Absolute Percentage Error Research Articles

Machine learning assisted mechanical properties prediction of fine denier polyester fiber

In recent years, more and more attention has been attracted by fine denier polyester fibers in electronic information industry due to their unique structural and excellent mechanical properties. As known, the mechanical properties of fine denier polyester fibers have a close relationship with their melt-spinning process parameters, thus, it is worth further exploring the influence of process parameters on fiber mechanical properties (namely breaking strength, elongation at break and CV of breaking strength). This study aims to develop a novel prediction model based on the artificial neural network (ANN) to predict the key mechanical properties of fine denier polyester fibers. For this purpose, firstly, different specifications of fine denier polyester fibers with different mechanical properties are produced by adjusting the key melt-spinning parameters, namely spinning temperature, take-roll speed and metering pump speed. Then the experimental data is collected, analyzed and augmented with effect to be used for modeling. Next, a novel prediction model based on ANN is developed by importing the two-streams structure and two attention matrices to improve the feature abstraction for mechanical properties prediction. Lastly, different predictive performance indicators, namely root mean square error (RMSE), mean absolute percent error (MAPE) and the coefficient of determination (R2) are imported to evaluate the proposed model. For the total of three mechanical properties, the predicted results of the proposed model in terms of RMSE, MAPE and R2 are respectively 0.3499, 0.0906 and 2.9516.

Modification of wavelet-based downsampling method to reduce computational error in nonlinear dynamic analysis

One of the significant obstacles in conducting linear and non-linear time history analysis is its time-consuming nature. In this article, a new downsamlping method based on discrete wavelet transform (DWT) and smoothing is proposed to overcome this problem. In order to assess the precision of this approach, 50,000 linear and non-linear dynamic analyses of single degree of freedom (SDOF) systems and 300 nonlinear dynamic analyses of frame structures have been performed. One hundred Fema440 records were utilized to generate approximate waves up to the third level and the outcomes of this method were then contrasted with those of DWT. It has been demonstrated that the third-level approximate wave produced by DWT, previously considered dependable in other research, generates significant errors in results and the average error (absolute error percentage of the acceleration spectrum) of its third-level approximate wave is approximately 17.5%. On the other hand, the proposed method generated approximate waves with an average error of less than 4.5% across all behavior coefficients and periods and the error rate decreases as the period and behavior coefficient increase. Analysis of steel moment-resisting frames indicated that the lowest error in both methods is achieved for the base shear and across different engineering demand parameters, the average error rate for the proposed method was below 7.5%. Furthermore, caution must be exercised when employing the proposed method for structures with periods shorter than 0.5 s.

Method for wind power forecasting based on support vector machines optimized and weighted composite gray relational analysis

This study proposes a weighted composite approach for grey relational analysis (GRA) that utilizes a numerical weather prediction (NWP) and support vector machine (SVM). The approach is optimized using an improved grey wolf optimization (IGWO) algorithm. Initially, the dimension of NWP data is decreased by t-distributed stochastic neighbor embedding (t-SNE), then the weight of sample coefficients is calculated by entropy-weight method (EWM), and the weighted grey relational of data points is calculated for different weather numerical time series data. At the same time, a new weighted composite grey relational degree is formed by combining the weighted cosine similarity of NWP values of the historical day and to be measured day. The SVM’s regression power prediction model is constructed by the time series data. To improve the accuracy of the system’s predictions, the grey relational time series data is chosen as the input variable for the SVM, and the influence parameters of the ideal SVM are discovered using the IGWO technique. According to the simulated prediction and analysis based on NWP, it can be observed that the proposed method in this study significantly improves the prediction accuracy of the data. Specifically, evaluation metrics such as root mean squared error (RMSE), regression correlation coefficient (r2), mean absolute error (MAE) and mean absolute percent error (MAPE) all show corresponding enhancements, while the computational burden remains relatively low.

