Quantification Model Research Articles

Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. We created an algorithm to predict cMRI-quantified PRF from echocardiography using machine learning (ML). We retrospectively performed echocardiographic measurements paired to cMRI within 3months in patients with ≥ mild PR from 2009 to 2022. Model inputs were vena contracta ratio, PR index, PR pressure half-time, main and branch pulmonary artery diastolic flow reversal (BPAFR), and transannular patch repair. A gradient boosted trees ML algorithm was trained using k-fold cross-validation to predict cMRI PRF by phase contrast imaging as a continuous number and at > mild (PRF ≥ 20%) and severe (PRF ≥ 40%) thresholds. Regression performance was evaluated with mean absolute error (MAE), and at clinical thresholds with area-under-the-receiver-operating-characteristic curve (AUROC). Prediction accuracy was compared to historical clinician accuracy. We externally validated prior reported studies for comparison. We included 243 subjects (median age 21years, 58% repaired tetralogy of Fallot). The regression MAE = 7.0%. For prediction of > mild PR, AUROC = 0.96, but BPAFR alone outperformed the ML model (sensitivity 94%, specificity 97%). The ML model detection of severe PR had AUROC = 0.86, but in the subgroup with BPAFR, performance dropped (AUROC = 0.73). Accuracy between clinicians and the ML model was similar (70% vs. 69%). There was decrement in performance of prior reported algorithms on external validation in our dataset. A novel ML model for echocardiographic quantification of PRF outperforms prior studies and has comparable overall accuracy to clinicians. BPAFR is an excellent marker for > mild PRF, and has moderate capacity to detect severe PR, but more work is required to distinguish moderate from severe PR. Poor external validation of prior works highlights reproducibility challenges.

Tracking the Cellular Degradation of Silver Nanoparticles: Development of a Generic Kinetic Model.

Understanding the degradation of nanoparticles (NPs) after crossing the cell plasma membrane is crucial in drug delivery designs and cytotoxicity assessment. However, the key factors controlling the degradable kinetics remain unclear due to the absence of a quantification model. In this study, subcellular imaging of silver nanoparticles (AgNPs) was used to determine the intracellular transfer of AgNPs, and single particle ICP-MS was utilized to track the degradation process. A cellular kinetic model was subsequently developed to describe the uptake, transfer, and degradation behaviors of AgNPs. Our model demonstrated that the intracellular degradation efficiency of AgNPs was much higher than that determined by mimicking testing, and the degradation of NPs was highly influenced by cellular factors. Specifically, deficiencies in Ca or Zn primarily decreased the kinetic dissolution of NPs, while a Ca deficiency also resulted in the retardation of NP transfer. The biological significance of these kinetic parameters was strongly revealed. Our model indicated that the majority of internalized AgNPs dissolved, with the resulting ions being rapidly depurated. The release of Ag ions was largely dependent on the microvesicle-mediated route. By changing the coating and size of AgNPs, the model results suggested that size influenced the transfer of NPs into the degradation process, whereas coating affected the degradation kinetics. Overall, our developed model provides a valuable tool for understanding and predicting the impacts of the physicochemical properties of NPs and the ambient environment on nanotoxicity and therapeutic efficacy.

Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework.

Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients' privacy. We developed a federated learning framework, named dGEM-disparity (decentralized algorithm for Generalized linear mixed Effect Model for disparity quantification). Consisting of 2 modules, dGEM-disparity first provides accurately estimated common effects and calibrated hospital-specific effects by requiring only aggregated data from each center and then adopts a counterfactual modeling approach to assess whether the graft failure rates differ if NHB patients had been admitted at transplant centers in the same distribution as NHW patients were admitted. Utilizing United States Renal Data System data from 39043 adult patients across 73 transplant centers over 10 years, we found that if NHB patients had followed the distribution of NHW patients in admissions, there would be 38 fewer deaths or graft failures per 10000 NHB patients (95% CI, 35-40) within 1year of receiving a kidney transplant on average. The proposed framework facilitates efficient collaborations in clinical research networks. Additionally, the framework, by using counterfactual modeling to calculate the event rate, allows us to investigate contributions to racial disparities that may occur at the level of site of care. Our framework is broadly applicable to other decentralized datasets and disparities research related to differential access to care. Ultimately, our proposed framework will advance equity in human health by identifying and addressing hospital-level racial disparities.

Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using Bayesian optimization

Guided wave propagation (GWP) is commonly employed for the design of SHM systems. However, GWP is sensitive to variations in the material properties, often leading to false alarms. To address this issue, uncertainty quantification (UQ) is employed to improve the reliability of the predictions. Computational structural mechanics is a useful tool for the simulation of GWP. Even so, the application of UQ methods requires numerous solutions, while large-scale, transient GWP simulations increase the computational cost. In this paper, we propose a machine learning (ML)-based reduced order model (ROM), annotated as BO-ML-ROM, to decrease the computational time of the GWP simulation. The ROM is integrated with a Bayesian optimization (BO) framework to adaptively sample the data for the ROM training. The results showed that BO outperforms one-shot sampling methods, both in terms of accuracy and speed. The developed ROM, which is orders of magnitude faster than the original solver, is then applied for forward uncertainty quantification of the GWP in an aluminum plate and a composite panel under varying material properties. The UQ analysis reveals the higher impact of the uncertainties in the wave reflections. Furthermore, to compute the influence of the material properties on the predictions, a sensitivity analysis (SA) is performed using the variance-based Sobol’ indices and the Shapley additive explanations, where it is shown that the uncertainties in the elastic properties have a varying influence across time. The predicted results reveal the efficiency of BO-ML-ROM for the simulation of the GWP and demonstrate its utility for UQ and SA.

Uncertainty qualification in seismic analysis of concrete dams based on model order reduction accelerated stochastic SBFEM

A pioneering framework for uncertainty quantification in seismic analysis of concrete dams rooted in the stochastic scaled boundary finite element method (SSBFEM) is proposed. For seismic analysis of dams, octree decomposition is used for mesh generation. SSBFEM is developed to predict the structural responses with randomly distributed material properties. The script in this paper can integrate octree mesh and SSBFEM into ABAQUS user element (UEL). Implementing octree SSBFEM on commercial software provides accurate and convenient full order models for uncertainty quantification. Monte Carlo simulation (MCs) is deployed to calculate the statistical characteristics of structural responses under random variables. Additionally, singular value decomposition (SVD) and radial basis function (RBF) are leveraged to refine traditional MCs. The solution space of MCs is decomposed into lower-order subspaces. Any structural responses can be rapidly evaluated from linear combinations of subspaces. Particularly, the method has been successfully applied to seismic analysis of concrete dams with different random material properties. Finally, several illustrative examples are provided to validate the accuracy and efficacy of the algorithm.

A novel attached-spring model for damage quantification and degradation evaluation of short/mid-span bridges

Short/mid-span bridges are the most commonly used infrastructure in highways, railways, and other transportation projects. With service time increasing, local damage and performance degradation of short/mid-span bridges are inevitable. Therefore, the assessment of the bridge condition is essential to ensure its safe operation. However, the existing methods mostly assume the damage region of the bridge as a uniform reduction in stiffness, which is not consistent with the actual bridge. Consequently, those methods can hardly achieve the evaluation of the damage. To address this issue, this paper proposes a novel attached-spring model for damage quantification and degradation evaluation of short/mid-span bridges. By assuming the damage region as a spring, an attached-spring model is proposed to describe the nonuniform local damage of the bridge. Then, the deflection influence line is used to identify the stiffness and position of the attached-spring, which achieves the damage detection and evaluation. This approach provides a general model to describe various bridge damage and evaluate the effect of the damage. Numerical simulation and experimental tests are used to verify the feasibility and accuracy of the proposed method. By comparing the results identified from the moving load test and those measured from the static test, the performance of the method is assessed.

