Development Of Modeling Approach Research Articles

Analysis and Optimal Control of Propagation Model for Malware in Multi-Cloud Environments with Impact of Brownian Motion Process

Today, cloud computing is a widely used technology that provides a wide range of services to numerous sectors around the world. This technology depends on the interaction and cooperation of virtual machines (VMs) to complete various computing tasks, propagating malware attacks quickly due to the complexity of cloud computing environments and users’ interfaces. As a result of the rising demand for cloud computing from multiple perspectives for complete analysis and decision-making across a range of life disciplines, multi-cloud environments (MCEs) are established. Therefore, in this work, we discuss impacted mathematical modeling for the MCEs’ network dynamics using two deterministic and stochastic approaches. In both approaches, appropriate assumptions are considered. Then, the proposed networks’ VMs are classified to have six different possible states covering media, healthcare, finance, and educational servers. After that, the two developed modeling approaches’ solution existence, uniqueness, equilibrium, and stability are carefully investigated. Using an optimal control strategy, both proposed models are tested for sustaining a certain level of security of the VMs’ states and reducing the propagation of malware within the networks. Finally, we verify the theoretical results by employing numerical simulations to track the malware’s propagation immunization. Results showed how the implemented control methods maintained the essential objectives of managing malware infections.

A hybrid symbolic learning approach for Ground-Motion model Development

A hybrid symbolic learning approach for Ground-Motion model Development

Editorial: Special Issue on Fatigue and Fracture Mechanics

Abstract The study of fatigue and fracture behaviors in engineered components remains a critical area across multiple disciplines. Limits on materials behavior are among the most significant technical challenges to enhancing the safety and reliability of engineering systems. Thus, accurately defining recent advancements in analytical methods and testing techniques within fatigue and fracture mechanics for engineered structures, components, and materials is essential. This analysis encompasses both experimental research and recent developments in modeling approaches. Key areas of interest include applications of emerging analytical tools and novel experimental techniques to assess and improve durability and damage tolerance using multiscale or multiphysics-based approaches; studies on the effects of additive manufacturing processes on fatigue and fracture properties; and implications of improved modeling and experimental capabilities on fatigue life forecasting and structural health monitoring strategies. In this context, a special issue in Materials Performance and Characterization offers readers valuable insights for their research. The call for papers attracted high-quality contributions from leading scientists and engineers resulting in 11 full-length manuscripts. The main topics covered include fatigue crack growth fracture, propagation, toughness fatigue, and damage failure inspection repair. The editorial teams extend their heartfelt thanks to the authors and reviewers for their dedication and to the ASTM staff for their support in bringing this issue to publication. We hope these papers provide valuable contributions to ongoing research efforts in fatigue and fracture mechanics.

THE DEVELOPMENT OF ELECTRONIC TOPIC MODULE WITH INTEGRATED 2D SIMULATION AND AUGMENTED REALITY APPLICATION: MODULE VALIDITY

The evolution of education has significantly transformed Teaching and Learning (T&L) sessions through the integration of technologies and digital tools, offering students new learning opportunities. This shift has led to changes in Malaysia’s educational curriculum, particularly in technical and vocational subjects. Design and Technology (D&T), a new subject introduced to replace Integrated Life Skills. Research shows that the electronics topic in D&T is particularly challenging, making it difficult for students to achieve their highest mastery levels as outlined in the Curriculum Standard and Assessment Documentation (DSKP). Both teachers and students often struggle with completing electronic projects due to limited technical skills and students’ poor visualization abilities. This study aims to validate the module validity in electronic topic module. The research adopts a quantitative research design with Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model approach, emphasizing the implementation phase. The module validity will be evaluated by nine experts in D&T and Technical and Vocational field. The study of the data will be analyzed by using the Content Validity Index (CVI). The results show that some items have a minimum CVI value of 0.78, and the S-CVI value is also 0.983, indicating the module validity is valid and acceptable. This demonstrates that the module is ready for pilot and actual studies to assess its usability and effectiveness. In conclusion, the module has been validated by experts, improving its quality and helping to identify areas for future research, particularly in evaluating its usability and effectiveness.

