Articles

Simulation-based learning (SBL) is widely recognized as an effective educational approach that bridges theory and practice in nursing education. Despite its global adoption, limited research has examined the experiences of Filipino nursing students with SBL, particularly in resource-constrained settings. This study explored the perspectives of Bachelor of Science in Nursing students from a university in Metro Manila, Philippines, on the impact of SBL on their skills, emotional responses, and challenges encountered. A descriptive qualitative design was employed using purposive sampling of ten students who had participated in at least one SBL activity. Data were collected through semi-structured interviews and short written reflections and analyzed thematically following Braun and Clarke’s framework to capture nuanced experiences. Three major themes emerged from the analysis. First, students reported initial anxiety, nervousness, and stress during their early SBL experiences, which gradually transformed into confidence, adaptability, and resilience as they gained familiarity and competence. Second, SBL enhanced technical and cognitive skills such as clinical judgment, decision-making, teamwork, and patient-centered care, supporting students’ readiness for real-world practice. Third, students identified resource limitations, insufficient equipment, and time constraints as significant barriers to optimal learning, though these challenges also fostered creativity and perseverance. The findings demonstrate that SBL fosters technical competence, critical thinking, and professional growth but requires institutional support to address resource constraints and faculty development needs. This study underscores the importance of expanding SBL in Philippine nursing curricula to align with international best practices and to contribute to Sustainable Development Goals 3 (good health and well-being), 4 (quality education), and 5 (gender equality).

The gut-brain axis (GBA) has emerged as a central focus in the study of neurodevelopmental disorders, particularly autism spectrum disorder (ASD). Research suggests that microbial composition and its metabolic byproducts influence neural development, synaptic plasticity, and behavior [1,2,3]. A structured bibliometric analysis of Scopus and Web of Science records was performed using Bibliometrix and VOSviewer to trace trends and thematic evolution of GBA–ASD literature [7,8]. In parallel, a data-driven pathway modeling approach maps microbial metabolites (e.g., short-chain fatty acids, tryptophan catabolites) to host signaling pathways including vagal stimulation, immune cytokine modulation, and blood–brain barrier (BBB) permeability [4,5]. Simulations implemented in Python’s NetworkX illustrate how perturbations in metabolite flux may influence CNS outcomes. The findings reveal growing emphasis on butyrate, serotonin, microglial priming, and maternal immune activation in ASD-related GBA studies, and highlight the need for rigorous empirical validation of computational predictions [9,10,11].

This article presents a mathematical model designed to simulate the impact of targeted interventions aimed at preventing HIV transmission among female sex workers (FSWs) and their clients, while also analyzing their effects on the health of the general population. The compartmental model distinguishes between high-risk populations (FSWs and their clients) and low-risk populations (sexually active men and women in the general population), and links prevention efforts in high-risk groups to the evolution of the epidemic in the low-risk population. The fundamental properties of the model, such as the positivity of solutions and the boundedness of the system, have been verified, and the basic reproduction number R<SUB>0</SUB> has been calculated. Finally, the stability of the model was studied using Varga’s theorem and the Lyapunov method. Simulation results show that targeted prevention among FSWs and their clients reduces HIV incidence in the general population. This framework provides a valuable tool for guiding policymakers in the design of effective strategies to combat the epidemic, especially relevant in the context of suspension of USAID funding.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

Groundwater is undeniably crucial to people's lives, particularly in coastal regions. Therefore, it is imperative to address this vital water source strategically and implement a management plan to maintain its optimal state. The salinization of groundwater poses a significant challenge for coastal communities, stemming from factors like excessive groundwater extraction from coastal aquifers, reduced recharge, rising sea levels, climate change, and other causes. Saltwater intrusion (SWI) is a prevalent issue that needs attention, as it significantly threatens groundwater quantity and quality. SWI happens when saline water infiltrates coastal aquifers, contaminating freshwater supplies. This review article aims to define SWI, explore its causes and influencing factors, and discuss various monitoring techniques. Additionally, it examines different modeling methods and management tools, including remote sensing, field surveys, modeling approaches, and optimization techniques. To mitigate the adverse effects of SWI, several control measures are outlined, along with their pros and cons. The final section reviews previous SWI studies and case studies from the Nile Delta, Sinai Peninsula, and North-West coast in Egypt. These studies offer suggestions, adaptations, and mitigation measures for future research.

