Discrete Variables Research Articles

Non-Negative Decomposition of Multivariate Information: From Minimum to Blackwell-Specific Information.

Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question at an arbitrary number of discrete random variables. This work proposes a solution with a non-negative PID that satisfies an inclusion-exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. Zonogons are the Neyman-Pearson region for an indicator variable of each target state, and f-information is the expected value of quantifying its boundary. We prove that the proposed decomposition satisfies the desired axioms and guarantees non-negative partial information results. Moreover, we demonstrate how the obtained decomposition can be transformed between different decomposition lattices and that it directly provides a non-negative decomposition of Rényi-information at a transformed inclusion-exclusion relation. Finally, we highlight that the decomposition behaves differently depending on the information measure used and how it can be used for tracing partial information flows through Markov chains.

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer.

The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions. We developed a novel machine-learning approach called GEP-CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP-CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis. We identified and validated 1184 three-SNP combinations that had the highest fitness function values. Notably, SERPINA1, ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1, EGF and MUC16. These SNPs-related proteins were confirmed to interact with each other in the protein-protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms. We successfully developed an effective deep-learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.

A modified cell-centered nodal integral scheme for the convection-diffusion equation

The nodal integral methods (NIMs) are very efficient and accurate coarse-mesh methods for solving partial differential equations. The cell-centered NIM (CCNIM) is a simplified variant of the NIMs that has recently shown its efficiency in solving fluid flow problems but has been hampered by issues such as inapplicability to one-dimensional problems, complexities in handling Neumann boundary conditions and the formulation of a system of differential-algebraic equations (DAEs) for discrete unknowns. Here, we present a modified version of the CCNIM designed to overcome the challenges associated with its previous version. Our novel development retains the essence of CCNIM while resolving these issues. The proposed scheme, grounded in the nodal framework, achieves second-order accuracy in both spatial and temporal dimensions. Unlike its precursor, the proposed method formulates algebraic equations for discrete variables per node, eliminating the cumbersome DAE system. Neumann boundary conditions are seamlessly incorporated through a straightforward flux definition, and applicability to one-dimensional problems is now feasible. We successfully apply our approach to one and two-dimensional convection-diffusion problems with known analytical solutions to validate our approach. The simplicity and robustness of the approach lay the foundation for its seamless extension to more complex fluid flow problems.

Gender Bias in Artificial Intelligence-Written Letters of Reference.

Letters of reference (LORs) play an important role in postgraduate residency applications. Human-written LORs have been shown to carry implicit gender bias, such as using more agentic versus communal words for men, and more frequent doubt-raisers and references to appearance and personal life for women. This can result in inequitable access to residency opportunities for women. Given the known gendered language often unconsciously inserted into human-written LORs, we sought to identify whether LORs generated by artificial intelligence exhibit gender bias. Observational study. Multicenter academic collaboration. Prompts describing identical men and women applying for Otolaryngology residency positions were created and provided to ChatGPT to generate LORs. These letters were analyzed using a gender-bias calculator which assesses the proportion of male- versus female-associated words. Regardless of the gender, school, research, or other activities, all LORs generated by ChatGPT showed a bias towardmale-associated words. There was no significant difference between the percentage of male-biased words in letters written for women versus men (39.15 vs37.85, P = .77). There were significant differences in gender bias found by each of the other discrete variables (school, research, and other activities) chosen. While ChatGPT-generated LORs all showed a male bias in the language used, there was no gender bias difference in letters produced using traditionally masculine versus feminine names and pronouns. Other variables did induce gendered language, however. ChatGPT is a promising tool for LOR drafting, but users must be aware of potential biases introduced or propagated through these technologies.

