Terms Of Set Research Articles

Three-way group consensus method based on probabilistic linguistic preference relations with acceptable inconsistency

In the realm of addressing consensus challenges within group decision-making (GDM) that encompass individual preferences, this article presents an innovative approach. This article introduces a three-way group consensus methodology grounded in probabilistic linguistic preference relations (PLPRs) that ensures an acceptable level of inconsistency by applying the three-way decision (TWD) principle. This methodology is referred to as the TWD-GC-AIC approach. The TWD-GC-AIC approach consists of two core components, each dedicated to distinct aspects: ensuring the internal consistency of individual expert preference relations and achieving consensus within the expert group. The first component is dedicated to enhancing the consistency of individual expert preferences. This process commences with the determination of expert weights and the development of a novel aggregation formula. Subsequently, the consistency metric is refined through the incorporation of a newly devised distance formula. Once the predefined consistency threshold is reached, this study seamlessly integrates a planning model with an iterative algorithm. This integration serves a dual purpose: minimizing adjustment costs while retaining as much of the original information provided by the experts as possible. In the consensus-reaching phase, known as the consensus reaching process (CRP) of the expert group, this study employs iterative algorithms that incorporate TWD within the feedback mechanism. This strategic approach aims to comprehensively consider the emotional attitudes of the experts when adjusting the evaluation information, ultimately leading to a more informed and rational final decision outcome. Finally, this paper empirically validates the effectiveness and efficiency of the TWD-GC-AIC method, particularly in scenarios involving probabilistic linguistic term sets (PLTSs). The method is applied to a practical example and rigorously benchmarked against various other consensus methodologies to assess its performance.

On growth and instability for semilinear evolution equations: an abstract approach

AbstractWe propose a new approach to the study of (nonlinear) growth and instability for semilinear abstract evolution equations with compact nonlinearities. We show, in particular, that compact nonlinear perturbations of linear evolution equations can be treated as linear ones as far as the growth of their solutions is concerned. We obtain exponential lower bounds of solutions for initial values from a dense set in resolvent or spectral terms. The abstract results are applied, in particular, to the study of energy growth for semilinear backward damped wave equations.

Figure and Field in the Anthropocene

The following thoughts will inevitably find themselves in the service of a central idea: the creation of a discursive common ground – that is, the production of a set of assumptions, beliefs and common terms that find acceptance and use, and which form the basis for conversation and dialogue. So, in the simple act of gathering a group of essays under the title “Making Ground”, we have created a discursive commons that allows discrete and unique thoughts and ideas to find expression. This notion – the creation of a discursive commons – is the guiding theme of this essay, where it begins and where it will end. It is also very important to note that (although I do not think it is the case in this instance) a discursive commons can be created through evasion as much as through attraction: by advancing or proposing a theme that is somehow repellant to an existing field of discourse, a new common ground can be created. Therefore, the overt rejection of a discursive commons, or the rejection of a conceptual assertion, in many ways serves the same purpose as its acceptance.

A novel integrated approach based on best–worst and VIKOR methods for green supplier selection under multi-granularity extended probabilistic linguistic environment

With consideration for the extensive resources consumption and environmental degradation being on the rise today, implementing green development strategy to pursue both socioeconomic growth and the coordinated of environment sustainability, has become an increasingly important issue in modern enterprise supply chain operations management. Hence, the appropriate green supplier selection (GSS), viewed as a core issue in green supply chain management (GSCM), requires continuous research in this field to obtain a complete perception on GSS practices. It can be regarded as a multi-attribute group decision-making (MAGDM) problem that involves many conflict and unmeasurable evaluation criteria. In view of the superiority of multi-granularity extended probabilistic linguistic term sets (MGEPLTSs) in modeling such issues on potential ambiguity, complexity and uncertainty in actual GSS practices, we propose a novel integrated MAGDM methodology for GSS problems, by integrating the BWM (best–worst method) with the VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) technique under the MGEPLTSs environment. First, by introducing the multi-granularity and probabilistic linguistic term sets, the MGEPLTSs are proposed to represent and quantify the decision information of GSCM practitioners. Then, the BWM is introduced to the MGEPLTSs environment to compute the weights of decision-making panels and evaluation attributes in GSS problems, by building the fuzzy mathematical programming model, respectively. Finally, we extend a multi-granularity extended probabilistic linguistic VIKOR method to calculate the compromise measure of alternatives considering the group utility maximization and the individual regret minimization, thereby achieving the full ranking of alternatives. A GSS case is conducted to illustrate the feasibility of the proposed approach, and the sensitivity analysis and comparative analysis with other similar approaches are presented to demonstrate its effectiveness and advantages.

