Competitive Metrics Research Articles

Multi-objective, multi-constraint high-throughput design, synthesis, and characterization of tungsten-containing refractory multi-principal element alloys

Refractory multi-principal element alloys (RMPEAs) have gained interest recently due to their superior properties at elevated temperatures, including outstanding yield and ultimate strengths, high thermal conductivity, and resistance to creep. RMPEAs can be designed to exhibit a wide range of properties by tailoring their composition. However, the vast chemical design space makes brute-force experimental screening inefficient and costly. In this work, we follow a closed-loop, iterative computational/experimental screening approach that combines computational alloy design methodologies with high-throughput synthesis and characterization tools to explore the vast RMPEAs space and design new RMPEAs that satisfy multiple objectives and constraints. In particular, we targeted compositions with yield strengths higher than 50 MPa at 2000 °C, W content of more than 30 at.% for high-temperature strength and operability up to 2000 °C, narrow solidification range for additive manufacturability, competitive ductility metrics, among other property constraints. We evaluated the mechanical properties and microstructure of 58 alloys designed in 5 batches, in both as-cast and homogenized conditions, synthesized using vacuum arc melting, utilizing scanning electron microscopy, X-ray diffraction, Vickers microhardness, nanoindentation, and high-temperature compression testing. Based on the microhardness screening experiments in each batch, the best-performing alloys were selected for scale-up. High-temperature compression at 1800 °C was performed in these alloys, demonstrating that the designed alloys exhibit up to five times higher yield strength than a pure tungsten benchmark. We conclude that W-containing RMPEAs designed in this study merit further consideration for next-generation structural materials for ultra-high temperature applications.

52335 An Analysis of Dermatology Match Statistics and Other Metrics of Competitiveness

52335 An Analysis of Dermatology Match Statistics and Other Metrics of Competitiveness

Sustainable Hard Carbon as Anode Materials for Na‐Ion Batteries: From Laboratory to Upscaling

Sodium‐ion batteries (NIBs) are an alternative to lithium‐ion batteries (LIBs), particularly in applications where cost, availability, and sustainability are more critical. Hard carbon is emerging as a promising anode material for NIBs, however, the scale up remains in developmental stages. In this study, we focus on the development and potential upscaling of sustainable hard carbon materials as anodes for NIBs. The synthesis of hard carbon starts from D‐glucose, a scalable and environmentally benign precursor. A facile process combining hydrothermal carbonisation and subsequent pyrolysis at 1500°C allows the hard carbon to become an industrially viable material. The resulting hard carbon demonstrates competitive performance metrics including a high initial Coulombic efficiency, high reversible capacity, long‐term cycling stability, and rate capability. This study concludes with a discussion of the techno‐economic analysis of adopting such sustainable materials in the battery industry, highlighting the potential for significant advancements in energy storage technologies.

Evaluating the transferability of airborne laser scanning derived stem size prediction models for Pinus taeda L. stem size estimation to two different locations and acquisition specifications

ABSTRACT Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS’s ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted.

A multiscale 3D network for lung nodule detection using flexible nodule modeling.

Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density. Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres. To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel point selection strategy (PSS) devised to accelerate the learning process of anisotropic representation. We further incorporate a composite loss function that combines the conventional L2 loss and cosine similarity loss, facilitating M3N to learn nodules' intensity distribution in three dimensions. Experiment results show that the M3N achieves 90.6% competitive performance metrics (CPM) with seven predefined false positives per scan on the LUNA 16 dataset. This performance appears to exceed that of other state-of-the-art deep learning-based networks reported in their respective publications. Individual test results also demonstrate that M3N excels in providing more accurate, adaptive bounding boxes surrounding the contours of target nodules. The newly developed nodule detection system reduces reliance on prior knowledge, such as the general size of objects in the dataset, thus it should enhance overall robustness and versatility. Distinct from traditional nodule modeling techniques, the ANM approach aligns more closely with the morphological characteristics of nodules. Time consumption and detection results demonstrate promising efficiency and accuracy which should be validated in clinical settings.

Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets

ABSTRACT This study investigates the performance of six machine learning (ML) models – Random Forest (RF), Adaptive Boosting (ADA), CatBoost (CAT), Support Vector Machine (SVM), Lasso Regression (LAS), and Artificial Neural Network (ANN) – against traditional empirical formulas for estimating maximum scour depth after sluice gates. Our findings indicate that ML models generally outperform empirical formulas, with correlation coefficients (CORR) ranging from 0.882 to 0.944 for ML models compared with 0.835–0.847 for empirical methods. Notably, ANN exhibited the highest performance, followed closely by CAT, with a CORR of 0.936. RF, ADA, and SVM performed competitive metrics around 0.928. Variable importance assessments highlighted the dimensionless densimetric Froude number (Fd) as significantly influential, particularly in RF, CAT, and LAS models. Furthermore, SHAP value analysis provided insights into each predictor's impact on model outputs. Uncertainty assessment through Monte Carlo (MC) and Bootstrap (BS) methods, with 1,000 iterations, indicated ML's capability to produce reliable uncertainty maps. ANN leads in performance with higher mean values and lower standard deviations, followed by CAT. MC results trend towards optimistic predictions compared with BS, as reflected in median values and interquartile ranges. This analysis underscores the efficacy of ML models in providing precise and reliable scour depth predictions.

An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition

In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stochastic optimization algorithms. These principles were successfully applied in the M6 forecasting competition under the team named AdaGaussMC. Our approach takes a unique path by embracing the Efficient Market Hypothesis (EMH) instead of trying to beat the market directly. We focus on evaluating the efficient market and emphasize the importance of online forecasting in adapting to the dynamic nature of financial markets. The three key points of our approach are: (a) apply the univariate time-varying volatility model AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We contend that the simplicity of our approach contributes to its robustness and consistency. Our performance in the M6 competition resulted in an overall 7th place, with a 5th place in the forecasting task. Considering our approach’s perceived simplicity, this achievement underscores the efficacy of our adaptive volatility method in probabilistic forecasting.

Multiphysics simulations of a cylindrical waveguide optical switch using phase change materials on silicon

This work presents the design and multiphysics simulation of a cylindrical waveguide-based optical switch using germanium-antimony-tellurium (GST) as an active phase change material. The innovative cylindrical architecture is theoretically analyzed and evaluated at 1550 nm wavelength for telecommunication applications. The dispersion relation is derived analytically for the first time to model the optical switch, while finite element method (FEM) and finite difference time domain (FDTD) techniques are utilized to simulate the optical modes, light propagation, and phase change dynamics. The fundamental TE01 and HE11 modes are studied in detail, enabling switching between low-loss amorphous and high-loss crystalline GST phases. Increasing the GST thickness is found to increase absorption loss in the crystalline state but also slows down phase transition kinetics, reducing switching speeds. A 10 nm GST layer results in competitive performance metrics of 0.79 dB insertion loss, 13.47 dB extinction ratio, 30 nJ average power consumption, and 3.5 Mb/s bit rate. The combined optical, thermal, and electrical simulation provides comprehensive insights towards developing integrated non-volatile photonic switches and modulators utilizing phase change materials.

A concise but high-performing network for image guided depth completion in autonomous driving

Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth map into a dense depth prediction. The sparse depth map serves as a partial reference for the actual depth, and the fusion of RGB images is frequently employed to augment the completion process owing to its inherent richness in semantic information. Image-guided depth completion confronts three principal challenges: (1) the effective fusion of the two modalities; (2) the enhancement of depth information recovery; and (3) the realization of real-time predictive capabilities requisite for practical autonomous driving scenarios. In response to these challenges, we propose a concise but high-performing network, named CHNet, to achieve high-performance depth completion with an elegant and straightforward architecture. Firstly, we use a fast guidance module to fuse the two sensor features, harnessing abundant auxiliary information derived from the color space. Unlike the prevalent complex guidance modules, our approach adopts an intuitive and cost-effective strategy. In addition, we find and analyze the optimization inconsistency problem for observed and unobserved positions. To mitigate this challenge, we introduce a decoupled depth prediction head, tailored to better discern and predict depth values for both valid and invalid positions, incurring minimal additional inference time. Capitalizing on the dual-encoder and single-decoder architecture, the simplicity of CHNet facilitates an optimal balance between accuracy and computational efficiency. In benchmark evaluations on the KITTI depth completion dataset, CHNet demonstrates competitive performance metrics and inference speeds relative to contemporary state-of-the-art methodologies. To assess the generalizability of our approach, we extend our evaluations to the indoor NYUv2 dataset, where CHNet continues to yield impressive outcomes. The code of this work will be available at https://github.com/lmomoy/CHNet.

