Key Performance Metrics Research Articles

Accelerate vaccine development using seamless phase 2/3 trial designs

ABSTRACT Background Traditional vaccine development, often a lengthy and costly process of three separated phases. However, the swift development of COVID-19 vaccines highlighted the critical importance of accelerating the approval of vaccines. This article showcases a seamless phase 2/3 trial design to expedite the development process, particularly for multi-valent vaccines. Research Design and Methods This study utilizes simulation to compare the performance of seamless phase 2/3 design with that of conventional trial design, specifically by re-envisioning a 9-valent HPV vaccine trial. Across three cases, several key performance metrics are evaluated: overall power, type I error rate, average sample size, trial duration, the percentage of early stop, and the accuracy of dose selection. Results On average, when the experimental vaccine was assumed to be effective, the seamless design that performed interim analyses based solely on efficacy saved 555.73 subjects, shortened trials by 10.29 months, and increased power by 3.70%. When the experimental vaccine was less effective than control, it saved an average of 887.73 subjects while maintaining the type I error rate below 0.025. Conclusion The seamless design proves to be a compelling strategy for vaccine development, given its versatility in early stopping, re-estimating sample sizes, and shortening trial durations.

Artificial Intelligence-Driven Model for Gold Price Prediction

This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)--driven models. Utilizing advanced machine learning techniques, including Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine (SVM), the research evaluates the predictive capabilities of these models through comprehensive assessments based on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error. A particular focus is placed on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data. These findings contribute valuable insights to the field of financial forecasting, emphasizing the potential of AI-driven models to inform stakeholders in gold investment and financial markets. The study concludes by advocating for ongoing research and continuous model refinement to adapt to dynamic market conditions and enhance the precision of gold price predictions. Keywords: gold price prediction artificial intelligence, MSE.

Study on the hydrodynamics of structured packing: Liquid holdup and pressure drop of a novel 3D printed packing

SpiroPak is a patented innovative structured packing and can be used in process industries for chemical separation purposes. The core of its design is to ensure an uninterrupted flow path for both liquids and gases, leading to remarkably low-pressure drops but with substantial surface areas for efficient mass transfer. Experimental studies using the wire-mesh sensor technique have showcased SpiroPak's ability to eliminate the channelling effect that is observed in commercial packings. Furthermore, its wide-ranging liquid holdup capability encompasses the functionalities of conventional packings, offering clients the feasibility of diverse applications. In this study, the pressure-drop correlations for SpiroPak in this work are based on Stichlmair empirical formulas, with the coefficients of C1, C2 and C3 tuned through the curve fitting method. The primary objective of this study was to establish packing selection criteria based on the key hydraulic performance metrics, such as pressure drop and liquid holdup. By swiftly identifying suitable packing solutions, the method accelerates the pre-selection process, significantly reducing computational complexity and time expenditures at the early stages of research and development.

Adaptive Random Mac Strategy for IoT Security Through Network Forensics Investigation

In the rapidly evolving landscape of digital systems and networks, ensuring robust cyber security measures is of paramount importance. This study delves into the evaluation of a proposed security method's efficiency through a comprehensive comparative analysis conducted under diverse security breach simulations. The objective is to discern the method's effectiveness in safeguarding sensitive information and maintaining system integrity.The research methodology involves the selection of representative security breach scenarios, each emulating distinct attack vectors and potential vulnerabilities. A benchmark security protocol, widely acknowledged for its reliability, serves as a reference for the proposed method's assessment. A series of controlled breach simulations are executed, simulating real-world cyber threats, ranging from sophisticated malware intrusions to social engineering attacks. Key performance metrics, such as response time, resource utilization, and data recovery rates, are meticulously recorded and analyzed for both the benchmark and proposed methods. Furthermore, the impact on system functionality and user experience is evaluated to comprehend the trade-off between heightened security and seamless user interaction. The experiments are conducted across multiple system architectures to account for variations in hardware and software configurations. The findings reveal nuanced insights into the proposed method's efficiency under distinct breach scenarios. It showcases commendable resilience against certain attack vectors while potentially exposing limitations in others. The comparative analysis provides a comprehensive overview of the method's overall efficacy and enables the identification of scenarios where enhancements might be necessary. The performance of the suggested approach under various security breach simulations is empirically demonstrated in this paper, adding to the body of current knowledge. The insights gained from this research aid in refining the method's design and implementation, bolstering its potential to address the dynamic and evolving landscape of cyber security threats. As digital systems continue to play an increasingly integral role in our lives, the significance of such evaluations cannot be overstated, ensuring that our vital digital infrastructure remains secure and resilient.