Angioplasty with ductal stent: experience at Garrahan Hospital

To describe and evaluate the outcomes of ductal angioplasty with stent placement at a single high-complexity center during the period 2016-2022. A retrospective descriptive cross-sectional study was conducted, including patients under 3 months of age who underwent ductal stent implantation as initial palliative treatment. Demographic, clinical, and anatomical data were collected before the intervention. Mortality, intra- and post-procedural complications, need for re-intervention, intensive care requirements, and hospital stay were recorded. The characteristics at the time of definitive surgery are described. Discrete variables are presented as percentages, and continuous variables are presented with their medians and respective interquartile ranges. Twenty patients who underwent this treatment were reviewed, revealing a success rate of 80%. Complications due to stent dysfunction required surgical resolution. 95% of patients were dischargedfrom the institution after the procedure, and 17 patients reached a second definitive surgical stage. Three patients died afterthe procedure, but with no direct relation to it. Indications for ductal angioplasty with stent as an alternative treatment to systemic-pulmonary anastomosis by surgery are not yet fully defined; the strategy represents a valid alternative in appropriately selected patients. The presented experience shows results similar to international reference centers.

Multi-strategy enhanced snake optimizer for quantitative structure-activity relationship modeling

Quantitative structure-activity relationship modeling techniques are widely employed in diverse areas of chemistry, including environmental risk assessment of compounds and virtual screening of drugs. The modeling process entails optimizing the hyper parameters of the model and selecting appropriate molecular descriptors. It is a typical optimization problem with high-dimensional, nonlinear, continuous and discrete variables coexisting. To address this challenge, this paper proposes an enhanced snake optimizer with multiple strategies. First, new food emergence and temperature change mechanisms are introduced to balance the exploration and exploitation. Second, a death and regeneration mechanism is employed to enhance the algorithm's ability to escape from local optima. Third, new formulas for food searching, fighting, mating and eating are designed to simplify the complexity of the algorithm and improve its search efficiency and accuracy. Fourth, the performance of the proposed algorithm is evaluated using the CEC2017 benchmark functions, eight engineering cases with continuous variables, and six feature selection cases with discrete variables. Experimental results demonstrate that the algorithm significantly outperforms comparison algorithms. Finally, the algorithm was applied to quantitative structure-activity relationship modeling for environmental toxicity prediction. Compared with other algorithms, the algorithm can better configure hyper-parameters and select fewer and more effective features for environmental toxicity prediction model. It will be an effective tool to improve the performance of quantitative structure-activity relationship models.

Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.

Real time aggregation control of P2H loads in a virtual power plant based on a multi-period stackelberg game

Power to Heat (P2H) technology is seen as an effective solution for integrating stochastic wind power. Particularly in real-time electricity markets, high-precision wind power forecasts enable the load to better accommodate its fluctuations. Nonetheless, the shorter time scales of real-time trading pose challenges for efficiently aggregating a large, heterogeneous, and dispersed loads and fully harnessing the thermal storage capabilities of loads to follow wind power fluctuations across multiple periods. Against this backdrop, this paper proposes an aggregation control approach for P2H loads with considering multi-period Stackelberg game strategy within the framework of a load-oriented virtual power plant (VPP). For multi-period real-time trading, a Stackelberg game model based on a dynamic price ceiling is established with considering dynamic supply-demand balance between wind power and P2H loads; To enhance load aggregation effectiveness, a dynamically updated temperature-power aggregation model is developed by employing model order reduction, variables continuousization, and linear superposition on second-order equivalent thermodynamic model; To improve load decomposition efficiency, a hierarchical and multi-step control strategy encompassing the continuousization of discrete variables is utilized to simplify the problem. Finally, a simulation example is presented using actual data from a power grid in northern China to validate the effectiveness of the proposed method.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In this study, we develop an ensemble algorithm based on the feature-oriented mean (FM) suitable for spatially discrete variables in precipitation ensembles. This method can adjust the locations of ensemble precipitation fields to reduce the location-related deviations among ensemble members, ultimately enhancing the ensemble mean forecast skill for TC precipitation. To evaluate the feasibility of the FM in TC precipitation ensemble forecasting, 18 landing TC cases in China from 2019 to 2021 were selected for validation. For precipitation forecasts of the landing TCs with a varying leading time, we conducted a comprehensive quantitative evaluation and comparison of the precipitation forecast skills of the FM and arithmetic mean (AM) algorithms. The results indicate that the field adjustment algorithm in the FM can effectively align with the TC precipitation structure and the location of the ensemble mean, reducing the spatial divergence among precipitation fields. The FM method demonstrates superior performance in the equitable threat score, probability of detection, and false alarm ratio compared with the AM, exhibiting an overall improvement of around 10%. Furthermore, the FM ensemble mean shows a higher pattern of the correlation coefficient with observations and has a smaller root mean square error than the AM ensemble mean, signifying that the FM method can better preserve the characteristics of the precipitation structure. Additionally, an object-based diagnostic evaluation method was used to verify forecast results, and the results suggest that the attribute distribution of FM forecast objects more closely resembles that of observed precipitation objects (including the area, longitudinal and latitudinal centroid locations, axis angle, and aspect ratio).