Electronic nose for odor monitoring at a landfill fenceline: Training and validation of a model for real-time odor concentration measurement

In recent years, electronic noses, or more generally Instrumental Odor Monitoring Systems (IOMS), have aroused increasing interest in the field of environmental monitoring.One of the most interesting applications of these instruments is the real-time estimation of the odor concentration at plant fencelines to continuously monitor odor emissions and identify anomalous conditions. In this type of application, it is possible to setting a “warning” threshold, enabling the continuous check of proper functioning of the plant and sudden intervention in case of malfunctions, preventing, at the same time, the risk of odor events at the receptors. For this purpose, it is necessary to provide a continuous, fast and reliable measurement of the odor concentration, which is nowadays one of the main challenges of this technology.In this context, this work proposes the development of a quantification model for quantifying odors detected at the fenceline of a landfill characterized by very different odor fingerprints. A double-step quantification model, firstly identifying the different odor classes to which the ambient air monitored at the fenceline by the IOMS belong to, and then developing different specific PLS regression models for each of the odor classes identified, was developed. The results of the proposed quantification model were compared to the ones obtained developing a “global” quantification model, which implements the regression on the globality of the training set, without differentiating between the odor classes. Then, they were further evaluated by comparison with the odor events detected at the sensitive receptor by another electronic nose. Moreover, the combined evaluation of the odor events at the plant fenceline and the receptor, respectively, together with the meteorological data highlighted the need of identifying variable warning thresholds for the odor concentrations at the fenceline according to effectively account for meteorological conditions and produce an output that is more correlated with the probability that an odor is perceived outside of the plant.

Analysis of Brain Regions Activated by Afferent Renal Nerves in DOCA-Salt Hypertensive Mice

We have reported that the neurogenic component of DOCA-salt HTN is dependent on afferent renal nerves (ARNs) that modulate central autonomic networks, however, the central pathways are poorly understood. Targeted recombination in active populations (TRAP2) mice provide a model for global visualization and quantification of neuronal activity via the expression of the fluorescent reporter protein tdTomato which is expressed under the immediate-early gene, c-Fos, a neuronal activation marker, via tamoxifen-inducible Cre. In combination with the deoxycorticosterone-acetate (DOCA)-salt model of hypertension (HTN), neuronal activation in central autonomic centers can be evaluated in TRAP2 mice during DOCA-salt-induced HTN to identify the role of ARNs in the control of blood pressure. We evaluated the expression of tdTomato in TRAP2 mice as a measure of c-Fos activation following 7 days of DOCA-salt HTN and compared it to immunolabeled c-Fos at day 21 of DOCA-salt HTN in brain regions known to be involved in blood pressure regulation. We hypothesized that DOCA will increase the expression of tdTomato and c-Fos in mice with intact renal nerves and that afferent renal denervation (ARDN) will attenuate this DOCA-induced neuronal activity. Three groups of TRAP2-DOCA mice were studied: normotensive (Vehicle-Sham), hypertensive (DOCA-Sham), hypertensive with ARDN (DOCA-ARDN). Mice received a nephrectomy, sham or ARDN, implant of 50mg DOCA and 0.9% saline drinking water for the length of the study (drug-free pellet and diH2O for vehicle). TRAP2-DOCA-salt mice received an injection of 5mg/mL 4-hydroxytamoxifen (4-OHT), a short acting tamoxifen metabolite, on day 7 to induce Cre recombination and expression of tdTomato during the development of HTN. On day 21, mice were anesthetized and perfused with 4% paraformaldehyde. Brains were immunolabeled for c-Fos expression indicating day 21 neuronal activation and compared to endogenous Cre-induced tdTomato expression from day 7 4-OHT. Preliminary data indicate increased neuronal activity in TRAP2 DOCA-Sham mice compared to Vehicle-Sham in the paraventricular nucleus of the hypothalamus, lateral parabrachial nucleus, and nucleus of the solitary tract. This activation pattern was reduced in TRAP2 DOCA-ARDN compared to TRAP2 DOCA-Sham mice. A future study will evaluate c-Fos expression in wild-type DOCA-salt mice at day 7 and 21 of DOCA-salt HTN development to validate the observations in the TRAP2-DOCA model. This experiment will also validate TRAPing as a method for measuring neuronal activation at two time points within the same animal. These preliminary results establish an anatomical foundation for future investigations into the physiological role of ARN input to central autonomic control centers that regulate blood pressure in a salt-induced hypertensive model. NIH R01 HL116476. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

An efficient uncertainty propagation method for nonlinear dynamics with distribution-free P-box processes

