Average Percentage Error Research Articles

Approximate fundamental frequency formula for cantilevers with weakly non-uniform sections and verification with experiments and other studies in the literature

The vibrational frequencies of beams with uniform cross-sections are easily derived from the analytical solutions of the Euler-Bernoulli equation (EBE). However, for beams with non-uniform cross-sections, complex and time-consuming numerical solutions are typically required for each cross-sectional geometry. This paper presents a formula that accurately predicts the vibrational frequencies in the presence of weak heterogeneities, regardless of how the cross-sectional shape varies along the beam's length. Experimental verification and comparisons with literature confirm that the formula reliably predicts frequency shifts in cases where a thin heterogeneous film is coated on a uniform beam, thin heterogeneous layers are removed from a uniform beam, and when a moving point load affects the vibrational frequency. The average absolute percentage error between the experimentally measured and calculated frequencies was <0.17 %. As such, this formula serves as a practical tool for various engineering applications involving weakly heterogeneous beams.

Multifrequency electrical impedance tomography system based on undersampling combined with a fast digital demodulation algorithm.

Multifrequency electrical impedance tomography (MFEIT) has shown great application prospects in the field of biomedical imaging. To realize high-precision multifrequency electrical impedance information acquisition, a high-precision MFEIT system with undersampling combined with a fast digital demodulation algorithm is proposed. The system is integrated with 16 electrodes, and semi-parallel acquisition is used. In addition, a novel multifrequency digital demodulation algorithm is applied to enhance the accuracy of multifrequency excitation signal demodulation and achieve rapid demodulation. This improvement is achieved by analyzing the process of the multifrequency digital demodulation algorithm and combining undersampling with a fast digital demodulation technique. To evaluate the proposed method, a systematic comparative experiment is conducted. The experimental results demonstrate that the demodulation error using the undersampling method is less than 0.7% within the frequency range of 5-500kHz. In addition, the system achieves a maximum signal-to-noise ratio of 62.92 dB, an average blur radius of 0.953, and an average position error percentage of 9.3%. The results indicate that the MFEIT system constructed based on the above research has good performance and a high signal-to-noise ratio.

Research of Shock Response Spectrum Prediction based on Machine Learning Method

Abstract Shock response spectrum (SRS) is the primary method of simulating explosive separation impact environment. SRS is obtained from shock experiments in traditional research, which consumes lots of materials. In this study, a prediction model based on machine learning (ML) is established to rapidly obtain SRS. 1053 sets of data obtained by shock experiments were used in the model. A total of 15 features are selected, in which 7 accelerations of different frequencies on the SRS are taken as target features. 7 types of ML algorithm are used for the prediction and the best one in each target is integrated in the completed model. The results indicate that the optimal average percentage errors at frequencies of 100Hz, 500Hz, 1000Hz, 3000Hz, 5000Hz, 7000Hz, and 10000Hz are 5.9%, 6.8%, 8.1%, 19.5%, 20.0%, 22.0%, and 19.7%, respectively. The error range of SRS fit by the 7 prediction targets is within ±20%. This study is contributed to improving the debugging efficiency of SRS.

Short‐term energy forecasting using deep neural networks: Prospects and challenges

AbstractThis study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

ANALYSIS OF THE INFLUENCE OF MONETARY INSTRUMENTS ON THE SIZE OF THE DOMESTIC PUBLIC DEBT

The article is devoted to highlighting the results of research aimed at improving existing approaches to conducting a comprehensive analysis of the nature of the relationships between the parameters of the state's monetary and debt policy. Among other things, such indicators as the size of the domestic public debt, the size of international reserves, the level of inflation, the exchange rate and the discount rate are taken into account.The proposed approach to the analysis of the influence of managed monetary indicators on the size of the internal state debt proved that the use of the ARDL model gives slightly better results than the VECM in terms of the adequacy of the description and forecasting of the studied process. At the same time, both models proved their ability to describe the change in the size of the internal state debt, and have high values of coefficients of determination and low values of the indicator of the average absolute percentage error. In the future, it is recommended to use it for the analysis of the causality of the influence, discriminant analysis and the expediency of changing monetary instruments independently of other instruments.On the basis of the developed models, the results were obtained, which allow us to draw conclusions that the system continues to remain stable after fluctuations of key factors capable of destabilizing it and will be able to return to its previous state after a shock for some time. This is confirmed by the fact that, on the basis of statistical tests, the existence of a long-term relationship between the investigated indicators and the impulse response function together with the decomposition of the variance of the VAR model was proved.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

