Model Predictions Research Articles

Mixotrophic growth of a ubiquitous marine diatom.

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium's metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium's metabolic flexibility and its potential role in global carbon cycling.

Predictive modeling for cell culture in commercial manufacturing of biotherapeutics.

The biopharmaceutical industry continually seeks advancements in the commercial manufacturing of therapeutic proteins, where mammalian cell culture plays a pivotal role. The current work presents a novel data-driven predictive modeling application designed to enhance the efficiency and predictability of cell culture processes in biotherapeutic production. The capability of the cloud-based digital data science application, developed using open-source tools, is demonstrated with respect to predicting bioreactor potency from at-line process parameters over a 5-day horizon. The uncertainty in model's prediction is quantified, providing valuable insights for process control and decision-making. Model validation on unseen data confirms the model's robust generalizability. An interactive dashboard, tailored to process scientist's requirements is also developed to streamline biopharmaceutical manufacturing processes, ultimately leading to enhanced productivity and product quality.

Effects of α-olefin sulfonate (AOS) on Tubifex tubifex: toxicodynamic-toxicokinetic inferences from the general unified threshold (GUTS) model, biomarker responses and molecular docking predictions.

We investigated the potential ecological risks and harm to aquatic organisms posed by anionic surfactants such as α-olefin sulfonate (AOS), which are commonly found in industrial and consumer products, including detergents. This study assessed acute (96-h) and subchronic (14-day) responses using antioxidant activity, protein levels, and histopathological changes in Tubifex tubifex exposed to different AOS concentrations (10% of the LC50, 20% of the LC50, and a control). Molecular docking was used to investigate the potential interactions between the key stress biomarker enzymes (superoxide dismutase, catalase, and cytochrome c oxidase) of Tubifex tubifex. Acute AOS exposure showed a concentration-dependent decrease in survival, and the general unified threshold (GUTS) model revealed that survivorship is linked to individual response patterns rather than random (stochastic) fluctuations. The GUTS model also revealed dose-dependent toxicity patterns in Tubifex tubifex exposed to α-olefin sulfonate (AOS), with adaptive mechanisms at lower concentrations but significant increases in mortality beyond a certain threshold, emphasizing the role of the AOS concentration in shaping its toxicological impact. Exposure to AOS disrupted antioxidant activity, inducing oxidative stress, with GST and GPx showing positive associations with surfactant concentration and increased lipid peroxidation (elevated MDA levels); moreover, AOS exposure decreased protein concentration, signifying disturbances in vital cellular processes. Histopathological examinations revealed various tissue-level alterations, including cellular vacuolation, cytoplasmic swelling, inflammation, necrosis, and apoptosis. Molecular docking analysis demonstrated interactions between AOS and enzymes (-catalase, superoxide dismutase, and cytochrome c oxidase) in Tubifex tubifex, including hydrophobic and hydrogen bond interactions, with the potential to disrupt enzyme structures and activities, leading to cellular process disruptions, oxidative stress, and tissue damage. According to the species sensitivity distribution (SSD), the difference in toxicity between Tilapia melanopleura (higher sensitivity) and Daphnia magna (low sensitivity) to AOS suggests distinct toxicokinetic and toxicodynamic mechanisms attributable to more complex physiology in Tilapia and efficient detoxification in Daphnia due to its smaller size.

PBPK Modeling of Entrectinib and Its Active Metabolite to Derive Dose Adjustments in Pediatric Populations Co-Administered with CYP3A4 Inhibitors.