Prahara Digital Labor dalam Bingkai Penafsiran Logika Waktu Pendek dan Kapitalisme Luwes

<p align="justify"><em>When the COVID-19 pandemic gradually subsided, digital disruption was considered a game changer for today's work, such as work from home (WFH). The prevalence of digital disruption in contemporary work has triggered the birth of a digital labor class, which broadly impacts intra- and inter-organizational communication, especially in dealing with work-related risks in an increasingly insecure labor market. In a macro context like this, digital labor within neoliberal higher education institutions became an interesting phenomenon, especially given the worsening conditions of intellectual workers and changes in academic behavior and practices. A recent study showed many practices violating academic integrity in Indonesian higher education institutions, such as plagiarism, data fabrication, improper claims of authorship, contract-cheating, and other violations of professional codes of ethics. To address these problems, the author employed a critical method of the political economy of communication and the sociology of work to analyze 'short-time logic' as an operational mechanism in flexible capitalism, commoditizing workers' time by blurring the boundaries between productive and leisure time and turning the illusion of worker autonomy into a 'sustained contestation.' The 'short time logic' of flexible capitalism deformed intellectual workers in higher education in Indonesia into digital labor because they were constantly conditioned to produce intellectual products with highly competitive metrics but lacking reflection and meaningfulness, which sharpened the gaps in competency-based inequalities and created a new hierarchy of social classes in higher education. </em><strong></strong></p>

Analysis and Benchmarking of feature reduction for classification under computational constraints

Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.

Octopus-inspired deception and signaling systems from an exceptionally-stable acene variant

Multifunctional platforms that can dynamically modulate their color and appearance have attracted attention for applications as varied as displays, signaling, camouflage, anti-counterfeiting, sensing, biomedical imaging, energy conservation, and robotics. Within this context, the development of camouflage systems with tunable spectroscopic and fluorescent properties that span the ultraviolet, visible, and near-infrared spectral regions has remained exceedingly challenging because of frequently competing materials and device design requirements. Herein, we draw inspiration from the unique blue rings of the Hapalochlaena lunulata octopus for the development of deception and signaling systems that resolve these critical challenges. As the active material, our actuator-type systems incorporate a readily-prepared and easily-processable nonacene-like molecule with an ambient-atmosphere stability that exceeds the state-of-the-art for comparable acenes by orders of magnitude. Devices from this active material feature a powerful and unique combination of advantages, including straightforward benchtop fabrication, competitive baseline performance metrics, robustness during cycling with the capacity for autonomous self-repair, and multiple dynamic multispectral operating modes. When considered together, the described exciting discoveries point to new scientific and technological opportunities in the areas of functional organic materials, reconfigurable soft actuators, and adaptive photonic systems.

Feature redundancy removal for text classification using correlated feature subsets

AbstractThe curse of high dimensionality in text classification is a worrisome problem that requires efficient and optimal feature selection (FS) methods to improve classification accuracy and reduce learning time. Existing filter‐based FS methods evaluate features independently of other related ones, which can then lead to selecting a large number of redundant features, especially in high‐dimensional datasets, resulting in more learning time and less classification performance, whereas information theory‐based methods aim to maximize feature dependency with the class variable and minimize its redundancy for all selected features, which gradually becomes impractical when increasing the feature space. To overcome the time complexity issue of information theory‐based methods while taking into account the redundancy issue, in this article, we propose a new feature selection method for text classification termed correlation‐based redundancy removal, which aims to minimize the redundancy using subsets of features having close mutual information scores without sequentially seeking already selected features. The idea is that it is not important to assess the redundancy of a dominant feature having high classification information with another irrelevant feature having low classification information and vice‐versa since they are implicitly weakly correlated. Our method, tested on seven datasets using both traditional classifiers (Naive Bayes and support vector machines) and deep learning models (long short‐term memory and convolutional neural networks), demonstrated strong performance by reducing redundancy and improving classification compared to ten competitive metrics.

