Model Parameters Research Articles

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features

Background and purpose Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Materials and methods Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Results Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. Conclusion The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma.

An Integrated Fuzzy Delphi and Fuzzy AHP Model for Evaluating Factors Affecting Human Errors in Manual Assembly Processes

Human errors (HEs) are prevalent issues in manual assembly, leading to product defects and increased costs. Understanding and knowing the factors influencing human errors in manual assembly processes is essential for improving product quality and efficiency. This study aims to determine and rank factors influencing HEs in manual assembly processes based on expert judgments. To achieve this objective, an integrated model was developed using two multi-criteria decision-making (MCDM) techniques—specifically, the fuzzy Delphi Method (FDM) and the fuzzy Analytic Hierarchy Process (FAHP). Firstly, two rounds of the FDM were conducted to identify and categorize the primary factors contributing to HEs in manual assembly. Expert consensus with at least 75% agreement determined that 27 factors with influence scores of 0.7 or higher significantly impact HEs in these processes. After that, the priorities of the 27 influencing factors in assembly HEs were determined using a third round of the FAHP method. Data analysis was performed using SPSS 22.0 to evaluate the reliability and normality of the survey responses. This study has divided the affecting factors on assembly HEs into two levels: level 1, called main factors, and level 2, called sub-factors. Based on the final measured weights for level 1, the proposed model estimation results revealed that the most influential factors on HEs in a manual assembly are the individual factor, followed by the tool factor and the task factor. For level 2, the model results showed a lack of experience, poor instructions and procedures, and misunderstanding as the most critical factors influencing manual assembly errors. Sensitivity analysis was performed to determine how changes in model inputs or parameters affect final decisions to ensure reliable and practical results. The findings of this study provide valuable insights to help organizations develop effective strategies for reducing worker errors in manual assembly. Identifying the key and root factors contributing to assembly errors, this research offers a solid foundation for enhancing the overall quality of final products.

Fractional Derivative-based Burger Creep Model for Soft Rocks and its Verification Using Tunnel Monitoring Results and Experimental Data

AbstractConsidering the creep behavior of soft and weak rocks is critical for analyzing the long-term stability of underground constructions. This paper introduces a novel creep constitutive model to characterize the creep behavior of rocks under uniaxial and triaxial stress states. The fractional derivative Abel dashpot was used to improve the Burger model, and a viscoplastic component was added in series with the modified Burgers model to replicate the tertiary phase of rock creep. The effectiveness of the model was verified using creep test data from various soft rocks and monitoring measurements from a tunnel excavated in heavily jointed weak rock masses. Furthermore, a sensitivity analysis was carried out to assess the impact of the model parameters on creep deformation, and a comparative study was performed to evaluate the efficacy of the suggested model in modeling the accelerated stage of rock creep compared with some existing models. The strong agreement observed between the calculated results and both the creep test data and tunnel monitoring measurements underscores the accuracy and validity of the proposed model. The comparative analysis further revealed that the proposed model offers the highest fitting efficiency for describing the tertiary stage of rock creep. These findings suggest that the model effectively captures the creep behavior of rocks and precisely represents the entire creep process.

Optimal sampling strategy for probability estimation: An application to the Agricultural Quarantine Inspection Monitoring program

AbstractImported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway‐level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container‐by‐container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision‐making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box‐level (beyond only container‐level) inspection data, which could be used to estimate this correlation and other model parameters.

Inference of Some Macroeconomic Variables on Nigeria Unemployment Rate

This research investigates the impact of foreign direct investment (FDI), government expenditure (GOE), and inflation rate (IFR) on the unemployment rate (UPR) in Nigeria from 1985 to 2021 through Autoregressive Distributed Lag (ARDL) modeling approach. An initial assessment to test the significance of signals between each independent variable and UPR was performed using rolling correlation analysis. Subsequently, the bounds test methodology to examine cointegration among between the FDI, GOE, IFR, and UPR was performed. Additionally, the causal relationship between these economic variables was performed through the Error Correction Model (ECM) approach. The estimated ARDL model parameters stability was determined using the cumulative sum (CUSUM) of squares chart. The Augmented Dickey Fuller unit root test suggests that the variables are stationary at first differences (I(1)). The bounds test revealed that the variables are cointegrated at 1%, 5%, and 10% indicating a long run relationship between UPR and FDI, GOE, and IFR. The ARDL results indicates that at 5%, a unit increase in FDI at lag one have a long run significant decreasing impact on UPR by 19.96%. But in the short run, the FDI at lag one has a significant increasing effect on UPR by 7.23%. However, the CUSUM of square chart shows unstable parameter estimation based on the Akaike Information Criteria selected model, ARDL (1, 3, 0, 0). The study concludes that UPR is being influenced by FDI in reducing UPR in the long run. Recommendation based on the findings is that FDI should be considered most important when policies are drafted for tackling the issue bordering UPR in Nigeria.