The distribution-free P-box process serves as an effective quantification model for time-varying uncertainties in dynamical systems when only imprecise probabilistic information is available. However, its application to nonlinear systems remains limited due to excessive computation. This work develops an efficient method for propagating distribution-free P-box processes in nonlinear dynamics. First, using the Covariance Analysis Describing Equation Technique (CADET), the dynamic problems with P-box processes are transformed into interval Ordinary Differential Equations (ODEs). These equations provide the Mean-and-Covariance (MAC) bounds of the system responses in relation to the MAC bounds of P-box-process excitations. They also separate the previously coupled P-box analysis and nonlinear-dynamic simulations into two sequential steps, including the MAC bound analysis of excitations and the MAC bounds calculation of responses by solving the interval ODEs. Afterward, a Gaussian assumption of the CADET is extended to the P-box form, i.e., the responses are approximate parametric Gaussian P-box processes. As a result, the probability bounds of the responses are approximated by using the solutions of the interval ODEs. Moreover, the Chebyshev method is introduced and modified to efficiently solve the interval ODEs. The proposed method is validated based on test cases, including a duffing oscillator, a vehicle ride, and an engineering black-box problem of launch vehicle trajectory. Compared to the reference solutions based on the Monte Carlo method, with relative errors of less than 3%, the proposed method requires less than 0.2% calculation time. The proposed method also possesses the ability to handle complex black-box problems.

Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases

BackgroundProsthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk...

Rapid quantification models for assessing melamine adulteration in sport nutrition supplements via benchtop and portable NIRS instruments

Sport nutrition supplements (SNS) are vulnerable to adulteration with melamine, artificially augmenting their protein content as determined by conventional assay methodologies. Vibrational spectroscopy techniques are suitable for the detection of adulteration because they allow rapid analysis, require minimal sample preparation, and can perform numerous analyses in a short time. The aim of this study was to develop rapid quantification models for the determination of melamine adulteration in a variety of SNS matrices using NIRS (near-infrared spectroscopy) in combination with multivariate data processing. Moreover, a comparison of benchtop and portable NIR instruments was carried out. Employing a stepwise approach involving OPLS-DA and PLS analysis, matrix discrimination and prediction ability were investigated. The benchtop instrument effectively discriminated among matrices (R2Y = 0.964, Q2 = 0.933), while the portable device, although showing a slightly altered pattern, maintained favorable discrimination capability (R2Y = 0.966, Q2 = 0.931). The quantitative PLS models for each SNS matrix exhibited comparable statistical indicators for both instruments with reasonable errors for melamine content estimation and prediction (RMSEE: 0.3–2.4 %, RMSEP: 0.98–2.99 %). Higher estimation and prediction errors were observed for protein-containing samples in both acquisition modes, probably due to the tendency of protein agglomeration and adhesion to different surfaces, which affects the homogeneity of the powder. Despite data loss due to the narrower spectral range and lower resolution of the portable instrument, all models were found to be suitable for predicting melamine content in sport nutrition supplements.

Testing two-step models of negative quantification using a novel machine learning analysis of EEG

ABSTRACT The sentences “More than half of the students passed the exam” and “Fewer than half of the students failed the exam” describe the same set of situations, and yet the former results in shorter reaction times in verification tasks. The two-step model explains this result by postulating that negative quantifiers contain hidden negation, which involves an extra processing stage. To test this theory, we applied a novel EEG analysis technique focused on detecting cognitive stages (HsMM-MVPA) to data from a picture-sentence verification task. We estimated the number of processing stages during reading and verification of quantified sentences (e.g. “Fewer than half of the dots are blue”) that followed the presentation of pictures containing coloured geometric shapes. We did not find evidence for an extra step during the verification of sentences with fewer than half. We provide an alternative interpretation of our results in line with an expectation-based pragmatic account.

Sampling, quantification and mathematical modeling in agricultural spray drift: A review

An effective spray of agrochemicals is inevitable for crop production for viable agriculture. Spraying inherently suffers from drift, which has always been one of the major concerns in agriculture, affecting the intent of agrochemical spraying and posing serious environmental hazards. Complete elimination of spray drift is impractical under field conditions but can be minimized using precision spraying techniques. Agricultural spray drift has several detrimental effects, such as environmental damage, polluting water bodies, human and animal health risks, chemical exposure, and economic losses, and may also lead to conflicts between neighboring farmers. Hence, the assessment of spray drift is a salient part of the design process of plant protection equipment to achieve maximum deposition in both chemical and biological pesticide applications. The different methods used to study the drift of a sprayer include test bench, wind tunnel and phase Doppler particle analyzer (PDPA) methods. In the field-level assessment, the fluorometric tracer sampling method conforming to ISO-22866:2005 was used. Plume dispersion, particle tracking and computational fluid dynamics (CFD) are the major mathematical modeling approaches for spray drift simulation studies. Among various methodologies and techniques, an appropriate method for spray drift assessment should be adopted in accordance with factors such as crop parameters, mode of application, and environmental conditions.

