Quantification Model Research Articles

Toward quantitative CEST imaging of glutamate in the mouse brain using a multi-pool exchange model calibrated by 1H-MRS.

To develop a CEST quantification model to map glutamate concentration in the mouse brain at 11.7 T, overcoming the limitations of conventional glutamate-weighted CEST (gluCEST) contrast (magnetization transfer ratio with asymmetric analysis). 1H-MRS was used as a gold standard for glutamate quantification to calibrate a CEST-based quantitative pipeline. Joint localized measurements of Z-spectra at B1 = 5 μT and quantitative 1H-MRS were carried out in two voxels of interest in the mouse brain. A six-pool Bloch-McConnell model was found appropriate to fit experimental data. Glutamate exchange rate was estimated in both regions with this dedicated multi-pool fitting model and using glutamate concentration determined by 1H-MRS. Glutamate exchange rate was estimated to be ˜1300 Hz in the mouse brain. Using this calibrated value, maps of glutamate concentration in the mouse brain were obtained by pixel-by-pixel fitting of Z-spectra at B1 = 5 μT. A complementary study of simulations, however, showed that the quantitative model has high sensitivity to noise, and therefore, requires high-SNR acquisitions. Interestingly, fitted [Glu] seemed to be overestimated compared to 1H-MRS measurements, although it was estimated with simulations that the model has no intrinsic fitting bias with our experimental level of noise. The hypothesis of an unknown proton-exchanging pool contributing to gluCEST signal is discussed. High-resolution mapping of glutamate in the brain was made possible using the proposed calibrated quantification model of gluCEST data. Further studying of the in vivo molecular contributions to gluCEST signal could improve modeling.

Raman spectroscopy for monitoring free sulfhydryl formation during monoclonal antibody manufacturing

During production, harvested cell culture fluid (HCCF) can degrade due to reductases breaking interchain disulfide bonds, forming low molecular weight (LMW) impurities that contain free sulfhydryl and high molecular weight (HMW) impurities through disulfide shuffling. Thus, detecting and quantifying the free sulfhydryl increase in HCCF is critical. Herein, Raman spectroscopy is implemented as a process analytical technology, and multivariate data analysis is applied to characterize and quantify sulfhydryl formation in HCCF with disulfide-containing indicator molecules. Raman spectra qualitatively probe the presence or absence of disulfide bond breakage in antibodies, consistent with offline non-reduced capillary electrophoresis sodium dodecyl sulfate results. Between two antibodies studied, mAb A was identified for a higher risk of antibody reduction where sulfhydryl formation was observed within 16 h, while mAb B did not show similar concerns even after 1 week. The offline measurement of redox potential is below –100 mV in HCCF for mAb A, while the stable mAb B HCCF shows redox potentials above +20 mV. A multivariate partial least squares (PLS) model for quantification is developed using an offline free sulfhydryl assay, applying Raman spectra to predict free sulfhydryl concentration with high accuracy (R2 > 0.98) and expected mean error of 0.677 mM from the offline Ellman’s Assay. This work confirms the use of Raman PAT to monitor real-time disulfide reduction, enabling improvements to process understanding and product quality.

MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either ‘best-mapped’ or ‘random-mapped’ locations and categorize them at the subfamily levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community.

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

AbstractIn the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non‐destructive moisture determination using near‐infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS‐DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS‐DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS‐EDNN, seamlessly integrated classification and quantification, revealing its potential for real‐time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.

Autoregressive models for quantification of time-averaging uncertainties in turbulent flows

Autoregressive models for quantification of time-averaging uncertainties in turbulent flows

A multi-scale uncertainty quantification model encompassing minimum-size unit cells for effective properties of plain woven composites