Efficient malicious node detection by multi-objective energy trust aware hybrid optimization based maximizing lifetime of wireless sensor networks

Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4.

Residential long-span timber floor typologies: a comprehensive performance assessment and opportunities for value adding to plantation hardwood

ABSTRACT High-performance engineered wood products (EWPs) and composite mass timber products (CMTPs) are being employed more frequently in residential projects with increasing interest in more sustainable systems that achieve long-spans. The relative performance of these systems is not readily apparent, with individual manufacturers offering proprietary products assembled from specific timber resources. In addition, the suitability of producing these long-span systems using plantation hardwoods is currently unknown. This research investigated the comparative performance of four typical EWPs and CMTPs; 1. solid slab, 2. thin-walled cassette, 3. T-sections, and 4. slab on beam. Key performance metrics of depth, mass, stiffness, vibration response, fire performance and global warming potential were assessed. The mechanical performance of two high-strength plantation hardwood varieties (Eucalyptus nitens and Eucalyptus globulus) were determined experimentally and subsequently used to re-calculate performance criteria for the previously assessed typologies. Substantial improvements over the typical softwood varieties were identified, particularly in structural efficiency, global warming potential and fire performance. This highlights the potential for value adding to plantation hardwoods by using them in high-performance long-span engineered floor products.

Performance Evaluation of VGG_19 Model for Brain Tumor Datasets

This paper presents a comprehensive assessment of the VGG19 model for detecting brain tumors using deep learning methods, particularly convolutional neural networks (CNNs). The study evaluates key performance metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC). The investigation delves into the influence of different activation functions, learning rates, and epochs on the VGG19 model's performance. Activation functions are crucial for feature extraction and model convergence, with a comparison made between ReLU and Leaky ReLU to determine their effectiveness in enhancing brain tumor detection accuracy. Additionally, the study examines the impact of various learning rates (0.01, 0.001, 0.0001) on the model's convergence speed and overall performance. Different numbers of training epochs (15, 25, 50) are also considered to strike a balance between computational efficiency and model effectiveness. Evaluation metrics encompass accuracy, precision, recall, F1 score, and AUC, providing a comprehensive assessment of the model's performance across different configurations. The goal is to identify optimal combinations of activation functions, learning rates, and epochs to maximize the VGG19 model's accuracy in detecting brain tumors. This research contributes to advancing efficient and accurate methodologies for brain tumor detection, facilitating early diagnosis and treatment planning. The insights gained from this study can guide the development of more effective deep learning-based systems for medical image analysis, benefiting both patients and healthcare professionals.

Infection control practices in public sector hospitals of Punjab: a critical analysis

IntroductionInfection prevention and control (IPC) is imperative towards patient safety and health. The Infection Prevention and Control Assessment Framework (IPCAF) developed by WHO provides a baseline assessment at the acute...

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

Performance and Emission Analysis of Ethanol-Petrol Blends in Single-Cylinder Engines: A Sustainability Perspective