A Metro Passenger Flow Forecasting Model Based On Time-series Evolving Interaction Graph Network

Passenger flow forecasting is an important task in metro operation management. In order to achieve more accurate metro passenger flow forecasting, this paper proposes a metro passenger flow forecasting model based on time-series evolution interaction graph. First, by introducing two kinds of inter-station interaction graphs, namely connectivity graph and temporal correlation graph, to capture the potential interaction relationship among metro passenger flow stations. Then, by using the time-series evolving graph, the weights of the graph convolutional neural network are dynamically evolved in time series. Finally, taking Suzhou Metro as an example, the short-term passenger flow of the metro is forecasted. The experimental results show that the Root Mean Squared Error (RMSE) of this model is 34.17, the Mean Absolute Error (MAE) is 16.35, the R-Squared is 0.94, the Mean Absolute Percent Error (MAPE) is 0.21. All evaluation metrics are better than the baseline models, thus verifying the effectiveness and applicability of the metro passenger flow forecasting model based on the time series evolution interaction graph.

Energy Expenditure Validation of an Exergame Platform: Ring Fit Adventure Use in Adults with Overweight and Obesity.

Objective: This study aims at comparing the energy expenditure (EE) and heart rate (HR) data from Ring Fit Adventure (RFA) with those from indirect calorimetry (COSMED) and a heart rate monitor (Polar FT7). A secondary goal is to evaluate self-reported enjoyment and perceived effort levels. Materials and Methods: Thirty participants (age = 21.8 ± 2.2; body mass index = 31.8 ± 4.4) were recruited for two laboratory visits. The first visit involved baseline measurements. In the second visit, participants performed a 55-minute ad libitum exercise session with the RFA in adventure mode with moderate difficulty. During this session, EE, HR, perceived effort, and enjoyment of physical activity were recorded. Results: Although no statistically significant overestimation of EE was found between the RFA and the metabolic cart, two-way analysis of variance results show a main effect of condition (RFA vs. Polar FT7) on HR (122.8 ± 20.1 bpm and 129.0 ± 18.6 bpm; P = 0.007; ᶯp2 = 0.235). Based on comprehensive statistical evaluations, including the mean absolute percent error, intraclass correlations, typical error of measurement, and limits of agreement, the data suggest that the RFA provides reliable estimates for EE and HR. Overall, participants enjoyed the game considerably (71.3 ± 5.9/80 arbitrary units), and their reported perceived exertion was low. Conclusion: This study underscores that the RFA values are relatively accurate and precise, and thus it can be safely suggested for individuals with overweight and obesity to adopt an active lifestyle.

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery.

In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model's adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.

Enhancing multi-scenario data-driven energy consumption prediction in campus buildings by selecting appropriate inputs and improving algorithms with attention mechanisms

Effective building energy prediction is vital for sustainable development, especially with an increasing focus on flexibility and elasticity in building energy usage. However, challenges persist in input and algorithm selection including suitable input selection and the balance of calculation time consumption and prediction precision. This study proposed a method for suitable input selection and introduced a CNN-LSTM-SE model for enhancing prediction precision. The CNN-LSTM-SE model integrates a convolutional neural network (CNN) process and a squeeze-and-excitation (SE) block attention mechanism into a long short-term memory (LSTM) neural network. Experiments across seven buildings demonstrate that selecting inputs improves the prediction performance with a 13.94 % decrease in MAPE (mean absolute percent error), a 3.36 % increase in R2 (coefficient of determination), and an 11.20 % decrease in CV-RMSE (coefficient-of-variation of root mean square error) on average compared to not selecting inputs. Moreover, implementing CNN-LSTM-SE yields a 21.44 % MAPE decrease, a 6.25 % R2 increase, and a 21.82 % CV-RMSE decrease compared to alternative algorithms. Simultaneously employing suitable inputs and CNN-LSTM-SE balances time consumption and prediction precision optimally.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

Multimodal machine learning for predicting heat transfer characteristics in micro-pin fin heat sinks