Self-supervised learning for hotspot detection and isolation from thermal images

Hotspot detection using thermal imaging has recently become essential in several industrial applications, such as security applications that require identification of suspicious activities or intruders by detecting hotspots generated by human body heat, health applications such as screening of individuals in quarantine environments for onset of fever, and equipment monitoring applications where ensuring smooth operation and preventing potential malfunction by assessing temperature distribution in equipment is important. Hotspot detection is of utmost importance in industrial safety where equipment can develop anomalies. Hotspots are early indicators of such anomalies. We address the problem of hotspot detection in thermal images by proposing a self-supervised learning approach. Self-supervised learning has shown potential as a competitive alternative to their supervised learning counterparts but their application to thermography has been limited. This has been due to lack of diverse data availability, domain specific pre-trained models, standardized benchmarks, etc. We propose a self-supervised representation learning approach followed by fine-tuning that improves detection of hotspots by classification. The SimSiam network based ensemble classifier decides whether an image contains hotspots or not. Detection of hotspots is followed by precise hotspot isolation. By doing so, we are able to provide a highly accurate and precise hotspot identification, applicable to a wide range of applications. We created a novel large thermal image dataset to address the issue of paucity of easily accessible thermal images. Our experiments with the dataset created by us and a publicly available segmentation dataset show the potential of our approach for hotspot detection and its ability to isolate hotspots with high accuracy. We achieve a Dice Coefficient of 0.736, the highest when compared with existing hotspot identification techniques. Our experiments also show self-supervised learning as a strong contender of supervised learning, providing competitive metrics for hotspot detection, with the highest accuracy of our approach being 97%.

Evaluating the feasibility of a normalized competitive index (NCI) to assess the competitiveness of general surgery residency

BackgroundGeneral surgery remains the predominant surgical training pathway; however, the trends in competitiveness over time are unknown. The aim of this study was to determine the feasibility of using a normalized competitive index (NCI) to assess competitiveness by analyzing 30 years of general surgery match data. MethodsData for general surgery programs were collected using the National Resident Matching Program (NRMP) Main Residency Match data from 1993 to 2022. Matched applicant metrics from 2007 to 2021 were collected via NRMP Charting Outcomes data. Metrics included USMLE Step 1 and 2 scores, research experiences and output, work experiences, and volunteer experiences. A competitive index was created by dividing number of positions by the match rate each year. The index was normalized, creating the NCI for trends over time. Linear regressions were performed on the NCI, match data, and applicant metrics across time. ResultsThe NCI significantly differed across time (p = 0.02) with an upward-trending NCI slope; however, there were substantial fluctuations over time. The overall match rate increased over time (p<0.001), and applicants per position decreased over time (p<0.001). The USMLE Step 1 and Step 2 scores of matched applicants increased over time (R2=0.92 and R2=0.95, p < 0.001). Research output has tripled over the 2007–2021 period (2.2 vs. 7, p < 0.001). Step 2 and research output correlated with NCI (Pearson r = 0.89 and r = 0.97) but did not correlate with the match rate. ConclusionThe competitiveness of general surgery residency programs was highly variable over time. Over the last ten years, there has been a significant increase in applicants per position and applicant metrics. The fluctuations in standard metrics of competitiveness (i.e., applicants per position, USMLE scores, research output) correlated with fluctuations in the NCI, while the match rate was relatively stable over time. This study demonstrates that the NCI may be a valuable metric for applicants to determine or predict the competitiveness of a residency program.

Common Institutional Ownership and Product Market Threats

The common ownership of firms can have anticompetitive effects by incentivizing collusive outcomes that maximize joint surpluses of the commonly held firms or procompetitive effects through enhanced knowledge spillovers. Using a difference-in-differences regression methodology that exploits mergers between financial institutions as exogenous shocks to common ownership, our baseline results suggest that higher common ownership leads to greater product market fluidity (a text-based metric of competition) and generally leads to more product development and higher investments. These findings suggest that, on average, common ownership spurs dynamism in product spaces rather than tacit collusion between cross-held competitors. This is especially true in economic environments in which it is easier to take advantage of knowledge spillovers. However, common ownership can also inhibit product market competition and dynamism, especially in industries more prone to quasi-monopoly outcomes in product spaces. Implementing a one-size-fits-all regulatory policy limiting common ownership may be harmful in industries with strong spillover opportunities. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4830 .