The uncertainty quantification is crucial to the high-precision prediction of composites’ effective properties. However, the unclear input uncertainties of multiscale parameters, complex uncertainty propagations of inter-scale correlations, and unaffordable computational cost of massive simulations are three primary problems at present. In this work, an innovative model with high accuracy and feasible cost for the mechanical property prediction of plain woven composites is developed encompassing minimum-size unit cells and multi-scale uncertainty quantification. For the accuracy holding, an uncertainty analysis process consists of the traceability description, inter-scale propagation and quantification is established. The uncertainties of geometry are described by uniform distributions for fiber, fiber bundle and composite scales, respectively; that of constituent properties is described by normal distributions for fiber and matrix, and its propagations to bundle and composite scales are realized by Nataf transformation methods with the consideration of parameter correlations. For the cost control, minimum-size unit cells are formulated by exhaustive analysis of structural symmetries to reduce the computational cost without accuracy compromising for the single simulation, and 1/8 and 1/16 unit cells compared with traditional full-size ones are obtained. The evolution convergence for statistical uncertainties of effective properties is finally obtained with totally reduced computational cost of 89%.

Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.

Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson's r; analysis of covariance; Bland-Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90min to 20s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/Rwithout/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

Uncertainty Quantification In The Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective was from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

ASLAM-FD: a multi-sensor adaptive collaborative fusion SLAM framework based on degradation detection and deep reinforcement learning

Abstract Aiming at the difficulty in ensuring accuracy and performance of existing Simultaneous Localization And Mapping (SLAM) technology based on multi-sensor fusion in complex dynamic environments, a multi-sensor adaptive fusion SLAM framework based on degradation detection and deep reinforcement learning (ASLAM-FD) is proposed. This framework can achieve adaptive collaborative precise adjustment of fusion weights (FWs) based on the real-time self-degradation states and relative degradation states quantified by each sensor, and can adapt to different tightly coupled SLAM algorithms based on FWs. In addition, within this framework, the continuous quantification models for the degradation states of internal/external sensors with certain versatility (we refer to them as EX-DM and IN-DM) is proposed. These quantitative models can achieve continuous quantification of degradation states of various mainstream internal/external sensors. Based on the above sensor degradation state quantification models, this paper further proposes a deep reinforcement learning (DRL) network suitable for adaptive collaborative adjustment of FWs. This network focuses more on the temporal nature of sensor observation data and degradation states, making it more suitable for modeling relationships between data with temporal features. In the experimental section, we adapted the proposed ASLAM-FD to different multi-sensor fusion SLAM algorithms on multiple datasets, and compared it with multiple advanced fusion SLAM algorithms. We found that adapting ASLAM-FD can effectively improve the accuracy and performance of fusion SLAM in complex dynamic environments.

Minimum Risk Quantification Method for Error Threshold of Wind Farm Equivalent Model Based on Bayes Discriminant Criterion

The error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) equivalent models, and can promote the standardization process of equivalent methodology. Due to differences in power system conditions and model evaluation standards in different countries, the form and indexes of error thresholds of WF equivalent models have not been unified yet. This paper proposes a theoretical method for quantifying the minimum risk of error threshold of WF equivalent models based on the Bayes discriminant criterion. Firstly, the Euclidean errors of WF equivalent models in different periods are quantified, and the probability density distributions of the errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the prior probability of a valid WF equivalent model, and the different losses caused by the missed judgment and misjudgment of the model validity to power systems are taken into account. Thirdly, the minimum risk quantification model of error threshold is established based on the Bayes discriminant criterion, and the feasibility of the proposed method is verified by an actual WF with numerical examples. Compared with the existing error thresholds, the proposed error threshold can more quickly and accurately determine the validity of WF equivalent models.

Rapid UV-Vis spectrophotometric method aided by firefly-PLS models for the simultaneous quantification of ciprofloxacin, lomefloxacin, and enrofloxacin in their laboratory mixture, dosage forms and water samples: greenness and blueness assessment

