Inaccurate Performance Research Articles

Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) sensors.Design/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for classification.FindingsThe findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification accuracy.Practical implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that neglecting DRA can lead to long-term issues such as back pain, pelvic floor dysfunction, and compromised core strength.Social implicationsDuring our data collection camps, there were educational sessions to raise awareness about the DRA problem and the benefits of rehabilitation exercises. This dual approach helped in building trust and encouraging participation. Moreover, the use of wearable sensors in this study provided a non-invasive and convenient way for new mothers to engage in rehabilitation exercises without needing frequent visits to a clinic, which is often impractical for them.Originality/valueThe utilization of discriminating features retrieved from the output layer of 1D CNN is a significant contribution to this work. The responses of this study indicate that 1D convolutional neural network (1D CNN) and Boosting algorithms used in a transfer learning strategy produce successful discrimination between accurate and inaccurate performance of exercises by achieving an accuracy of 96%.

Anomalous CatalyticPerformance on Non-Active-Site Carbon Substrate.

Carbon-based nanomaterials are gaining attention in electrocatalysis. This study investigates the inherent nitrate reduction activity (NO3RR) of commercial carbon paper as a substrate. Results showed that carbon paper, without additional catalysts, achieved approximately 80.42% NH3Faradaic efficiency (FE) at -2.1 V vs. Hg/HgO in alkaline conditions, 83.51% NH3FE at -1.9 V vs. Ag/AgCl in neutral conditions, and 14.53% NH3FE at -1.9 V vs. MSE in acidic conditions. Density Functional Theory (DFT) calculations revealed energy barriers of 2.66 eV, 0.95 eV, and 1.37 eV, respectively. Molecular physisorption on the carbon paper surface generates an induced electric field, promoting charge transfer between the carbon paper and the adsorbed molecules, thus enhancing the activity of the carbon paper. These findings highlight the importance of considering the intrinsic catalytic properties of carbon substrates in catalyst design and evaluation, as overlooking these properties can lead to inaccurate performance assessments. This study emphasizes the need for a comprehensive approach to optimize catalytic systems.

Robust retrofits for rural house envelope considering construction quality and occupant behavior uncertainties: A MOO-integrated Taguchi method

Due to the underdeveloped socio-economic environment, the uncertainties in construction quality and occupant behavior are more prominent for rural house than urban residential buildings. If these uncertainties are not considered, there are higher risks of inaccurate performance prediction, unrealistic retrofit decisions and causing serious performance gap. To address this challenge, this paper proposes a robust envelope retrofitting workflow that combine deterministic multi objective optimization (MOO) and Taguchi method. This workflow stands out for its utilizing mature deterministic MOO to fast approximate Alternative schemes, and utilizing Taguchi method to handle uncertainties, reducing some key disadvantages of traditional robust design optimization (RDO), such as heavy calculating burden, overly conservative solution and difficulty to predefine proper robust objectives or performance target value. The proposed workflow consists of four main steps, formulating Alternative schemes by deterministic MOO, screening highly sensitive uncertain parameters by uncertainty and global sensitivity analysis, formulating Typical uncertain scenarios by orthogonal experiments (OE) and calculating the comprehensive robustness indicators. Results show that by employing the proposed workflow, the selected robust optimal solution not only proves to be robust but also leads to preferable performance enhancements. Compared with the reference mode, the energy efficiency has been improved by 33.2% to 49.4%, the discomfort hours have been reduced by 0.4% to 26.5%, and the robustness has been enhanced by 36.1%.

Enhancing capacity estimation of retired electric vehicle lithium-ion batteries through transfer learning from electrochemical impedance spectroscopy

