Al al-Bayt University

The impact of advertising and sales promotion on firm value and sales performance within the Jordanian manufacturing sector was examined, recognizing the significant role of advertising in enhancing competitive market outcomes. The study aimed to investigate the effect of advertising and sales promotion on firm value within the manufacturing Jordanian firms that holds a benefit for deciphering several challenges and opportunities that firms face within an emerging market context. Data from 64 Jordanian manufacturing firms listed on Amman Stock Exchange between 2014 and 2022 were analyzed. Regression analysis was applied across two models: one focused on the relationship between advertising expenditures and firm value, while the other assessed sales performance. Firm size and return on equity served as control variables across both models. The results revealed that advertising and sales promotion expenses had a significant and positive effect on both firm value and sales performance. Specifically, advertising's impact on firm value was characterized by a coefficient of 0.107 and a t-value of 3.640, while its effect on sales performance yielded a coefficient of 0.321 and a t-value of 9.372, both with p-values of 0.00, highlighting a strong statistical significance. Additionally, firm size demonstrated a robust positive effect on both outcomes, underscoring its role as a critical control factor. Return on equity, however, did not yield a significant effect. These findings underscore the importance of advertising as a driver of firm growth and market position, particularly in larger firms. Investment in advertising appears to foster sustainable value and performance enhancements, offering firms in competitive sectors a strategic path for growth.

Nowadays, diseases have a high rate of incidence and mortality worldwide. On the other side, the drawbacks of conventional modalities in the suppression of diseases have encountered serious problematic issues for the health of human beings. For instance, although various approaches have been applied for the treatment of cancer, it has an ever‐increasing rate of incidence and mortality throughout the globe. Thus, there is a fundamental requirement for the development of breakthrough technologies in the inhibition of diseases. Hyaluronic acid (HA) is one of the most practical biopolymers in the suppression of diseases. HA has lots of potential physicochemical (like rheological, structural, molecular weight, and ionization, etc.) and biomedical properties (bioavailability, biocompatibility, CD44 targeting and signaling pathways, components of biological organs, mucoadhesion, immunomodulation, etc.), which made it a potential candidate for the development of breakthrough tools in pharmaceutical and biomedical sciences. The ease of surface modification (carboxylation, amidation, hydroxylation, and esterification), high bioavailability and synthesis routes, and various administration routes are considered as other merits of HA‐based vehicles. These mucopolysaccharide HA‐based materials have been considerably developed for use in drug delivery systems (DDSs), cancer therapy, wound healing, antiaging, and tissue engineering. This review summarizes the advantages of HA‐based DDS and scaffolds in the treatment of diseases.

This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques. A dataset of 1759 samples (987 breast cancer patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, and ElasticNet for feature selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, and transformer-based embeddings (BioBERT, DNABERT), were applied to enhance model interpretability. Classifiers such as XGBoost, LightGBM, ensemble voting, Multi-Layer Perceptron, and Stacking were trained using grid search and cross-validation. Model evaluation was conducted using accuracy, AUC, MCC, Kappa Score, ROC, and PR curves, with external validation performed on an independent dataset of 175 samples. XGBoost and LightGBM achieved the highest test accuracies (0.91 and 0.90) and AUC values (up to 0.92), particularly with NMF and BioBERT. The ensemble Voting method exhibited the best external accuracy (0.92), confirming its robustness. Transformer-based embeddings and advanced feature selection techniques significantly improved model performance compared to conventional approaches like PCA and Decision Trees. The proposed ML framework enhances diagnostic accuracy and interpretability, demonstrating strong generalizability on an external dataset. These findings highlight its potential for precision oncology and personalized breast cancer diagnostics.