Evaluating Seismocardiography as a Non-Exercise Method for Estimating Maximal Oxygen Uptake.

The value of maximal oxygen uptake (VO2MAX) is a key health indicator. Usually, VO2MAX is determined with cardiopulmonary exercise testing (CPET), which is cumbersome and time-consuming, making it impractical in many testing scenarios. The aim of this study is to validate a novel seismocardiography sensor (Seismofit®, VentriJect DK, Hellerup, Denmark) for non-exercise estimation of VO2MAX. A cohort of 94 healthy subjects (52% females, 48.2 (8.7) years old) were included in this study. All subjects performed an ergometer CPET. Seismofit® measurements were obtained 10 and 5 min before CPET in resting condition and 5 min after exhaustion. The CPET VO2MAX was 37.2 (8.6) mL/min/kg, which was not different from the two first Seismofit® estimates at 37.5 (8.1) mL/min/kg (p = 0.28) and 37.3 (7.8) mL/min/kg (p = 0.66). Post-exercise Seismofit® was 33.8 (7.1) mL/min/kg (p < 0.001). The correlation between the CPET and the Seismofit® was r = 0.834 and r = 0.832 for the two first estimates, and the mean average percentage error was 11.4% and 11.2%. Intraclass correlation coefficients between the first and second Seismofit® measurement was 0.993, indicating excellent test-retest reliability. The novel Seismofit® VO2MAX estimate correlates well with CPET VO2MAX, and the accuracy is acceptable for general health assessment. The repeatability of Seismofit® estimates obtained at rest was very high.

Regional agricultural drought vulnerability prediction based on interpretable Random Forest.

Drought is one of the leading natural disasters in the world. It severely restricts the normal functioning of societies and economies. The purpose of this paper is to predict the drought vulnerability index in Henan Province so that timely measures can be taken to mitigate the impact of drought and to ensure the continued stability of people's lives and social development. First, the drought vulnerability index of Henan Province for 2010-2022 was calculated based on relevant literature and the regional situation of Henan Province. In addition, the main factors of sensibility and resilience are selected, the prediction indicator system is established, and the grey correlation analysis model is applied to select the top 10 main factors of drought vulnerability. Second, the Random Forest model was trained and simulated, and the drought vulnerability index of Henan Province was predicted for 2023-2025. Finally, the SHapley Additive exPlanations model was used to explain the specific effects of the influences of the Random Forest model. The interpretable Random Forest model not only accurately predicts the drought vulnerability index in Henan Province but also alleviates concerns about the "black box" problem of indirect interpretation of the Random Forest model. The results of the study show that the Average Percentage Error of the drought vulnerability index in Henan Province from 2010 to 2022 simulated by the Random Forest model is as low as 3.0457%, and the simulation accuracy is as high as 96.9643%. Drought vulnerability prediction for Henan Province was also made for the period 2023-2025, showing an increasing trend. Relevant sectors should enhance awareness and early warning of drought, improve resilience, and take effective measures to reduce the socio-economic impact of drought.

Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran.

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989; coefficient of determination 0.9612 & 0.9980; root mean square error 0.3036 & 0.3340; Nash-Sutcliffe error 0.9980 & 0.9979; and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predictions that outperform traditional methods in handling complex, nonlinear environmental data.

Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R2) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs.