The low economic feasibility caused by inefficient testing and inaccurate performance estimation is one of the main bottlenecks in the echelon utilization of large-scale retired batteries. This study proposes a fast and accurate capacity estimation method for retired batteries based on electrochemical impedance spectroscopy (EIS). Firstly, the EIS of the batteries that experience multi-condition aging in the laboratory are collected. EIS characteristic parameter sequences highly related to battery performance, including real part and magnitude, are directly extracted to establish a base bi-directional long short-term memory model. Secondly, a transfer learning method based on feature matching is designed, which applies a linear transformation layer to map the features between the source and target domains. The proposed transfer learning method has been effectively validated on laboratory battery data measured at different temperatures and retired battery datasets of different material types. The improvements are especially notable for retired batteries. The detection time has been reduced, with each cell requiring only 1.67 min. And using only a small amount of data as input for transfer learning can achieve an accuracy improvement of over 90 %, indicating an effective transfer channel from the base model established on laboratory small-capacity battery aging data to large-capacity retired battery data is successfully established for the first time. For retired nickel-cobalt-manganese batteries, the mean absolute percentage error (MAPE) and the root mean square percentage error (RMSPE) are 2.33 % and 2.75 %, respectively, while for retired lithium-iron-phosphate batteries, the MAPE and RMSPE reached 4.12 % and 5.04 %, respectively. The results demonstrate the proposed method reduces the cost of repeated testing, modeling, and training for specific retired batteries while maintaining the accuracy of capacity estimation. This advancement helps to improve the efficiency of large-scale retired battery grading, and injects new momentum into facilitating more effective decision-making processes.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

Investigation of non-line-of-sight underwater optical wireless communications with wavy surface.

Underwater optical wireless communication (UOWC) systems have been widely researched to achieve high-speed and secure wireless communications. The non-line-of-sight (NLOS) UOWC system that uses the water surface to reflect signal light is widely studied to overcome the line-of-sight (LOS) channel limitation, particularly the channel blockage issue by marine biology or complex underwater topography. However, most previous NLOS UOWC studies have assumed a flat water surface or a general sine or cosine surface wave model for simplicity, leading to inaccurate performance estimations. In this paper, we build a theoretical NLOS UOWC framework with the Pierson wave model which considers both spatial correlation and time relativity information of wave incorporating wind speed, and investigate the signal-noise-ratio (SNR) and bit-error-rate (BER) performance. Results show that compared with the previous flat surface, the wavy surface can reduce the probability of achieving a satisfying signal level by up to 70%, affecting the performance of NLOS UOWC systems. Furthermore, we investigate the multiple-input-multiple-output (MIMO)-based NLOS UOWC under wavy surfaces. Results show that the MIMO principle can reduce the impact of the wavy surface, where the probability of achieving a satisfying signal level can be increased by up to 50% using the 2 × 4 MIMO configuration. However, results also show that further increasing the number of receivers may not further improve the system performance. The proposed model enables more accurate design and analysis of NLOS UOWC systems by accounting for the overlooked impact of wavy surfaces.

Modified Induction Machine Equivalent Circuit Including Solid Shaft Eddy Currents

The shaft eddy currents cause a significant saturation in two-pole induction machines (IMs) as they generate an opposing field and repulse the main flux, thus tightening the flux path. This results in inaccurate performance estimations with the magnetizing inductance measured in no-load conditions when the machine is loaded. This article presents a modified IM equivalent circuit considering the rotor back iron saturation effects caused by the solid shaft eddy currents using experimental measurements and recursive parameter estimation techniques. The classical equivalent circuit (CEC) parameters are determined with the standard test techniques followed by the parameter estimation of the newly introduced modified equivalent circuit (MEC) parameters. The proposed modified equivalent circuit is benchmarked with CEC and finite element analysis (FEA) simulations with and without considering eddy effects. The proposed MEC model and the FEA that consider eddy effects performed better than the other models and yielded a negligibly small error over a wide range of loading conditions. Compared to the FEA, the proposed MEC estimates the IM performance much faster, which makes it more appealing for IM performance estimations.

Long-term stable wireless smart contact lens for robust digital diabetes diagnosis

Wearable devices for digital continuous glucose monitoring (CGM) have attracted great attention as a new paradigm medical device for diabetes management. However, the relatively inaccurate performance and instability of CGM devices have limited their wide applications in the clinic. Here, we developed hyaluronate (HA) modified Au@Pt bimetallic electrodes for long-term accurate and robust CGM of smart contact lens. After glucose oxidation reaction, the bimetallic electrodes facilitated the rapid decomposition of hydrogen peroxide and charge transfer for robust CGM. The passivation of Au@Pt bimetallic electrode with branch-type thiolated HA prevented the dissolution of Au electrode by chloride ions in tears. In diabetic and normal rabbits, the smart contact lens with HA-Au@Pt bimetallic electrodes enabled the high correlation (ρ = 0.88) CGM with 98.6% clinically acceptable data for 3 weeks. Taken together, we could confirm the feasibility of our smart contact lens for long-term CGM for further clinical development.

Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs

Edge embedding is a technique for constructing low-dimensional feature vectors of edges in heterogeneous graphs, which are also called heterogeneous information networks (HINs). However, edge embedding research is still in its early stages, and few well-developed models exist. Moreover, existing models often learn features on the edge graph, which is much larger than the original network, resulting in slower speed and inaccurate performance. To address these issues, a multi-view learning-based fast edge embedding model is developed for HINs in this paper, called MVFEE. Based on the “divide and conquer” strategy, our model divides the global feature learning into multiple separate local intra-view features learning and inter-view features learning processes. More specifically, each vertex type in the edge graph (each edge type in HIN) is first treated as a view, and a private skip-gram model is used to rapidly learn the intra-view features. Then, a cross-view learning strategy is designed to further learn the inter-view features between two views. Finally, a multi-head attention mechanism is used to aggregate these local features to generate accurate global features of each edge. Extensive experiments on four datasets and three network analysis tasks show the advantages of our model.

Quantifying and mitigating uncertainties in design optimization including off-the-shelf components: Application to an electric multirotor UAV

This article proposes an approach to quantify and manage uncertainty in the conceptual design of aerial vehicles. A growing number of aerial vehicles, such as unmanned aerial vehicles (UAVs), are being wholly or partly designed with off-the-shelf components to streamline development and certification costs. The design optimization of systems typically relies on component design models in the continuous domain. In addition to the inaccuracies in the design models, the continuous domain condition is inconsistent with the finite number of components available in the market. This inconsistency yields uncertainty about the actual characteristics of the components defined by the continuous design process, hence inaccurate performance predictions at the system's level. The proposed approach is developed within the scope of the conceptual design of multirotor UAVs and covers the uncertainties related to both the design models and off-the-shelf components availability. Simple criteria are established to determine the importance of uncertainties according to the density of products in an off-the-shelf component database. The uncertainties are propagated through the system's models to evaluate the variance of the outputs and identify the most critical design parameters. A multidisciplinary design optimization framework enabling custom, off-the-shelf, and hybrid design is introduced and illustrated with a multirotor UAV case study. This case study demonstrates that switching from a custom design to an off-the-shelf selection for the most critical component, namely the propeller, results in a 69% reduction in the standard deviation of estimated hover endurance. The results obtained demonstrate that the proposed approach can contribute to the emergence of future aerial vehicles and systems that must meet demanding economic and performance targets by combining custom and off-the-shelf components in their design.

Distribution of relaxation times as an accessible method to optimize the electrode structure of anion exchange membrane fuel cells

Recent significant progress in performance of anion exchange membrane fuel cells (AEMFCs) is proving this technology to be a meaningful low-cost alternative to commercial proton-conducting fuel cells. This progress has been largely driven by improved water management through electrode layer engineering. The plethora of membrane and ionomer chemistries makes the optimization of the electrode composition often unreproducible, leading to inaccurate performance comparisons. In this study we introduce the distribution of relaxation times (DRT) as an accessible and convenient tool to diagnose the limiting polarization processes in AEMFCs, which significantly simplifies the electrode optimization. To demonstrate the potential of DRT diagnosis, we fabricate cells with different anode electrode compositions simulating the effects of water transport limitations, such as anode flooding and cathode dry-out. The DRT separates and quantifies the contributions to the polarization resistance of the following processes: cathode and anode charge transfer, ion transport in the catalyst layer and gas diffusion. The anode catalyst layer optimization using information from the DRT has helped to increase the peak power density of the current cells from 550 to 690 mW/cm2. We foresee a large potential in using the DRT approach for the diagnosis and optimization of AEMFCs’ operational, structural, and compositional parameters.