CEO’s Salary Hike: Harnessing the Factors Behind the Scene

A chief executive officer (CEO) is typically the highest-ranking executive, and they are in general accountable to the organisation’s stakeholders and the public. Major business-related decisions are made at this position, and this makes it strategically one of the most revered and sought-after positions in the organisational hierarchy. Thus, no doubt, a CEO’s salary has been seeing an upper trend for the past 50 years. It is a complex and highly controversial topic which has received great attention across the academia, business and research fraternities. Many factors have been identified and studied to understand the reasons behind this trend. Magazines such as Forbes and Business Week have annual publications pertaining to the CEO’s compensation. This article also looks at CEO salaries; a sample size of 300 CEOs from three important sectors was taken for a multi-factor analysis to determine the most significant factors. Various discrete, continuous and categorical variables such as ‘CEO’s salary’, ‘firm’s size’, ‘No. of Directors on Board’, ‘Percentage of executive directors on Board’, ‘CEO and Chairperson are the same person’, ‘Sales growth’ and ‘Excess return of the firm over industry’s average’ and ‘sector’ were chosen for the analysis. Further, multiple regression analysis was conducted, and it was found that the ‘firm’s size’ and ‘proportion of executive directors on board’ were irrelevant in determining the CEO’s salary, while ‘dummy variable’, ‘sales growth’ and ‘excess return’ were highly significant. Additionally, the ANOVA test suggested that CEO’s mean salaries differ significantly across sectors.

A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching

Optimal energy transmission dispatching of microgrid systems involves complicated transmission energy allocation and battery charging/discharging management and remains a difficult and challenging research problem subject to complex operation conditions and action constraints due to the randomness and volatility of new energy. Traditional microgrid transmission dispatching mainly considers the matching of the demand side and the supply side from a macro perspective, without considering the impact of line loss. Therefore, a Hierarchical Deep Q-network (HDQN) approach for microgrid energy dispatching is proposed to address this issue. The approach takes the power flow of each line and the battery charging/discharging behavior as decision variables to minimize the system operation cost. The proposed approach employs a two-layer agent optimization architecture for simultaneously processing the discrete and continuous variables, with one agent making upper layer decisions on the charging and discharging behavior of the batteries, and the other agent making lower layer decisions on the transmission energy allocation for the line. The experimental results indicate that the proposed approach achieves better performance than existing approaches.

An algebraic approach to simulation and verification for cyber-physical systems with shared-variable concurrency

Cyber-Physical systems (CPS), containing discrete behaviors of the cyber and continuous behaviors of the physical, have gained wide applications in many fields. Since CPS subsume the intersection of cyber systems and physical processes, the traditional modeling languages which merely include discrete variables are no longer applicable to CPS. Accordingly, a shared variable language called CPSLsc was proposed to specify CPS. In this paper, we elaborate the algebraic semantics for this language, so that every program of CPSLsc can be converted into a unified form called guarded choice form and the sequentialization of parallel programs is achieved. Additionally, we formalize the algebraic semantics in the rewriting engine Real-Time Maude. With the algebraic laws constructed, for every program specified with CPSLsc, we can simulate its execution step by step. Furthermore, automatic transformation and execution are attained. As a consequence, if the program and its initial data state are provided, the corresponding trace of data states during execution can be generated. In the light of the generated trace, automatic verification can be carried out as well.