This study aims to assess a hybrid framework that combines radiomic features with deep learning and attention mechanisms to improve the accuracy of classifying lung cancer subtypes using CT images. A dataset of 2725 lung cancer images was used, covering various subtypes: adenocarcinoma (552 images), SCC (380 images), small cell lung cancer (SCLC) (307 images), large cell carcinoma (215 images), and pulmonary carcinoid tumors (180 images). The images were extracted as 2D slices from 3D CT scans, with tumor-containing slices selected from scans obtained across five healthcare centers. The number of slices per patient varied between 7 and 30, depending on tumor visibility. CT images were preprocessed using standardization, cropping, and Gaussian smoothing to ensure consistency across scans from different imaging instruments used at the centers. Radiomic features, including first-order statistics (FOS), shape-based, and texture-based features, were extracted using the PyRadiomics library. A DeepCNN architecture, integrated with attention mechanisms in the second convolutional block, was used for deep feature extraction, focusing on diagnostically important regions. The dataset was split into training (60%), validation (20%), and testing (20%) sets. Various feature selection techniques, such as Non-negative Matrix Factorization (NMF) and Recursive Feature Elimination (RFE), were used, and multiple machines learning models, including XGBoost and Stacking, were evaluated using accuracy, sensitivity, and AUC metrics. The model’s reproducibility was validated using ICC analysis across different imaging conditions. The hybrid model, which integrates DeepCNN with attention mechanisms, outperformed traditional methods. It achieved a testing accuracy of 92.47%, an AUC of 93.99%, and a sensitivity of 92.11%. XGBoost with NMF showed the best performance across all models, and the combination of radiomic and deep features improved classification further. Attention mechanisms played a key role in enhancing model performance by focusing on relevant tumor areas, reducing misclassification from irrelevant features. This also improved the performance of the 3D Autoencoder, boosting the AUC to 93.89% and accuracy to 93.24%. This study shows that combining radiomic features with deep learning—especially when enhanced by attention mechanisms—creates a powerful and accurate framework for classifying lung cancer subtypes. Clinical trial number Not applicable.

In this study, a combination of ab initio calculation (density functional theory) and a thermodynamic approach was applied to investigate the properties of arsenic in exhaust gas emitted from coal-based power plants in various temperature ranges. Also, the mechanism of interaction of aluminum phosphorus nanotube (AlPNT) with various arsenic moieties in the gas phase was studied. The stock gas is rich in trivalent arsenic (As³⁺), while the temperature can remarkably alter its morphological distribution. In the case of temperature < 850 K, the trigonal bipyramid form is the governing structure for trioxide moieties. On the other hand, for temperature > 850 K, the dominant structure is chain type rather than trigonal bipyramid. This work is devoted to confirming the possibility of arsenic removal from the exhaust gas by using AlPNT as an adsorbent. Also, it should be mentioned that compared with the AlPNTs surface’s performance is high.

Background Orthopaedic procedures often cause intense postoperative pain, posing challenges for effective management. Brachial plexus blocks offer relief but optimising analgesia with minimal local anaesthetic is still challenging. Perineural dexamethasone, with anti-inflammatory effects, shows promise in lower doses but lacks sufficient research. Objective The study aims to assess low-dose perineural dexamethasone in ultrasonography-guided brachial plexus blocks for extending analgesia and reducing opioid use in upper limb surgeries. Methods Double-blinded trial on 90 American Society of Anaesthesiologists class I or II patients undergoing upper limb procedures. The patients were divided into two groups and received bupivacaine with either 4 mg dexamethasone (Group D) or saline (Group C). Analgesia duration was evaluated via the Numerical Pain Rating Scale and adverse events were recorded. Findings The dexamethasone group showed significantly longer analgesia (1253.33 ± 41.00 vs. 714.67 ± 32.80 min, p < 0.001) and lower Numerical Pain Rating Scale scores at 4, 8, 12, and 24 h postoperatively. Minimal adverse events were observed in both groups, with mild nausea being the only event reported. Conclusions In upper limb procedures, low-dose perineural dexamethasone improves postoperative pain management with few side effects. It presents a viable adjunct for enhancing pain management techniques.

Data security in mobile environments has become a critical concern, driven by the growing demand for mobile services and the proliferation of data-intensive applications such as online gaming, virtual reality, and augmented reality. These applications generate massive amounts of data, challenging the storage, computational capacity, and battery life of mobile devices. Cloud environments offer a solution through task offloading, but centralized architectures introduce latency and potential vulnerabilities. Edge computing-based cloudlet networks have emerged as a promising alternative, providing localized resources to enhance service quality. However, their proximity to users increases susceptibility to security threats, posing barriers to widespread adoption. This paper presents a novel approach to addressing these challenges by integrating blockchain technology with cloudlet networks, bolstered by an agent-layer concept. The proposed architecture features an agent between mobile devices and cloudlets, utilizing a unique "proof of trust" consensus mechanism. This mechanism evaluates trust and experience based on the number of coins held by nodes, selecting miners for message verification using an elliptic curve cryptography scheme. In cases of dispute, a third miner resolves conflicts, with incorrect verifications resulting in penalties that deter malicious behavior. Experimental results demonstrate that this solution significantly enhances security, mitigates latency, and improves network performance compared to existing methods. These findings highlight the potential of blockchain-integrated cloudlet networks to revolutionize mobile data processing, offering robust security and reliable interactions between mobile devices and cloudlets.