Herein, a novel UV spectrophotometric method coupled with chemometric tools was developed for the simultaneous determination of three fluoroquinolone antibiotics: ciprofloxacin, lomefloxacin, and enrofloxacin. Such integration of UV spectroscopy and chemometric analysis proved to be a simple, rapid, and cost-effective approach for the quantification of these clinically important pharmaceutical compounds and aid in their quality control analysis. The method employed firefly algorithm for variable selection and partial least squares (PLS) regression for model calibration. The developed method was validated by independent test set in addition the accuracy, intra and inter-day precision as per ICH guidelines which showed a satisfactory performance with mean recovery ranged between 98.18 and 101.83 with %RSD < 2. Besides, the developed method displayed ultrasensitive levels with LODs (0.0803, 0.1125, 0.1309 µg/mL) and LOQs (0.2434, 0.3409, 0.3968 µg/mL) for ciprofloxacin, lomefloxacin, and enrofloxacin, respectively. The greenness and blueness of the developed method were also evaluated using the recently proposed Analytical GREEnness metric approach (AGREE) and Blue applicability grade index (BAGI) tools, which showed a high AGREE score of 0.79 and a BAGI score of 77.5. These results indicate that the developed method provides an environmentally friendly alternative to the traditionally used chromatographic techniques, while maintaining high analytical practicability. Finally, the application of the developed methodology was demonstrated on real pharmaceutical and tap water samples, and the results were in good agreement with those obtained by the reference HPLC method indicating the reliability and suitability of the proposed spectrophotometric method for routine analysis of fluoroquinolone antibiotics.

Maximal information coefficient and geodetector coupled quantification model: a new data-driven approach to coalbed methane reservoir potential evaluation

Maximal information coefficient and geodetector coupled quantification model: a new data-driven approach to coalbed methane reservoir potential evaluation

Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

This paper addresses the computational challenges inherent in the stochastic characterization and uncertainty quantification of Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometers. Traditional methods, such as Markov Chain Monte Carlo (MCMC) algorithms, are often constrained by the computational intensity required for high-fidelity (e.g., finite element) simulations. To overcome these limitations, we propose to use supervised learning-based surrogate models, specifically artificial neural networks, to effectively approximate the response of MEMS capacitive accelerometers. Our approach involves training the surrogate models with data derived from initial high-fidelity finite element analyses (FEA), providing rich datasets to be generated in an offline phase. The surrogate models replicate the FEA accuracy in predicting the behavior of the accelerometer under a wide range of fabrication parameters, thereby reducing the online computational cost without compromising accuracy. This enables extensive and efficient stochastic analyses of complex MEMS devices, offering a flexible framework for their characterization. A key application of our framework is demonstrated in estimating the sensitivity of an accelerometer, accounting for unknown mechanical offsets, over-etching, and thickness variations. We employ an MCMC approach to estimate the posterior distribution of the device’s unknown fabrication parameters, informed by its response to transient voltage signals. The integration of surrogate models for mapping fabrication parameters to device responses, and subsequently to sensitivity measures, greatly enhances both backward and forward uncertainty quantification, yielding accurate results while significantly improving the efficiency and effectiveness of the characterization process. This process allows for the reconstruction of device sensitivity using only voltage signals, without the need for direct mechanical acceleration stimuli.

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studied IE modeling for small molecules, there are limited reports on peptide IEs. In this study, we leverage the stoichiometric relationship in trypsin digests of well-characterized monoclonal antibodies (mAbs) to compile a data set of relative ionization efficiencies (RIEs) for 241 peptides. From each peptide's sequence, we computed a set of physiochemical descriptors, which were then used to train machine learning regression models to predict RIEs. Peptides shorter than 20 amino acids had RIEs that were highly correlated to their molecular weight. A random forest (RF) model was able to best predict the RIEs of a test data set with a mean relative error of 23.9%. For larger peptides, a multilayer perceptron (MLP) model improved RIE prediction compared to current best practices, reducing mean relative error from 60.5% to 32.0%. Finally, we also show the application of the RF model in label-free relative protein quantification and improving the quantification of peptide post-translational modifications (PTMs). This approach to predicting peptide IEs from their sequences enables the development of accurate label-free quantification workflows for peptide and protein analysis.

Detection and quantification of groundnut oil adulteration with machine learning using a comparative approach with NIRS and UV–VIS

Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet–visible (UV–Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV–Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV–Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV’s up to 0.8799 and RMSECV’s lower than 3 ml/100 ml for NIR spectra and R2CV’s up to 0.81 and RMSECV’s lower than 4 ml/100 ml for UV–Vis spectra. NIR spectra produced better models as compared to UV–Vis spectra.