Inter-rater reliability and agreement of the General Movement Assessment and Motor Optimality Score-Revised in a large population-based sample

BackgroundPrechtl's General Movement Assessment (GMA) at fidgety age (3–5 months) is a widely used tool for early detection of cerebral palsy. Further to GMA classification, detailed assessment of movement patterns at fidgety age is conducted with the Motor Optimality Score-Revised (MOS-R). Inter-rater reliability and agreement are properties that inform test application and interpretation in clinical and research settings. This study aims to establish the inter-rater reliability and agreement of the GMA classification and MOS-R in a large population-based sample. MethodsA cross-sectional study of 773 infants from birth-cohort in Perth, Western Australia. GMA was conducted on home-recorded videos collected between 12 + 0 and 16 + 6 weeks post term age. Videos were independently scored by two masked experienced assessors. Inter-rater reliability and agreement were assessed using intraclass correlation coefficient and limits of agreement respectively for continuous variables, and Cohen's Kappa and Gwet's Agreement Coefficient, and percentage agreement respectively for discrete variables. ResultsThe classification of GMA showed almost perfect reliability (AC1 = 0.999) and agreement (99.9 %). Total MOS-R scores showed good-excellent reliability (ICC 0.857, 95 % CI 0.838–0.876) and clinically acceptable agreement (95 % limits of agreement of ±2.5 points). Substantial to almost perfect reliability and agreement were found for all MOS-R domain subscores. While MOS-R domains with higher redundancy in their categorisation have higher reliability and agreement, inter-rater reliability and agreement are substantial to almost perfect at the item level and are consistent across domains. ConclusionGMA at fidgety age shows clinically acceptable inter-rater reliability and agreement for GMA classification and MOS-R for population-based cohorts assessed by experienced assessors.

Determination of the Frequency of BCL-2 Polymorphisms (c.-717C>A and c.*2364G>A) and LIF Polymorphism (c.*1414T>G) in Patients with Congenital Anomalies of the Kidney and Urinary Tract

Introduction: Congenital anomalies of the kidney and urinary tract (CAKUT) are characterized by several malformations. Its prevalence is 0.3–0.6% in live births. The B-cell lymphoma (BCL-2) gene regulates apoptosis, and the Leukemia Inhibitory Factor (LIF) gene plays a role in many biological processes, such as blastocyst growth and uterine preparation for implantation. In this study, two single nucleotide polymorphisms (SNPs) of the BCL-2 gene (rs2279115 and rs4987856) and one SNP of the LIF gene (rs929271) were investigated in CAKUT patients for the first time. Methods: Hundred and twenty-nine CAKUT patients and 105 controls were enrolled in this study. We used polymerase chain reaction-restriction fragment length polymorphism for rs2279115 and rs929271 and SNaPshot for rs4987856. The χ2 test was used to compare discrete variables, and the independent sample t test was used to compare continuous variables. Results: The allele frequencies for the rs2279115 and rs4987856 polymorphisms of BCL-2 and the rs929271 polymorphism of LIF were not significantly different between the patient and control groups (p = 0.162, p = 0.053, p = 0.635, respectively). However, the co-segregation analysis revealed a significant difference in the distribution of allele frequencies between the patient and control groups for two genetic variations: LIF rs929271 SNP and BCL-2 rs4987856 SNP (p = 0.034). The relative odds ratio was 2.444 (95% Confidence Interval (CI) 1.054–5.671). Conclusion: This study, which is the first time in the literature, showed that changes in BCL-2 and LIF genes are associated with CAKUT disease.

Classification feature selection and dimensionality reduction based on logical binary sine-cosine function arithmetic optimization algorithm

Arithmetic optimization algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operations. AOA has been diffusely used for optimization issues on continuous domains, but few scholars have studied discrete optimization problems. In this paper, we proposed Binary AOA (BAOA) based on two strategies to handle the feature selection problem. The first strategy used S-shaped and V-shaped shift functions to map continuous variables to discrete variables. The second strategy was to combine four logical operations (AND, OR, XOR, XNOR) on the basis of the transfer function, and constructed a parameter model based on the sine and cosine function. An enhanced logic binary sine–cosine function arithmetic optimization algorithm (LBSCAOA) was proposed to realize the position update of variables. Its purpose was to improve the algorithm's global search capabilities and local exploitation capabilities. In the simulation experiments, 20 datasets were selected to testify the capability of the proposed algorithm. Since KNN had the advantages of easy understanding and low training time complexity, this classifier was selected for evaluation. The performance of the improved algorithm was comprehensively evaluated by comparing the average classification accuracy, the average number of selected features, the average fitness value and the average running time. Simulation results showed LBSCAOA with V-Shaped “V4” stood out among many improved algorithms. On the other hand, LBSCAOA with V-Shaped “V4” was used as a representative to compare with other typical feature selection algorithms to verify its competitivenes.