The proliferation of fake news has become a significant threat, influencing individuals, institutions, and societies at large. This issue has been exacerbated by the pervasive integration of social media into daily life, directly shaping opinions, trends, and even the economies of nations. Social media platforms have struggled to mitigate the effects of fake news, relying primarily on traditional methods based on human expertise and knowledge. Consequently, machine learning (ML) and deep learning (DL) techniques now play a critical role in distinguishing fake news, necessitating their extensive deployment to counter the rapid spread of misinformation across all languages, particularly Arabic. Detecting fake news in Arabic presents unique challenges, including complex grammar, diverse dialects, and the scarcity of annotated datasets, along with a lack of research in the field of fake news detection compared to English. This study provides a comprehensive review of fake news, examining its types, domains, characteristics, life cycle, and detection approaches. It further explores recent advancements in research leveraging ML, DL, and transformer-based techniques for fake news detection, with a special attention to Arabic. The research delves into Arabic-specific pre-processing techniques, methodologies tailored for fake news detection in the language, and the datasets employed in these studies. Additionally, it outlines future research directions aimed at developing more effective and robust strategies to address the challenge of fake news detection in Arabic content.

Background Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. Methods This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. Results The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. Conclusions The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

In regions facing water scarcity, such as Jordan, accurate measuring and tracking of water usage is crucial to prevent depletion of water resources. This can be done by implementing water accounting to reveal opportunities for reuse and recycling. In this study, water accounting plus (WA+) and open-access remote sensing data from the FAO water productivity portal (WaPOR) were applied to develop agricultural water accounting (AWA) and quantify the inflows, outflows, and water consumption in the Amman Zarqa Basin (AZB) for the period 2014–2022. An assessment is made for WaPOR data utility in AWA. Results showed positive correlations between WaPOR precipitation data and rainfall station records and WAPOR actual evapotranspiration (ET) data with standard ET calculated by FAO56PM method. Results of the AWA showed considerable non-consumed water that could be recovered, with the beneficial fraction surpasses the non-beneficial fraction. Findings showed that Utilized Land Use controls the water balance of the AZB with the highest water consumption around 63%. The analysis of (P - ETa) revealed that the AZB is a water net generator with precipitation consistently being greater than total ET. It is crucial to investigate the pathways and processes involved in the movement of excess rainfall into underground basins. This study highlights the importance to Jordan of leveraging remote sensing datasets such as WaPOR to quantify National Water Budget parameters in addition to bridging data gaps and thus improving water availability and consumption.

In this work, the interactions of ethylene oxide (C2H4O) molecule over the MoS2 monolayers functionalized with different clusters of Ag atoms were investigated using density functional theory outlook. Our obtained results confirmed that Ag cluster–modified MoS2 nanosheets had excellent adsorption capacity for ethylene oxide molecules. The variations in the electronic properties were explained based on the band structure and charge density redistribution analyses. Our charge density distribution calculations represented the large collection of atomic charges above the adsorbed molecules. By plotting the projected density of states, we described the interaction occurred between the oxygen atoms of ethylene oxide molecules and Ag clusters. Adsorption distance, energies, angles, and other structural factors were also calculated for describing the results. Therefore, based on our results, we can propose the Ag cluster–modified MoS2 systems as effective ethylene oxide (C2H4O) detection devices for real phase applications.

Precise pressure control in shell-and-tube steam condensers is crucial for ensuring efficiency in thermal power plants. However, traditional controllers (PI, PD, PID) struggle with nonlinearities and external disturbances, while classical tuning methods (Ziegler-Nichols, and Cohen-Coon) fail to provide optimal parameter selection. These challenges lead to slow response, high overshoot, and poor steady-state performance. To address these limitations, this study proposes a cascaded PI-PDN control strategy optimized using the electric eel foraging optimizer (EEFO). EEFO, inspired by the prey-seeking behavior of electric eels, efficiently tunes controller parameters, ensuring improved stability and precision. A comparative analysis against recent metaheuristic algorithms (SMA, GEO, KMA, QIO) demonstrates superior performance of EEFO in regulating condenser pressure. Additionally, validation against documented studies (CSA-based FOPID, RIME-based FOPID, GWO-based PI, GA-based PI) highlights its advantages over existing methods. Simulation results confirm that EEFO reduces settling time by 22.7%, overshoot by 78.7%, steady-state error by three orders of magnitude, and ITAE by 81.2% compared to metaheuristic based methods. The EEFO-based controller achieves faster convergence, enhanced robustness to disturbances, and precise tracking, making it a highly effective solution for real-world applications. These findings contribute to optimization-based control strategies in thermal power plants and open pathways for further bio-inspired control innovations.