SAMA: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

Geophysical data is a form of spatial data that suffers from various limitations when applying conventional machine learning algorithms and evaluation techniques. A key limitation facing models trained on geophysical data is their inability to generalize well when deployed to predict from new unseen data. We address the problem of inaccurate performance assessments of machine learning models, that stems from violating independence assumptions during the feature selection and evaluation phases of the learning process. Our proposed spatially-aware and model-agnostic (SAMA) framework provides a suite of spatially-aware feature generation, feature selection, and model validation algorithms that account for spatial characteristics of geophysical data. The framework is model agnostic, as it tackles data-related challenges that are not affected by the specific machine learning algorithm used to fit the data. To demonstrate the effectiveness of the proposed approach, it is applied to the water saturation mapping problem using a novel geophysical dataset to train a prediction model. The proposed spatially-aware models obtains an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.620, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> of 0.220 for predicting water saturation for the Whole Region of the reservoir model box and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.161, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> of 0.263 for the Interwell Region. Extensive experiments on 5 additional unseen datasets show that the model maintains stable performance across different datasets, which demonstrates the ability of the SAMA framework to produce robust models that are transferable to new datasets.

System dynamics characterisation and synthesis of floating photovoltaics in terms of energy, environmental and economic parameters with WELF nexus sustainability features

The invention of floatovoltaic technologies brought new meaning to the theoretical framing of sustainability as the technology delivers extended natural resource conservation benefits. However, planning assessments for this novel sustainable energy technology exposed knowledge gaps jeopardising global financial investments and regulatory approvals. Knowledge and methodological gaps cause inaccurate performance predictions in current geospatial-engineering modelling tools and fail to adequately quantify the diverse range of layered performance qualities and impacts of floating photovoltaics. This paper advances a geo-sensitive system dynamics framework to systemically integrate the energy, environmental and economic domain object functions in characterising the behaviour and sustainability of floating photovoltaic systems theoretically. The framework serves as computer logic in a geospatial digital twin to synthesise floatovoltaic operations to predict the technology’s sustainability impact and offset attributes in balanced scorecard metrics and water–energy–land–food nexus indicators. Experimental evaluations with the proposed framework in a real-world setting demonstrate the value of the holistically integrated framework in analytical floatovoltaic project appraisal and planning support. The results highlight significant advantages when comparing a 1000 m2 floatovoltaic system with a similar-sized conventional photovoltaic alternative, including a 19.3% lifetime energy gain, a carbon emission displacement of CO2e=5168t and a freshwater evaporation benefit of 983 kL. Predictive energy, environmental and economic modelling also offers water–energy–land–food–resource analysis parameters, thus delivering multi-attribute performance profiles that solve many of the current problems with the “technology unknowns” of floatovoltaics that impede energy project commissioning and licensing approvals.

Modeling of liquid film thickness around Taylor bubbles rising in vertical stagnant and co-current slug flowing liquids

The hydrodynamic study of the liquid film around Taylor bubbles in slug flow has great significance for understanding parallel flow and interaction between Taylor bubbles. The prediction models for liquid film thickness mainly focus on stagnant flow, and some of them remain inaccurate performance. However, in the industrial process, the slug flow essentially is co-current flow. Therefore, in this paper, the liquid film thickness is studied by theoretical analysis and experimental methods under two conditions of stagnant and co-current flow. Firstly, under the condition of stagnant flow, the present work is based on Batchelor's theory, and modifies Batchelor’s liquid film thickness model, which effectively improves its prediction accuracy. Under the condition of co-current flow, the prediction model of average liquid film thickness in slug flow is established by force and motion analysis. Taylor bubble length is introduced into the model as an important parameter. Dynamic experiments were carried out in the pipe with an inner diameter of 20 mm. The liquid film thickness, Taylor bubble velocity and length were measured by distributed ultrasonic sensor and intrusive cross-correlation conductivity sensor. Comparing the predicted value of the model with the measured results, the relative error is controlled within 10%.

