Zarqa University

Introduction: The research demonstrates the transforming role of AI in email marketing. This points towards the need for advanced strategies which will serve to improve open rates and consumer engagement. The objective of the research is to study the impact of AI on automated emails and consumer familiarity. Literature Review: The key messages emerge on how AI improves engagement through making things timely and personalized, while on the other side, ethical issues reduce consumers who are in a state of mistrust over data privacy or integrity. There is a knowledge gap with regard to AI's long-term impact on trust and its aftermath of ethics. Methodology: This can be a quantitative study through questionnaires; it also includes statistical testing by SPSS. The sample size is 100 for generalization through simple random sampling. Key findings and analyses: While AI increases the rate of engagement and helps people develop trust in a certain brand, it has raised a number of problems in directing privacy concerns. Regression and Chi-square tests confirm significant impacts of personalization on engagement. Conclusion and Limitations: This chapter finally concluded that AI improves the effectiveness of email marketing while ensuring considerate information handling for long-lasting customer relationships. The limitations can be determined by illustrating the sample size which is small with 100 only.

This paper introduces DualConnect, an innovative mobility management protocol for 5G networks that utilizes dual connectivity to facilitate seamless handovers. A make-before-break handover execution technique is suggested in which the UE keeps connections to both the source and target base stations during the handover. This stops any loss of packets or service interruptions. Improvements have been made to measurement reporting, target cell selection, conditional handover, and coordination between base stations to make the dual connectivity model work better. An analytical framework determines the ideal duplication time to reduce packet loss. Extensive simulations show that handover interruption time is over 95% shorter, failure rates are 10 times lower, and latency is 50% lower compared to LTE. DualConnect’s ability to provide almost uninterrupted connectivity while on the move is a good sign for supporting new ultra-reliable low latency use cases in 5G networks.

Stroke prediction and prevention is an important focus in healthcare due to the significant morbidity and mortality associated with strokes. In this study, we investigate using Generative Adversarial Networks (GANs) to augment a stroke dataset and evaluate the effects on prediction performance. The original dataset contained patient medical records and demographics used to predict stroke occurrence. We trained a GAN on these data and generated synthetic samples to augment the training set. Five machine learning models were developed on the original and augmented datasets, including decision tree, k-nearest neighbors, random forest, Support Vector Machine (SVM), and logistic regression classifiers. Experiments indicate statistically significant improvements in prediction accuracy, F1 score, specificity, and sensitivity with GAN augmentation across all models. The random forest classifier achieved the highest average accuracy of 0.981 on augmented data, versus 0.967 on original data. GANs prove effective for tackling class imbalance and enabling more robust stroke prediction from limited real-world data. This demonstrates the potential of data augmentation and generative models to enhance healthcare Artificial Intelligence (AI) applications.

Fault detection in IoT-enabled machinery involves identifying defects in the operation of industrial equipment to foil breakdowns that ensure reliability. It typically relies on analysing data from IoT sensors that monitor key parameters such as temperature, vibration, pressure, and speed in machinery. Fault detection systems in industrial settings often face challenges due to the inconsistent distribution of critical sensor data, affecting monitoring systems reliability. Thus, this research aims to design an Optimized Robust PCA-based Ensemble framework for Machine Learning model to find the abnormalities in the IoT-enabled machinery and ensure robust performance by analysing the distribution patterns of critical sensors. This frameworkA is used to extract the essential samples based on voltage, speed, temperature and vibrations. The ensemble learning model includes K-Nearest Neighbour (KNN) and Adaboost, which are optimized by Bayesian optimization. The importance of optimization technique used to fine-tune the parameters of ensemble learning model. The improved ensembled learning model detects the fault in the IoT enabled machinery can be ensured by the performance metrics such as accuracy, precision, and detection rate and false negative rate. The effectiveness of the proposed model is evaluated by varying operational conditions and viability is analysed using an extensive dataset is IoT enabled machinery operations. Thus, the experimentation of the proposed model significantly improves fault detection reliability, ensuring operational efficiency and reducing downtime in industrial infrastructures.