Influencia de las percepciones y actitudes gerenciales en la oferta enoturística en empresas vitivinícolas

This article sets out to analyse the perceptions and attitudes of the managers of the wine and viticul‑ tural companies of the Guadalupe Valley (Mexico) regarding wine tourism activities, that has experienced steady growth in the northwest of the country wth the wine routes in Baja California. To this end, we used quantitative methods to construct and validate a measurement instrument to gauge levels of reliability based on three prem‑ ises: perceptions, attitudes and wine tourism activities. This was applied to a representative sample of 64 com‑ panies, using structural equations with partial least squares (PLS). As a result, it can be seen how managerial perceptions are competitively focused on perceived threats posed by new competitors and products, with attitudes centring on their client portfolio and employee productivity, and wine tourism activities of tastings and tours. Consequently, this research contributes to the scientific debate on the organisation and business management of the wine tourism sector, by highlighting the specific metrics of business competitiveness in SMEs.

Estimating competition in metacommunities: accounting for biases caused by dispersal

Abstract Estimating the strength of interactions among species in natural communities has always been a challenge for empirical ecologists. Sessile organisms, like plants or corals, often occur in metacommunities where they compete only with their immediate neighbours but disperse propagules over a wider area. To estimate the strength of competitive interactions, ecologists often count abundances in cells on a spatial grid for at least two time‐points. This data is then analysed using regression, by modelling the change in population size as a function of local densities, using cells as independent data‐points: a technique known as space‐for‐time substitution. These analyses generate estimates of competition coefficients; however, the method ignores dispersal among cells. To determine the impact of ignoring dispersal, we derived the bias that would arise when we apply regression methods to a metacommunity in which a fraction of seeds disperse beyond their natal cells but this dispersal is ignored in the model fitting process. We present results from a range of population models that make different assumptions about the nature of competition and assess the performance of our bias formulae by analysing data from simulated metacommunities. We reveal that: estimates of competition coefficients are biased when dispersal is not properly accounted for; and the resulting bias is often correlated with abundance, with rare species suffering the greatest overestimation. We also provide a standardized metric of competition that allows the bias to be calculated for a broad range of other population models. Our study suggests that regression methods that ignore dispersal produce biased estimates of competition coefficients when using space‐for‐time substitution. Our analytical bias formula allows empirical ecologists to potentially correct for biases, but it requires either tailored experiments in controlled conditions or an estimate of the average dispersal rate in a natural community, so may be challenging to apply to real datasets.

Analyzing trends in obstetrics and gynecology fellowship training over the last decade using the normalized competitive index

BACKGROUNDThe objective and relative competitiveness of obstetrical and gynecologic subspecialty training programs remain understudied. Traditional metrics, such as match rate or program fill rate, fail to standardize the application environment. This limits their applicability when examining demographic trends or when comparing data between different fellowship matches. The normalized competitive index was introduced to serve as a comprehensive metric of competitiveness by incorporating disparate indicators and normalizing to enable more detailed analyses.OBJECTIVEThis study aimed to analyze trends in the competitiveness across obstetrical and gynecologic subspecialty fellowship matches during the last decade.STUDY DESIGNThe results and data reports from the National Resident Match Program fellowship for 2010 to 2019 were used to collect data on multiple metrics of competitiveness for 6 obstetrical and gynecologic subspecialties. These data were used to determine the normalized competitive index. Subanalyses were conducted to identify trends over the last decade.RESULTSAmong fellowship programs in obstetrics and gynecology, the overall specialty match rate was 67.6%. The overall specialty program fill rate was 95.7%. According to the normalized competitive index metric, minimally invasive gynecologic surgery was the most competitive fellowship match (normalized competitive index=1.31; P=.002). Maternal-fetal medicine was the least competitive (normalized competitive index=0.94; P≤.005). When comparing the first and second half of the decade, no specialty experienced a significant decrease in match rate. The only significant increase in match rates occurred for female pelvic medicine and reconstructive surgery (P=.035). Subanalyses of the normalized competitive index metric and other indicators of competitiveness demonstrated a strong negative correlation between the normalized competitive index and the subspecialty match rate (r=−0.9444) and a moderately positive correlation between the normalized competitive index and the program fill rate (r=0.4047).CONCLUSIONThe normalized competitive index offers trainees a more quantitative understanding of the fellowship application environment. By incorporating multiple metrics and normalizing the result, it uniquely enables comparison between the subspecialty matches and the match process over time. The same standardization offers the potential for future comparisons of competitiveness within a single subspecialty match based on geographic region, applicant demographics, and other important determinants of a diverse and vibrant training environment.