Cross-modal scenario generation for stock price forecasting using Wasserstein GAN and GCN

The forecasting of stock price has always been a difficult problem, as the various inputs like company performance, technical innovation, political factors are intricate and often assessed with uncertainty. While most of the existing studies belong to deterministic forecast that focused on either the trend or exact value of future stock price, the forecasted results contain little uncertain information, thus bringing certain risks to investors. In this study, an effective scenario generation method based on wasserstein generative adversarial network (WGAN) is proposed for stock price forecasting, which can characterize the temporally and spatially correlations among different stocks, so as to help investors make proper investment strategies in complicated market environments. To the best of our knowledge, the scenario generation of stock price has been rarely studied by existing publications. Specifically, considering the inherent correlation among stocks, graph convolutional network (GCN) is used to capture the features of related stocks, whereafter the features of target stocks and related stocks are combined. In terms of feature fusion, this paper further uses a pre-trained model to obtain the key information of news headlines as text features, and fully integrates them with historical data through the attention mechanism, thus improving the overall performance of scenario generation. In summary, the proposed model can generate promising scenarios for future stock prices over time based on cross-modal information like historical data and news headlines. Numerical experiments show that our method outperforms other baseline methods in terms of root-mean-square error and average absolute percentage error (the average of several scenarios). For each part of the method, stock correlation is the most helpful for the results, followed by cross-modal information, with scenario generation contributing less to the results. Besides, experiments on a real-life portfolio selection problem also demonstrate that our method brings the highest returns, which proves its effectiveness in practical application.

A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.

Design and testing of a microwave Doppler-based yield estimation system for machine-picked seed cotton

Abstract Accurately obtaining crop yields is an important part of the precision agriculture technology system; with the increased mechanisation of cotton planting and harvesting, it has become particularly important to accurately obtain seed cotton yield data. In order to achieve accurate estimation of machine-picked seed cotton yield, this paper designs a machine-picked seed cotton yield estimation system based on microwave Doppler principle. Based on LabVIEW software, the signal acquisition circuit is designed; the power spectrum of the echo signal is estimated; the signal echo power is obtained; the relationship model between echo power and seed cotton mass is established by using multiple regression equations; and the reliability of the model is verified by using statistical methods. By adjusting the rotational speed of the fan, the wind speed at the inlet of the cotton pipeline was 10 m s−1, 15 m s−1 and 20 m s−1, and the yield estimation tests were carried out at the three wind speeds, and the data showed that the average absolute percentage errors of the estimation at the three wind speeds were 7.36%, 7.72% and 8.17%, and the mean squared errors were 2.699, 4.938 and 4.026, respectively. The test results show that the accuracy of seed cotton mass estimation under the three wind speeds basically meets the needs of the yield measurement system, and the systematic error of seed cotton yield estimation is the smallest when the wind speed is 10 m s−1.

Distributed photovoltaic power forecasting based on personalized federated adversarial learning

Existing distributed photovoltaic (PV) power forecasting methods fail to address the impact of sample scarcity and heterogeneity in PV power data. Moreover, training a single prediction model proves challenging to meet the personalized forecasting needs of different PV stations in distributed environments. This paper proposes a personalized federated generative adversarial network (PFedGAN)-based DPV power forecasting method. Leveraging the federated learning (FL) framework, it achieves collaborative training of prediction models among DPV stations while preserving data privacy. y introducing generative adversarial networks (GAN) and personalized strategy optimization into the FL training process, it alleviates data scarcity issues and reduces the impact of data heterogeneity. A TimesNet-DeepAR (TNE-DeepAR) power prediction model is designed, where the TimesNet module extracts correlations between PV power data from different time periods, and the DeepAR module facilitates PV power prediction, mitigating the effects of meteorological factors' multi-periodic variations on PV power. Experimental results show that the proposed hybrid prediction model reduces the average mean absolute percentage error (MAPE) by 30–40 % compared to single models. The proposed approach reduces the MAPE by 9.79 % compared to traditional methods and by 49.62 % for PV stations with scarce data.