Pulsatile blood flow in a subject-specific human aortic arch and its major branches is studied computationally for a peak Reynolds number of 1553 and a Womersley number of 22.74. The aortic geometry is constructed from the CT-scan images of a subject. The aorta has out-of-plane curvature and significant area variation along the flow direction. A physiologically representative pulsatile velocity waveform is applied as boundary condition at the inlet of the aorta. The primary velocity profiles are skewed towards the inner wall of the ascending aorta during the entire cardiac cycle. In the decelerating phase, reverse flow is noted along the inner wall and the magnitude of maximum velocity is about 50 % of the peak flow condition. Flow separation is observed in the inner wall of the ascending aorta during the decelerating and reverse flow phases of the cardiac cycle. In the accelerating phase, however, flow separation does not occur. The major observation of the present work is the existence of complex and asymmetrical vortical flow structures which are not observed either in simple curved pipes or in idealized aortic arch computational studies. The relative strength of the secondary flow with respect to the primary flow is quantified by means of Relative Secondary Kinetic Energy whose highest value is evaluated to be 1.202 occurring near the entrance of the right carotid artery during the maximum reverse flow condition. High values of wall shear stress is observed at distal of the left and right subclavian arteries, the bifurcation of brachiocephalic artery between right subclavian artery and right carotid artery, and proximal inner wall of descending aorta during the cardiac cycle. The wall shear stress at the bifurcations of the branches are low and oscillatory and generally correlates with the preferential sites for atherosclerosis. The flow structures on the aorta wall are explicitly highlighted by the limiting streamlines. The application of limiting streamlines to clearly elucidate the complex on-wall flow structures is one of the key contributions of the present study. During the decelerating and reverse flow phases several critical points are observed on the aortic wall. These complex flow structures vanish during the accelerating phase. The observations made in the present study will be helpful in creating accurate and clinically useful computational models.

There is a growing body of evidence that the number of individuals suffering from chronic and acute pain is under-reported and the burden of the veteran, aging, athletic, and working populations is rising. Current pain management is limited by our capacity to collaborate with individuals continuing normal daily functions and self-administration of pain treatments outside of traditional healthcare appointments and hospital settings. In this review, the current gap in clinical care for real-time feedback and guidance with pain management decision-making for chronic and post-operative pain treatment is defined. We examine the recent and future applications for predictive analytics of opioid use after surgery and implementing real-time neural networks for personalized pain management goal setting for particular individuals on the path to discharge to normal function. Integration of personalized neural networks with longitudinal data may enable the development of future treatment personalizations paired with electrical simulations.

SARS-COV-19 is a serious respiratory infection created by a devastating coronavirus family (2019-nCoV) that has become the first global epidemic of the last one hundred years. It is a highly transmissible virus transmitted by inhalation or contact with the droplet core produced by infected people when they sneeze, cough, and speak. SARS-COV-2 transmission in the air is possible even in a confined space near the infected person. This study aimed to evaluate the effectiveness of using a shield or mask as a barrier to a patient’s face against the spread of virus particles. For the present simulation, the discrete phase model (DPM) is used; Because this model allows us to study the particle’s mass discretely in a fluid space with the continuous phase. Due to the choice of this model, the virus particles secreted from the patient’s mouth are considered a discrete phase, and the open airflow in the computational area is considered a continuous phase. The present study uses fluent 2019R3 software to simulate the virus transmission to model the transient flows numerically. The analysis found that the masks or shields can be an effective method of protecting the participants of a conversation in the presence of an infected person.

This research explores the complex relationship between user-perceived Quality of Experience (QoE) and underlying network performance for multimedia traffic. As video streaming, online gaming, and interactive media dominate modern networks, ensuring consistent QoE has become a key challenge. The study develops a network performance model that integrates objective Quality of Service (QoS) parameters—such as delay, jitter, packet loss, and throughput—with subjective QoE metrics like Mean Opinion Score (MOS) and perceptual quality indices. Using simulation-based and analytical approaches, the paper evaluates how network conditions affect multimedia traffic behavior and user satisfaction. The results highlight critical thresholds for QoE degradation, enabling predictive modeling for adaptive multimedia delivery and real-time optimization. This work contributes to designing intelligent, user-centered network management systems capable of balancing resource efficiency and end-user satisfaction.

Biopharmaceuticals, or biologics, are a burgeoning sector in the pharmaceutical industry, predicted to reach $239.4 billion by 2025. This unparalleled growth is often attributed to the enhanced specificity offered by large molecules over small molecules. The large size of the constituent proteins necessitates the continuous implementation of big data predictive analytics to elucidate the most effective candidates in the lead optimization process. These same methodologies can be applied, and with the advent of machine learning and automated predictive analytics, this is becoming an increasingly facile task, to the augmentation and optimization of the downstream production processes that comprise the majority of the development cost of any biologic. In this work, big data from cell line generation, product and process design, and large-scale lead validation studies have been used to compare the applicability of simple statistical models against these black-box approaches for the rapid acceleration of enzymes to the pilot plant stage. This research can be expanded upon to exploit the big datasets generated as part of the progression of biologics through the development pipeline to further optimize production outcomes. Over the coming months, data from the project will be used to probe which approaches are amenable to which processes and, as a result, more amenable to various economic simulations. The computed optimization objective for the HIT must include the cost of acquiring, storing, and analyzing data to construct these predictive models, alongside the expected commercial reward of choosing an optimally ranked candidate. In this vein, perspective must be taken in the probable future price, capability outputs, and ownership issues of increasingly sophisticated data analysis software as superstructures become more frequent. It is frequently stated that decisions made to reduce production costs are data-driven, but that is not because more economically or energetically costly experiments or production methods are employed; to truly evaluate production steps, dynamic energy, and economic models need to become more commonplace. Conversion of process quality approaches from large questionnaires, risk analysis, and expert opinion-driven methods to statistical and thus more reliable approaches is an area of future research in analytics used herein.