Background The pharmacy profession has significantly changed over the years. Pharmacy students’ perceptions of their coursework and future career aspirations may vary in relation to gender. Objectives The present study explored the motivations of pharmacy students to enter pharmacy school, their satisfaction with the academic program, future plans after graduation, and perceptions about the pharmacy profession in relation to gender. Methods Data were collected using a cross-sectional descriptive validated questionnaire built by the research team. The study was conducted at twelve public and private universities offering pharmacy programs. Results In total, 918 pharmacy students have completed the online questionnaire, with a 98% response rate. Most participants reported that family encouragement was a motive to enter pharmacy school. The results of the Chi-Squared Test indicated a significant difference between female and male participants with respect to the following motives: High school grades (p = 0.009), being good at science (p = 0.013), working with patients(p = 0.024), professional status (p = 0.014), working in a family business (p = 0.001) and job opportunities (p = 0.001). The majority of male participants and female participants perceived pharmacy jobs as prestigious jobs. In addition, male and female students believed that it was a profession with well-paid jobs. Conclusion Females were significantly more motivated by their high school degrees, goodness at science, working with patients, and professional status to enter pharmacy school. Pharmacy students are satisfied enough with the academic program. Male and female pharmacists have different career aspirations in the pharmaceutical sectors. It is recommended that students be educated about career planning to help them accomplish their goals. Future research could benefit from longitudinal studies to explore changes in pharmacy students’ motivations, satisfaction, and career aspirations over time.

Developing new devices to the early detection of breast cancer via specific molecules is the key to delivering better breast cancer handling and a greater opportunity for living. Plasmonic nanoparticles (NPs) offer useful potential for early breast cancer detection due to their specific optical properties. These NPs, naturally composed of gold or silver metals, display optical properties like localized surface plasmon resonance (LSPR), where combined oscillations of electrons make intense light scattering and absorption. LSPR enables sensitive detection of biomarkers, including peptides, DNA, aptamers, and other small biomolecules. One useful application of plasmonic NPs is surface-enhanced Raman scattering (SERS), where NPs intensify the Raman signal of molecules. In this review after an introduction of breast cancer biomarkers and plasmonic NPs, a comprehensive study was presented about the application of plasmonic NPs for the primary detection of breast cancer biomarkers and their advantages in this line. Graphical Abstract

The Marburg virus (MARV), a member of the Filoviridae family, is a highly lethal pathogen that causes Marburg virus disease (MVD), a severe hemorrhagic fever with high fatality rates.Despite recurrent outbreaks, no licensed vaccine is currently available. This review explores MARV’s genomic architecture, structural proteins, and recent advancements in vaccine development. It highlights the crucial role of MARV’s seven monocistronic genes in viral replication and pathogenesis, with a focus on structural proteins such as nucleoprotein (NP), glycoprotein (GP), and viral proteins VP35, VP40, and VP24. These proteins are essential for viral entry, immune evasion, and replication. The review further examines various vaccine platforms, including multi-epitope vaccines, DNA-based vaccines, viral vector vaccines, virus-like particles (VLPs), and mRNA vaccines. Cutting-edge immunoinformatics approaches are discussed for identifying conserved epitopes critical for broad-spectrum protection. The immunological responses induced by these vaccine candidates, particularly their efficacy in preclinical trials, are analyzed, showcasing promising results in generating both humoral and cellular immunity. Moreover, the review addresses challenges and future directions in MARV vaccine development, emphasizing the need for enhanced immunogenicity, safety, and global accessibility. The integration of omics technologies (genomics, transcriptomics, proteomics) with immunoinformatics is presented as a transformative approach for next-generation vaccine design. Innovative platforms such as mRNA and VLP-based vaccines offer rapid and effective development opportunities. In this study, underscores the urgent need for a licensed MARV vaccine to prevent future outbreaks and strengthen global preparedness. By synthesizing the latest research and technological advancements, it provides a strategic roadmap for developing safe, effective, and broadly protective vaccines. The fight against MARV is a global priority, requiring coordinated efforts from researchers, policymakers, and public health organizations. Graphical abstract