Soil Moisture-Latent Heat Flux Coupling in Climate Modeling: Insights and Implications from the Offline Land Model

Abstract Understanding the relationship between soil moisture (SM) and latent heat flux (LE) is crucial for forecasting seasonal extremes like heat waves and droughts and grasping long-term climate systems. Despite its importance, substantial disparities exist among various models’ interpretations of this relationship. This study defines four SM-LE indicators: wilting point, critical SM, saturated LE, and SM-LE slope, characterizing SM-LE regimes and corresponding breakpoints delineating the regimes using segmented regression and scrutinizes model disparities by contrasting conventional coupled simulations with matching offline land model (LM) simulations within the CMIP6 framework. It is revealed that those indicators have lower variability among ensemble members from the same model than among different models. Moreover, all indicators except for SM-LE slope demonstrate strong correlations between the coupled and uncoupled simulations using the same LMs. While the spatial distributions of breakpoint values correspond only moderately well with the indicators’ values, they improve after adjusting for model atmospheric biases of precipitation and downward radiation. The impact of the atmospheric fields on the four SM-LE indicators is well correlated between the coupled and offline experiments, except for the relationship between precipitation and the wilting point. Consequently, this indicates a significant constraint of the SM-LE relationship in coupled model simulations determined by the LM's behavior. Thus, one may determine much about the land-atmosphere coupling behavior parsimoniously in a model system without running the fully coupled model, justifying a hierarchical approach for model development and assessment.

Life cycle assessment and modeling approaches in silvopastoral systems: A case study of egg production integrated in an organic apple orchard

This paper aimed to assess the environmental impacts of two organic silvopastoral farms in Austria, using a Life Cycle Assessment approach. The two farms (F1, F2), with egg production integrated into an apple orchard, were compared to standard practices for each product. The functional unit was ‘1 kg fresh Class I apples’ and ‘1 kg fresh Class I eggs’. The assessment covered two scopes: cradle-to-farm gate and cradle-to-retail for each product. Effects on climate (including carbon sequestration in the soil and woody biomass), eutrophication potential (EP), acidification potential (AP), and land occupation (LO) were assessed. Feed, manure, and land were three resource loops included in the system boundary. Two modeling approaches were used from cradle-to-farm gate for distributing the impacts of the entire system between apples and eggs: model 1 (M1) used economic allocation, while model 2 (M2) divided the system into two subsystems. Results varied considerably by model. M1 consistently showed higher impacts for apples and considerably lower for eggs compared to M2. At farm gate, the carbon footprint (CF) ranged from 0.09 to 0.17 kg CO2-eq/kg apple and 0.19–1.62 kg CO2-eq/kg egg across all analyzed systems and models. Carbon sequestration reduced emissions by 22–42% for apples and by 0.4–39% for eggs. Sequestration was mainly associated with the carbon contributions from plant biomass from apple production (84–99%), with manure contributing 0.7–9%. EP ranged from 0.19 to 1.7 g PO4-eq/kg apple and 0.7–35 g PO4-eq/kg egg and AP ranged from 0.8 to 2.9 g SO2-eq/kg apple and 2–36 g SO2-eq/kg egg across all analyzed systems and models. LO ranged from 0.3 to 0.6 m2/kg apple and 0.8–9 m2/kg egg across all analyzed systems and models. Post-harvest activities accounted for up to 29% of the total impacts for EP and AP, and up to 57% for CF from cradle-to-retail. In general, the impacts per kg egg or kg apple in F1 and F2 were lower in most impact categories relative to their reference systems, driven mainly by management factors and the production phase of the value chain. Further development of modeling approaches is needed.

A Reduced Order Modeling Approach to Capture Transport within a Three-Electrode System Towards Fast Charge Applications

In support of GM's traction battery efforts, we derive and implement a reduced order modeling method to describe the electrochemical performance of a battery cell through the combination of a modified Newman Pseudo-2-Dimensional model and a three-electrode experimental apparatus. To assess the capability of the method, we compare the modeling results with experimental data for a lithiated graphite electrode and a lithiated nickel rich electrode system. The model is applied to simulate the electrochemical and transport processes within the battery cell to predict the negative electrode potential and positive electrode potential with respect to a lithium iron phosphate reference electrode, as well as the terminal voltage.The manufacture and set up of the three-electrode system, model development and reduction approaches, as well as a discussion of applicability to fast charge considerations to avoid lithium plating at the anode during a charging event will be presented.

A Model Development Approach Based on Point Cloud Reconstruction and Mapping Texture Enhancement

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by 20% in terms of point cloud density and number of model facets, while the hardware cost was only 1% of that associated with LiDAR.

Linking theory and practice to advance sustainable healthcare: the development of maturity model version 1.0.

Climate change and increased awareness of planetary health have made reducing ecological footprints a priority for healthcare organizations. However, improving healthcare's environmental impact remains difficult. Numerous researchers argue these difficulties are caused by healthcare's environmental impact being multidimensional, influenced throughout the healthcare chain, and often has downstream consequences that are hard to identify or to measure. Even though existing research describes many successful approaches to reduce healthcare's environmental impact, a robust multidimensional framework to assess this impact is lacking. This research aims at developing a maturity model for sustainable healthcare that could be used for self-assessment by healthcare professionals to identify improvement actions and for sharing best practices in environmental sustainability. A design-oriented approach for maturity model development was combined with an expert panel and six case studies to develop, refine and expand the maturity model for environmentally sustainable healthcare. A maturity model was developed containing four domains: 'Governance', 'Organization Structures', 'Processes', and 'Outcomes and Control'. Applying the model in real-world environments demonstrated the model's understandability, ease of use, usefulness, practicality and ability to identify improvement actions for environmental sustainability in healthcare organizations. This study found that healthcare practitioners could apply the maturity model developed and tested in this study in several hours without training to help them gain valuable insights into the environment footprint of the healthcare setting they worked in. Systematically implementing the model developed in this study could help address the urgent need to mitigate the substantial environmental impact of healthcare. These implementations can help evaluate and improve the maturity model.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Managing platelets supply chain under uncertainty: A two-stage collaborative robust programming approach