A multi-objective optimization scheduling approach of integrated energy system considering the exergy efficiency using the variable step-size approximation method

As an important part of the new power system, it is crucial to consider the efficiency, economy and environment of the integrated energy system simultaneously. In order to improve the efficiency of solving multi-objective optimization scheduling problems for integrated energy systems, a variable step-size approximation method is proposed in this paper. Firstly, a model of integrated energy system is constructed, and the source-side and load-side exergy equations of different energy networks are given, as well as the mathematical expressions of equipment by combining the idea of unification. Secondly, in comparison with the ergodic weights approach, the variable step-size approximation method theoretical minimum reduction percentage of solution time is deduced by mathematical methodology under the m objective functions. Finally, the results from an integrated energy system including a modified New England 30-bus system, a 6-node heat network and a 7-node gas network demonstrate that the multi-objective function proposed compromises the economy, environment and efficiency of integrated energy system compared to the traditional optimization problem. In comparison with the ergodic weights approach, the step size approximation method can save over 97 % of the solution time, and the percentage of average error can be as low as 0 % under the multi-objective functions.

Development, validation, and application of a generic image-based noise addition method for simulating reduced dose computed tomography images.

Major efforts in computed tomography (CT) have been focused on reducing radiation dose to patients while maintaining adequate diagnostic quality. To that end, research tools have been developed to simulate reduced-dose images via either image-based or projection-based methods. The former is limited to fully capturing realistic texture, streak, and non-stationary characteristics of reduced dose, while the latter is impractical clinically. To develop and validate an image-based noise addition method that accounts for such attributes while being practical in clinical settings. A noise addition method was developed to add realistic noise in the image domain. The method first estimates the noise power spectrum (NPS) of CT images, which are also forward-projected to form synthetic projections. The projection data are supplemented with random white noise proportional to their attenuation values. The noise sinogram is then back-projected onto the image, filtered by the NPS, and scaled according to the desired dose reduction level. The tool was evaluated using both phantom images and patient data. The phantom images were acquired using a multi-sized image quality phantom (Mercury Phantom 3.0, Duke University), and a thorax anthropomorphic phantom (Lungman Phantom, Kyoto Kagaku) at different dose levels and reconstruction settings. The patient images consisted of two dose levels of various CT examinations and reconstruction settings. The simulated and real reduced-dose images were compared in terms of the noise magnitude and texture (i.e., NPS average frequency, NPS-fav). The utility of this methodology was also assessed for routine clinical use for CT protocol review. For the phantom images, the percent errors in the noise magnitude between the simulated images and the actual images of the Mercury Phantom and anthropomorphic phantom images were 3.34% and 3.50%, respectively. The difference in fav was 0.07mm-1 for the Mercury Phantom and 0.06mm-1 for the anthropomorphic phantom between the simulated and actual images. The average noise magnitude percent error between the simulated and actual patient images was 4.61% with noise texture judged to be visually comparable with some kernel dependencies. When implemented clinically, the tool proved practical to simplify the process of estimating radiation dose reduction for CT protocols, resulting in a 50% dose reduction of our multiple myeloma protocol. The method generated simulated CT images with realistic noise properties similar to images acquired at the same radiation exposure without needing access to raw projection data.

Reconstructing Tibetan Plateau lake bathymetry using ICESat-2 photon-counting laser altimetry

Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry reconstruction by combining ICESat-2/ATLAS (Advanced Topography Laser Altimetry System) data with a numerical model. An improved grid-based photon noise removal method is used to address the photon signal buried in the background noise during the local daytime. The developed model was validated for seven lakes on the Tibetan Plateau and showed good agreement between simulated and measured lake volumes, with an average absolute percentage error of 8.0 % for maximum water depth and 19.7 % for lake volume simulations. The model was then utilized to estimate the water volume of other lakes by combining it with the self-affine theory. The lake depths obtained from ICESat-2/ATLAS show good agreement (RMSE = 0.69 m; rRMSE = 10.3 %) with available in-situ measurements for lakes with depths <16.5 m, demonstrating the potential of ICESat-2/ATLAS for improved reconstruction of the bathymetry of clear water inland lakes. Our study reveals for the first time, that the Tibetan Plateau has an estimated total lake water volume of 1043.69 ± 341.31 km3 for 33,477 lakes (>0.01 km2) in 2022. Over 70 % (∼734.8 km3) of the lake water storage is concentrated in the Inner Plateau, with the Yellow River basin accounting for 10.9 % (∼113.9 km3), followed by the Indus River basin with 7.2 % (∼75.1 km3). Our study provides a robust method for estimating total lake volumes where in-situ measurements are scarce and can be extended to other clear water lakes, thus contributing to more accurate global assessments and towards comprehensive quantification of Earth's surface water resources distribution.