Altered Sweat Composition Due to Changes in Tight Junction Expression of Sweat Glands in Cholinergic Urticaria Patients.

In cholinergic urticaria (CholU), small, itchy wheals are induced by exercise or passive warming and reduced sweating has been reported. Despite the described reduced muscarinic receptor expression, sweat duct obstruction, or sweat allergy, the underlying pathomechanisms are not well understood. To gain further insights, we collected skin biopsies before and after pulse-controlled ergometry and sweat after sauna provocation from CholU patients as well as healthy controls. CholU patients displayed partially severely reduced local sweating, yet total sweat volume was unaltered. However, sweat electrolyte composition was altered, with increased K+ concentration in CholU patients. Formalin-fixed, paraffin-embedded biopsies were stained to explore sweat leakage and tight junction protein expression. Dermcidin staining was not found outside the sweat glands. In the secretory coils of sweat glands, the distribution of claudin-3 and -10b as well as occludin was altered, but the zonula occludens-1 location was unchanged. In all, dermcidin and tight junction protein staining suggests an intact barrier with reduced sweat production capability in CholU patients. For future studies, an ex vivo skin model for quantification of sweat secretion was established, in which sweat secretion could be pharmacologically stimulated or blocked. This ex vivo model will be used to further investigate sweat gland function in CholU patients and decipher the underlying pathomechanism(s).

Quantitative analysis and stochastic modeling of osteophyte formation and growth process on human vertebrae based on radiographs: a follow-up study

Osteophytes are frequently observed in elderly people and most commonly appear at the anterior edge of the cervical and lumbar vertebrae body. The anterior osteophytes keep developing and will lead to neck/back pain over time. In clinical practice, the accurate measurement of the anterior osteophyte length and the understanding of the temporal progression of anterior osteophyte growth are of vital importance to clinicians for effective treatment planning. This study proposes a new measuring method using the osteophyte ratio index to quantify anterior osteophyte length based on lateral radiographs. Moreover, we develop a continuous stochastic degradation model with time-related functions to characterize the anterior osteophyte formation and growth process on cervical and lumbar vertebrae over time. Follow-up data of anterior osteophytes up to 9 years are obtained for measurement and model validation. The agreement test indicates excellent reproducibility for our measuring method. The proposed model accurately fits the osteophyte growth paths. The model predicts the mean time to onset of pain and obtained survival function of the degenerative vertebrae. This research opens the door to future quantification and mathematical modeling of the anterior osteophyte growth on human cervical and lumbar vertebrae. The measured follow-up data is shared for future studies.

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems.

Online and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, a new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges, including compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time-consuming. Here, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensor cell parameters for targeted gas-phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other processes.

An entropy‐based model for quantifying multi‐dimensional traffic scenario complexity

AbstractQuantifying the complexity of traffic scenarios not only provides an essential foundation for constructing the scenarios used in autonomous vehicle training and testing, but also enhances the robustness of the resulting driving decisions and planning operations. However, currently available quantification methods suffer from inaccuracies and coarse‐granularity in complexity measurements due to issues such as insufficient specificity or indirect quantification. The present work addresses these challenges by proposing a comprehensive entropy‐based model for quantifying traffic scenario complexity across multiple dimensions based on a consideration of the essential components of the traffic environment, including traffic participants, static elements, and dynamic elements. In addition, the limitations of the classical information entropy models applied for assessing traffic scenarios are addressed by calculating magnitude entropy. The proposed entropy‐based model is analyzed in detail according to its application to simulated traffic scenarios. Moreover, the model is applied to real world data within a naturalistic driving dataset. Finally, the effectiveness of the proposed quantification model is illustrated by comparing the complexity results obtained for three typical traffic scenarios with those obtained using an existing multi‐factor complexity quantification method.