Research on stock prediction based on CED-PSO-StockNet time series model

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R2 metric. To further assess the model’s generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction.

Mathematical Modeling and Optimization of Enzyme‐Based Amperometric Screen‐Printed Biosensors Using Horseradish Peroxidase as a Model

AbstractIn this study, a mathematical model was formulated to characterize the functioning of third‐generation enzyme‐based amperometric biosensors. This study utilized a horseradish peroxidase (HRP)‐based, screen‐printed biosensor. The model parameters were determined using the Environment for Modeling Simulation and Optimization (EMSO), validated with experimental data, and exhibited high determination coefficients, indicating the accuracy of the models in capturing biosensor behavior. Simulations based on this model emphasize diffusion as a limiting factor. Further sensitivity analysis was conducted using simulated response surfaces and biosensor performance optimization, which confirmed that higher concentrations of HRP in the thinnest polymeric film resulted in enhanced biosensor sensitivity.

External evaluation of intravenous vancomycin population pharmacokinetic models in adults receiving high‐flux intermittent haemodialysis

AimsPatients undergoing haemodialysis (HD) are at greater risk of methicillin‐resistant Staphylococcus aureus infections requiring intravenous vancomycin. Close vancomycin therapeutic drug monitoring is warranted in HD patients as renal clearance is the primary elimination pathway. Clinically, population pharmacokinetics (popPK) model‐informed dosing is commonly used. This study aimed to perform an external evaluation of published vancomycin popPK models developed for adults undergoing high‐flux intermittent HD, and to create a dosing nomogram derived from the model that performed best.MethodsA literature review was conducted through PubMed and EMBASE to identify relevant popPK models. an external dataset was collected retrospectively from patients of 2 healthcare centres in Quebec, Canada. Selected models were implemented in NONMEM (v7.5; ICON Development Solutions). Predictive performance was assessed through prediction and simulation‐based diagnostics.ResultsIn total, 2386 vancomycin concentrations were collected from 274 patients and 476 antibiotic courses. Four vancomycin popPK models were selected for evaluation. None of the models demonstrated overall satisfactory or clinically acceptable predictive performance. Nonetheless, Bae et al.’s model performed best with a median prediction error of 16.25% and median absolute prediction error of 34.66%. Different predictive performance was also observed for vancomycin concentrations from samples collected during and between HD sessions.ConclusionAll evaluated models presented poor overall predictive performance. Further studies are required, through existing popPK model parameter re‐estimation or new model development, to adequately describe vancomycin pharmacokinetics for our high‐flux intermittent HD patient cohort.

Conditional parametric bootstrap in GLARMA models

Due to situations where asymptotic results are not valid or when we do not know the true distributions of the data set, the bootstrap technique stands out as an effective alternative to make inferences about model parameters. In this context, we introduce the conditional parametric bootstrap, applied to time series data, specifically using the Generalized Linear Autoregressive Moving Average (GLARMA) model. Our interest is to use the procedure to correct possible biases and construct confidence intervals and hypothesis testing for the parameters of the GLARMA model. We conducted simulation studies to evaluate the performance of the procedure in finite samples. The results show that the proposed technique is promising and can be applied in practical situations. An example is the analysis of the monthly number of chronic obstructive pulmonary disease (COPD) cases, which illustrates the potential of the proposed approach in a real situation.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

Combined carbapenem resulted in a 4.48-fold increase in valproic acid clearance: a population pharmacokinetic model in Chinese children and adults with epilepsy or after neurosurgery