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

A survey on the practice of phlebotomy in Lithuania and adherence to the EFLM-COLABIOCLI recommendations: continuous training and clear standard operating procedures as tools for better quality.

The aim of this study was to determine the level of compliance of venous blood sampling (VBS) in Lithuania with the joint recommendations of the European Federation of Clinical Chemistry and Laboratory Medicine and the Latin American Confederation of Clinical Biochemistry (EFLM-COLABIOCLI) and to analyse possible causes of errors. A survey was conducted between April and September 2022. A self-designed questionnaire was distributed to the Lithuanian National Societies. Error frequencies and compliance score were computed. Differences between groups were analysed using Pearson's chi-square, Fisher's exact criterion, Mann-Whitney U (for two groups), or Kruskal-Wallis (for more than two groups) for categorical and discrete indicators. The association between ordinal and discrete variables was assessed using Spearman's rank correlation coefficient. Statistical significance was determined at P < 0.05. A total of 272 respondents completed the questionnaire. Median error rate and compliance score were 31.5% and 13/19, respectively. Significant differences were found among professional titles, standard operating procedures availability, training recency, and tourniquet purpose opinions. A negative correlation was noted between compliance and time since training (rs = - 0.28, P < 0.001). The findings of this study indicate that there is a significant need for improvement in compliance with the EFLM-COLABIOCLI recommendations on VBS among specialists in Lithuania. Essential measures include prioritizing ongoing phlebotomy training and establishing national guidelines. Harmonisation of blood collection practices across healthcare institutions is crucial.

RPEM: Randomized Monte Carlo parametric expectation maximization algorithm.

Inspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis-Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it Randomized Parametric Expectation Maximization (RPEM). We compared RPEM with NONMEM's Importance Sampling Method (IMP), Monolix's Stochastic Approximation Expectation Maximization (SAEM), and Certara's Quasi-Random Parametric Expectation Maximization (QRPEM) for a realistic two-compartment voriconazole model with ordinary differential equations using simulated data. We show that RPEM is as fast and as accurate as the algorithms IMP, QRPEM, and SAEM for the voriconazole model in reconstructing the population parameters, for the normal and log-normal cases.

HFN: Heterogeneous feature network for multivariate time series anomaly detection

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies.

Emergence characteristics comparing endotracheal tube to reinforced laryngeal mask airway during endoscopic sinus surgery - A randomised controlled study.

During endoscopic sinus surgery, anaesthetic conditions significantly impact the intraoperative surgical field and bleeding during emergence. While the endotracheal tube (ETT) has been traditionally used in sinus surgery, a reinforced laryngeal mask airway (RLMA) that produces less upper airway stimulation may result in smoother emergence. A randomised controlled trial of 72 patients undergoing elective sinus surgery was conducted, with the allocation of airway technique to either ETT with a throat pack or RLMA. The primary outcome measure was emergence time, measured by time to opening eyes on commands at the cessation of anaesthesia, and the secondary outcomes were time to removal of airway device, remifentanil use, procedure times, mean arterial pressure (MAP) and the RLMA grade of blood contamination. The continuous variables were analysed using Student's t-tests and discrete variables, count tables were analysed using Fisher's exact tests. There was no significant difference in the emergence time between the ETT and RLMA groups (P = 0.83). Remifentanil use was significantly higher in the ETT group than in the RLMA group (P = 0.022). The ETT group showed a significantly increased total anaesthetic time (P = 0.01). MAP was not significant during preinduction, maintenance or post-RMLA removal. The highest grade of contamination was grade 2 in RLMA. RLMA had lower rates of postoperative adverse events. RLMA comparable to ETT in terms of emergence time. The RMLA group had lower remifentanil use, anaesthesia duration and fewer postoperative adverse events such as cough and throat pain.