The mitogen-activated protein kinase (MAPK) signaling pathway plays a key role in regulating a number of cellular processes, including proliferation, differentiation, apoptosis, and cellular responses to stress. In cancer, abnormal activation of some or all pathways of the MAPK cascade, particularly the RAS/RAF/MEK/ERK and p38/JNK signaling pathways, is common across many cancers as well as in an equal or disproportional measure in tumor initiation and progression. In the context of the tumor microenvironment (TME), the MAPK pathway facilitates complex interactions between cancer cells, stroma, and immune and endothelial cells. These interactions allow for elements of tumorigenesis, such as driving angiogenesis, immune suppression, and remodeling of the extracellular matrix, to all contribute to tumor survival and invasion. Additionally, MAPK signaling programs the response to cytokines and growth factor secretion by the TME beyond direct cellular responses, ultimately remodeling the TME in a favorable way for tumorigenesis. While targeting components of MAPK-related pathways has shown promise in the clinic, intrinsic and acquired resistance continues to develop in response to drug therapy composed of compensatory convoluted signaling, the activation of alternate signaling pathways, and TME-induced secondary feedback response. Recent data demonstrate that immune- and stroma-derived signals within the TME can sustain MAPK activity even under pharmacologic inhibition, providing the complexity of achieving durable therapeutic responses. As a result, it is crucial to gain consideration of the connections between MAPK signaling and the TME that may impart for sensitive strategies to elicit sustained drug-resistant treatment responses. Integrative therapies that incorporate MAPK inhibitors in conjunction with immunotherapy approaches, anti-angiogenic therapies, or metabolic modulators are also an important therapeutic strategy to elicit these adaptive pathways. This review will summarize our current understanding of the complex role of MAPK signaling in the TME, as well as propose broad ideas and potential future manipulation of MAPK-coordinated TME approaches for novel, durable cancer therapies.

The transition from bedside nurse to nurse manager can be challenging, and it needs more studies in Jordan. Therefore, this qualitative study aimed to explore the transition experience from a bedside nurse to a nurse manager in a governmental hospital in Jordan. The researcher utilized an interpretive phenomenological approach. Data collection included audio‐recorded face‐to‐face semistructured interviews with 12 full‐time nurse managers. The findings of this study identified six themes representing the experience of transition from bedside nurse to nurse manager: (1) swapping their nursing uniforms for administrative suits seeking transformation, (a) guidance and support: hand in hand and (b) nurse manager is a servant leader; (2) struggled to make a difference while being pulled in all directions with subthemes (a) single‐handed, (b) accountability without authority; and (3) sewing the executive suits with empowerment. In conclusion, the findings of this study indicated that the experience of transition from bedside nurse to nurse manager includes positive and negative aspects that are manifested by a willingness to wear the administrative suits, support, and guidance received either from the manager or from the staff nurses, and the presence of networking and empowerment to be effective as a new nurse manager. At the same time, the negative experiences were a lack of preparedness, lack of autonomy, frustration, and meeting the needs of top and lower managerial levels. Healthcare administrators must implement specialized training programs and assign mentors to facilitate the transition from bedside nurse to nurse manager, ensuring that new nurse managers are qualified to improve patient outcomes.

This article analyzes the process of digitalization in the global energy industry, and describes the use of digital IT solutions, new achievements in technology, as well as modern marketing tools. The objective of this article is to assess the impact of new digital technologies on operational activities efficiency, investment appeal and competitiveness of energy sector companies. The analysis uses econometric techniques to industry-level data and the financial performance of leading natural resource firms. The findings reveal that the applications of IT are closely related to enhancements in operational productivity and business process transparency. The digital elements help establish more developed management practices which are centered on accountability and data driven decision-making. Digital marketing instruments and analytical tools also enhance customer engagement and loyalty, as well as enable more precise energy demand predictions. In the end, digital energy transformation acts as a stimulator of investment scaling and sustainable development in the sector, but also provides impetus for technological and managerial innovation. Practically, implications emphasize that energy firms need to concentrate when implementing IT and marketing strategies, indicating interrelated use of them could directly improve firm performance while indirectly enable innovative practices, and sustained development in the long run for energy companies.