A group consensus model with prospect theory under probabilistic linguistic term sets

The challenges encountered in the realm of multi-attribute group decision-making (MAGDM) involving probabilistic linguistic term sets (PLTSs) have garnered substantial attention. Within the PLTS context, this study introduces a consensus reaching process (CRP) that iteratively refines the weights assigned to decision-makers (DMs) by leveraging the principles of prospect theory (PT). The primary goal of this iterative weight adjustment process is to enhance the overall decision-making procedure when dealing with PLTSs. To circumvent any data loss during transformation and compute the prospect values of PLTSs directly, a novel transformation formula is developed. Acknowledging the distinct cognitive levels among different DMs, the integration of multiple weights into the consensus process is characterized by its dynamic and iterative nature. Concerning the measurement of consensus, this study employs a method based on the gap between prospect values, which enhances objectivity while overcoming the limitations associated with the distance formula of PLTSs. Furthermore, the feedback mechanism incorporated into the modification process incorporates dynamic adjustment parameters that are tailored to different evaluation values, thereby preventing excessive adjustments that are either too low or too high. By utilizing the newly proposed prospect value function, this research aggregates the group evaluation value and identifies the optimal alternative. In conclusion, this paper concludes with a comparative analysis involving various counterparts, shedding light on the feasibility and validity of the proposed model.

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface.

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.

Cascade multi-resonant disturbance observer design. Application to a distillation column

In this work, we present a novel approach for input disturbance estimation design and implementation for dynamical processes under the influence of unknown disturbances that present a clear periodical behaviour of a known frequency. Both the design and the implementation are focused on simplicity. The observer consists of a set of transfer functions fed by the process manipulated variable and the sensor measurement that are implemented through a mixture of cascade and series connections. For the synthesis of the disturbance observer, we just need an input–output model of the process and one tuning parameter in each of the transfer functions. We present simple rules for the design considering the trade-off between the transient time needed to estimate changes in the behaviour of the disturbance and the robustness against measurement noise effects. We show the benefits in using this disturbance estimation as a feed-forward signal in both open loop and closed loop control applications, and we quantify the robustness modification when used together with a closed loop controller. The transfer functions of the observer include both a low pass filter and a set of resonant terms. We quantify the effect of the different terms and their parameter tuning. Our implementation method uses standard tools available in industrial control systems and we have applied it to a real distillation column under ambient temperature disturbances. The main contribution of this work is the simplicity of the design and implementation of the disturbance observer, making it suitable for the process industry and to be managed by non-experts in control systems. Another contribution is the a priori design based in intuitive engineering performance indices.

A rare first case of prosthetic knee joint infection by Citrobacter koseri

Citrobacter generally colonizes the gastrointestinal and urogenital tracts of humans, but musculoskeletal infection by Citrobacter in the elderly is extremely rare. To our knowledge, less than 15 cases of causing musculoskeletal infection have been reported in the literature. In the last few years, there has been a considerable increase in the incidence of prosthetic knee joint infection in the United States, with a higher risk of infection in the first two years following surgery. Although gram-positive bacteria like Staphylococcus and Streptococci are more common cause of PJI, gram-negative bacteria can also be found in infected joints. We report the first case of PJI in the left knee due to in an 82-year-old adult two years after bilateral knee arthroplasty. This case report intends to educate and raise awareness for causing PJI as a long term post-op complication even in healthy individuals. We searched articles on Pubmed and Medline for PJI and citrobacter infection. We report clinically and microbiologically confirmed in a synovial fluid sample of the left knee joint. should be considered as a possible pathogen for PJI in long term setting even in an otherwise healthy individual.