Our study aims to explore the pharmacokinetics of valproic acid (VPA) in Chinese patients with epilepsy or after neurosurgery and establish a robust population pharmacokinetics (PPK) model. The PPK model was developed using nonlinear mixed-effects modeling, incorporating a total of 615 VPA plasma concentration data points from 443 Chinese epilepsy or after neurosurgery patients. A one-compartment model with an additive residual model was established. Forward addition and backward elimination strategies were used to assess the impact of covariates on the model parameters. Goodness-of-fit plots, bootstrap, visual predict check and normalized prediction distribution errors were used for model validation. In the final model, the apparent clearance (CL) was estimated using the following formula: CL L/h=0.430×BW/600.787×Cr/50.3−0.253×ALB/39−0.873×egender×eCBP×eIND2 ×eηCL (gender = 0.121 when is female, otherwise = 0; CBP = 1.50 when combined with carbapenems, otherwise = 0; IND2 = 0.15 when combined with oxcarbazepine, carbamazepine, phenobarbital, or phenytoin, otherwise = 0). The volume of distribution (Vd) was estimated using the formula: Vd L=8.66×BW/600.751. Comedication with carbapenems could increase VPA clearance by 4.48 times, and comedication with oxcarbazepine could enhance VPA clearance by 116%. Besides, creatinine and albumin could affect VPA clearance. Goodness-of-fit plots, bootstrap, visual predict check and normalized prediction distribution showed acceptable data fit, stability, and predictability of the model. In our study, a PPK model was utilized to attain a more comprehensive insight into these variables, improving the accuracy and individualization of VPA therapy in Chinese patients with epilepsy or after neurosurgery.

Modeling demand responsive service for the elderly healthcare activities considering temporal and spatial variations

ABSTRACT This paper develops a cost-effective demand-responsive transportation (DRT) model aimed at enhancing the accessibility and convenience of clinical visits for the elderly by optimizing two NP-hard problems. First, a set of pick-up stops is optimized based on potential stop locations, spatial and temporal demand, and acceptable walking distance. Next, vehicle routing and scheduling are optimized, considering vehicle capacity, users’ desired arrival times, and travel companions, to minimize total costs. An elite genetic algorithm (EGA) is developed to effectively search for the optimal solution. In the case study, demands are generated from a survey on elderly clinical visits and population census data in Xi’an. The proposed EGA is applied to different clustered demands classified by time interval to identify optimal solutions. Sensitivity analysis is conducted to explore the relationships between model parameters and decision variables for the studied DRT system.

Dynamics of Shoreline Changes Along the Coast of Subarnarekha and Budhabalanga River Estuary, North Eastern Coast of India Using DSAS Technique: A Geospatial Technology Approach

ABSTRACTThe coastal regions represent one of the most significant environmental and economic resources, offering critical ecosystems that support biodiversity. Shoreline change analysis offers critical insights into coastal dynamics, providing trends in erosion and accretion, enabling effective coastal management and hazard mitigation. In the current study, shoreline change is assessed utilizing the Digital Shoreline Analysis System (DSAS) model for Subarnarekha and Budhabalanga river estuaries in Baleswar district, Odisha, India. The shoreline was extracted using multitemporal satellite images like Landsat‐5 and Landsat‐7 for the years 1991, 2000, and 2011. Similarly Sentinel‐2 and Landsat‐8 satellite imagery were used for the year 2022. The satellite data from 1991 to 2022 were processed using Envi, ArcGIS softwares and the shoreline is extracted for each year using band ratioing methods to demarcate shoreline. In the current research, the net accretion and erosion along the coast were analyzed using the GIS (geographic information systems) technique and the DSAS model. The four important statistical parameters of the DSAS model utilized in the study area are end point rate (EPR), net shoreline movement (NSM), linear regression rate (LRR), and least median of squares (LMS). The shoreline change analysis for the coast of Subarnarekha and Budhabalanga river estuary area from 1991 to 2022 reveals that 44% of the coast is under accretion, 7% is under stable coast, and 23% is under erosion. According to the findings, the coastal area of the recent study is both progressive and regressive in nature. For the study area, the average rates of shoreline accretion and erosion are 1.05 m and 0.45 m per year, respectively. The study gives information on erosion and accretion near the Subarnarekha and Budhabalanga river estuaries, which will aid in the development of an adaptive shoreline management strategy and coastal vulnerability assessment.

Distribution of Pb, Zn, Fe, As, Sn, Sb, Bi, and Ni Between Oxide Liquid and Metal in the ‘CuO0.5’-CaO-AlO1.5 System in Equilibrium with Cu Metal at 1400 °C