Physiologically based pharmacokinetic (PBPK) models of entrectinib and its equipotent metabolite, M5, were established in healthy adult subjects and extrapolated to pediatric patients to predict increases in steady-state systemic exposure on co-administration of strong and moderate CYP3A4 inhibitors (itraconazole at 5 mg/kg, erythromycin at 7.5-12.5 mg/kg and fluconazole at 3-12 mg/kg, respectively). Adult model establishment involved the optimization of fraction metabolized by CYP3A4 (0.92 for entrectinib and 0.98 for M5) using data from an itraconazole DDI study. This model captured well the exposure changes of entrectinib and M5 seen in adults co-administered with the strong CYP3A4 inducer rifampicin. In pediatrics, reasonable prediction of entrectinib and M5 pharmacokinetics in ≧2 year olds was achieved when using the default models for physiological development and enzyme ontogenies. However, a two to threefold misprediction of entrectinib and M5 exposures was seen in <2 year olds which may be due to missing mechanistic understanding of gut physiology and/or protein binding in very young children. Model predictions for ≧2 year olds showed that entrectinib AUC(0-t) was increased by approximately sevenfold and five to threefold by strong and high-moderate and low-moderate CYP3A4 inhibitors, respectively. Based on these victim DDI predictions, dose adjustments for entrectinib when given concomitantly with strong and moderate CYP3A4 inhibitors in pediatric subjects were recommended. These simulations informed the approved entrectinib label without the need for additional clinical pharmacology studies.

An externally validated resting-state brain connectivity signature of pain-related learning

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

Applying natural language processing to patient messages to identify depression concerns in cancer patients.

This study aims to explore and develop tools for early identification of depression concerns among cancer patients by leveraging the novel data source of messages sent through a secure patient portal. We developed classifiers based on logistic regression (LR), support vector machines (SVMs), and 2 Bidirectional Encoder Representations from Transformers (BERT) models (original and Reddit-pretrained) on 6600 patient messages from a cancer center (2009-2022), annotated by a panel of healthcare professionals. Performance was compared using AUROC scores, and model fairness and explainability were examined. We also examined correlations between model predictions and depression diagnosis and treatment. BERT and RedditBERT attained AUROC scores of 0.88 and 0.86, respectively, compared to 0.79 for LR and 0.83 for SVM. BERT showed bigger differences in performance across sex, race, and ethnicity than RedditBERT. Patients who sent messages classified as concerning had a higher chance of receiving a depression diagnosis, a prescription for antidepressants, or a referral to the psycho-oncologist. Explanations from BERT and RedditBERT differed, with no clear preference from annotators. We show the potential of BERT and RedditBERT in identifying depression concerns in messages from cancer patients. Performance disparities across demographic groups highlight the need for careful consideration of potential biases. Further research is needed to address biases, evaluate real-world impacts, and ensure responsible integration into clinical settings. This work represents a significant methodological advancement in the early identification of depression concerns among cancer patients. Our work contributes to a route to reduce clinical burden while enhancing overall patient care, leveraging BERT-based models.

Using the Haken-Strobl-Reineker Model to Determine the Temperature Dependence of the Diffusion Coefficient.

Stochastic quantum Liouville equations (SQLE) are widely used to model energy and charge dynamics in molecular systems. The Haken-Strobl-Reineker (HSR) SQLE is a particular paradigm in which the dynamical noise that destroys quantum coherences arises from a white noise (i.e., constant-frequency) spectrum. A system subject to the HSR SQLE thus evolves to its "high-temperature" limit, whereby all the eigenstates are equally populated. This result would seem to imply that the predictions of the HSR model, e.g., the temperature dependence of the diffusion coefficient, have no validity for temperatures lower than the particle bandwidth. The purpose of this paper is to show that this assumption is incorrect for translationally invariant systems. In particular, provided that the diffusion coefficient is determined via the mean-squared-displacement, considerations about detailed-balance are irrelevant. Consequently, the high-temperature prediction for the temperature dependence of the diffusion coefficient may be extrapolated to lower temperatures, provided that the bath remains classical. Thus, for diagonal dynamical disorder the long-time diffusion coefficient, D∞(T) = c1/T, while for both diagonal and off-diagonal disorder, D∞(T) = c1/T + c2T, where c2 ≪ c1. An appendix discusses an alternative interpretation from the HSR model of the "quantum to classical" dynamics transition, whereby the dynamics is described as stochastically punctuated coherent motion.

Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.

The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

Parkinsonian Tremor as Unstable Feedback in a Physiologically Consistent Control Framework.