Predicting pressure loss of turbulent non-Newtonian fluids in annuli under temperature and pipe rotation effects using optimization algorithms

Accurate estimation of frictional pressure loss in an annulus is crucial for selecting appropriate pump systems, determining horsepower requirements, and controlling hole cleaning. Generally, when calculating the friction factor, the temperature of the fluid and the rotation of the inner pipe are not considered, which can lead to errors in estimating the pressure difference in the annulus. In this study, to ensure accurate pressure drop calculations, the effects of temperature and inner pipe rotation, along with other parameters, were included. An extensive experimental study was conducted at Izmir Katip Celebi University’s Flow Loop using two Carboxymethyl cellulose (CMC) solutions, across various fluid velocities, fluid temperatures, diameter ratios, and inner pipe rotation speeds. A dimensional analysis was performed to identify the parameters that affect friction factors in the annulus. Finally, friction factor equations were developed based on the experimental data using optimization algorithms such as interior point (fmincon), genetic, minimax, and simulated annealing. The findings showed that the predictions from all the algorithms tested in the study closely matched the experimental results; however, the interior point algorithm (fmincon) provided the best performance, with an average absolute percentage error of 9.4%.

Quick and reagent-free monitoring of edible oil saponification values using a handheld Raman device

Saponification value, the average molecular weight of fatty acids, is a crucial parameter for detecting adulteration of edible oils. Conventionally, it is determined in a laboratory setup through a time-consuming, laborious titration process using chemical reagents. Herein, the application of Raman spectroscopy for quick SV estimation of oils is demonstrated. It was hypothesized that the SV can be predicted from Raman spectra since the spectral patterns reflect the composition of fatty acid triglycerides. Two model oil systems were studied: coconut-gingelly oil and coconut-sunflower oil. Univariate models built from Raman spectra were successful only for the specific oil system; hence, PLS-Regression was executed across the two systems. The PLSR model on the validation set returned the average error, percentage error, and root mean square error of prediction as 2.1, 0.99 %, and 2.4, respectively. This method offers several advantages of portability, little reagent use, minimal sample preparation, and reduced analysis time.

Implant Cost Awareness Among Orthopaedic Surgeons at a Single Academic Institution

Introduction Despite the increasing costs of implants, surgeons’ awareness of these costs varies widely. The purpose of this study is to determine baseline awareness of sports-medicine-related implant cost among orthopaedic surgeons. Methods A survey including images of 10 different orthopaedic implants related to sports medicine was sent to 18 surgeons. All included surgeons practice in a single ambulatory surgery center and routinely perform sports-medicine-related procedures. Surgeons were asked to estimate the cost of each implant and answer questions about surgeon characteristics. Cost estimates were compared with implant cost found in the institutional charge master database. Results Cost estimates varied widely among providers regardless of fellowship training. The average percent error among all providers was 54.3%. Surgeons that rated knowledge of implant cost to be important most or all of the time had an average cost estimate error of 60.7% versus 48.0% error in their counterparts that stated that knowledge of cost was important to them only some of the time, though this did not reach statistical significance. Low-cost implants were more likely to be overestimated while high-cost implants were more often underestimated. Years of experience, sports medicine fellowship training, and involvement in contract negotiations had no effect on implant cost accuracy. Conclusion Implant cost awareness is dishearteningly low sports medicine orthopaedists despite the large economic impact of these devices. There is considerable room for improving cost awareness in sports medicine.