection methods are used to enable attack and defence agents for adaptive behaviours. The MAS architecture implements realistic attack vectors and response strategies. A set of experiments demonstrate that the AI-driven MAS achieves much higher simulation realism and responsiveness than the traditional static systems. This method also has higher detection accuracy with minimal mitigation times. The model undergoes rigorous validation and acceptance testing to assess robustness and generalizability.

Polycystic ovary syndrome (PCOS) is a global health concern for women of reproductive age, as 6.5% of women worldwide are affected by this syndrome. PCOS is marked by hyperandrogenism, anovulation, menstrual abnormalities, and polycystic ovaries. Metals such as arsenic, cadmium, lead and mercury are considered to be systemic toxicants/human carcinogens and seem to have devastating effects on humans, even at minimal exposures. One of the probable aetiological factors for PCOS has been identified as oxidative stress. In view of the probable associations among oxidative stress, metal toxicity and PCOS, the present study examined the role of heavy metals in the generation of oxidative stress among females. This prospective study included 106 women (56 women diagnosed with PCOS and 50 women who were not diagnosed with PCOS as control women). There were no significant differences in the sociodemographic characteristics between the two groups except for the irregularity of menses and the presence of acne. The serum As, Cd, Pb, and Hg levels increased and the serum glutathione (GSH) and superoxide dismutase (SOD) levels diminished significantly in the PCOS group compared to the control group at P  < 0.001. The SOD levels were negatively correlated with the As and Pb levels at P  < 0.05. Additionally, the PCOS group exhibited a strong negative correlation between the GSH and As levels ( P  < 0.01), GSH and Pb levels ( P  < 0.05) and GSH and Hg levels ( P  < 0.01). Furthermore, the As levels were positively correlated with increased levels of Cd, Pb and Hg among PCOS women. Significant positive correlations were observed between Pb and Cd and between Cd and Hg at P  < 0.001. The outcome of the study provides clear insight into the role of metal-induced oxidative stress, which plays a vital role in the pathophysiology underlying PCOS and suggests the use of these markers as prognostic tools to reduce the consequences of high-risk exposure to these metals among females.

This paper discusses the generalized non-linear (3+1)-dimensional wave equation by modeling and analyzing the dynamics of multi-dimensional nonlinear waves with the WAS-neural network technique. The suggested framework accurately models various wave forms such as bright, singular, and bright-dark solitons. Insofar as we know such neural network based solutions of this model are not reported before. To ensure the reliability and proficiency of the WAS neural network technique. The gain solutions are stable or not by executing the stability analysis on them. The graphical visualization in three-dimensional surface and two-dimensional plots are used. The findings validate that the WAS neural network technique is an efficient and strong alternative to classical techniques of higher-dimensional nonlinear wave equations, and has applications in fluid mechanics and engineering systems that have to deal with gas liquid interactions.

As organizations become more dependent on information technology, security breaches caused by employee negligence and non-compliance have been increasing. Thus, motivating compliant behavior via persuasive communication is of critical importance. An essential form of persuasive communication is awareness messages that are often utilized in awareness campaigns. Existing literature examines different strategies to improve the effectiveness of awareness messages. This dissertation consists of two studies. The first study is a systematic literature review of awareness messages persuasive strategies that have been examined in the literature. The review proposes a classification of these strategies that contribute to the literature by providing a holistic view of the current state of the literature and potential directions for future research. The classification also contributes to the practice by providing theoretically-founded building blocks for constructing persuasive awareness messages. The second study extends the literature by examining the potential effects of narratives in security awareness messages guided by the Theory of Planned Behavior (TPB) and the Narrative Policy Framework (NPF). The results suggest that narratives increase the effectiveness of security awareness messages through narrative transportation and narrator trust. This study contributes to the theory by providing a model that explains the effectiveness of narratives in persuasive communication for information security policy compliance. Additionally, it contributes to practice by shedding light on the importance of crafting engaging narrative-based security awareness messages.

Successful e-government requires planning and unrelenting political commitment and should not focus solely on technological solutions. In the Arab world, citizens receive most of their services from government agencies which are highly bureaucratic in nature; wasting work days confronting employees who are rarely trained in customer service or reprimanded for inefficient work. Between the year 2000-2007, Internet usage worldwide has grown 265.6% while in the Middle East, the growth was 920.2 % and its egovernment readiness rose to higher than the world average of 0.4514. Studies on worldwide e-municipal Web sites, most Arab cities have been clearly absent. In this study an evaluation checklist for municipal Web sites was used to evaluate the only six Arab capitals with official municipal Web sites. The study found that these Web sites were not citizen centered, suffered from fundamental problems, lack basic requirements for any municipal Web site with some feature inoperable and limited interactive services.

The importance of m-Government models lies in their offering a basis to measure and guide m-Government. There is still no agreement on how to assess a government online. Most of the m-Government models are not based on research, nor are they validated. In most countries, m-Government has not reached higher stages of growth. Several scholars have shown a confusing picture of m-Government. What is lacking is an in-depth analysis of m-Government models. Responding to the need for such an analysis, this study identifies the strengths and weaknesses of major national and local m-Government evaluation models. The common limitations of most models are focusing on the government and not the citizen, missing qualitative measures, constructing the e-equivalent of a bureaucratic administration, and defining general criteria without sufficient validations. In addition, this study has found that the metrics defined for national m-Government are not suitable for municipalities, and most of the existing studies have focused on national m-Governments even though local ones are closer to citizens. There is a need for developing a good theoretical model for both national and local municipal m-Government.

The overall purpose of this research was to develop and validate an evaluation instrument and to test various hypotheses using the instrument to determine its value in eliciting more information from users who participate in the design and development of a multimedia Web-based interface. This evaluation instrument or tool, the Multimedia User Interface Usability (MUIU), is proposed to be used as an aid for designers or developers in conducting formative evaluations to locate usability problems of the online user interface. The focus of the MUIU evaluation tool is the quality of media elements used and the efficacy of their integration. The MUIU evaluation tool provides users with an opportunity to accurately convey their attitudes or perceptions on certain design features. The results of the evaluation are quantitative data that may be interpreted and applied to the redesign of the user interface. The original and successive versions of the MUIU evaluation tool were developed based on; (1) the review of the literature and other evaluation instruments; (2) the review and recommendations of expert and novices; and (3) a formal importance rating by experts. Data analyses from expert and novice evaluations of two educational Web sites using the MUIU evaluation tool revealed that the MUIU evaluation tool was reliable and had content validity. The usefulness of the MUIU evaluation tool was addressed by comparing one novice group evaluations of an educational multimedia Web site, using their own words and expressions, to another novice group that used the MUIU evaluation tool. Content analysis of their comments and statements revealed that novices who had previously used the MUIU evaluation tool provided more meaningful and quantifiable results regarding improved usability and design defects with the interface than the novices who evaluated the same Web site but were not exposed to the MUIU evaluation tool.

During the period between December 1988 and February 1995, a total of 279,482 newborn infants were screened in the regional neonatal screening program for congenital hypothyroidism (CH) in Riyadh province, Saudi Arabia. Eighty-one infants were confirmed to have CH giving an incidence of 1 in 3,450. Variable congenital anomalies, other than those of the thyroid gland, were present in 16 (19.8%). The anomalies most frequently encountered were congenital heart defects (7), unclassified multiple congenital anomalies (5) and Down's syndrome (2). The results of our study confirm this association, and emphasize the need to search for such anomalies in infants born with CH. Nationwide studies, however, on birth defects in the general population and those associated with CH are still needed to help us understanding the role of local genetic and environmental factors.