This study investigates the blending of ethanol and petrol in the Indian subcontinent, addressing the urgent need for sustainable fuel alternatives amid rising energy demands and environmental concerns. Ethanol, as a renewable biofuel, presents a viable option for enhancing the efficiency of internal combustion engines while reducing harmful emissions. Given the significant air pollution issues prevalent in urban areas of India, optimizing fuel blends can substantially improve engine performance and contribute to cleaner air. The primary objectives of this research include analyzing the impact of different ethanol-petrol blends on the performance of a single-cylinder petrol engine, assessing key performance metrics such as brake power, Indicated Mean Effective Pressure (IMEP), Brake Mean Effective Pressure (BMEP), and Brake Thermal Efficiency (BTHE). Emission analysis was conducted to measure hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO₂), oxygen (O₂), and nitrogen oxides (NOₓ) across various blends, quantifying the environmental impact of each. Grey Relation Analysis (GRA) was employed to rank the fuel blends based on their performance and emissions, helping identify the most effective blend for optimal engine operation. Additionally, the sustainability of ethanol blending as an alternative fuel solution in the Indian automotive sector was assessed. The study recorded key performance indicators and emissions using standardized techniques, with GRA calculating Grey relational coefficients to measure the degree of similarity between observed data and desired outcomes. This approach facilitated the ranking of blends, ultimately identifying the optimal ethanol-petrol blend. The findings revealed significant variations in both performance and emissions across different blends, indicating that ethanol blending holds substantial promise as a sustainable solution for the Indian automotive sector. These insights align with recent studies on the benefits of biofuels in enhancing engine performance and reducing emissions, providing valuable guidance for optimizing ethanol-petrol blends to improve sustainability in energy consumption.

Enhancing Network Attack Detection Accuracy through the Integration of Large Language Models and Synchronized Attention Mechanism

In this study, we propose a novel method for detecting cyberattack behaviors by leveraging the combined strengths of large language models and a synchronized attention mechanism. Extensive experiments conducted on diverse datasets, including server logs, financial behaviors, and comment data, demonstrate the significant advantages of this method over existing models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B in key performance metrics such as precision, recall, and accuracy. For instance, on the server log dataset, the method achieved a precision of 93%, a recall of 91%, and an accuracy of 92%; on the financial behavior dataset, it reached a precision of 90%, a recall of 87%, and an accuracy of 89%; and on the comment data dataset, it excelled with a precision of 95%, a recall of 93%, and an accuracy of 94%. The introduction of a synchronized attention mechanism and a newly designed synchronized loss function proved especially effective, enhancing the method’s ability to process multi-source data and providing superior performance in identifying complex cyberattack patterns. Ablation experiments further validated the crucial roles of these innovations in boosting model performance: the synchronous attention mechanism substantially improved the model’s precision, recall, and accuracy to 93%, 89%, and 91% respectively, far exceeding other attention mechanisms. Similarly, the synchronized loss showcased a significant advantage, achieving the best performance across all tested metrics compared to traditional cross-entropy loss, focal loss, and MSE. These results underscore the method’s ability to deeply mine and analyze semantic information and contextual relationships within text data as well as to effectively integrate and process multimodal data, thereby offering strong technical support for the accurate and efficient detection of cyberattack behaviors.

Review of energy-efficient HVAC technologies for sustainable buildings

This review paper provides a comprehensive examination of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) technologies for sustainable buildings. The focus is on exploring the latest advancements, benefits, challenges, and future prospects in the realm of HVAC systems designed to enhance energy efficiency and promote sustainability in building operations. The review begins with an overview of the importance of energy-efficient HVAC technologies in the context of sustainable building design and operation. It underscores the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, and enhancing occupant comfort and well-being. The paper delves into various categories of energy-efficient HVAC technologies, including advanced heating and cooling systems, ventilation strategies, and smart controls. Each category is examined in detail, highlighting key features, performance metrics, and real-world applications. Furthermore, the review evaluates the benefits associated with energy-efficient HVAC technologies, such as significant energy savings, reduced operating costs, improved indoor air quality, and enhanced building resilience. It also discusses the potential challenges and barriers to adoption, such as high upfront costs, complexity of implementation, and retrofitting existing buildings. The paper explores emerging trends and innovations in the field of energy-efficient HVAC technologies, including the integration of renewable energy sources, smart sensors and actuators, and predictive maintenance algorithms. These advancements have the potential to further optimize energy performance, increase system reliability, and enable proactive management of HVAC systems. In conclusion, the review emphasizes the importance of energy-efficient HVAC technologies as a cornerstone of sustainable building practices. It underscores the need for continued research, innovation, and collaboration among stakeholders to overcome existing challenges and unlock the full potential of energy-efficient HVAC systems in contributing to a more sustainable built environment. Overall, this comprehensive review provides valuable insights into the current state of energy-efficient HVAC technologies, their applications, benefits, and challenges, while also highlighting opportunities for future advancements and their crucial role in achieving sustainability goals in the building sector.