New terminology of medication adherence enabling and supporting activities: ENABLE terminology.

Introduction: Medication non-adherence negatively affects the effectiveness of evidence-based therapies and sustainability of healthcare systems. Lack of agreed terminology of medication adherence enabling and supporting activities leads to underuse of the available tools. The ENABLE COST Action was aimed at proposing a new terminology for these activities in order to help both scientific research and its clinical application. Methods: Initial discussions within the ENABLE Working Groups allowed for the conceptualization of four interlinked terms related to adherence, i.e., "medication adherence technology", "medication adherence enhancing intervention", "best practice" and "reimbursement". The iterative process of internal discussion was structured around two dedicated international workshops. Moreover, extensive stakeholder consultations have been organised, including an interactive online survey used to assess the level of agreement with, and the clarity of relevant terms and definitions proposed. Results: Detailed analysis of the results of this process allowed for fine-tuning of the items, and finally, for proposing the final set of definitions. Across all the three phases of this process, the definitions were substantially modified to better reflect the concepts, simplify the language, and assure completeness and cohesiveness of terminology. Feedback obtained from the stakeholders helped this process and confirmed that the final terms and definitions were well received by the experts active in the field of medication adherence. Discussion: Covering the gap in the existing terminology, this work proposes a cohesive set of terms and definitions applicable to medication adherence enabling and supporting activities. Promoting evidence-based approach to this field, this terminology may help research, clinical practice and policy.

Method for classroom teaching quality evaluation in college English based on the probabilistic uncertain linguistic multiple-attribute group decision-making

College English is an important basic course in higher education. Promoting the reform of college English teaching and constructing a scientific and standardized quality assurance system for college English teaching are important guarantees and powerful means to improve the quality of talent cultivation in higher education. In terms of ideology, it is necessary to attach great importance to strengthening the awareness of monitoring and ensuring the quality of college English teaching system construction; In terms of management, ensure the operation of a diversified governance and evaluation system for college English teaching; Practice in improving the quality monitoring and guarantee system of college English teaching in action; In terms of overall coordination, build a high-quality and efficient college English teaching resource sharing system. The classroom teaching quality evaluation in college English is classical multiple-attribute group decision-making (MAGDM) issues. Recently, the Exponential TODIM (ExpTODIM) method has been used to solve MAGDM issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information during the classroom teaching quality evaluation in college English. In this paper, the probabilistic uncertain linguistic ExpTODIM (PUL-ExpTODIM) method is defined to solve the MAGDM under PULTSs. In the end, a numerical case study for classroom teaching quality evaluation in college English is given to validate the proposed method.

A comparison of hesitant fuzzy VIKOR methods for supplier selection

Supplier selection is a complex decision-making process which is influenced by uncertainty and DM hesitation. To deal with this problem, various studies have proposed the use of methods based on Fuzzy Set Theory and its extensions. There are several varieties of Hesitant Fuzzy Linguistic VIKOR (HFLVIKOR) methods in the literature which can use two or more linguistic terms in each evaluation and can generate solutions based on the group’s utility or individual regret. Despite these benefits, we have not found comparative studies involving HFLVIKOR methods to supplier selection. Given this gap, this study applies and compares Extended Hesitant Fuzzy Linguistic VIKOR (EHFLVIKOR) methods and Hesitant Fuzzy Linguistic Term Set VIKOR (PDHFLVIKOR) methods with Possibility Distributions within the supplier selection context. This application was realized using a service company. We also performed 8 scenario simulations to analyze the effect of including other criteria and alternatives. Despite the identified limitations of each method, we conclude that both are appropriate for dealing with group decision making under uncertainty and require little effort in their data collection. The PDHFLVIKOR results were more consistent in maintaining the ranking and the compromise solution in the real application as well as simulated scenarios. Unlike PDHFLVIKOR, EHFLVIKOR loses information when there is a consensus. On the other hand, because it makes it possible to create subgroups of DMs, EHFLVIKOR appears to be more appropriate for companies in which there is more than one DM involved in supplier selection for each functional area. This study’s results will help managers and academics in selecting the most appropriate method for supplier selection.