Optical multiscale model for quantification of photon recycling including incoherent light scattering

A comprehensive multiscale modeling framework for photon recycling in textured solar cells and LEDs is introduced, treating light absorption and emission on an equal footing under consideration of the full internal mode spectrum in the device. The framework seamlessly merges a coherent emission model, free from unphysical divergence and ensuring consistency with detailed balance principles, with an incoherent net-radiation model applied to optically thick layers. The scattering of light at nonplanar interfaces is taken into consideration through four characteristic mappings that depict the microscopic scattering process, which are calculated analytically or by using a Monte-Carlo ray-tracing method. The final output of the model consists of energy- and angle-resolved local emission, re-absorption, and energy flux rates for further coupling to electronic transport. To validate the model, it has been compared with analytical solutions for the re-absorption probability in an ideal semiconductor slab with either ideal Lambertian or flat surfaces. The comparison between analytical and numerical calculations shows excellent agreement.

Dual-energy CT Radiomics Combined with Quantitative Parameters for Differentiating Lung Adenocarcinoma From Squamous Cell Carcinoma: A Dual-center Study

Rationale and ObjectivesTo evaluate the ability of dual-energy CT(DECT)-based quantitative parameters and radiomics features to differentiate solid lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC). MethodsThis study included 213 patients diagnosed with ADC and SCC who underwent DECT scans at two centers from November 2022 to December 2023. Patients at center 1 were randomly divided into training(n=114) and internal test set (n=50) in a 7:3 ratio, with center 2 serving as the external test set (n=49). Radiologic and clinical data were combined to establish a clinical-radiologic model. Ten types of DECT energy images including conventional images, iodine density (ID), effective atomic number (Zeff), electron density, and virtual monoenergetic images (VMI) were reconstructed in both arterial phases (AP) and venous phases (VP). Quantitative parameters were measured at the uniform enhanced solid portion of the tumor and normalized to the aorta, used to develop a quantification model and calculate the quantitative score (quantscore). Radiologists manually delineated the tumor ROI at the largest level for extracting radiomics features in these 10 energy images. These features were used to establish ten uni-energy models from which the best-performing features were selected to construct the final radiomics model and calculate a radiomics score (radscore). Then, a combined model was developed using the akaike information criterion(AIC) and compared with the clinical-radiological model to test its diagnostic validity. ResultsThe independent predictors of the clinical-radiological model included age, gender, and central or peripheral location, and the AUCs for the training set, internal test set, and external test set were 0.808, 0.837, and 0.802. The quantification model incorporated 40keV CT values, Zeff, normalized Zeff, and the slope of the spectral attenuation curve (λHU) in the AP and normalized ID, Zeff, and λHU in the VP. Uni-energy models based on AP ID maps, AP Zeff maps, and VP VMI 65keV significantly outperformed AUC = 0.5, and 11 radiomics features were selected from these three models to construct the final radiomics model. The combined model, incorporating age, gender, quantscore, and radscore, significantly outperformed the clinical-radiological model in the training set (AUC = 0.952 vs 0.808, P < 0.001), and demonstrated higher performance in both the internal and external test sets, although these differences did not reach statistical significance (AUC = 0.870 vs 0.837, for the internal test set [P = 0.542], 0.888 vs 0.802 for the external test sets [P = 0.128]). The evaluation of the combined model demonstrated good discriminative ability and potential for generalization. ConclusionsThe combined model, integrating quantitative parameters and radiomics features from DECT multi-energy images with clinical-radiological characteristics, can be used as a non-invasive tool to differentiate ADC from SCC.

A performance-based ballistic design framework for RC panels and a probabilistic model for crater quantification

Ballistic design is crucial for widely utilised concrete structures in today’s unpredictable world. In the past, extensive experimental and numerical studies have investigated concrete panels, resulting in design guidelines and empirical formulations for local damage parameters. However, with the advent of performance-based design, notably in seismic design, the ballistic design of concrete structures lacks a comprehensive design philosophy. Additionally, deterministic empirical formulations for local damage parameters, particularly in the case of reinforced concrete (RC) panels’ crater formation, necessitate a probabilistic treatment. Consequently, this paper addresses the need for a well-defined ballistic design philosophy for concrete structures and introduces a probabilistic approach for crater quantification. Firstly, a novel multi-level and multi-parameter performance-based design framework for RC panels is proposed, centred on damage states, namely depth of penetration and crater area. This framework establishes an innovative design philosophy featuring four damage levels for each state. Secondly, using dimensionless explanatory functions, a Bayesian inference model is developed to estimate the crater diameter in RC panels under hard projectile impact, accounting for the associated uncertainties. LS-Dyna is used for numerical modelling of RC panels and rigid projectiles. The numerical models are validated with the experimental data and are utilised to develop the probabilistic model. This study reveals the profound influence of projectile and concrete properties in addition to the incidence angle of the projectile on crater formation. The developed probabilistic model is validated with experimental results demonstrating its reliability and accuracy. Consequently, a reliable design formula for estimating crater diameter for RC panels under hard projectile impact is proposed in addition to the novel ballistic design framework.

GHG emission quantification and reduction pathway of subway shield tunnel engineering: a case study on Guangzhou Metro, China.

The shield method is a commonly used construction technique in subway tunnel engineering. However, studies on greenhouse gas (GHG) emissions specifically in subway shield tunnel engineering are lacking. This study aims to investigate the GHG emission characteristics and GHG reduction pathways during the construction period of subway shield tunnels. Firstly, based on the life cycle assessment (LCA) method, a greenhouse gas (GHG) emission quantification model for the shield tunnel construction period was developed using a multi-level decomposition of construction. Then, the GHG emission level and intensity during the construction period of a case project are quantified, and its emission characteristics and GHG reduction potential points are assessed. Finally, a comprehensive path for GHG reduction in subway shield tunnel engineering is proposed. The research results indicate that constructing 1 km of subway shield tunnel can generate 19,294.28 t CO2eq. Among these, material production element dominates the emissions with a percentage of 89.05%, while transportation and mechanical construction elements contribute 1.81% and 9.14%, respectively. From the structure perspective, the main structure contributes 88.73% of total emissions, while the ancillary structure contributes 11.27%. Among them, the working shaft and tunnel segments are the main sources of emissions for the main structure, accounting for 23.65% and 65.08%, respectively. Connecting channel and end reinforcement are the main emission sources of the ancillary structures, accounting for 43.63% and 31.30%, respectively. These findings provide a scientific foundation for the environmentally friendly transformation of urban railway development regarding pursuing "carbon peaking and carbon neutrality" strategic goals.

Quantitative modeling and uncertainty estimation for small-sample LIBS using Gaussian negative log-likelihood and monte carlo dropout methods

Laser-induced breakdown spectroscopy (LIBS) has emerged as a powerful analytical technique widely applied in materials science, environmental monitoring, and other fields. In traditional quantitative analysis, models are established for the sample-to-element content based on acquired characteristic spectra combined with specific modeling methods. Small-sample LIBS poses significant challenges to researchers due to its high dimensionality and limited sample size, making it difficult for traditional modeling methods to establish effectively generalized and robust models. Addressing the issues of overfitting during training and the uncertainty of LIBS data, we developed an artificial neural network (ANN) model. Unlike traditional ANN which uses mean squared error functions as training losses, Gaussian negative log-likelihood (GLL) functions were used to train the model. This approach allows for simultaneous consideration of the quantitative prediction of sample element concentrations and the uncertainty of the quantification model prediction. Moreover, the Monte Carlo Dropout (MCDropout) method was applied to reduce prediction bias, resulting in a more robust, generalizable, and uncertainty-aware quantitative model. Compared to traditional quantitative methods, the method proposed in this paper achieved quantitative accuracies of 0.9877, 0.9939, 0.9876, and 0.9899 for four elements (Mn, Mo, Cr, Cu) in stainless steel, respectively. The uncertainty output effectively informs data comprehension and spectral analysis. To the best of our knowledge, this study is the first to propose utilizing model-quantified spectral uncertainty in LIBS quantitative tasks. This innovative study not only provides solutions for small-sample LIBS learning but also enhances the stability and accuracy of quantitative modeling by addressing the impact of uncertainty on training and reducing uncertainty. It is crucial for overcoming the limitations of traditional methods in small-sample LIBS quantification and achieving reliable real-time, high-precision LIBS detection, taking LIBS technology a step further towards practical reliability and precision.