As three-dimensional integrated circuit (3D-IC) chip technology advances, thermal management has become increasingly important because of increasing heat flux from thermal stacking. Micro-pin fin-embedded cooling has emerged as a promising solution for 3D-ICs, offering better thermal and hydraulic performance than conventional microchannel heat sinks. It is also easy to integrate into existing 3D-IC structures, such as through-silicon vias between stacks. The utilization of two-phase flow in micro-pin fins further enhances temperature uniformity and improves the heat transfer coefficient by leveraging latent heat. Nevertheless, predicting thermal performance in micro-pin fin heat sinks under boiling conditions remains challenging owing to intricate geometric shapes and diverse operating conditions. The present lack of correlation or theoretical models poses a significant obstacle. To address this problem, our study employed a Multimodal machine-learning (ML) approach, combining image data capturing boiling patterns of two-phase flow and information about geometric shape and operating conditions, to predict heat transfer characteristics in micro-pin fin heat sinks. We utilized experimental data comprising 155 types of boiling heat transfer data with the dielectric fluid FC-72 in two micro-fin shapes directly etched on Si. Four ML algorithms (XGBoost, LightGBM, Multilayer perceptron (MLP), and Multimodal ML) were employed to predict thermal performance. The correlation coefficient analysis before learning revealed the influence of each type of measurement data on the heater surface temperature during two-phase flow. Prediction accuracy was measured using mean absolute percent error (MAPE), and the results were compared in terms of maximum and average temperature depending on the characteristics of each ML algorithm. Overall, the Multimodal approach demonstrated superior capability in predicting temperature distributions with spatial details, surpassing conventional decision-tree algorithms and MLP in performance. When trained with boiling images, the Multimodal ML model achieved remarkable precision, evidenced by a MAPE of 1.81% for the maximum temperature and 0.84% for the average temperature, highlighting its exceptional accuracy in mapping the heated surface temperature profile. By contrast, the traditional MLP model, which lacked training on boiling images, showed diminished accuracy, with a MAPE of 2.54% for the maximum temperature and 1.77% for the average temperature, indicating a comparative shortfall against the Multimodal model results.

Developing a hybrid technique for energy demand forecasting based on optimized improved SVM by the boosted multi-verse optimizer: Investigation on affecting factors

Electricity demand prediction accuracy is crucial for operational energy resource management and strategy. In this study, we provide a multi-form model for electricity demand prediction in China that based on incorporating of an upgraded Support Vector Machine (SVM) and a Boosted Multi-Verse Optimizer (BMVO). The suggested model is proposed to address the shortcomings of existing prediction approaches, which frequently fail to internment the complicated nonlinear interactions between demand for electricity and the variables that influence it. The improved SVM algorithm incorporates a modified genetic algorithm based on kernel function for enhancing the stability of the model. The BMVO technique is employed to optimize the combined model's weights and increase its generalization effectiveness. The suggested approach is tested by real-world Chinese energy demand data. The findings show that it outperforms existing prediction approaches in terms of reliability and precision. Further, the number of samples chosen affects how well the proposed BMVO linked with the Incremental SVM (ISVM) predicts outcomes. Particularly, when 1735 samples are chosen, the lowest level of Mean Absolute Percent Error (MAPE) was noted. The Root Mean Square Error (RMSE) and MAPE values under the proposed BMVO/ISVM model are reduced by 53.72% and 55.22%, respectively, compared to the Artificial Neural Network (ANN) approach reported in literature. Finally, the suggested model is capable of accurately predicting the electricity demand in China and has the potential to be applied to other energy-demand prediction applications.

Optimization Control Strategy for Transition Season Blinds Balancing Daylighting, Thermal Discomfort, and Energy Efficiency

Natural lighting in office buildings is often affected by excessive solar heat gain and discomfort glare, and the transitional seasons are no exception. Therefore, addressing the issue of natural lighting in buildings has always been a challenge in developing shading control strategies. This study designed a model-based calculation method for shading blind control indicators. The method combines existing physical models for sky conditions, solar radiation, heat transfer, thermal comfort, glare, and illuminance calculations and incorporates modifications and additions. Additionally, an equivalent energy-saving model for shading was established. A shading blind control strategy balancing indoor light and the thermal environment with energy savings during transitional seasons was proposed. In transitional seasons, this method can eliminate 100% of glare, reduce discomfort illuminance duration by 81.3% and heat discomfort duration by 87.5% and save 81.3% of lighting energy consumption when air conditioning is used. By comparing the simulation results of an office building in Xi’an with the results obtained from this calculation method, the absolute error percentage was found to be 6.83%, verifying the reliability of the calculation method. Finally, the proposed control strategy was compared with common methods such as no blinds, end-angle control, and fixed-angle control to evaluate its performance in terms of daylighting, thermal comfort, and energy savings. The results showed that the control strategy proposed in this study has significant advantages with respect to various performance indicators.