Prioritizing real estate enterprises based on credit risk assessment: an integrated multi-criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness. In the context of real estate enterprises, credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects. Additionally, it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry. However, Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns, making subjective evaluation approaches inevitable, occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks. To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises, this study proposes an integrated multi-criteria group decision-making approach. First, a credit risk assessment index for Chinese real estate enterprises is established. Then, the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods. This approach is suitable for processing large amounts of data with high uncertainty, which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjective evaluation information. Finally, the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility. This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment.

Convex economic model predictive control for blade loads mitigation on wind turbines

AbstractEconomic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade‐off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real‐time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC‐IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid‐fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made.

Towards a standardised cross-sectoral data access agreement template for research: a core set of principles for data access within trusted research environments.

Trusted Research Environments (TREs) are secure computing environments that provide access to data for approved researchers to use in studies that can save and improve lives. TREs rely on Data Access Agreements (DAAs) to bind researchers and their organisations to the terms and conditions of accessing the infrastructure and data use. However, DAAs can be overly lengthy, complex, and can contain outdated terms from historical data sharing agreements for physical exchange of data. This is often cited as a cause of significant delays to legal review and research projects starting. The aim was to develop a standardised DAA optimised for data science in TREs across the UK and framed around the 'Five Safes framework' for trustworthy data use. The DAA is underpinned by principles of data access in TREs, the development of which is described in this paper. The Pan-UK Data Governance Steering Group of the UK Health Data Research Alliance led the development of a core set of data access principles. This was informed by a benchmarking exercise of DAAs used by established TREs and consultation with public members and stakeholders. We have defined a core set of principles for TRE data access that can be mapped to a common set of DAA terms for UK-based TREs. Flexibility will be ensured by including terms specific to TREs or specific data/data owners in customisable annexes. Public views obtained through public involvement and engagement (PIE) activities are also reported. These principles provide the foundation for a standardised UK TRE DAA template, designed to support the growing ecosystem of TREs. By providing a familiar structure and terms, this template aims to build trust among data owners and the UK public and to provide clarity to researchers on their obligations to protect the data. Widespread adoption is intended to accelerate health data research by enabling faster approval of projects, ultimately enabling more timely and effective research.

Z hesitant fuzzy linguistic term set and their applications to multi-criteria decision making problems

With the constant changes in the decision-making environment, various limitations have emerged in traditional decision-making methods, such as the inability to simultaneously consider multiple factors such as randomness, hesitation, and ambiguity, thus limiting a comprehensive grasp of the decision-maker’s cognitive inputs. To address the above problems, we propose a multi-criteria decision-making (MCDM) model based on a new fuzzy set (FS). Firstly, we define a new Z hesitant fuzzy linguistic term set (ZHFLTS) and propose a visualization metric for mapping ZHFLTS into T-spherical fuzzy (T-SF) space to simplify the computation. The relevant properties of ZHFLTS are further investigated. In order to accurately measure the uncertainty of FSs, we propose the probabilistic-hesitation-fuzzy (PHF) entropy and use it as a basis to derive the potential probability distribution using the fruit fly optimization algorithm (FOA). Subsequently, an MCDM model is established based on the proposed ZHFLTS, visualization metric, and PHF entropy. The model can better solve complex decision-making problems such as TCM through example studies and comparative analyses. The research in this paper aims to cope with today’s increasingly complex decision-making environments, and the introduced ZHFLTS framework and the corresponding methodology provide new ways to solve complex decision problems.

