Simulation learning

Treemaps are a powerful tool for representing hierarchical data in a space-efficient manner and are used in various domains, including network security or software development. However, interpreting the topology encoded by nested rectangles can be challenging, particularly compared to tree-structured representations like node-link diagrams or icicle plots. To address this challenge, we introduce TreEducation, a visual education platform designed to improve the visualization literacy skills required for reading treemaps among non-expert users...

Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment...

This article introduces neural graph distance embedding (nGDE), a method for generating 3D molecular geometries. Leveraging a graph neural network trained on the OE62 dataset of molecular geometries, nGDE predicts interatomic distances based on molecular graphs. These distances are then used in multidimensional scaling to produce 3D geometries, subsequently refined with standard bioorganic forcefields. The machine learning-based graph distance introduced herein is found to be an improvement over the conventional shortest path distances used in graph drawing...

OBJECTIVE: This scoping review will aim to map the existing academic literature on simulation-based activities that are designed with and delivered for health professionals in geographically rural and remote contexts in high-income countries. INTRODUCTION: Simulation-based health care activities are implemented in health services to increase patient safety because they allow health professionals to prepare, learn, practice, rehearse, and improve clinical performance and teamwork...

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative...

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start...

Coronavirus disease (COVID-19) and pulmonary hypertension (PH) are closely correlated. However, the mechanism is still poorly understood. In this article, we analyzed the molecular action network driving the emergence of this event. Two datasets (GSE113439 and GSE147507) from the GEO database were used for the identification of differentially expressed genes (DEGs).Common DEGs were selected by VennDiagram and their enrichment in biological pathways was analyzed. Candidate gene biomarkers were selected using three different machine-learning algorithms (SVM-RFE, LASSO, RF)...

INTRODUCTION: Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications...

Majority voting is a simple mathematical function that returns the most frequently occurring value within a given set. As a popular decision fusion technique (DFT), the majority voting function (MVF) finds applications in resolving conflicts, where several independent voters report their opinions on a classification problem. Despite its importance and its various applications in ensemble learning, data crowdsourcing, remote sensing, and data oracles for blockchains, the accuracy of the MVF for the general multiclass classification problem has remained unknown...

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status...

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation...

ConspectusElectrical neuromodulation has achieved significant translational advancements, including the development of deep brain stimulators for managing neural disorders and vagus nerve stimulators for seizure treatment. Optoelectronics, in contrast to wired electrical systems, offers the leadless feature that guides multisite and high spatiotemporal neural system targeting, ensuring high specificity and precision in translational therapies known as "photoelectroceuticals". This Account provides a concise overview of developments in novel optoelectronic nanomaterials that are engineered through innovative molecular, chemical, and nanostructure designs to facilitate neural interfacing with high efficiency and minimally invasive implantation...

BACKGROUND: The application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of high-count bone scintigraphy images from low-count counterparts. Few studies have been conducted and validated on real clinical pairs due to the misalignment inherent in multiple scan procedures. PURPOSE: To generate high quality whole-body bone images from 2× and 3× fast scans using deep learning based enhancement method...

INTRODUCTION: Many educators have adopted the Promoting Excellence and Reflective Learning in Simulation (PEARLS) model to guide debriefing sessions in simulation-based learning. The PEARLS Debriefing Checklist (PDC), a 28-item instrument, and the PEARLS Debriefing Adherence Rubric (PDAR), a 13-item instrument, assess facilitator adherence to the model. The aims of this study were to collect evidence of concurrent validity and to evaluate their unique strengths. METHODS: A review of 130 video recorded debriefings from a synchronous high-fidelity mannequin simulation event involving third-year medical students was undertaken...

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities...

INTRODUCTION: Surgical cricothyroidotomy (SC) is a vital skill that combat first responders must master as airway obstruction is the third most preventable cause of death on the battlefield. Degradation of skills over time is a known problem, and there is inadequate knowledge regarding the rate of SC skill retention. Our prior study showed that simulation-based mastery learning was effective in training 89 novices how to reliably perform an en route SC to mastery performance standards...

Given the past limitations on clinical practice training during the COVID-19 pandemic, a hybrid format program was developed, combining a time-lapse unfolding case study and high-fidelity simulation. This study assesses the effectiveness of a new form of clinical training from the perspective of student nurses. A questionnaire was administered to 159 second-year nursing students enrolled in the "Basic Nursing Practice II" course. Text mining was performed using quantitative text analysis for the following items: (1) aspects that were learned more deeply, (2) benefits, and (3) difficulties encountered with the new practice format...

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model...

BACKGROUND: Artificial intelligence (AI) enabled tools have been proposed as 1 solution to improve health care delivery. However, research on downstream effects of AI integration into the clinical workflow is lacking. OBJECTIVE: We aim to analyze how integration of an automated basal cell carcinoma detection and tumor mapping algorithm in a Mohs micrographic surgery unit impacts the work efficiency of clinical and laboratory staff. METHODS: Slide, staff, and histotechnician waiting times were analyzed over a 20-day period in a Mohs micrographic surgery unit...

BACKGROUND: The study aimed to assess the impact of different training modalities on otoscopy performance during a practical exam using a high-fidelity simulator and to determine if objective evaluation of otoscopy is feasible using a simulator that records insertion depth and tympanic membrane coverage. METHODS: Participants were assigned to one of four groups: control and three intervention groups with varying training approaches. Participants received otoscopy training and then were assessed through a practical exam on a high-fidelity simulator that uses virtual reality to visualize the ear canal and middle ear...