Real-time hybrid testing for active mass driver system based on an improved control strategy against control-structure-interaction effects

General real-time hybrid testing (RTHT) for active mass driver (AMD) system requires shake table to achieve boundary conditions between the numerical and experimental substructures, which may be unavailable in some situations. Simplified RTHT is free of shake table, but it cannot consider control-structure-interaction (CSI) effects, and may result in an inaccurate performance evaluation of AMD system due to a significant influence of CSI on control performance. In this study, to eliminate the CSI effects, an improved control strategy is proposed, by incorporating the acceleration of control floor into the AMD controller and transforming all the control commands and feedbacks from the geocentric coordinate system to the relative coordinate system. Based on this strategy, the implementations of general and simplified RTHTs for AMD system are described. Experimental investigations are conducted on a two-story structure equipped with an AMD. It is confirmed that the general RTHT can be used to evaluate the actual performances of different control strategies. The CSI effects are essentially eliminated by the improved control strategy, but adversely affect the performance of traditional control strategy. In addition, the simplified RTHT is inappropriate for the performance evaluation of the traditional control strategy, but is available to that of the improved control strategy. Finally, an AMD control scheme is presented for a seismically excited 20-story benchmark building, and is evaluated through the simplified RTHT. Experimental results demonstrate the effectiveness of the proposed AMD control scheme. The maximum roof displacement and acceleration decrease from 0.175 m and 3.059 m/s2 to 0.116 m and 1.356 m/s2, respectively, when subjected to the El Centro earthquake.

A NEW METHOD TO ENHANCE THE DIFFERENTIAL PROTECTION OF THE MICROGRID BY SELF-BACKUP PROTECTION

Although the differential protection schemes consider as one of the effective types to protect the microgrid, this method may result in inaccurate performance because of the loss of communication link connecting the two ends of the line or other reasons. Therefore, the use of backup protection can lead to more accurate results. In this paper, self-backup protection is presented to cover the mal-operation of main protection. In this scheme, a new R-ratio method is defined based on the summation of the three-phase currents and the minimum of the three-phase voltages to overcome the low fault current during the islanded microgrid. The proposed scheme can detect all fault types during both operation modes of microgrid, grid-connected and islanded for radial and loop configuration. The validation of the proposed scheme is performed using PSCAD/EMTDC software. The simulation results proved that the proposed scheme could provide adequate protection against various fault types in grid-connected and islanded operation modes for a radial and loop configuration.

Known and Unknown Mental Illness: Uncovering the Multiple Routes to Workplace Inequities

This article examines the hidden nature of mental illness in the workplace and the resulting inequities. We examine inequities that may occur when mental illness is known to others and when it is concealed in the workplace. When mental illness is known to others, assumptions about capabilities, dangerousness, validity of mental illness, and the health consequences of work can drive a variety of employment inequities, including unfair or inaccurate hiring decisions, underemployment, inaccurate performance appraisals and judgments about potential, social shunning resulting in weak social networks, fewer resources to deal with discrimination, bullying, and difficulty in obtaining accommodations. When mental illness is concealed, misattributions about behavior and performance, self-stigma, and identity management dynamics can lead to similar inequities. Research in other fields has mainly examined inequities occurring when others know about the mental illness. Furthermore, this research conducted outside the management field does not usually address the more proximal causes and workplace dynamics behind inequities. The purpose of this article is to uncover the multiple routes to workplace inequities facing employees with mental illness and to spur management researchers to devote more research attention to these issues to promote the full inclusion of people with mental illness into the workforce.

The Upside of Sugarcoating: Inaccurate Performance Reporting and Exploration in Organizations

The Upside of Sugarcoating: Inaccurate Performance Reporting and Exploration in Organizations

Superstar Productivity and Pay: Evidence from the Australian Football League

We use game‐level data from the Australian Football League (AFL) to examine superstar workers' productivity and pay. By exploiting teams' injury‐induced line‐up changes between games, we show that, compared with replacement‐level players, superstars increase their teams' likelihood of winning away games by approximately 15 percentage points. While we then show that betting markets appear to appropriately price superstars' marginal productivity, we present back‐of‐the‐envelope calculations that suggest that teams underpay superstar players by at least 30 per cent. We discuss how inaccurate performance evaluations, labour market regulations, long‐term back‐loaded contracts, clubs' attempts to reduce harmful intra‐team pay disparities and on‐field success as a form of payment‐in‐kind may explain our findings.