Medicinal Plant Identification using Machine Learning Algorithm

The identification of medicinal plants plays a crucial role in various fields including pharmacology, traditional medicine, and biodiversity conservation. Traditional methods of plant identification are often time-consuming, labor-intensive, and require expertise in botanical taxonomy. In recent years, the application of Convolutional Neural Networks (CNNs) has shown promising results in automating the process of plant species recognition. This paper provides a comprehensive review of recent advances in medicinal plant identification using CNNs. This study discuss the methodology, challenges, and opportunities associated with CNN-based approaches, as well as their potential applications in pharmacological research and healthcare. Furthermore, we highlight key datasets, architectures, and performance metrics used in CNN-based plant identification systems. Finally, we identify future research directions and potential areas for improvement in this rapidly evolving field

The Effect of Sectoral Diversification on Return and Risk in Portfolio Management: An Application in Borsa Istanbul

This paper investigates sectoral diversification within Borsa Istanbul, aiming to elucidate its impact on portfolio risk and return dynamics. Spanning the timeframe from 2020 to 2022, the study meticulously analyzes stocks from pivotal sectors, including banking, energy, and iron and steel. Employing a robust methodology, the research harnesses Monte Carlo simulations to generate many hypothetical portfolios, subsequently evaluating them on the Efficient Frontier to identify optimal risk-return trade-offs. Key performance metrics, such as the Sharpe Ratio, Sortino Ratio, and Maximum Drawdown, further enrich the analysis, providing a granular view of portfolio behaviors. The significance of this study lies in its bridging of theoretical constructs of diversification with the tangible realities of an emerging market like Borsa Istanbul. Our main findings underscore the potential benefits of sectoral diversification while highlighting the complexities inherent in portfolio construction. The insights gleaned offer valuable guidance for investors, emphasizing the delicate balance between risk mitigation and return optimization in a diversified portfolio.

Deutsche Bank Restart: Goodbye Goldman Sachs of Europe?

This public-sourced case describes the latest restructuring efforts by Deutsche Bank (DB) and gives a short history of prior restructuring efforts from the decade before. In July 2019, Christian Sewing, the new CEO of DB, announced a series of measures that included, among others, the elimination of global equity trading, the layoff of 18,000 employees, the creation of a “bad bank” to transfer noncore assets, and the suspension of dividends until 2022. The case describes key decisions a bank CEO makes when a bank needs to change course to return to profitability and growth. The case offers an opportunity to debate these key decisions, as well as discuss some of the prior ones during earlier restructuring efforts, and put the students in the CEO's shoes: What would you do and why? The case also describes key banking performance metrics (e.g., ROE, ROA) and other critical variables such as those reflecting capital health (Tier 1 ratio), as well as gives an overview of the bank business model and factors impacting bank profitability and value.

GPT for medical entity recognition in Spanish

AbstractIn recent years, there has been a remarkable surge in the development of Natural Language Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER). Models such as BERT have demonstrated exceptional performance, leveraging annotated corpora for accurate entity identification. However, the question arises: Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR). This study utilized a dataset of Spanish EHRs related to breast cancer and implemented both a traditional NER method using BERT, and a contemporary approach that combines few shot learning and integration of external knowledge, driven by LLMs using GPT, to structure the data. The analysis involved a comprehensive pipeline that included these methods. Key performance metrics, such as precision, recall, and F-score, were used to evaluate the effectiveness of each method. This comparative approach aimed to highlight the strengths and limitations of each method in the context of structuring Spanish EHRs efficiently and accurately.The comparative analysis undertaken in this article demonstrates that both the traditional BERT-based NER method and the few-shot LLM-driven approach, augmented with external knowledge, provide comparable levels of precision in metrics such as precision, recall, and F score when applied to Spanish EHR. Contrary to expectations, the LLM-driven approach, which necessitates minimal data annotation, performs on par with BERT’s capability to discern complex medical terminologies and contextual nuances within the EHRs. The results of this study highlight a notable advance in the field of NER for Spanish EHRs, with the few shot approach driven by LLM, enhanced by external knowledge, slightly edging out the traditional BERT-based method in overall effectiveness. GPT’s superiority in F-score and its minimal reliance on extensive data annotation underscore its potential in medical data processing.