Parkinson's disease (PD) is characterized by decreased dopamine in the basal ganglia that causes excessive tonic inhibition of the thalamus. This excessive inhibition seems to explain inhibitory motor symptoms in PD, but the source of tremor remains unclear. This paper investigates how neural inhibition may change the closed-loop characteristics of the human motor control system to determine how this established pathophysiology could produce tremor. The rate-coding model of neural signals suggests increased inhibition decreases signal amplitude, which could create a mismatch between the closed-loop dynamics and the internal models that overcome proprioceptive feedback delays. This paper aims to identify a candidate model structure with decreased-amplitude-induced tremor in PD that also agrees with previously recorded movements of healthy and cerebellar patients. The optimal feedback control theory of human motor control forms the basis of the model. Key additional elements include gating of undesired movements via the basal ganglia-thalamus-motor cortex circuit and the treatment of the efferent copy of the control input as a measurement in the state estimator. Simulations confirm the model's ability to capture tremor in PD and also demonstrate how disease progression could affect tremor and other motor symptoms, providing insight into the existence of tremor and non-tremor phenotypes. Altogether, the physiological underpinnings of the model structure and the agreement of model predictions with clinical observations provides support for the hypothesis that unstable feedback produces parkinsonian tremor. Consequently, these results also support the associated framework for the neuroanatomy of human motor control.

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LETd) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LETdis associated with variability in the relative biological effectiveness (RBE) of protons, we also evaluate the implications of using NN predictions for normal tissue complication probability (NTCP) models within a variable-RBE context.&#xD;&#xD;Approach: The predictive performance of three-dimensional NN architectures was evaluated using five-fold cross-validation on a cohort of brain tumor patients (n=151). The best-performing model was identified and externally validated on patients from a different center (n=107). LETdpredictions were compared to MC-simulated results in clinically relevant regions of interest. We assessed the impact on NTCP models by leveraging LETdpredictions to derive RBE-weighted doses, using the Wedenberg RBE model.&#xD;&#xD;Main results: We found NNs based solely on the planned dose profile, i.e. without additional usage of CT images, can approximate MC-based LETddistributions. Root mean squared errors (RMSE) for the median LETdwithin the brain, brainstem, CTV, chiasm, lacrimal glands (ipsilateral/contralateral) and optic nerves (ipsilateral/contralateral) were 0.36, 0.87, 0.31, 0.73, 0.68, 1.04, 0.69 and 1.24~keV/μm, respectively. Although model predictions showed statistically significant differences from MC outputs, these did not result in substantial changes in NTCP predictions, with RMSEs of at most 3.2 percentage points.&#xD;&#xD;Significance: The ability of NNs to predict LETdbased solely on planned dose profiles suggests a viable alternative to the compute-intensive MC simulations in a variable-RBE setting. This is particularly useful in scenarios where MC simulation data are unavailable, facilitating resource-constrained proton therapy treatment planning, retrospective patient data analysis and further investigations on the variability of proton RBE.

An improved random forest model for music genre classification algorithm based on sparrow search algorithm

In this study, a random forest model optimised based on the sparrow search algorithm is explored for the music genre classification problem. By introducing the sparrow search algorithm, the model is able to search the feature space more comprehensively during the training process, which improves the learning ability of complex data relationships. After 15 iterations of optimisation, the fitness value of the model is improved from 0.2002 to 0.197, showing better convergence. The performance on the training set shows that the model predicts 2,543 music genres with an accuracy of 99.59% and only 10 incorrect predictions, which verifies its excellent performance on the training data. While on the test set, the model's prediction accuracy for 623 music genres is 79.78%, which is slightly lower than the result of the training set, but still reflects good generalisation ability. The results of this paper imply that the improved random forest model based on the sparrow search algorithm has potential application prospects in the music genre classification task. By optimising the parameter settings and feature selection, we not only improve the prediction accuracy and generalisation ability of the model, but also provide new ideas for solving other complex data classification problems. In the future, we can further expand the sample size, try different feature combinations, and conduct in-depth research by combining more domain knowledge in order to further improve the model performance and promote the development of related fields.