Sunlight-Daylight Signature: a Novel Concept to Assess Sunlight and Daylight Availability at Urban Scale

Daylighting and solar availability at urban scale has come to play a crucial role in the perception of discomfort conditions for people, both in outdoor and indoor spaces, and on the energy consumption of buildings. Daylighting and solar analyses are typically done separately. The paper presents a novel method, called the ‘sunlight-daylight signature’ (SDS), which allows the qualitative analysis of urban settings with respect to sunlight and daylight. The method can be used to classify different urban settings in terms of daylight/sunlight access or to test new development proposals by referring to existing locations and confirm whether a certain daylight quality is met. The SDS relies on a new analysis tool, called ‘sunlight-daylight wedge’ (SDW), which combines obstruction (through the vertical sky component VSC) and sunlight access (through the annual probable sunlight hours PASH and the winter probable sunlight hours PWSH). The orientation of the façade at each point is also included as it will affect the times of the day when the sun-hours from PASH and PWSH occur, thus affecting the character of the corresponding sunlight. The SDS approach is based on a clustering technique to subdivide large datasets (in this case, daylight data points across entire cities or major urban areas) into smaller groups, using machine learning by way of the k-medoids clustering technique. This is used to derive typical daylight and sunlight scenarios representing groups of data points with similar conditions. Additional data is included to account for urban density and daylight availability in public areas. Final output of the clustering process consists of a map showing areas with the same daylight signature (SDS), which means areas with the same sunlight and daylight conditions. The SDS can be useful for urban planners and building practitioners to predict the access to both daylight and sunlight of large urban settings to optimize comfort for people and energy usage.

A statistical approach to large-scale group decision making with incomplete hesitant fuzzy linguistic information by incorporating risk attitude

In response to the lack of information and the arguments among the decision-makers (DMs) in large-scale group decision making (LSGDM), this study proposes a method that integrates decision risk and risk attitude into LSGDM with hesitant fuzzy linguistic preference relations (HFLPRs) based on statistical estimation. Firstly, the DMs are arranged into several subgroups via their risk attitudes. Next, the whole or part of the five-number summary for subgroups from incomplete information provided by DMs are collected. After that, the optimization model and Monte Carlo simulation are utilized to obtain mean and standard deviation estimators of the decision information, and the mean estimator is regarded as the evaluation information of subgroup. Then, the hesitant fuzzy linguistic term sets (HFLTSs) with different number of linguistic variables are normalized according to their risk attitudes and confidence interval. Meanwhile, the number of members in subgroups and the decision risk level of subgroups are combined to determine the weights of subgroups. Moreover, a minimum deviation model is derived to rank alternatives. Finally, a case study, sensitivity analysis and comparison are used to guarantee the rationality and availability of the proposed method.

Fuzzy C-means clustering with hesitant fuzzy linguistic preference relation

With the advancement of technology and growing social demand, large scale group decision making has gained significant importance in the field of decision making. Clustering analysis plays a crucial role in enhancing the efficiency of large scale group decision making processes. Linguistic evaluation is more in line with people’s cognitive and expressive habits. The hesitant fuzzy linguistic term set (HFLTS) offers more flexibility in expressing evaluation information. This paper is dedicated to designing a fuzzy C-means clustering model that is specifically applicable for the hesitant fuzzy linguistic preference relation (HFLPR). The objective function of the model is built based on the dissimilarity between HFLPRs and the initial cluster centers to obtain the fuzzy membership matrix and cluster centers. Since initializing cluster centers is a crucial step to produce a reasonable cluster result, three methods are proposed for generating initial centers for HFLPRs. The first and second methods are improvements over existing approaches that dealt with the clustering problem with numerical values. The third approach considers both the preference relation of preferring an alternative and the distribution of the actual preference relations. Based on this, a fuzzy C-means clustering algorithm with HFLPR is designed to obtain the cluster centers and membership matrix for there types of initializing clustering centers. Finally, based on the quality and speed of clustering, a numerical example and comparative analyses illustrate that the proposed clustering algorithm is efficient and effective.

Pregnancy Outcomes of Conservative Management in Preeclampsia with Severe Features.