ANN prediction model of final construction cost at an early stage

ABSTRACT Previous studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. However, relying on models with relatively few inputs will reduce the accuracy of the prediction results. This paper aims to develop an artificial neural network (ANN) model to predict the FCC based on contract cost (CC), contract duration, and project sector at an early stage of a project. The data collected and used for the ANN model was 135 Saudi Arabian construction projects. The Zavadskas and Turskis logarithmic approaches, and the Pasini method were utilized to overcome the limited data. Then, the ANN model was developed through two stages. The purpose of the first stage was to enhance the data by identifying the abnormal data using absolute percentage errors (APE). The enhanced data was used to develop the ANN in the second stage. The finding showed that the ANN model provided an average MAPE (mean absolute percentage error) of 18.7%. The MAPE of the ANN model is decreased to 8.7% on average by deleting data with an APE higher than 35%. The model allows stakeholders to evaluate the financial importance of potential risks and develop appropriate risk management strategies.

Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions

Cavitation, a physical phenomenon that detrimentally affects pump performance and reduces pump life, can cause wear on pump elements. Various engineering methods have been developed to identify the initiation and full development of the cavitation process. One such method is the determination of the net positive suction head (NPSH) through a 3% decrease in total head (Hm) at a constant flow rate. In radial pumps, commonly used in agricultural irrigation and industry, cavitation conditions result in a sudden drop in the Hm-Q curve, making it challenging to detect the 3% Hm value drop. This study differs from others in the literature by modelling NPSH, noise, and vibration levels using three machine learning models, specifically artificial neural networks (ANN), support vector machines (SVM), and decision tree regression (DTR). The best-performing model predicts NPSH, noise, and vibration levels corresponding to a 3% decrease in Hm level. The present study determined the NPSH values of a horizontal shaft centrifugal pump at different flow rates and constant operating speed, and the vibration and noise levels were measured for these NPSH values. For each of the NPSH, noise, and vibration levels, ANN, SVM and DTR models were created. The performances of these models were evaluated using criteria such as root mean squared error (RMSE), Mean Absolute Error (MAE) and mean absolute percentage error (MAPE). In addition, Taylor and error box diagrams were created. The ANN model and DTR yielded high accuracy predictions for NPSH values (R2 = 0.86 and R2 = 0.8, respectively). The ANN model provided the best prediction performance for noise and vibration levels. By entering the level of 3% drop in the Hm value of the pump as external data input to the ANN model, NPSH3, noise, and vibration levels were determined. The ANN models can be effectively employed to determine NPSH3, noise, and vibration levels, particularly in radial flow pumps, where detecting 3% reductions in manometric height value is challenging.

Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers.

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Morphometric and clinical comparison of MRI-based synthetic CT to conventional CT of the hip in children.