Energy Demand for Pyrolysis of Mixed Thermoplastics and Waste Plastics in Chemical Recycling: Model Prediction and Pilot-Scale Validation

Energy Demand for Pyrolysis of Mixed Thermoplastics and Waste Plastics in Chemical Recycling: Model Prediction and Pilot-Scale Validation

An improved efficientnet B7 underwater marine organism image classification model based on the spatial group enhancement attention mechanisms

In this paper, we adopt the Efficientnet B7 model, which is an excellent performer in deep learning algorithms, and improve it by combining with the Spatial Group Enhancement attention mechanism to provide an innovative method for underwater biological image classification. After a reasonable division of the dataset, we introduced the improved Efficientnet B7 model for training. By observing the change of the loss curve on the training set, it can be seen that the loss value of the model gradually decreases from 0.6 to 0.05 and tends to be stable, which indicates that the classification prediction of the model is gradually close to the real result, and it can classify the underwater marine organisms images effectively. The accuracy on the training set gradually increases from the initial 80.12% to 98.31%, and the recall also grows from 75.82% to 98.93%, showing that the model predicts well. The performance on the test set shows an accuracy of 84.86%, a recall of 82.40%, and an F1 value of 80.2%, indicating that the model has good generalisation ability. This means that our proposed improved methods and ideas have achieved significant results in the task of underwater biological image classification, providing a useful reference for research in related fields. By comparing the experimental results, it can be seen that the optimisation of the Efficientnet B7 model by introducing the Spatial Group Enhancement attention mechanism achieves better performance in the underwater marine organisms image classification task, which lays the foundation for the future application of deep learning in underwater environments. This study is of positive significance for the promotion of underwater biology research, marine protection and other fields, and provides new ideas and methods for carrying out more in-depth related research.

Cyclic Refiner: Object-Aware Temporal Representation Learning for Multi-view 3D Detection and Tracking

AbstractWe propose a unified object-aware temporal learning framework for multi-view 3D detection and tracking tasks. Having observed that the efficacy of the temporal fusion strategy in recent multi-view perception methods may be weakened by distractors and background clutters in historical frames, we propose a cyclic learning mechanism to improve the robustness of multi-view representation learning. The essence is constructing a backward bridge to propagate information from model predictions (e.g., object locations and sizes) to image and BEV features, which forms a circle with regular inference. After backward refinement, the responses of target-irrelevant regions in historical frames would be suppressed, decreasing the risk of polluting future frames and improving the object awareness ability of temporal fusion. We further tailor an object-aware association strategy for tracking based on the cyclic learning model. The cyclic learning model not only provides refined features, but also delivers finer clues (e.g., scale level) for tracklet association. The proposed cycle learning method and association module together contribute a novel and unified multi-task framework. Experiments on nuScenes show that the proposed model achieves consistent performance gains over baselines of different designs (i.e., dense query-based BEVFormer, sparse query-based SparseBEV and LSS-based BEVDet4D) on both detection and tracking evaluation. Codes and models will be released.

Mapping the distribution of sandflies and sandfly-associated pathogens in China.

Understanding and mapping the distribution of sandflies and sandfly-associated pathogens (SAPs) is crucial for guiding the surveillance and control effort. However, their distribution and the related risk burden in China remain poorly understood. We mapped the distribution of sandflies and SAPs using literature data from 1940 to 2022. We also mapped the human visceral leishmaniasis (VL) cases using surveillance data from 2014 to 2018. The ecological drivers of 12 main sandfly species and VL were identified by applying machine learning, and their distribution and risk were predicted in three time periods (2021-2040, 2041-2060, and 2061-2080) under three scenarios of climate and socioeconomic changes. In the mainland of China, a total of 47 sandfly species have been reported, with the main 12 species classified into three clusters according to their ecological niches. Additionally, 6 SAPs have been identified, which include two protozoa, two bacteria, and two viruses. The incidence risk of different VL subtypes was closely associated with the distribution risk of specific vectors. The model predictions also revealed a substantial underestimation of the current sandfly distribution and VL risk. The predicted areas affected by the 12 major species of sandflies and the high-risk areas for VL were found to be 37.9-1121.0% and 136.6% larger, respectively, than the observed range in the areas. The future global changes were projected to decrease the risk of mountain-type zoonotic VL (MT-ZVL), but anthroponotic VL (AVL) and desert-type zoonotic VL (DT-ZVL) could remain stable or slightly increase. Current field observations underestimate the spatial distributions of main sandfly species and VL in China. More active surveillance and field investigations are needed where high risks are predicted, especially in areas where the future risk of VL is projected to remain high or increase.