Objective: To study the pregnancy outcomes of conservative treatment for preeclampsia with severe features. Methods: A retrospective study was conducted on pregnancies with preeclampsia with severe features at gestational age 23-34 weeks and that received conservative management at Chiang Mai University Hospital between January 2014 and August 2020. The women were divided into two groups: (1) pregnancy prolongation of at least 48 h and (2) pregnancy prolongation of less than 48 h. Results: Of the 100 recruited pregnancies, the median gestational age was 29 weeks (range 23-34). Of these, 65 cases (65%) had pregnancy prolongation of at least 48 h, and 35 cases (35%) had prolongation of less than 48 h. The median pregnancy prolongation was 2.9 days (range 4 h-27.7 days). Eighty-seven (88%) pregnant women experienced no complications. Multivariate analysis shows that high urine protein/creatinine ratio (UPCI) at admission was significantly associated with pregnancy prolongation of less than 48 h with an odds ratio for prolongation for at least 48 h of 0.86 (95% CI 0.75-0.99: p-value 0.04). Kaplan-Meier analysis shows that the mean time of prolongation was 3.6 days vs. 6.7 days, and median time of prolongation was 2.1 days vs. 4.4 days in the group of high and low UPCI (using cut-off 1.0), respectively. The number of prolonged days was significantly lower in the high UPCI group than in the low UPCI group (log-rank test, p = 0.01). The maternal and fetal outcomes between the two groups were not significantly different. The cesarean section rate was also comparable. The mean birth weight and gestational age at delivery were not significantly different, though they had a higher trend in the group of successful conservative management. Conclusion: The rate of pregnancy prolongation of at least 48 h with conservative management was 65%, with a median prolongation time of 2.9 days. A new insight gained from this study is that high UPCI at admission is an independent factor for prolongation of less than 48 h with conservative treatment. Nevertheless, the maternal and fetal outcomes between the two groups were not significantly different. Therefore, the benefit and risk of expectant management in actual practice of service settings in terms of maternal and fetal morbidity is still unclear.

50-W vs 40-W During High-Power Short-Duration Ablation for Paroxysmal Atrial Fibrillation: A Multicenter Prospective Study

BackgroundHigh-power short-duration (HPSD) radiofrequency ablation of atrial fibrillation (AF) increases first-pass pulmonary vein isolation (PVI) and freedom from atrial arrhythmias while decreasing procedural time. However, the optimal power setting in terms of safety and efficacy has not been determined. ObjectivesThis study compared the procedural characteristics and clinical outcomes of 50-W vs 40-W during HPSD ablation of paroxysmal AF. MethodsPatients from the REAL-AF prospective multicenter registry (Real-World Experience of Catheter Ablation for Treatment of Symptomatic Paroxysmal and Persistent Atrial Fibrillation) undergoing HPSD ablation of paroxysmal AF, either using 50-W or 40-W, were included. The primary efficacy outcome was freedom from all-atrial arrhythmias. The primary safety outcome was the occurrence of any procedural complication at 12 months. Secondary outcomes included procedural characteristics, AF-related symptoms, and the occurrence of transient ischemic attack or stroke at 12 months. ResultsA total of 383 patients were included. Freedom from all-atrial arrhythmias at 12 months was 80.7% in the 50-W group and 77.3% in the 40-W group (Log-rank P = 0.387). The primary safety outcome occurred in 3.7% of patients in the 50-W group vs 2.8% in the 40-W group (P = 0.646). The 50-W group had a higher rate of first-pass PVI (82.3% vs 76.2%; P = 0.040) as well as shorter procedural (67 minutes [IQR: 54-87.5 minutes] vs 93 minutes [IQR: 80.5-111 minutes]; P < 0.001) and radiofrequency ablation times (15 minutes [IQR: 11.4-20 minutes] vs 27 minutes [IQR: 21.5-34.6 minutes]; P < 0.001) than the 40-W group. ConclusionsThere was no significant difference in freedom from all-atrial arrhythmias or procedural safety outcomes between 50-W and 40-W during HPSD ablation of paroxysmal AF. The use of 50-W was associated with a higher rate of first-pass PVI as well as shorter procedural times.