MRI-based synthetic CT (sCT) generates CT-like images from MRI data. To evaluate equivalence, inter- and intraobserver reliability, and image quality of sCT compared to conventional (cCT) for assessing hip morphology and maturity in pediatric patients. We prospectively enrolled patients <21 years old with cCT and 3T MRI of the hips/pelvis. A dual-echo gradient-echo sequence was used to generate sCT via a commercially available post-processing software (BoneMRI v1.5 research version, MRIguidance BV, Utrecht, NL). Two pediatric musculoskeletal radiologists measured seven morphologic hip parameters. 3D surface distances between cCT and sCT were computed. Physeal status was established at seven locations with cCT as reference standard. Images were qualitatively scored on a 5-point Likert scale regarding diagnostic quality, signal-to-noise ratio, clarity of bony margin, corticomedullary differentiation, and presence and severity of artifacts. Quantitative evaluation of Hounsfield units (HU) was performed in bone, muscle, and fat tissue.Inter- and intraobserver reliability were measured by intraclass correlation coefficients. The cCT-to-sCT intermodal agreement was assessed via Bland-Altman analysis. The equivalence between modalities was tested using paired two one-sided tests. The quality parameter scores of each imaging modality were compared via Wilcoxon signed-rank test. For tissue-specific HU measurements, mean absolute error and mean percentage error values were calculated using the cCT as the reference standard. Thirty-eight hips in 19 patients were included (16.6 ± 3 years, range 9.9-20.9; male = 5). cCT- and sCT-based morphologic measurements demonstrated good to excellent inter- and intraobserver correlation (0.77<ICC<0.98). Measurements were statistically equivalent (P< 0.05), with average intermodal differences <2°. Mean surface distance between cCT and sCT was 0.23 ± 0.18mm. Accuracy to determine physeal maturity status by sCT was 98.4% (242/246, 95% CI 96.0-99.6). No significant differences were found on overall diagnostic quality, bony margin clarity, corticomedullary differentiation, and artifacts between sCT and cCT; signal-to-noise ratio was higher on sCT (P< 0.001). HU were within reference values on both sCT and cCT. sCT is equivalent to cCT for the assessment of hip morphology, physeal status, and radiodensity assessment in pediatric patients.

A metaheuristic-based method for photovoltaic temperature computation under tropical conditions

Tropical climates have favorable irradiation levels for the development of photovoltaic systems; however, high temperatures have a negative impact on the efficiency of solar cells. Since direct measurement of cell temperature is not common, mathematical models are needed to make predictions. Numerous models have been documented, highlighting the challenge of applying a universal model to different climatic conditions. The main contribution of this study is the proposal of a metaheuristic algorithm to accurately compute the temperature of solar cells. This method is simple and effective in exploring numerous potential states of the reference parameters (i.e., irradiance and ambient temperature). Data collected over a 23-month period in two photovoltaic installations with an output power of 2.2 MW of multicrystalline silicon technology were used to develop the proposed method and validate it. The proposed model was compared with 19 previously reported models in the literature. Compared to the model recommended by the International Electrotechnical Commission (IEC Standard 61215-1), the mean square error, mean absolute error (MAE) and mean absolute percentage error were reduced by 4.9, 4.8, and 2.4 times, respectively. The accuracy of the proposed method is demonstrated by MAE errors ranging from 0.56 °C to 1.88 °C, obtained by considering three different daily profiles of irradiance and ambient temperature. Therefore, the proposed method is recommended to more accurately calculate the temperature of the photovoltaic cell in tropical areas.

Subgroup identification-based model selection to improve the predictive performance of individualized dosing.

Currently, model-informed precision dosing uses one population pharmacokinetic model that best fits the target population. We aimed to develop a subgroup identification-based model selection approach to improve the predictive performance of individualized dosing, using vancomycin in neonates/infants as a test case. Data from neonates/infants with at least one vancomycin concentration was randomly divided into training and test dataset. Population predictions from published vancomycin population pharmacokinetic models were calculated. The single best-performing model based on various performance metrics, including median absolute percentage error (APE) and percentage of predictions within 20% (P20) or 60% (P60) of measurement, were determined. Clustering based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm was used to group neonates/infants according to their best-performing model. Subsequently, classification trees to predict the best-performing model using clinical and demographic characteristics were developed. A total of 208 vancomycin treatment episodes in training and 88 in test dataset was included. Of 30 identified models from the literature, the single best-performing model for training dataset had P20 26.2-42.6% in test dataset. The best-performing clustering approach based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm had P20 44.1-45.5% in test dataset, whereas P60 was comparable. Our proof-of-concept study shows that the prediction of the best-performing model for each patient according to the proposed model selection approaches has the potential to improve the predictive performance of model-informed precision dosing compared with the single best-performing model approach.