AbstractThe increasing complexity of ore resources and recycled materials in the feed of pyrometallurgical processes present a technical challenge to the metallurgical engineers working on maximizing the recovery of the valuable elements and minimizing the environmental impact of the processes. To address this challenge, the availability of computational tools that can predict the mass and energy balance in complex systems is required. Then, the accurate description of phase equilibria in the complex multicomponent systems describing the chemistry of the pyrometallurgical processes becomes critical for the correct implementation of the indicated tools and facing the outlined industrial challenges. In the present study, the distribution of selected elements (Pb, Zn, Fe, As, Sn, Sb, Bi, and Ni) between oxide liquid and metal in the ‘CuO0.5’–CaO–AlO1.5 system in equilibrium with Cu metal at 1400 °C (liquidus of CaAl2O4) was experimentally studied using the equilibration and quenching technique followed by the electron probe X-ray microanalysis of the resulted samples. The study covered a wide range of effective p(O2) over the system from 10−11 to 10−3.5 (corresponding to formation of immiscible CuO0.5-rich slag). To avoid loss of volatile elements (Pb, Zn, As, Sn, Sb, and Bi), a correlation between ‘CuO0.5’ in oxide liquid and p(O2) in open system was obtained first, followed by studying the volatile elements distribution in closed conditions (Al2O3 crucible sealed in SiO2 ampoule), where ‘CuO0.5’ concentration was used as a marker to evaluate the effective p(O2) over the system. The experimental results were then used for the optimization of the thermodynamic model parameters of the system as part of the integrated experimental and self-consisting thermodynamic modeling research program of phase equilibria in the Cu–Pb–Zn–Fe–Ca–Si–Al–Mg–O–S–(As, Sn, Sb, Bi, Ag, Au, Ni, Cr, Co, and Na) gas/oxide liquid/matte/speiss/metal/solids system. Graphical Abstract

Synthetic Wind Estimation for Small Fixed-Wing Drones

Wind estimation is crucial for studying the atmospheric boundary layer. Traditional methods such as weather balloons offer limited in situ capabilities; besides an Air Data System (ADS) combined with inertial measurements and satellite positioning is required to estimate the wind on fixed-wing drones. As pressure probes are an important constituent of an ADS, they are susceptible to malfunctioning or failure due to blockages, thus affecting the capability of wind sensing and possibly the safety of the drone. This paper presents a novel approach, using low-fidelity aerodynamic models of drones to estimate wind synthetically. In our work, the aerodynamic model parameters are derived from post-processed flight data, in contrast to existing approaches that use expensive wind tunnel calibration for identifying the same. In sum, our method integrates aerodynamic force and moment models into a Vehicle Dynamic Model (VDM)-based navigation filter to yield a synthetic wind estimate without relying on an airspeed sensor. We validate our approach using two geometrically distinct drones, each characterized by a unique aerodynamic model and different quality of inertial sensors, altogether tested across several flights. Experimental results demonstrate that the proposed cross-platform method provides a synthetic wind velocity estimate, thus offering a practical backup to traditional techniques.

One-way reflection waveform inversion with depth-dependent gradient preconditioning

Summary Reflection Waveform Inversion (RWI) is a technique that uses pure reflection data to estimate subsurface background velocity, relying on evolving seismic images. Conventional RWI operates in a cyclic workflow, with two key components in each cycle—migration and reflection tomography. Conventional RWI may result in suboptimal background velocity estimation, partly due to limited or unresolved resolution within each component in each cycle. While gradient preconditioning with the reciprocal of Hessian information helps resolve this issue in both components of RWI, it becomes impractical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform inversion technique in which the forward modeling scheme operates in one direction (downward and then upward) via virtual parallel depth levels within the medium. Leveraging the ORWI framework, we decompose and reduce the linear Hessian operator (also known as the approximate Hessian or Gauss-Newton Hessian) into multiple smaller sub-operators. In particular, the diagonal blocks of the mono-frequency approximate Hessian operators, each corresponding to a single depth level within the medium, are extracted and inverted to precondition the corresponding mono-frequency gradients in both the migration and reflection tomography components of ORWI. This depth-dependent gradient preconditioning transforms standard ORWI into a high-resolution, yet computationally feasible version aimed at addressing suboptimal velocity estimation, referred to as high-resolution ORWI (HR-ORWI). The effectiveness of the proposed approach is demonstrated through successful applications to synthetic data examples.

An investigation to improve a nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose–response data

ABSTRACT Dose–response relationships are important in assessing the efficacy and potency of compounds, which can usually be characterized by a 4-parameter logistic (4-PL) model estimating EC50, slope factor, lower asymptote, and upper asymptote. EC50, the concentration of a compound that induces a response halfway between the baseline and maximum, is a key quantity to evaluate compound potency. For multi-donor dose–response data, it is often of interest to estimate the overall EC50 (i.e. the average EC50 of the population of donors) and its 95% confidence interval (CI). A few multi-donor EC50 estimation methods have been proposed in the literature. Jiang and Kopp-Schneider (2014) systematically compared the meta-analysis approach and the nonlinear mixed-effects approach and concluded that the meta-analysis approach is simple and robust to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments, while the nonlinear mixed-effects approach has the issue of convergence failures probably due to overparameterization. In this article, we propose a modification of the nonlinear mixed-effects approach by using the stochastic approximation expectation-maximization (SAEM) algorithm to estimate model parameters and using multiple starting points to search for globally optimal values, which can substantially alleviate the issue of convergence failures even for small number of donors (e.g. n = 3), and achieve a smaller absolute median bias and better coverage probability of 95% confidence interval than the meta-analysis approach when the number of donors is not too small (e.g. n ≥ 7).