Bound and resonating multiquark configurations

Abstract We review the predictions of quark models for multiquark configurations that are bound or resonant states, and compare different methods for estimating the properties of resonances.

Why Single-Chain Nanoparticles from Weak Polyelectrolytes Can Be Synthesized at Large Scale in Concentrated Solution?

Here, the unresolved question of why single-chain nanoparticles (SCNPs) prepared from a weak polyelectrolyte (PE) precursor can be synthesized on a large is addresses, unlike SCNPs obtained from an equivalent neutral (nonamphiphilic) polymer precursor. The combination of the standard elastic single-chain nanoparticles (ESN) model -developed for neutral chains- with the classical scaling theory of PE solutions provides the key. Essentially, the long-range repulsion between electrostatic blobs in a weak PE precursor restricts the cross-linking process during SCNPs formation to the interior of each blob. Consequently, the maximum concentration at which PE-SCNPs can be prepared without interchain cross-linking is not determined by the full size of the PE precursor but, instead, by the smaller size of its electrostatic blobs. Therefore, PE-SCNPs can be synthesized up to a critical concentration where electrostatic blobs from different chains touch each other. This concentration can be 30 times higher than that for non-PE polymer precursors. Upon progressive dilution, the size of PE-SCNPs synthesized in concentrated solution increases until it reaches the bigger size of PE-SCNPs prepared under highly diluted conditions. PE-SCNPs do not adopt a globular conformation either in concentrated or in diluted solution. It shows that the main model predictions agree with experimental results.

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Optically Quiet, But FUV Loud: Results from comparing the far-ultraviolet predictions of flare models with TESS and HST

Abstract The far-ultraviolet (FUV) flare activity of low-mass stars has become a focus in our understanding of the exoplanet atmospheres and how they evolve. However, direct detection of FUV flares and measurements of their energies and rates are limited by the need for space-based observations. The difficulty of obtaining such observations may push some works to use widely available optical data to calibrate multi-wavelength spectral models that describe UV and optical flare emission. These models either use single temperature blackbody curves to describe this emission, or combine a blackbody curve with archival spectra. These calibrated models would then be used to predict the FUV flare rates of low-mass stars of interest. To aid these works, we used TESS optical photometry and archival HST FUV spectroscopy to test the FUV predictions of literature flare models. We tested models for partially (M0-M2) and fully convective (M4-M5) stars, 40 Myr and field age stars, and optically quiet stars. We calculated FUV energy correction factors that can be used to bring the FUV predictions of tested models in line with observations. A flare model combining optical and NUV blackbody emission with FUV emission based on HST observations provided the best estimate of FUV flare activity, where others underestimated the emission at all ages, masses and activity levels, by up to a factor of 104 for combined FUV continuum and line emission and greater for individual emission lines. We also confirmed previous findings that showed optically quiet low-mass stars exhibit regular FUV flares.

A New Method for Estimating Oil Well Production Rates Based on Enthalpy Balance Utilizing Temperature Data

Abstract The fundamental objective of this study is to utilize a temperature dataset to achieve accurate rate estimation. This study introduces a graphical method, incorporating suitable assumptions, for predicting flow rates in single-phase vertical oil flow. The method takes into consideration the fouling factor and addresses the steady-state flow of heat in the wellbore, while also accounting for the transient heat conduction in the formation. To evaluate and validate the proposed model, two field cases are examined, and associated uncertainties are discussed. The model's predictions are compared with those obtained from Hassan and Kabir's approach, as well as actual separator flow rate data. The comparisons reveal that the model exhibits a high level of accuracy within its specified range of application. Statistical analyses indicate that most of the models perform comparably, although the suggested graphical model stands out for its higher accuracy and user-friendly nature. The performance of uncertainty is found to be more reliant on the accuracy of data measurement rather than the features of the model itself.