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by electrochemical sensors can be used to offer quantitative information about drug concentrations. Several approaches have been proposed for single-concentration quantification based on CV results and machine learning (ML) models with lower training difficulty. However, insufficient measured dataset hinders the application of diverse large-scale ML algorithms in this field. A new method for automatic parameter estimation by ML of measured CV samples is here illustrated with the aim to generate a large simulated dataset for generalized drug concentration quantification model training in this paper. We present an ML-based approach that combines k-means clustering, polynomial regression, and Gaussian Mixture Model (GMM), which automates parameter estimation and simulation using peak detection, baseline subtraction, and peak Gaussian fitting with a small number of measurements. Large simulation datasets constructed on the basis of the estimation results open the possibility of training ML models for more generalized drug concentration quantification. The simulated dataset is processed to assess the efficiency of the proposed method. The Mean Average Percentage Error (MAPE) was 0.32% for etoposide (ETO) and 4.78% for methotrexate (MTX).

CarbonKG: Industrial Carbon Emission Knowledge Graph-Based Modeling and Application for Carbon Traceability of Complex Manufacturing Process

Abstract In the context of the 2030 carbon dioxide emissions peak target, achieving carbon neutrality in manufacturing is essential. However, the complex and extensive supply chain, ranging from raw materials to the final product, presents significant challenges in assessing the carbon footprint throughout the lifecycle. This paper concentrates on the manufacturing process and introduces a modeling approach for quantifying and predicting carbon emissions, utilizing industrial intelligent technologies like process mining and knowledge graphs. First, it is crucial to establish a comprehensive carbon emission quantification model for each manufacturing stage, encompassing the energy, material, personnel, and carbon flow. Subsequently, this paper proposes an industrial carbon emission knowledge graph-based model (CarbonKG) to record and compute emissions at each production stage. Furthermore, process mining technology aids in analyzing the global distribution and movement of carbon emissions within the manufacturing process. Finally, this research presents a two-stage predictive approach for manufacturing process carbon emissions based on CarbonKG. The first stage involves initializing a local order carbon graph for prediction and developing a model to find similar cases. The second stage uses a graph-matching model to identify the Top-K similar order cases, using their carbon emission data for comprehensive prediction. Moreover, the feasibility of the proposed method was verified using actual production process data from industrial companies.

REHEATFUNQ (REgional HEAT-Flow Uncertainty and aNomaly Quantification) 2.0.1: a model for regional aggregate heat flow distributions and anomaly quantification

Abstract. Surface heat flow is a geophysical variable that is affected by a complex combination of various heat generation and transport processes. The processes act on different lengths scales, from tens of meters to hundreds of kilometers. In general, it is not possible to resolve all processes due to a lack of data or modeling resources, and hence the heat flow data within a region is subject to residual fluctuations. We introduce the REgional HEAT-Flow Uncertainty and aNomaly Quantification (REHEATFUNQ) model, version 2.0.1. At its core, REHEATFUNQ uses a stochastic model for heat flow within a region, considering the aggregate heat flow to be generated by a gamma-distributed random variable. Based on this assumption, REHEATFUNQ uses Bayesian inference to (i) quantify the regional aggregate heat flow distribution (RAHFD) and (ii) estimate the strength of a given heat flow anomaly, for instance as generated by a tectonically active fault. The inference uses a prior distribution conjugate to the gamma distribution for the RAHFDs, and we compute parameters for a uninformed prior distribution from the global heat flow database by Lucazeau (2019). Through the Bayesian inference, our model is the first of its kind to consistently account for the variability in regional heat flow in the inference of spatial signals in heat flow data. Interpretation of these spatial signals and in particular their interpretation in terms of fault characteristics (particularly fault strength) form a long-standing debate within the geophysical community. We describe the components of REHEATFUNQ and perform a series of goodness-of-fit tests and synthetic resilience analyses of the model. While our analysis reveals to some degree a misfit of our idealized empirical model with real-world heat flow, it simultaneously confirms the robustness of REHEATFUNQ to these model simplifications. We conclude with an application of REHEATFUNQ to the San Andreas fault in California. Our analysis finds heat flow data in the Mojave section to be sufficient for an analysis and concludes that stochastic variability can allow for a surprisingly large fault-generated heat flow anomaly to be compatible with the data. This indicates that heat flow alone may not be a suitable quantity to address fault strength of the San Andreas fault.