A novel friction coefficient model parameters identification method for needle-tissue insertion procedure

Abstract In endoscopic liver vascular insertion surgeries, during the process of angiographic operation, the success of vascular staining depends on precise needle insertion control which heavily relies on experienced surgeons. Endoscopic vascular insertion surgical navigation system shows the potential to improve position precision, however it relies on needle-tissue interaction model and parameter identification to provide essential information for improving needle insertion accuracy, in which the friction coefficient is important parameters but difficult to determine. In this paper, a novel needle-tissue friction coefficient identification method was proposed with unknown tissue Young's modulus under endoscopic liver surgery scenarios. A modified friction coefficient model was proposed including the adhesion and elastic friction component to describe needle-tissue dynamic interaction process which can predict the friction coefficient more precisely. The proposed parameter estimation method based on the modified friction model can simultaneously estimate friction coefficient and Young's modulus. The proposed method was demonstrated by the friction coefficient measurement experiment. The results showed that the friction coefficient model prediction results agreed well with expected value. The proposed method can be applied to provide essential tissue-needle interactive information to improve needle insertion precision in the endoscopic liver vascular insertion surgery scenarios.

Model-based characterization of total serum bilirubin dynamics in preterm infants

Abstract Objectives This study aims to characterize the age-related natural dynamics of total serum bilirubin (TSB) in preterm infants through a mathematical model and to study the model parameters as potential biomarkers for detecting associated morbidities. Methods We proposed an exponential decay model and applied it to each infant. Patient-specific parameters were obtained by minimizing the error between measured TSB and model output. Modeling evaluation was based on root-mean-square error (RMSE). The occurrence of high-risk clinical events was analyzed based on RMSE. Results In a subset of the CARESS-Premi study involving 373 preterm infants (24–32 weeks’ gestation), 72 patient-specific models were fitted. RMSE ranged from 1.20 to 40.25 µmol/L, with a median [IQR] of 8.74 [4.89, 14.25] µmol/L. Conclusions Our model effectively characterized TSB dynamics for 72 patients, providing valuable insights from model parameters and fitting errors. To our knowledge, this is the first long-term mathematical description of natural TSB decay in preterm infants. Furthermore, the model was able to estimate the occurrence of clinical events such as necrotizing enterocolitis, as reflected by the relatively high RMSE. Future implications include the development of model-based clinical decision support systems for optimizing NICU monitoring and detecting high-risk events. Impact The study characterizes the natural dynamics of total serum bilirubin in preterm infants (24–32 weeks’ gestation) using a patient-specific exponential decay model. The model describes patient-specific patterns of TSB evolution from day three to the first weeks, providing a median [IQR] root-mean-squared error of 8.74 [4.89, 14.25] µmol/L. Complementary to previous studies focusing on the first 72–96 h, our study emphasizes the later decay course, contributing to a comprehensive long-term characterization of the natural TSB dynamics in preterm infants. The proposed model holds potential for clinical decision support systems for the optimization of NICU monitoring and high-risk event detection.

Reparameterization of the mW model to accurately predict the experimental phase diagram of methane hydrate.

Due to their high computational efficiency, the coarse-grained water models are of particular importance for practical molecular simulations of gas hydrates. In these models, the mW model is successfully used to study many thermodynamics and dynamics of methane hydrate. Yet, despite several decades of intense research, the mW model is still found to overestimate the melting temperature of methane hydrate. We here employ the minimum mean squared error estimation to revisit the key parameter of the mW model, which determines the strength of the tetrahedral angle of the water system. Relying on the free energy calculations, we first estimate the chemical potentials of water in the liquid phase for temperatures at which methane hydrate forms. We then turn to the mean squared error to describe the chemical potential deviation between the mW model and the TIP4P/ice model (the latter could reproduce the experimental phase diagram of methane hydrate). By minimizing the mean squared error, we finally have an optimized parameter for the mW model. In this part, we also discuss the pressure effect on such reparameterization procedure. Moreover, relying on the direct coexistence method, the melting temperature determined using the reparameterized mW model is found to be consistent with the experimental data. This strategy provides a means to improve the coarse-grained model to match the experimental observations for temperatures in the range of interest.