Background: Medical-surgical nurses are responsible of providing competent care to clients with a wide-array of acute and chronic health problems. This challenging task requires arming nurses with advanced competencies of health literacy to effectively educate their clients. However, evidence about medical-surgical nurse’s health literacy-related knowledge and experience is limited.  Purposes: This study aimed to determine the level of the health literacy-related knowledge and experience among medical-surgical nurses.Design: A descriptive-cross-sectional study was conducted among a total sample of 177 nurses who were practicing in medical-surgical wards in teaching hospitals in Iraq. A convenience sampling method was used to sele

This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise J

Substantial research has been performed on Building Information Modeling (BIM) in various topics, for instance, the use and benefit of BIM in design, construction, sustainable environment building, and Facility assets over the past several years. Although there are various studies on these topics, Building Information Modeling (BIM) awareness through facilities management is still relatively poor. The researcher's interest is increased in BIM study is based heavily upon the perception that it can facilitate the exchange and reuse of information during various project phases. This property and others can be used in the Iraqi Construction industry to motivate the government to eliminate the change resistance to use innovat

Several studies have claimed that AgNPs’ safety is not completely verified. The current work studied the CNS and renal genetic abnormalities (as manifested by modifications in NF-κB transcription) in mice treated with diverse AgNP concentrations. The findings revealed that as the amount of AgNPs was elevated, NF-κB production increased considerably.

The fiscal position of governments in rentier economies depends heavily on oil revenues. The relationship between oil prices and the budget surplus or deficit is often nonlinear and characterized by complex temporal dependencies, which may limit the predictive capability of conventional econometric models. Accordingly, this study aims to forecast the Iraqi budget surplus and deficit and compare the predictive performance of the ARDL, NARDL, LSTM, 1D-CNN, and hybrid 1D-CNN-LSTM models using oil prices as the primary predictive variable. The hybrid model integrates the feature-extraction capability of One-Dimensional Convolutional Neural Networks (1D-CNN) with the ability of Long Short-Term Memory (LSTM) networks to capture long-term

In architectural learning, it is difficult to stimulate cultural awareness through the traditional education approaches, which results in historic places being neglected as knowledge sources. This research explores the premise that sketch-based visual storytelling may act as a generative approach to connect cognition, emotion, and behavior in historical contexts. The study adopts a qualitative methodology to explore a learning experience comprising two phases: the first is a formal educational setting, and the second is a historical and cultural context, aiming to investigate the role of sketch-based storytelling in enhancing cultural awareness. MAXQDA was employed to code the students’ storyboards on three levels of cultural awareness, m

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr