Techno-economic analysis (TEA) and life-cycle assessment (LCA) are essential tools for evaluating manufacturing processes, but the use of proprietary software creates barriers to accessibility and reproducibility. We ...
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Techno-economic analysis (TEA) and life-cycle assessment (LCA) are essential tools for evaluating manufacturing processes, but the use of proprietary software creates barriers to accessibility and reproducibility. We present the BioProcessNexus, an open-source platform that democratizes access to process modeling through surrogate models trained on Monte Carlo data from proprietary TEA software. The platform facilitates model generation, analysis, and optimization while promoting standardization and collaboration across the scientific community. We demonstrate BioProcessNexus's capabilities through a comprehensive analysis of enzymatic PET recycling, comparing three surrogate modeling types: partial least squares, random forest, and Gaussian Process regression. Our analysis revealed that enzymatic PET recycling faces economic challenges, with a unit production cost of $1.74 kg⁻¹ TPA and an expected negative gross margin of -49.5 %. Sensitivity analysis identified feedstock cost and purification strategy as key areas for optimization. BioProcessNexus enables accessible, reproducible process modeling even when proprietary software was used for the initial model development. This approach advances the open innovation initiative and promotes transparent scientific collaboration while reducing barriers to advanced process modeling and optimization techniques. In this article, we will (i) introduce the BioProcessNexus platform and (ii) showcase a use case of an enzymatic PET recycling process.
Testing the predictive performance of energy models (EMs) is necessary to evaluate their accuracies. This paper investigates the adequacy of existing statistical metrics that are often used by professionals and resear...
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Testing the predictive performance of energy models (EMs) is necessary to evaluate their accuracies. This paper investigates the adequacy of existing statistical metrics that are often used by professionals and researchers to test EMs. It discerns that coefficient of variance of rootmeansquarederror (CVRMSE) and mean bias error (MBE), which are prescribed in ASHRAE guideline 14, are not suitable for system-level energy model testing. It points out the limitations of CVRMSE, MBE, and also rootmeansquarederror (RMSE). The analysis shows that the normalizing term of statistical metrics influences its accuracy in determining the predictive performance of EMs. An alternative metric (range normalized root mean squared error, RN_RMSE) is proposed that normalizes the RMSE by the range of the data as a replacement for CVRMSE. It is shown that RN_RMSE when used in tandem with can provide more meaningful and accurate representation of the performance of system-level EMs.
This work focuses on interpolation methods which are proposed as solutions to the EEG source localization. First, a low pass and a high pass filter were applied to the EEG signal in order to remove EEG artifacts. Then...
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This work focuses on interpolation methods which are proposed as solutions to the EEG source localization. First, a low pass and a high pass filter were applied to the EEG signal in order to remove EEG artifacts. Then, classical interpolation techniques such as three-dimensional (3D) K-nearest neighbor and 3D spline were implemented. The major contribution of this article is to develop a new interpolation method called 3D multiquadratic technique which is based on the Euclidean distances between the electrodes. A substitution of the Euclidean distance by the corresponding arc length was realized to promote the 3D spherical multiquadratic interpolation. Based on measured EEG recordings from 19 electrodes mounted on the scalp, these interpolation methods (3D K-nearest neighbor, 3D spline, 3D multiquadratic and spherical multiquadratic) were applied to EEG recordings of 15 healthy subjects at rest and with closed eyes. The aim of EEG interpolation is to reach the maximum of the spatial resolution of EEG mapping by predicting the brain activity distribution of 109 virtual points located on the scalp surface. The evaluation of the different interpolation methods was achieved by measuring the means of the normalized root mean squared error (NRMSE) and processing time. The results showed that the multiquadratic and 3D spline interpolation methods gave the minimum normalized root mean squared error, but the multiquadratic method was characterized by the minimal processing time compared with 3D K-nearest neighbor, 3D spline, and 3D spherical multiquadratic methods. Finally, a Spectral density variation mapping of different cerebral waves (delta, theta, alpha and beta) with 128 electrodes was generated by applying the Fast Fourier Transform (FFT). (c) 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 191-198, 2015
Background: Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, mos...
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Background: Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate. Methods: In this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step. Results: We evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of normalizedrootmean Square error (NRMSE), especially for datasets with large missing rates and less complete genes. Conclusions: It is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.
Microarray gene expression data often contains multiple missing values due to various reasons. However, most of gene expression data analysis algorithms require complete expression data. Therefore, accurate estimation...
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