Car Parking Occupancy Detection Using Deep Learning

It can be difficult to find a parking space in a busy neighborhood because you never know when one will become available. Due to the rise of automobiles and fall in parking spots, this task even more challenging. To increase the dependability of smart parking systems, numerous researchers have sought to improve them. In smart parking systems, computer vision is a more advantageous method for detecting parking spaces than sensors. Many parking spots may be monitored by a single smart camera, which is also reasonably easy to install and maintain. To identify parking occupancy in camera-captured photos, a CNN model is employed. The CNN model will be trained using the CNRpark+Ext and PKLot datasets, which contain 4081 and 12,416 parking lot photos under various weather scenarios, respectively. The CNN architectures, CmAlexNet and mAlexNet, are five-layer designs built on the eight-layer AlexNet architecture, in which processing speed is increased by removing the completely connected layer and the third and fourth convolutional layers. To increase accuracy, the first convolutional layer of CmAlexNet's filter is resized from 11x11 to 13x13, and batch normalization [1] is used instead of the original method. Three key performance metrics are evaluated between CmAlexNet and mAlexNet: recall, precision, and accuracy in classification. Keywords: Car Parking , Computer Vision , Deep learning , CNN, mAlexnet , cmAlexnet , CNRpark dataset , PKLot dataset.

Hydrogen Bond Network Regulation in Electrolyte Structure for Zn‐based Aqueous Batteries

AbstractElectrolyte regulation in Zn‐based aqueous batteries (ZABs) has been extensively reported, and a broad range of strategies has been proposed. However, there is currently a lack of systematic summaries and a comprehensive understanding of the impact of hydrogen bond (H‐bond) networks on electrolyte performance. This work presents the electrolyte structure model, encompassing solvation structure, electrolyte/Zn anode interface, and H‐bond network. Through emphasizing and summarizing the reconstruction, strengthening, and breaking of the H‐bond network within the electrolyte structure, various specific regulation strategies are identified, such as high Gutmann donor number solvent, organic co‐solvent, molecular crowding additives, structure‐breaking ions, and solid‐state design. A critical appraisal is then provided on the key performance metrics influenced by these regulation methods, including Coulomb efficiency, voltage hysteresis, freezing point, and lifespan. This work is expected to illustrate electrolyte structure design to improve the comprehensive performance of ZABs. Last, a data‐driven summary and outlook on hydrogen bond network regulation in electrolyte structure is provided, to objectively evaluate the overall performances of ZABs.

Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis.

Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.

Short waits, happy patients and expert care, moving basic musculoskeletal care from the emergency department to a physiotherapist-led diversion pathway.

Patients with musculoskeletal conditions (MSKCs) are highly prevalent in ED. This project explores the impact of the pilot phase of a 'diversion pathway', which directed patients with MSKCs from the ED waiting room to an outpatient clinic led by advanced-scope physiotherapists. A prospective intervention study comparing care outcomes between patients in the 'diversion pathway' with usual ED care. The characteristics of patients considered eligible and non-eligible are described. Between May and December 2022, 1099 patients were diverted. For diverted patients, mean length of stay (LOS) in ED was reduced by 110 (95% confidence interval [CI]: 99-120) min and 4 h rule compliance improved by 19.3% compared to usual ED care. There were fewer patients who 'did not wait' (DNW) with the diversion pathway. The diverted group was young (median age 22 years and 41% paediatric), mostly low urgency, self-referred and arrived by private transport with minor limb trauma. The diversion pathway triage process appropriately identified 182 patients ineligible for diversion. 96.7% of patients reported satisfaction with care received from the diversion pathway. There was no change in ED representation rates for diverted patients. A new pathway resulted in reduced LOS, reduced DNW, high patient satisfaction and more people being discharged within 4 h for diverted patients compared to usual ED care. The pathway increased ED capacity, improved key ED performance metrics and safely expedited care delivery for patients.