This study investigates the role of horizontal collaboration among blood centers in mitigating the negative impacts of fluctuations in blood platelet supply and demand during normal circumstances and after sudden-onset disasters. Specifically, four scenarios are studied: no collaboration, collaboration among blood centers, collaboration among hospitals, and collaboration among blood centers and hospitals. To formulate these scenarios, we propose a two-stage robust programming model that enables decision-makers to monitor the collaborative behavior of blood centers and hospitals before and after changes in supply and demand occur. In the first stage, a robust optimization approach is developed to cope with supply and demand uncertainties, where collaboration is limited to blood centers. In the second stage, the uncertain parameters (supply and demand) are treated as deterministic. The key decision is how to transfer platelets among hospitals located in a collaborative cluster. This research uses benchmark data from previous studies to test and validate the developed model and solution approach. Comparing the results obtained in different scenarios, we systematically select the most favorable scenario in terms of model performance under normal and emergency situations. The final results indicate that the network achieves the highest performance when blood centers and hospitals participate in the grand coalition.

Development of Web-Based Learning Media: Physical Education, Sports, and Health (PJOK) Volleyball Subjects

The aim of this research is to determine the feasibility of web-based learning media in the PJOK volleyball subject and the effectiveness of web-based learning media in the PJOK volleyball subject. The research uses the ADDIE Research and Development model approach (Analysis, Design, Development, Implementation, and Evaluation). The research results show: (a) web-based learning media from media expert validation with a score of 4.45, 89%, very feasible category; design expert validation with a score of 4.6 percentage 92%, very feasible category; material expert validation with a score of 4.5 percentage 90%, very feasible category; individual student trials with a score of 4.3, percentage 86%, very high category; student group trials with a score of 4.5, with a percentage of 90%, very high category. (b) The effectiveness of web-based learning media shows that there is a significant difference between student learning outcomes in experimental classes and classes without using web-based learning media at a significance level of 0.05 or 5%. The results of the T-test calculations on the posttest scores for the experimental class and control class show tcount > ttable, namely 3,225 > 1,677. It is evident from the average value of the posttest learning results for the experimental class that it is 88% higher than the posttest learning results for the control class at 64%. Thus, it can be concluded that the web-based learning media developed is effectively used to improve student learning outcomes.

Therapeutic Potential of Molsidomine-Loaded Liquid Crystal Nanoparticles for the Treatment and Management of Niacin-Induced Varicose Veins: In Vitro and In Vivo Studies.

Varicose vein therapy has historically relied significantly on invasive surgical procedures, which frequently resulted in poor compliance among patients. The tendency could stem from the past use of abrasive surgical procedures and a lack of documented drug-induced animal models. To address this challenge, we envisaged an innovative approach for animal model development that uses niacin-induced recurrent flushing. And to further treat the condition, we used liquid crystal nanoparticles (LCNPs) as carriers for the antiangiogenic, cardio protective, and anti-inflammatory drug molsidomine. After the successful initiation of reticular perforant varicose veins, the animals were administered and treated with molsidomine-loaded liquid crystal nanoparticles (MD-LCNPs) that were simultaneously synthesized via a straightforward homogenization method. The preparation of MD-LCNPs involved inducing the disruption of a cubic-phase gel of glyceryl monostearate (GMS) by Milli-Q water in the presence of a Tween-80. Characterization of MD-LCNPs encompassed an assessment of their physicochemical properties. Microscopic studies revealed monodispersity with an average size of 195 ± 55.94 nm. In vitro evaluations demonstrated commendable antioxidant potential, excellent swelling behavior, and sustained release behavior of MD-LCNPs. Furthermore, MD-LCNPs exhibited nontoxicity toward cells, with minimal generation of reactive oxygen species (ROS) or nitric oxide (NO). Histopathological and hematological analysis indicated the efficacy of MD-LCNPs in ameliorating niacin-induced varicose veins, the absence of detrimental and toxic effects on blood cells and visceral organs, and safety for intravenous administration. Following the administration of nanoparticles, the formulation demonstrated appropriate levels of prostaglandins (PGDs), vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), and vascular endothelial growth factor (VEGF). This substantiates the formulation's suitability for the treatment and management of varicose veins. In conclusion, our work shows an efficient method that induces varicose veins in rodents, and also a promising nanocarrier-based drug delivery approach using MD-LCNPs for effective and safe varicose vein therapy.

Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters.

Untreated periodontitis significantly increases the risk of tooth loss, often delaying treatment due to asymptomatic phases. Recent studies have increasingly associated poor dental health with conditions such as rheumatoid arthritis, diabetes, obesity, pneumonia, cardiovascular disease, and renal illness. Despite these connections, limited research has investigated the relationship between appendicitis and periodontal disease. This study aims to predict appendicitis in patients with periodontal disease using biochemical and clinical parameters through the application of a light gradient boost tree classifier. Data from 125 patient records at Saveetha Institute of Dental College and Medical College were pre-processed and analyzed. We utilized data preprocessing techniques, feature selection methods, and model development approaches to estimate the risk of appendicitis in patients with periodontitis. Both Random Forest and Light Gradient Boosting algorithms were evaluated for accuracy using confusion matrices to assess their predictive performance. The Random Forest model achieved an accuracy of 94%, demonstrating robust predictive capability in this context. In contrast, the Light Gradient Boost algorithms achieved a significantly higher accuracy of 98%, underscoring their superior predictive efficiency. This substantial difference highlights the importance of algorithm selection and optimization in developing reliable predictive models. The higher accuracy of Light Gradient Boost algorithms suggests effective minimization of prediction errors and improved differentiation between appendicitis with periodontitis and healthy states. Our study identifies age, white blood cell count, and symptom duration as pivotal predictors for detecting concurrent periodontitis in acute appendicitis cases. The newly developed prediction model introduces a novel and promising approach, providing valuable insights into distinguishing between periodontitis and acute appendicitis. These findings highlight the potential to improve diagnostic accuracy and support informed clinical decision-making in patients presenting with both conditions, offering new avenues for optimizing patient care strategies.

Data-driven visual model development and 3D visual analytics framework for underground mining

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations.

DEVELOPMENT OF INFORMATION SYSTEM STUDENT PERIOD COMPLETION MOBILE-BASED

This research aims to develop a mobile-based student study period completion information system in JPTE. The system developed uses the fountain development model approach with 5 stages, namely analysis, subsystem requirements, design, implementation, and testing. The purpose of choosing the fountain model is to ensure flexibility in development and adaptation to user needs. Black Box testing is carried out to ensure that each main feature, such as login, thesis/final project upload, journal upload notification, input and display of graduation dates, as well as management of student and KAPRODI data, functions according to the specifications set. The test results show that all 100% of the features successfully meet the expected functional criteria, with a system that is able to provide easy access, flexibility, and responsiveness. Overall, the Black Box test results show that the system is ready to be used by users and meets all the functional requirements that have been set. This mobile-based information system is expected to improve the efficiency of academic administration management and support the smooth process of completing the student study period.

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

BackgroundRapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.ResultsWe develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.ConclusionsWe demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.

Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.

Drivers and epidemiological patterns of West Nile virus in Serbia.

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023. We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country. During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections. Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.

Development of an Online Nursing Leadership Program for Global Health in the Western Pacific Region.

Global health highlights the transnational determinants, issues, and possible solutions for improving health outcomes. Addressing global health issues requires population-based approaches coupled with individualized healthcare across settings, including strategies for achieving health equity. Designing and implementing global health solutions requires competent nurse leaders who can facilitate multi-disciplinary collaborations, engage in policy development, and lead advocacies that support the global health agenda. Thus, this paper examined the stakeholder perspectives that informed the development of a leadership program on global health for nurse leaders in the Western Pacific Region (WPR). The study used a descriptive qualitative approach to generate key recommendations for a context-appropriate, multi-country capacity-building program for nurse leaders on global health. The Analysis Design, Development, Implementation, and Evaluation (ADDIE) model and reflexive approach were employed to guide the development of the training design. Twenty-five nurse leaders from World Health Organization Collaborating Centers (WHO-CC) in the Western Pacific participated. Qualitative data were collected from participant feedback and post-activity sessions throughout program implementation. Thematic analysis of stakeholder data revealed that the program (1) capitalized on equipping nurse leaders with the concepts related to global health and developing competencies in leadership, policy engagement, and advocacy; (2) ensured learning strategies by having participants from varied contexts and experiences; and (3) reflected on the strengths and limitations of the use of the online platform. This paper contributed to the growing literature on global health and programs that support addressing global health issues. The findings underscored the urgent need to capacitate nurses in leadership positions who contribute to addressing emerging issues in global health. The paper recommended improvements in the design and implementation of the Global Health Nursing Leadership Program to engage more nurse leaders across the region and enhance content and delivery.